(upbeat music) >> Okay, welcome back everyone in live coverage here at theCUBE, Boston, Massachusetts, for AWS re:inforce 22 security conference for Amazon Web Services. Obviously reinvent the end of the years' the big celebration, "re:Mars" is the new show that we've covered as well. The res are here with theCUBE. I'm John Furrier, host with a great guest, Ameesh Divatia, co-founder, and CEO of a company called "Baffle." Ameesh, thanks for joining us on theCUBE today, congratulations. >> Thank you. It's good to be here. >> And we got the custom encrypted socks. >> Yup, limited edition >> 64 bitter 128. >> Base 64 encoding. >> Okay.(chuckles) >> Secret message in there. >> Okay.(chuckles) Secret message.(chuckles) We'll have to put a little meme on the internet, figure it out. Well, thanks for comin' on. You guys are goin' hot right now. You guys a hot startup, but you're in an area that's going to explode, we believe. >> Yeah. >> The SuperCloud is here, we've been covering that on theCUBE that people are building on top of the Amazon Hyperscalers. And without the capex, they're building platforms. The application tsunami has come and still coming, it's not stopping. Modern applications are faster, they're better, and they're driving a lot of change under the covers. >> Absolutely. Yeah. >> And you're seeing structural change happening in real time, in ops, the network. You guys got something going on in the encryption area. >> Yes >> Data. Talk about what you guys do. >> Yeah. So we believe very strongly that the next frontier in security is data. We've had multiple waves in security. The next one is data, because data is really where the threats will persist. If the data shows up in the wrong place, you get into a lot of trouble with compliance. So we believe in protecting the data all the way down at the field, or record level. That's what we do. >> And you guys doing all kinds of encryption, or other things? >> Yes. So we do data transformation, which encompasses three different things. It can be tokenization, which is format preserving. We do real encryption with counter mode, or we can do masked views. So tokenization, encryption, and masking, all with the same platform. >> So pretty wide ranging capabilities with respect to having that kind of safety. >> Yes. Because it all depends on how the data is used down the road. Data is created all the time. Data flows through pipelines all the time. You want to make sure that you protect the data, but don't lose the utility of the data. That's where we provide all that flexibility. >> So Kurt was on stage today on one of the keynotes. He's the VP of the platform at AWS. >> Yes. >> He was talking about encrypts, everything. He said it needs, we need to rethink encryption. Okay, okay, good job. We like that. But then he said, "We have encryption at rest." >> Yes. >> That's kind of been there, done that. >> Yes. >> And, in-flight? >> Yeah. That's been there. >> But what about in-use? >> So that's exactly what we plug. What happens right now is that data at rest is protected because of discs that are already self-encrypting, or you have transparent data encryption that comes native with the database. You have data in-flight that is protected because of SSL. But when the data is actually being processed, it's in the memory of the database or datastore, it is exposed. So the threat is, if the credentials of the database are compromised, as happened back then with Starwood, or if the cloud infrastructure is compromised with some sort of an insider threat like a Capital One, that data is exposed. That's precisely what we solve by making sure that the data is protected as soon as it's created. We use standard encryption algorithms, AES, and we either do format preserving, or true encryption with counter mode. And that data, it doesn't really matter where it ends up, >> Yeah. >> because it's always protected. >> Well, that's awesome. And I think this brings up the point that we want been covering on SiliconAngle in theCUBE, is that there's been structural change that's happened, >> Yes. >> called cloud computing, >> Yes. >> and then hybrid. Okay. Scale, role of data, higher level abstraction of services, developers are in charge, value creations, startups, and big companies. That success is causing now, a new structural change happening now. >> Yes. >> This is one of them. What areas do you see that are happening right now that are structurally changing, that's right in front of us? One is, more cloud native. So the success has become now the problem to solve - >> Yes. >> to get to the next level. >> Yeah. >> What are those, some of those? >> What we see is that instead of security being an afterthought, something that you use as a watchdog, you create ways of monitoring where data is being exposed, or data is being exfiltrated, you want to build security into the data pipeline itself. As soon as data is created, you identify what is sensitive data, and you encrypt it, or tokenize it as it flows into the pipeline using things like Kafka plugins, or what we are very clearly differentiating ourselves with is, proxy architectures so that it's completely transparent. You think you're writing to the datastore, but you're actually writing to the proxy, which in turn encrypts the data before its stored. >> Do you think that's an efficient way to do it, or is the only way to do it? >> It is a much more efficient way of doing it because of the fact that you don't need any app-dev resources. There are many other ways of doing it. In fact, the cloud vendors provide development kits where you can just go do it yourself. So that is actually something that we completely avoid. And what makes it really, really interesting is that once the data is encrypted in the data store, or database, we can do what is known as "Privacy Enhanced Computation." >> Mm. >> So we can actually process that data without decrypting it. >> Yeah. And so proxies then, with cloud computing, can be very fast, not a bottleneck that could be. >> In fact, the cloud makes it so. It's very hard to - >> You believe that? >> do these things in static infrastructure. In the cloud, there's infinite amount of processing available, and there's containerization. >> And you have good network. >> You have very good network, you have load balancers, you have ways of creating redundancy. >> Mm. So the cloud is actually enabling solutions like this. >> And the old way, proxies were seen as an architectural fail, in the old antiquated static web. >> And this is where startups don't have the baggage, right? We didn't have that baggage. (John laughs) We looked at the problem and said, of course we're going to use a proxy because this is the best way to do this in an efficient way. >> Well, you bring up something that's happening right now that I hear a lot of CSOs and CIOs and executives say, CXOs say all the time, "Our", I won't say the word, "Our stuff has gotten complicated." >> Yes. >> So now I have tool sprawl, >> Yeah. >> I have skill gaps, and on the rise, all these new managed services coming at me from the vendors who have never experienced my problem. And their reaction is, they don't get my problem, and they don't have the right solutions, it's more complexity. They solve the complexity by adding more complexity. >> Yes. I think we, again, the proxy approach is a very simple. >> That you're solving that with that approach. >> Exactly. It's very simple. And again, we don't get in the way. That's really the the biggest differentiator. The forcing function really here is compliance, right? Because compliance is forcing these CSOs to actually adopt these solutions. >> All right, so love the compliance angle, love the proxy as an ease of use, take the heavy lifting away, no operational problems, and deviations. Now let's talk about workloads. >> Yeah. >> 'Cause this is where the use is. So you got, or workloads being run large scale, lot a data moving around, computin' as well. What's the challenge there? >> I think it's the volume of the data. Traditional solutions that we're relying on legacy tokenizations, I think would replicate the entire storage because it would create a token wall, for example. You cannot do that at this scale. You have to do something that's a lot more efficient, which is where you have to do it with a cryptography approach. So the workloads are diverse, lots of large files in the workloads as well as structured workloads. What we have is a solution that actually goes across the board. We can do unstructured data with HTTP proxies, we can do structured data with SQL proxies. And that's how we are able to provide a complete solution for the pipeline. >> So, I mean, show about the on-premise versus the cloud workload dynamic right now. Hybrid is a steady state right now. >> Yeah. >> Multi-cloud is a consequence of having multiple vendors, not true multi-cloud but like, okay, they have Azure there, AWS here, I get that. But hybrid really is the steady state. >> Yes. >> Cloud operations. How are the workloads and the analytics the data being managed on-prem, and in the cloud, what's their relationship? What's the trend? What are you seeing happening there? >> I think the biggest trend we see is pipelining, right? The new ETL is streaming. You have these Kafka and Kinesis capabilities that are coming into the picture where data is being ingested all the time. It is not a one time migration. It's a stream. >> Yeah. >> So plugging into that stream is very important from an ingestion perspective. >> So it's not just a watchdog. >> No. >> It's the pipelining. >> It's built in. It's built-in, it's real time, that's where the streaming gets another diverse access to data. >> Exactly. >> Data lakes. You got data lakes, you have pipeline, you got streaming, you mentioned that. So talk about the old school OLTP, the old BI world. I think Power BI's like a $30 billion product. >> Yeah. >> And you got Tableau built on OLTP building cubes. Aren't we just building cubes in a new way, or, >> Well. >> is there any relevance to the old school? >> I think there, there is some relevance and in fact that's again, another place where the proxy architecture really helps, because it doesn't matter when your application was built. You can use Tableau, which nobody has any control over, and still process encrypted data. And so can with Power BI, any Sequel application can be used. And that's actually exactly what we like to. >> So we were, I was talking to your team, I knew you were coming on, and they gave me a sound bite that I'm going to read to the audience and I want to get your reaction to. >> Sure. >> 'Cause I love this. I fell out of my chair when I first read this. "Data is the new oil." In 2010 that was mentioned here on theCUBE, of course. "Data is the new oil, but we have to ensure that it does not become the next asbestos." Okay. That is really clever. So we all know about asbestos. I add to the Dave Vellante, "Lead paint too." Remember lead paint? (Ameesh laughs) You got to scrape it out and repaint the house. Asbestos obviously causes a lot of cancer. You know, joking aside, the point is, it's problematic. >> It's the asset. >> Explain why that sentence is relevant. >> Sure. It's the assets and liabilities argument, right? You have an asset which is data, but thanks to compliance regulations and Gartner says 75% of the world will be subject to privacy regulations by 2023. It's a liability. So if you don't store your data well, if you don't process your data responsibly, you are going to be liable. So while it might be the oil and you're going to get lots of value out of it, be careful about the, the flip side. >> And the point is, there could be the "Grim Reaper" waiting for you if you don't do it right, the consequences that are quantified would be being out of business. >> Yes. But here's something that we just discovered actually from our survey that we did. While 93% of respondents said that they have had lots of compliance related effects on their budgets. 75% actually thought that it makes them better. They can use the security postures as a competitive differentiator. That's very heartening to us. We don't like to sell the fear aspect of this. >> Yeah. We like to sell the fact that you look better compared to your neighbor, if you have better data hygiene, back to the. >> There's the fear of missing out, or as they say, "Keeping up with the Joneses", making sure that your yard looks better than the next one. I get the vanity of that, but you're solving real problems. And this is interesting. And I want to get your thoughts on this. I found, I read that you guys protect more than a 100 billion records across highly regulated industries. Financial services, healthcare, industrial IOT, retail, and government. Is that true? >> Absolutely. Because what we are doing is enabling SaaS vendors to actually allow their customers to control their data. So we've had the SaaS vendor who has been working with us for over three years now. They store confidential data from 30 different banks in the country. >> That's a lot of records. >> That's where the record, and. >> How many customers do you have? >> Well, I think. >> The next round of funding's (Ameesh laughs) probably they're linin' up to put money into you guys. >> Well, again, this is a very important problem, and there are, people's businesses are dependent on this. We're just happy to provide the best tool out there that can do this. >> Okay, so what's your business model behind? I love the success, by the way, I wanted to quote that stat to one verify it. What's the business model service, software? >> The business model is software. We don't want anybody to send us their confidential data. We embed our software into our customers environments. In case of SaaS, we are not even visible, we are completely embedded. We are doing other relationships like that right now. >> And they pay you how? >> They pay us based on the volume of the data that they're protecting. >> Got it. >> That in that case which is a large customers, large enterprise customers. >> Pay as you go. >> It is pay as you go, everything is annual licenses. Although, multi-year licenses are very common because once you adopt the solution, it is very sticky. And then for smaller customers, we do base our pricing also just on databases. >> Got it. >> The number of databases. >> And the technology just reviewed low-code, no-code implementation kind of thing, right? >> It is by definition, no code when it comes to proxy. >> Yeah. >> When it comes to API integration, it could be low code. Yeah, it's all cloud-friendly, cloud-native. >> No disruption to operations. >> Exactly. >> That's the culprit. >> Well, yeah. >> Well somethin' like non-disruptive operations.(laughs) >> No, actually I'll give an example of a migration, right? We can do live migrations. So while the databases are still alive, as you write your. >> Live secure migrations. >> Exactly. You're securing - >> That's the one that manifests. >> your data as it migrates. >> Awright, so how much funding have you guys raised so far? >> We raised 36 and a half, series A, and B now. We raised that late last year. >> Congratulations. >> Thank you. >> Who's the venture funders? >> True Ventures is our largest investor, followed by Celesta Capital, National Grid Partners is an investor, and so is Engineering Capital and Clear Vision Ventures. >> And the seed and it was from Engineering? >> Seed was from Engineering. >> Engineering Capital. >> And then True came in very early on. >> Okay. >> Greenspring is also an investor in us, so is Industrial Ventures. >> Well, privacy has a big concern, big application for you guys. Privacy, secure migrations. >> Very much so. So what we are believe very strongly in the security's personal, security is yours and my data. Privacy is what the data collector is responsible for. (John laughs) So the enterprise better be making sure that they've complied with privacy regulations because they don't tell you how to protect the data. They just fine you. >> Well, you're not, you're technically long, six year old start company. Six, seven years old. >> Yeah. >> Roughly. So yeah, startups can go on long like this, still startup, privately held, you're growing, got big records under management there, congratulations. What's next? >> I think scaling the business. We are seeing lots of applications for this particular solution. It's going beyond just regulated industries. Like I said, it's a differentiating factor now. >> Yeah >> So retail, and a lot of other IOT related industrial customers - >> Yeah. >> are also coming. >> Ameesh, talk about the show here. We're at re:inforce, actually we're live here on the ground, the show floor buzzing. What's your takeaway? What's the vibe this year? What if you had to share what your opinion the top story here at the show, what would be the two top things, or three things? >> I think it's two things. First of all, it feels like we are back. (both laugh) It's amazing to see people on the show floor. >> Yeah. >> People coming in and asking questions and getting to see the product. The second thing that I think is very gratifying is, people come in and say, "Oh, I've heard of you guys." So thanks to digital media, and digital marketing. >> They weren't baffled. They want baffled. >> Exactly. >> They use baffled. >> Looks like, our outreach has helped, >> Yeah. >> and has kept the continuity, which is a big deal. >> Yeah, and now you're a CUBE alumni, welcome to the fold. >> Thank you. >> Appreciate you coming on. And we're looking forward to profiling you some day in our startup showcase, and certainly, we'll see you in the Palo Alto studios. Love to have you come in for a deeper dive. >> Sounds great. Looking forward to it. >> Congratulations on all your success, and thanks for coming on theCUBE, here at re:inforce. >> Thank you, John. >> Okay, we're here in, on the ground live coverage, Boston, Massachusetts for AWS re:inforce 22. I'm John Furrier, your host of theCUBE with Dave Vellante, who's in an analyst session, right? He'll be right back with us on the next interview, coming up shortly. Thanks for watching. (gentle music)

(cheerful music) >> Hello everyone, welcome to today's session of theCUBE's presentation of AWS Startup Showcase, New Breakthroughs in DevOps, Data Analytics, Cloud Management Tools, featuring Okera from the cloud management migration track. I'm John Furrier, your host. We've got two great special guests today, Nong Li, founder and CTO of Okera, and Sanjeev Mohan, principal @SanjMo, and former research vice president of big data and advanced analytics at Gartner. He's a legend, been around the industry for a long time, seen the big data trends from the past, present, and knows the future. Got a great lineup here. Gentlemen, thank you for this, so, life in the trenches, lessons learned across compliance, cloud migration, analytics, and use cases for Fortune 1000s. Thanks for joining us. >> Thanks for having us. >> So Sanjeev, great to see you, I know you've seen this movie, I was saying that in the open, you've at Gartner seen all the visionaries, the leaders, you know everything about this space. It's changing extremely fast, and one of the big topics right out of the gate is not just innovation, we'll get to that, that's the fun part, but it's the regulatory compliance and audit piece of it. It's keeping people up at night, and frankly if not done right, slows things down. This is a big part of the showcase here, is to solve these problems. Share us your thoughts, what's your take on this wide-ranging issue? >> So, thank you, John, for bringing this up, and I'm so happy you mentioned the fact that, there's this notion that it can slow things down. Well I have to say that the old way of doing governance slowed things down, because it was very much about control and command. But the new approach to data governance is actually in my opinion, it's liberating data. If you want to democratize or monetize, whatever you want to call it, you cannot do it 'til you know you can trust said data and it's governed in some ways, so data governance has actually become very interesting, and today if you want to talk about three different areas within compliance regulatory, for example, we all know about the EU GDPR, we know California has CCPA, and in fact California is now getting even a more stringent version called CPRA in a couple of years, which is more aligned to GDPR. That is a first area we know we need to comply to that, we don't have any way out. But then, there are other areas, there is insider trading, there is how you secure the data that comes from third parties, you know, vendors, partners, suppliers, so Nong, I'd love to hand it over to you, and see if you can maybe throw some light into how our customers are handling these use cases. >> Yeah, absolutely, and I love what you said about balancing agility and liberating, in the face of what may be seen as things that slow you down. So we work with customers across verticals with old and new regulations, so you know, you brought up GDPR. One of our clients is using this to great effect to power their ecosystem. They are a very large retail company that has operations and customers across the world, obviously the importance of GDPR, and the regulations that imposes on them are very top of mind, and at the same time, being able to do effective targeting analytics on customer information is equally critical, right? So they're exactly at that spot where they need this customer insight for powering their business, and then the regulatory concerns are extremely prevalent for them. So in the context of GDPR, you'll hear about things like consent management and right to be forgotten, right? I, as a customer of that retailer should say "I don't want my information used for this purpose," right? "Use it for this, but not this." And you can imagine at a very, very large scale, when you have a billion customers, managing that, all the data you've collected over time through all of your devices, all of your telemetry, really, really challenging. And they're leveraging Okera embedded into their analytics platform so they can do both, right? Their data scientists and analysts who need to do everything they're doing to power the business, not have to think about these kind of very granular customer filtering requirements that need to happen, and then they leverage us to do that. So that's kind of new, right, GDPR, relatively new stuff at this point, but we obviously also work with customers that have regulations from a long long time ago, right? So I think you also mentioned insider trading and that supply chain, so we'll talk to customers, and they want really data-driven decisions on their supply chain, everything about their production pipeline, right? They want to understand all of that, and of course that makes sense, whether you're the CFO, if you're going to make business decisions, you need that information readily available, and supply chains as we know get more and more and more complex, we have more and more integrated into manufacturing and other verticals. So that's your, you're a little bit stuck, right? You want to be data-driven on those supply chain analytics, but at the same time, knowing the details of all the supply chain across all of your dependencies exposes your internal team to very high blackout periods or insider trading concerns, right? For example, if you knew Apple was buying a bunch of something, that's maybe information that only a select few people can have, and the way that manifests into data policies, 'cause you need the ability to have very, very scalable, per employee kind of scalable data restriction policies, so they can do their job easier, right? If we talk about speeding things up, instead of a very complex process for them to get approved, and approved on SEC regulations, all that kind of stuff, you can now go give them access to the part of the supply chain that they need, and no more, and limit their exposure and the company's exposure and all of that kind of stuff. So one of our customers able to do this, getting two orders of magnitude, a 100x reduction in the policies to manage the system like that. >> When I hear you talking like that, I think the old days of "Oh yeah, regulatory, it kind of slows down innovation, got to go faster," pretty basic variables, not a lot of combination of things to check. Now with cloud, there seems to be combinations, Sanjeev, because how complicated has the regulatory compliance and audit environment gotten in the past few years, because I hear security in a supply chain, I hear insider threats, I mean these are security channels, not just compliance department G&A kind of functions. You're talking about large-scale, potentially combinations of access, distribution, I mean it seems complicated. How much more complicated is it now, just than it was a few years ago? >> So, you know the way I look at it is, I'm just mentioning these companies just as an example, when PayPal or Ebay, all these companies started, they started in California. Anybody who ever did business on Ebay or PayPal, guess where that data was? In the US in some data center. Today you cannot do it. Today, data residency laws are really tough, and so now these organizations have to really understand what data needs to remain where. On top of that, we now have so many regulations. You know, earlier on if you were healthcare, you needed to be HIPAA compliant, or banking PCI DSS, but today, in the cloud, you really need to know, what data I have, what sensitive data I have, how do I discover it? So that data discovery becomes really important. What roles I have, so for example, let's say I work for a bank in the US, and I decide to move to Germany. Now, the old school is that a new rule will be created for me, because of German... >> John: New email address, all these new things happen, right? >> Right, exactly. So you end up with this really, a mass of rules and... And these are all static. >> Rules and tools, oh my god. >> Yeah. So Okera actually makes a lot of this dynamic, which reduces your cloud migration overhead, and Nong used some great examples, in fact, sorry if I take just a second, without mentioning any names, there's one of the largest banks in the world is going global in the digital space for the first time, and they're taking Okera with them. So... >> But what's the point? This is my next topic in cloud migration, I want to bring this up because, complexity, when you're in that old school kind of data center, waterfall, these old rules and tools, you have to roll this out, and it's a pain in the butt for everybody, it's a hassle, huge hassle. Cloud gives the agility, we know that, and cloud's becoming more secure, and I think now people see the on-premise, certainly things that'd be on-premises for secure things, I get that, but when you start getting into agility, and you now have cloud regions, you can start being more programmatic, so I want to get you guys' thoughts on the cloud migration, how companies who are now lifting and shifting, replatforming, what's the refactoring beyond that, because you can replatform in the cloud, and still some are kind of holding back on that. Then when you're in the cloud, the ones that are winning, the companies that are winning are the ones that are refactoring in the cloud. Doing things different with new services. Sanjeev, you start. >> Yeah, so you know, in fact lot of people tell me, "You know, we are just going to lift and shift into the cloud." But you're literally using cloud as a data center. You still have all the, if I may say, junk you had on-prem, you just moved it into the cloud, and now you're paying for it. In cloud, nothing is free. Every storage, every processing, you're going to pay for it. The most successful companies are the ones that are replatforming, they are taking advantage of the platform as a service or software as a service, so that includes things like, you pay as you go, you pay for exactly the amount you use, so you scale up and scale down or scale out and scale in, pretty quickly, you know? So you're handling that demand, so without replatforming, you are not really utilizing your- >> John: It's just hosting. >> Yeah, you're just hosting. >> It's basically hosting if you're not doing anything right there. >> Right. The reason why people sometimes resist to replatform, is because there's a hidden cost that we don't really talk about, PaaS adds 3x to IaaS cost. So, some organizations that are very mature, and they have a few thousand people in the IT department, for them, they're like "No, we just want to run it in the cloud, we have the expertise, and it's cheaper for us." But in the long run, to get the most benefit, people should think of using cloud as a service. >> Nong what's your take, because you see examples of companies, I'll just call one out, Snowflake for instance, they're essentially a data warehouse in the cloud, they refactored and they replatformed, they have a competitive advantage with the scale, so they have things that others don't have, that just hosting. Or even on-premise. The new model developing where there's real advantages, and how should companies think about this when they have to manage these data lakes, and they have to manage all these new access methods, but they want to maintain that operational stability and control and growth? >> Yeah, so. No? Yeah. >> There's a few topics that are all (indistinct) this topic. (indistinct) enterprises moving to the cloud, they do this maybe for some cost savings, but a ton of it is agility, right? The motor that the business can run at is just so much faster. So we'll work with companies in the context of cloud migration for data, where they might have a data warehouse they've been using for 20 years, and building policies over that time, right? And it's taking a long time to go proof of access and those kind of things, made more sense, right? If it took you months to procure a physical infrastructure, get machines shipped to your data center, then this data access taking so long feels okay, right? That's kind of the same rate that everything is moving. In the cloud, you can spin up new infrastructure instantly, so you don't want approvals for getting policies, creating rules, all that stuff that Sanjeev was talking about, that being slow is a huge, huge problem. So this is a very common environment that we see where they're trying to do that kind of thing. And then, for replatforming, again, they've been building these roles and processes and policies for 20 years. What they don't want to do is take 20 years to go migrate all that stuff into the cloud, right? That's probably an experience nobody wants to repeat, and frankly for many of them, people who did it originally may or may not be involved in this kind of effort. So we work with a lot of companies like that, they have their, they want stability, they got to have the business running as normal, they got to get moving into the new infrastructure, doing it in a new way that, you know, with all the kind of lessons learned, so, as Sanjeev said, one of these big banks that we work with, that classical story of on-premise data warehousing, maybe a little bit of Hadoop, moved onto AWS, S3, Snowflake, that kind of setup, extremely intricate policies, but let's go reimagine how we can do this faster, right? What we like to talk about is, you're an organization, you need a design that, if you onboarded 1000 more data users, that's got to be way, way easier than the first 10 you onboarded, right? You got to get it to be easier over time, in a really, really significant way. >> Talk about the data authorization safety factor, because I can almost imagine all the intricacies of these different tools creates specialism amongst people who operate them. And each one might have their own little authorization nuance. Trend is not to have that siloed mentality. What's your take on clients that want to just "Hey, you know what? I want to have the maximum agility, but I don't want to get caught in the weeds on some of these tripwires around access and authorization." >> Yeah, absolutely, I think it's real important to get the balance of it, right? Because if you are an enterprise, or if you have diversive teams, you want them to have the ability to use tools as best of breed for their purpose, right? But you don't want to have it be so that every tool has its own access and provisioning and whatever, that's definitely going to be a security, or at least, a lot of friction for you to get things going. So we think about that really hard, I think we've seen great success with things like SSO and Okta, right? Unifying authentication. We think there's a very, very similar thing about to happen with authorization. You want that single control plane that can integrate with all the tools, and still get the best of what you need, but it's much, much easier (indistinct). >> Okta's a great example, if people don't want to build their own thing and just go with that, same with what you guys are doing. That seems to be the dots that are connecting you, Sanjeev. The ease of use, but yet the stability factor. >> Right. Yeah, because John, today I may want to bring up a SQL editor to go into Snowflake, just as an example. Tomorrow, I may want to use the Azure Bot, you know? I may not even want to go to Snowflake, I may want to go to an underlying piece of data, or I may use Power BI, you know, for some reason, and come from Azure side, so the point is that, unless we are able to control, in some sort of a centralized manner, we will not get that consistency. And security you know is all or nothing. You cannot say "Well, I secured my Snowflake, but if you come through HTFS, Hadoop, or some, you know, that is outside of my realm, or my scope," what's the point? So that is why it is really important to have a watertight way, in fact I'm using just a few examples, maybe tomorrow I decide to use a data catalog, or I use Denodo as my data virtualization and I run a query. I'm the same identity, but I'm using different tools. I may use it from home, over VPN, or I may use it from the office, so you want this kind of flexibility, all encompassed in a policy, rather than a separate rule if you do this and this, if you do that, because then you end up with literally thousands of rules. >> And it's never going to stop, either, it's like fashion, the next tool's going to come out, it's going to be cool, and people are going to want to use it, again, you don't want to have to then move the train from the compliance side this way or that way, it's a lot of hassle, right? So we have that one capability, you can bring on new things pretty quickly. Nong, am I getting it right, this is kind of like the trend, that you're going to see more and more tools and/or things that are relevant or, certain use cases that might justify it, but yet, AppSec review, compliance review, I mean, good luck with that, right? >> Yeah, absolutely, I mean we certainly expect tools to continue to get more and more diverse, and better, right? Most innovation in the data space, and I think we... This is a great time for that, a lot of things that need to happen, and so on and so forth. So I think one of the early goals of the company, when we were just brainstorming, is we don't want data teams to not be able to use the tools because it doesn't have the right security (indistinct), right? Often those tools may not be focused on that particular area. They're great at what they do, but we want to make sure they're enabled, they do some enterprise investments, they see broader adoption much easier. A lot of those things. >> And I can hear the sirens in the background, that's someone who's not using your platform, they need some help there. But that's the case, I mean if you don't get this right, there are some consequences, and I think one of the things I would like to bring up on next track is, to talk through with you guys is, the persona pigeonhole role, "Oh yeah, a data person, the developer, the DevOps, the SRE," you start to see now, developers and with cloud developers, and data folks, people, however they get pigeonholed, kind of blending in, okay? You got data services, you got analytics, you got data scientists, you got more democratization, all these things are being kicked around, but the notion of a developer now is a data developer, because cloud is about DevOps, data is now a big part of it, it's not just some department, it's actually blending in. Just a cultural shift, can you guys share your thoughts on this trend of data people versus developers now becoming kind of one, do you guys see this happening, and if so, how? >> So when, John, I started my career, I was a DBA, and then a data architect. Today, I think you cannot have a DBA who's not a developer. That's just my opinion. Because there is so much of CICD, DevOps, that happens today, and you know, you write your code in Python, you put it in version control, you deploy using Jenkins, you roll back if there's a problem. And then, you are interacting, you're building your data to be consumed as a service. People in the past, you would have a thick client that would connect to the database over TCP/IP. Today, people don't want to connect over TCP/IP necessarily, they want to go by HTTP. And they want an API gateway in the middle. So, if you're a data architect or DBA, now you have to worry about, "I have a REST API call that's coming in, how am I going to secure that, and make sure that people are allowed to see that?" And that was just yesterday. >> Exactly. Got to build an abstraction layer. You got to build an abstraction layer. The old days, you have to worry about schema, and do all that, it was hard work back then, but now, it's much different. You got serverless, functions are going to show way... It's happening. >> Correct, GraphQL, and semantic layer, that just blows me away because, it used to be, it was all in database, then we took it out of database and we put it in a BI tool. So we said, like BusinessObjects started this whole trend. So we're like "Let's put the semantic layer there," well okay, great, but that was when everything was surrounding BusinessObjects and Oracle Database, or some other database, but today what if somebody brings Power BI or Tableau or Qlik, you know? Now you don't have a semantic layer access. So you cannot have it in the BI layer, so you move it down to its own layer. So now you've got a semantic layer, then where do you store your metrics? Same story repeats, you have a metrics layer, then the data centers want to do feature engineering, where do you store your features? You have a feature store. And before you know, this stack has disaggregated over and over and over, and then you've got layers and layers of specialization that are happening, there's query accelerators like Dremio or Trino, so you've got your data here, which Nong is trying really hard to protect, and then you've got layers and layers and layers of abstraction, and networks are fast, so the end user gets great service, but it's a nightmare for architects to bring all these things together. >> How do you tame the complexity? What's the bottom line? >> Nong? >> Yeah, so, I think... So there's a few things you need to do, right? So, we need to re-think how we express security permanence, right? I think you guys have just maybe in passing (indistinct) talked about creating all these rules and all that kind of stuff, that's been the way we've done things forever. We get to think about policies and mechanisms that are much more dynamic, right? You need to really think about not having to do any additional work, for the new things you add to the system. That's really, really core to solving the complexity problem, right? 'Cause that gets you those orders of magnitude reduction, system's got to be more expressive and map to those policies. That's one. And then second, it's got to be implemented at the right layer, right, to Sanjeev's point, close to the data, and it can service all of those applications and use cases at the same time, and have that uniformity and breadth of support. So those two things have to happen. >> Love this universal data authorization vision that you guys have. Super impressive, we had a CUBE Conversation earlier with Nick Halsey, who's a veteran in the industry, and he likes it. That's a good sign, 'cause he's seen a lot of stuff, too, Sanjeev, like yourself. This is a new thing, you're seeing compliance being addressed, and with programmatic, I'm imagining there's going to be bots someday, very quickly with AI that's going to scale that up, so they kind of don't get in the innovation way, they can still get what they need, and enable innovation. You've got cloud migration, which is only going faster and faster. Nong, you mentioned speed, that's what CloudOps is all about, developers want speed, not things in days or hours, they want it in minutes and seconds. And then finally, ultimately, how's it scale up, how does it scale up for the people operating and/or programming? These are three major pieces. What happens next? Where do we go from here, what's, the customer's sitting there saying "I need help, I need trust, I need scale, I need security." >> So, I just wrote a blog, if I may diverge a bit, on data observability. And you know, so there are a lot of these little topics that are critical, DataOps is one of them, so to me data observability is really having a transparent view of, what is the state of your data in the pipeline, anywhere in the pipeline? So you know, when we talk to these large banks, these banks have like 1000, over 1000 data pipelines working every night, because they've got that hundred, 200 data sources from which they're bringing data in. Then they're doing all kinds of data integration, they have, you know, we talked about Python or Informatica, or whatever data integration, data transformation product you're using, so you're combining this data, writing it into an analytical data store, something's going to break. So, to me, data observability becomes a very critical thing, because it shows me something broke, walk me down the pipeline, so I know where it broke. Maybe the data drifted. And I know Okera does a lot of work in data drift, you know? So this is... Nong, jump in any time, because I know we have use cases for that. >> Nong, before you get in there, I just want to highlight a quick point. I think you're onto something there, Sanjeev, because we've been reporting, and we believe, that data workflows is intellectual property. And has to be protected. Nong, go ahead, your thoughts, go ahead. >> Yeah, I mean, the observability thing is critically important. I would say when you want to think about what's next, I think it's really effectively bridging tools and processes and systems and teams that are focused on data production, with the data analysts, data scientists, that are focused on data consumption, right? I think bridging those two, which cover a lot of the topics we talked about, that's kind of where security almost meets, that's kind of where you got to draw it. I think for observability and pipelines and data movement, understanding that is essential. And I think broadly, on all of these topics, where all of us can be better, is if we're able to close the loop, get the feedback loop of success. So data drift is an example of the loop rarely being closed. It drifts upstream, and downstream users can take forever to figure out what's going on. And we'll have similar examples related to buy-ins, or data quality, all those kind of things, so I think that's really a problem that a lot of us should think about. How do we make sure that loop is closed as quickly as possible? >> Great insight. Quick aside, as the founder CTO, how's life going for you, you feel good? I mean, you started a company, doing great, it's not drifting, it's right in the stream, mainstream, right in the wheelhouse of where the trends are, you guys have a really crosshairs on the real issues, how you feeling, tell us a little bit about how you see the vision. >> Yeah, I obviously feel really good, I mean we started the company a little over five years ago, there are kind of a few things that we bet would happen, and I think those things were out of our control, I don't think we would've predicted GDPR security and those kind of things being as prominent as they are. Those things have really matured, probably as best as we could've hoped, so that feels awesome. Yeah, (indistinct) really expanded in these years, and it feels good. Feels like we're in the right spot. >> Yeah, it's great, data's competitive advantage, and certainly has a lot of issues. It could be a blocker if not done properly, and you're doing great work. Congratulations on your company. Sanjeev, thanks for kind of being my cohost in this segment, great to have you on, been following your work, and you continue to unpack it at your new place that you started. SanjMo, good to see your Twitter handle taking on the name of your new firm, congratulations. Thanks for coming on. >> Thank you so much, such a pleasure. >> Appreciate it. Okay, I'm John Furrier with theCUBE, you're watching today's session presentation of AWS Startup Showcase, featuring Okera, a hot startup, check 'em out, great solution, with a really great concept. Thanks for watching. (calm music)

