>> Live from Washington DC. It's the Cube. Covering AWS Public Sector Summit. Brought to you by Amazon Web Services. >> Welcome to the Cube's live coverage of AWS Public Sector, here in our nation's capital. I'm your host Rebecca Knight, along with my co-host, John Furrier. We are joined by Sanjay Sardar, he is the VP Modernization and Digital Transformation at SAIC. Thank you so much for coming on the Cube. >> Thank you for having me. >> So, you are a twenty-five year veteran of data management. Why don't I start by asking you to... Sort of break down the principles of good data management. This is what we're here to talk about. >> Yeah. So... When you say it that way it makes me feel very old. I've done data management for a long time. The key to data management... Some of the principles are understanding, kind of what data you have. Where it is. What's the value of the data. That's the key that everyone's trying to bring. You know in the last twenty years, we've seen an explosion in the amount of data that we were handling. So, really, how do you get through all that data? How do you understand how to manage it? Where do you put it? And then really understand how to use it. What is that value of all of it coming through? Some of if is just machine data and noise. That you're looking at. That's important for certain aspects, but doesn't really add much value to the overall working of the agency or organization that you're with. And others are very valuable data, that you cannot really do anything with, unless you manipulate it in some way, or some fashion. So, data management takes a lot of different practices. And different ways to look at it. So, we've been doing master data management, meta data management for a long time, which helps understand what that data is. But then, what's the provenance of the data? What's the governance of data? What policies surround it? Where's the security of the data? All those factors play into, when you're looking at data as an enterprise. >> Sanjay, talk about SAIC specifically. I mean in long history working with the government and many, many contracts with broad range of services. But now at the modernization focus. The conversation is about agility, speed, modernizing government private, public sponsorships... Partnerships. Responsibility and accountability. All these things are in a melting pot. What is SAIC like today? What's your specific role here in Washington DC for Public Sector? >> Fair enough. So the SAIC is almost a fifty year old company. We've been around the government sector for about that long. We've done everything. We do everything from, data management, to software development, to infrastructure and hardware. Pretty much the whole gamut of IT services. And we've worked with almost every federal agency in the area, in the country. From a modernization perspective, what we're looking at is, the federal government is at this tipping point. We have a lot of legacy systems. We have a lot old aging infrastructure that... That needs to be replaced. That needs to be upgraded and modernized. This is a national security issue. We're getting into a point where things... If they start failing, it would be catastrophic for the US as a whole. So, where we are right now, as we're trying to work with the government, to bring in new technologies. As you said it's a melting pot of things that are happening. Not only has data exploded, but the technologies that are being used, have also exploded. You're seeing a massive consumerization happening. Biggest example is the apple iPhone. When the iPhone came out, that consumer... That model of the Apple iStore... Or, being able to do everything from your phone, is something the government has to get to. That's where you're looking at the UIUX models. That's where you're looking at different workflows being moved to the cloud. How do you handle all that? >> They used to be a government. They used to be a consumer of technology. Now they are a regulator of technology. That's what the discussions are. They're looking at using data and technology for their workload. So, it's not so much a supplier consumption relationship. They're much more active participants in the technology scene. The question is, do they really understand, what's going on? Cause, if you don't understand it, you can't control it, you can't regulate it, you can't utilize it properly. This is the number one conversation around modernization. What are the key factors in your opinion? The discovered needs to do better. Is it the procurement? Is it just awareness? (Sanjay laughing) What's your thoughts? >> That's a lot of questions. A lot of things going on there. And you're right. The government has become a consumer of technology. I mean it used to be back in the days when we were launching... Missions into space and putting men on the moon. The government was a leader in technology. Now with the commercialization, government has actually become a consumer of all these types of technologies, and a creator of tons of data. So, managing that data. Managing and understanding that data is very critical. How do you use it to add value to what the government is doing? And then further down the road, to what the citizens are doing. How do you add value to the citizens' life? In doing that, there's a lot of different things that have to come into play. One. As I said, technology is a big part of it. Understanding what technology to apply. It's not just about replacing technology. That's not what modernization is. Modernization, is how do you change and digitally transform your workloads. Your workflow. How you do business. That's really where the value add comes in. To get there, yeah you have to look at the technology. You have to look at the procurement practices. You have to look at different pricing and consumption models that the government hasn't been used to in a long time. When you look at these, traditional contracting models, they may not apply to some of the new ways of consuming technology. >> The world has changed for the government. >> The world has absolutely changed. >> What will it take though, for the government to become a more savvy buyer? I mean what are some of the things that... >> I think the government is already starting to become a more savvy buyer. Again. Remember the far, as when they talk about it, the federal acquisitions regulations. It's a massive volume that's probably, you know, a thousand pages long. So, there's a lot of opportunity to interpret that correctly. Where we're changing now, is how do you interpret it, so that there's fair practices for all competitors in the government market. And you're starting to see that. You're starting to see procurement officers looking at things differently. You're starting to see CIO's demand different services. They almost cannot do it. The compete in storage powers necessary? It's way too hard to go the old traditional route. >> You know what's interesting Rebecca, we talk about data all the time. We just read Infomatica World, they're kind of a supplier. They do the catalog and stuff for here at Amazon. Multi clouds of big countries, so Amazon is one of the biggest cloud. Andy Jackson who was just on stage last night in Arizona at a conference. Talking about response on recognition. All these hot AI data issues. Everything is a data problem. Right? But, yet we talk about government, but it's not just government. It's public sector. It's federal. But it's also international nation states. Competitiveness. So, there's a lot going on in such a short period in time, where analytics and data are key part, around the future value. So, it's almost the whole world is twisted upside down, from just ten years ago. >> Oh. Easily! >> Your thoughts on what's going on, and what the public sector community... Because a lot of these environments, don't have huge IT budgets. But now we're seeing things like Ground Station. Satellite. New stuff happening. >> So you're right. The explosion of data has really caused government... And in fact, every industry to change. More industries are becoming digital industries than when they were manufacturing ones You know, things like Uber, and all those industries that popped up because of the data. That's where government is also turning into. They are starting to understand that all the decisions that government makes, has to be done through a data driven model. They have to have this evidence based decision making process. And you're seeing that, because of the federal data practices. The data management act. The creation of CDOs in every agency. This is really pushing. The government is really recognizing, data is an asset. It's a value added asset, that they have to use better, to add value to the citizens life. To what they're providing. >> And it wasn't necessarily front and center on the... Quote, "data balance sheet". If you will.. Or the evaluation of data wasn't always looked at that way. >> No. >> Cause that changed the perspective. Understanding and... >> It's a huge shift. Like I said. When you look at the rise of the CDO. The Chief Data Officer in the federal government. That's a really big indication that data is now become and looked at as an asset. The CIO was responsible for all the technology and... They're governing all the technology. And they're the... Owner of that. The Chief Data Officer's now doing the same thing from the data side. The governance. The policy. The usage. The cooperation across multiple agencies. Multiple countries, as you said. >> Are agencies deploying CDOs across all agencies now? >> I think you're seeing more and more of the CDO being put out there. In fact almost all the agencies that I work with, have a CDO already in place, or are hiring one in the next three months. >> Why is modernization such a contentious topic? Is it because everyone has a different definition of what modernization is? It seems to be contentious when I talk about it with folks. It's like, what does it mean? >> I don't know if modernization is a contentious topic in the sense of... I think everybody recognizes that they have to modernize. It's how do you do it? You know, we are in a world where we have so much legacy infrastructure, legacy applications, that are tied so closely to mission. There's a risk of how do you modernize. You don't modernize correctly, you might in fact mission. And when you're talking about thing like in the DOD, where that leads to potential, you know, in theater situations and problems. That's a big problem from the DOD side. In the civilian side of the house, same thing. If your taxes go up by forty five percent because someone messed up on the modernization side, that's a problem. So, we have to be careful. Every agency has a personal journey. SAIC, when we look at this working with our partner systems, we look at an agency's personal journey. Everybody's going to do it differently. So, I think the contention comes in is, how do you do it? When do you do it? What do you attack first? Where do you look at the challenges and value adds are? Because everybody has to do it. Budgets are shrinking, and security is important. >> And workload has kicked around a lot. Applications used to be the old worry. Now an application sits on a server. It runs kind of monolithic. But, the applications are what... And the workloads are what really is the goal. Agency's got their own unique solution. That taxes is for taxes. Make that go better. So. Data and cloud, is different per