>>Welcome back to HPD discovered 2021. My name is Dave Volonte and you're watching the cubes virtual coverage of discover we're going to dig into the most pressing topic not only for I. T. But entire organizations and that's cyber security with me. Miss O'Neil James, senior Director of security engineering at Hewlett Packard Enterprise. So Neil welcome to the cube. Come on in. >>Dave, thank you for having me. I appreciate it. >>Hey, you talked about Project Aurora today. Tell us about project Aurora. What is that? >>So I'm glad you asked. Project Aurora is a new framework that we're working on that attempts to provide the underpinnings for Zero Trust architectures inside of everything that we build at. Hp. Zero Trust is a way of providing a mechanism for enterprises to allow for everything in their enterprise. Whether it's a server, a human or anything in between to be verified and attested to before they're allowed to access or transact in certain ways. That's what we announced today. >>Well, so in response to a spate of damaging cyber attacks last month, President biden issued an executive order designed to improve the United States security posture and in that order essentially issued a zero trust mandate. You know, it's interesting. Zero Trust has gone from a buzzword to a critical part of a security strategy. So in thinking about a zero trust architecture, how do you think about that and how does project Aurora fit in? >>Yeah, Zero Trust architecture as a concept has has been around for quite some time now and over the last few years you've seen many a company attempting to provide technologies that they purport to be. Zero trust. Zero Trust is a framework. It's not one technology, it's not one tool, it's not one product. It is an entire framework of thinking and applying cyber security principles uh to everything that we just talked about beforehand. Project Aurora, as I said before hand, is designed to provide a way for our ourselves and our customers to be able to measure a test and verify every single piece of technology that we sell to them, whether it's a server or everything else in between. Now, we've got a long way to go before we're able to cover everything that HP sells. But for us these capabilities are the root of Zero Trust architectures, you need to be able to at any given moments notice, verify measure and a test and this is what we're doing with Project Aurora. >>So you founded a company called citadel and sold out to HPD last year. And my understanding is you were really the driving force behind the secure production identity framework, but you said zero Trust is really a framework, uh that's an open source project. Maybe you can explain what that is. I mean people talk about the nist framework for cybersecurity. How does that relate? What why is this important and how does Aurora fit into it? >>Yeah, so it's a good question. The next framework is a broader framework for cybersecurity that couples and covers many aspects of thinking about the security posture of an enterprise, whether it's network security, host based intrusion detection capabilities in response things of that sort Spiffy. What you're referring to secure production identity framework for everyone is an open source framework and technology base that we did work on when I was the ceo of Seattle. That was designed to provide a platform agnostic way to assign identity to anything that runs in a network. And so think about yourself or myself, we are uh, we have identities in our back pocket driver's license, passports, things of that sort. They provide a unique assertion of who we are and what we're allowed to do that does not exist in the world of software. And what spiffy does is it provides that mechanism so that you can actually use frameworks like project Aurora that can verify the underpinning infrastructure on top of which software workloads run to be able to verify the spiffy identities even better than before >>is the intensive product ties this capability within this framework. How do you approach this from HP standpoint >>suspicion inspire will and always will be. As far as I'm concerned, remain an open source project held by the cloud Native Computing Foundation. It's for the world. And we want that to be the case because we think that more of our enterprise customers are not living in the world of one vendor or two vendors. They have multiple vendors. And so we need to give them the tools and the flexibility to be able to allow for open source capabilities like Spiffy inspire to provide a way for them to assign these identities and assign policies and control regardless of the infrastructure choices they make today or tomorrow. H P E recognizes that this is a key differentiating capability for our customers. And our goal is to be able to look at our offerings that power the next generation of workloads, kubernetes instances, containers, serverless and anything that comes after that. And our responsibility to say, how can we actually take what we have and be able to provide those kinds of assertions, those underpinnings for zero trust that are going to be necessary to distribute those identities to others workloads and to do so in a scalable, effective and automated manner, which is one of the most important things that project Wara does. >>So a lot of companies senior will set up a security division, uh and and so, but is the IS HPV strategy to essentially uh embed security across its entire portfolio? How