Hello, everyone, and welcome back to the special presentation from theCUBE, where we're exploring the future of cloud and its business impact in the coming decade, kind of where we've come from and where we're going. My name is Dave Vellante, and with me is a CIO/CTO/COO, and longtime colleague, Dan Sheehan. Hello, Dan, how're you doing? >> Hey, Dave, how are you doing? Thank you for having me. >> Yeah, you're very welcome. So folks, Dan has been in the technology industry for a number of years. He's overseen, you know, large-multi, tens of millions of dollar ERP application development efforts, He was a CIO of a marketing, you know, direct mail company. Dan, we met at ADVO, it seems like such a (snickers) long time ago. >> Yeah, that was a long time ago, back in Connecticut. Back in the early 2000s. >> Yeah, ancient days. But pretty serious data for back then, you know, the early 2000s, and then you did a six-year stint as a EVP and CIO at Dunkin' Brands. I remember I came out to see you when I was starting Wikibon and trying to understand. >> Oh yeah. >> You know, what the CIOs cared about. You were so helpful and thanks for that. And that was a big deal. I mean, Dunkin', 17,000 points of distribution. I mean, that was sort of a complicated situation, right? >> Oh yeah. >> So, great experience. >> I mean, when you get involved with franchisees and trying to make everybody happy, yes, that was a lot of fun. >> And then you had a number of other roles, one was as COO at Modell's, and then to fast-forward, Beacon Health. You were EVP and CIO there. And you also, it looked like you had a kind of a business and operational role. You helped the company get acquired by Anthem Blue Cross. So awesome, congrats on that. That must've been a great experience. >> It was. A year of my life, yes. (both laugh) >> You're still standing. So anyway, you can see Dan, he's like this multi-tool star, he's seen a lot of changes in the technology business. So Dan, again, welcome back. Dan Sheehan. >> Oh, thank you. >> So when you started in your career, you know, there was no cloud, right? I mean, you had to do everything. It's funny, I remember I was... You probably know Bill Rucci, CIO of Hartford Steam Boiler. I remember we were talking one day, and this again was pre-cloud and he said, you know, I'm thinking, do I really need to manage my own email? I mean, back then, we did everything. So you had to provision infrastructure so you could write apps, and that was important. That frustrated CFOs, but it was a necessary piece of the value chain. So how have you seen that sort of IT value contribution shift over the years? Let's start there. >> Ah, well, I think it comes down to demand versus capacity. If you look at where companies want to go, they want to do a lot with technology. Technology has taken on a larger role. It's no longer and has not been a, so to speak, cost center. So I think the demand for making change and driving a company forward or reducing costs, there are other executives, peers to the CIO, to the CTO that are looking to do more, and when it comes to doing more, that means more demand, and you step back and you look at what the CIO has for capacity. Looking at Quick Solution's data, solutions in the cloud is appealing, and there are, you know, times where other functions talk to a vendor and see that they can get a vertical solution done pretty quickly. They go off and take that on, or it could be, you know, a ServiceNow capability that you want to implement across the company, and you do that just like an ERP type of roll up. But the bottom line is there are solutions out there that have pushed, I would say the IT organization to look at their capacity versus demand, and sometimes you can get things done quicker with a cloud type of solution. >> So how did you look at that shadow IT as a CIO? Was it something that kind of ticked you off or like you're sort of implying that it made you better? >> Well, I think it does ultimately make you better, but I think you have to partner with the functions because if you don't, you get these types of scenarios, and I've been involved in these just as well. You are busy with, you know, fulfilling your objectives as the leader of IT, and then you get a knock on the door from, let's say marketing or operations, and they say, hey, we just purchased this X solution and we want to integrate it with A, B and C. Well, that was not on the budget or on the IT roadmap or the IT strategy that was linked to the IT, I'm sorry, to the business strategy, and all of a sudden now you have more demand versus the capacity, and then you have to go start reprioritizing. So it's more of, yeah, kind of disrupted, but at the same time, it pushed, you know, the needle of the company forward. But it's all about just working together to make it happen. And that's a lot of, you know, hard conversations when you have to start reprioritizing capacity. >> Well, so let's talk about that alignment. I mean, there's always been a sort of a schism between IT and its ability to deliver, manage demand, and the business will always want you to go faster. They want IT to develop the systems, you know, of course, for less and then they want you to eat the cost of maintaining them, so (chuckles) there's been that tension. But in many ways, that CIO's job is alignment. I mean, it seems to me anyway that schism has certainly narrowed and the cloud's been been part of that, but what do you see as that trajectory over the years and where do you see it going? >> Well, I think it's going to continue to move forward, and depending upon the service, you know, companies are going to take advantage of those services. So yes, some of the non-mission critical capabilities that you would want to move out to the cloud or have somebody else do it, so to speak, that's going to continue to happen because they should be able to do it a lot cheaper than you can, just like use you mentioned a few moments ago about email. I did not want to maintain, you know, exchange service and keeping that all up and running. I moved quickly to Microsoft 365 and that's been a world of difference, but that's just one example. But when you have mission critical apps, you're going to have to make a decision if you want to continue to house them in-house or push them out to an AWS and house them there. So maybe you don't need a large data center and you can utilize some of the best and brightest around security, around managing size of the infrastructure and getting some of their engineering help, which can help. So it just depends upon the application, so to speak, or a function that you're trying to support. And you got to really look at your enterprise architecture and see where that makes sense. So you got to have a hybrid. I see and I have, you know, managed towards a hybrid way of looking at your architecture. >> Okay, so obviously the cloud played a role in that change, and of course, you were in healthcare too so you had to be somewhat careful, >> Yep. >> With the cloud. But you mentioned this hybrid architecture. I mean, from a technologist standpoint and a business standpoint, what do you want out of, you know, you hear a hybrid, multi, all the buzz words. What are you looking for then? Is it a consistent experience? Is it a consistent security? Or is it sort of more horses for courses, where you're trying to run a workload in the right place? What's your philosophy on that? >> Well, I mean, all those things matter, but you're looking at obviously, cost, you're looking at engagement. How does these services engage? Whether it's internal employees or external clients who you're servicing, and you want to get to a cost structure that makes sense in terms of managing those services as well as those mission critical apps. So it comes down to looking at the dollars and cents, as well as what type of services you can provide. In many cases, if you can provide a cheaper and increase the overall services, you're going to go down that path. And just like we did with ServiceNow, I did that at Beacon and also at DentaQuest two healthcare companies. We were able to, you know, remove duplicated, so to speak, ticketing systems and move to one and allow a better experience for the internal employee. They can do self-service, they can look at metrics, they can see status, real-time status on where their request was. So that made a bigger difference. So you engaged the employee differently, better, and then you also reduce your costs. >> Well, how about the economics? I mean, your experience that cloud is cheaper. You hear a lot of the, you know, a lot of the legacy players are saying, oh, no cloud's super expensive. Wait till you get that Amazon bill. (laughs) What's the truth? >> Well, I think there's still a lot of maturing that needs to go on, because unfortunately, depending upon the company, so let's use a couple of examples. So let's look at a startup. You look at a startup, they're probably going to look at all their services being in the cloud and being delivered through a SaaS model, and that's going to be an expense, that's going to be most likely a per user expense per month or per year, however, they structure the contract. And right out of the gate, that's going to be a top line expense that has to be managed going forward. Now you look at companies that have been around for a while, and two of the last companies I worked with, had a lot of technical debt, had on-prem applications. And when you started to look at how to move forward, you know, you had CFOs that were used to going to buy software, capitalize in that software over, you know, five years, sometimes three years, and using that investment to be capitalized, and that would sit below the line, so to speak. Now, don't get me wrong, you still have to pay for it, it's just a matter of where it sits. And when you're running a company and you're looking at the financials, not having that cost on your operational expenses, so to speak, if you're not looking at the depreciation through those numbers, that was advantageous to a CFO many years ago. Now you come to them and say, hey, we're going to move forward with a new HR system, and it's all increasing the expense because there's nothing else to capitalize. Those are different conversations, and all of a sudden your expenses have increased, and yes, you have to make sure that the businesses behind you, with respects to an ROI and supporting it. >> Yeah, so as long as the value is there, and that's a part of the alignment. I want to ask you about cloud pricing strategies because you mentioned ServiceNow, you know, Salesforce is in there, Workday. If you look at the way these guys price, it's really not true cloud pricing in a way, cause they're going to have you sign up for an annual license, you know, a lot of times you got pay up front, or if you want a discount, you're going to have to sign up for two years or three years. But now you see guys like Snowflake coming in, you know, big high-profile IPO. They actually charge you on a consumption-based model. What are your thoughts on that? Do you see that as sort of a trend in the coming decade? >> No, I absolutely think it's going to be on a trend, because consumption means more transactions and more transactions means more computing, and they're going to look at charging it just like any other utility charges. So yes, I see that trend continuing. Did a big deal with UltiPro HR, and yeah, that was all based upon user head count, but they were talking about looking at their payroll and changing their costing on payroll down the road. With their merger, or they went from being a public company to a private company, and now looking to merge with Kronos. I can see where time and attendance and payroll will stop being looked at as a transaction, right? It's a weekly or bi-weekly or monthly, however the company pays, and yes, there is dollars to be made there. >> Well, so let me ask you as a CIO and a business, you know, COO. One of the challenges that you hear with the cloud is okay, if I get my Amazon bill, it's something that Snowflake has talked about, where you know, to me, it's the ideal model, but on the other hand, the transparency is not necessarily there. You don't know what it's going to be at the end of (mumbles) Would you rather have more certainty as to what that bill's going to look like? Or would you rather have it aligned with consumption and the value to the business? >> Well, you know, that's a great question, because yes, I mean, budgets are usually built upon a number that's fixed. Now, no, don't get me wrong. I mean, when I look at the wide area network, the cost for internet services, yes, sometimes we need to increase and that means an increase in the overall cost, but that consumption, that transactional, that's going to be a different way of having to go ahead and budget. You have to budget now for the maximum transactions you anticipate with a growth of a company, and then you need to take a look at that you know, if you're budgeting. I know we were on a calendar fiscal year, so we started up budgeting process in August and we finalized at sometime in the end of October, November for the proceeding year, and if that's the case, you need to get a little bit better on what your consumptions are going to be, because especially if you're a public company, going out on the street with some numbers, those numbers could vary based upon a high transaction volume and the cost, and maybe you're not getting the results on the top end, on the revenue side. So I think, yeah, it's going to be an interesting dilemma as we move forward. >> Yeah. So, I mean, it comes back to alignment, doesn't it? I mean, I know in our small example, you know, we're doing now, we were used to be physical events with theCUBE, now it's all virtual events and our Amazon bill is going through the roof because we're supporting all these users on these virtual events, and our CFO's like, well, look at this Amazon bill, and you say, yeah, but look at the revenue, it's supporting. And so to your point, if the revenue is there, if the ROI is there, then it makes sense. You can kind of live with it because you're growing with it, but if not, then you really got to question it. >> Yeah. So you got to need to partner with your financial folks and come up with better modeling around some of these transactional services and build that into your modeling for your budget and for your, you know, your top line and your expenses. >> So what do you think of some of these SaaS companies? I mean, you've had a lot of experience. They're really coming at it from largely an application perspective, although you've managed a lot of infrastructure too. But we've talked about ServiceNow. They've kind of mopped up in the ITSM. I mean, there's nobody left. I mean, ServiceNow has sort of taken over the whole (mumbles) You know, Salesforce, >> Yeah. >> I guess, sort of similarly, sort of dominating the CRM space. You hear a lot of complaints now about, you know, ServiceNow pricing. There is somebody the other day called them the Oracle of ITSM. Do you see that potentially getting disrupted by maybe some cloud native developers who are developing tools on top? You see in, like, for instance, Datadog going after Splunk and LogRhythm. And there seem to be examples popping up. Well, what's your take on all this? >> No, absolutely. I think cause, you know, when we were talking about back when I first met you, when I was at the ADVO, I mean, Oracle was on it's, you know, rise with their suite of capabilities, and then before you know it, other companies were popping up and took over, whether it was Firstbeat, PeopleSoft, Workday, and then other companies that just came into play, cause it's going to happen because people are going to get, you know, frustrated. And yes, I did get a little frustrated with ServiceNow when I was looking at a couple of new modules because the pricing was a little bit higher than it was when I first started out. So yes, when you're good and you're able to provide the right services, they're going to start pricing it that way. But yes, I think you're going to get smaller players, and then those smaller players will start grabbing up, so to speak, market share and get into it. I mean, look at Salesforce. I mean, there are some pretty good CRMs. I mean, even, ServiceNow is getting into the CRM space big time, as well as a company like Sugar and a few others that will continue to push Salesforce to look at their pricing as well as their services. I mean, they're out there buying up companies, but you just can't automatically assume that they're going to, you know, integrate day one, and it's going to take time for some of their services to come and become reality, so to speak. So yes, I agree that there will be players out there that will push these lager SaaS companies, and hopefully get the right behaviors and right pricing. >> I've said for years, Dan, that I've predicted that ServiceNow and Salesforce are on a collision course. It didn't really happen, but it's starting to, because ServiceNow, the valuation is so huge. They have to grow into other markets much in the same way that Salesforce has. So maybe we'll see McDermott start doing some acquisitions. It's maybe a little tougher for ServiceNow given their whole multi-instance architecture and sort of their own cloud. That's going to be interesting to see how that plays out. >> Yeah. Yeah. You got to play in that type of architecture, let's put it that way. Yes, it'll be interesting to see how that does play out. >> What are your thoughts on the big hyperscalers; Amazon, Microsoft, Google? What's the right strategy there? Do you go all in on one cloud like AWS or are you more worried about lock-in? Do you want to spread your bets across clouds? How real is multi-cloud? Is it a strategy or more sort of a reality that you get M and A and you got shadow IT? What's your take on all that? >> Yeah, that's a great question because it does make you think a little differently around you know, where to put all your eggs. And it's getting tougher because you do want to distribute those eggs out to multiple vendors, if you would, service providers. But, you know, for instance we had a situation where we were building a brand new business intelligence data warehouse, and we decided to go with Microsoft as its core database. And we did a bake-off on business analytic tools. We had like seven of them at Beacon and we ended up choosing Microsoft's Power BI, and a good part of that reason, not all of it, but a good part of it was because we felt they did everything else that the Tableau's and others did, but, you know, Microsoft would work to give, you know, additional capabilities to Power BI if it's sitting on their database. So we had to take that into consideration, and we did and we ended up going with Power BI. With Amazon, I think Amazon's a little bit more, I'll put it horizontal, whereby they can help you out because of the database and just kind of be in that data center, if you would, and be able to move some of your homegrown applications, some of your technical debt over to that, I'll say cloud. But it'll get interesting because when you talk about integration, when you talk about moving forward with a new functionality, yeah, you have to put your architecture in a somewhat of a center point, and then look to see what is easier, cheaper, cost-effective, but, you know, what's happening to my functionality over the next three to five years. >> But it sounds like you'd subscribe to a horses for courses approach, where you put the right workload in the right cloud, as opposed to saying, I'm going to go all in on one cloud and it's going to be, you know, same skillset, same security, et cetera. It sounds like you'd lean toward the former versus going all in with, you know, MANO cloud. >> Yeah, I guess again, when I look at the architecture. There will be major, you know, breaks if you would. So yes, there is somewhat of a, you know, movement to you know, go with one horse. But, you know, I could see looking back at the Beacon architecture that we could, you know, lift and put the claims adjudication capabilities up in Amazon and then have that conduct, you know, the left to right claims processing, and then those transactions could then be moved into Microsoft's data warehouse. So, you know, there is ways to go about spreading it out so that you don't have all those eggs in one basket and that you reduce the amount of risk, but that weighed heavily on my mind. >> So I was going to ask you, how much of a factor lock-in is it? It sounds like it's more, you know, spreading your eggs around, as you say and reducing your risk as opposed to, you know, worried about lock-in, but as a CIO, how worried are you about lock-in? Where is that fit in the sort of decision tree? >> Ah, I mean, I would say it's up there, but unfortunately, there's no number one, there's like five number ones, if you would. So it's definitely up there and it's something to consider when you're looking at, like you said, the cost, risk integration, and then time. You know, sometimes you're up against the time. And again, security, like I said. Security is a big key in healthcare. And actually security overall, whether you're retail, you're going to always have situations no matter what industry, you got to protect the business. >> Yeah, so I want to ask you about security. That's the other number one. Well, you might've been a defacto CSO, but kind of when we started in this business security was the problem of the security teams, and you know, it's now a team sport. But in thinking about the cloud and security, how big of a concern is the cloud? Is it just more, you're looking for consistency and be able to apply the corporate edicts? Are there other concerns like the shared responsibility model? What are your thoughts on security in the cloud? >> Well, it probably goes back to again, the industry, but when I looked at the past five years in healthcare, doing a lot of work with the CMS and Medicaid, Medicare, they had certain requirements and certain restrictions. So we had to make sure that we follow those requirements. And when you got audited, you needed to make sure that you can show that you are adhering to their requirements. So over the past, probably two years with Amazon's government capabilities that those restrictions have changed, but we were always looking to make sure that we owned and managed how we manage the provider and member data, because yes, we did not want to have obviously a breach, but we wanted to make sure we were following the guidelines, whether it's state or federal, and then and even some cases healthcare guidelines around managing that data. So yes, top of mind, making sure that we're protecting, you know, in my case so we had 37 million members, patients, and we needed to make sure that if we did put it in the cloud or if it was on-prem, that it was being protected. And as you mentioned, recently come off of, I was going to say Amazon, but it was an acquisition. That company that was looking at us doing the due diligence, they gave us thumbs up because of how we were managing the data at the lowest point and all the different levels within the architecture. So Anthem who did the acquisition, had a breach back in, I think it was 2015. That was top of mind for them. We had more questions during the due diligence around security than any other functional area. So it is critical, and I think slowly, some of that type of data will get up into the cloud, but again, it's going to go through some massive risk management and security measures, and audits, because how fragile that is. >> Yeah, I mean, that could be a deal breaker in an acquisition. I got two other questions for you. One is, you know, I know you follow the technologies very closely, but there's all the buzz words, the digital transformation, the AI, these new SaaS models that we talked about. You know, a lot of CIOs tell me, look, Dave, get the business right and the technology is the easy part. It's people, it's process. But what are you seeing in terms of some of this new stuff coming out, there's machine learning, you know, obviously massive scale, new cloud workloads. Anything out there that really excites you and that you could see on the horizon that could be, you know, really change agents for the next decade? >> Yeah, I think we did some RPA, robotics on some of the tasks that, you know, where, you know, if the analysis types of situations. So I think RPA is going to be a game changer as it continues to evolve. But I agree with what you just said. Doing this for quite a while now, it still comes down to the people. I can get the technology to do what it needs to do as long as I have the right requirements, so that goes back to people. Making sure we have the partnership that goes back to leadership and the people. And then the change management aspects. Right out of the gate, you should be worrying about how is it going to affect and then the adoption and engagement. Because adoption is critical, because you can go create the best thing you think from a technology perspective, but if it doesn't get used correctly, it's not worth the investment. So I agree, whether it's digital transformation or innovation, it still comes down to understanding the business model and injecting and utilizing technology to grow or reduce costs, grow the business or reduce costs. >> Yeah, usage really means value. Sorry, my last question. What's the one thing that vendors shouldn't do? What's the vendor no-no that'll alienate CIO's? >> To this day, I still don't like, there's a company out there that starts with an O. I still don't like it to that, every single technology module, if you would, has a separate sales rep. I want to work with my strategic partners and have one relationship and that single point of contact that spark and go back into their company and bring me whatever it is that we're looking at so that I don't get, you know, for instance from that company that starts with an O, you know, 17 calls from 17 different sales reps trying to sell me 17 different things. So what irritates me is, you know, you have a company that has a lot of breadth, a lot of, you know, capability and functional, you know that I may want. Give me one person that I can deal with. So a single point of contact, then that makes my life a lot easier. >> Well, Dan Sheehan, I really appreciate you spending some time on theCUBE, it's always a pleasure catching up with you and really appreciate you sharing your insights with our audience. Thank you. >> Oh, thank you, David. I appreciate the opportunity. You have a great day. >> All right. You too. And thank you for watching everybody. This is Dave Vellante for theCUBE on Cloud. Keep it right there. We'll be back with our next guest right after the short break. Awesome, Dan.

>> Go on my lead. >> Dan: All right, very good. >> Five, four. Hello, everyone, and welcome back to the special presentation from theCUBE, where we're exploring the future of cloud and its business impact in the coming decade, kind of where we've come from and where we're going. My name is Dave Vellante, and with me is a CIO/CTO/COO, and longtime colleague, Dan Sheehan. Hello, Dan, how're you doing? >> Hey, Dave, how are you doing? Thank you for having me. >> Yeah, you're very welcome. So folks, Dan has been in the technology industry for a number of years. He's overseen, you know, large-multi, tens of millions of dollar ERP application development efforts, He was a CIO of a marketing, you know, direct mail company. Dan, we met at ADVO, it seems like such a (snickers) long time ago. >> Yeah, that was a long time ago, back in Connecticut. Back in the early 2000s. >> Yeah, ancient days. But pretty serious data for back then, you know, the early 2000s, and then you did a six-year stint as a EVP and CIO at Dunkin' Brands. I remember I came out to see you when I was starting Wikibon and trying to understand. >> Oh yeah. >> You know, what the CIOs cared about. You were so helpful and thanks for that. And that was a big deal. I mean, Dunkin', 17,000 points of distribution. I mean, that was sort of a complicated situation, right? >> Oh yeah. >> So, great experience. >> I mean, when you get involved with franchisees and trying to make everybody happy, yes, that was a lot of fun. >> And then you had a number of other roles, one was as COO at Modell's, and then to fast-forward, Beacon Health. You were EVP and CIO there. And you also, it looked like you had a kind of a business and operational role. You helped the company get acquired by Anthem Blue Cross. So awesome, congrats on that. That must've been a great experience. >> It was. A year of my life, yes. (both laugh) >> You're still standing. So anyway, you can see Dan, he's like this multi-tool star, he's seen a lot of changes in the technology business. So Dan, again, welcome back. Dan Sheehan. >> Oh, thank you. >> So when you started in your career, you know, there was no cloud, right? I mean, you had to do everything. It's funny, I remember I was... You probably know Bill Rucci, CIO of Hartford Steam Boiler. I remember we were talking one day, and this again was pre-cloud and he said, you know, I'm thinking, do I really need to manage my own email? I mean, back then, we did everything. So you had to provision infrastructure so you could write apps, and that was important. That frustrated CFOs, but it was a necessary piece of the value chain. So how have you seen that sort of IT value contribution shift over the years? Let's start there. >> Ah, well, I think it comes down to demand versus capacity. If you look at where companies want to go, they want to do a lot with technology. Technology has taken on a larger role. It's no longer and has not been a, so to speak, cost center. So I think the demand for making change and driving a company forward or reducing costs, there are other executives, peers to the CIO, to the CTO that are looking to do more, and when it comes to doing more, that means more demand, and you step back and you look at what the CIO has for capacity. Looking at Quick Solution's data, solutions in the cloud is appealing, and there are, you know, times where other functions talk to a vendor and see that they can get a vertical solution done pretty quickly. They go off and take that on, or it could be, you know, a ServiceNow capability that you want to implement across the company, and you do that just like an ERP type of roll up. But the bottom line is there are solutions out there that have pushed, I would say the IT organization to look at their capacity versus demand, and sometimes you can get things done quicker with a cloud type of solution. >> So how did you look at that shadow IT as a CIO? Was it something that kind of ticked you off or like you're sort of implying that it made you better? >> Well, I think it does ultimately make you better, but I think you have to partner with the functions because if you don't, you get these types of scenarios, and I've been involved in these just as well. You are busy with, you know, fulfilling your objectives as the leader of IT, and then you get a knock on the door from, let's say marketing or operations, and they say, hey, we just purchased this X solution and we want to integrate it with A, B and C. Well, that was not on the budget or on the IT roadmap or the IT strategy that was linked to the IT, I'm sorry, to the business strategy, and all of a sudden now you have more demand versus the capacity, and then you have to go start reprioritizing. So it's more of, yeah, kind of disrupted, but at the same time, it pushed, you know, the needle of the company forward. But it's all about just working together to make it happen. And that's a lot of, you know, hard conversations when you have to start reprioritizing capacity. >> Well, so let's talk about that alignment. I mean, there's always been a sort of a schism between IT and its ability to deliver, manage demand, and the business will always want you to go faster. They want IT to develop the systems, you know, of course, for less and then they want you to eat the cost of maintaining them, so (chuckles) there's been that tension. But in many ways, that CIO's job is alignment. I mean, it seems to me anyway that schism has certainly narrowed and the cloud's been been part of that, but what do you see as that trajectory over the years and where do you see it going? >> Well, I think it's going to continue to move forward, and depending upon the service, you know, companies are going to take advantage of those services. So yes, some of the non-mission critical capabilities that you would want to move out to the cloud or have somebody else do it, so to speak, that's going to continue to happen because they should be able to do it a lot cheaper than you can, just like use you mentioned a few moments ago about email. I did not want to maintain, you know, exchange service and keeping that all up and running. I moved quickly to Microsoft 365 and that's been a world of difference, but that's just one example. But when you have mission critical apps, you're going to have to make a decision if you want to continue to house them in-house or push them out to an AWS and house them there. So maybe you don't need a large data center and you can utilize some of the best and brightest around security, around managing size of the infrastructure and getting some of their engineering help, which can help. So it just depends upon the application, so to speak, or a function that you're trying to support. And you got to really look at your enterprise architecture and see where that makes sense. So you got to have a hybrid. I see and I have, you know, managed towards a hybrid way of looking at your architecture. >> Okay, so obviously the cloud played a role in that change, and of course, you were in healthcare too so you had to be somewhat careful, >> Yep. >> With the cloud. But you mentioned this hybrid architecture. I mean, from a technologist standpoint and a business standpoint, what do you want out of, you know, you hear a hybrid, multi, all the buzz words. What are you looking for then? Is it a consistent experience? Is it a consistent security? Or is it sort of more horses for courses, where you're trying to run a workload in the right place? What's your philosophy on that? >> Well, I mean, all those things matter, but you're looking at obviously, cost, you're looking at engagement. How does these services engage? Whether it's internal employees or external clients who you're servicing, and you want to get to a cost structure that makes sense in terms of managing those services as well as those mission critical apps. So it comes down to looking at the dollars and cents, as well as what type of services you can provide. In many cases, if you can provide a cheaper and increase the overall services, you're going to go down that path. And just like we did with ServiceNow, I did that at Beacon and also at DentaQuest two healthcare companies. We were able to, you know, remove duplicated, so to speak, ticketing systems and move to one and allow a better experience for the internal employee. They can do self-service, they can look at metrics, they can see status, real-time status on where their request was. So that made a bigger difference. So you engaged the employee differently, better, and then you also reduce your costs. >> Well, how about the economics? I mean, your experience that cloud is cheaper. You hear a lot of the, you know, a lot of the legacy players are saying, oh, no cloud's super expensive. Wait till you get that Amazon bill. (laughs) What's the truth? >> Well, I think there's still a lot of maturing that needs to go on, because unfortunately, depending upon the company, so let's use a couple of examples. So let's look at a startup. You look at a startup, they're probably going to look at all their services being in the cloud and being delivered through a SaaS model, and that's going to be an expense, that's going to be most likely a per user expense per month or per year, however, they structure the contract. And right out of the gate, that's going to be a top line expense that has to be managed going forward. Now you look at companies that have been around for a while, and two of the last companies I worked with, had a lot of technical debt, had on-prem applications. And when you started to look at how to move forward, you know, you had CFOs that were used to going to buy software, capitalize in that software over, you know, five years, sometimes three years, and using that investment to be capitalized, and that would sit below the line, so to speak. Now, don't get me wrong, you still have to pay for it, it's just a matter of where it sits. And when you're running a company and you're looking at the financials, not having that cost on your operational expenses, so to speak, if you're not looking at the depreciation through those numbers, that was advantageous to a CFO many years ago. Now you come to them and say, hey, we're going to move forward with a new HR system, and it's all increasing the expense because there's nothing else to capitalize. Those are different conversations, and all of a sudden your expenses have increased, and yes, you have to make sure that the businesses behind you, with respects to an ROI and supporting it. >> Yeah, so as long as the value is there, and that's a part of the alignment. I want to ask you about cloud pricing strategies because you mentioned ServiceNow, you know, Salesforce is in there, Workday. If you look at the way these guys price, it's really not true cloud pricing in a way, cause they're going to have you sign up for an annual license, you know, a lot of times you got pay up front, or if you want a discount, you're going to have to sign up for two years or three years. But now you see guys like Snowflake coming in, you know, big high-profile IPO. They actually charge you on a consumption-based model. What are your thoughts on that? Do you see that as sort of a trend in the coming decade? >> No, I absolutely think it's going to be on a trend, because consumption means more transactions and more transactions means more computing, and they're going to look at charging it just like any other utility charges. So yes, I see that trend continuing. Did a big deal with UltiPro HR, and yeah, that was all based upon user head count, but they were talking about looking at their payroll and changing their costing on payroll down the road. With their merger, or they went from being a public company to a private company, and now looking to merge with Kronos. I can see where time and attendance and payroll will stop being looked at as a transaction, right? It's a weekly or bi-weekly or monthly, however the company pays, and yes, there is dollars to be made there. >> Well, so let me ask you as a CIO and a business, you know, COO. One of the challenges that you hear with the cloud is okay, if I get my Amazon bill, it's something that Snowflake has talked about, where you know, to me, it's the ideal model, but on the other hand, the transparency is not necessarily there. You don't know what it's going to be at the end of (mumbles) Would you rather have more certainty as to what that bill's going to look like? Or would you rather have it aligned with consumption and the value to the business? >> Well, you know, that's a great question, because yes, I mean, budgets are usually built upon a number that's fixed. Now, no, don't get me wrong. I mean, when I look at the wide area network, the cost for internet services, yes, sometimes we need to increase and that means an increase in the overall cost, but that consumption, that transactional, that's going to be a different way of having to go ahead and budget. You have to budget now for the maximum transactions you anticipate with a growth of a company, and then you need to take a look at that you know, if you're budgeting. I know we were on a calendar fiscal year, so we started up budgeting process in August and we finalized at sometime in the end of October, November for the proceeding year, and if that's the case, you need to get a little bit better on what your consumptions are going to be, because especially if you're a public company, going out on the street with some numbers, those numbers could vary based upon a high transaction volume and the cost, and maybe you're not getting the results on the top end, on the revenue side. So I think, yeah, it's going to be an interesting dilemma as we move forward. >> Yeah. So, I mean, it comes back to alignment, doesn't it? I mean, I know in our small example, you know, we're doing now, we were used to be physical events with theCUBE, now it's all virtual events and our Amazon bill is going through the roof because we're supporting all these users on these virtual events, and our CFO's like, well, look at this Amazon bill, and you say, yeah, but look at the revenue, it's supporting. And so to your point, if the revenue is there, if the ROI is there, then it makes sense. You can kind of live with it because you're growing with it, but if not, then you really got to question it. >> Yeah. So you got to need to partner with your financial folks and come up with better modeling around some of these transactional services and build that into your modeling for your budget and for your, you know, your top line and your expenses. >> So what do you think of some of these SaaS companies? I mean, you've had a lot of experience. They're really coming at it from largely an application perspective, although you've managed a lot of infrastructure too. But we've talked about ServiceNow. They've kind of mopped up in the ITSM. I mean, there's nobody left. I mean, ServiceNow has sort of taken over the whole (mumbles) You know, Salesforce, >> Yeah. >> I guess, sort of similarly, sort of dominating the CRM space. You hear a lot of complaints now about, you know, ServiceNow pricing. There is somebody the other day called them the Oracle of ITSM. Do you see that potentially getting disrupted by maybe some cloud native developers who are developing tools on top? You see in, like, for instance, Datadog going after Splunk and LogRhythm. And there seem to be examples popping up. Well, what's your take on all this? >> No, absolutely. I think cause, you know, when we were talking about back when I first met you, when I was at the ADVO, I mean, Oracle was on it's, you know, rise with their suite of capabilities, and then before you know it, other companies were popping up and took over, whether it was Firstbeat, PeopleSoft, Workday, and then other companies that just came into play, cause it's going to happen because people are going to get, you know, frustrated. And yes, I did get a little frustrated with ServiceNow when I was looking at a couple of new modules because the pricing was a little bit higher than it was when I first started out. So yes, when you're good and you're able to provide the right services, they're going to start pricing it that way. But yes, I think you're going to get smaller players, and then those smaller players will start grabbing up, so to speak, market share and get into it. I mean, look at Salesforce. I mean, there are some pretty good CRMs. I mean, even, ServiceNow is getting into the CRM space big time, as well as a company like Sugar and a few others that will continue to push Salesforce to look at their pricing as well as their services. I mean, they're out there buying up companies, but you just can't automatically assume that they're going to, you know, integrate day one, and it's going to take time for some of their services to come and become reality, so to speak. So yes, I agree that there will be players out there that will push these lager SaaS companies, and hopefully get the right behaviors and right pricing. >> I've said for years, Dan, that I've predicted that ServiceNow and Salesforce are on a collision course. It didn't really happen, but it's starting to, because ServiceNow, the valuation is so huge. They have to grow into other markets much in the same way that Salesforce has. So maybe we'll see McDermott start doing some acquisitions. It's maybe a little tougher for ServiceNow given their whole multi-instance architecture and sort of their own cloud. That's going to be interesting to see how that plays out. >> Yeah. Yeah. You got to play in that type of architecture, let's put it that way. Yes, it'll be interesting to see how that does play out. >> What are your thoughts on the big hyperscalers; Amazon, Microsoft, Google? What's the right strategy there? Do you go all in on one cloud like AWS or are you more worried about lock-in? Do you want to spread your bets across clouds? How real is multi-cloud? Is it a strategy or more sort of a reality that you get M and A and you got shadow IT? What's your take on all that? >> Yeah, that's a great question because it does make you think a little differently around you know, where to put all your eggs. And it's getting tougher because you do want to distribute those eggs out to multiple vendors, if you would, service providers. But, you know, for instance we had a situation where we were building a brand new business intelligence data warehouse, and we decided to go with Microsoft as its core database. And we did a bake-off on business analytic tools. We had like seven of them at Beacon and we ended up choosing Microsoft's Power BI, and a good part of that reason, not all of it, but a good part of it was because we felt they did everything else that the Tableau's and others did, but, you know, Microsoft would work to give, you know, additional capabilities to Power BI if it's sitting on their database. So we had to take that into consideration, and we did and we ended up going with Power BI. With Amazon, I think Amazon's a little bit more, I'll put it horizontal, whereby they can help you out because of the database and just kind of be in that data center, if you would, and be able to move some of your homegrown applications, some of your technical debt over to that, I'll say cloud. But it'll get interesting because when you talk about integration, when you talk about moving forward with a new functionality, yeah, you have to put your architecture in a somewhat of a center point, and then look to see what is easier, cheaper, cost-effective, but, you know, what's happening to my functionality over the next three to five years. >> But it sounds like you'd subscribe to a horses for courses approach, where you put the right workload in the right cloud, as opposed to saying, I'm going to go all in on one cloud and it's going to be, you know, same skillset, same security, et cetera. It sounds like you'd lean toward the former versus going all in with, you know, MANO cloud. >> Yeah, I guess again, when I look at the architecture. There will be major, you know, breaks if you would. So yes, there is somewhat of a, you know, movement to you know, go with one horse. But, you know, I could see looking back at the Beacon architecture that we could, you know, lift and put the claims adjudication capabilities up in Amazon and then have that conduct, you know, the left to right claims processing, and then those transactions could then be moved into Microsoft's data warehouse. So, you know, there is ways to go about spreading it out so that you don't have all those eggs in one basket and that you reduce the amount of risk, but that weighed heavily on my mind. >> So I was going to ask you, how much of a factor lock-in is it? It sounds like it's more, you know, spreading your eggs around, as you say and reducing your risk as opposed to, you know, worried about lock-in, but as a CIO, how worried are you about lock-in? Where is that fit in the sort of decision tree? >> Ah, I mean, I would say it's up there, but unfortunately, there's no number one, there's like five number ones, if you would. So it's definitely up there and it's something to consider when you're looking at, like you said, the cost, risk integration, and then time. You know, sometimes you're up against the time. And again, security, like I said. Security is a big key in healthcare. And actually security overall, whether you're retail, you're going to always have situations no matter what industry, you got to protect the business. >> Yeah, so I want to ask you about security. That's the other number one. Well, you might've been a defacto CSO, but kind of when we started in this business security was the problem of the security teams, and you know, it's now a team sport. But in thinking about the cloud and security, how big of a concern is the cloud? Is it just more, you're looking for consistency and be able to apply the corporate edicts? Are there other concerns like the shared responsibility model? What are your thoughts on security in the cloud? >> Well, it probably goes back to again, the industry, but when I looked at the past five years in healthcare, doing a lot of work with the CMS and Medicaid, Medicare, they had certain requirements and certain restrictions. So we had to make sure that we follow those requirements. And when you got audited, you needed to make sure that you can show that you are adhering to their requirements. So over the past, probably two years with Amazon's government capabilities that those restrictions have changed, but we were always looking to make sure that we owned and managed how we manage the provider and member data, because yes, we did not want to have obviously a breach, but we wanted to make sure we were following the guidelines, whether it's state or federal, and then and even some cases healthcare guidelines around managing that data. So yes, top of mind, making sure that we're protecting, you know, in my case so we had 37 million members, patients, and we needed to make sure that if we did put it in the cloud or if it was on-prem, that it was being protected. And as you mentioned, recently come off of, I was going to say Amazon, but it was an acquisition. That company that was looking at us doing the due diligence, they gave us thumbs up because of how we were managing the data at the lowest point and all the different levels within the architecture. So Anthem who did the acquisition, had a breach back in, I think it was 2015. That was top of mind for them. We had more questions during the due diligence around security than any other functional area. So it is critical, and I think slowly, some of that type of data will get up into the cloud, but again, it's going to go through some massive risk management and security measures, and audits, because how fragile that is. >> Yeah, I mean, that could be a deal breaker in an acquisition. I got two other questions for you. One is, you know, I know you follow the technologies very closely, but there's all the buzz words, the digital transformation, the AI, these new SaaS models that we talked about. You know, a lot of CIOs tell me, look, Dave, get the business right and the technology is the easy part. It's people, it's process. But what are you seeing in terms of some of this new stuff coming out, there's machine learning, you know, obviously massive scale, new cloud workloads. Anything out there that really excites you and that you could see on the horizon that could be, you know, really change agents for the next decade? >> Yeah, I think we did some RPA, robotics on some of the tasks that, you know, where, you know, if the analysis types of situations. So I think RPA is going to be a game changer as it continues to evolve. But I agree with what you just said. Doing this for quite a while now, it still comes down to the people. I can get the technology to do what it needs to do as long as I have the right requirements, so that goes back to people. Making sure we have the partnership that goes back to leadership and the people. And then the change management aspects. Right out of the gate, you should be worrying about how is it going to affect and then the adoption and engagement. Because adoption is critical, because you can go create the best thing you think from a technology perspective, but if it doesn't get used correctly, it's not worth the investment. So I agree, whether it's digital transformation or innovation, it still comes down to understanding the business model and injecting and utilizing technology to grow or reduce costs, grow the business or reduce costs. >> Yeah, usage really means value. Sorry, my last question. What's the one thing that vendors shouldn't do? What's the vendor no-no that'll alienate CIO's? >> To this day, I still don't like, there's a company out there that starts with an O. I still don't like it to that, every single technology module, if you would, has a separate sales rep. I want to work with my strategic partners and have one relationship and that single point of contact that spark and go back into their company and bring me whatever it is that we're looking at so that I don't get, you know, for instance from that company that starts with an O, you know, 17 calls from 17 different sales reps trying to sell me 17 different things. So what irritates me is, you know, you have a company that has a lot of breadth, a lot of, you know, capability and functional, you know that I may want. Give me one person that I can deal with. So a single point of contact, then that makes my life a lot easier. >> Well, Dan Sheehan, I really appreciate you spending some time on theCUBE, it's always a pleasure catching up with you and really appreciate you sharing your insights with our audience. Thank you. >> Oh, thank you, David. I appreciate the opportunity. You have a great day. >> All right. You too. And thank you for watching everybody. This is Dave Vellante for theCUBE on Cloud. Keep it right there. We'll be back with our next guest right after the short break. Awesome, Dan.