workload. Per environment. Per mission. >> It very well could be. I think it's ubiquitous that there is a compute and storage factor, that everybody has to use. But the workloads that really transform the digital mission, are very different from agency to agency. So, you have to look at, what are they valuing, and where they are going with it. So, agencies like PTO, they're looking at, how do I more effectively our examiner's time? Versus, agencies like NASA, which are looking at, how do I do higher level compute, and HPC type work? So. >> One of the things you talked about when we first began our conversation. Is not only the explosion in data, but the explosion around the technologies and tools that are used to store and manipulate, and execute decisions on the data. Can you talk a little about what you're seeing. For example AI. I mean this is all the buzz, and all the big technology shows that we go to around the country. And it's maturing... But there's not a lot of adoption in the government. >> So, you're right. Along with this data explosion, we've seen a technology explosion. And with the different types of tools, handling the different sectors of managing data. Storage is one we talk about all the time. Because you have so much data, you can't actually access all that data at once. So, there's segmentation in the data that you have to look at. Companies at Cohesity are doing a good job of handling and managing that segmentation, in their hyper converged storage architectures. But we're also looking at in the AI world. Yes. AI is artificial intelligence. Deep learning. Machine learning. These are all techniques that are working very well for certain types of data usage and data problems. But the adoption is not as wide spread. Because, they're new technologies. I mean AI is where data was, like I said, twenty years ago. So, they're starting to understand, how do I use it. What do I use it for? You know that natural... That learning process that AI goes through. To say, "Okay, I'm going to make something more efficient." How do I do posturing of that data? Where do I actually use that? When you have large volumes of data. Security for example, is a great example. When you look at security logs, lots of volume of data coming out of that. But to use AI to learn which vectors the next security threat's going to to come through? That's a pretty daunting challenge, and not an easy one. And you have to find used cases like that. So, artificial intelligence I think has a large promise in the world. There's image recognition that's working very very well. Image recognition and classification. Natural language processing to look at different core sets of data in the research community. Or, in the pattern community. Those are very good examples of how AI is being used today. But there's a long way to go. And there's a lot to be learnt still. >> There's a lot of technology behind storing, and one of our sponsors that sponsors the Cube, Rebecca's cohesity. They sponsor us and invest in events. I think, always thank the sponsors. They're in the business of scaling up storage. So, it's not that easy to store it. So, you have to not only figure out the business model behind how to use the data. There's also the technology around storing it cleanly without hiring away. Talk about the dynamics around tech, in terms of managing the data. >> Well, so as you said it. There's a storage aspect of it. There's a retrieval aspect of it. There's a time aspect of it. All of that leads to... Yes, data is so valuable and so large and so limitless now. Doing all of those things matter. I mean if you're waiting, even nowadays... If you're waiting even three seconds for any response to come back? You're going to look at it and be like, I got to change my computer out cause it's too slow. That's the kind of area where we're in. When you look at the segmentation of data, nearline storage versus online storage. Well, the nearline has to be almost as fast as the online, cause now we're looking at things where, as you put it. The AI models are looking across vast amounts of data. They're looking at everything. How do you do that well? So that... All of that technology factor plays into it. >> One final thing. And this is just about the mindset of the government right now. Because what you're talking about, is a lot of exploration, and a lot of experimentation that's needed. How would you describe, sort of the federal approach to this? I mean, in fail fast is the motto of Silicone Valley. (Sanjay laughing) But that's a lot harder to do in the government. When lives are at stake. >> Well yeah. And it's cautious to be fair. It's not only lives at stake, but it's tax per dollars. Everybody is putting in there. And we want to make sure that we're doing right. To be fair. The government is looking at a fail fast prototype type models. That do work with, like you know, hackathons, and competitions. That really bring together public sector and private companies, like SAIC and others. To do different things that help kind of with this technology explosion. So for example, We work with USDA. We did multiple hackathons for precision agriculture. That kind of work is... It helps understand, what do we need to do with precision agriculture? What tools make sense? So, we have something we called our innovation factory. Where we have contracted out with multiple Silicone Valley. So we bring that to us, and then we bring that to government. That way the government does not, you know, not precluded by some of the rules that they have. But those type of things really help, that public, private partnership... It has to happen. >> I just want to... On that point real quick. Then we got to break. >> One of the things that you mentioned there is that this new generation kind of mindset. Talk about that dynamic, because there seems to be a new generation, digital natives, emerging into the work force. >> Absolutely. >> Enforcing the change, within the government. Can you validate that? Can you see... Can you share your opinion on how that's impacting everyone? >> Absolutely. Since I joined government over, God, now it's over twelve or thirteen years ago. And I left four years ago. We've been talking about this cliff that's coming up in the human resources side of the house. Where thirty-five percent of the top tier leadership is retiring. That's all getting replaced by new folks entering the market. And all these folks grew up in the iPhone era. None of these guys do anything that is... They are all mobile. They'll work anytime, anywhere. >> Very impatient too. >> Very different mindset. >> Cut the red tape. >> Right. Very different mindset and how to make government work. And that's a good thing. That kind of shake up is actually necessary. As these folks grow into leadership positions. They're going to change how government works. So we got to be ready for it. >> Great. Well Sanjay, thank you so much for coming on the Cube. >> Absolutely. Thank you for having me. >> We'll have more from AWS public sector. I'm Rebecca Knight, for John Furrier. Stay tuned. (theme music)

(upbeat music) >> Live, from Las Vegas it's theCUBE. Covering Informatica World 2019. Brought to you by Informatica. >> Welcome back everyone to theCUBE's live coverage of Informatica World 2019. I'm your host Rebecca Knight along with my co-host John Furrier. We are joined by Adam Mariano, he is the Vice-President Health Informatics at HighPoint Solutions. Thanks for coming on theCUBE! >> Thank you for having me. >> So tell our viewers a little bit about HighPoint Solutions, what the company does and what you do there. >> Sure, HighPoint is a consulting firm in the Healthcare and Life Sciences spaces. If it's data and it moves we probably can assist with it. We do a lot of data management, we implement the full Infomatica stack. We've been an Infomatica partner for about 13 years, we were their North American partner of the year last year. We're part of a much larger organization, IQVIA, which is a merger of IMS quintiles, large data asset holder, big clinical research organization. So we're very much steeped in the healthcare data space. >> And what do you do there as Vice President of Health and Formatics? >> I'm in an interesting role. Last year I was on the road 51 weeks. So I was at over a hundred facilities, I go out and help our customers or prospective customers or just people we've met in the space, get strategic about how they're going to leverage data as a corporate asset, figure out how they're going to use it for clinical insight, how they're going to use it for operational support in payer spaces. And really think about how they're going to execute on their next strategy for big data, cloud strategy, digital re-imaginment of the health care space and the like. >> So we know that healthcare is one of the industries that has always had so much data, similar to financial services. How are the organizations that you're working with, how are they beginning to wrap their brains around this explosion of data? >> Well it's been an interesting two years, the last augur two years there isn't a single conversation that hasn't started with governance. And so it's been an interesting space for us. We're a big MDM proponent, we're a big quality proponent, and you're seeing folks come back to basics again, which is I need data quality, I need data management from a metadata perspective, I need to really get engaged from a master data management perspective, and they're really looking for integrated metadata and governance process. Healthcare's been late to the game for about five or six years behind other industries. I think now that everybody's sort of gone through meaningful use and digital transformation on some level, we're now arcing towards consumerism. Which really requires a big deep-dive in the data. >> Adam, data governance has been discussed at length in the industry, certainly recently everyone knows GDPR's one year anniversary, et cetera, et cetera. But the role of data is really critical applications for SAS and new kinds of use cases, and the term Data Provisioning as a service has been kicked around. So I'd love to get your take on what that means, what is the definition, what does it mean? Data Provisioning as a service. >> The industry's changed. We've sort of gone through that boomerang, alright, we started deep in the sort of client server, standard warehouse space. Everything was already BMS. We then, everybody moved to appliances, then everybody came back and decided Hadoop, which is now 15 year old technology, was the way to go. Now everybody's drifting to Cloud, and you're trying to figure out how am I going to provision data to all these self-service users who are now in the sort of bring your own tools space. I'd like to use Tablo, I'd like to use Click. I like SAS. People want to write code to build their own data science. How can you provision to all those people, and do so through a standard fashion with the same metadata with the same process? and there isn't a way to do that without some automation at this point. It's really just something you can't scale, without having an integrated data flow. >> And what's the benefits of data provisioning as a service? What's the impact of that, what does it enable? >> So the biggest impact is time to market. So if you think about warehousing projects, historically a six month, year-long project, I can now bring data to people in three weeks. In two days, in a couple of