do you, how should we think about HP strategy in cyber? >>Yeah, so it's a it's a great question. Hp has a long history, uh security and other domains, networking and servers and storage and beyond. Uh the way we think about what we're building with project or this is plumbing, this is plumbing that must be and everything we built, customers don't buy one product from us and they think it's one custom, one company and something else from us and they think it's another company, they're buying HPV products. And our goal with Project Aurora is to ensure that this plumbing is widely and uniformly distributed and made available. So whether you're buying in Aruba device, a primary storage device or per alliance server. Project Aurora's capabilities are going to provide a consistent way to do the things that I've mentioned beforehand To allow for those zero trust architectures to become real. >>So it's I alluded to President biden's executive order previously, I mean you're a security practitioner or an expert in this area. It just seems as though, and I'd love to get your comments on this. I mean the adversaries are well funded. You know, they're either organized crime, their nation states, uh they're they're extracting a lot of very valuable information, they're monetizing that you've seen things like ransomware as a service now, so any any knucklehead can, can be in the ransomware business. Um it's just this endless escalation game. Um how do you see the industry approaching this? What needs to happen? So obviously I like what you're saying about the plumbing, you're not trying to attack this with a bunch of point tools, which is part of the problem. How do you see the industry coming together to solve this problem? >>Yeah, it's uh if you operate in the world of security, you have to operate from the standpoint of humility. And the reason why you have to operate from a standpoint of humility is because the attack landscape is constantly changing the things and tools and investments and techniques that you thought were going to thwart an attacker. Today, there quickly outdated within a week, a month, a quarter or whatever it might be. And so you have to be able to consistently and continuously evolve and adapt towards what customers are facing on any given moments notice I think to be able to as an industry tackle these issues more and more. So you need to be able to have all of us start to abide, not abide, but start to adopt these open source patterns. We recognize that every company hB included is here to serve customers and to make money for its shareholders as well. But in order for us to do that, we have to also recognize that they've got other technologies in their infrastructure as well. And so it's our belief, it's my belief that allowing for us to support open standards with spiffy inspire and perhaps with some of the aspects of what we're doing with project Aurora, I think allows for other people to be able to kind of deliver the same underpinning capabilities, the plumbing if you will, regardless of whether it's an HP product or somebody else along those lines as well. We need more of that generally across our industry and I think we're far from it. >>I mean this sounds like a war. I mean, it's it's more than a battle. It's a war that actually is never gonna end. Uh, and I don't think there is an end in sight. And you hear, see, so let's talk about the shortage of talent. Uh, they're getting inundated with point products and tools and then that just creates more technical debt. It's been interesting to watch interesting. Maybe it's not the right word, but the pivot 20 trust, endpoint security, cloud security and the exposure that we've now seen as a result of the pandemic was sort of rushed. And then of course, we've seen, you know, the the adversaries really take advantage of that. So, I mean, what you're describing is this ongoing, never ending battle, >>isn't it? Yeah, yeah, no, it's it's it's going to be ongoing. And by the way, Zero Trust is not the end state, right. I mean, there was things that we called the final nail in the coffin Five years ago, 10 years ago and yet the Attackers persevered. And that's because there's a lot of innovation out there. There's a lot of uh, infrastructure moving to dynamic architecture is like cloud and others that are going to be poorly configured and are going to not have necessarily the best and brightest providing security around that. So we have to remain vigilant. We have to work as hard as we can to help customers deploy Zero Trust architecture, but we have to be thinking about what's next. We have to be watching, studying and evolving to be able to prepare ourselves to be able to go after whatever the next capabilities are. >>What I like about what you're saying is, you're right. You have to have humility. I don't want to say. I mean it's it's hard because I do feel like a lot of times the vendor community says, okay, we have the answer to your point. You know, okay. We have a zero trust solution or we have a security solution and there is no silver bullet in this game. And I think what I'm hearing from you is look, we're providing infrastructure, Plumbing is the substrate, but it's an open system. It's got to evolve. We've anything you didn't say, but I love your thoughts on this is we got to collaborate with who some of you might think is your competitor because they're still, they're the good guys. >>Yeah. I mean our our customers are customers