>> Narrator: From around the globe it's theCUBE, with digital coverage of AWS re:Invent 2020. Special coverage sponsored by AWS Global Partner Network. >> Hello and welcome to theCUBE Virtual and our coverage of AWS re:Invent 2020 with special coverage of APN partner experience. We are theCUBE Virtual and I'm your host, Justin Warren. And today I'm joined by Ed Walsh, CEO of ChaosSearch. Ed, welcome to theCUBE. >> Well thank you for having me, I really appreciate it. >> Now, this is not your first time here on theCUBE. You're a regular here and I've loved it to have you back. >> I love the platform you guys are great. >> So let's start off by just reminding people about what ChaosSearch is and what do you do there? >> Sure, the best way to say is so ChaosSearch helps our clients know better. We don't do that by a special wizard or a widget that you give to your, you know, SecOp teams. What we do is a hard work to give you a data platform to get insights at scale. And we do that also by achieving the promise of data lakes. So what we have is a Chaos data platform, connects and indexes data in a customer's S3 or glacier accounts. So inside your data lake, not our data lake but renders that data fully searchable and available for analysis using your existing tools today 'cause what we do is index it and publish open API, it's like API like Elasticsearch API, and soon SQL. So give you an example. So based upon those capabilities were an ideal replacement for a commonly deployed, either Elasticsearch or ELK Stack deployments, if you're hitting scale issues. So we talk about scalable log analytics, and more and more people are hitting these scale issues. So let's say if you're using Elasticsearch ELK or Amazon Elasticsearch, and you're hitting scale issues, what I mean by that is like, you can't keep enough retention. You want longer retention, or it's getting very expensive to keep that retention, or because the scale you hit where you have availability, where the cluster is hard to keep up running or is crashing. That's what we mean by the issues at scale. And what we do is simply we allow you, because we're publishing the open API of Elasticsearch use all your tools, but we save you about 80% off your monthly bill. We also give you an, and it's an and statement and give you unlimited retention. And as much as you want to keep on S3 or into Glacier but we also take care of all the hassles and management and the time to manage these clusters, which ends up being on a database server called leucine. And we take care of that as a managed service. And probably the biggest thing is all of this without changing anything your end users are using. So we include Kibana, but imagine it's an Elastic API. So if you're using API or Kibana, it's just easy to use the exact same tools used today, but you get the benefits of a true data lake. In fact, we're running now Elasticsearch on top of S3 natively. If that makes it sense. >> Right and natively is pretty cool. And look, 80% savings, is a dramatic number, particularly this year. I think there's a lot of people who are looking to save a few quid. So it'd be very nice to be able to save up to 80%. I am curious as to how you're able to achieve that kind of saving though. >> Yeah, you won't be the first person to ask me that. So listen, Elastic came around, it was, you know we had Splunk and we also have a lot of Splunk clients, but Elastic was a more cost effective solution open source to go after it. But what happens is, especially at scale, if it's fall it's actually very cost-effective. But underneath last six tech ELK Stack is a leucine database, it's a database technology. And that sits on our servers that are heavy memory count CPU count in and SSDs. So you can do on-prem or even in the clouds, so if you do an Amazon, basically you're spinning up a server and it stays up, it doesn't spin up, spin down. So those clusters are not one server, it's a cluster of those servers. And typically if you have any scale you're actually having multiple clusters because you don't dare put it on one, for different use cases. So our savings are actually you no longer need those servers to spin up and you don't need to pay for those seen underneath. You can still use Kibana under API but literally it's $80 off your bill that you're paying for your service now, and it's hard dollars. So it's not... And we typically see clients between 70 and 80%. It's up to 80, but it's literally right within a 10% margin that you're saving a lot of money, but more importantly, saving money is a great thing. But now you have one unified data lake that you can have. You used to go across some of the data or all the data through the role-based access. You can give different people. Like we've seen people who say, hey give that, help that person 40 days of this data. But the SecOp up team gets to see across all the different law. You know, all the machine generated data they have. And we can give you a couple of examples of that and walk you through how people deploy if you want. >> I'm always keen to hear specific examples of how customers are doing things. And it's nice that you've thought of drawn that comparison there around what what cloud is good for and what it isn't is. I'll often like to say that AWS is cheap to fail in, but expensive to succeed. So when people are actually succeeding with this and using this, this broad amount of data so what you're saying there with that savings I've actually got access to a lot more data that I can do things with. So yeah, if you could walk through a couple of examples of what people are doing with this increased amount of data that they have access to in EKL Search, what are some of the things that people are now able to unlock with that data? >> Well, literally it's always good for a customer size so we can go through and we go through it however it might want, Kleiner, Blackboard, Alert Logic, Armor Security, HubSpot. Maybe I'll start with HubSpot. One of our good clients, they were doing some Cloud Flare data that was one of their clusters they were using a lot to search for. But they were looking at to look at a denial service. And they were, we find everyone kind of at scale, they get limited. So they were down to five days retention. Why? Well, it's not that they meant to but basically they couldn't cost-effectively handle that in the scale. And also they're having scale issues with the environment, how they set the cluster and sharding. And when they also denial service tech, what happened that's when the influx of data that is one thing about scale is how fast it comes out, yet another one is how much data you have. But this is as the data was coming after them at denial service, that's when the cluster would actually go down believe it or not, you know right. When you need your log analysis tools. So what we did is because they're just using Kibana, it was easy swap. They ran in parallel because we published the open API but we took them from five days to nine days. They could keep as much as they want but nine days for denial services is what they wanted. And then we did save them in over $4 million a year in hard dollars, What they're paying in their environment from really is the savings on the server farm and a little bit on the Elasticsearch Stack. But more importantly, they had no outages since. Now here's the thing. Are you talking about the use case? They also had other clusters and you find everyone does it. They don't dare put it on one cluster, even though these are not one server, they're multiple servers. So the next use case for CloudFlare was one, the next QS and it was a 10 terabyte a day influx kept it for 90 days. So it's about a petabyte. They brought another use case on which was NetMon, again, Network Monitoring. And again, I'm having the same scale issue, retention area. And what they're able to do is easily roll that on. So that's one data platform. Now they're adding the next one. They have about four different use cases and it's just different clusters able to bring together. But now what they're able to do give you use cases either they getting more cost effective, more stability and freedom. We say saves you a lot of time, cost and complexity. Just the time they manage that get the data in the complexities around it. And then the cost is easy to kind of quantify but they've got better but more importantly now for particular teams they only need their access to one data but the SecOP team wants to see across all the data. And it's very easy for them to see across all the data where before it was impossible to do. So now they have multiple large use cases streaming at them. And what I love about that particular case is at one point they were just trying to test our scale. So they started tossing more things at it, right. To see if they could kind of break us. So they spiked us up to 30 terabytes a day which is for Elastic would even 10 terabytes a day makes things fall over. Now, if you think of what they just did, what were doing is literally three steps, put your data in S3 and as fast as you can, don't modify, just put it there. Once it's there three steps connect to us, you give us readability access to those buckets and a place to write the indexy. All of that stuff is in your S3, it never comes out. And then basically you set up, do you want to do live or do you want to do real time analysis? Or do you want to go after old data? We do the rest, we ingest, we normalize the schema. And basically we give you our back and the refinery to give the right people access. So what they did is they basically throw a whole bunch of stuff at it. They were trying to outrun S3. So, you know, we're on shoulders of giants. You know, if you think about our platform for clients what's a better dental like than S3. You're not going to get a better cross curve, right? You're not going to get a better parallelism. And so, or security it's in your, you know a virtual environment. But if you... And also you can keep data in the right location. So Blackboard's a good example. They need to keep that in all the different regions and because it's personal data, they, you know, GDPR they got to keep data in that location. It's easy, we just put compute in each one of the different areas they are. But the net net is if you think that architecture is shoulders of giants if you think you can outrun by just sheer volume or you can put in more cost-effective place to keep long-term or you think you can out store you have so much data that S3 and glacier can't possibly do it. Then you got me at your bigger scale at me but that's the scale we'r&e talking about. So if you think about the spiked our throughput what they really did is they try to outrun S3. And we didn't pick up. Now, the next thing is they tossed a bunch of users at us which were just spinning up in our data fabric different ways to do the indexing, to keep up with it. And new use cases in case they're going after everyone gets their own worker nodes which are all expected to fail in place. So again, they did some of that but really they're like you guys handled all the influx. And if you think about it, it's the shoulders of giants being on top of an Amazon platform, which is amazing. You're not going to get a more cost effective data lake in the world, and it's continuing to fall in price. And it's a cost curve, like no other, but also all that resiliency, all that security and the parallelism you can get, out of an S3 Glacier is just a bar none is the most scalable environment, you can build an environment. And what we do is a thin layer. It's a data platform that allows you to have your data now fully searchable and queryable using your tools >> Right and you, you mentioned there that, I mean you're running in AWS, which has broad experience in doing these sorts of things at scale but on that operational management side of things. As you mentioned, you actually take that off, off the hands of customers so that you run it on their behalf. What are some of the areas that you see people making in trying to do this themselves, when you've gone into customers, and brought it into the EKL Search platform? >> Yeah, so either people are just trying their best to build out clusters of Elasticsearch or they're going to services like Logz.io, Sumo Logic or Amazon Elasticsearch services. And those are all basically on the same ELK Stack. So they have the exact same limits as the same bits. Then we see people trying to say, well I really want to go to a data lake. I want to get away from these database servers and which have their limits. I want to use a data Lake. And then we see a lot of people putting data into environments before they, instead of using Elasticsearch, they want to use SQL type tools. And what they do is they put it into a Parquet or Presto form. It's a Presto dialect, but it into Parquet and structure it. And they go a lot of other way to, Hey it's in the data lake, but they end up building these little islands inside their data lake. And it's a lot of time to transform the data, to get it in a format that you can go after our tools. And then what we do is we don't make you do that. Just literally put the data there. And then what we do is we do the index and a polish API. So right now it's Elasticsearch in a very short time we'll publish Presto or the SQL dialect. You can use the same tool. So we do see people, either brute forcing and trying their best with a bunch of physical servers. We do see another group that says, you know, I want to go use an Athena use cases, or I want to use a there's a whole bunch of different startups saying, I do data lake or data lake houses. But they are, what they really do is force you to put things in the structure before you get insight. True data lake economics is literally just put it there, and use your tools natively to go after it. And that's where we're unique compared to what we see from our competition. >> Hmm, so with people who have moved into ChaosSearch, what's, let's say pick one, if you can, the most interesting example of what people have started to do with, with their data. What's new? >> That's good. Well, I'll give you another one. And so Armor Security is a good one. So Armor Security is a security service company. You know, thousands of clients doing great I mean a beautiful platform, beautiful business. And they won Rackspace as a partner. So now imagine thousand clients, but now, you know massive scale that to keep up with. So that would be an example but another example where we were able to come in and they were facing a major upgrade of their environment just to keep up, and they expose actually to their customers is how their customers do logging analytics. What we're able to do is literally simply because they didn't go below the API they use the exact same tools that are on top and in 30 days replaced that use case, save them tremendous amount of dollars. But now they're able to go back and have unlimited retention. They used to restrict their clients to 14 days. Now they have an opportunity to do a bunch of different things, and possible revenue opportunities and other. But allow them to look at their business differently and free up their team to do other things. And now they're, they're putting billing and other things into the same environment with us because one is easy it's scale but also freed up their team. No one has enough team to do things. And then the biggest thing is what people do interesting with our product is actually in their own tools. So, you know, we talk about Kibana when we do SQL again we talk about Looker and Tableau and Power BI, you know, the really interesting thing, and we think we did the hard work on the data layer which you can say is, you know I can about all the ways you consolidate the performance. Now, what becomes really interesting is what they're doing at the visibility level, either Kibana or the API or Tableau or Looker. And the key thing for us is we just say, just use the tools you're used to. Now that might be a boring statement, but to me, a great value proposition is not changing what your end users have to use. And they're doing amazing things. They're doing the exact same things they did before. They're just doing it with more data at bigger scale. And also they're able to see across their different machine learning data compared to being limited going at one thing at a time. And that getting the correlation from a unified data lake is really what we, you know we get very excited about. What's most exciting to our clients is they don't have to tell the users they have to use a different tool, which, you know, we'll decide if that's really interesting in this conversation. But again, I always say we didn't build a new algorithm that you going to give the SecOp team or a new pipeline cool widget that going to help the machine learning team which is another API we'll publish. But basically what we do is a hard work of making the data platform scalable, but more importantly give you the APIs that you're used to. So it's the platform that you don't have to change what your end users are doing, which is a... So we're kind of invisible behind the scenes. >> Well, that's certainly a pretty strong proposition there and I'm sure that there's plenty of scope for customers to come and and talk to you because no one's creating any less data. So Ed, thanks for coming out of theCUBE. It's always great to see you here. >> Know, thank you. >> You've been watching theCUBE Virtual and our coverage of AWS re:Invent 2020 with special coverage of APN partner experience. Make sure you check out all our coverage online, either on your desktop, mobile on your phone, wherever you are. I've been your host, Justin Warren. And I look forward to seeing you again soon. (soft music)

>> We're here at the Data Cloud Summit 2020, tracking the rise of the data cloud. And we're talking about the ecosystem powering the next generation of innovation in cloud, you know, for decades, the technology industry has been powered by great products. Well, the cloud introduced a new type of platform that transcended point products and the next generation of cloud platforms is unlocking data-centric ecosystems where access to data is at the core of innovation, tapping the resources of many versus the capabilities of one. Casey McGee is here. He's the vice president of global ISV sales at Microsoft, and he's joined by Colleen Kapase, who is the VP of partnerships and global alliances at Snowflake. Folks, welcome to theCUBE. It's great to see you. >> Thanks Dave, good to see you. Thank you. >> Thanks for having us here. >> You're very welcome. So, Casey, let me start with you please. You know, Microsoft's got a long heritage, of course, working with partners, you're renowned in that regard, built a unbelievable ecosystem, the envy of many in the industry. So if you think about as enterprises, they're speeding up their cloud adoption, what are you seeing as the role and the importance of ecosystem, the ISV ecosystem specifically, in helping make customers' outcomes successful? >> Yeah, let me start by saying we have a 45 year history of partnership, so from our very beginning as a company, we invested to build these partnerships. And so let me start by saying from day one, we looked at a diverse ecosystem as one of the most important strategies for us, both to bring innovation to customers and also to drive growth. And so we're looking to build that environment even today. So 45 years later, focused on how do we zero in on the business outcomes that matter most to customers, usually identified by the industry that they're serving. So really building an ecosystem that helps us serve both the customers and the business outcomes they're looking to drive. And so we're building that ecosystem of ISVs on the Microsoft cloud and focused on bringing that innovation as a platform provider through those companies. >> So Casey, let's stay on that for a moment, if we can. I mean, you work with a lot of ISVs and you got a big portfolio of your own solutions. Now, sometimes they overlap with the ISV offerings of your partners. How do you balance the focus on first party solutions and third-party ISV partner solutions? >> Yeah, first and foremost, we're a platform company. So our whole intent is to bring value to that partner ecosystem. Well, sometimes that means we may have offers in market that may compliment one another. Our focus is really on serving the customer. So anytime we see that, we're looking at what is the most desired outcome for our customer, driving innovation into that specific business requirement. So for us, it's always focusing on the customer, and really zeroing in on making sure that we're solving their business problems. Sometimes we do that together with partners like Snowflake. Sometimes that means we do that on our own, but the key for us is really deeply understanding what's important to the customer and then bringing the best of the Microsoft and Snowflake scenarios to bear. >> You know, Casey, I appreciate that. A lot times people say "Dave, don't ask me that question. It's kind of uncomfortable." So Colleen, I want to bring you into the discussion. How does Snowflake view this dynamic, where you're simultaneously partnering and competing sometimes with some of the big cloud companies on the planet? >> Yeah, Dave, I think it's a great question, and really in this era of innovation, so many large companies like Microsoft are so diverse in their product set, it's almost impossible for them to not have some overlap with most of their ecosystem. But I think Casey said it really well, as long as we stay laser focused on the customer, and there are a lot of very happy Snowflake customers and happy Azure customers, we really win together. And I think we're finding ways in which we're working better and better together, from a technology standpoint, and from a field standpoint. And customers want to see us come together and bring best of breed solutions. So I think we're doing a lot better, and I'm looking forward to our future, too. >> So Casey, Snowflake, you know, they're really growing, they've got a pretty large footprint on Azure. You're talking hundreds of customers here that are active on that platform. I wonder if you could talk about the product integration points that you kind of completed initially, and then kind of what's on the horizon that you see as particularly important for your joint customers? >> You have to say, so one of the things that I love about this partnership is that, well, we start with what the customer wants. We bring that back into the engineering-level relationship that we have between the two companies. And so that's produced some pretty incredibly rich functionality together. So let me start by saying, you know, we've got eight Azure regions today with nine coming on soon. And so we have a geographic diversity that is important for many of our customers. We've also got a series of engineering-level integrations that we've already built. So that's functionality for Azure Private Link, as well as integration between Power BI, Azure Data Factory, and Azure Data Lake, all of this back again to serve the business outcomes that are required for our customers. So it's this level of integration that I think really speaks to the power of the partnership. So we are intently focused on the democratization of data. So we know that Snowflake is the premier partner to help us do that. So getting that right is key to enabling high concurrency use cases with large numbers of businesses, users coming together, and getting the performance they expect. >> Yeah, I appreciate that Casey, because a lot of times I'll, you know, I'll look at the press release. Sometimes we laugh, we call them Barney deals. You know, "I love you. You love me." But I listen for the word engineering and integration. Those are sort of important triggers. Colleen, or Casey too, but I want to start with Colleen. I mean, anything you would add to that, are there things that you guys have worked on together that you're particularly proud of, or maybe that have pushed the envelope and enabled new capabilities for customers where they've given you great feedback? Any examples you can share? >> Great question. And we're definitely focusing on making sure stability is a core value for both of us, so that what we offer, that our customers can trust, is going to work well and be dependable, so that's a key focus for us. We're also looking at how can we advance into the future, what can we do around machine learning, it's an area that's really exciting for a lot of the CXO-level leadership at our customers, so we're certainly focused on that. And also looking at Power BI and the visualization of how do we bring these solutions together as well. I'd also say at the same time, we're trying to make the buying experience frictionless for our customers, so we're also leveraging and innovating with Azure's Marketplace, so that our customers can easily acquire Snowflake together with Azure. And even that is being helpful for our customers. Casey, what are your thoughts, too? >> Yeah, let me add to that. I think the work that we've done with Power BI is pretty, pretty powerful. I mean, ultimately, we've got customers out there that are looking to better visualize the data, better inform decisions that they're making. So as much as AI and ML and the inherent power of the data that's being stored within Snowflake is important in and of itself, Power BI really unlocks that and helps drive better decisions, better visualization, and help drive to decision outcomes that are important to the customer. So I love the work that we're doing on Power BI and Snowflake. >> Yeah, and you guys both mentioned, you know, machine learning. I mean, they really are an ecosystem of tools. And the thing to me about Azure, it's all about optionality. You mentioned earlier, Casey, you guys are a platform. So, you know, customer A may want to use Power BI. Another customer might want to use another visualization tool, fine, from a platform perspective, you really don't care, do you? So I wonder Colleen, if we could, and again, maybe Casey can chime in afterwards. You guys, obviously everybody these days, but you in particular, you're focused on customer outcomes. That's the sort of starting point, and Snowflake for sure has built pretty significant experience working with large enterprises and working alongside of Microsoft to get other partners. In your experience, what are customers really looking for out of the two joint companies when they engage with Snowflake and Microsoft, so that one plus one is, you know, much bigger than two. Maybe Colleen, you could start. >> Yeah, I definitely think that what our customers are looking for is both trust and seamlessness. They just want the technology to work. The beauty of Snowflake is our ease of use. So many customers have questions about their business, more so now in this pandemic world than ever before. So the seamlessness, the ease of use, the frictionless, all of these things really matter to our joint customers, and seeing our teams come together, too, in the field, to show here's how Snowflake and Azure are better together, in your local area, and having examples of customers where we've had win-wins, which I'd say Casey, we're getting more and more of those every day, frankly, so it's pretty exciting times. And having our sales teams work as a partnership, even though we compete, we know where we play well together, and I see us doing that over and over again, more and more, around the world, too, which is really important as Snowflake pushes forward, beyond the North America geographies into stronger and stronger in the global regions, where frankly, Microsoft's had a long, storied history at. That's very exciting, especially in Europe and Asia. >> Casey, anything you'd add to that? >> Yeah. Colleen, it's well said. I think ultimately, what customers are looking for is that when our two companies come together, we bring new innovation, new ideas, new ways to solve old problems. And so I think what I love about this partnership is ultimately when we come together, whether it's engineering teams coming together to build new product, whether it's our sales and marketing teams out in front of the customers, across that spectrum, I think customers are looking for us to help bring new ideas. And I love the fact that we've engineered this partnership to do just that. And ultimately we're focused on how do we come together and build something new and different. And I think we can solve some of the most challenging problems with the power of the data and the innovation that we're bringing to the table. >> I mean, you know, Casey, I mean, everybody's really quite in awe and amazed at Microsoft's transformation, and really openness and willingness to really, change and lean into some of the big waves. I wonder if you could talk about your multi-platform strategy and what problems that you're solving in conjunction with Snowflake. >> Yeah, let me start by saying, you know, I think as much as we appreciate that feedback on the progress that we've been striving for, I mean, we're still learning every day, looking for new opportunities to learn from customers, from partners, and so a lot of what you see on the outside is the result of a really focused culture, really focusing on what's important to our customers, focusing on how do we build diversity and inclusion to everything we do, whether that's within Microsoft, with our partners, our customers, and ultimately, how do we show up as one Microsoft, I call one Microsoft kind of the partner's gift. It's ultimately how do our companies show up together? So I think if you look multi-platform, we have the same concept, right? We have the Microsoft cloud that we're offering out in the marketplace. The Microsoft cloud consists of what we're serving up as far as the platform, consists of what we're serving up for data and AI, modern workplace and business applications. And so this multi-cloud strategy for us is really focused on how do we bring innovation across each of the solution areas that matter most to customers. And so I see really the power of the Snowflake partnership playing in there. >> Awesome. Colleen, are there any examples you can share where, maybe this partnership has unlocked the customer opportunity or unique value? >> Yeah, I can't speak about the customer-specific, but what I can do and say is, Casey and I play very corporate roles in terms of we're thinking about the long-term partnership, we're driving the strategy. But hey, look, we'll get called in, we're working a deal right now, it's almost close of the quarter for us, we're literally working on an opportunity right now, how can we win together, how can we be competitive, the customers, the CIO has asked us to come together, to work on that solution. Very large, well-known brand. And we're able to get up to the very senior levels of our companies very quickly to make decisions on what do we need to do to be better and stronger together. And that's really what a partnership is about, you can do the long-term plans and the strategics and you can have great products, but when your executives can pick up the phone and call each other to work on a particular deal, for a particular customer's need, I think that's where the power of the partnership really comes together, and that's where we're at. And that's been a growth opportunity for us this year, is, wasn't necessarily where we were at, and I really have to thank Casey for that. He's done a ton, getting us the right glue between our executives, making sure the relationships are there, and making sure the trust is there, so when our customers need us to come together, that dialogue and that shared diction of putting customers first is there between both companies. So thank you, Casey. >> Oh, thanks, Colleen, the feeling's mutual. >> Well, I think this is key because as I said up front, we've gone from sort of very product-focused to platform-focused. And now we're tapping the power of the ecosystem. That's not always easy to get all the parts moving together, but we live in this API economy. You could say "Hey, I'm a company, everything's going to be homogeneous. Everything is going to be my stack." And maybe that's one way to solve the problem, but really that's not how customers want to solve the problem. Casey, I'll give you the last word. >> Yeah, let me just end by saying, you know, first off the cultures between our two companies couldn't be more well aligned. So I think ultimately when you ask yourself the question, "What do we do to best show up in front of our customers?" It is, focus on their business outcomes, focus on the things that matter most to them. And this partnership will show up well. And I think ultimately our greatest opportunity is to tap into that need, to that interest. And I couldn't be happier about the partnership and the fact that we are so well aligned. So thank you for that. >> Well guys, thanks very much for coming on theCUBE and unpacking some of the really critical aspects of the ecosystem. It was really a pleasure having you. >> Thank you so much for having us. >> Okay, and thank you for watching. Keep it right there. We've got more great content coming your way at the Data Cloud Summit.

>> Voice Over: Data is at the heart of transformation, and the change every company needs to succeed. But it takes more than new technology. It's about teams, talent and cultural change. Empowering everyone on the front lines to make decisions, all at the speed of digital. The transformation starts with you, it's time to lead the way, it's time for thought leaders. (soft upbeat music) >> Welcome to Thought.Leaders a digital event brought to you by ThoughtSpot, my name is Dave Vellante. The purpose of this day is to bring industry leaders and experts together to really try and understand the important issues around digital transformation. We have an amazing lineup of speakers, and our goal is to provide you with some best practices that you can bring back and apply to your organization. Look, data is plentiful, but insights are not, ThoughtSpot is disrupting analytics, by using search and machine intelligence to simplify data analysis and really empower anyone with fast access to relevant data. But in the last 150 days, we've had more questions than answers. Creating an organization that puts data and insights at their core, requires not only modern technology but leadership, a mindset and a culture, that people often refer to as data-driven. What does that mean? How can we equip our teams with data and fast access to quality information that can turn insights into action? And today we're going to hear from experienced leaders who are transforming their organizations with data, insights, and creating digital first cultures. But before we introduce our speakers, I'm joined today by two of my co-hosts from ThoughtSpot. First, chief data strategy officer of the ThoughtSpot is Cindi Howson, Cindi is an analytics and BI expert with 20 plus years experience, and the author of Successful Business Intelligence: Unlock the Value of BI & Big Data. Cindi was previously the lead analyst at Gartner for the data and analytics Magic Quadrant. In early last year, she joined ThoughtSpot to help CEOs and their teams understand how best to leverage analytics and AI for digital transformation. Cindi great to see you, welcome to the show. >> Thank you Dave, nice to join you virtually. >> Now our second cohost and friend of theCUBE is ThoughtSpot CEO Sudheesh Nair Hello Sudheesh, how are you doing today? >> I'm well, good to talk to you again. >> That's great to see you, thanks so much for being here. Now Sudheesh, please share with us why this discussion is so important to your customers and of course to our audience, and what they're going to learn today. (upbeat music) >> Thanks Dave, I wish you were there to introduce me into every room that I walk into because you have such an amazing way of doing it. It makes me feel also good. Look, since we have all been you know, cooped up in our homes, I know that the vendors like us, we have amped up our sort of effort to reach out to you with, invites for events like this. So we are getting very more invites for events like this than ever before. So when we started planning for this, we had three clear goals that we wanted to accomplish. And our first one, that when you finish this and walk away, we want to make sure that you don't feel like it was a waste of time, we want to make sure that we value your time, then this is going to be used. Number two, we want to put you in touch with industry leaders and thought leaders, generally good people, that you want to hang around with long after this event is over. And number three, as we plan through this, you know we are living through these difficult times we want this event to be more of an uplifting and inspiring event too. Now, the challenge is how do you do that with the team being change agents, because teens and as much as we romanticize it, it is not one of those uplifting things that everyone wants to do or likes to do. The way I think of it, changes sort of like, if you've ever done bungee jumping, and it's like standing on the edges, waiting to make that one more step you know, all you have to do is take that one step and gravity will do the rest, but that is the hardest step today. Change requires a lot of courage, and when we are talking about data and analytics, which is already like such a hard topic not necessarily an uplifting and positive conversation most businesses, it is somewhat scary, change becomes all the more difficult. Ultimately change requires courage, courage to first of all, challenge the status quo. People sometimes are afraid to challenge the status quo because they are thinking that you know, maybe I don't have the power to make the change that the company needs, sometimes they feel like I don't have the skills, sometimes they may feel that I'm probably not the right person to do it. Or sometimes the lack of courage manifest itself as the inability to sort of break the silos that are formed within the organizations when it comes to data and insights that you talked about. You know, that are people in the company who are going to have the data because they know how to manage the data, how to inquire and extract, they know how to speak data, they have the skills to do that. But they are not the group of people who have sort of the knowledge, the experience of the business to ask the right questions off the data. So there is the silo of people with the answers, and there is a silo of people with the questions, and there is gap, this sort of silos are standing in the way of making that necessary change that we all know the business needs. And the last change to sort of bring an external force sometimes. It could be a tool, it could be a platform, it could be a person, it could be a process but sometimes no matter how big the company is or how small the company is you may need to bring some external stimuli to start the domino of the positive changes that are necessary. The group of people that we are brought in, the four people, including Cindi that you will hear from today are really good at practically telling you how to make that step, how to step off that edge, how to dress the rope, that you will be safe and you're going to have fun, you will have that exhilarating feeling of jumping for a bungee jump, all four of them are exceptional, but my owner is to introduce Michelle. And she's our first speaker, Michelle I am very happy after watching our presentation and reading your bio that there are no country vital worldwide competition for cool parents, because she will beat all of us. Because when her children were small, they were probably into Harry Potter and Disney and she was managing a business and leading change there. And then as her kids grew up and got to that age where they like football and NFL, guess what? She's the CIO of NFL, what a cool mom. I am extremely excited to see what she's going to talk about. I've seen this slides, a bunch of amazing pictures, I'm looking to see the context behind it, I'm very thrilled to make that client so far, Michelle, I'm looking forward to her talk next. Welcome Michelle, it's over to you. (soft upbeat music) >> I'm delighted to be with you all today to talk about thought leadership. And I'm so excited that you asked me to join you because today I get to be a quarterback. I always wanted to be one, and I thought this is about as close as I'm ever going to get. So I want to talk to you about quarterbacking our digital revolution using insights data, and of course as you said, leadership. First a little bit about myself, a little background as I said, I always wanted to play football, and this is something that I wanted to do since I was a child, but when I grew up, girls didn't get to play football. I'm so happy that that's changing and girls are now doing all kinds of things that they didn't get to do before. Just this past weekend on an NFL field, we had a female coach on two sidelines, and a female official on the field. I'm a lifelong fan and student of the game of football, I grew up in the South, you can tell from the accent and in the South is like a religion and you pick sides. I chose Auburn University working in the Athletic Department, so I'm testament to you can start the journey can be long it took me many, many years to make it into professional sports. I graduated in 1987 and my little brother, well, not actually not so little, he played offensive line for the Alabama Crimson Tide. And for those of you who know SEC football you know, this is a really big rivalry. And when you choose sides, your family is divided, so it's kind of fun for me to always tell the story that my dad knew his kid would make it to the NFL he just bet on the wrong one. My career has been about bringing people together for memorable moments at some of America's most iconic brands. Delivering memories and amazing experiences that delight from Universal Studios, Disney to my current position as CIO of the NFL. In this job I'm very privileged to have the opportunity to work with the team, that gets to bring America's game to millions of people around the world. Often I'm asked to talk about how to create amazing experiences for fans, guests, or customers. But today I really wanted to focus on something different and talk to you about being behind the scenes and backstage. Because behind every event every game, every awesome moment is execution, precise repeatable execution. And most of my career has been behind the scenes, doing just that, assembling teams to execute these plans, and the key way that companies operate at these exceptional levels, is making good decisions, the right decisions at the right time and based upon data, so that you can translate the data into intelligence and be a data-driven culture. Using data and intelligence is an important way that world-class companies do differentiate themselves. And it's the lifeblood of collaboration and innovation. Teams that are working on delivering these kinds of world-class experiences are often seeking out and leveraging next generation technologies and finding new ways to work. I've been fortunate to work across three decades of emerging experiences, which each required emerging technologies to execute. A little bit first about Disney, in the 90s I was at Disney, leading a project called destination Disney, which it's a data project, it was a data project, but it was CRM before CRM was even cool. And then certainly before anything like a data-driven culture was ever brought up. But way back then we were creating a digital backbone that enabled many technologies for the things that you see today, like the magic band, just these magical express. My career at Disney began in finance, but Disney was very good about rotating you around, and it was during one of these rotations that I became very passionate about data. I kind of became a pain in the butt to the IT team, asking for data more and more data. And I learned that all of that valuable data was locked up in our systems, all of our point of sales systems, our reservation systems, our operation systems, and so I became a shadow IT person in marketing, ultimately leading to moving into IT, and I haven't looked back since. In the early 2000s I was at Universal Studios Theme Park as their CIO, preparing for and launching the wizarding world of Harry Potter. Bringing one of history's most memorable characters to life required many new technologies and a lot of data. Our data and technologies were embedded into the rides and attractions. I mean, how do you really think a wand selects you at a wine shop. As today at the NFL, I am constantly challenged to do leading edge technologies using things like sensors, AI, machine learning, and all new communication strategies, and using data to drive everything from player performance, contracts to where we build new stadiums and hold events. With this year being the most challenging, yet rewarding year in my career at the NFL. In the middle of a global pandemic, the way we are executing on our season is leveraging data from contract tracing devices joined with testing data. Talk about data, actually enabling your business without it we wouldn't be having a season right now. I'm also on the board of directors of two public companies, where data and collaboration are paramount. First RingCentral, it's a cloud based unified communications platform, and collaboration with video message and phone, all in one solution in the cloud. And Quotient Technologies, whose product is actually data. The tagline at quotient is the result in knowing. I think that's really important, because not all of us are data companies, where your product is actually data. But we should operate more like your product is data. I'd also like to talk to you about four areas of things to think about, as thought leaders in your companies. First just hit on it is change, how to be a champion and a driver of change. Second, how to use data to drive performance for your company, and measure performance of your company. Third, how companies now require intense collaboration to operate, and finally, how much of this is accomplished through solid data-driven decisions. First let's hit on change. I mean, it's evident today more than ever, that we are in an environment of extreme change. I mean, we've all been at this for years and as technologists we've known it, believed it, lived it, and thankfully for the most part knock on wood we were prepared for it. But this year everyone's cheese was moved, all the people in the back rooms, IT, data architects and others, were suddenly called to the forefront. Because a global pandemic has turned out to be the thing that is driving intense change in how people work and analyze their business. On March 13th, we closed our office at the NFL in the middle of preparing for one of our biggest events, our kickoff event, the 2020 Draft. We went from planning, a large event in Las Vegas under the bright lights red carpet stage to smaller events in club facilities. And then ultimately to one where everyone coaches, GMs, prospects and even our commissioner were at home in their basements. And we only had a few weeks to figure it out. I found myself for the first time being in the live broadcast event space, talking about bungee dress jumping, this is really what it felt like. It was one in which no one felt comfortable, because it had not been done before. But leading through this, I stepped up, but it was very scary, it was certainly very risky but it ended up being Oh, so rewarding when we did it. And as a result of this, some things will change forever. Second, managing performance. I mean, data should inform how you're doing and how to get your company to perform at this level, highest level. As an example, the NFL has always measured performance obviously, and it is one of the purest examples of how performance directly impacts outcome. I mean, you can see performance on the field, you can see points being scored and stats, and you immediately know that impact, those with the best stats, usually win the games. The NFL has always recorded stats, since the beginning of time, here at the NFL a little this year as our 100 and first year and athletes ultimate success as a player has also always been greatly impacted by his stats. But what has changed for us, is both how much more we can measure, and the immediacy with which it can be measured. And I'm sure in your business, it's the same, the amount of data you must have has got to have quadrupled recently and how fast you need it and how quickly you need to analyze it, is so important. And it's very important to break the silos between the keys to the data and the use of the data. Our next generation stats platform is taking data to a next level, it's powered by Amazon Web Services, and we gathered this data real time from sensors that are on players' bodies. We gather it in real time, analyze it, display it online and on broadcast, and of course it's used to prepare week to week in addition to what is a normal coaching plan would be. We can now analyze, visualize, route patterns speed, matchups, et cetera, so much faster than ever before. We're continuing to roll out sensors too, that we'll gather more and more information about player's performance as it relates to their health and safety. The third trend is really I think it's a big part of what we're feeling today and that is intense collaboration. And just for sort of historical purposes it's important to think about for those of you that are IT professionals and developers, you know more than 10 years ago, agile practices began sweeping companies or small teams would work together rapidly in a very flexible, adaptive and innovative way, and it proved to be transformational. However today, of course, that is no longer just small teams the next big wave of change, and we've seen it through this pandemic is that it's the whole enterprise that must collaborate and be agile. If I look back on my career when I was at Disney, we owned everything 100%, we made a decision, we implemented it, we were a collaborative culture but it was much easier to push change because you own the whole decision. If there was buy in from the top down, you got the people from the bottom up to do it, and you executed. At Universal, we were a joint venture, our attractions and entertainment was licensed, our hotels were owned and managed by other third parties. So influence and collaboration and how to share across companies became very important. And now here I am at the NFL and even the bigger ecosystem. We have 32 clubs that are all separate businesses 31 different stadiums that are owned by a variety of people. We have licensees, we have sponsors, we have broadcast partners. So it seems that as my career has evolved centralized control has gotten less and less and has been replaced by intense collaboration not only within your own company, but across companies. The ability to work in a collaborative way across businesses and even other companies that has been a big key to my success in my career. I believe this whole vertical integration and big top down decision making is going by the wayside in favor of ecosystems that require cooperation, yet competition to coexist. I mean the NFL is a great example of what we call coopertition, which is cooperation and competition. When in competition with each other, but we cooperate to make the company the best it can be. And at the heart of these items really are data-driven decisions and culture. Data on its own isn't good enough, you must be able to turn it to insights, partnerships between technology teams who usually hold the keys to the raw data, and business units who have the knowledge to build the right decision models is key. If you're not already involved in this linkage, you should be, data mining isn't new for sure. The availability of data is quadrupling and it's everywhere. How do you know what to even look at? How do you know where to begin? How do you know what questions to ask? It's by using the tools that are available for visualization and analytics and knitting together strategies of the company. So it begins with first of all making sure you do understand the strategy of the company. So in closing, just to wrap up a bit, many of you joined today looking for thought leadership on how to be a change agent, a change champion, and how to lead through transformation. Some final thoughts are be brave, and drive, don't do the ride along program, it's very important to drive, driving can be high risk but it's also high reward. Embracing the uncertainty of what will happen, is how you become brave, get more and more comfortable with uncertainty be calm and let data be your map on your journey, thanks. >> Michelle, thank you so much. So you and I share a love of data, and a love of football. You said you want to be the quarterback, I'm more an old wine person. (Michelle laughing) >> Well, then I can do my job without you. >> Great, and I'm getting the feeling now you know, Sudheesh is talking about bungee jumping. My boat is when we're past this pandemic, we both take them to the Delaware Water Gap and we do the cliff jumping. >> That sounds good, I'll watch. >> You'll watch, okay, so Michelle, you have so many stakeholders when you're trying to prioritize the different voices, you have the players, you have the owners you have the league, as you mentioned to the broadcasters your, your partners here and football mamas like myself. How do you prioritize when there's so many different stakeholders that you need to satisfy? I think balancing across stakeholders starts with aligning on a mission. And if you spend a lot of time understanding where everyone's coming from, and you can find the common thread ties them all together you sort of do get them to naturally prioritize their work, and I think that's very important. So for us at the NFL, and even at Disney, it was our core values and our core purpose is so well known, and when anything challenges that we're able to sort of lay that out. But as a change agent, you have to be very empathetic, and I would say empathy is probably your strongest skill if you're a change agent. And that means listening to every single stakeholder even when they're yelling at you, even when they're telling you your technology doesn't work and you know that it's user error, or even when someone is just emotional about what's happening to them and that they're not comfortable with it. So I think being empathetic and having a mission and understanding it, is sort of how I prioritize and balance. >> Yeah, empathy, a very popular word this year. I can imagine those coaches and owners yelling. So I thank you for your metership here. So Michelle, I look forward to discussing this more with our other customers and disruptors joining us in a little bit. (soft upbeat music) >> So we're going to take a hard pivot now and go from football to Chernobyl, Chernobyl, what went wrong? 