hours. So thinking about how I do ingestion, if you think about the Informatica stack, something like EDC using enterprise data catalog to automatically ingest data, pushing that out into IDQ for quality. Proving that along to AXON for data governance and process and then looking at enterprise data lake for actual self-service provisioning. Allowing users to go in and look at their own data assets like a store, pick things off the shelf, combine them, and then publish them to their favorite tools. That premise is going to have to show up everywhere. It's going to have to show up on AWS, and on Amazon, and on Azure. It's going to have to show up on Google, it's going to have to show up regardless of what tool you're using. And if you're going to scale data science in a real meaningful way without having to stack a bunch of people doing data munging, this is the way it's going to have to go. >> Now you are a former nurse, and you now-- >> I'm still a nurse, technically. >> You're still a nurse! >> Once a nurse, always a nurse. Don't upset the nurses. >> I've got an ear thing going on, can you help me out here? (laughter) >> So you have this really unique vantage point, in the sense that you are helping these organizations do a better job with their data, and you also have a deep understanding of what it's like to be the medical personnel on the other side, who has to really implement these changes, and these changes will really change how they get their jobs done. How would you say, how does that change the way you think about what you do? And then also what would you say are the biggest differences for the nurses that are on the floor today, in the hospital serving patients? >> I think, in America we think about healthcare we often talked about Doctors, we only talk about nurses in nursing shortages. Nurses deliver all the care. Physicians see at this point, the way that medicine is running, physicians see patients an average two to four minutes. You really think about what that translates to if you're not doing a surgery on somebody, it's enough time to talk to them about their problem, look at their chart and leave. And so nursing care is the point of care, we have a lot of opportunity to create deflection and how care is delivered. I can change quality outcomes, I can change safety problems, I can change length of stay, by impacting how long people keep IVs in after they're no longer being used. And so understanding the way nursing care is delivered, and the lack of transparency that exists with EMR systems, and analytics, there's an opportunity for us to really create an open space for nursing quality. So we're talking a lot now to chief nursing officers, who are never a target of analytics discussion. They don't necessarily have the budget to do a lot of these things, but they're the people who have the biggest point of control and change in the way care is delivered in a hospital system. >> Care is also driven by notifications and data. >> Absolutely. >> So you can't go in a hospital without hearing all kinds of beeps and things. In AI and all the things we've been hearing there's now so many signals, the question is what they pay attention to? >> Exactly. >> This becomes a really interesting thing, because you can get notifications, if everything's instrumented, this is where kind of machine learning, and understanding workflows, outcomes play a big part. This is the theme of the show. It's not just the data and coding, it's what are you looking for? What's the problem statement or what's the outcome or scenario where you want the right notification, at the right time or a resource, is the operating room open? Maybe get someone in. These kinds of new dynamics are enabled by data, what's your take on all this? >> I think you've got some interesting things going on, there's a lot of signal to noise ratio in healthcare. Everybody is trying to build an algorithm for something. Whether that's who's going to overstay their visit, who's going to be readmitted, what's the risk for somebody developing sepsis? Who's likely to follow up on a pharmacy refill for their medication? We're getting into the space where you're going to have to start to accept correlation as opposed to causation, right? We don't have time to wait around for a six month study, or a three year study where you employ 15,000 patients. I've got three years of history, I've got a current census for the last year. I want to figure out, when do I have the biggest risk for falls in a hospital unit? Low staffing, early in their career physicians and nurses? High use of psychotropic meds? There are things that, if you've been in the space, you can pretty much figure out which should go into the algorithm. And then being pragmatic about what data hospitals can actually bring in to use as part of that process. >> So what you're getting at is really domain expertise is just as valuable as coding and wrangling data, and engineering data. >> In healthcare if you don't have SMEs you're not going to get anything practical done. And so we take a lot of these solutions, as one of the interesting touch points of our organization, I think it's where we shine, is bringing that subject matter expertise into a space where pure technology is not going to get it done. It's great if you know how to do MDM. But if you don't know how to do MDM in healthcare, you're going to miss all the critical use cases. So it really - being able to engage that user base, and the SMEs and bring people like nurses to the forefront of the conversation around analytics and how data will be used to your point, which signals to pay attention to. It's critical. >> Supply chains, another big one. >> Yeah. >> Impact there? >> Well it's the new domain in MDM. It's the one that was ignored for a long time. I think people had a hard time seeing the value. It's funny I spoke at 10 o'clock today, about supply chain, that was the session that I had with Nathan Rayne from BJC. We've been helping them embark on their supply chain journey. And from all the studies you look at it's one of the easiest places to find ROI with MBM. There's an unbelievable amount of ways- >> Low hanging fruit. >> $24.5 billion in waste a year in supply chain. It's just astronomical. And it's really easy things, it's about just in time supplies, am I overstocking, am I losing critical supplies for tissue samples, that cost sometimes a $100,000, because a room has been delayed. And therefore that tissue sits out, it ends up expiring, it has to be thrown away. I'll bring up Nathan's name again, but he speaks to a use case that we talked about, which is they needed a supply at a hospital within the system, 30 miles away another hospital had that supply. The supply costs $40,000. You can only buy them in packs of six. The hospital that needed the supply was unaware that one existed in the system, they ordered a new pack of six. So you have a $240,000 price that you could have resolved with a $100 Uber ride, right? And so the reality is that supply could have been shipped, could have been used, but because that wasn't automated and because there was no awareness you couldn't leverage that. Those use cases abound. You can get into the length of stay, you can get into quality of safety, there's a lot of great places to create wins with supply chain in the MDM space. >> One of the conversations we're having a lot in theCUBE, and we're having here at Informatica World, it centers around the skills gap. And you have a interesting perspective on this, because you are also a civil rights attorney who is helping underserved people with their H1B visas. Can you talk a little bit about the visa situation, and what you're seeing particularly as it relates to the skills gap? >> We're in an odd time. We'll leave it at that. I won't make a lot of commentary. >> Yes. >> I'm a civil rights and immigration attorney, and on the immigration side I do a lot of pro bono work with primarily communities of color, but communities at risk looking to help adjust their immigration status. And what you've had is a lot of fear. And so you have, well you might have an H1B holder here, you may have somebody who's on a provisional visa, or family members, and because those family members can no longer come over, people are going home. And you're getting people who are now returning. So we're seeing a negative immigration of places like Mexico, you're seeing a lot of people take their money, and their learnings and go back to India and start companies there and work remotely. So we're seeing a big up-tick in people who are looking for staffing again. I think the last quarter or so has been a pretty big ramp-up. And I think there's going to continue to be this hole, we're going to have to find new sources of talent if we can't bring people in to do the jobs. We're still also, I think it just speaks to our STEM education the fact that we're not teaching kids. I have a 28 year old daughter who loves technology, but I can tell you, her education when she was a kid, was lacking in this technology space. I think it's really an opportunity for us to think about how do we train young people to be in the new data economy. There's certainly an opportunity there today. >> And what about the, I mean you said you were talking about your daughter's education. What would you have directed her toward? What kinds of, when you look ahead to the jobs of the future, particularly having had various careers yourself, what would you say the kids today should be studying? >> That's two questions. So my daughter, I told her do what makes you happy. But I also made her learn Sequel. >> Be happy, but learn Sequel. >> But learn sequel. >> Okay! >> And for kids today I would say look, if you have an affinity and you think you enjoy the computer space, so you think about coding, you like HTML, you like social media. There are a plethora of jobs in that space and none of them require you to be an architect. You can be a BA, you can be a quality assurance person, you can be a PM. You can do analysis work. You can do data design, you can do interface design, there's a lot of space in there. I think we often reject kids who don't go to college, or don't have that opportunity. I think there's an opportunity for us to reach down into urban centers and really think about how we make alternate pathways for kids to get into the space. I think all the academies out there, you're seeing rise, Udemy, and a of of these other places that are offering academy based programs that are three, six months long and they're placing all of their students into jobs. So I don't think that the arc that we've always chased which is you've got to come from a brand named school to get into the space, I don't think it's that important. I think what's important is can I get you the clinical skill, so that you've understood how to move data around, how to process it, how to do testing, how to do design, and then I can bring you into the space and bring you in as an entry level employee. That premise I think is not part of the American dream but it should be. >> Absolutely, looking for talent in these unexpected places. >> College is not the only in point. We're back to having I think vocational schools for the new data economy, which don't exist yet. That's an opportunity for sure. >> And you said earlier, domain expertise, in healthcare as an example, points to what we've been hearing here at the conference, is that with data understanding outcomes and value of the data actually is just as important, as standing up, wrangling data, because if you don't have the data-- >> You make a great point. The other thing I tell young people in my practice, young people I interact with, people who are new to the space is, okay I hear you want to be a data scientist. Learn the business. So if you don't know healthcare get a healthcare education. Come be on this project as a BA. I know you don't want to be a BA, that's fine. Get over it. But come be here and learn the business, learn the dialogue, learn the economy of the business, learn who the players are, learn how data moves through the space, learn what is the actual business about. What does delivering care actually look like? If you're on the payer side, what does claims processing look like from an end to end perspective? Once you understand that I can put you in any role. >> And you know digital four's new non-linear ways to learn, we've got video, I see young kids on YouTube, you can learn anything now. >> Absolutely. >> And scale up your learning at a pace and if you get stuck you can just keep getting through it no-- >> And there are free courses everywhere at this point. Google has a lot of free courses, Amazon will let you train for free on their platform. It's really an opportunity-- >> I think you're right about vocational specialism is actually a positive trend. You know look at the college University scandals these days, is it really worth it? (laughter) >> I got my nursing license through a vocational school originally. But the nursing school, they didn't have any technology at that point. >> But you're a great use case. (laughter) Excellent Adam, thank you so much for coming on theCUBE it's been a pleasure talking to you. >> Thank you. >> I'm Rebecca Knight for John Furrier. You are watching theCUBE. (techno music)

>> Live from Las Vegas, it's theCUBE. Covering Informatica World 2019. Brought to you by Informatica. >> Welcome back, everyone to theCUBE's live coverage of Informatica World 2019 I'm your host, Rebecca Knight, along with my cohost, John Furrier. We are joined by Sudhir Hasbe. He is the director of product management at Google Cloud. Thank you so much for coming on theCUBE. >> Thank you for inviting me. (laughing) >> So, this morning we saw Thomas Kurian up on the main stage to announce the expanded partnership. Big story in Wall Street Journal. Google Cloud and Informatica Team Up to Tame Data. Tell us more about this partnership. >> So if you take a look at the whole journey of data within organizations, lot of data is still siloed in different systems within different environments. Could be a hybrid on-prem. It could be multi-cloud and all. And customers need this whole end-to-end experience where you can go ahead and take that data, move it to Cloud, do data cleansing on it, do data preparation. You want to be able to go ahead and govern the data, know what data you have, like a catalog. Informatica provides all of those capabilities. And if you look at Google Cloud, we have some highly differentiated services like Google BigQuery, which customers love across the globe, to go ahead and use for analytics. We can do large scale analytics. We have customers from few terabytes to 100-plus petabytes, and storing that amount of data in BigQuery, analyzing, getting value out of it. And from there, all the A.I. capabilities that we have built on top of it. This whole journey of taking data from wherever it is, moving it, cleansing it, and then actually getting value out of it with Big Query, as with our A.I. capabilities. That whole end-to-end experience is what customers need. And with this partnership, I think we are bringing all the key components our customers need together for a perfect fit. >> Sadhir, first of all, great to see you. Since Google Next, we just had a great event by the way this year, congratulations. >> Thanks. >> A lot of great momentum in the enterprise. Explain for a minute. What is the relationship, what is the partnership? Just take a quick minute to describe what it is with Informatica that you're doing. >> Yeah, that's great. I think if you take a look at it, you can bring two key areas together in this partnership. There's data management. How do you get data into Cloud, how do you govern it, manage it, understand it. And then there is analyze the data and AI. So the main thing that we're bring together is these two capabilities. What do I mean by that? The two key components that will be available for our customers is the Intelligent Cloud services from Informatica, which will be available on GCP, will run on GCP. This will basically make sure that the whole end-to-end capability for that platform, like data pipelines and data cleansing and preparation, everything is now available natively on GCP. That's one thing. What that will also do is, Informatica team has actually optimized the execution as part of this migration. What that means is, now you'll be able to use products like Data Cloud, Dataproc. You'll be able to use some of the AI capabilities in BigQuery to actually go do the data cleansing and preparation and process-- >> So when you say "execute", you mean "running." >> Yeah, just running software. >> Not executing, go to market, but executing software. >> Executing software. If you have a data pipeline, you can literally layer this Dataproc underneath to go ahead and run some of the key processes. >> And so the value to the customer is seamless-- >> Seamless integration. >> Okay, so as you guys get more enterprise savvy, and it's clear you guys are doing good work, and obviously Thomas has got the chops there. We've covered that on theCUBE many times. As you go forward, this Cloud formula seems to be taking shape. Amazon, Azure, Google, coming in, providing onboarding to Cloud and vice-versa, so those relationships. The customers are scratching their heads, going, "Okay, where do I fit in that?" So, when you talk to customers, how do you explain that? Because, unlike the old days in computer science and the computer industry, there was known practices. You built a data center, you provisioned some servers, you did some things. It was the general-purpose formula. But every company is different. Their journey's different. Their software legacy make-up's different. Could be born in the cloud with on-prem compliance needs. So, how do customers figure this out? What's the playbook? >> I think the big thing is this: There's a trend in the industry, across the board, to go ahead and be more data-driven, build a culture that is data-driven culture. And as customers are looking at it, what they are seeing is, "Hey, traditionally I was doing a lot of stuff. "Managing infrastructure. Let me go build a data center. "Let me buy machines." That is not adding that much value. It is because. "I need to go do that." That's why they did that. But the real value is, if I can get the data, I can go analyze it, I can get better decisions from it. If I can use machine learning to differentiate my services, that's where the value is. So, most customers are looking at it and saying, "Hey, I know what I need to do in the industry now, "is basically go ahead and focus more on insights "and less on infrastructure." But as doing this, the most important thing is, data is still, as you mentioned, siloed. It's different applications, different data centers, still sitting in different places. So, I think what is happening with what we announced today is making it easy to get that data into Google Cloud and then leveraging that to go ahead and get insights. That's where the focus is for us. And as you get more of these capabilities in the cloud as native services, from Infomatica and Google, customers can now focus more on how to derive value from the data. Putting the data into Cloud, cleansing it, and data preparation, and all of that, that becomes easier. >> Okay, so that brings the solution question to the table. With the solutions that you see with Infomatica, because again, they have a broad space, a horizontal, on-prem and cloud, and they have a huge customer base with enterprise, 25 years, and big data is their thing. What us case is their low-hanging fruit right now? Where are people putting their toe in the water? Where are they jumping full in? Where do you see that spectrum of solutions? >> Great question. There are two or three key scenarios that I see across the board with talking to a lot of customers. Even today, I spoke to a lot of customers at this show. And the first main thing I hear is this whole thing, modedernization of the data warehousing and analytics infrastructure. Lot of data is still siloed and stuck into these different data systems that are there within organizations. And, if you want to go ahead and leverage that data to build on top of the data, democratize it with everybody within the organization, or to leverage AI and machine learning on top of it, you need to unwind what you've done and just take that data and put into Cloud and all. I think modernization of data warehouses and analytics infrastructure is one key play across the IT systems and IT operations. >> Before you go on to the next one, I just want to drill down on that. Because one of the things we're hearing, obviously here and all of the places, is that if you constrain the data, machine learning and AI application ultimately fails. >> Yes. >> So, legacy silos. You mentioned that. But also regulatory things. I got to have privacy now, forget my customer, GDPR first-year anniversary, new regulatory things around, all kinds of data, nevermind outside the United States. But the cloud is appealing, of just throwing it in there as one thing. It's an agility lag issue. Because lagging is not good for AI. You want real-time data. You need to have it fast. How does a customer do that? Is it best to store it in the cloud first, on-premise, with mechanisms? What's your take on this? >> I think it's different in different scenarios. I talk a lot of customers on this. Not all data is restricted from going anywhere. I think there are some data sets you want to have good governance in place. For example, if you have PII data, if you have important customer information, you want to make sure that you take the right steps to govern it. You want to anonymize it. You want to make sure that the right amount of data, per the policies within the organization, only gets into the right systems. And I think this is where, also, the partnership is helpful, because with Infomatica, the tooling that they're provided, or as you mentioned over 25 years, allows customers to understand what these data sets are, what value they're providing. And so, you can do anonymization of data before it lands into Cloud and all of that. So I think one thing is the tooling around that, which is critical. And the second thing is, if you can identify data sets that are real-time, and they don't have business-critical or PII-critical data, that you're fine as a business process to be there, then you can derive a lot of data in real time from all the data sets. >> Tell me about Google's big capabilities, because you guys have a lot of internal power platform features. BigQuery is one of them. Is BigQuery the secret weapon? Is that the big power source for managing the data? >> I would just say: Our customers love BigQuery, primarily because of the capability it provides. There are different capabilities. Let me just list a few. One is: We can do analytics at scale. So as organizations