1986, as the reactors were melting down they had the data to say, this is going to be catastrophic and yet the culture said, "No, we're perfect, hide it. Don't dare tell anyone," which meant they went ahead and had celebrations in Kiev. Even though that increased the exposure the additional thousands getting cancer, and 20,000 years before the ground around there and even be inhabited again, This is how powerful and detrimental a negative culture, a culture that is unable to confront the brutal facts that hides data. This is what we have to contend with, and this is why I want you to focus on having fostering a data-driven culture. I don't want you to be a laggard, I want you to be a leader in using data to drive your digital transformation. So I'll talk about culture and technology, isn't really two sides of the same coin, real-world impacts and then some best practices you can use to disrupt and innovate your culture. Now, oftentimes I would talk about culture and I talk about technology, and recently a CDO said to me, "You know Cindi, I actually think this is two sides of the same coin. One reflects the other, what do you think?" Let me walk you through this, so let's take a laggard. What is the technology look like? Is it based on 1990s BI and reporting largely parameterized reports on-premises data warehouses, or not even that operational reports, at best one enterprise data warehouse very slow moving and collaboration is only email. What does that culture tell you? Maybe there's a lack of leadership to change, to do the hard work that Sudheesh referred to. Or is there also a culture of fear, afraid of failure, resistance to change complacency and sometimes that complacency it's not because people are lazy, it's because they've been so beaten down every time a new idea is presented. It's like, no we're measured on least cost to serve. So politics and distrust, whether it's between business and IT or individual stakeholders is the norm. So data is hoarded, let's contrast that with a leader, a data and analytics leader, what is their technology look like? Augmented analytics, search and AI-driven insights not on-premises, but in the cloud and maybe multiple clouds. And the data is not in one place, but it's in a data lake, and in a data warehouse, a logical data warehouse. The collaboration is being a newer methods whether it's Slack or teams allowing for that real time decisioning or investigating a particular data point. So what is the culture in the leaders? It's transparent and trust, there is a trust that data will not be used to punish, that there is an ability to confront the bad news. It's innovation, valuing innovation in pursuit of the company goals, whether it's the best fan experience and player safety in the NFL or best serving your customers. It's innovative and collaborative. There's none of this, oh, well, I didn't invent that, I'm not going to look at that. There's still pride of ownership, but it's collaborating to get to a better place faster. And people feel empowered to present new ideas to fail fast, and they're energized, knowing that they're using the best technology and innovating at the pace that business requires. So data is democratized and democratized, not just for power users or analysts, but really at the point of impact what we like to call the new decision makers. Or really the frontline workers. So Harvard business review partnered with us to develop this study to say, just how important is this? They've been working at BI and analytics as an industry for more than 20 years. Why is it not at the front lines? Whether it's a doctor, a nurse, a coach, a supply chain manager a warehouse manager, a financial services advisor. 87% said they would be more successful if frontline workers were empowered with data-driven insights, but they recognize they need new technology to be able to do that. It's not about learning hard tools, the sad reality only 20% of organizations are actually doing this, these are the data-driven leaders. So this is the culture and technology, how did we get here? It's because state of the art keeps changing. So the first generation BI and analytics platforms were deployed on-premises, on small datasets really just taking data out of ERP systems that were also on-premises, and state of the art was maybe getting a management report, an operational report. Over time visual based data discovery vendors, disrupted these traditional BI vendors, empowering now analysts to create visualizations with the flexibility on a desktop, sometimes larger data sometimes coming from a data warehouse, the current state of the art though, Gartner calls it augmented analytics, at ThoughtSpot, we call it search and AI-driven analytics. And this was pioneered for large scale data sets, whether it's on-premises or leveraging the cloud data warehouses, and I think this is an important point. Oftentimes you, the data and analytics leaders, will look at these two components separately, but you have to look at the BI and analytics tier in lockstep with your data architectures to really get to the granular insights, and to leverage the capabilities of AI. Now, if you've never seen ThoughtSpot I'll just show you what this looks like, instead of somebody's hard coding a report, it's typing in search keywords and very robust keywords contains rank, top, bottom getting to a visualization that then can be pinned to an existing Pinboard that might also contain insights generated by an AI engine. So it's easy enough for that new decision maker, the business user, the non analyst to create themselves. Modernizing the data and analytics portfolio is hard, because the pace of change has accelerated. You used to be able to create an investment, place a bet for maybe 10 years. A few years ago, that time horizon was five years, now it's maybe three years, and the time to maturity has also accelerated. So you have these different components the search and AI tier, the data science tier, data preparation and virtualization. But I would also say equally important is the cloud data warehouse. And pay attention to how well these analytics tools can unlock the value in these cloud data warehouses. So ThoughtSpot was the first to market with search and AI-driven insights. Competitors have followed suit, but be careful if you look at products like Power BI or SAP Analytics Cloud, they might demo well, but do they let you get to all the data without moving it in products like Snowflake, Amazon Redshift or Azure Synapse or Google BigQuery, they do not. They require you to move it into a smaller in memory engine. So it's important how well these new products inter operate. The pace of change, it's acceleration, Gartner recently predicted that by 2022, 65% of analytical queries will be generated using search or NLP or even AI, and that is roughly three times the prediction they had just a couple years ago. So let's talk about the real world impact of culture. And if you've read any of my books or used any of the maturity models out there whether the Gartner IT score that I worked on, or the data warehousing institute also has a maturity model. We talk about these five pillars to really become data-driven, as Michelle spoke about, it's focusing on the business outcomes, leveraging all the data, including new data sources. It's the talent, the people, the technology, and also the processes, and often when I would talk about the people in the talent, I would lump the culture as part of that. But in the last year, as I've traveled the world and done these digital events for thought leaders you have told me now culture is absolutely so important. And so we've pulled it out as a separate pillar, and in fact, in polls that we've done in these events, look at how much more important culture is, as a barrier to becoming data-driven. It's three times as important as any of these other pillars. That's how critical it is, and let's take an example of where you can have great data but if you don't have the right culture there's devastating impacts. And I will say, I have been a loyal customer of Wells Fargo for more than 20 years, but look at what happened in the face of negative news with data, that said, "Hey, we're not doing good cross selling, customers do not have both a checking account and a credit card and a savings account and a mortgage." They opened fake accounts, facing billions in fines, change in leadership, that even the CEO attributed to a toxic sales culture, and they're trying to fix this. But even recently there's been additional employee backlash saying that culture has not changed. Let's contrast that with some positive examples, Medtronic a worldwide company in 150 countries around the world, they may not be a household name to you, but if you have a loved one or yourself, you have a pacemaker, spinal implant, diabetes you know, this brand. And at the start of COVID when they knew their business would be slowing down, because hospitals would only be able to take care of COVID patients, they took the bold move of making their IP for ventilators publicly available, that is the power of a positive culture. Or Verizon, a major telecom organization, looking at late payments of their customers, and even though the US federal government said "Well, you can't turn them off." They said, "We'll extend that even beyond the mandated guidelines," and facing a slow down in the business because of the tough economy, he said, "You know what? We will spend the time upskilling our people giving them the time to learn more about the future of work, the skills and data and analytics," for 20,000 of their employees, rather than furloughing them. That is the power of a positive culture. So how can you transform your culture to the best in class? I'll give you three suggestions, bring in a change agent identify the relevance, or I like to call it WIIFM, and organize for collaboration. So the CDO whatever your title is, chief analytics officer chief digital officer, you are the most important change agent. And this is where you will hear, that oftentimes a change agent has to come from outside the organization. So this is where, for example in Europe, you have the CDO of Just Eat takeout food delivery organization, coming from the airline industry or in Australia, National Australian Bank, taking a CDO within the same sector from TD Bank going to NAB. So these change agents come in disrupt, it's a hard job. As one of you said to me, it often feels like Sisyphus, I make one step forward and I get knocked down again, I get pushed back. It is not for the faint of heart, but it's the most important part of your job. The other thing I'll talk about is WIIFM, what is in it for me? And this is really about understanding the motivation, the relevance that data has for everyone on the frontline as well as those analysts, as well as the executives. So if we're talking about players in the NFL they want to perform better, and they want to stay safe. That is why data matters to them. If we're talking about financial services this may be a wealth management advisor, okay, we could say commissions, but it's really helping people have their dreams come true whether it's putting their children through college, or being able to retire without having to work multiple jobs still into your 70s or 80s. For the teachers, teachers, you asked them about data, they'll say, "We don't need that, I care about the student." So if you can use data to help a student perform better that is WIIFM. And sometimes we spend so much time talking the technology, we forget what is the value we're trying to deliver with it. And we forget the impact on the people that it does require change. In fact, the Harvard Business Review Study, found that 44% said lack of change management is the biggest barrier to leveraging both new technology but also being empowered to act on those data-driven insights. The third point, organize for collaboration. This does require diversity of thought, but also bringing the technology, the data and the business people together. Now there's not a single one size fits all model for data and analytics. At one point in time, even having a BICC, a BI Competency Center was considered state of the art. Now for the biggest impact, what I recommend is that you have a federated model, centralized for economies of scale, that could be the common data, but then in bed, these evangelists, these analysts of the future, within every business unit, every functional domain, and as you see this top bar, all models are possible but the hybrid model has the most impact, the most leaders. So as we look ahead to the months ahead, to the year ahead, an exciting time, because data is helping organizations better navigate a tough economy lock in the customer loyalty, and I look forward to seeing how you foster that culture that's collaborative with empathy and bring the best of technology, leveraging the cloud, all your data. So thank you for joining us at thought leaders, and next I'm pleased to introduce our first change agent Thomas Mazzaferro, chief data officer of Western Union, and before joining Western Union, Tom made his mark at HSBC and JP Morgan Chase spearheading digital innovation in technology operations, risk compliance, and retail banking. Tom, thank you so much for joining us today. (soft upbeat music) >> Very happy to be here and looking forward to talking to all of you today. So as we look to move organizations to a data-driven capability into the future, there is a lot that needs to be done on the data side, but also how does data connect and enable, different business teams and technology teams into the future. As we look across our data ecosystems and our platforms and how we modernize that to the cloud in the future, it all needs to basically work together, right? To really be able to drive over the shift from a data standpoint, into the future. That includes being able to have the right information with the right quality of data at the right time to drive informed business decisions, to drive the business forward. As part of that, we actually have partnered with ThoughtSpot to actually bring in the technology to help us drive that, as part of that partnership, and it's how we've looked to integrated into our overall business as a whole. We've looked at how do we make sure that our business and our professional lives, right? Are enabled in the same ways as our personal lives. So for example, in your personal lives, when you want to go and find something out, what do you do? You go on to google.com or you go on to Bing, or go to Yahoo and you search for what you want, search to find an answer. ThoughtSpot for us as the same thing, but in the business world. So using ThoughtSpot and other AI capability is allowed us to actually enable our overall business teams in our company, to actually have our information at our fingertips. So rather than having to go and talk to someone or an engineer to go pull information or pull data, we actually can have the end users or the business executives, right? Search for what they need, what they want, at the exact time that action needed, to go and drive the business forward. This is truly one of those transformational things that we've put in place. On top of that, we are on the journey to modernize our larger ecosystem as a whole. That includes modernizing our underlying data warehouses, our technology or our (indistinct) environments, and as we move that we've actually picked to our cloud providers going to AWS and GCP. We've also adopted Snowflake to really drive into organize our information and our data, then drive these new solutions and capabilities forward. So big portion of us though is culture, so how do we engage with the business teams and bring the IT teams together to really drive these holistic end to end solutions and capabilities, to really support the actual business into the future. That's one of the keys here, as we look to modernize and to really enhance our organizations to become data-driven, this is the key. If you can really start to provide answers to business questions before they're even being asked, and to predict based upon different economic trends or different trends in your business, what does is be made and actually provide those answers to the business teams before they're even asking for it. That is really becoming a data-driven organization. And as part of that, it's really then enables the business to act quickly and take advantage of opportunities as they come in based upon industries, based upon markets, based upon products, solutions, or partnerships into the future. These are really some of the keys that become crucial as you move forward right into this new age, especially with COVID, with COVID now taking place across the world, right? Many of these markets, many of these digital transformations are celebrating, and are changing rapidly to accommodate and to support customers in these very difficult times. As part of that, you need to make sure you have the right underlying foundation, ecosystems and solutions to really drive those capabilities, and those solutions forward. As we go through this journey, both of my career but also each of your careers into the future, right? It also needs to evolve, right? Technology has changed so drastically in the last 10 years, and that change is only a celebrating. So as part of that, you have to make sure that you stay up to speed, up to date with new technology changes both on the platform standpoint, tools, but also what our customers want, what do our customers need, and how do we then surface them with our information, with our data, with our platform, with our products and our services, to meet those needs and to really support and service those customers into the future. This is all around becoming a more data-driven organization such as how do you use your data to support the current business lines. But how do you actually use your information your data, to actually better support your customers better support your business, better support your employees, your operations teams and so forth, and really creating that full integration in that ecosystem is really when you start to get large dividends from these investments into the future. With that being said I hope you enjoyed the segment on how to become and how to drive a data-driven organization, and looking forward to talking to you again soon, thank you. >> Tom, that was great, thanks so much. Now I'm going to have to brag on you for a second, as a change agent you've come in disrupted, and how long have you been at Western Union? >> Only nine months, I just started this year, but there'd be some great opportunities and big changes, and we have a lot more to go, but we're really driving things forward in partnership with our business teams, and our colleagues to support those customers forward. >> Tom, thank you so much that was wonderful. And now I'm excited to introduce you to Gustavo Canton, a change agent that I've had the pleasure of working with meeting in Europe, and he is a serial change agent. Most recently with Schneider Electric, but even going back to Sam's Club, Gustavo welcome. (soft upbeat music) >> So hi everyone my name is Gustavo Canton and thank you so much Cindi for the intro. As you mentioned, doing transformations is a you know, high effort, high reward situation. I have empowerment in transformation and I have led many transformations. And what I can tell you is that it's really hard to predict the future, but if you have a North Star and you know where you're going, the one thing that I want you to take away from this discussion today, is that you need to be bold to evolve. And so in today, I'm going to be talking about culture and data, and I'm going to break this down in four areas. How do we get started barriers or opportunities as I see it, the value of AI, and also how do you communicate, especially now in the workforce of today with so many different generations, you need to make sure that you are communicating in ways that are nontraditional sometimes. And so how do we get started? So I think the answer to that is, you have to start for you, yourself as a leader and stay tuned. And by that, I mean you need to understand not only what is happening in your function or your field, but you have to be very into what is happening in society, socioeconomically speaking, wellbeing, you know, the common example is a great example. And for me personally, it's an opportunity because the number one core value that I have is wellbeing. I believe that for human potential, for customers and communities to grow, wellbeing should be at the center of every decision. And as somebody mentioned, it's great to be you know, stay in tune and have the skillset and the courage. But for me personally, to be honest to have this courage is not about not being afraid. You're always afraid when you're making big changes and your swimming upstream. But what gives me the courage is the empathy part, like I think empathy is a huge component because every time I go into an organization or a function, I try to listen very attentively to the needs of the business, and what the leaders are trying to do, what I do it thinking about the mission of how do I make change for the bigger, you know workforce so the bigger good, despite the fact that this might have a perhaps implication, so my own self interest in my career, right? Because you have to have that courage sometimes to make choices, that are not well seeing politically speaking what are the right thing to do, and you have to push through it. So the bottom line for me is that, I don't think they're transforming fast enough. And the reality is I speak with a lot of leaders and we have seen stories in the past, and what they show is that if you look at the four main barriers, that are basically keeping us behind budget, inability to add, cultural issues, politics, and lack of alignment, those are the top four. But the interesting thing is that as Cindi has mentioned, this topic about culture is actually gaining more and more traction, and in 2018, there was a story from HBR and it was for about 45%. I believe today, it's about 55%, 60% of respondents say that this is the main area that we need to focus on. So again, for all those leaders and all the executives who understand, and are aware that we need to transform, commit to the transformation and set us deadline to say, "Hey, in two years, we're going to make this happen, what do we need to do to empower and enable these search engines to make it happen?" You need to make the tough choices. And so to me, when I speak about being bold is about making the right choices now. So I'll give you samples of some of the roadblocks that I went through, as I think the intro information most recently as Cindi mentioned in Schneider. There are three main areas, legacy mindset, and what that means is that we've been doing this in a specific way for a long time, and here is how we have been successful. We're working the past is not going to work now, the opportunity there is that there is a lot of leaders who have a digital mindset, and their up and coming leaders that are perhaps not yet fully developed. We need to mentor those leaders and take bets on some of these talents, including young talent. We cannot be thinking in the past and just wait for people you know, three to five years for them to develop, because the world is going to in a way that is super fast. The second area and this is specifically to implementation of AI is very interesting to me, because just example that I have with ThoughtSpot, right? We went to an implementation and a lot of the way the IT team functions, so the leaders look at technology, they look at it from the prism of the prior or success criteria for the traditional BIs, and that's not going to work. Again, your opportunity here is that you need to really find what success look like, in my case, I want the user experience of our workforce to be the same as your experience you have at home. It's a very simple concept, and so we need to think about how do we gain that user experience with this augmented analytics tools, and then work backwards to have the right talent, processes and technology to enable that. And finally, and obviously with COVID a lot of pressure in organizations and companies to do more with less, and the solution that most leaders I see are taking is to just minimize cost sometimes and cut budget. We have to do the opposite, we have to actually invest some growth areas, but do it by business question. Don't do it by function, if you actually invest in these kind of solutions, if you actually invest on developing your talent, your leadership, to see more digitally, if you actually invest on fixing your data platform is not just an incremental cost, it's actually this investment is going to offset all those hidden costs and inefficiencies that you have on your system, because people are doing a lot of work in working very hard but it's not efficiency, and it's not working in the way that you might want to work. So there is a lot of opportunity there, and you just to put it into some perspective, there have been some studies in the past about you know, how do we kind of measure the impact of data? And obviously this is going to vary by organization, maturity there's going to be a lot of factors. I've been in companies who have very clean, good data to work with, and I think with companies that we have to start basically from scratch. So it all depends on your maturity level, but in this study what I think is interesting is, they try to put a tagline or attack price to what is a cost of incomplete data. So in this case, it's about 10 times as much to complete a unit of work, when you have data that is flawed as opposed to have imperfect data. So let me put that just in perspective, just as an example, right? Imagine you are trying to do something and you have to do 100 things in a project, and each time you do something it's going to cost you a dollar. So if you have perfect data, the total cost of that project might be a $100. But now let's say you have any percent perfect data and 20% flow data, by using this assumption that flow data is 10 times as costly as perfect data, your total costs now becomes $280 as opposed to $100, this just for you to really think about as a CIO, CTO, you know CSRO, CEO, are we really paying attention and really closing the gaps that we have on our infrastructure? If we don't do that, it's hard sometimes to see the snowball effect or to measure the overall impact, but as you can tell, the price tag goes up very, very quickly. So now, if I were to say, how do I communicate this? Or how do I break through some of these challenges or some of these barriers, right? I think the key is I am in analytics, I know statistics obviously, and love modeling and you know, data and optimization theory and all that stuff, that's what I can do analytics, but now as a leader and as a change agent, I need to speak about value, and in this case, for example for Schneider, there was this tagline coffee of your energy. So the number one thing that they were asking from the analytics team was actually efficiency, which to me was very interesting. But once I understood that I understood what kind of language to use, how to connect it to the overall strategy and basically how to bring in the right leaders, because you need to, you know, focus on the leaders that you're going to make the most progress. You know, again, low effort, high value, you need to make sure you centralize all the data as you can, you need to bring in some kind of augmented analytics, you know, solution, and finally you need to make it super simple for the you know, in this case, I was working with the HR teams and other areas, so they can have access to one portal. They don't have to be confused and looking for 10 different places to find information. I think if you can actually have those four foundational pillars, obviously under the guise of having a data-driven culture, that's when you can actually make the impact. So in our case, it was about three years total transformation but it was two years for this component of augmented analytics. It took about two years to talk to, you know, IT, get leadership support, find the budgeting, you know, get everybody on board, make sure the success criteria was correct. And we call this initiative, the people analytics, I pulled up, it was actually launched in July of this year. And we were very excited and the audience was very excited to do this. In this case, we did our pilot in North America for many, many manufacturers, but one thing that is really important is as you bring along your audience on this, you know, you're going from Excel, you know in some cases or Tableau to other tools like you know, ThoughtSpot, you need to really explain them, what is the difference, and how these two can truly replace some of the spreadsheets or some of the views that you might have on these other kind of tools. Again, Tableau, I think it's a really good tool, there are other many tools that you might have in your toolkit. But in my case, personally I feel that you need to have one portal going back to seeing these points that really truly enable the end user. And I feel that this is the right solution for us, right? And I will show you some of the findings that we had in the pilot in the last two months. So this was a huge victory, and I will tell you why, because it took a lot of effort for us to get to these stations. Like I said it's been years for us to kind of lay the foundation, get the leadership and chasing culture, so people can understand why you truly need to invest what I meant analytics. And so what I'm showing here is an example of how do we use basically, you know a tool to capturing video, the qualitative findings that we had, plus the quantitative insights that we have. So in this case, our preliminary results based on our ambition for three main metrics, hours saved, user experience and adoption. So for hours saved, our ambition was to have 10 hours per week per employee save on average, user experience or ambition was 4.5 and adoption 80%. In just two months, two months and a half of the pilot we were able to achieve five hours, per week per employee savings. I used to experience for 4.3 out of five, and adoption of 60%. Really, really amazing work. But again, it takes a lot of collaboration for us to get to the stage from IT, legal, communications obviously the operations things and the users, in HR safety and other areas that might be basically stakeholders in this whole process. So just to summarize this kind of effort takes a lot of energy, you are a change agent, you need to have a courage to make these decision and understand that, I feel that in this day and age with all this disruption happening, we don't have a choice. We have to take the risk, right? And in this case, I feel a lot of satisfaction in how we were able to gain all these very souls for this organization, and that gave me the confidence to know that the work has been done, and we are now in a different stage for the organization. And so for me it safe to say, thank you for everybody who has believed obviously in our vision, everybody who has believed in, you know, the word that we were trying to do and to make the life for, you know workforce or customers that are in community better. As you can tell, there is a lot of effort, there is a lot of collaboration that is needed to do something like this. In the end, I feel very satisfied with the accomplishments of this transformation, and I just want to tell for you, if you are going right now in a moment that you feel that you have to swim upstream you know, what would mentors what people in this industry that can help you out and guide you on this kind of a transformation is not easy to do is high effort but is well worth it. And with that said, I hope you are well and it's been a pleasure talking to you, talk to you soon, take care. >> Thank you Gustavo, that was amazing. All right, let's go to the panel. (soft upbeat music) >> I think we can all agree how valuable it is to hear from practitioners, and I want to thank the panel for sharing their knowledge with the community, and one common challenge that I heard you all talk about was bringing your leadership and your teams along on the journey with you. We talk about this all the time, and it is critical to have support from the top, why? Because it directs the middle, and then it enables bottoms up innovation effects from the cultural transformation that you guys all talked about. It seems like another common theme we heard, is that you all prioritize database decision making in your organizations, and you combine two of your most valuable assets to do that, and create leverage, employees on the front lines, and of course the data. That was rightly pointed out, Tom, the pandemic has accelerated the need for really leaning into this. You know, the old saying, if it ain't broke, don't fix it, well COVID's broken everything. And it's great to hear from our experts, you know, how to move forward, so let's get right into it. So Gustavo let's start with you if I'm an aspiring change agent, and let's say I'm a budding data leader. What do I need to start doing? What habits do I need to create for long lasting success? >> I think curiosity is very important. You need to be, like I say, in tune to what is happening not only in your specific field, like I have a passion for analytics, I can do this for 50 years plus, but I think you need to understand wellbeing other areas across not only a specific business as you know, I come from, you know, Sam's Club Walmart retail, I mean energy management technology. So you have to try to push yourself and basically go out of your comfort zone. I mean, if you are staying in your comfort zone and you want to use lean continuous improvement that's just going to take you so far. What you have to do is and that's what I tried to do is I try to go into areas, businesses and transformations that make me, you know stretch and develop as a leader. That's what I'm looking to do, so I can help transform the functions organizations, and do these change management and decisions mindset as required for these kinds of efforts. >> Thank you for that is inspiring and Cindi, you love data, and the data is pretty clear that diversity is a good business, but I wonder if you can add your perspectives to this conversation. >> Yeah, so Michelle has a new fan here because she has found her voice, I'm still working on finding mine. And it's interesting because I was raised by my dad, a single dad, so he did teach me how to work in a predominantly male environment. But why I think diversity matters more now than ever before, and this is by gender, by race, by age, by just different ways of working and thinking is because as we automate things with AI, if we do not have diverse teams looking at the data and the models, and how they're applied, we risk having bias at scale. So this is why I think I don't care what type of minority, you are finding your voice, having a seat at the table and just believing in the impact of your work has never been more important. And as Michelle said more possible >> Great perspectives thank you, Tom, I want to go to you. I mean, I feel like everybody in our businesses in some way, shape or form become a COVID expert but what's been the impact of the pandemic on your organization's digital transformation plans? >> We've seen a massive growth actually you know, in a digital business over the last 12 months really, even in celebration, right? Once COVID hit, we really saw that in the 200 countries and territories that we operate in today and service our customers and today, that there's been a huge need, right? To send money, to support family, to support friends and loved ones across the world. And as part of that, you know, we are very honored to support those customers that we across all the centers today. But as part of that celebration, we need to make sure that we had the right architecture and the right platforms to basically scale, right? To basically support and provide the right kind of security for our customers going forward. So as part of that, we did do some pivots and we did celebrate some of our plans on digital to help support that overall growth coming in, and to support our customers going forward. Because there were these times during this pandemic, right? This is the most important time, and we need to support those that we love and those that we care about. And in doing that, it's one of those ways is actually by sending money to them, support them financially. And that's where really are part of that our services come into play that, you know, I really support those families. So it was really a great opportunity for us to really support and really bring some of our products to this level, and supporting our business going forward. >> Awesome, thank you. Now I want to come back to Gustavo, Tom, I'd love for you to chime in too. Did you guys ever think like you were pushing the envelope too much and doing things with data or the technology that was just maybe too bold, maybe you felt like at some point it was failing, or you pushing your people too hard, can you share that experience and how you got through it? >> Yeah, the way I look at it is, you know, again, whenever I go to an organization I ask the question, Hey, how fast you would like to conform?" And, you know, based on the agreements on the leadership and the vision that we want to take place, I take decisions and I collaborate in a specific way. Now, in the case of COVID, for example, right? It forces us to remove silos and collaborate in a faster way, so to me it was an opportunity to actually integrate with other areas and drive decisions faster. But make no mistake about it, when you are doing a transformation, you are obviously trying to do things faster than sometimes people are comfortable doing and you need to be okay with that. Sometimes you need to be okay with tension, or you need to be okay, you know debating points or making repetitive business cases onto people connect with the decision because you understand, and you are seeing that, hey, the CEO is making a one, two year, you know, efficiency goal, the only way for us to really do more with less is for us to continue this path. We cannot just stay with the status quo, we need to find a way to accelerate transformation... >> How about you Tom, we were talking earlier was Sudheesh had said about that bungee jumping moment, what can you share? >> Yeah you know, I think you hit upon it. Right now, the pace of change will be the slowest pace that you see for the rest of your career. So as part of that, right? That's what I tell my team is that you need to feel comfortable being uncomfortable. I mean, that we have to be able to basically scale, right? Expand and support that the ever changing needs the marketplace and industry and our customers today and that pace of change that's happening, right? And what customers are asking for, and the competition the marketplace, it's only going to accelerate. So as part of that, you know, as we look at what how you're operating today in your current business model, right? Things are only going to get faster. So you have to plan into align, to drive the actual transformation, so that you can scale even faster into the future. So as part of that, so we're putting in place here, right? Is how do we create that underlying framework and foundation that allows the organization to basically continue to scale and evolve into the future? >> We're definitely out of our comfort zones, but we're getting comfortable with it. So, Cindi, last question, you've worked with hundreds of organizations, and I got to believe that you know, some of the advice you gave when you were at Gartner, which is pre COVID, maybe sometimes clients didn't always act on it. You know, they're not on my watch for whatever variety of reasons, but it's being forced on them now, but knowing what you know now that you know, we're all in this isolation economy how would you say that advice has changed, has it changed? What's your number one action and recommendation today? >> Yeah well, first off, Tom just freaked me out. What do you mean this is the slowest ever? Even six months ago, I was saying the pace of change in data and analytics is frenetic. So, but I think you're right, Tom, the business and the technology together is forcing this change. Now, Dave, to answer your question, I would say the one bit of advice, maybe I was a little more, very aware of the power in politics and how to bring people along in a way that they are comfortable, and now I think it's, you know what? You can't get comfortable. In fact, we know that the organizations that were already in the cloud, have been able to respond and pivot faster. So if you really want to survive as Tom and Gustavo said, get used to being uncomfortable, the power and politics are going to happen. Break the rules, get used to that and be bold. Do not be afraid to tell somebody they're wrong and they're not moving fast enough. I do think you have to do that with empathy as Michelle said, and Gustavo, I think that's one of the key words today besides the bungee jumping. So I want to know where's Sudheesh going to go on bungee jumping? (all chuckling) >> That's fantastic discussion really. Thanks again to all the panelists and the guests, it was really a pleasure speaking with you today. Really virtually all of the leaders that I've spoken to in theCUBE program recently, they tell me that the pandemic is accelerating so many things, whether it's new ways to work, we heard about new security models and obviously the need for cloud. I mean, all of these things are driving true enterprise wide digital transformation, not just as I said before lip service. And sometimes we minimize the importance and the challenge of building culture and in making this transformation possible. But when it's done right, the right culture is going to deliver tremendous results. Yeah, what does that mean getting it right? Everybody's trying to get it right. My biggest takeaway today, is it means making data part of the DNA of your organization. And that means making it accessible to the people in your organization that are empowered to make decisions that can drive you revenue, cut costs, speed, access to critical care, whatever the mission is of your organization. Data can create insights and informed decisions that drive value. Okay, let's bring back Sudheesh and wrap things up. Sudheesh please bring us home. >> Thank you, thank you Dave, thank you theCUBE team, and thanks goes to all of our customers and partners who joined us, and thanks to all of you for spending the time with us. I want to do three quick things and then close it off. The first thing is I want to summarize the key takeaways that I had from all four of our distinguished speakers. First, Michelle, I was simply put it, she said it really well, that is be brave and drive. Don't go for a drive along, that is such an important point. Often times, you know that I think that you have to do to make the positive change that you want to see happen. But you wait for someone else to do it, why not you? Why don't you be the one making that change happen? That's the thing that I picked up from Michelle's talk. Cindi talked about finding the importance of finding your voice, taking that chair, whether it's available or not and making sure that your ideas, your voices are heard and if it requires some force then apply that force, make sure your ideas are good. Gustavo talked about the importance of building consensus, not going at things all alone sometimes building the importance of building the courtroom. And that is critical because if you want the changes to last, you want to make sure that the organization is fully behind it. Tom instead of a single take away, what I was inspired by is the fact that a company that is 170 years old, 170 years old, 200 companies and 200 countries they're operating in, and they were able to make the change that is necessary through this difficult time. So in a matter of months, if they could do it, anyone could. The second thing I want to do is to leave you with a takeaway that is I would like you to go to thoughtspot.com/nfl because our team has made an app for NFL on Snowflake. I think you will find this interesting now that you are inspired and excited because of Michelle's talk. And the last thing is, please go to thoughtspot.com/beyond, our global user conferences happening in this December, we would love to have you join us. It's again, virtual, you can join from anywhere, we are expecting anywhere from five to 10,000 people, and we would love to have you join and see what we would have been up to since the last year. We have a lot of amazing things in store for you, our customers, our partners, our collaborators, they will be coming and sharing, you'll be sharing things that you have been working to release something that will come out next year. And also some of the crazy ideas for engineers I've been cooking up. All of those things will be available for you at ThoughtSpot Beyond, thank you, thank you so much.

(speaks in foreign language) >> Narrator: Data is at the heart of transformation and the change every company needs to succeed, but it takes more than new technology. It's about teams, talent, and cultural change. Empowering everyone on the front lines to make decisions, all at the speed of digital. The transformation starts with you. It's time to lead the way, it's time for thought leaders. >> Welcome to Thought Leaders, a digital event brought to you by ThoughtSpot. My name is Dave Vellante. The purpose of this day is to bring industry leaders and experts together to really try and understand the important issues around digital transformation. We have an amazing lineup of speakers and our goal is to provide you with some best practices that you can bring back and apply to your organization. Look, data is plentiful, but insights are not. ThoughtSpot is disrupting analytics by using search and machine intelligence to simplify data analysis, and really empower anyone with fast access to relevant data. But in the last 150 days, we've had more questions than answers. Creating an organization that puts data and insights at their core, requires not only modern technology, but leadership, a mindset and a culture that people often refer to as data-driven. What does that mean? How can we equip our teams with data and fast access to quality information that can turn insights into action. And today, we're going to hear from experienced leaders, who are transforming their organizations with data, insights and creating digital-first cultures. But before we introduce our speakers, I'm joined today by two of my co-hosts from ThoughtSpot. First, Chief Data Strategy Officer for ThoughtSpot is Cindi Hausen. Cindi is an analytics and BI expert with 20 plus years experience and the author of Successful Business Intelligence Unlock The Value of BI and Big Data. Cindi was previously the lead analyst at Gartner for the data and analytics magic quadrant. And early last year, she joined ThoughtSpot to help CDOs and their teams understand how best to leverage analytics and AI for digital transformation. Cindi, great to see you, welcome to the show. >> Thank you, Dave. Nice to join you virtually. >> Now our second cohost and friend of theCUBE is ThoughtSpot CEO Sudheesh Nair. Hello Sudheesh, how are you doing today? >> I am well Dave, it's good to talk to you again. >> It's great to see you. Thanks so much for being here. Now Sudheesh, please share with us why this discussion is so important to your customers and of course, to our audience and what they're going to learn today? (gentle music) >> Thanks, Dave, I wish you were there to introduce me into every room that I walk into because you have such an amazing way of doing it. It makes me feel also good. Look, since we have all been cooped up in our homes, I know that the vendors like us, we have amped up our, you know, sort of effort to reach out to you with invites for events like this. So we are getting way more invites for events like this than ever before. So when we started planning for this, we had three clear goals that we wanted to accomplish. And our first one that when you finish this and walk away, we want to make sure that you don't feel like it was a waste of time. We want to make sure that we value your time, and this is going to be useful. Number two, we want to put you in touch with industry leaders and thought leaders, and generally good people that you want to hang around with long after this event is over. And number three, as we plan through this, you know, we are living through these difficult times, we want an event to be, this event to be more of an uplifting and inspiring event too. Now, the challenge is, how do you do that with the team being change agents? Because change and as much as we romanticize it, it is not one of those uplifting things that everyone wants to do or likes to do. The way I think of it, change is sort of like, if you've ever done bungee jumping. You know, it's like standing on the edges, waiting to make that one more step. You know, all you have to do is take that one step and gravity will do the rest, but that is the hardest step to take. Change requires a lot of courage and when we are talking about data and analytics, which is already like such a hard topic, not necessarily an uplifting and positive conversation, in most businesses it is somewhat scary. Change becomes all the more difficult. Ultimately change requires courage. Courage to to, first of all, challenge the status quo. People sometimes are afraid to challenge the status quo because they are thinking that, "You know, maybe I don't have the power to make the change that the company needs. Sometimes I feel like I don't have the skills." Sometimes they may feel that, I'm probably not the right person to do it. Or sometimes the lack of courage manifest itself as the inability to sort of break the silos that are formed within the organizations, when it comes to data and insights that you talked about. You know, there are people in the company, who are going to hog the data because they know how to manage the data, how to inquire and extract. They know how to speak data, they have the skills to do that, but they are not the group of people who have sort of the knowledge, the experience of the business to ask the right questions off the data. So there is this silo of people with the answers and there is a silo of people with the questions, and there is gap. These sort of silos are standing in the way of making that necessary change that we all I know the business needs, and the last change to sort of bring an external force sometimes. It could be a tool, it could be a platform, it could be a person, it could be a process, but sometimes no matter how big the company is or how small the company is. You may need to bring some external stimuli to start that domino of the positive changes that are necessary. The group of people that we have brought in, the four people, including Cindi, that you will hear from today are really good at practically telling you how to make that step, how to step off that edge, how to trust the rope that you will be safe and you're going to have fun. You will have that exhilarating feeling of jumping for a bungee jump. All four of them are exceptional, but my honor is to introduce Michelle and she's our first speaker. Michelle, I am very happy after watching her presentation and reading her bio, that there are no country vital worldwide competition for cool patents, because she will beat all of us because when her children were small, you know, they were probably into Harry Potter and Disney and she was managing a business and leading change there. And then as her kids grew up and got to that age, where they like football and NFL, guess what? She's the CIO of NFL. What a cool mom. I am extremely excited to see what she's going to talk about. I've seen the slides with a bunch of amazing pictures, I'm looking to see the context behind it. I'm very thrilled to make the acquaintance of Michelle. I'm looking forward to her talk next. Welcome Michelle. It's over to you. (gentle music) >> I'm delighted to be with you all today to talk about thought leadership. And I'm so excited that you asked me to join you because today I get to be a quarterback. I always wanted to be one. This is about as close as I'm ever going to get. So, I want to talk to you about quarterbacking our digital revolution using insights, data and of course, as you said, leadership. First, a little bit about myself, a little background. As I said, I always wanted to play football and this is something that I wanted to do since I was a child but when I grew up, girls didn't get to play football. I'm so happy that that's changing and girls are now doing all kinds of things that they didn't get to do before. Just this past weekend on an NFL field, we had a female coach on two sidelines and a female official on the field. I'm a lifelong fan and student of the game of football. I grew up in the South. You can tell from the accent and in the South football is like a religion and you pick sides. I chose Auburn University working in the athletic department, so I'm testament. Till you can start, a journey can be long. It took me many, many years to make it into professional sports. I graduated in 1987 and my little brother, well not actually not so little, he played offensive line for the Alabama Crimson Tide. And for those of you who know SEC football, you know this is a really big rivalry, and when you choose sides your family is divided. So it's kind of fun for me to always tell the story that my dad knew his kid would make it to the NFL, he just bet on the wrong one. My career has been about bringing people together for memorable moments at some of America's most iconic brands, delivering memories and amazing experiences that delight. From Universal Studios, Disney, to my current position as CIO of the NFL. In this job, I'm very privileged to have the opportunity to work with a team that gets to bring America's game to millions of people around the world. Often, I'm asked to talk about how to create amazing experiences for fans, guests or customers. But today, I really wanted to focus on something different and talk to you about being behind the scenes and backstage. Because behind every event, every game, every awesome moment, is execution. Precise, repeatable execution and most of my career has been behind the scenes doing just that. Assembling teams to execute these plans and the key way that companies operate at these exceptional levels is making good decisions, the right decisions, at the right time and based upon data. So that you can translate the data into intelligence and be a data-driven culture. Using data and intelligence is an important way that world-class companies do differentiate themselves, and it's the lifeblood of collaboration and innovation. Teams that are working on delivering these kind of world class experiences are often seeking out and leveraging next generation technologies and finding new ways to work. I've been fortunate to work across three decades of emerging experiences, which each required emerging technologies to execute. A little bit first about Disney. In '90s I was at Disney leading a project called Destination Disney, which it's a data project. It was a data project, but it was CRM before CRM was even cool and then certainly before anything like a data-driven culture was ever brought up. But way back then we were creating a digital backbone that enabled many technologies for the things that you see today. Like the MagicBand, Disney's Magical Express. My career at Disney began in finance, but Disney was very good about rotating you around. And it was during one of these rotations that I became very passionate about data. I kind of became a pain in the butt to the IT team asking for data, more and more data. And I learned that all of that valuable data was locked up in our systems. All of our point of sales systems, our reservation systems, our operation systems. And so I became a shadow IT person in marketing, ultimately, leading to moving into IT and I haven't looked back since. In the early 2000s, I was at Universal Studio's theme park as their CIO preparing for and launching the Wizarding World of Harry Potter. Bringing one of history's most memorable characters to life required many new technologies and a lot of data. Our data and technologies were embedded into the rides and attractions. I mean, how do you really think a wand selects you at a wand shop. As today at the NFL, I am constantly challenged to do leading edge technologies, using things like sensors, AI, machine learning and all new communication strategies, and using data to drive everything, from player performance, contracts, to where we build new stadiums and hold events. With this year being the most challenging, yet rewarding year in my career at the NFL. In the middle of a global pandemic, the way we are executing on our season is leveraging data from contact tracing devices joined with testing data. Talk about data actually enabling your business. Without it we wouldn't be having a season right now. I'm also on the board of directors of two public companies, where data and collaboration are paramount. First, RingCentral, it's a cloud based unified communications platform and collaboration with video message and phone, all-in-one solution in the cloud and Quotient Technologies, whose product is actually data. The tagline at Quotient is The Result in Knowing. I think that's really important because not all of us are data companies, where your product is actually data, but we should operate more like your product is data. I'd also like to talk to you about four areas of things to think about as thought leaders in your companies. First, just hit on it, is change. how to be a champion and a driver of change. Second, how to use data to drive performance for your company and measure performance of your company. Third, how companies now require intense collaboration to operate and finally, how much of this is accomplished through solid data-driven decisions. First, let's hit on change. I mean, it's evident today more than ever, that we are in an environment of extreme change. I mean, we've all been at this for years and as technologists we've known it, believed it, lived it. And thankfully, for the most part, knock on wood, we were prepared for it. But this year everyone's cheese was moved. All the people in the back rooms, IT, data architects and others were suddenly called to the forefront because a global pandemic has turned out to be the thing that is driving intense change in how people work and analyze their business. On March 13th, we closed our office at the NFL in the middle of preparing for one of our biggest events, our kickoff event, The 2020 Draft. We went from planning a large event in Las Vegas under the bright lights, red carpet stage, to smaller events in club facilities. And then ultimately, to one where everyone coaches, GMs, prospects and even our commissioner were at home in their basements and we only had a few weeks to figure it out. I found myself for the first time, being in the live broadcast event space. Talking about bungee jumping, this is really what it felt like. It was one in which no one felt comfortable because it had not been done before. But leading through this, I stepped up, but it was very scary, it was certainly very risky, but it ended up being also rewarding when we did it. And as a result of this, some things will change forever. Second, managing performance. I mean, data should inform how you're doing and how to get your company to perform at its level, highest level. As an example, the NFL has always measured performance, obviously, and it is one of the purest examples of how performance directly impacts outcome. I mean, you can see performance on the field, you can see points being scored and stats, and you immediately know that impact. Those with the best stats usually win the games. The NFL has always recorded stats. Since the beginning of time here at the NFL a little... This year is our 101st year and athlete's ultimate success as a player has also always been greatly impacted by his stats. But what has changed for us is both how much more we can measure and the immediacy with which it can be measured and I'm sure in your business it's the same. The amount of data you must have has got to have quadrupled recently. And how fast do you need it and how quickly you need to analyze it is so important. And it's very important to break the silos between the keys to the data and the use of the data. Our next generation stats platform is taking data to the next level. It's powered by Amazon Web Services and we gather this data, real-time from sensors that are on players' bodies. We gather it in real time, analyze it, display it online and on broadcast. And of course, it's used to prepare week to week in addition to what is a normal coaching plan would be. We can now analyze, visualize, route patterns, speed, match-ups, et cetera, so much faster than ever before. We're continuing to roll out sensors too, that will gather more and more information about a player's performance as it relates to their health and safety. The third trend is really, I think it's a big part of what we're feeling today and that is intense collaboration. And just for sort of historical purposes, it's important to think about, for those of you that are IT professionals and developers, you know, more than 10 years ago agile practices began sweeping companies. Where small teams would work together rapidly in a very flexible, adaptive and innovative way and it proved to be transformational. However today, of course that is no longer just small teams, the next big wave of change and we've seen it through this pandemic, is that it's the whole enterprise that must collaborate and be agile. If I look back on my career, when I was at Disney, we owned everything 100%. We made a decision, we implemented it. We were a collaborative culture but it was much easier to push change because you own the whole decision. If there was buy-in from the top down, you got the people from the bottom up to do it and you executed. At Universal, we were a joint venture. Our attractions and entertainment was licensed. Our hotels were owned and managed by other third parties, so influence and collaboration, and how to share across companies became very important. And now here I am at the NFL an even the bigger ecosystem. We have 32 clubs that are all separate businesses, 31 different stadiums that are owned by a variety of people. We have licensees, we have sponsors, we have broadcast partners. So it seems that as my career has evolved, centralized control has gotten less and less and has been replaced by intense collaboration, not only within your own company but across companies. The ability to work in a collaborative way across businesses and even other companies, that has been a big key to my success in my career. I believe this whole vertical integration and big top-down decision-making is going by the wayside in favor of ecosystems that require cooperation, yet competition to co-exist. I mean, the NFL is a great example of what we call co-oppetition, which is cooperation and competition. We're in competition with each other, but we cooperate to make the company the best it can be. And at the heart of these items really are data-driven decisions and culture. Data on its own isn't good enough. You must be able to turn it to insights. Partnerships between technology teams who usually hold the keys to the raw data and business units, who have the knowledge to build the right decision models is key. If you're not already involved in this linkage, you should be, data mining isn't new for sure. The availability of data is quadrupling and it's everywhere. How do you know what to even look at? How do you know where to begin? How do you know what questions to ask? It's by using the tools that are available for visualization and analytics and knitting together strategies of the company. So it begins with, first of all, making sure you do understand the strategy of the company. So in closing, just to wrap up a bit, many of you joined today, looking for thought leadership on how to be a change agent, a change champion, and how to lead through transformation. Some final thoughts are be brave and drive. Don't do the ride along program, it's very important to drive. Driving can be high risk, but it's also high reward. Embracing the uncertainty of what will happen is how you become brave. Get more and more comfortable with uncertainty, be calm and let data be your map on your journey. Thanks. >> Michelle, thank you so much. So you and I share a love of data and a love of football. You said you want to be the quarterback. I'm more an a line person. >> Well, then I can't do my job without you. >> Great and I'm getting the feeling now, you know, Sudheesh is talking about bungee jumping. My vote is when we're past this pandemic, we both take him to the Delaware Water Gap and we do the cliff jumping. >> Oh that sounds good, I'll watch your watch. >> Yeah, you'll watch, okay. So Michelle, you have so many stakeholders, when you're trying to prioritize the different voices you have the players, you have the owners, you have the league, as you mentioned, the broadcasters, your partners here and football mamas like myself. How do you prioritize when there are so many different stakeholders that you need to satisfy? >> I think balancing across stakeholders starts with aligning on a mission and if you spend a lot of time understanding where everyone's coming from, and you can find the common thread that ties them all together. You sort of do get them to naturally prioritize their work and I think that's very important. So for us at the NFL and even at Disney, it was our core values and our core purpose is so well known and when anything challenges that, we're able to sort of lay that out. But as a change agent, you have to be very empathetic, and I would say empathy is probably your strongest skill if you're a change agent and that means listening to every single stakeholder. Even when they're yelling at you, even when they're telling you your technology doesn't work and you know that it's user error, or even when someone is just emotional about what's happening to them and that they're not comfortable with it. So I think being empathetic, and having a mission, and understanding it is sort of how I prioritize and balance. >> Yeah, empathy, a very popular word this year. I can imagine those coaches and owners yelling, so thank you for your leadership here. So Michelle, I look forward to discussing this more with our other customers and disruptors joining us in a little bit. >> (gentle music) So we're going to take a hard pivot now and go from football to Chernobyl. Chernobyl, what went wrong? 1986, as the reactors were melting down, they had the data to say, "This is going to be catastrophic," and yet the culture said, "No, we're perfect, hide it. Don't dare tell anyone." Which meant they went ahead and had celebrations in Kiev. Even though that increased the exposure, additional thousands getting cancer and 20,000 years before the ground around there can even be inhabited again. This is how powerful and detrimental a negative culture, a culture that is unable to confront the brutal facts that hides data. This is what we have to contend with and this is why I want you to focus on having, fostering a data-driven culture. I don't want you to be a laggard. I want you to be a leader in using data to drive your digital transformation. So I'll talk about culture and technology, is it really two sides of the same coin? Real-world impacts and then some best practices you can use to disrupt and innovate your culture. Now, oftentimes I would talk about culture and I talk about technology. And recently a CDO said to me, "You know, Cindi, I actually think this is two sides of the same coin, one reflects the other." What do you think? Let me walk you through this. So let's take a laggard. What does the technology look like? Is it based on 1990s BI and reporting, largely parametrized reports, on-premises data warehouses, or not even that operational reports. At best one enterprise data warehouse, very slow moving and collaboration is only email. What does that culture tell you? Maybe there's a lack of leadership to change, to do the hard work that Sudheesh referred to, or is there also a culture of fear, afraid of failure, resistance to change, complacency. And sometimes that complacency, it's not because people are lazy. It's because they've been so beaten down every time a new idea is presented. It's like, "No, we're measured on least to serve." So politics and distrust, whether it's between business and IT or individual stakeholders is the norm, so data is hoarded. Let's contrast that with the leader, a data and analytics leader, what does their technology look like? Augmented analytics, search and AI driven insights, not on-premises but in the cloud and maybe multiple clouds. And the data is not in one place but it's in a data lake and in a data warehouse, a logical data warehouse. The collaboration is via newer methods, whether it's Slack or Teams, allowing for that real-time decisioning or investigating a particular data point. So what is the culture in the leaders? It's transparent and trust. There is a trust that data will not be used to punish, that there is an ability to confront the bad news. It's innovation, valuing innovation in pursuit of the company goals. Whether it's the best fan experience and player safety in the NFL or best serving your customers, it's innovative and collaborative. There's none of this, "Oh, well, I didn't invent that. I'm not going to look at that." There's still pride of ownership, but it's collaborating to get to a better place faster. And people feel empowered to present new ideas, to fail fast and they're energized knowing that they're using the best technology and innovating at the pace that business requires. So data is democratized and democratized, not just for power users or analysts, but really at the point of impact, what we like to call the new decision-makers or really the frontline workers. So Harvard Business Review partnered with us to develop this study to say, "Just how important is this? We've been working at BI and analytics as an industry for more than 20 years, why is it not at the front lines? Whether it's a doctor, a nurse, a coach, a supply chain manager, a warehouse manager, a financial services advisor." 87% said they would be more successful if frontline workers were empowered with data-driven insights, but they recognize they need new technology to be able to do that. It's not about learning hard tools. The sad reality only 20% of organizations are actually doing this. These are the data-driven leaders. So this is the culture and technology, how did we get here? It's because state-of-the-art keeps changing. So the first generation BI and analytics platforms were deployed on-premises, on small datasets, really just taking data out of ERP systems that were also on-premises and state-of-the-art was maybe getting a management report, an operational report. Over time, visual based data discovery vendors disrupted these traditional BI vendors, empowering now analysts to create visualizations with the flexibility on a desktop, sometimes larger data, sometimes coming from a data warehouse. The current state-of-the-art though, Gartner calls it augmented analytics. At ThoughtSpot, we call it search and AI driven analytics, and this was pioneered for large scale data sets, whether it's on-premises or leveraging the cloud data warehouses. And I think this is an important point, oftentimes you, the data and analytics leaders, will look at these two components separately. But you have to look at the BI and analytics tier in lock-step with your data architectures to really get to the granular insights and to leverage the capabilities of AI. Now, if you've never seen ThoughtSpot, I'll just show you what this looks like. Instead of somebody hard coding a report, it's typing in search keywords and very robust keywords contains rank, top, bottom, getting to a visual visualization that then can be pinned to an existing pin board that might also contain insights generated by an AI engine. So it's easy enough for that new decision maker, the business user, the non-analyst to create themselves. Modernizing the data and analytics portfolio is hard because the pace of change has accelerated. You used to be able to create an investment, place a bet for maybe 10 years. A few years ago, that time horizon was five years. Now, it's maybe three years and the time to maturity has also accelerated. So you have these different components, the search and AI tier, the data science tier, data preparation and virtualization but I would also say, equally important is the cloud data warehouse. And pay attention to how well these analytics tools can unlock the value in these cloud data warehouses. So ThoughtSpot was the first to market with search and AI driven insights. Competitors have followed suit, but be careful, if you look at products like Power BI or SAP analytics cloud, they might demo well, but do they let you get to all the data without moving it in products like Snowflake, Amazon Redshift, or Azure Synapse, or Google BigQuery, they do not. They require you to move it into a smaller in-memory engine. So it's important how well these new products inter-operate. The pace of change, its acceleration, Gartner recently predicted that by 2022, 65% of analytical queries will be generated using search or NLP or even AI and that is roughly three times the prediction they had just a couple of years ago. So let's talk about the real world impact of culture and if you've read any of my books or used any of the maturity models out there, whether the Gartner IT Score that I worked on or the Data Warehousing Institute also has a maturity model. We talk about these five pillars to really become data-driven. As Michelle spoke about, it's focusing on the business outcomes, leveraging all the data, including new data sources, it's the talent, the people, the technology and also the processes. And often when I would talk about the people in the talent, I would lump the culture as part of that. But in the last year, as I've traveled the world and done these digital events for thought leaders. You have told me now culture is absolutely so important, and so we've pulled it out as a separate pillar. And in fact, in polls that we've done in these events, look at how much more important culture is as a barrier to becoming data-driven. It's three times as important as any of these other pillars. That's how critical it is. And let's take an example of where you can have great data, but if you don't have the right culture, there's devastating impacts. And I will say I have been a loyal customer of Wells Fargo for more than 20 years, but look at what happened in the face of negative news with data. It said, "Hey, we're not doing good cross-selling, customers do not have both a checking account and a credit card and a savings account and a mortgage." They opened fake accounts facing billions in fines, change in leadership that even the CEO attributed to a toxic sales culture and they're trying to fix this, but even recently there's been additional employee backlash saying the culture has not changed. Let's contrast that with some positive examples. Medtronic, a worldwide company in 150 countries around the world. They may not be a household name to you, but if you have a loved one or yourself, you have a pacemaker, spinal implant, diabetes, you know this brand. And at the start of COVID when they knew their business would be slowing down, because hospitals would only be able to take care of COVID patients. They took the bold move of making their IP for ventilators publicly available. That is the power of a positive culture. Or Verizon, a major telecom organization looking at late payments of their customers and even though the U.S. Federal Government said, "Well, you can't turn them off." They said, "We'll extend that even beyond the mandated guidelines," and facing a slow down in the business because of the tough economy, They said, "You know what? We will spend the time upskilling our people, giving them the time to learn more about the future of work, the skills and data and analytics for 20,000 of their employees rather than furloughing them. That is the power of a positive culture. So how can you transform your culture to the best in class? I'll give you three suggestions. Bring in a change agent, identify the relevance or I like to call it WIIFM and organize for collaboration. So the CDO, whatever your title is, Chief Analytics Officer, Chief Digital Officer, you are the most important change agent. And this is where you will hear that oftentimes a change agent has to come from outside the organization. So this is where, for example, in Europe you have the CDO of Just Eat, a takeout food delivery organization coming from the airline industry or in Australia, National Australian Bank taking a CDO within the same sector from TD Bank going to NAB. So these change agents come in, disrupt. It's a hard job. As one of you said to me, it often feels like. I make one step forward and I get knocked down again, I get pushed back. It is not for the faint of heart, but it's the most important part of your job. The other thing I'll talk about is WIIFM What's In It For Me? And this is really about understanding the motivation, the relevance that data has for everyone on the frontline, as well as those analysts, as well as the executives. So, if we're talking about players in the NFL, they want to perform better and they want to stay safe. That is why data matters to them. If we're talking about financial services, this may be a wealth management advisor. Okay, we could say commissions, but it's really helping people have their dreams come true, whether it's putting their children through college or being able to retire without having to work multiple jobs still into your 70s or 80s. For the teachers, teachers you ask them about data. They'll say, "We don't need that, I care about the student." So if you can use data to help a student perform better, that is WIIFM and sometimes we spend so much time talking the technology, we forget, what is the value we're trying to deliver with this? And we forget the impact on the people that it does require change. In fact, the Harvard Business Review study found that 44% said lack of change management is the biggest barrier to leveraging both new technology, but also being empowered to act on those data-driven insights. The third point, organize for collaboration. This does require diversity of thought, but also bringing the technology, the data and the business people together. Now there's not a single one size fits all model for data and analytics. At one point in time, even having a BICC, a BI competency center was considered state of the art. Now for the biggest impact, what I recommend is that you have a federated model centralized for economies of scale. That could be the common data, but then embed these evangelists, these analysts of the future within every business unit, every functional domain. And as you see this top bar, all models are possible, but the hybrid model has the most impact, the most leaders. So as we look ahead to the months ahead, to the year ahead, an exciting time because data is helping organizations better navigate a tough economy, lock in the customer loyalty and I look forward to seeing how you foster that culture that's collaborative with empathy and bring the best of technology, leveraging the cloud, all your data. So thank you for joining us at Thought Leaders. And next, I'm pleased to introduce our first change agent, Tom Mazzaferro Chief Data Officer of Western Union and before joining Western Union, Tom made his Mark at HSBC and JP Morgan Chase spearheading digital innovation in technology, operations, risk compliance and retail banking. Tom, thank you so much for joining us today. (gentle music) >> Very happy to be here and looking forward to talking to all of you today. So as we look to move organizations to a data-driven capability into the future, there is a lot that needs to be done on the data side, but also how does data connect and enable different business teams and the technology teams into the future? As we look across our data ecosystems and our platforms, and how we modernize that to the cloud in the future, it all needs to basically work together, right? To really be able to drive an organization from a data standpoint, into the future. That includes being able to have the right information with the right quality of data, at the right time to drive informed business decisions, to drive the business forward. As part of that, we actually have partnered with ThoughtSpot to actually bring in the technology to help us drive that. As part of that partnership and it's how we've looked to integrate it into our overall business as a whole. We've looked at, how do we make sure that our business and our professional lives, right? Are enabled in the same ways as our personal lives. So for example, in your personal lives, when you want to go and find something out, what do you do? You go onto google.com or you go onto Bing or you go onto Yahoo and you search for what you want, search to find an answer. ThoughtSpot for us is the same thing, but in the business world. So using ThoughtSpot and other AI capability is it's allowed us to actually enable our overall business teams in our company to actually have our information at our fingertips. So rather than having to go and talk to someone, or an engineer to go pull information or pull data. We actually can have the end users or the business executives, right. Search for what they need, what they want, at the exact time that they actually need it, to go and drive the business forward. This is truly one of those transformational things that we've put in place. On top of that, we are on a journey to modernize our larger ecosystem as a whole. That includes modernizing our underlying data warehouses, our technology, our... The local environments and as we move that, we've actually picked two of our cloud providers going to AWS and to GCP. We've also adopted Snowflake to really drive and to organize our information and our data, then drive these new solutions and capabilities forward. So a big portion of it though is culture. So how do we engage with the business teams and bring the IT teams together, to really help to drive these holistic end-to-end solutions and capabilities, to really support the actual business into the future. That's one of the keys here, as we look to modernize and to really enhance our organizations to become data-driven. This is the key. If you can really start to provide answers to business questions before they're even being asked and to predict based upon different economic trends or different trends in your business, what decisions need to be made and actually provide those answers to the business teams before they're even asking for it. That is really becoming a data-driven organization and as part of that, it really then enables the business to act quickly and take advantage of opportunities as they come in based upon industries, based upon markets, based upon products, solutions or partnerships into the future. These are really some of the keys that become crucial as you move forward, right, into this new age, Especially with COVID. With COVID now taking place across the world, right? Many of these markets, many of these digital transformations are celebrating and are changing rapidly to accommodate and to support customers in these very difficult times. As part of that, you need to make sure you have the right underlying foundation, ecosystems and solutions to really drive those capabilities and those solutions forward. As we go through this journey, both in my career but also each of your careers into the future, right? It also needs to evolve, right? Technology has changed so drastically in the last 10 years, and that change is only accelerating. So as part of that, you have to make sure that you stay up to speed, up to date with new technology changes, both on the platform standpoint, tools, but also what do our customers want, what do our customers need and how do we then service them with our information, with our data, with our platform, and with our products and our services to meet those needs and to really support and service those customers into the future. This is all around becoming a more data-driven organization, such as how do you use your data to support your current business lines, but how do you actually use your information and your data to actually better support your customers, better support your business, better support your employees, your operations teams and so forth. And really creating that full integration in that ecosystem is really when you start to get large dividends from these investments into the future. With that being said, I hope you enjoyed the segment on how to become and how to drive a data-driven organization, and looking forward to talking to you again soon. Thank you. >> Tom, that was great. Thanks so much and now going to have to drag on you for a second. As a change agent you've come in, disrupted and how long have you been at Western Union? >> Only nine months, so just started this year, but there have been some great opportunities to integrate changes and we have a lot more to go, but we're really driving things forward in partnership with our business teams and our colleagues to support those customers going forward. >> Tom, thank you so much. That was wonderful. And now, I'm excited to introduce you to Gustavo Canton, a change agent that I've had the pleasure of working with meeting in Europe and he is a serial change agent. Most recently with Schneider Electric but even going back to Sam's Clubs. Gustavo, welcome. (gentle music) >> So, hey everyone, my name is Gustavo Canton and thank you so much, Cindi, for the intro. As you mentioned, doing transformations is, you know, a high reward situation. I have been part of many transformations and I have led many transformations. And, what I can tell you is that it's really hard to predict the future, but if you have a North Star and you know where you're going, the one thing that I want you to take away from this discussion today is that you need to be bold to evolve. And so, in today, I'm going to be talking about culture and data, and I'm going to break this down in four areas. How do we get started, barriers or opportunities as I see it, the value of AI and also, how you communicate. Especially now in the workforce of today with so many different generations, you need to make sure that you are communicating in ways that are non-traditional sometimes. And so, how do we get started? So, I think the answer to that is you have to start for you yourself as a leader and stay tuned. And by that, I mean, you need to understand, not only what is happening in your function or your field, but you have to be very in tune what is happening in society socioeconomically speaking, wellbeing. You know, the common example is a great example and for me personally, it's an opportunity because the number one core value that I have is wellbeing. I believe that for human potential for customers and communities to grow, wellbeing should be at the center of every decision. And as somebody mentioned, it's great to be, you know, stay in tune and have the skillset and the courage. But for me personally, to be honest, to have this courage is not about not being afraid. You're always afraid when you're making big changes and you're swimming upstream, but what gives me the courage is the empathy part. Like I think empathy is a huge component because every time I go into an organization or a function, I try to listen very attentively to the needs of the business and what the leaders are trying to do. But I do it thinking about the mission of, how do I make change for the bigger workforce or the bigger good despite the fact that this might have perhaps implication for my own self interest in my career. Right? Because you have to have that courage sometimes to make choices that are not well seen, politically speaking, but are the right thing to do and you have to push through it. So the bottom line for me is that, I don't think we're they're transforming fast enough. And the reality is, I speak with a lot of leaders and we have seen stories in the past and what they show is that, if you look at the four main barriers that are basically keeping us behind budget, inability to act, cultural issues, politics and lack of alignment, those are the top four. But the interesting thing is that as Cindi has mentioned, these topic about culture is actually gaining more and more traction. And in 2018, there was a story from HBR and it was about 45%. I believe today, it's about 55%, 60% of respondents say that this is the main area that we need to focus on. So again, for all those leaders and all the executives who understand and are aware that we need to transform, commit to the transformation and set a deadline to say, "Hey, in two years we're going to make this happen. What do we need to do, to empower and enable these change agents to make it happen? You need to make the tough choices. And so to me, when I speak about being bold is about making the right choices now. So, I'll give you examples of some of the roadblocks that I went through as I've been doing transformations, most recently, as Cindi mentioned in Schneider. There are three main areas, legacy mindset and what that means is that, we've been doing this in a specific way for a long time and here is how we have been successful. What worked in the past is not going to work now. The opportunity there is that there is a lot of leaders, who have a digital mindset and they're up and coming leaders that are perhaps not yet fully developed. We need to mentor those leaders and take bets on some of these talents, including young talent. We cannot be thinking in the past and just wait for people, you know, three to five years for them to develop because the world is going in a way that is super-fast. The second area and this is specifically to implementation of AI. It's very interesting to me because just the example that I have with ThoughtSpot, right? We went on implementation and a lot of the way the IT team functions or the leaders look at technology, they look at it from the prism of the prior or success criteria for the traditional BIs, and that's not going to work. Again, the opportunity here is that you need to redefine what success look like. In my case, I want the user experience of our workforce to be the same user experience you have at home. It's a very simple concept and so we need to think about, how do we gain that user experience with these augmented analytics tools and then work backwards to have the right talent, processes, and technology to enable that. And finally and obviously with COVID, a lot of pressure in organizations and companies to do more with less. And the solution that most leaders I see are taking is to just minimize costs sometimes and cut budget. We have to do the opposite. We have to actually invest on growth areas, but do it by business question. Don't do it by function. If you actually invest in these kind of solutions, if you actually invest on developing your talent and your leadership to see more digitally, if you actually invest on fixing your data platform, it's not just an incremental cost. It's actually this investment is going to offset all those hidden costs and inefficiencies that you have on your system, because people are doing a lot of work and working very hard but it's not efficient and it's not working in the way that you might want to work. So there is a lot of opportunity there and just to put in terms of perspective, there have been some studies in the past about, you know, how do we kind of measure the impact of data? And obviously, this is going to vary by organization maturity, there's going to be a lot of factors. I've been in companies who have very clean, good data to work with and I've been with companies that we have to start basically from scratch. So it all depends on your maturity level. But in this study, what I think is interesting is they try to put a tagline or a tag price to what is the cost of incomplete data. So in this case, it's about 10 times as much to complete a unit of work when you have data that is flawed as opposed to having perfect data. So let me put that just in perspective, just as an example, right? Imagine you are trying to do something and you have to do 100 things in a project, and each time you do something, it's going to cost you a dollar. So if you have perfect data, the total cost of that project might be $100. But now let's say you have 80% perfect data and 20% flawed data. By using this assumption that flawed data is 10 times as costly as perfect data, your total costs now becomes $280 as opposed to $100. This just for you to really think about as a CIO, CTO, you know CHRO, CEO, "Are we really paying attention and really closing the gaps that we have on our data infrastructure?" If we don't do that, it's hard sometimes to see the snowball effect or to measure the overall impact, but as you can tell, the price tag goes up very, very quickly. So now, if I were to say, how do I communicate this or how do I break through some of these challenges or some of these barriers, right? I think the key is, I am in analytics, I know statistics obviously and love modeling, and, you know, data and optimization theory, and all that stuff. That's what I came to analytics, but now as a leader and as a change agent, I need to speak about value and in this case, for example, for Schneider. There was this tagline, make the most of your energy. So the number one thing that they were asking from the analytics team was actually efficiency, which to me was very interesting. But once I understood that, I understood what kind of language to use, how to connect it to the overall strategy and basically, how to bring in the right leaders because you need to, you know, focus on the leaders that you're going to make the most progress, you know. Again, low effort, high value. You need to make sure you centralize all the data as you can, you need to bring in some kind of augmented analytics, you know, solution. And finally, you need to make it super-simple for the, you know, in this case, I was working with the HR teams and other areas, so they can have access to one portal. They don't have to be confused and looking for 10 different places to find information. I think if you can actually have those four foundational pillars, obviously under the guise of having a data-driven culture, that's when you can actually make the impact. So in our case, it was about three years total transformation, but it was two years for this component of augmented analytics. It took about two years to talk to, you know, IT, get leadership support, find the budgeting, you know, get everybody on board, make sure the success criteria was correct. And we call this initiative, the people analytics portal. It was actually launched in July of this year and we were very excited and the audience was very excited to do this. In this case, we did our pilot in North America for many, many, many factors but one thing that is really important is as you bring along your audience on this, you know. You're going from Excel, you know, in some cases or Tableu to other tools like, you know, ThoughtSpot. You need to really explain them what is the difference and how this tool can truly replace some of the spreadsheets or some of the views that you might have on these other kinds of tools. Again, Tableau, I think it's a really good tool. There are other many tools that you might have in your toolkit but in my case, personally, I feel that you need to have one portal. Going back to Cindi's points, that really truly enable the end user. And I feel that this is the right solution for us, right? And I will show you some of the findings that we had in the pilot in the last two months. So this was a huge victory and I will tell you why, because it took a lot of effort for us to get to this stage and like I said, it's been years for us to kind of lay the foundation, get the leadership, initiating culture so people can understand, why you truly need to invest on augmented analytics. And so, what I'm showing here is an example of how do we use basically, you know, a tool to capturing video, the qualitative findings that we had, plus the quantitative insights that we have. So in this case, our preliminary results based on our ambition for three main metrics. Hours saved, user experience and adoption. So for hours saved, our ambition was to have 10 hours per week for employee to save on average. User experience, our ambition was 4.5 and adoption 80%. In just two months, two months and a half of the pilot, we were able to achieve five hours per week per employee savings, a user experience for 4.3 out of five and adoption of 60%. Really, really amazing work. But again, it takes a lot of collaboration for us to get to the stage from IT, legal, communications, obviously the operations things and the users. In HR safety and other areas that might be basically stakeholders in this whole process. So just to summarize, this kind of effort takes a lot of energy. You are a change agent, you need to have courage to make this decision and understand that, I feel that in this day and age with all this disruption happening, we don't have a choice. We have to take the risk, right? And in this case, I feel a lot of satisfaction in how we were able to gain all these great resource for this organization and that give me the confident to know that the work has been done and we are now in a different stage for the organization. And so for me, it's just to say, thank you for everybody who has belief, obviously in our vision, everybody who has belief in, you know, the work that we were trying to do and to make the life of our, you know, workforce or customers and community better. As you can tell, there is a lot of effort, there is a lot of collaboration that is needed to do something like this. In the end, I feel very satisfied with the accomplishments of this transformation and I just want to tell for you, if you are going right now in a moment that you feel that you have to swim upstream, you know, work with mentors, work with people in the industry that can help you out and guide you on this kind of transformation. It's not easy to do, it's high effort, but it's well worth it. And with that said, I hope you are well and it's been a pleasure talking to you. Talk to you soon. Take care. >> Thank you, Gustavo. That was amazing. All right, let's go to the panel. (light music) Now I think we can all agree how valuable it is to hear from practitioners and I want to thank the panel for sharing their knowledge with the community. Now one common challenge that I heard you all talk about was bringing your leadership and your teams along on the journey with you. We talk about this all the time and it is critical to have support from the top. Why? Because it directs the middle and then it enables bottoms up innovation effects from the cultural transformation that you guys all talked about. It seems like another common theme we heard is that you all prioritize database decision making in your organizations. And you combine two of your most valuable assets to do that and create leverage, employees on the front lines, and of course the data. Now as as you rightly pointed out, Tom, the pandemic has accelerated the need for really leaning into this. You know, the old saying, if it ain't broke, don't fix it, well COVID has broken everything and it's great to hear from our experts, you know, how to move forward, so let's get right into it. So Gustavo, let's start with you. If I'm an aspiring change agent and let's say I'm a budding data leader, what do I need to start doing? What habits do I need to create for long-lasting success? >> I think curiosity is very important. You need to be, like I said, in tune to what is happening, not only in your specific field, like I have a passion for analytics, I've been doing it for 50 years plus, but I think you need to understand wellbeing of the areas across not only a specific business. As you know, I come from, you know, Sam's Club, Walmart retail. I've been in energy management, technology. So you have to try to push yourself and basically go out of your comfort zone. I mean, if you are staying in your comfort zone and you want to just continuous improvement, that's just going to take you so far. What you have to do is, and that's what I try to do, is I try to go into areas, businesses and transformations, that make me, you know, stretch and develop as a leader. That's what I'm looking to do, so I can help transform the functions, organizations, and do the change management, the essential mindset that's required for this kind of effort. >> Well, thank you for that. That is inspiring and Cindi you love data and the data is pretty clear that diversity is a good business, but I wonder if you can, you know, add your perspectives to this conversation? >> Yeah, so Michelle has a new fan here because she has found her voice. I'm still working on finding mine and it's interesting because I was raised by my dad, a single dad, so he did teach me how to work in a predominantly male environment, but why I think diversity matters more now than ever before and this is by gender, by race, by age, by just different ways of working and thinking, is because as we automate things with AI, if we do not have diverse teams looking at the data, and the models, and how they're applied, we risk having bias at scale. So this is why I think I don't care what type of minority you are, finding your voice, having a seat at the table and just believing in the impact of your work has never been more important and as Michelle said, more possible. >> Great perspectives, thank you. Tom, I want to go to you. So, I mean, I feel like everybody in our businesses is in some way, shape, or form become a COVID expert, but what's been the impact of the pandemic on your organization's digital transformation plans? >> We've seen a massive growth, actually, in our digital business over the last 12 months really, even acceleration, right, once COVID hit. We really saw that in the 200 countries and territories that we operate in today and service our customers in today, that there's been a huge need, right, to send money to support family, to support friends, and to support loved ones across the world. And as part of that we are very honored to be able to support those customers that, across all the centers today, but as part of the acceleration, we need to make sure that we have the right architecture and the right platforms to basically scale, right? To basically support and provide the right kind of security for our customers going forward. So as part of that, we did do some pivots and we did accelerate some of our plans on digital to help support that overall growth coming in and to support our customers going forward, because during these times, during this pandemic, right, this is the most important time and we need to support those that we love and those that we care about. And doing that some of those ways is actually by sending money to them, support them financially. And that's where really our products and our services come into play that, you know, and really support those families. So, it was really a great opportunity for us to really support and really bring some of our products to the next level and supporting our business going forward. >> Awesome, thank you. Now, I want to come back to Gustavo. Tom, I'd love for you to chime in too. Did you guys ever think like you were pushing the envelope too much in doing things with data or the technology that it was just maybe too bold, maybe you felt like at some point it was failing, or you're pushing your people too hard? Can you share that experience and how you got through it? >> Yeah, the way I look at it is, you know, again, whenever I go to an organization, I ask the question, "Hey, how fast you would like to conform?" And, you know, based on the agreements on the leadership and the vision that we want to take place, I take decisions and I collaborate in a specific way. Now, in the case of COVID, for example, right, it forces us to remove silos and collaborate in a faster way. So to me, it was an opportunity to actually integrate with other areas and drive decisions faster, but make no mistake about it, when you are doing a transformation, you are obviously trying to do things faster than sometimes people are comfortable doing, and you need to be okay with that. Sometimes you need to be okay with tension or you need to be okay, you know, debating points or making repetitive business cases until people connect with the decision because you understand and you are seeing that, "Hey, the CEO is making a one, two year, you know, efficiency goal. The only way for us to really do more with less is for us to continue this path. We can not just stay with the status quo, we need to find a way to accelerate the transformation." That's the way I see it. >> How about Utah, we were talking earlier with Sudheesh and Cindi about that bungee jumping moment. What can you share? >> Yeah, you know, I think you hit upon it. Right now, the pace of change will be the slowest pace that you see for the rest of your career. So as part of that, right, this is what I tell my team, is that you need to be, you need to feel comfortable being uncomfortable. Meaning that we have to be able to basically scale, right? Expand and support the ever changing needs in the marketplace and industry and our customers today, and that pace of change that's happening, right? And what customers are asking for and the competition in the marketplace, it's only going to accelerate. So as part of that, you know, as you look at how you're operating today in your current business model, right? Things are only going to get faster. So you have to plan and to align and to drive the actual transformation, so that you can scale even faster into the future. So it's part of that, that's what we're putting in place here, right? It's how do we create that underlying framework and foundation that allows the organization to basically continue to scale and evolve into the future? >> Yeah, we're definitely out of our comfort zones, but we're getting comfortable with it. So Cindi, last question, you've worked with hundreds of organizations and I got to believe that, you know, some of the advice you gave when you were at Gartner, which was pre-COVID, maybe sometimes clients didn't always act on it. You know, not my watch or for whatever, variety of reasons, but it's being forced on them now. But knowing what you know now that, you know, we're all in this isolation economy, how would you say that advice has changed? Has it changed? What's your number one action and recommendation today? >> Yeah, well first off, Tom, just freaked me out. What do you mean, this is the slowest ever? Even six months ago I was saying the pace of change in data and analytics is frenetic. So, but I think you're right, Tom, the business and the technology together is forcing this change. Now, Dave, to answer your question, I would say the one bit of advice, maybe I was a little more very aware of the power in politics and how to bring people along in a way that they are comfortable and now I think it's, you know what, you can't get comfortable. In fact, we know that the organizations that were already in the cloud have been able to respond and pivot faster. So, if you really want to survive, as Tom and Gustavo said, get used to being uncomfortable. The power and politics are going to happen, break the rules, get used to that and be bold. Do not be afraid to tell somebody they're wrong and they're not moving fast enough. I do think you have to do that with empathy, as Michelle said and Gustavo, I think that's one of the key words today besides the bungee jumping. So I want to know where Sudheesh is going to go bungee jumping. (all chuckling) >> Guys, fantastic discussion, really. Thanks again to all the panelists and the guests, it was really a pleasure speaking with you today. Really, virtually all of the leaders that I've spoken to in theCUBE program recently, they tell me that the pandemic is accelerating so many things. Whether it's new ways to work, we heard about new security models and obviously the need for cloud. I mean, all of these things are driving true enterprise-wide digital transformation, not just as I said before, lip service. You know, sometimes we minimize the importance and the challenge of building culture and in making this transformation possible. But when it's done right, the right culture is going to deliver tournament results. You know, what does that mean? Getting it right. Everybody's trying to get it right. My biggest takeaway today is it means making data part of the DNA of your organization. And that means making it accessible to the people in your organization that are empowered to make decisions, decisions that can drive new revenue, cut costs, speed access to critical care, whatever the mission is of your organization, data can create insights and informed decisions that drive value. Okay, let's bring back Sudheesh and wrap things up. Sudheesh, please bring us home. >> Thank you, thank you, Dave. Thank you, theCUBE team, and thanks goes to all of our customers and partners who joined us, and thanks to all of you for spending the time with us. I want to do three quick things and then close it off. The first thing is I want to summarize the key takeaways that I heard from all four of our distinguished speakers. First, Michelle, I will simply put it, she said it really well. That is be brave and drive, don't go for a drive alone. That is such an important point. Often times, you know the right thing that you have to do to make the positive change that you want to see happen, but you wait for someone else to do it, not just, why not you? Why don't you be the one making that change happen? That's the thing that I picked up from Michelle's talk. Cindi talked about finding, the importance of finding your voice. Taking that chair, whether it's available or not, and making sure that your ideas, your voice is heard and if it requires some force, then apply that force. Make sure your ideas are heard. Gustavo talked about the importance of building consensus, not going at things all alone sometimes. The importance of building the quorum, and that is critical because if you want the changes to last, you want to make sure that the organization is fully behind it. Tom, instead of a single takeaway, what I was inspired by is the fact that a company that is 170 years old, 170 years old, 200 companies and 200 countries they're operating in and they were able to make the change that is necessary through this difficult time in a matter of months. If they could do it, anyone could. The second thing I want to do is to leave you with a takeaway, that is I would like you to go to ThoughtSpot.com/nfl because our team has made an app for NFL on Snowflake. I think you will find this interesting now that you are inspired and excited because of Michelle's talk. And the last thing is, please go to ThoughtSpot.com/beyond. Our global user conference is happening in this December. We would love to have you join us, it's, again, virtual, you can join from anywhere. We are expecting anywhere from five to 10,000 people and we would love to have you join and see what we've been up to since last year. We have a lot of amazing things in store for you, our customers, our partners, our collaborators, they will be coming and sharing. We'll be sharing things that we have been working to release, something that will come out next year. And also some of the crazy ideas our engineers have been cooking up. All of those things will be available for you at ThoughtSpot Beyond. Thank you, thank you so much.