grow, even if data sets are small within organization, what I have seen is, over a period of time, when you derive a lot of value from data, you will start collecting more data within organization. And so, you have to think about scale, whether you are starting with one terabyte or one petabyte or 100 petabytes, it doesn't matter. Analyzing data at scale is what we're really good at, at different types of scale. Second is: democratizing data. We have done a good job of making data available through different tooling, existing tooling that customers have invested in and our tooling, to make it available to everybody. AirAsia is a good example. They have been able to go ahead and give right insights to everybody within the organization, which has helped them go save 5 to 10% in their operational costs. So that's one great example of democratizing access to insights. The third big thing is machine learning and AI. We all know there are just lack of resources to do, at once, analytics with AI and machine learning in the industry. So our goal has been democratize it. Make it easy within an organization. So investments that we have done with BigQuery ML, where you can do machine learning with just simple SQL statements or AutoML tables, which basically allows you to just, within the UI, map and say, "That's table in BigQuery, here's a column that I want to predict, and just automatically figure out what model you want to create, and then we can use neural networks to go do that. I think that kind of investments is what customers love about it from the platform side. >> What about the partnership from a particular functional part of the company, marketing? There's the old adage: 50% of my marketing budget is wasted. I just don't know which one. This one could really change that. >> Exactly right. >> So talk a little bit about the impact of it on marketing. >> I think the main thing is, if you think about the biggest challenge that CMOs have within organizations is how do you better marketing analytics and optimize the spend? So, one of the thing that we're doing with the partnership is not just breaking the silos, getting the data in BigQuery, all of that side and data governance. But another thing is with master data management capability that Infomatica brings to table. Now you can have all of your data in BigQuery. You leverage the Customer 360 that MDM provides and now CMOs can actually say, "Hey, I have a complete view of my customer. "I can do better segmentation. I can do better targeting. "I can give them better service." So that is actually going to derive lot of value with our customers. >> I want to just touch on that once, see if I can get this right. What you just said, I think might be the question I was just about to ask, which is: What is unique about Google's analytical portfolio with Infomatica specifically? Because there's other cloud deals they have. They have Azure and AWS. What's unique about you guys and Infomatica? Was it that piece? >> Yeah, I think there are a few things. One is the whole end-to-end experience of basically getting the data, breaking the silos, doing data governance, this tight integration between our product portfolio, where now you can get a great experience within the native GCP environment. That's one. And then on the other side, Cloud for Marketing is a big, big initiative for us. We work with hundreds of thousand of customers across the globe on their marketing spend and optimizing their marketing. And this is one of the areas where we can work together to go ahead and help those CMOs to get more value from their marketing investments. >> One of the conversations we're having here on theCUBE, and really that we're having in the technology industry, is about the skills gap. I want to hear what you're doing at Google to tackle this problem. >> I think one of the big things that we're doing is just trying to-- I have this team internally. In planning, I use "radical simplicity." And radical simplicity is: How do we take things that we are doing today and make it extremely simple for the next generation of innovation that we're doing? All the investments and BigQuery ML, you SQL for mostly everything. One of the other things that we announced at Next was SQL for data flow, SQL pipelines. What that means is, instead of writing Beam or Java code to build data flow pipelines, now you can write SQL commands to go ahead and create a whole pipeline. Similarly, machine learning with SQL. This whole aspect of simplifying capabilities so that you can use SQL and then AutoML, that's one part of it. And the second, of course, we are working with different partners to go ahead and have a lot of training that is available online, where customers don't have to go take classes, like traditional classes, but just go online. All the assets are available, examples are available. One of the big things in BigQuery we have is we have 70-plus public data sets, where you can go, with BigQuery sandbox, without credit card, you can start using it. You can start trying it out. You can use 70-plus data sets that already available and start learning the product. So I think that should help drive more-- >> Google's a real cultural tech company, so you guys obviously based that from Stanford. Very academic field, so you do hire a lot of smart people. But there's a lot of people graduating middle school, high school, college. Berkeley just graduated their first, inaugural class in data science and analytics. What's the skills, specifically, that young kids or people who are either retraining should either reboot, hone, or dial up? Is there any things that you see from people that are successful inside Google? I mean, sometimes you don't have to have that traditional math background or computer science, although math does help; it's key. But what is your observation? What's your personal view on this? >> I think the biggest thing I've noticed is the passion for data. I fundamentally believe that, in the next three to five years, most organizations will be driven with data and insights. Machine learning and AI is going to become more and more important. So this understanding and having the passion for understanding data, answering questions based on data is the first thing that you need to have. And then you can learn the technologies and everything else. They will become simpler and easier to use. But the key thing is this passion for data and having this data-driven decision-making is the biggest thing, so my recommendation to everybody who is going to college today and learning is: Go learn more about how to make better decisions with data. Learn more about tooling around data. Focus on data, and then-- >> It's like an athlete. If you're not at the gym shooting hoops, if you don't love it, if you're not living it, you're probably not going to be any-- (laughing) It's kind of like that. >> Sudhir, thank you so much for coming on theCUBE. It's a pleasure talking to you. >> Thank you. Thanks a lot for having me. >> I'm Rebecca Knight for John Furrier. You are watching theCUBE. (techno music)

>> Live from Las Vegas. It's theCUBE. Covering Informatica World 2019. Brought to you by Informatica. >> Welcome back everyone to theCUBE's live coverage of Informatica World 2019. I'm your host, Rebecca Knight. We are joined by Sanjeev Vhora. He is the group technology officer and global data business lead at Accenture. Thank you so much for coming on theCUBE. >> Thanks. Thanks for having me here. >> We're hearing so much about AI lead data intelligence, and the other buzz word of course, that we hear so much of, is digital transformation. I'd love to hear your thoughts about data first approach to digital transformation. First of all, what does that mean? >> I think what we are seeing is that, if you... I think we do see that we are getting into a post digital era. Which means that in the last seven years, most bigger companies and businesses have invested in building a better customer engagement. What they did was they created properties, like portals, mobile applications, you name it, to just get better sense and touch their customers better than they were touching earlier. That was a whole investment that went in the last six, seven years. What they feel is that what's next. You do that, but does it really translate into revenue growth? Is it really translating into the experience in a sustained basis? Not one time, but on sustained basis. Every time when you touch a customer, they feel the same passion towards you. They feel that they are still engaged with you, and they want to come again to you for whatever your offering, your services or your goods. They felt that that's not actually happening. The reason why it's not happening is because the underlying data is not complete or comprehensive enough, or not accurate enough, for giving that experience. That realization is seeping up right now. They are asking for ensuring instead of looking at a use-case base approach of solving one problem for one business or one geography, is there a way to do it enterprise-wide? That a (mumbles). Point which is coming out is that they looked at that technology process that's old tradition model of looking at new businesses. Technology people processes and those three. But now they're looking to fourth element, which is foundation-call data. That's what we are calling data-first approach. You have to look at data as well, while looking at reforming your business services, and offers to the client. >> I want to touch on something you said earlier, and that is to make the customer feel passionate about interacting with you. I mean that's such a loaded, and almost romantic word to describe a customer interacting with a company. Why is it that companies are trying invoke passion, and insight passion, inspire passion? >> I think it's a way to differentiate yourself from the competition, so I think that's what in my view the businesses are doing right. Let me give an example to you to make it real, it may address your first question as well to some extent. We are working with a cruise company, one of the largest cruise companies North America based. They obviously are trying to make sure the experience of the customer is much better than had earlier. Which can resinate to a much higher revenue for them obviously, and inquisition of more customers. The friends of friends, friends of customers if you may. They had done a great job creating that digital property, and the transformation of the program. But they also realize that they are now, they realize that they don't really have a sense of who's the customer? Now that's a good question, after all this investment you still don't know who's the customer. That's where they came and talked about can I get a single view of my customer? The reason why they don't have a single view of customer is because they actually don't own all their individual customers. They only own their own individual customers, but they also work with their partners. As you can see Experian and others actually own that same customer. So they are not able to have a sense of that customer, their habits, and their behavior in one single place. They can really provide their accommodations, saying... well guest, if you're going to Italy we can probably help you this summer. >> So yes, exactly that's what I want to know; Is what, if you do have a sense of who your customer is, and that is everything from their basic demographic information, to what they do on Sunday