>> Narrator: From around the globe, it's theCUBE. With digital coverage of smart data marketplaces. Brought to you by Io-Tahoe. >> Digital transformation has really gone from a buzzword to a mandate, but digital business is a data business. And for the last several months we've been working with Io-Tahoe on an ongoing content series, focused on smart data and automation to drive better insights and outcomes, essentially putting data to work. And today we're going to do a deeper dive on automating data discovery. And one of the thought leaders in this space is Ajay Vohora, who's the CEO of Io-Tahoe. Once again, joining me, Ajay good to see you. Thanks for coming on. >> Great to be here, David, thank you. >> So let's, let's start by talking about some of the business realities and what are the economics that are driving automated data discovery? Why is that so important? >> Yeah, on this one, David it's a number of competing factors. We've got the reality of data which may be sensitive. So there's control. Three other elements wanting to drive value from that data to innovation. You can't really drive a lot of value without exchanging data. So the ability to exchange data and to manage those cost overheads and data discovery is at the root of managing that in an automated way to classify that data and set some policies to put that automation in place. >> Yeah, look, we have a picture of this. If we could bring it up guys, cause I want to, Ajay, help the audience understand kind of where data discovery fits in here. This is, as we talked about, this is a complicated situation for a lot of customers. They've got variety of different tools and you've really laid it out nicely here in this diagram. So, take us through sort of where that piece fits. >> Yeah, I mean, we're at the right hand side of this exchange, you know. We're really now in a data driven economy that is everything's connected through APIs that we consume online through mobile apps. And what's not apparent is the chain of activities and tasks that have to go into serving that data to an API at the outset. They may be many legacy systems, technologies, platforms On-premise, in cloud, hybrid, you name it and across those silos, getting to a unified view is the heavy lifting. I think we've seen some, some great impacts that BI tools, such as Power BI, Tableau, Looker, and so on, and Qlik have had, and they're in our ecosystem on visualizing Data and, you know, CEOs, managers, people that are working in companies day-to-day get a lot of value from saying, "What's the real time activity? "What was the trend over this month versus last month?" The tools to enable that, you know, we hear a lot of good things that we're doing with Snowflake, MongoDB on the public Cloud platforms, GCP Azure about enabling building those pipelines to feed into those analytics. But what often gets hidden is how do you source that data that could be locked into a mainframe, a data warehouse, IOT data, and pull over all of that together. And that is the reality of that is it's a lot of heavy lifting. It's hands on work that can be time consuming. And the issue there is that data may have value. It might have potential to have an impact on the top line for a business, on outcomes for consumers, but you're never really sure unless you've done the investigation, discovered it, unified that, and be able to serve that through to other technologies. >> Guys, if you would bring that picture back up again, because Ajay you made a point and I want to land on that for a second. There's a lot of manual curating. An example would be the data catalog. You know, data scientists complain all the time that they're manually wrangling data. And so you're trying to inject automation into the cycle. And then the other piece that I want you to address is the importance of APIs. You really can't do this without an architecture that allows you to connect things together that sort of enables some of the automation. >> Yep, I mean, I'll take that in two parts, David, the APIs, so virtual machines connected by APIs, business rules, and business logic driven by APIs, applications, so everything across the stack from infrastructure down to the network, hardware is all connected through APIs and the work of serving data through to an API, building those pipelines, is often miscalculated, just how much manual effort that takes and that manual effort, we've got a nice list here of what we automate down at the bottom, those tasks of indexing, labeling, mapping across different legacy systems, all of that takes away from the job of a data scientist or data engineer, looking to produce value, monetize data, and to help that business convey to consumers. >> Yeah, it's that top layer that the business sees, of course, there's a lot of work that has to go into achieving that. I want to talk about some of the key tech trends that you're seeing. And one of the things that we talk about a lot is metadata. The importance of metadata, you know, can't be understated. What are some of the big trends that you're seeing metadata and others? >> Yeah, I'll summarize it as five. There's a trend now look at metadata more holistically across the enterprise. And that really makes sense from trying to look across different data silos and apply a policy to manage that data. So that's the control piece. That's that lever. The other side, sometimes competing with that control around sensitive data around managing the cost of data is innovation. Innovation being able to speculate and experiment and try things out where you don't really know what the outcome is if you're a data scientist and engineer, you've got a hypothesis and therefore you've got that tension between control over data and innovation and driving value from it. So enterprise wide metadata management is really helping to unlock where might that latent value be across that sets of data. The other piece is adaptive data governance. Those controls that stick from the data policemen, data stewards, where they're trying to protect the organization, protect the brand, protect consumers data necessary, but in different use cases, you might want to nuance and apply a different policy to govern that data relevant to the context where you might have data that is less sensitive, that can be used for innovation and adapting the style of governance to fit the context is another trend that we're seeing coming up here. A few others is where we're sitting quite extensively in working with automating data discovery. We're now breaking that down into what can we direct? What do we know is a business outcome is a known upfront objective and direct that data discovery to towards that. And that means applying our algorithms around technology and our tools towards solving a known problem. The other one is autonomous data discovery. And that means, you know, trying to allow background processes to understand what changes are happening with data over time, flagging those anomalies. And the reason that's important is when you look over a length of time to see different spikes, different trends and activity, that's really giving a data ops team the ability to manage and calibrate how they're applying policies and controls the data. And the last two, David, that we're seeing is this huge drive towards self-service. So re-imagining how to apply policy data governance into the hands of a data consumer inside a business, or indeed the consumer themselves, to self-service if they're a banking customer or healthcare customer and the policies and the controls and rules, making sure that those are all in place to adaptively serve those data marketplaces that when are involved in creating. >> I want to ask you about the autonomous data discovering, the adaptive data governance, is the problem we're addressing there one of quality, in other words, machines are better than humans are at doing this? Is it one of scale? That humans just don't don't scale that well? Is it both? Can you add some color to that? >> Yeah, honestly, it's the same equation that existed 10 years ago, 20 years ago, it's being exacerbated, but it's that equation of how do I control all the things that I need to protect? How do I enable innovation where it is going to deliver business value? How do I exchange data between a customer, somebody in my supply chain safely, and do all of that whilst managing the fourth leg, which is cost overheads. There's not an open checkbook here. I've got to figure out if I'm the CIO and CDO, how I do all of this within a fixed budget. So those aspects have always been there, now with more choices, infrastructure in the Cloud, API driven applications, On-premises, and that is expanding the choices that a business has and how they put their data to work. It's also then creating a layer of management and data governance that really has to now manage those four aspects, control, innovation, exchange of data, and the cost overhead. >> That top layer of the first slide that we showed was all about the business value. So, I wonder if we could drill into the business impact a little bit. What are your customers seeing specifically in terms of the impact of all this automation on their business? >> Yeah, so we've had some great results. I think a few of the biggest have been helping customers move away from manually curating their data and their metadata. It used to be a time where if data initiatives or data governance initiatives, there'd be teams of people manually feeding a data catalog. And it's great to have that inventory of classified data to be able to understand single version of the truth, but having 10, 15 people manually process that, keep it up to date, when it's moving feet, the reality of it is what's true about data today, add another few sources and a few months time to your business, start collaborating with new partners, suddenly the landscape has changed. The amount of work has gone up, but what we're finding is through automating, creating that data discovery, feeding our data catalog, that's releasing a lot more time for our customers to spend on innovating and managing their data. A couple of others is around self service data analytics, moving the choices of what data might have business value into the hands of business users and data consumers to have faster cycle times around generating insights. And we're really helping them by automating the creation of those data sets that are needed for that. And the last piece, I'd have to say where we're seeing impacts more recently is in the exchange of data. There are a number of marketplaces out there who are now being compelled to become more digital, to rewire their business processes and everything from an RPA initiative to automation involving digital transformation is having CIOs, chief data officers and enterprise architects rethink how do they, how do they rewire the pipelines for their data to feed that digital transformation? >> Yeah, to me, it comes down to monetization. Now, of course, that's for a for-profit industry. For non-profits, for sure, the cost cutting or in the case of healthcare, which we'll talk about in a moment, I mean, it's patient outcomes, but the job of a Chief Data Officer has gone from data quality and governance and compliance to really figuring out how data can be monetized, not necessarily selling the data, but how it contributes to the monetization of the company. And then really understanding specifically for that organization, how to apply that. And that is a big challenge. We sort of chatted about 10 years ago, the early days of a dupe. And then 1% of the companies had enough engineers to figure it out, but now the tooling is available. The technology is there and the practices are there. And that really, to me is the bottom line, Ajay, is it's show me the money. >> Absolutely. It's definitely is focusing in on the single view of that customer and where we're helping there is to pull together those disparate, siloed sources of data to understand what are the needs of the patient, of the broker of the, if it's insurance? What are the needs of the supply chain manager, if it's manufacturing? And providing that 360 view of data is helping to see, helping that individual unlock the value for the business. So data's providing the lens provided, you know which data it is that can assist in doing that. >> And, you know, you mentioned RPA before, I had an RPA customer tell me she was a Six Sigma expert and she told me, "We would never try to apply Six Sigma "to a business process, "but with RPA we can do so very cheaply." Well, what that means is lower costs. It means better employee satisfaction and really importantly, better customer satisfaction and better customer outcomes. Let's talk about healthcare for a minute because it's a really important industry. It's one that is ripe for disruption and has really been, up until recently, pretty slow to adopt a lot of the major technologies that have been made available. But what are you seeing in terms of this theme we're using a putting data to work in healthcare specifically? >> Yeah, I mean, health care's has had a lot thrown at it. There's been a lot of change in terms of legislation recently, particularly in the U.S. market, in other economies, healthcare is on a path to becoming more digital. And part of that is around transparency of price. So, to be operating effectively as a healthcare marketplace, being able to have that price transparency around what an elective procedure is going to cost before taking that step forward. It's super important to have an informed decision around that. So if we look at the U.S., for example, we've seen that healthcare costs annually have risen to $4 trillion, but even with all of that cost, we have healthcare consumers who are reluctant sometimes to take up healthcare even if they have symptoms. And a lot of that is driven through not knowing what they're opening themselves up to. And, you know, I think David, if you or I were to book travel a holiday, maybe, or trip, we'd want to know what we're in for, what we're paying for upfront. But sometimes in healthcare that choice, the option might be the plan, but the cost that comes with it isn't. So recent legislation in the U.S. is certainly helpful to bring forward that price transparency. The underlying issue there though is the disparate different format types of data that are being used from payers, patients, employers, different healthcare departments to try and make that work. And where we're helping on that aspect in particular related to price transparency is to help make that data machine readable. So, sometimes with data, the beneficiary might be a person, but in a lot of cases, now we're seeing the ability to have different systems interact and exchange data in order to process the workflow to generate online lists of pricing from a provider that's been negotiated with a payer is really an enabling factor. >> So guys, I wonder if you could bring up the next slide, which is kind of the nirvana. So, if you saw the previous slide that the middle there was all different shapes and presumably to disparate data, this is the outcome that you want to get, where everything fits together nicely. And you've got this open exchange. It's not opaque as it is today. It's not bubble gum, band-aids and duct tape, but describe this sort of outcome that you're trying to achieve and maybe a little bit about what it's going to take to get there. >> Ajay: Yeah, that that's the culmination of a number of things. It's making sure that the data is machine readable, making it available to APIs, that could be RPA tools. We're working with technology companies that employ RPA for healthcare, and specifically to manage that patient and payer data to bring that together. In our data discovery, what we're able to do is to classify that data and have it made available to a downstream tool technology or person to apply that, that workflow to the data. So this looks like nirvana, it looks like utopia, but it's, you know, the end objective of a journey that we can see in different economies, that are at different stages of maturity in turning healthcare into a digital service even so that you can consume it from where you live, from home with telemedicine and tele care. >> Yeah, so, and this is not just for healthcare, but you know, you want to achieve that self-service data marketplace in virtually any industry. You're working with TCS, Tata Consulting Services to achieve this. You know, a company like Io-Tahoe has to have partnerships with organizations that have deep industry expertise. Talk about your relationship with TCS and what you guys are doing specifically in this regard. >> Yeah, we've been working with TCS now for a long while and we'll be announcing some of those initiatives here where we're now working together to reach their customers where they've got a brilliant framework of business, 4.0, where they're re-imagining with the clients, how their business can operate with AI, with automation and become more agile and digital. Our technology, now, the reams of patients that we have in our portfolio, being able to apply that at scale, on a global scale across industries, such as banking, insurance and healthcare is really allowing us to see a bigger impact on consumer outcomes, patient outcomes. And the feedback from TCS is that we're really helping in those initiatives remove that friction. They talk a lot about data friction. I think that's a polite term for the image that we just saw with the disparate technologies that the legacy that has built up. So if we want to create a transformation, having that partnership with TCS across industries is giving us that reach and that impact on many different people's day-to-day jobs and lives. >> Let's talk a little bit about the Cloud. It's a topic that we've hit on quite a bit here in this content series. But, but you know, the Cloud companies, the big hyper-scalers, they've put everything into the Cloud, right? But customers are more circumspect than that. But at the same time, machine intelligence, ML, AI, the Cloud is a place to do a lot of that. That's where a lot of the innovation occurs. And so what are your thoughts on getting to the Cloud, putting data to work, if you will, with machine learning, stuff that you're doing with AWS, what's your fit there? >> Yeah, we, David, we work with all of the Cloud platforms, Microsoft Azure, GCP, IBM, but we're expanding our partnership now with AWS. And we're really opening up the ability to work with their Greenfield accounts, where a lot of that data, that technology is in their own data centers at the customer. And that's across banking, healthcare, manufacturing, and insurance. And for good reason, a lot of companies that have taken the time to see what works well for them with the technologies that the Cloud providers are offering, and a lot of cases, testing services or analytics using the Cloud to move workloads to the Cloud to drive data analytics is a real game changer. So there's good reason to maintain a lot of systems On-premise. If that makes sense from a cost, from a liability point of view and the number of clients that we work with that do have, and will keep their mainframe systems when in Cobra is no surprise to us, but equally they want to tap into technologies that AWS has such as SageMaker. The issue is as a Chief Data Officer, I didn't have the budget to move everything to the Cloud they want, I might want to show some results first upfront to my business users and work closely with my Chief Marketing Officer to look at what's happening in terms of customer trends and customer behavior> What are the customer outcomes, patient outcomes and partner outcomes that you can achieve through analytics, data science? So, working with AWS and with clients to manage that hybrid topology of some of that data being in the Cloud, being put to work with AWS SageMaker and Io-Tahoe being used to identify where is the data that needs to be amalgamated and curated to provide the dataset for machine learning, advanced analytics to have an impact for the business. >> So what are the critical attributes of what you're looking at to help customers decide what to move and what the keep if you will? >> Well, one of the quickest outcomes that we help customers achieve is to buy that business glossary, you know, that the items of data, that means something to them across those different silos and pull all of that together into a unified view. Once they've got that data engineer working with a business manager to think through, how do we want to create this application? Now, what is the churn model, the loyalty or the propensity model that we want to put in place here? How do we use predictive analytics to understand what needs for a patient that sort of innovation is what we're unlocking, applying a tools such as SageMaker on AWS to then do the computation and to build those models to deliver that outcome is across that value chain. And it goes back to the first picture that we put up, David, you know, the outcome is that API on the back of it, you've got a machine learning model that's been developed in a tool such as Databricks or Jupiter notebook. That data has to be sourced from somewhere. Somebody has to say that, "Yep, "You've got permission to do what you're trying to do without falling foul "of any compliance around data." And it all goes back to discovering that data, classifying it, indexing it in an automated way to cut those timelines down to hours and days. >> Yeah, it's the innovation part of your data portfolio, if you will, that you're going to put into the Cloud, apply tools like SageMaker and others, your tool Azure. I mean, whatever your favorite tool is, you don't care. The customer's going to choose that. And you know, the Cloud vendors, maybe they want you to use their tool, but they're making their marketplaces available to everybody, but it's that innovation piece, the ones that you, where you want to apply that self-service data marketplace to, and really drive, as I said before, monetization, All right, give us your final thoughts. Ajay, bring us home. >> So final thoughts on this, David, is at the moment, we're seeing a lot of value in helping customers discover their data using automation, automatically curating a data catalog. And that unified view is then being put to work through our API is having an open architecture to plug in whatever tool technology our clients have decided to use. And that open architecture is really feeding into the reality of what CIOs and Chief Data Officers are managing, which is a hybrid On-premise Cloud approach to use best of breed. But business users wanting to use a particular technology to get their business outcome, having the flexibility to do that no matter where your data is sitting On-premise, on Cloud is where self-service comes in so that sales service view of what data I can plug together, jive exchange, monetizing that data is where we're starting to see some real traction with customers. Now accelerating, becoming more digital to serve their own customers. >> Yeah, we really have seen a cultural mind shift going from sort of complacency, and obviously COVID has accelerated this, but the combination of that cultural shift, the Cloud machine intelligence tools give me a lot of hope that the promises of big data will ultimately be lived up to in this next 10 years. So Ajay Vohora, thanks so much for coming back on theCUBE. You're a great guest and appreciate your insights. >> Appreciate it, David. See you next time. >> All right, keep it right there, everybody, right back after this short break. 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(ambient music) >> Narrator: From the cube studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is a CUBE conversation. >> Hello welcome to this cube conversation. I'm John Furrier, host of theCUBE here in our Palo Alto studios. During this quarantine, crew doing all the interviews, getting all the top story especially during this COVID pandemic. Great conversation here Jitesh Ghai, Senior Vice President and General Manager of Data Management with Informatica, CUBE alumni multi time. We can't be in person this year, because of the pandemic but a lot of great content. We've been doing a lot of interviews with you guys. Jitesh great to see you. Thanks for coming on. >> Hey, great to see you again. We weren't able to make it happen in person this year, >> but if not in person, >> virtually will have to work. >>In our past conversations on theCUBE and through all the Informatica employees it's always been kind of an inside baseball, kind of inside the ropes conversation in the industry >> about data. >> Now more than ever, with the pandemic, you starting to see people seeing it. Oh, I get it now. I get why data is important. I can see why Cloud First, Mobile First, Data First strategies and now Virtual First, is now this transformational scene. Everyone's feeling it, you can't help not ignore it. It's happening. It's also highlighting what's working, what's not. I have to ask you in the current environment Jitesh what are you seeing as some of those opportunities that your customers are dealing with approach to data? 'Cause clearly, you're working with that data layer, there's a lot of innovation opportunities, you've got CLAIRE on the AI side, all great. But now with the pandemic, it's really forcing that conversation. I got to rethink about what's going to happen after and have a really good strategy. >> Yeah, you're exactly right. There's a broad based realization that, I'll take a step back. First, we all know that as global 2000 organizations or in general, we all need to be data driven, we need to make fact based decisions. And there is a lot of that good work that's happened over the last few years as organizations have realized just how important data is to innovate and to deliver new products and services, new business models. What's really happened is that, during this COVID pandemic, there is a greater appreciation for trust in data. Historically, organizations became data driven, we're on the journey of being increasingly data driven. However, there was some element of Oh, gut or experience and that combined with data will get us to the outcomes we're looking for, will enable us to make the decisions. In this pandemic world of great uncertainty, supply chains falling apart on occasion, groceries not getting delivered on time et cetra, et cetra. The appreciation and critical importance on the quality on the trust of data is greater than ever to drive the insights for organizations. Leaders are less hesitant or sorry, leaders are more hesitant to just go with your gut type of approaches. There is a tremendous reliance on data. And we're seeing it in particular, more than ever, as you can imagine in the healthcare provider sector, in the public sector with federal state and local, as all of these organizations are having to make very difficult decisions, and are increasingly relying on high quality, trustworthy governed data to help them make what can be life or death decision. So a big shift and appreciation for the importance and trustworthiness in their data, their data state and their insights. >> So as the GM of data management and Senior Vice President at Informatica, you get a good view of things. I got to ask you love this data 4.0 concept. Talk about what that means to you because you got customers have been doing data management with you guys for a while, but now it's data 4.0 that has a feeling of agility to it. It's got kind of a DevOps vibe. It feels like a lot of automation being discussed and you mentioned trust. What is data 4.0 mean? >> So data 4.0 for us is where AI and ML is powering data management. And so what do I mean by that? There is a greater insight and appreciation for high quality trustworthy data to enable organizations to make fact based decisions to be more data driven. But how do you do that when data is exponentially growing in volume, where data types are increasing, where data is moving increasingly between Clouds, between On-premises and Clouds between various ecosystems, new data sources are emerging, the internet of things is yet another exploding source of data. This is a lot of different types of data, a lot of volume of data, a lot of different locations, and gravity of data where data resides. So the question becomes how do you practically manage this data without intelligence and automation. And that's what the era of data 4.0 is. Where AI and ML is powering data management, making it more intelligent, automating more and more of what was historically manual to enable organizations to scale, to enable them to scale to the breadth of data that they need to get a greater understanding of their data landscape within the enterprise, to get a greater understanding of the quality of the data within their landscape, how it's moving, and the associated privacy implications of how that data is being used, how effectively it's protected, so on and so forth. All underpinned by our CLAIRE engine, which is AI and ML applied to metadata, to deliver the intelligence and enable the automation of the data management operations. >> Awesome. Thanks for taking the time to define that, love that. The question I want to ask you, I'll put you on the spot here because I think this is an important conversation we've been having and also writing a lot about it on siliconangle.com and that is customers say to us, "Hey, John, I'm investing in Cloud Native technologies, using Cloud data warehouse as a data lakes. I need to make this work because this is a scale opportunity. I need to come out of this pandemic with really agile, scalable solutions that I can move fast on my applications." How do you comment on that? What's your thoughts on this because, you guys are in the middle of all this with the data management. >> I couldn't agree more. Increasingly, data workloads are moving to the Cloud. It's projected that by 2022, 75% of all databases will be in the Cloud, and COVID-19 is really accelerating it. It's opening the eyes of leadership of decision makers to be truly Cloud First and Cloud Native, now more than ever. And so organizations, traditional banking organizations, highly regulated industries that have been hesitant to move to the cloud, are now aggressively embarking on that journey. And industries that were early adopters of the Cloud are now accelerating that journey. I mentioned earlier that, we had a very seamless transition as we moved to a work from home environment, and that's because our IT is Cloud First Cloud Native. And why is that? It's because it's through being Cloud First and Cloud Native that you get the resiliency, the agility, the flexibility benefits in these uncertain times. And we're seeing that with the data and analytics stack as well. Customers are accelerating the move to Cloud data warehouses to Cloud data lakes, and become Cloud Native for their data management stack in addition to the data analytics platforms. >> Great stuff which I agree with hundred percent. Cloud Native is where it goes but you aren't they're (laughs) yet. Still on Hybrid and Multi-cloud is a big discussion. I want to get your thoughts >> Completely. >> On how that's going to play up because if you put Hybrid cloud and Multi-cloud I see Public cloud it's amazing, we know that. But Hybrid and Multi-cloud as the next generation of kind of interoperability framework of Cloud services, you're going to have to overlay and manage data governance and privacy. It's going to get more complicated, right? So how are you seeing your customers approach that piece, on the Public side, and then with Hybrid, because that's become a big discussion point. >> So Hybrid is an absolutely critical enabling capability as organizations modernize their on premise estate into the Cloud. You need to be able to move and connect to your On-premise applications, databases, and migrate the data that's important into the Cloud. So Hybrid is an essential capability. When I say Informatica is Cloud First Cloud Native, being Cloud First Cloud Native as a data management as a service provider if you will, requires essentially capabilities of being able to connect to On-premise data sources and therefore, be Hybrid. So Hybrid architecture is an essential part of that. Equally, it's important to enable organizations to understand what needs to go to the Cloud. As you're modernizing your infrastructure, your applications, your data and analytics stack. You don't need to bring everything to the Cloud with you. So there's an opportunity for organizations to introduce efficiencies. And that's done by enabling organizations to really scan the data landscape On-premise, scan the data that already exists in the various Public clouds that they partner with, and understand what's important, what's not, what can be decommissioned and left behind to realize savings and what is important for the business and needs to be moved into a Cloud Native analytic stack. And that's really where our CLAIRE metadata intelligence capabilities come to bear. And that's really what serves as the foundation of data governance, data cataloging and data privacy, to enable organizations to get the right data into the Cloud. To do so, while ensuring privacy. And to ensure that they govern that data in their new now Cloud Native analytics stack, whether it's AWS, Azure, GCP, snowflake data, bricks, all partners, all deep partnerships that we have. >> Jitesh, I want to get your thoughts on something. I was having a Zoom call a couple weeks ago, with a bunch of CXO friends, people, practitioners, probably some of them are probably your customers. It was kind of a social get together. But we were talking about, how the world we're living in pandemic, from COVID data, fake news, and one of the comments was, finally the whole world now realized what my life like. And in referring to how we're seeing fake news and misinformation kind of screw up an election and you got COVID's got 10 zillion different data points and people are making it to tell stories. And what does it really mean? There's a lot of trust involved. People are confused, and all that's going on. Again, in that backdrop, he said that that's my world. >> Right. This is back down to some of the things you're talking about, trust. We've talked about metadata services in the past. This authenticity, the duck democratization has been around for a while in the enterprise, so that dealing with bad data or fake data or too much data, you can make data (laughs) into whatever you want. You got to make sense of it. What's your thoughts on the reaction to his comment? I mean, what does it make you feel? >> Completely agree, completely agree. And that goes back to the earlier comment I made about making fact based decisions that you can have confidence in because the insight is based on trusted data. And so you mentioned data democratization. Our point of view is to democratize data, you have to do it on a foundational governance, right? There's a reason why traffic lights exist, it's to facilitate or at least attempt to facilitate the optimal free flow of traffic without getting into accidents, without causing congestion, so on and so forth. Equally, you need to have a foundation of governance. And I realized that there's an optical tension of democratized data, which is, free data for everybody consume it whenever and however you want, and then governance, which seems to imply, locking things down controlling them. And really, when I say you need a foundation of data governance, you need to enable for organizations to implement guardrails so that data can be effectively democratized. So that data consumers can easily find data. They can understand how trustworthy it is, what the quality of it is, and they can access it in easy way and consume it, while adhering to the appropriate privacy policies that are fit for the use of that particular set of data that a data and data consumer wants to access. And so, how do you practically do that? That's where data 4.0 AI power data management comes into play. In that, you need to build a foundation of what we call intelligent data governance. A foundation of scanning metadata, combining it with business metadata, linking it into an enterprise knowledge graph that gives you an understanding of an organization and enterprises data language. It auto tags auto curates, it gives you insight into the quality of the data, and now enables organizations to publish these curated data sets into a capability, what we call a data marketplace, so that much like Amazon.com, you can shop for the data, you can browse home and garden, electronics various categories. You can identify the data sets that are interesting to you, when you select them, you can look at the quality dimensions that have already been analyzed and associated with the data set. And you can also review the privacy policies that govern the use of that data set. And if you're interested in it, find the data sets, add them to your shopping cart, like you would do with Amazon.com, and check out. And when you do that triggers off an approval workflow to enable organizations to that last mile of governing access. And once approved, we can automatically provision the datasets to wherever you want to analyze them, whether it's in Tableau Power BI, an S3 market, what have you. And that is what I mean by a foundation of intelligent data governance. That is enabling data democratization. >> A common metadata layer gives you capabilities to use AI, I get that, There's a concept that you guys are talking a lot about, this augmentation to the data. This augmented data management activities that go on. What does that mean? Can you describe and explain that further and unpack that? This augmented data management activity? >> Yeah, and what do we mean by augmented data management, it's a really a first step into full blown automation of data management. In the old world, a developer would connect to a source, parse the source schema, connect to another source, parse its source schema, connect to the target, understand the target schema, and then pick the appropriate fields from the various sources, structure it through a mapping and then run a job that transforms the data and delivers it to a target database, in its structure, in its schema, in its format. Now that we have enterprise scale metadata intelligence, we know what source of data looks like, we know what targets exist as you simply pick sources and targets, we're able to automatically generate the mappings and automate this development part of the process so that organizations can more rapidly build out data pipelines to support their AI to operationalize AIML, to enable data science, and to enable analytics. >> Jitesh great insight. I really appreciate you explaining all this concept and unpacking that with me. Final point, I'd love you to have you just take a minute to put the plug in there for Informatica, what you're working on? What are your customers doing? What are some of the best practices coming out of the current situation? Take a minute to talk about that. >> Yeah, thank you, I'm happy to. It really comes down to focusing on enabling organizations to have a complete understanding of their data landscape. And that is, where we're enabling organizations to build an enterprise knowledge graph of technical metadata, business metadata, operational usage metadata, social metadata to understand and link and develop the necessary context to understand what data exists, where how it's used, what its purpose is and whether or not you should be using. And that's where we're building the Google for the enterprise to help organizations develop that. Equally, leveraging that insight, we're building out the necessary that insight and intelligence through CLAIRE, we're building out the automation in the data quality capabilities, in the data integration capabilities, in the metadata management capabilities, in the master data management capabilities, as well as the data privacy capability. So things that our tooling historically used to do manually, we're just automating it so that organizations can more productively access data, understand it and scale their understanding and insight and analytics initiatives with greater trust greater insight. It's all built on a foundation of our intelligent data platform. >> Love it, scaling data. It's that's really the future fast, available, highly available, integrated to the applications for AI. That's the future. >> Exactly right. Data 4.0, (laughs) AI power data management. >> I love talking about data in the future, because I think that's really valuable. And I think developers, and I've always been saying for over a decade now data is a critical piece for the applications, and AI really unlocks that of having it available, and surface is critical. You guys doing a great job. Thanks for the insight, appreciate you Jitesh. Thank you for coming on. >> Thanks for having me. Pleasure to be here. >> You couldn't do it in person with Informatica world but we're getting the conversations here on the remote CUBE, CUBE virtual. I'm John Furrier, you're watching CUBE conversation with Jitesh Ghai Senior Vice President General Manager, Data Manager at Informatica. Thanks for watching. (upbeat music)

(ambient music) >> Narrator: From the cube studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is a CUBE conversation. >> Hello welcome to this cube conversation. I'm John Furrier, host of theCUBE here in our Palo Alto studios. During this quarantine, crew doing all the interviews, getting all the top story especially during this COVID pandemic. Great conversation here Jitesh Ghai, Senior Vice President and General Manager of Data Management with Informatica, CUBE alumni multi time. We can't be in person this year, because of the pandemic but a lot of great content. We've been doing a lot of interviews with you guys. Jitesh great to see you. Thanks for coming on. >> Hey, great to see you again. We weren't able to make it happen in person this year, but if not in person, virtually will have to work. >> One of the things, I'm a half glass half full kind of guy but you can't look at this without saying man, it's bad. But it really highlights how things are going on. So first, how are you doing? How's everyone Informatica doing over there? You guys are doing okay? >> We are well, we are well, families well, the Informatica family is well. So overall, can't complain can't complain, I think it was remarkable how quickly we were able to transition to a work from home environment for our global 5000 plus organization. And really, the fact that we're Cloud First Cloud Native, both in our product offerings, as well as an IT organization really helped make that transition seamless. >> In our past conversations on theCUBE and through all the Informatica employees it's always been kind of an inside baseball, kind of inside the ropes conversation in the industry about data. Now more than ever, with the pandemic, you starting to see people seeing it. Oh, I get it now. I get why data is important. I can see why Cloud First, Mobile First, Data First strategies and now Virtual First, is now this transformational scene. Everyone's feeling it, you can't help not ignore it. It's happening. It's also highlighting what's working, what's not. I have to ask you in the current environment Jitesh what are you seeing as some of those opportunities that your customers are dealing with approach to data? 'Cause clearly, you're working with that data layer, there's a lot of innovation opportunities, you've got CLAIRE on the AI side, all great. But now with the pandemic, it's really forcing that conversation. I got to rethink about what's going to happen after and have a really good strategy. >> Yeah, you're exactly right. There's a broad based realization that, I'll take a step back. First, we all know that as global 2000 organizations or in general, we all need to be data driven, we need to make fact based decisions. And there is a lot of that good work that's happened over the last few years as organizations have realized just how important data is to innovate and to deliver new products and services, new business models. What's really happened is that, during this COVID pandemic, there is a greater appreciation for trust in data. Historically, organizations became data driven, we're on the journey of being increasingly data driven. However, there was some element of Oh, gut or experience and that combined with data will get us to the outcomes we're looking for, will enable us to make the decisions. In this pandemic world of great uncertainty, supply chains falling apart on occasion, groceries not getting delivered on time et cetra, et cetra. The appreciation and critical importance on the quality on the trust of data is greater than ever to drive the insights for organizations. Leaders are less hesitant or sorry, leaders are more hesitant to just go with your gut type of approaches. There is a tremendous reliance on data. And we're seeing it in particular, more than ever, as you can imagine in the healthcare provider sector, in the public sector with federal state and local, as all of these organizations are having to make very difficult decisions, and are increasingly relying on high quality, trustworthy governed data to help them make what can be life or death decision. So a big shift and appreciation for the importance and trustworthiness in their data, their data state and their insights. >> So as the GM of data management and Senior Vice President at Informatica, you get a good view of things. I got to ask you love this data 4.0 concept. Talk about what that means to you because you got customers have been doing data management with you guys for a while, but now it's data 4.0 that has a feeling of agility to it. It's got kind of a DevOps vibe. It feels like a lot of automation being discussed and you mentioned trust. What is data 4.0 mean? >> So data 4.0 for us is where AI and ML is powering data management. And so what do I mean by that? There is a greater insight and appreciation for high quality trustworthy data to enable organizations to make fact based decisions to be more data driven. But how do you do that when data is exponentially growing in volume, where data types are increasing, where data is moving increasingly between Clouds, between On-premises and Clouds between various ecosystems, new data sources are emerging, the internet of things is yet another exploding source of data. This is a lot of different types of data, a lot of volume of data, a lot of different locations, and gravity of data where data resides. So the question becomes how do you practically manage this data without intelligence and automation. And that's what the era of data 4.0 is. Where AI and ML is powering data management, making it more intelligent, automating more and more of what was historically manual to enable organizations to scale, to enable them to scale to the breadth of data that they need to get a greater understanding of their data landscape within the enterprise, to get a greater understanding of the quality of the data within their landscape, how it's moving, and the associated privacy implications of how that data is being used, how effectively it's protected, so on and so forth. All underpinned by our CLAIRE engine, which is AI and ML applied to metadata, to deliver the intelligence and enable the automation of the data management operations. >> Awesome. Thanks for taking the time to define that, love that. The question I want to ask you, I'll put you on the spot here because I think this is an important conversation we've been having and also writing a lot about it on siliconangle.com and that is customers say to us, "Hey, John, I'm investing in Cloud Native technologies, using Cloud data warehouse as a data lakes. I need to make this work because this is a scale opportunity. I need to come out of this pandemic with really agile, scalable solutions that I can move fast on my applications." How do you comment on that? What's your thoughts on this because, you guys are in the middle of all this with the data management. >> I couldn't agree more. Increasingly, data workloads are moving to the Cloud. It's projected that by 2022, 75% of all databases will be in the Cloud, and COVID-19 is really accelerating it. It's opening the eyes of leadership of decision makers to be truly Cloud First and Cloud Native, now more than ever. And so organizations, traditional banking organizations, highly regulated industries that have been hesitant to move to the cloud, are now aggressively embarking on that journey. And industries that were early adopters of the Cloud are now accelerating that journey. I mentioned earlier that, we had a very seamless transition as we moved to a work from home environment, and that's because our IT is Cloud First Cloud Native. And why is that? It's because it's through being Cloud First and Cloud Native that you get the resiliency, the agility, the flexibility benefits in these uncertain times. And we're seeing that with the data and analytics stack as well. Customers are accelerating the move to Cloud data warehouses to Cloud data lakes, and become Cloud Native for their data management stack in addition to the data analytics platforms. >> Great stuff which I agree with hundred percent. Cloud Native is where it goes but you aren't they're (laughs) yet. Still on Hybrid and Multi-cloud is a big discussion. I want to get your thoughts >> Completely. >> On how that's going to play up because if you put Hybrid cloud and Multi-cloud I see Public cloud it's amazing, we know that. But Hybrid and Multi-cloud as the next generation of kind of interoperability framework of Cloud services, you're going to have to overlay and manage data governance and privacy. It's going to get more complicated, right? So how are you seeing your customers approach that piece, on the Public side, and then with Hybrid, because that's become a big discussion point. >> So Hybrid is an absolutely critical enabling capability as organizations modernize their on premise estate into the Cloud. You need to be able to move and connect to your On-premise applications, databases, and migrate the data that's important into the Cloud. So Hybrid is an essential capability. When I say Informatica is Cloud First Cloud Native, being Cloud First Cloud Native as a data management as a service provider if you will, requires essentially capabilities of being able to connect to On-premise data sources and therefore, be Hybrid. So Hybrid architecture is an essential part of that. Equally, it's important to enable organizations to understand what needs to go to the Cloud. As you're modernizing your infrastructure, your applications, your data and analytics stack. You don't need to bring everything to the Cloud with you. So there's an opportunity for organizations to introduce efficiencies. And that's done by enabling organizations to really scan the data landscape On-premise, scan the data that already exists in the various Public clouds that they partner with, and understand what's important, what's not, what can be decommissioned and left behind to realize savings and what is important for the business and needs to be moved into a Cloud Native analytic stack. And that's really where our CLAIRE metadata intelligence capabilities come to bear. And that's really what serves as the foundation of data governance, data cataloging and data privacy, to enable organizations to get the right data into the Cloud. To do so, while ensuring privacy. And to ensure that they govern that data in their new now Cloud Native analytics stack, whether it's AWS, Azure, GCP, snowflake data, bricks, all partners, all deep partnerships that we have. >> Jitesh, I want to get your thoughts on something. I was having a Zoom call a couple weeks ago, with a bunch of CXO friends, people, practitioners, probably some of them are probably your customers. It was kind of a social get together. But we were talking about, how the world we're living in pandemic, from COVID data, fake news, and one of the comments was, finally the whole world now realized what my life like. And in referring to how we're seeing fake news and misinformation kind of screw up an election and you got COVID's got 10 zillion different data points and people are making it to tell stories. And what does it really mean? There's a lot of trust involved. People are confused, and all that's going on. Again, in that backdrop, he said that that's my world. >> Right. This is back down to some of the things you're talking about, trust. We've talked about metadata services in the past. This authenticity, the duck democratization has been around for a while in the enterprise, so that dealing with bad data or fake data or too much data, you can make data (laughs) into whatever you want. You got to make sense of it. What's your thoughts on the reaction to his comment? I mean, what does it make you feel? >> Completely agree, completely agree. And that goes back to the earlier comment I made about making fact based decisions that you can have confidence in because the insight is based on trusted data. And so you mentioned data democratization. Our point of view is to democratize data, you have to do it on a foundational governance, right? There's a reason why traffic lights exist, it's to facilitate or at least attempt to facilitate the optimal free flow of traffic without getting into accidents, without causing congestion, so on and so forth. Equally, you need to have a foundation of governance. And I realized that there's an optical tension of democratized data, which is, free data for everybody consume it whenever and however you want, and then governance, which seems to imply, locking things down controlling them. And really, when I say you need a foundation of data governance, you need to enable for organizations to implement guardrails so that data can be effectively democratized. So that data consumers can easily find data. They can understand how trustworthy it is, what the quality of it is, and they can access it in easy way and consume it, while adhering to the appropriate privacy policies that are fit for the use of that particular set of data that a data and data consumer wants to access. And so, how do you practically do that? That's where data 4.0 AI power data management comes into play. In that, you need to build a foundation of what we call intelligent data governance. A foundation of scanning metadata, combining it with business metadata, linking it into an enterprise knowledge graph that gives you an understanding of an organization and enterprises data language. It auto tags auto curates, it gives you insight into the quality of the data, and now enables organizations to publish these curated data sets into a capability, what we call a data marketplace, so that much like Amazon.com, you can shop for the data, you can browse home and garden, electronics various categories. You can identify the data sets that are interesting to you, when you select them, you can look at the quality dimensions that have already been analyzed and associated with the data set. And you can also review the privacy policies that govern the use of that data set. And if you're interested in it, find the data sets, add them to your shopping cart, like you would do with Amazon.com, and check out. And when you do that triggers off an approval workflow to enable organizations to that last mile of governing access. And once approved, we can automatically provision the datasets to wherever you want to analyze them, whether it's in Tableau Power BI, an S3 market, what have you. And that is what I mean by a foundation of intelligent data governance. That is enabling data democratization. >> A common metadata layer gives you capabilities to use AI, I get that, There's a concept that you guys are talking a lot about, this augmentation to the data. This augmented data management activities that go on. What does that mean? Can you describe and explain that further and unpack that? This augmented data management activity? >> Yeah, and what do we mean by augmented data management, it's a really a first step into full blown automation of data management. In the old world, a developer would connect to a source, parse the source schema, connect to another source, parse its source schema, connect to the target, understand the target schema, and then pick the appropriate fields from the various sources, structure it through a mapping and then run a job that transforms the data and delivers it to a target database, in its structure, in its schema, in its format. Now that we have enterprise scale metadata intelligence, we know what source of data looks like, we know what targets exist as you simply pick sources and targets, we're able to automatically generate the mappings and automate this development part of the process so that organizations can more rapidly build out data pipelines to support their AI to operationalize AIML, to enable data science, and to enable analytics. >> Jitesh great insight. I really appreciate you explaining all this concept and unpacking that with me. Final point, I'd love you to have you just take a minute to put the plug in there for Informatica, what you're working on? What are your customers doing? What are some of the best practices coming out of the current situation? Take a minute to talk about that. >> Yeah, thank you, I'm happy to. It really comes down to focusing on enabling organizations to have a complete understanding of their data landscape. And that is, where we're enabling organizations to build an enterprise knowledge graph of technical metadata, business metadata, operational usage metadata, social metadata to understand and link and develop the necessary context to understand what data exists, where how it's used, what its purpose is and whether or not you should be using. And that's where we're building the Google for the enterprise to help organizations develop that. Equally, leveraging that insight, we're building out the necessary that insight and intelligence through CLAIRE, we're building out the automation in the data quality capabilities, in the data integration capabilities, in the metadata management capabilities, in the master data management capabilities, as well as the data privacy capability. So things that our tooling historically used to do manually, we're just automating it so that organizations can more productively access data, understand it and scale their understanding and insight and analytics initiatives with greater trust greater insight. It's all built on a foundation of our intelligent data platform. >> Love it, scaling data. It's that's really the future fast, available, highly available, integrated to the applications for AI. That's the future. >> Exactly right. Data 4.0, (laughs) AI power data management. >> I love talking about data in the future, because I think that's really valuable. And I think developers, and I've always been saying for over a decade now data is a critical piece for the applications, and AI really unlocks that of having it available, and surface is critical. You guys doing a great job. Thanks for the insight, appreciate you Jitesh. Thank you for coming on. >> Thanks for having me. Pleasure to be here. >> You couldn't do it in person with Informatica world but we're getting the conversations here on the remote CUBE, CUBE virtual. I'm John Furrier, you're watching CUBE conversation with Jitesh Ghai Senior Vice President General Manager, Data Manager at Informatica. Thanks for watching. (upbeat music)

>> Narrator: From around the globe it's theCUBE! With digital coverage of UiPath Live, the release show. Brought to you by UiPath. >> Hi everybody this is Dave Valenti, welcome back to our RPA Drill Down. Ted Kummert is here he is Executive Vice President for Products and Engineering at UiPath. Ted, thanks for coming on, great to see you. >> Dave, it's great to be here, thanks so much. >> Dave your background is pretty interesting, you started as a Silicon Valley Engineer, they pulled you out, you did a huge stint at Microsoft. You got experience in SAS, you've got VC chops with Madrona. And at Microsoft you saw it all, the NT, the CE Space, Workflow, even MSN you did stuff with MSN, and then the all important data. So I'm interested in what attracted you to UiPath? >> Yeah Dave, I feel super fortunate to have worked in the industry in this span of time, it's been an amazing journey, and I had a great run at Microsoft it was fantastic. You mentioned one experience in the middle there, when I first went to the server business, the enterprise business, I owned our Integration and Workflow products, and I would say that's the first I encountered this idea. Often in the software industry there are ideas that have been around for a long time, and what we're doing is refining how we're delivering them. And we had ideas we talked about in terms of Business Process Management, Business Activity Monitoring, Workflow. The ways to efficiently able somebody to express the business process in a piece of software. Bring systems together, make everybody productive, bring humans into it. These were the ideas we talked about. Now in reality there were some real gaps. Because what happened in the technology was pretty different from what the actual business process was. And so lets fast forward then, I met Madrona Venture Group, Seattle based Venture Capital Firm. We actually made a decision to participate in one of UiPath's fundraising rounds. And that's the first I really came encountered with the company and had to have more than an intellectual understanding of RPA. 'Cause when I first saw it, I said "oh, I think that's desktop automation" I didn't look very close, maybe that's going to run out of runway, whatever. And then I got more acquainted with it and figured out "Oh, there's a much bigger idea here". And the power is that by really considering the process and the implementation from the humans work in, then you have an opportunity really to automate the real work. Not that what we were doing before wasn't significant, this is just that much more powerful. And that's when I got really excited. And then the companies statistics and growth and everything else just speaks for itself, in terms of an opportunity to work, I believe, in one of the most significant platforms going in the enterprise today, and work at one of the fastest growing companies around. It was like almost an automatic decision to decide to come to the company. >> Well you know, you bring up a good point you think about software historically through our industry, a lot of it was 'okay here's this software, now figure out how to map your processes to make it all work' and today the processes, especially you think about this pandemic, the processes are unknown. And so the software really has to be adaptable. So I'm wondering, and essentially we're talking about a fundamental shift in the way we work. And is there really a fundamental shift going on in how we write software and how would you describe that? >> Well there certainly are, and in a way that's the job of what we do when we build platforms for the enterprises, is try and give our customers a new way to get work done, that's more efficient and helps them build more powerful applications. And that's exactly what RPA does, the efficiency, it's not that this is the only way in software to express a lot of this, it just happens to be the quickest. You know in most ways. Especially as you start thinking about initiatives like our StudioX product to what we talk about as enabling citizen developers. It's an expression that allows customers to just do what they could have done otherwise much more quickly and efficient. And the value on that is always high, certainly in an unknown era like this, it's even more valuable, there are specific processes we've been helping automate in the healthcare, in financial services, with things like SBA Loan Processing, that we weren't thinking about six months ago, or they weren't thinking about six months ago. We're all thinking about how we're reinventing the way we work as individuals and corporations because of what's going on with the coronavirus crisis, having a platform like this that gives you agility and mapping the real work to what your computer state and applications all know how to do, is even more valuable in a climate like that. >> What attracted us originally to UiPath, we knew Bobby Patrick CMO, he said "Dave, go download a copy, go build some automations and go try it with some other companies". So that really struck us as wow, this is actually quite simple. Yet at the same time, and so you've of course been automating all these simple tasks, but now you've got real aspiration, you're glomming on to this term of Hyperautomation, you've made some acquisitions, you've got a vision, that really has taken you beyond 'paving the cow path' I sometimes say, of all these existing processes. It's really trying to discover new processes and opportunities for automation, which you would think after 50 or whatever years we've been in this industry, we'd have attacked a lot of it, but wow, seems like we have a long way to go. Again, especially what we're learning through this pandemic. Your thoughts on that? >> Yeah, I'd say Hyperautomation. It's actually a Gartner term, it's not our term. But there is a bigger idea here, built around the core automation platform. So let's talk for a second just what's not about the core platform and then what Hyperautomation really means around that. And I think of that as the bookends of how do I discover and plan, how do I improve my ability to do more automations, and find the real opportunities that I have. And how do I measure and optimize? And that's a lot of what we delivered in 20.4 as a new capability. So let's talk about discover and plan. One aspect of that is the wisdom of the crowd. We have a product we call Automation Hub that is all about that. Enabling people who have ideas, they're the ones doing the work, they have the observation into what efficiencies can be. Enabling them to either with our Ask Capture Utility capture that and document that, or just directly document that. And then, people across the company can then collaborate eventually moving on building the best ideas out of that. So there's capturing the crowd, and then there's a more scientific way of capturing actually what the opportunities are. So we've got two products we introduced. One is process mining, and process mining is about going outside in from the, let's call it the larger processes, more end to end processes in the enterprise. Things like order-to-cash and procure-to-pay, helping you understand by watching the events, and doing the analytics around that, where your bottle necks, where are you opportunities. And then task mining said "let's watch an individual, or group of individuals, what their tasks are, let's watch the log of events there, let's apply some machine learning processing to that, and say here's the repetitive things we've found." And really helping you then scientifically discover what your opportunities are. And these ideas have been along for a long time, process mining is not new. But the connection to an automation platform, we think is a new and powerful idea, and something we plan to invest a lot in going forward. So that's the first bookend. And then the second bookend is really about attaching rich analytics, so how do I measure it, so there's operationally how are my robots doing? And then there's everything down to return on investment. How do I understand how they are performing, verses what I would have spent if I was continuing to do them the old way. >> Yeah that's big 'cause (laughing) the hero reports for the executives to say "hey, this is actually working" but at the same time you've got to take a systems view. You don't want to just optimize one part of the system at the detriment to others. So you talk about process mining, which is kind of discovering the backend systems, ERP and the like, where the task mining it sounds like it's more the collaboration and front end. So that whole system thinking, really applies, doesn't it? >> Yeah. Very much so. Another part of what we talked about then, in the system is, how do we capture the ideas and how do we enable more people to build these automations? And that really gets down to, we talk about it in our company level vision, is a robot for every person. Every person should have a digital assistant. It can help you with things you do less frequently, it can help you with things you do all the time to do your job. And how do we help you create those? We've released a new tool we call StudioX. So for our RPA Developers we have Studio, and StudioX is really trying to enable a citizen developer. It's not unlike the art that we saw in Business Intelligence there was the era where analytics and reporting were the domain of experts, and they produced formalized reports that people could consume. But the people that had the questions would have to work with them and couldn't do the work themselves. And then along comes ClickView and Tableau and Power BI enabling the self services model, and all of a sudden people could do that work themselves, and that enabled powerful things. We think the same arch happens here, and StudioX is really our way of enabling that, citizen developer with the ideas to get some automation work done on their own. >> Got a lot in this announcement, things like document understanding, bring your own AI with AI fabric, how are you able to launch so many products, and have them fit together, you've made some acquisitions. Can you talk about the architecture that enables you to do that? >> Yeah, it's clearly in terms of ambition, and I've been there for 10 weeks, but in terms of ambition you don't have to have been there when they started the release after Forward III in October to know that this is the most ambitious thing that this company has ever done from a release perspective. Just in terms of the surface area we're delivering across now as an organization, is substantive. We talk about 1,000 feature improvements, 100's of discreet features, new products, as well as now our automation cloud has become generally available as well. So we've had muscle building over this past time to become world class at offering SAS, in addition to on-premises. And then we've got this big surface area, and architecture is a key component of how you can do this. How do you deliver efficiently the same software on-premises and in the cloud? Well you do that by having the right architecture and making the right bets. And certainly you look forward, how are companies doing this today? It's really all about Cloud-Native Platform. But it's about an architecture such that we can do that efficiently. So there is a lot about just your technical strategy. And then it's just about a ton of discipline and customer focus. It keeps you focused on the right things. StudioX was a great example of we were led by customers through a lot of what we actually delivered, a couple of the major features in it, certainly the out of box templates, the studio governance features, came out of customer suggestions. I think we had about 100 that we have sitting in the backlog, a lot of which we've already done, and really being disciplined and really focused on what customers are telling. So make sure you have the right technical strategy and architecture, really follow your customers, and really stay disciplined and focused on what matters most as you execute on the release. >> What can we learn from previous examples, I think about for instance SQL Server, you obviously have some knowledge in it, it started out pretty simple workloads and then at the time we all said "wow, it's a lot more powerful to come from below that it is, if a Db2, or an Oracle sort of goes down market", Microsoft proved that, obviously built in the robustness necessary, is there a similar metaphor here with regard to things like governance and security, just in terms of where UiPath started and where you see it going? >> Well I think the similarities have more to do with we have an idea of a bigger platform that we're now delivering against. In the database market, that was, we started, SQL Server started out as more of just a transactional database product, and ultimately grew to all of the workloads in the data platform, including transaction for transactional apps, data warehousing and as well as business intelligence. I see the same analogy here of thinking more broadly of the needs, and what the ability of an integrated platform, what it can do to enable great things for customers, I think that's a very consistent thing. And I think another consistent thing is know who you are. SQL Server knew exactly who it had to be when it entered the database market. That it was going to set a new benchmark on simplicity, TCO, and that was going to be the way it differentiated. In this case, we're out ahead of the market, we have a vision that's broader than a lot of the market is today. I think we see a lot of people coming in to this space, but we see them building to where we were, and we're out ahead. So we are operating from a leadership position, and I'm not going to tell you one's easier that the other, and both you have to execute with great urgency. But we're really executing out ahead, so we've got to keep thinking about, and there's no one's tail lights to follow, we have to be the ones really blazing the trail on what all of this means. >> I want to ask you about this incorporation of existing systems. Some markets they take off, it's kind of a one shot deal, and the market just embeds. I think you guys have bigger aspirations than that, I look at it like a service now, misunderstood early on, built the platform and now really is fundamental part of a lot of enterprises. I also look at things like EDW, which again, you have some experience in. In my view it failed to live up to a lot of it's promises even though it delivered a lot of value. You look at some of the big data initiatives, you know EDW still plugs in, it's the system of record, okay that's fine. How do you see RPA evolving? Are we going to incorporate, do we have to embrace existing business process systems? Or is this largely a do-over in your opinion? >> Well I think it's certainly about a new way of building automation, and it's starting to incorporate and include the other ways, for instance in the current release we added support for long running workflow, it was about human workflow based scenarios, now the human is collaborating with the robot, and we built those capabilities. So I do see us combining some of the old and new way. I think one of the most significant things here, is also that impact that AI and ML based technologies and skills can have on the power of the automations that we deliver. We've certainly got a surface area that, I think about our AI and ML strategy in two parts, that we are building first class first party skills, that we're including in the platform, and then we're building a platform for third parties and customers to bring their what their data science teams have delivered, so those can also be a part of our ecosystem, and part of automations. And so things like document understanding, how do I easily extract data from more structured, semi-structured and completely unstructured documents, accurately? And include those in my automations. Computer vision which gives us an ability to automate at a UI level across other types of systems than say a Windows and a browser base application. And task mining is built on a very robust, multi layer ML system, and the innovation opportunity that I think just consider there, you know continue there. You think it's a macro level if there's aspects of machine learning that are about captured human knowledge, well what exactly is an automation that captured where you're capturing a lot of human knowledge. The impact of ML and AI are going to be significant going out into the future. >> Yeah, I want to ask you about them, and I think a lot of people are just afraid of AI, as a separate thing and they have to figure out how to operationalize it. And I think companies like UiPath are really in a position to embed UI into applications AI into applications everywhere, so that maybe those folks that haven't climbed on the digital bandwagon, who are now with this pandemic are realizing "wow, we better accelerate this" they can actually tap machine intelligence through your products and others as well. Your thoughts on that sort of narrative? >> Yeah, I agree with that point of view, it's AI and ML is still maturing discipline across the industry. And you have to build new muscle, and you build new muscle and data science, and it forces you to think about data and how you manage your data in a different way. And that's a journey we've been on as a company to not only build our first party skills, but also to build the platform. It's what's given us the knowledge that to help us figure out, well what do we need to include here so our customers can bring their skills, actually to our platform, and I do think this is a place where we're going to see the real impact of AI and ML in a broader way. Based on the kind of apps it is and the kind of skills we can bring to bear. >> Okay last question, you're ten weeks in, when you're 50, 100, 200 weeks in, what should we be watching, what do you want to have accomplished? >> Well we're listening, we're obviously listening closely to our customers, right now we're still having a great week, 'cause there's nothing like shipping new software. So right now we're actually thinking deeply about where we're headed next. We see there's lots of opportunities and robot for every person, and that initiative, and so we're launched a bunch of important new capabilities there, and we're going to keep working with the market to understand how we can, how we can add additional capability there. We've just got the GA of our automation cloud, I think you should expect more and more services in our automation cloud going forward. I think this area we talked about, in terms of AI and ML and those technologies, I think you should expect more investment and innovation there from us and the community, helping our customers, and I think you will also see us then, as we talked about this convergence of the ways we bring together systems through integrate and build business process, I think we'll see a convergence into the platform of more of those methods. I look ahead to the next releases, and want to see us making some very significant releases that are advancing all of those things, and continuing our leadership in what we talk about now as the Hyperautomation platform. >> Well Ted, lot of innovation opportunities and of course everybody's hopping on the automation bandwagon. Everybody's going to want a piece of your RPA hide, and you're in the lead, we're really excited for you, we're excited to have you on theCUBE, so thanks very much for all your time and your insight. Really appreciate it. >> Yeah, thanks Dave, great to spend this time with you. >> All right thank you for watching everybody, this is Dave Velanti for theCUBE, and our RPA Drill Down Series, keep it right there we'll be right back, right after this short break. (calming instrumental music)

>> Narrator: From theCUBE studios in Palo Alto and Boston, connecting with thought leaders all around the world, this is a CUBE conversation. >> Hi everybody, welcome to this CUBE conversation. This is Dave Vellante, and as part of my CEO and CXO series I've been bringing in leaders around the industry and I'm really pleased to have Sudheesh Nair, who is the CEO of ThoughtSpot Cube alum. Great to see you against Sudheesh, thanks for coming on. >> My pleasure Dave. Thank you so much for having me. I hope everything is well with you and your family. >> Yeah ditto back at you. I know you guys were in a hot spot for a while so you know we power on together, so I got to ask you. You guys are AI specialists, maybe sometimes you can see things before they happen. At what point did you realize that this COVID-19 was really going to be something that would affect businesses globally and then specifically your business. >> Yeah it's amazing, isn't it? I mean we used to think that in Silicon Valley we are sitting at the top of the world. AI and artificial intelligence, machine learning, Cloud, IOT and all of a sudden this little virus comes in and put us all in our places basically. We are all waiting for doctors and others to figure these things out so we can actually go outside. That tells you all about what is really important in life sometimes. It's been a hard journey for most people because of what a huge health event this has been. From a Silicon Valley point of view and specifically from artificial intelligence point of view, there is not a lot of history here that we can use to predict the future, however early February we had our sales kick off and we had a lot of our sellers who came from Asia and it became sort of clear to us immediately during our sales kick off in Napa Valley that this is not like any other event. The sort of things that they were going through in Asia we sort of realized immediately that us and when it gets to the shores of the US, this is going to really hurt. So we started hunkering down as a company, but as you mentioned early when we were talking, California in general had a head start, so we've been hunkered down for almost five weeks now, as a company and as the people and the results are showing. You know it is somewhat contained. Now obviously the real question is what next? How do we go out? But that's probably the next journey. >> So a lot of the executives that I've talked to, of course they start with the number one importance is the health and well-being of our employees. We set up the work from home infrastructure, et cetera. So that's I think, been fairly well played in the media and beginning to understand that pretty well. Also, you saw I talked to Frank Slootman and he's sort of joked about the Sequoia memos, that you know eliminate unnecessary expenses and practices. I've always eliminated unnecessary expenses, keep it to the essentials, but one of the things that I haven't probed with CEOs and I'd love your thoughts on this is, did you have to rethink sort of the ideal customer profile and your value proposition in the specific context of COVID? Was that something that you deliberately did? >> Yeah so it's a really important question that you asked, and I saw the Frank interview and I a 100% agree with that. Inside the company we have this saying, and our co-founder Ajeet actually coined the phrase of living like a middle-class company, and we've always lived that, even though we have, 300 plus million dollars in the bank and we raised a big round last year. It is important to know that as a growth stage company, we are not measured on what's in the bank. It's about the value that we are delivering and how much I'll be able to collect from customers to run the business. The living like a middle-class family has always been the ethos of the company and that has been a good thing. However, I've been with ThoughtSpot for a little more than 18 months. I joined as the CEO. I was an early investor in the company and there are a couple of big changes that we made in the last 18 months, and one of is moving to Cloud which we can talk. The other one has been around narrowing our focus on who we sell to, because one of the things that, as you know very well Dave, is that the world of data is extremely complex. Every company can come in and say, "We have the best solution out there" and it can just be in the world, but the reality is no single product is going to solve every problem for a customer when it comes to a data analytics issue. All we can hope for is that we become part of a package or solution that solves a very specific problem, so in that context there's a lot of services involved, a lot of understanding of customer problems involved. We are not a bi-product in the sense of Tableau or click on Microsoft, but they do. We are about a use case based outcomes, so we knew that we can't be everywhere. So the second change we made is actually a narrower focus, exclusively sell to global. That class, the middle class mentality, really paid off now because almost all the customers we sell to are very large customers and the four work verticals that we were seeing tremendous progress, one was healthcare, second was financial sector, the third was telecom and manufacturing and the last one is repair. Out of these four, I would say manufacturing is the one where we have seen a slowdown, but the other verticals have been, I would say cautiously spending. Being very responsible and thus far, I'm not here to say that everything is fine, but the impact if you take Zoom as a spectrum, on one end of the spectrum, where everything is doing amazingly well, because they are a good product market fit to hospitality industry on the other side. I would say ThoughtSpot and our approach to data analytics is closer to this than that. >> That's very interesting Sudheesh because, of course health care, I don't think they have time to do anything right now. I mean they're just so overwhelmed so that's obviously an interesting area that's going to continue to do well I would think. And they, the Financial Services guys, there's a lot of liquidity in the system and after 2009 the FinTech guys or the financial, the banks are doing quite well. They may squeeze you a little bit because they're smart negotiators, but as you say manufacturing with the supply chains, and in retail, look, if your ecommerce I mean Amazon hit, all-time highs today up whatever, 20% in the last two weeks. I mean just amazing what's happening, so it's really specific parts of those sectors will continue to do well, won't they? >> Absolutely, I think look, I saw this joke on Twitter, what's the number one cost? What is in fact (mic cuts out). Very soon people will say it is COVID and even businesses that have been tried to, sort of relatively, reluctant to really embrace the transformation that the customers have been asking for. This has become the biggest forcing function and that's actually a good thing because consumers are going to ultimately win because once you get groceries delivered to you into your front doors, it's going to be hard to sort of go back to standing in the line in Costco, when InstaCart can actually deliver it for you and you get used to it, so there are some transformation that is going to happen because of COVID. I don't think that society will go back from, but having said that, it's also not transformation for the sake of transformation. So speaking from our point of view on data analytics, I sort of believe that the last three to four years we have been sort of living in the Renaissance of enterprise data analytics and that's primarily because of three things. The first thing, every consumer is expecting, no matter how small or the big business, is to get to know them. You know, I don't want you to treat me like an average. I don't want you treat me like a number. Treat me like a person, which means understand me but personalize the services you are delivering and make sure that everything that you send me are relevant. If there's a marketing campaign or promo or customer support call, make sure it's relevant. The relevance and personalization. The second is, in return for that. customers are willing to give you all sorts of data. The privacy, be damned, so to a certain extent they are giving you location information, medical information,-- And the last part is with Cloud, the amount of data that you can collect and free plus in data warehouse like Snowflakes, like Redshift. It's been fundamentally shifted, so when you toggle them together the customers demand for better actors from the business, then amount of data that they're willing to give and collect to IOT and variables and then cloud-based technologies that allows you to process and store this means that analyzing this data and then delivering relevant actions to the consumers is no longer a nice to have and that I think is part of the reason why ThoughtSpot is finding sort of a tailwind, even with all this global headwind that we are all in. >> Well I think too, the innovation formula really has changed in our industry. I've said many times, it's not Moore's law anymore, it's the combination of data plus AI applied to that data and Cloud for scale and you guys are at the heart of that, so I want to talk about the market space a little bit. You look at BI and analytics, you look at the market. You know the Gartner Magic Quadrant and to your point, you know the companies on there are sort of chalk and cheese, to borrow a phrase from our friends across the pond. I mean, you're not power BI, you're not SaaS. I mean you're sort of search led. You're turning natural language into complex sequel queries. You're bringing in artificial intelligence and machine intelligence to really simplify and dramatically expand and put into the hands of business people analytics. So explain a little bit. First of all, do I have that sort of roughly right? And help us frame the market space how you think about it. >> Yeah I mean first of all, it is amazing that the diverse industry and technologies that you speak to and how you are able to grasp all of them and summarize them within a matter of seconds is a term to understand in itself. You and Stew, you both have that. You are absolutely right. So the way I think of this is that BI technologies have been around and it's played out really well. It played it's part. I mean if you look at it the way I think of BI, the most biggest BI tool is still Excel. People still want to use Excel and that is the number one BI tool ever. Then 10 years ago Tableau came in and made visualizations so delightful and a pic so to speak. That became the better way to consume complex data. Then Microsoft came in Power BI and then commoditized and the visualization to a point that, you know Tableau had to fight and it ended up selling to the Salesforce. We are not trying to play there because I think if you chase the idea of visualization it is going to be a long hard journey for ThoughtSpot to catch Tableau in visualization. That's not what we are trying to do. What we are trying to do is that you have a lot of data on one hand and you have a consumer sitting here and saying data doesn't mean you treated me well. What is my action that is this quote, very customized action quote. And our question is, how does beta turn into bespoke action inside a business? The insurance company is calling. You are calling an insurance company's customer support person. How do you know that the impact that you are getting from them is customized. But turning data into insight is an algorithmic process. That's what BI does, but that's like a few people in an organization can do that. Think of them like oil. They don't mix with water, that's the business people. The merchandising specialist who figures out which one should become site and what should be the price what should be ranking. That's the merchandiser. Their customer support person, that's a business user. They don't necessarily do Python or SQL, so what happens is in businesses you have the data people like water and the business people who touch the customer and interact with them every day, they're like the water. They don't mix. The idea of ThoughtSpot is very simple. We don't want this demarcation. We don't want this chasm. We want to break it so that every single person who interact with the customer should be able to have an interactive storytelling with the data, so that every decision that they make takes data into insight to knowledge to action, and that decision-making pipeline cannot be gut driven alone. It has to be enabled by data science and human experience coming together. So in our view, a well deployed data platform, decision-making platform, will enhance and augment human experience, as opposed to human experience says, this data says that, so you've got to pick one. That's an old model and that has been the approach with natural language based interactive access with the BI being done automated through AI in the backend, parts what we are able to put very complex data science in front of a 20 year experienced merchandising specialist in a large e-commerce website without learning Python, without learning people, without understanding data warehouse >> Right so, a couple of things I want to pick up on. I mean data is plentiful, insights aren't. That's really the takeaway from one of the things that you mentioned and this notion of storytelling is very, very important. I mean, all business people, they better be storytellers in some way shape or form and what better way to tell stories than with data, and so, because as you say it's no longer gut feel, it's not the answer anymore. So it seems to me Sudheesh, that you guys are transformative. The decision to focus on the global 2000 and really not, get washed up in the Excel, well I could just do it in Excel, or I'm going to go get Power BI, it's good enough. It's really, you're trying to be transformative and you've got a really disruptive model that we talked about before, search led and you're speaking to the system, or, typing in a way that's more natural, I wonder if you could comment on that and particularly that disruption of that transformation. >> Remember we are selling to global 2000. Almost all of them will have Tableau or one of these power BI or one of these solutions already, so you're not trying to go right and change that. What we have done is very clearly focus on use cases. We're transforming data into action. We will move the needle for the bit, but for example with the COVID situation going on, one of the most popular use cases for us is around working capital management. Now a CFO who's been in the business for 20 or 30 years is an expert and have the right kind of gut feeling about how her business is running when it comes to working capital. However, imagine now she can do 20 what-if scenarios in the next five seconds or next 10 minutes without going to the SPN 18, without going to the BI team. She can say what if we reduce hiring in Japan and instead we focus them on Singapore? What if we move 20% of marketing dollars from Germany to New York? What would be the impact of AR going up by 1% versus AP going down by 1%? She needs to now do complex scenarios, but without delay. It's sort of like how do I find a restaurant through Yelp versus going to the lobby to talk to a specialist who tells me the local restaurant. This interactive database storytelling for gut enhances the decision-making is very powerful. This is why, customer have, our largest customer has spent more than $26 million with ThougthSpot and this is not small. Our average is around close to 700k. This week for example, we are having a webinar where Verizon's SVP of Analytics specifically focused on finance. He's actually going to be on a webinar with our CFO. Our CFO Sophie, one of our financial specialists and Jeff Noto from Verizon are going to be on this talking about working capital management. What parts ThoughtSpot is a portion of, but they are sharing their experience of how do we manage, so that kind of varies, like extremely rigid focus on use cases, supply chain, modeling different things so that someone who knows Asia can really interact with the data to figure out if our supply chain from Bangladesh is going to be impacted because of COVID can we go to Ecuador? What will that look like? What will be the cost? What's the transportation cost, the fuel cost, Business has become so complex you don't have time to take five, six days to look at the report, no matter how pretty that report is, you have to make it efficient. You need to be able to make a lightning fast decision and something like COVID is really exposing all of that because day by day situation on the ground is changing. You know, employees are calling in sick. The virus is breaking out in one place, other place. If it's not, curves are going up and down so you cannot have any sort of delay between human experience and data signs and all of that comes down to your point telling visual stories so that the organization can rally behind the changes that they want to make. >> So these are mission-critical use cases. They are big problems that you're solving and attacking. As you said, you're not all things to all people. One of the things you're not is a data store, right? So you've got a partner, you've got to have an ecosystem, whether it's cloud databases, the cloud itself. I wonder if you could talk about some of the key partnerships that you're forming and how you're going to market and how that's affecting your business. >> Yeah, I mean one of the things that I've always believed in Silicon Valley is that companies die out of indigestion, not out of starvation. You try to do everything. That's how you end up dying and for us in the space of data, it's an extremely humbling space because there is so much to do, data prep, data warehousing, you know a mash-up of data, hosting of data, We have clearly decided that our ability is best spent on making artificial intelligence to work, interactive storytelling for business use and that's it. With that said, we needed a high velocity agility partner in the back end and Cloud based data warehouse have become a huge tailwind for us because our entire customer deployments are on Cloud, and the number one, obviously as you know from Frank's thing, the Snowflake has actually given, customers have seen Snowflakes plus ThoughtSpot is actually a good thing and we