afternoon with their families. What kinds of things then can the cruise company do to make that customer more passionate toward the cruise. >> They can do a lot, but I can tell you another example of another cruise company. Was looking at customer files and they did a fantastic job, and I'm assuming that you may have also experienced yourself. This customer they had covered the single view of customer obviously, but what they did was use a lot of IoT or sensors in their ships. They actually transformed the entire ship. Like the entire ship has been transformed to understand the customer movement, and give that flawless and seamless (mumbles) to customers. Which can help them have a pretty great on their vessels if you may. That's what they, from the day that you order the tickets for this service... From that time onward they actually send you a (mumbles). That tracks you as a person moving into the ship, and they can offer much more seamless services, and also reduces a friction of the operations staff. The staff is not in a hurry and hassle. They're actually able to understand who's actually the customer, what they want, and they are able to provide that service. So that's how they're using that feature of knowing the customer, to better serve them; being a better engagement with them. Plus also eases the operational friction in their own staff. >> So the customer wins because they feel the company gets them, and knows them, and understands them; and then the company wins because they're able to make more money off that customer, because they already have predicted what that customer wants and needs at every moment. >> And they can do more with less. They can do more with less staff, less resources. >> So one of that we are also talking a lot about here on theCUBE it's the tenth anniversary of theCUBE. So we've had a lot of these conversations, is how data is becoming a C-suite discussion, and there's this growing need to appoint a chief data officer to drive data strategy. What do you see as the evolving role of the CDO, at your company; and then also at the companies that you work with? >> We see this is a very significant step in the future. There are a lot of predictions from (mumbles) An analyst saying that there will be more and more roles, like three-fourth of the companies would have a CDO (mumbles). But I think our point is likely, you know, to augment that point I think what we believe is that, we do believe in respective of who actually owns (mumbles) That a chief data officer or a CI, or a CO. They definitely need a person at the C-suite, not below C-suite. To have that discussion at the table, and show that their data strategy is attached to their business strategy, and that's not true in many cases right now. So the data is (mumbles) which is two levels down in (mumbles), and that's why it's not getting that attention as a corporate asset as a (mumbles) asset from where you can actually extract value that you're looking at right. That's what we see; so we see a very broadened role, we see who so is in that role, we think there are a few qualities that person needs to have. The first one the person has to have a seat at the table. The second, is that person should be able to understand business quite well. (mumbles) He or she should have an insider business innovation, and if the person is tech savy it's good to have, but it's not must to have. We do believe that person should be able to prepare a strategy, and the governance of data across his or her peers. So they know that what value they are able to get from that data, and how they can share it across their functions. That's where the value comes in. Plus, beyond that the last point would be making sure whatever they do, they do responsibly. Do they actually make things work; whether it's using A.I., whether it's using any machine learning or anything else they have. They make sure that it's responsible data, and make it secure for themselves, for their enterprise, and for their customer. >> Well that is certainly a theme that we're hearing a lot about at Infomatica world. Tell me about the relationship between Accenture and Informatica. >> It's quite good, it's been good for years. We have been working together for years. The last two years, or two in a half years I think it has really taken a different shape within the new companies, and that's largely because we have really gone into a strategic discussion with the companies, and seeing what is the future. I think one thing that they are doing very well with their leadership. Anil himself is CEO; and Amit, and Tracy, and everybody else. And with our leadership is that we do believe that we are on the surface of un-tapping the value, one. Second thing is I don't think that used cases will draw the benefit which large organizations are looking at. It has to be something done at enterprise level. So think about like I think there another talk in the morning about enterprise data catalog. Amit was talking about, You need that. You need that to not do one used case for one particular business, for one particular country, or one particular customer segment. We need to do that for entire businesses across the enterprise. That can only happen if you have a sense of data, and you know how to do it effectively at scale. That's what I think that people are looking. Companies are going to be looking at the solution base, and I think it's the right timing for having the discussion. >> And there are going to be learnings that you can derive from financial services, and apply to retail, and healthcare, and all sorts of (mumble). Is that what you're finding here at Informatica World? Are you having those in conversations to learn the best practices? >> Oh yeah, I think we have our customers here; Accenture, as we have our customers here. we're presenting in different session. We had (mumbles) present today morning at eleven a.m. about how master data management can actually help you drive a better strategy on transforming your operations system like ESPE. That was never talked earlier, two years back nobody talked about saying how can MDM help you have a better transformation of your ESPE systems. Well that's where we are going. We are saying that, okay you have a trandiction systems, but you also need a system of right governance. Because all of your data, customer data or other data maybe sitting in ESPE or maybe sitting in sales force. How would you connect the dots? You need something to connect that dot so you have a single source of truth, and make sure that you know your customer, or vendor, or location, or everything else in the right fashion. >> Know your customer. So another thing I want to ask you about is the skills gap. I know that workforce of the future is something that you've worked passionately on. Passion keeps coming up in our conversation. (laughs amusingly) At Accenture. Tell us your story first in terms how you came to terms with this skill gap, and what you did at Accenture to remedy it. >> So this is four years back, and we were looking at our tech strategy, and our strategy to (mumbles) our business going forward, or where do we invest? And we are a people centric company so we are 470,000 people, that's a lot of people. In my role, one of the thing in my role is to make sure that I look at all the investment we do on our people. As CDO of our technology business, I need to make sure that we are investing in the right places. So this came to me saying that okay, will we be relevant as 470,000 people ten years from now? That's the question right? Because of A.I., because machine in our name, because people plus machine. What happens to our work force? So that's what I was trying to solve. Instead he's saying, what do we do next, and that was the whole point about workforce of the future. We will work more closely with the machines, and how will that happen. So what skills we will need as humans to work with machines, and everything else. What's going to happen in terms of automation going forward. And plus new talent which is required for the future. So we worked hard on this we built a strategy on what we need, then we did a very simple thing, we actually went to a high speed excursion, and agile sprints. We get it the few of principles actually. I can say a couple of them to use to resinate. One is the principle saying there's only (mumbles) available in the market. So don't spend creating stuff, but spend learning stuff. The second thing the chains of (mumbles) are a vision of our people vision, employee vision. It used to be saying, That you need to preform and grow. Something like that, if you preform high in our company, you'll grow faster. We changed the saying to learn and grow. So we said learning is more fundamental because performance will become automatic when you learn more. What we did was we changed. We worked really hard on the cultural aspect. And one of the things (mumbles) used to always say in the past ten years back, you used to learn a day in a month. Well that may not be enough today. Just because (mumbles) and the change of technology is much faster. It's 10x speed. So you can learn at 10x level, that doesn't mean you need to be learning at deep level for ten things, that's going to be hard for humans to do that. But you can use some help. That's what we do a 2 pronged approach. One is what we call a (mumble) training. Which means we make you more aware of everything that's happening in the world, and we give you a chance to support people-- >> I mean how do you do that, I mean that's a tall order. >> So what I did was we went to the market, and we looked at a lot of platforms. Okay you need technology to do everything. You get it right. You will be sitting here talking (mumbles). Using right technologies, right? Maybe show that our what we're talking is for (mumbles) people to watch us right. But the same thing there when we were looking at all the platforms. I looked at all things and I felt everything was great. (mumbles) It was not something which is exponential So I had to build a platform off it all, so I spent 6 months writing a whole platform. It was a really smart team, and all the logic I used was build a platform which treats or ploy a human in the center of your (mumbles) design. So we made a very personalized platform, where it helps a person to get there, and attracts you to come back. So it's very user friendly, or a very exponential platform. We call it Accenture Future Talent Platform. We deployed it across our entire businesses, we have 70+ number of people who are already being certified to their platform. They feel goof that they've gone to the next stage of their career. And now we are actually using the same platform for our clients. So we are giving them platforms so clients can use that effectively. >> From what I am hearing from you, it's about having technology skills, know how, and expertise. But also having this mindset of learning, and a hungry for learning, and wanting to know more. How do you make sure that, that culture is cultivated in the right way? >> We did some of the campaigns, so a very simple principle that we use is that like you do a marketing campaign to attract a customer. Whether he is selling a (mumbles), or selling a cruise experience, or vacation, or whatever. Use a similar principles for our own employers, and use it as learning campaigns. So marketing campaigns are learning campaigns. So one of the campaigns that we ran was, How important was it for you to be learning fit? So just like we always measure ourselves on health everyday, instead you measure yourself in learning. So our app was actually given to everybody, so you can see whether you are learning enough or not. We're in the culture of seeing how I'm doing against my own goals, but how am I doing against Rebecca's goal. >> Gameafying it, making it a little more fun. Making it a little competition. >> We also did (mumbles) as well, Because we felt that people look at their own models and say, well this person is very sexist, why would I want to be that person. That's a normal human. That's what people see so we made sure that our leaders do what they are saying. And they can buckle it down, they should start learning faster itself, from top management perspective. So people see them learning, they would say, I want to be like him. So that means I need to have the same behavior as this person. >> No, those are critical people in companies. Well, Sanjeev thank you so much for coming on theCUBE. It's been a pleasure having you. >> Same here, it was nice talking to you. >> I'm Rebecca Knight. You are watching theCUBE Informatica World 2019. (funky techno music)