are exclusive in global 2000 and the Snowflake is climbing up there and we are able to build a good mutual partnership, but we are also seeing a really creative partnership all the way from product design to go to market and compensation alignment with Amazon on their push on Redshift as well. Google, we have announced partnership. There is a little bit of (mic cuts out) in the beginning we are getting, and just a couple of weeks ago we started working with Microsoft on their Azure Synapse algo. Now I would say that it's lagging, we still have work to do but Amazon and Snowflake are really pushing in terms of what customers want to see, and it completely aligns with our value popular, one plus one equals three. It really works well for our customers >> And Google is what, BigQuery plus Google Cloud, or what are you doing there? >> Yep so both Amazon and Google. Well, what we are doing at three different pieces. One if obviously the hosting of their cloud platforms. Second is data warehouse and enterprise data warehouse, which is Redshift and BigQuery. Third, we are also pretty good at taking machine learning algorithms that they have built for specific verticals. We're going to take those and then ingest them and deliver better. So for example if you are one of the largest supply companies in the world and you want to know what's the shipment rate from China and it shows and then the next thing you want to know is what the failure rate on this based on last behavior when you compressed a shipment rate, and that probably could use a bit of specific algorithms and you know Google and others have actually built a library of algorithms that can be injected into ThoughtSpot. We will simply answer the question of we may have gotten that algorithm from the Google library, sort of the business use is concerned. It doesn't really matter, so we have made all that invisible and we are able to deliver democratized access to Bespoke Insights to a business user, who are too sort of been afraid to deal with the sector data. >> Since you mentioned that you've got obviously several hundred million dollars in cash. You've raised over half a billion. You've talked previously about potential acquisitions, about IPO, are you considering acquisitions? M&A at this point in time? I mean there may be some deals out there. There's certainly some talent out there, but boy the market is changing so fast. I mean, it seems to, certain sectors are actually doing quite well. Will you consider M&A at this point? >> Yes, so I think IPO and M&A are two different-- IPO definitely, it will be foolish to say that this hasn't pushed our clients back a little bit because this is a huge event. I think there will be a correction across valuation and all of that. However, it is also important for us we use this opportunity to look at how we are investing our resources and investment for long-term versus the short-term and make sure that we are more focused and more tightening at the belt. We are doing that internally. Having said that, being a private company our valuation is, you know at least in theory, frozen, and then we have a pretty good cash position of close to $300 million, which means that it is absolutely an opportunity for us to seriously consider M&A. The important thing going back to my adage of, companies don't die out of starvation. It is critical to make sure that whatever we do, we do it with clarity. Are we doing it for talent? Are we doing it for tech? Or are we doing it for market? When you have a massive event like this, it is a poor idea to go after new market. It is important to go to our existing customers who are very large global 2000 firms and then identify problems that we cannot solve otherwise and then add technology to solve those problems, so technology acquisitions are absolutely something to consider, but it needs some more time to settle in because, the first two weeks were all people who were blindsided by this, then the last two weeks we have now gotten the mojo back in sales and mojo back in engineering, and now I think it is time for us to digest and prepare for these next two, three quarters of event and as part of that, companies like us who are fortunate enough to be on a good cash position, we'll absolutely look for interesting and good deals in the M&S space. >> Yeah, it makes sense, is tell and tech and, post IPO you can worry about Tam expansion. You'll be under pressure to do that as the CEO, but for now that's a very pragmatic approach. My last question is, there's some things when you think about, you say five weeks now you've been essentially on lockdown. You must, as many of us start thinking about wow, a lot of this work from home which came so fast people wouldn't even think about it earlier. You know, some companies mandated the beehive approach. Now everybody's open to that. There are certain things that are likely to remain permanent post COVID. Have you thought much about that? Generally and specifically how it might affect your business, the permanence of post COVID. Your thoughts. >> Yeah I've thought a lot about it. In fact, this morning I was speaking with our CRO Brian McCarthy about this. I think the change will happen, think of like an onion's inner most layer, I think the most, my hope is, that the biggest change will be in every one of us internally, as a what sort of a person am I and what does my position in the world means. The ego of each one of us that we carry because if this global event in one shot did not make you rethink your own sort of position in this big universe I think that's a mess. So the first thing has to be about being a better person. The second thing is, I had this two, three days of fever which was negative for COVID but I isolated myself, but that gave me sort of an idea of dipping in the dark room where I'm hoping my family won't get infected and you know my parents are in India so I sort of also realized that what is really important for you in life and how much family should mean to you, so that goes to the first, yourself second, your relationship with family, but having said that, the third thing when it comes to business building is also the importance for building with quality people, because when things go wrong it is so critical to have people who believe in the purpose of what you are trying to build. People with good faith and unshakable faith, personal faith and unshakable faith in the purpose of the company and most importantly you mentioned something which is the story telling. People, leaders who can absolutely communicate with clarity and certainty. It becomes the most important thing to lead an organization. I mean, you are a small business owner. You know we are in a small company with around 500 people. There is nothing like sitting at home waiting to see how the company is doing over email if you're a friend line engineer or a seller. Communication becomes so critical, so having the trust and the respect of organization and have the ability to clearly and transparently communicate is the most important thing for the company and over communicating due to the time of crisis. These things are so useful even after this crisis is over. Obviously from a technology point of view, you know people have been speaking a lot about working remotely and technology changes, security, those things will happen but I think if these three things were to happen in that order. Be a better person, be a better family member and be a better leader, I think the world will be better off and the last thing I'll also tell you, that you know in Silicon Valley sometimes we have this disregard for arts and literature and fight over science. I hope that goes away, because I can't imagine living without books, without movies, without Netflix and everything. Art makes yourself creative and enriches our lives. You know, sports is no longer there on TV and the fact that people are able to immerse their imagination in books and fiction and watch TV. That also reminds you how important it is to have a good balance between arts and science in this world, so I have a long list of things that I hope we as a people and as a society will get better. >> Yeah, a lot more game playing in our household and it's good to reconnect in that regard. Well Sudheesh, you've always been a very clear thinker and you're in a great spot and an awesome leader. Thanks so much for coming on theCUBE. It was really great to see you again. All the best to you, your family and the broader community in your area. >> Dave, you've been very kind with this. Thank you so much, I wish you the same and hopefully we'll get to see face-to-face in the near future. Thanks a lot. >> I hope so, thank you. All right and thank you for watching everybody. This is Dave Vellante for theCUBE and we'll see you next time. (upbeat music)

>> Narrator: Hi, everyone, thanks for joining us today for the Virtual Vertica BDC 2020. Today's breakout session is entitled toward Zero Unplanned Downtime of Medical Imaging Systems using Big Data. My name is Sue LeClaire, Director of Marketing at Vertica, and I'll be your host for this webinar. Joining me is Mauro Barbieri, lead architect of analytics at Philips. Before we begin, I want to encourage you to submit questions or comments during the virtual session. You don't have to wait. Just type your question or comment in the question box below the slides and click Submit. There will be a Q&A session at the end of the presentation. And we'll answer as many questions as we're able to during that time. Any questions that we don't get to we'll do our best to answer them offline. Alternatively, you can also visit the vertical forums to post your question there after the session. Our engineering team is planning to join the forums to keep the conversation going. Also a reminder that you can maximize your screen by clicking the double arrow button in the lower right corner of the slide. And yes, this virtual session is being recorded, and we'll be available to view on demand this week. We'll send you a notification as soon as it's ready. So let's get started. Mauro, over to you. >> Thank you, good day everyone. So medical imaging systems such as MRI scanners, interventional guided therapy machines, CT scanners, the XR system, they need to provide hospitals, optimal clinical performance but also predictable cost of ownership. So clinicians understand the need for maintenance of these devices, but they just want to be non intrusive and scheduled. And whenever there is a problem with the system, the hospital suspects Philips services to resolve it fast and and the first interaction with them. In this presentation you will see how we are using big data to increase the uptime of our medical imaging systems. I'm sure you have heard of the company Phillips. Phillips is a company that was founded in 129 years ago in actually 1891 in Eindhoven in Netherlands, and they started by manufacturing, light bulbs, and other electrical products. The two brothers Gerard and Anton, they took an investment from their father Frederik, and they set up to manufacture and sale light bulbs. And as you may know, a key technology for making light bulbs is, was glass and vacuum. So when you're good at making glass products and vacuum and light bulbs, then there is an easy step to start making radicals like they did but also X ray tubes. So Philips actually entered very early in the market of medical imaging and healthcare technology. And this is what our is our core as a company, and it's also our future. So, healthcare, I mean, we are in a situation now in which everybody recognize the importance of it. And and we see incredible trends in a transition from what we call Volume Based Healthcare to Value Base, where, where the clinical outcomes are driving improvements in the healthcare domain. Where it's not enough to respond to healthcare challenges, but we need to be involved in preventing and maintaining the population wellness and from a situation in which we episodically are in touch with healthcare we need to continuously monitor and continuously take care of populations. And from healthcare facilities and technology available to a few elected and reach countries we want to make health care accessible to everybody throughout the world. And this of course, has poses incredible challenges. And this is why we are transforming the Philips to become a healthcare technology leader. So from Philips has been a concern realizing and active in many sectors in many sectors and realizing what kind of technologies we've been focusing on healthcare. And we have been transitioning from creating and selling products to making solutions to addresses ethical challenges. And from selling boxes, to creating long term relationships with our customers. And so, if you have known the Philips brand from from Shavers from, from televisions to light bulbs, you probably now also recognize the involvement of Philips in the healthcare domain, in diagnostic imaging, in ultrasound, in image guided therapy and systems, in digital pathology, non invasive ventilation, as well as patient monitoring intensive care, telemedicine, but also radiology, cardiology and oncology informatics. Philips has become a powerhouse of healthcare technology. To give you an idea of this, these are the numbers for, from 2019 about almost 20 billion sales, 4% comparable sales growth with respect to the previous year and about 10% of the sales are reinvested in R&D. This is also shown in the number of patents rights, last year we filed more than 1000 patents in, in the healthcare domain. And the company is about 80,000 employees active globally in over 100 countries. So, let me focus now on the type of products that are in the scope of this presentation. This is a Philips Magnetic Resonance Imaging Scanner, also called Ingenia 3.0 Tesla is an incredible machine. Apart from being very beautiful as you can see, it's a it's a very powerful technology. It can make high resolution images of the human body without harmful radiation. And it's a, it's a, it's a complex machine. First of all, it's massive, it weights 4.6 thousand kilograms. And it has superconducting magnets cooled with liquid helium at -269 degrees Celsius. And it's actually full of software millions and millions of lines of code. And it's occupied three rooms. What you see in this picture, the examination room, but there is also a technical room which is full of of of equipment of custom hardware, and machinery that is needed to operate this complex device. This is another system, it's an interventional, guided therapy system where the X ray is used during interventions with the patient on the table. You see on the left, what we call C-arm, a robotic arm that moves and can take images of the patient while it's been operated, it's used for cardiology intervention, neurological intervention, cardiovascular intervention. There's a table that moves in very complex ways and it again it occupies two rooms, this room that we see here and but also a room full of cabinets and hardwood and computers. This is another another characteristic of this machine is that it has to operate it as it is used during medical interventions, and so it has to interact with all kind of other equipment. This is another system it's a, it's a, it's a Computer Tomography Scanner Icon which is a unique, it is unique due to its special detection technology. It has an image resolution up to 0.5 millimeters and making thousand by thousand pixel images. And it is also a complex machine. This is a picture of the inside of a compatible device not really an icon, but it has, again three rotating, which waits two and a half turn. So, it's a combination of X ray tube on top, high voltage generators to power the extra tube and in a ray of detectors to create the images. And this rotates at 220 right per minutes, making 50 frames per second to make 3D reconstruction of the of the body. So a lot of technology, complex technology and this technology is made for this situation. We make it for clinicians, who are busy saving people lives. And of course, they want optimal clinical performance. They want the best technology to treat the patients. But they also want predictable cost of ownership. They want predictable system operations. They want their clinical schedules not interrupted. So, they understand these machines are complex full of technology. And these machines may have, may require maintenance, may require software update, sometimes may even say they require some parts, horrible parts to be replaced, but they don't want to have it unplanned. They don't want to have unplanned downtime. They would hate send, having to send patients home and to have to reschedule visits. So they understand maintenance. They just want to have a schedule predictable and non intrusive. So already a number of years ago, we started a transition from what we call Reactive Maintenance services of these devices to proactive. So, let me show you what we mean with this. Normally, if a system has an issue system on the field, and traditional reactive workflow would be that, this the customer calls a call center, reports the problem. The company servicing the device would dispatch a field service engineer, the field service engineer would go on site, do troubleshooting, literally smell, listen to noise, watch for lights, for, for blinking LEDs or other unusual issues and would troubleshoot the issue, find the root cause and perhaps decide that the spare part needs to be replaced. He would order a spare part. The part would have to be delivered at the site. Either immediately or the engineer would would need to come back another day when the part is available, perform the repair. That means replacing the parts, do all the needed tests and validations. And finally release the system for clinical use. So as you can see, there is a lot of, there are a lot of steps, and also handover of information from one to between different people, between different organizations even. Would it be better to actually keep monitoring the installed base, keep observing the machine and actually based on the information collected, detect or predict even when an issue is is going to happen? And then instead of reacting to a customer calling, proactively approach the customer scheduling, preventive service, and therefore avoid the problem. So this is actually what we call Corrective Service. And this is what we're being transitioning to using Big Data and Big Data is just one ingredient. In fact, there are more things that are needed. The devices themselves need to be designed for reliability and predictability. If the device is a black box does not communicate to the outside world the status, if it does not transmit data, then of course, it is not possible to observe and therefore, predict issues. This of course requires a remote service infrastructure or an IoT infrastructure as it is called nowadays. The passivity to connect the medical device with a data center in enterprise infrastructure, collect the data and perform the remote troubleshooting and the predictions. Also the right processes and the right organization is to be in place, because an organization that is, you know, waiting for the customer to call and then has a number of few service engineers available and a certain amount of spare parts and stock is a different organization from an organization that actually is continuously observing the installed base and is scheduling actions to prevent issues. And in other pillar is knowledge management. So in order to realize predictive models and to have predictive service action, it's important to manage knowledge about failure modes, about maintenance procedures very well to have it standardized and digitalized and available. And last but not least, of course, the predictive models themselves. So we talked about transmitting data from the installed base on the medical device, to an enterprise infrastructure that would analyze the data and generate predictions that's predictive models are exactly the last ingredient that is needed. So this is not something that I'm, you know, I'm telling you for the first time is actually a strategic intent of Philips, where we aim for zero unplanned downtime. And we market it that way. We also is not a secret that we do it by using big data. And, of course, there could be other methods to to achieving the same goal. But we started using big data already now well, quite quite many years ago. And one of the reasons is that our medical devices already are wired to collect lots of data about the functioning. So they collect events, error logs that are sensor connecting sensor data. And to give you an idea, for example, just as an order of magnitudes of size of the data, the one MRI scanner can log more than 1 million events per day, hundreds of thousands of sensor readings and tens of thousands of many other data elements. And so this is truly big data. On the other hand, this data was was actually not designed for predictive maintenance, you have to think a medical device of this type of is, stays in the field for about 10 years. Some a little bit longer, some of it's shorter. So these devices have been designed 10 years ago, and not necessarily during the design, and not all components were designed, were designed with predictive maintenance in mind with IoT, and with the latest technology at that time, you know, progress, will not so forward looking at the time. So the actual the key challenge is taking the data which is already available, which is already logged by the medical devices, integrating it and creating predictive models. And if we dive a little bit more into the research challenges, this is one of the Challenges. How to integrate diverse data sources, especially how to automate the costly process of data provisioning and cleaning? But also, once you have the data, let's say, how to create these models that can predict failures and the degradation of performance of a single medical device? Once you have these models and alerts, another challenge is how to automatically recommend service actions based on the probabilistic information on these possible failures? And once you have the insights even if you can recommend action still recommending an action should be done with the goal of planning, maintenance, for generating value. That means balancing costs and benefits, preventing unplanned downtimes without of course scheduling and unnecessary interventions because every intervention, of course, is a disruption for the clinical schedule. And there are many more applications that can be built off such as the optimal management of spare parts supplies. So how do you approach this problem? Our approach was to collect into one database Vertica. A large amount of historical data, first of all historical data coming from the medical devices, so event logs, parameter value system configuration, sensor readings, all the data that we have at our disposal, that in the same database together with records of failures, maintenance records, service work orders, part replacement contracts, so basically the evidence of failures and once you have data from the medical devices, and data from the failures in the same database, it becomes possible to correlate event logs, errors, signal sensor readings with records of failures and records of part replacement and maintenance operations. And we did that also with a specific approach. So we, we create integrated teams, and every integrated team at three figures, not necessarily three people, they were actually multiple people. But there was at least one business owner from a service organization. And this business owner is the person who knows what is relevant, which use case are relevant to solve for a particular type of product or a particular market. What basically is generating value or is worthwhile tackling as an organization. And we have data scientists, data scientists are the one who actually can manipulate data. They can write the queries, they can write the models and robust statistics. They can create visualization and they are the ones who really manipulate the data. Last but not least, very important is subject matter experts. Subject Matter Experts are the people who know the failure modes, who know about the functioning of the medical devices, perhaps they're even designed, they come from the design side, or they come from the service innovation side or even from the field. People who have been servicing the machines in real life for many, many years. So, they are familiar with the failure models, but also familiar with the type of data that is logged and the processes and how actually the systems behave, if you if you if you if you allow me in, in the wild in the in the field. So the combination of these three secrets was a key. Because data scientist alone, just statisticians basically are people who can all do machine learning. And they're not very effective because the data is too complicated. That's why you more than too complex, so they will spend a huge amount of time just trying to figure out the data. Or perhaps they will spend the time in tackling things that are useless, because it's such an interesting knows much quicker which data points are useful, which phenomenon can be found in the data or probably not found. So the combination of subject matter experts and data scientists is very powerful and together gathered by a business owner, we could tackle the most useful use cases first. So, this teams set up to work and they developed three things mainly, first of all, they develop insights on the failure modes. So, by looking at the data, and analyzing information about what happened in the field, they find out exactly how things fail in a very pragmatic and quantitative way. Also, they of course, set up to develop the predictive model with associated alerts and service actions. And a predictive model is just not an alert is just not a flag. Just not a flag, only flag that turns on like a like a traffic light, you know, but there's much more than that. It's such an alert is to be interpreted and used by highly skilled and trained engineer, for example, in a in a call center, who needs to evaluate that error and plan a service action. Service action may involve the ordering a replacement of an expensive part, it may involve calling up the customer hospital and scheduling a period of downtime, downtime to replace a part. So it has an impact on the clinical practice, could have an impact. So, it is important that the alert is coupled with sufficient evidence and information for such a highly skilled trained engineer to plan the service session efficiently. So, it's it's, it's a lot of work in terms of preparing data, preparing visualizations, and making sure that old information is represented correctly and in a compact form. Additionally, These teams develop, get insight into the failure modes and so they can provide input to the R&D organization to improve the products. So, to summarize these graphically, we took a lot of historical data from, coming from the medical devices from the history but also data from relational databases, where the service, work orders, where the part replacement, the contact information, we integrated it, and we set up to the data analytics. From there we don't have value yet, only value starts appearing when we use the insights of data analytics the model on live data. When we process live data with the module we can generate alerts, and the alerts can be used to plan the maintenance and the maintenance therefore the plant maintenance replaces replacing downtime is creating value. To give an idea of the, of the type of I cannot show you the details of these modules, all of these predictive models. But to give you an idea, this is just a picture of some of the components of our medical device for which we have models for which we have, for which we call the failure modes, hard disk, clinical grade monitoring, monitors, X ray tubes, and so forth. This is for MRI machines, a lot of custom hardware and other types of amplifiers and electronics. The alerts are then displayed in a in a dashboard, what we call a Remote monitoring dashboard. We have a team of remote monitoring engineers that basically surveyors the install base, looks at this dashboard picks up these alerts. And an alert as I said before is not just one flag, it contains a lot of information about the failure and about the medical device. And the remote monitor engineer basically will pick up these alerts, they review them and they create cases for the markets organization to handle. So, they see an alert coming in they create a case. So that the particular call center in in some country can call the customer and schedule and make an appointment to schedule a service action or it can add it preventive action to the schedule of the field service engineer who's already supposed to go to visit the customer for example. This is a picture and high-level picture of the overall data person architecture. On the bottom we have install base install base is formed by all our medical devices that are connected to our Philips and more service network. Data is transmitted in a in a secure and in a secure way to our enterprise infrastructure. Where we have a so called Data Lake, which is basically an archive where we store the data as it comes from, from the customers, it is scrubbed and protected. From there, we have a processes ETL, Extract, Transform and Load that in parallel, analyze this information, parse all these files and all this data and extract the relevant parameters. All this, the reason is that the data coming from the medical device is very verbose, and in legacy formats, sometimes in binary formats in strange legacy structures. And therefore, we parse it and we structure it and we make it magically usable by data science teams. And the results are stored in a in a vertica cluster, in a data warehouse. In the same data warehouse, where we also store information from other enterprise systems from all kinds of databases from SQL, Microsoft SQL Server, Tera Data SAP from Salesforce obligations. So, the enterprise IT system also are connected to vertica the data is inserted into vertica. And then from vertica, the data is pulled by our predictive models, which are Python and Rscripts that run on our proprietary environment helps with insights. From this proprietary environment we generate the alerts which are then used by the remote monitoring application. It's not the only application this is the case of remote monitoring. We also have applications for particular remote service. So whenever we cannot prevent or predict we cannot predict an issue from happening or we cannot prevent an issue from happening and we need to react on a customer call, then we can still use the data to very quickly troubleshoot the system, find the root cause and advice or the best service session. Additionally, there are reliability dashboards because all this data can also be used to perform reliability studies and improve the design of the medical devices and is used by R&D. And the access is with all kinds of tools. So Vertica gives the flexibility to connect with JDBC to connect dashboards using Power BI to create dashboards and click view or just simply use RM Python directly to perform analytics. So little summary of the, of the size of the data for the for the moment we have integrated about 500 terabytes worth of data tables, about 30 trillion data points. More than eighty different data sources. For our complete connected install base, including our customer relation management system SAP, we also have connected, we have integrated data from from the factory for repair shops, this is very useful because having information from the factory allows to characterize components and devices when they are new, when they are still not used. So, we can model degradation, excuse me, predict failures much better. Also, we have many years of historical data and of course 24/7 live feeds. So, to get all this going, we we have chosen very simple designs from the very beginning this was developed in the back the first system in 2015. At that time, we went from scratch to production eight months and is also very stable system. To achieve that, we apply what we call Exhaustive Error Handling. When you process, most of people attending this conference probably know when you are dealing with Big Data, you have probably you face all kinds of corner cases you feel that will never happen. But just because of the sheer volume of the data, you find all kinds of strange things. And that's what you need to take care of, if you want to have a stable, stable platform, stable data pipeline. Also other characteristic is that, we need to handle live data, but also be able to, we need to be able to reprocess large historical datasets, because insights into the data are getting generated over time by the team that is using the data. And very often, they find not only defects, but also they have changed requests for new data to be extracted to distract in a different way to be aggregated in a different way. So basically, the platform is continuously crunching data. Also, components have built-in monitoring capabilities. Transparent transparency builds trust by showing how the platform behaves. People actually trust that they are having all the data which is available, or if they don't see the data or if something is not functioning they can see why and where the processing has stopped. A very important point is documentation of data sources every data point as a so called Data Provenance Fields. That is not only the medical device where it comes from, with all this identifier, but also from which file, from which moment in time, from which row, from which byte offset that data point comes. This allows to identify and not only that, but also when this data point was created, by whom, by whom meaning which version of the platform and of the ETL created a data point. This allows us to identify issues and also to fix only the subset of when an issue is identified and fixed. It's possible then to fix only subset of the data that is impacted by that issue. Again, this grid trusts in data to essential for this type of applications. We actually have different environments in our analytic solution. One that we call data science environment is more or less what I've shown so far, where it's deployed in our Philips private cloud, but also can be deployed in in in public cloud such as Amazon. It contains the years of historical data, it allows interactive data exploration, human queries, therefore, it is a highly viable load. It is used for the training of machine learning algorithms and this design has been such that we it is for allowing rapid prototyping and for large data volumes. In other environments is the so called Production Environment where we actually score the models with live data from generation of the alerts. So this environment does not require years of data just months, because a model to make a prediction does not need necessarily years of data, but maybe some model even a couple of weeks or a few months, three months, six months depending on the type of data on the failure which has been predicted. And this has highly optimized queries because the applications are stable. It only only change when we deploy new models or new versions of the models. And it is designed optimized for low latency, high throughput and reliability is no human intervention, no human queries. And of course, there are development staging environments. And one of the characteristics. Another characteristic of all this work is that what we call Data Driven Service Innovation. In all this work, we use the data in every step of the process. The First business case creation. So, basically, some people ask how did you manage to find the unlocked investment to create such a platform and to work on it for years, you know, how did you start? Basically, we started with a business case and the business case again for that we use data. Of course, you need to start somewhere you need to have some data, but basically, you can use data to make a quantitative analysis of the current situation and also make it as accurate as possible estimate quantitative of value creation, if you have that basically, is you can justify the investments and you can start building. Next to that data is used to decide where to focus your efforts. In this case, we decided to focus on the use cases that had the maximum estimated business impact, with business impact meaning here, customer value, as well as value for the company. So we want to reduce unplanned downtime, we want to give value to our customers. But it would be not sustainable, if for creating value, we would start replacing, you know, parts without any consideration for the cost of it. So it needs to be sustainable. Also, then we use data to analyze the failure modes to actually do digging into the data understanding of things fail, for visualization, and to do reliability analysis. And of course, then data is a key to do feature engineering for the development of the predictive models for training the models and for the validation with historical data. So data is all over the place. And last but not least, again, these models is architecture generates new data about the alerts and about the how good the alerts are, and how well they can predict failures, how much downtime is being saved, how money issues have been prevented. So this also data that needs to be analyzed and provides insights on the performance of this, of this models and can be used to improve the models found. And last but not least, once you have performance of the models you can use data to, to quantify as much as possible the value which is created. And it is when you go back to the first step, you made the business value you you create the first business case with estimates. Can you, can you actually show that you are creating value? And the more you can, have this fitness feedback loop closed and quantify the better it is for having more and more impact. Among the key elements that are needed for realizing this? So I want to mention one about data documentation is the practice that we started already six years ago is proven to be very valuable. We document always how data is extracted and how it is stored in, in data model documents. Data Model documents specify how data goes from one place to the other, in this case from device logs, for example, to a table in vertica. And it includes things such as the finish of duplicates, queries to check for duplicates, and of course, the logical design of the tables below the physical design of the table and the rationale. Next to it, there is a data dictionary that explains for each column in the data model from a subject matter expert perspective, what that means, such as its definition and meaning is if it's, if it's a measurement, the use of measure and the range. Or if it's a, some sort of, of label the spec values, or whether the value is raw or or calculated. This is essential for maximizing the value of data for allowing people to use data. Last but not least, also an ETL design document, it explains how the transformation has happened from the source to the destination including very important the failure and the strategy. For example, when you cannot parse part of a file, should you load only what you can parse or drop the entire file completely? So, import best effort or do all or nothing or how to populate records for which there is no value what are the default values and you know, how to have the data is normalized or transform and also to avoid duplicates. This again is very important to provide to the users of the data, if full picture of all the data itself. And this is not just, this the formal process the documents are reviewed and approved by all the stakeholders into the subject matter experts and also the data scientists from a function that we have started called Data Architect. So to, this is something I want to give about, oh, yeah and of course the the documents are available to the end users of the data. And we even have links with documents of the data warehouse. So if you are, if you get access to the database, and you're doing your research and you see a table or a view, you think, well, it could be that could be interesting. It looks like something I could use for my research. Well, the data itself has a link to the document. So from the database while you're exploring data, you can retrieve a link to the place where the document is available. This is just the quick summary of some of the of the results that I'm allowed to share at this moment. This is about image guided therapy, using our remote service infrastructure for remotely connected system with the right contracts. We can achieve we have we have reduced downtime by 14% more than one out of three of cases are resolved remotely without an engineer having to go outside. 82% is the first time right fixed rate that means that the issue is fixed either remotely or if a visit at the site is needed, that visit only one visit is needed. So at that moment, the engineer we decided the right part and fix this straightaway. And this result on average on 135 hours more operational availability per year. This therefore, the ability to treat more patients for the same costs. I'd like to conclude with citing some nice testimonials from some of our customers, showing that the value that we've created is really high impact and this concludes my presentation. Thanks for your attention so far. >> Thank you Morrow, very interesting. And we've got a number of questions that we that have come in. So let's get to them. The first one, how many devices has Philips connected worldwide? And how do you determine which related center data workloads get analyzed with protocols? >> Okay, so this is just two questions. So the first question how many devices are connected worldwide? Well, actually, I'm not allowed to tell you the precise number of connected devices worldwide, but what I can tell is that we are in the order of tens of thousands of devices. And of all types actually. And then, how would we determine which related sensor gets analyzed with vertica well? And a little bit how I set In the in the presentation is a combination of two approaches is a data driven approach and the knowledge driven approach. So a knowledge driven approach because we make maximum use of our knowledge of the failure modes, and the behavior of the medical devices and of their components to select what we think are promising data points and promising features. However, from that moment on data science kicks in, and it's actually data science is used to look at the actual data and come up with quantitative information of what is really happening. So, it could be that an expert is convinced that the particular range of value of a sensor are indicative of a particular failure. And it turns out that maybe it was too optimistic on the other way around that in practice, there are many other situations situation he was not aware of. That could happen. So thanks to the data, then we, you know, get a better understanding of the phenomenon and we get the better modeling. I bet I answered that, any question? >> Yeah, we have another question. Do you have plans to perform any analytics at the edge? >> Now that's a good question. So I can't disclose our plans on this right now, but at the edge devices are certainly one of the options we look at to help our customers towards Zero Unplanned Downtime. Not only that, but also to facilitate the integration of our solution with existing and future hospital IT infrastructure. I mean, we're talking about advanced security, privacy and guarantee that the data is always safe remains. patient data and clinical data remains does not go outside the parameters of the hospital of course, while we want to enhance our functionality provides more value with our services. Yeah, so edge definitely very interesting area of innovation. >> Another question, what are the most helpful vertica features that you rely on? >> I would say, the first that comes to mind, to me at this moment is ease of integration. Basically, with vertica, we will be able to load any data source in a very easy way. And also it really can be interfaced very easily with old type of ions as an application. And this, of course, is not unique to vertica. Nevertheless, the added value here is that this is coupled with an incredible speed, incredible speed for loading and for querying. So it's basically a very versatile tool to innovate fast for data science, because basically we do not end up another thing is multiple projections, advanced encoding and compression. So this allows us to perform the optimizations only when we need it and without having to touch applications or queries. So if we want to achieve high performance, we Basically spend a little effort on improving the projection. And now we can achieve very often dramatic increases in performance. Another feature is EO mode. This is great for for cloud for cloud deployment. >> Okay, another question. What is the number one lesson learned that you can share? >> I think that would my advice would be document control your entire data pipeline, end to end, create positive feedback loops. So I hear that what I hear often is that enterprises I mean Philips is one of them that are not digitally native. I mean, Philips is 129 years old as a company. So you can imagine the the legacy that we have, we will not, you know, we are not born with Web, like web companies are with with, you know, with everything online and everything digital. So enterprises that are not digitally native, sometimes they struggle to innovate in big data or into to do data driven innovation, because, you know, the data is not available or is in silos. Data is controlled by different parts of the organ of the organization with different processes. There is not as a super strong enterprise IT system, providing all the data, you know, for everybody with API's. So my advice is to, to for the very beginning, a creative creating as soon as possible, an end to end solution, from data creation to consumption. That creates value for all the stakeholders of the data pipeline. It is important that everyone in the data pipeline from the producer of the data to the to the consumers, basically in order to pipeline everybody gets a piece of value, piece of the cake. When the value is proven to all stakeholders, everyone would naturally contribute to keep the data pipeline running, and to keep the quality of the data high. That's the students there. >> Yeah, thank you. And in the area of machine learning, what types of innovations do you plan to adopt to help with your data pipeline? >> So, in the error of machine learning, we're looking at things like automatically detecting the deterioration of models to trigger improvement action, as well as connected with active learning. Again, focused on improving the accuracy of our predictive models. So active learning is when the additional human intervention labeling of difficult cases is triggered. So the machine learning classifier may not be able to, you know, classify correctly all the time and instead of just randomly picking up some cases for a human to review, you, you want the costly humans to only review the most valuable cases, from a machine learning point of view, the ones that would contribute the most in improving the classifier. Another error is is deep learning and was not working on it, I mean, but but also applications of more generic anomaly detection algorithms. So the challenge of anomaly detection is that we are not only interested in finding anomalies but also in the recommended proper service actions. Because without a proper service action, and alert generated because of an anomaly, the data loses most of its value. So, this is where I think we, you know. >> Go ahead. >> No, that's, that's it, thanks. >> Okay, all right. So that's all the time that we have today for questions. I want to thank the audience for attending Mauro's presentation and also for your questions. If you weren't able to, if we weren't able to answer your question today, I'd ask let we'll let you know that we'll respond via email. And again, our engineers will be at the vertica, on the vertica quorums awaiting your other questions. It would help us greatly if you could give us some feedback and rate the session before you sign off. Your rating will help us guide us as when we're looking at content to provide for the next vertica BTC. Also, note that a replay of today's event and a PDF copy of the slides will be available on demand, we'll let you know when that'll be by email hopefully later this week. And of course, we invite you to share the content with your colleagues. Again, thank you for your participation today. This includes this breakout session and hope you have a wonderful day. Thank you. >> Thank you