(funky guitar music) >> From our studios, in the heart of Silicon Valley, Palo Alto, California, This is theCUBE conversation. >> Everyone welcome to this CUBE conversation here in Palo Alto, California CUBE studios, I'm John Furrier, the host of theCUBE. Were with CUBE alumni, special guest Amit Walia, President of Products & Marketing at Informatica. Amit, it's great to see you. It's been a while. It's been a couple of months, how's things? >> Good to be back as always. >> Welcome back. Okay, Informatica worlds is coming up, we have a whole segment on that but we have been covering you guys for a long long time, data is at the center of the value proposition again and again, it's more amplified now, the fog is lifting. >> Sure. >> And the world is now seeing what we were talking about four years ago. (giggles) >> Yeah. >> With data, what's new? What's the big trends that going on that you guys are doubling down on? What's new, what's changed? Give us the update. >> Sure. I think we have been talking the last couple of years, I think your right, data has becoming more and more important. I think, three things we see a lot. One is obviously, you saw this whole world of digital transformation. I think that has de faintly has picked up so much steam now. I mean, every company is going digital and obviously that creates a whole new paradigm shift for companies to carry out almost recreate themselves, rebuild them, so data becomes the new definition. And that's what we call those things you saw at Infomatica even before data3.org, but data is the center of everything, right? And you see the volume of data growth, you know, the utilization of data to make decisions, whether it's, you know, decisions on the shop floor, decisions basically related to cyber security or whatever it is. And the key to what you see different now is the whole AI assisted data management. I mean the scale of complexity, the scale of growth, you know, multi-cloud, multi-platform, all the stuff that is in front of us, it's really difficult to run the old way of doing things, so that's why we see one thing that we see a whole lot is AI is becoming a lot more mainstream, still early days but it's assisting the whole ability for companies, what I call, exploit data to really become a lot more transformative. >> You have been on this for a while, again we can go back to theCUBE archives, we can almost pull out clips from two years ago, be relevant today, you know, the data control, understanding >> Yeah. >> Understanding where the data governance is-- >> Sure. >> That's always a foundational thing but you guys nailed the chat bots, you have been doing AI was previous announcements, this is putting a lot of pressure on you, the president of the products, you got to get this out there. >> What's new? What's happening inside Informatica? pedaling as fast as you can? What is some of the updates? >> No. >> Gives us the-- >> The best example always is like a duck, right? Your really swimming and feel things are calm at the top and then you are really paddling. No, I think it's great for us. I think, I look at AI's, AI is like, there is so much FUD [fear, uncertainty and doubt] around it and machine learning AI. We look at it as two different ways. One is how we leverage machine learning within our products to help our customers. Making it easy for them, like I said, so many different data types, think of IOT data, unstructured data, streaming data, how do you bring all that stuff together and marry it with your existing transactional data to make sense. So, we're leveraging a lot of machine learning to make the internal products a lot more easier to consume, a lot more smarter, a lot more richer. The second thing is that, we're what we call it our AI, CLAIRE, which we unveiled, if you remember, a couple of years ago at the Informatica World. How that then helps our customers make smarter decisions, you know, in data science and all of these data workbenches, you know, the old statistical models is only as good as they can ever be. So, we leveraging helping our customers see the value proposition of our AI, CLAIRE, then to what I make things that, you know, find patterns, you know, statistical models cannot. So, to me I look at both of those really, leveraging ML to shape our products, which is where we do a lot of innovation and then creating our AI, CLAIRE, to help customers to make smarter decisions, easier decisions, complex decisions, which I called the humans or statistical models, really cannot. >> Well this is the balance with machines and humans. >> Right. >> working together, you guys have nailed this before and I'm, I think this was two years ago. I started to hear the words, land, adopt, expand, form you guys, right? Which is, you got to get adoption. >> Right. >> And so, as you're iterating on this product focus, you got to getting working, making secure your products-- >> Big, big maniacal focus on that one. >> So, tell me what you have learned there because that's a hard thing. >> Right. >> You guy are doing well at it. You got to get adoption, which means you got to listen customers, you got to do the course correction. >> Yeah. >> what's the learnings coming out of that piece of that. >> That's actually such a good point. We've made such, we've always been a customer centric company but as you said, like, as whole world shifted towards a new subscription cloud model, we've really focused on helping our customers adopt our products and you know, in this new world, customers are struggling with new architectures and everything, so we doubled down on what we called customer success. Making sure we can help our customers adopt the products and by the way it's to our benefit. Our customers get value really quickly and of course we believe in what we call a customer for life. Our ability to then grow with our customers and help them deliver value becomes a lot better. So, we really focused, so, we have globally across the board customers, success managers, we really invest in our customers, the moment a customer buys a product from us, we directly engage with them to help them understand for this use case, how you implement the product. >> It's not just self service, that's one thing that I appreciate 'cause I know how hard it is to build products these days, especially with the velocity of change but it's also when you have a large scale data. >> Yeah. >> You need automation, you got to have machine learning, you got to have these disciplines. >> Sure. >> And this is both on your end and but also on the customer. >> Yes. >> Any on the updates on the CLAIRE and some customer learnings you're seeing that are turning into use cases or best practices, what are some of them? >> So many of them. So take a simple example, right? I mean, we think of, we take these things for granted, right? I mean, take note, we don't talk about IOB these days right? All these cell cells, we were streaming data, right? Or even robots on the shop floor. So much of that data has no schema, no structure, no definition, it's coming, right? Netflix data and for customers there is a lot of volume in it, a lot of it could be junk, right? So, how do you first take that volume of data? Create some structure to it for you to do analytics. You can only do analytics if you put some structure to it, right? So, first thing is I've leverage CLAIRE, we help our customers to create, what I call, schema and you can create some structure to it. Then what we do allow is basically CLAIRE through CLAIRE, it can naturally bring what we have the data quality on top of it, like how much of it is irrelevant, how much of it is noise, how much of it really makes sense, so, then, as you said it, signal from the noise We are helping our customers get signal from the noise of data. That's where it AI comes very handy because it's very manual, cumbersome, time consuming and sometimes very difficult to do. So, that's a area we have leveraged creating structure and data quality on top and finding rules that didn't naturally probably didn't exist, that you and me wouldn't be able to see. Machines are able to do it and to your point, our belief is, this is my 100% belief, we believe AI assisting the humans. We have given the value of CLAIRE to our users, so it complements you and that's where we are trying to help our users get more productive and deliver more value to you faster. >> Productivity is multifold, it's like, also, efficiency, people wasting time on project that can be automated, so you can focus that valuable resource somewhere else. >> Yeah. >> Okay, let's shift gears onto Informatica World coming up. Let's spend some time on that. What's the focus this year, the show, it's coming up, right around the corner, what's going to be the focus? What's going to be the agenda? What's on the plate? >> Give you a quick sense on how it's shape up, it's probably going to be our Informatica World. So, it's 20th year, again back in Waze, you know, we love Waze of course. We have obviously, a couple of days lined up over there, I know you guys will be there too. A great set of speakers. Obviously, we will have me on stage, speakers like, we'll have some, the CEO of Google Cloud, Thomas Kurian is going to be there, we'll have on the main stage with Anil, we'll have the CEO of Databricks, Ali, with me, we'll also have CMO of AWS, Ariel, there, then we have a couple of customers lined up, Simon from Credit Suisse, Daniel is the CDO of Nissan, we also have the Head of AI, Simon Guggenheimer from Microsoft as well as the Chief Product Officer of Tableau, Francois Ajenstat, so, we have a great line up of speakers, customers and some of our very very strategic partners with us. If you remember last year, We also had Scott Guthrie there main stage. 80 plus sessions, pretty much 90% lead by customers. We have 70 to 80 customers presenting. >> Technical sessions or going to be a Ctrack? >> Technical, business, we have all kinds of tracks, we have hands on labs, we have learnings, customers really want to learn our products, talk with the experts, some want to the product managers, some want to talk to the engineers, literally so many hands on labs, so, it's going to be a full blown couple of days for us. >> What's the pitch for someone watching that never been Informatica World? Why should they come for the show? >> I'll always tell them three things. Number one is that, it's a user conference for our customers to learn all things about data management and of course in that context they learn a lot about. So, they learn a lot about the industry. So, day one we kick it off by market perspectives. We are giving a sense on how the market is going, how everybody is stepping back from the day to and understanding, where are these digital transformation, AI, where is all the world of data going. We've got some great annalists coming, talkings, some customers talking, we are talking about futures over there. Then it is all about hands on learning, right?, learning about the product. Hearing from some of these experts, right?, from the industry experts as well as our customers, teaching what to do and what not to do and networking, it's always go to network, right, it's a great place for people to learn from each other. So, it's a great forum for all those three things but the theme this year is all about AI. I talked about CLAIRE, I'll in fact our tagline this year is, Clarity Unleashed. We really want, basically, AI has been developing over the last couple of years, it's becoming a lot more mainstream, for us in our offerings and this year we're really taking it mainstream, so, it's kind of like, unleashing it for everybody can genuinely use it, truly use it, for the day to day data management activities. >> Clarity is a great theme, I mean, it plays on CLAIRE but this is what we're starting to see some visiblility into some clear >> Yeah. >> Economic benefits, business benefits. >> Yep. >> Technical benefits, >> Yep. >> Kind of all starting to come in. How would you categorize those three areas because you know, generally that's the consensus these days that what was once a couple years ago was, like, foggy when you see, now you're starting to see that lift, you're seeing economic, business and technical benefits. >> To me it's all about economic and business. So, technology plays a role in driving value for the business, right, I'm a full believer in that, right, and if you think about some of the trends today, right, a billion users are coming into play that will be assisted by AI. Data is doubling every year, you know the volume of data, >> Yep. >> The amount of, and I always say business users today, I mean, I run a business, I want, I always say, tomorrow data, yesterday to make a decision today. It's just in time and that's where AI comes into play. So our goal is to help organizations transform themselves, truly be more productive, reduce operation cost, by the way governance and compliance, that's becoming such a mainstream topic. It's not just basically making analytical decisions. How do you make sure your data is safe and secure, you don't want to get basically get hit by all of these cyber attacks, they're all are coming after data. So, governance, compliance of data that's becoming very, so, those-- >> Again you guys are right on the data thing. >> Yeah. >> I want to get your reaction, you mentioned some stats. >> Sure. >> I've got some stats here. Data explosion, 15.3 zettabytes per year >> Yeah, in global traffic. >> Yeah. >> 500 million business data users and growing 20 billion in connected devices, one billion workers will be assisted by machine learning, so, thanks for plugging those stats but I want to get your reaction to some of these other points here. 80% of enterprises are looking at multicloud, their really evaluating where the data sits in that equation >> Sure. And the other thing is the responsibility and role of the Chief Data Officer >> Yes. >> These are new dynamics, I think you guys will be addressing that into the event. >> Absolutely, absolutely. >> Because organizational dynamics, skill gaps are issues but also you have multicloud. So your thoughts on those to. >> That's a big thing, look at, in the old world, John, Hidrantes is always still in large enterprises, right, and it's going to stay here. In fact I think it's not just cloud, think of it this way, on-premise is still here, it's not going a way. It's reducing in scope but then you have this multicloud world, SAS apps, PAS apps, infrastructure, if I'm a customer, I want to do all of it but the biggest problem is that my data is everywhere, how do I make sense of it and then how do I govern it, like my customer data is sitting somewhere in this SAS app, in that platform, on this on-prem application transaction app I'm running, how do I connect the three and how do I make sense it doesn't get, I can have a governance control around it. That's when data management becomes more important but more complex but that's why AI comes in to making it easier. What are the things we've seen a lot, as you touched upon, is the rise of CDO. In fact we have Daniel from Nissan, she is the CDO of Nissan North America, on main stage, talking about her role and how they have leveraged data to transform themselves. That is something we're seeing a lot more because you know, the role of the CDO is making sure that is not only a sense of governance and compliance, a sense of how do we even understand the value of data across an enterprise. Again, I see, one of the things we going to talk about is system thinking around data. We call it System Thinking 3.0, data is becoming a platform. See, there was OSA-D hardware layer whether it is server, or compute, we believe that data is becoming a platform in itself. Whether you think about it in terms of scale, in terms of governance, in terms of AI, in terms of privacy, you have to think of data as a platform. That's the other big thing. >> I think that is a very powerful statement and I like to get your thoughts, we had many conversations on camera, off camera, around product, Silicon Valley, Venture Capital, how can startups create value. On of the old antigens use to be, build a platform, that's your competitive strategy, you were a platform company and that was a strategic competitive advantage. >> Yes. >> That was unique to the company, they created enablement, Facebook is a great example. >> Yeah. >> They monetized all the data from the users, look where they are. >> Sure. >> If you think about platforms today. >> Sure. >> It seems to be table steaks, not as a competitive advantage but more of a foundational. >> Sure. >> Element of all businesses. >> Yeah. >> Not just startups and enterprises. This seems to be a common thread, do you agree with that, that platforms becoming table steaks, 'cause of if we have to think like systems people >> Mm-hmm. >> Whether it's an enterprise. >> Sure. >> Or a supplier, then holistically the platform becomes table steaks on premer or cloud. Your reaction to that. Do you agree? >> No, I think I agree. I'll say it slightly differently, yes. I think platform is a critical component for any enterprise when they think of their end to end technology strategy because you can't do piece meals otherwise you become a system integrator of your own, right? But it's no easy to be a platform player itself, right, because as a platform player, the responsibility of what you have to offer your customer becomes a lot bigger. So, we obviously has this intelligent data platform but the other thing is that the rule of the platform is different too. It has to be very modular and API driven. Nobody wants to buy a monolithic platform. I don't want to, as a enterprise, I don't buy all now, I'm going to implement five years of platform. You want it, it's going to be like a Lego block, okay you, it builds by itself. Not monolithic, very API driven, maybe microservices based and that's our belief that in the new world, yes, platform is very critical for to accelerate your transformational journeys or data driven transformational journeys but the platform better be API driven, microservices based, very nimble that is not a percussor to value creation but creates value as you go along. >> It's all, kind of up to, depends on the customer it could have a thin foundational data platform, from you guys for instance, then what you're saying, compose. >> Of different components. >> On whatever you need. >> For example you have data integration platform, you can do data quality on top, you can do master data management on top, you can provide governance, you can provide privacy, you can do cataloging, it all builds. >> Yeah. >> It's not like, oh my gosh, I have go do all these things over the course of five years, then I get value. You got to create value all along. >> Yeah. >> Today's customers want value like, in two months, three months, you don't want to wait for a year or two. >> This is the excatly the, I think, the operating system, systems mindset. >> Yes. >> You were referring too, this is kind of how enterprises are behaving now. There is the way you see on-premise, >> Yep. >> Thinking around data, cloud, multicloud emerging, it's a systems view distributed computing, with the right Lego blocks. >> That's what our belief is. That's what we heard from customers. See our, I spend most of my time talking to customers and are we trying to understand what customers want today and you know, some of this latent demands that they have, sometimes can't articulate, my job, I always end up on the road most of the time, just hearing customers, that's what they want. They want exactly to your point, a platform that builds, not monolithic, but they do want a platform. They do want to make it easy for them not to do everything piece meal. Every project is a data project. Whether it's a customer experience project, whether it's a governance project, whether it's nothing else but a analytical project, it's a data project. You don't repeat it every time. That's what they want. >> I know you got a hard stop but I want to get your thoughts on this because I have heard the word, workload, mentioned so many more times in the past year, if there was a tag cloud of all theCUBE conversations where the word workload was mentioned, it would be the biggest font. (laughs) >> Yes. >> Workload has been around for a while but now you are seeing more workloads coming on. >> Yeah. >> That's more important for data. >> Yes. >> Workloads being tied into data. >> Absolutely. >> And then sharing data across multiple workloads, that's a big focus, do you see that same thing? >> We absolutely see that and the unique thing we see also is that newer workloads are being created and the old workloads are not going away, which is where the hybrid becomes very important. See, we serve large enterprises and their goal is to have a hybrid. So, you know, I'm running a old transaction workload order here, I want to have a experimental workload, I want to start a new workload, I want all of them to talk to each other, I don't want them to become silos and that's when they look to us to say connect the dots for me, you can be in the cloud, as an example, our cloud platform, you know last time, we talked about a 5 trillion transactions a month, today is double that, eight to ten trillion transactions a month. Growing like crazy but our traditional workload is also still there so we connect the dots for our customers. >> Amit, thank you for coming on sharing your insights, obviously you guys are doing well. You've got 300,000 developers, billions in revenue, thanks for coming on, appreciate the insight and looking forward to your Informatica World. >> Thank you very much. >> Amit Walia here inside theCUBE, with theCUBE conversation, in Palo Alto, thanks for watching.