don't care that we're competitors with anybody. They care that we're helping them solve their problems for their business. So our responsibility is to figure out what we need to do to work together to provide the basic capabilities that allow for our customers to to remain in business. Right. If cybersecurity issues plague any of our customers, that doesn't affect just HP. That affects all of the companies that are serving that customer itself. So I think we have a shared responsibility to be able to protect our customers >>and you've been in cyber for much, if not most of your career. Right, correct. Let's go. So I got to ask you, did you have a superhero when you were a kid? Did you have sort of uh, you know, save the world thing going? >>Did I have to say, you know, I I didn't have to save the world thing going. But I had um I had, I had two parents that cared for for the world in many, many ways. They were both in the world of health care and so every day I saw them taking care of other people. And I think that probably rubbed off in some of the decisions that I made too >>Well. It's awesome. You can do a great work, really appreciate you coming on the cube and and thank you so much for your insights. >>I appreciate that. Thanks >>All right. Thank you for being with us for our ongoing coverage. HPD discovered 21. This is Dave Volonte. You're watching the cube. The leader in digital tech coverage will be right back. Mhm.

[Music] [Applause] hi I'm Peter Burris and welcome again to another cube conversation from our wonderful studios here in Palo Alto California great conversation today branching out into the world of data governance a lot of things going on in the industry around data and what does it mean for digital business and how do we treat data increasingly as an asset and that obviously raises a lot of questions about how we govern those assets improve their value share them appropriately at the same time privatizing and make sure that they are corrupted and to have this conversation we've got two great guests first off we've got Kelly O'Neil who's the CEO of first San Francisco partners is an information management consultancy here in the Bay Area and Saatchi on sangani welcome back to the cube CEO of relation welcome so let's get started I kind of said in the preamble that this notion of data governance becomes especially acute especially important because we're now trying to treat data as an asset so we're not governing the resources to manage data we're actually trying to govern data itself utilizing resources so Sachin why don't we start with you what does data governance mean from a tool and process standpoint and then Kelly on rescue and let's go deeper into that process part >> yeah I mean I think so there's lots of different definitions of data governance that a wide variety of experts have put out and I'm not sure that I want to sort of put a new definition in the debate very generically it's a set of processes that institutions use to manage the data that's at their disposal and if you think about that generically in terms of where the problem is broadly stated how do I manage my information and in the consumption and the production and the storage of that information you know that is a super hard problem to deal with when you have you know hundreds of thousands of data sources potentially millions of different data sets and thousands of people who are constantly consuming that information and limited resources and so the process of data governance now in a world to your point where every business is trying to become a digital business and where the monetization of data is a huge part of that business is the fundamental problem right how do we have people discover the data how do we have people understand the data that they're seeing how do we have people trust the data that they're seeing that is the consort of province of data governance and that is what people are coming to realize but >> we do have tooling now that is specifically being built including elation which is a great catalog for performing some of these or to facilitate some of these governance activities so there's a enough of a standard set of definitions that we actually can put tooling in place which means now we can really liberate the power and the talent of people to appropriately govern data and use data so Kelly what are you doing with your clients to help them take the tools for data governance and turn it into ideally a strategic capability that really drives the digital business forward yeah >> absolutely so as a services organization we really focus on ensuring that the people in the process are in place so that they can take advantage of the technology right so you've got accountability around who has who's responsible to ensure the data is of a certain sort of quality or a certain sort of standard as well as who has the ability to access that data and use that data and I think one of the things that sahteen brought up is there's just this onslaught of data that's coming in so if you think about that as a construct it's entirely overwhelming there's too much data to be able to say this person owns this data field this person defines that data field it has to be much more organic it has to be much more shared and tools much more communal yeah and so it really is this concept of how do we have a certain level of trust in the data and what does trust mean to the organization to take advantage of that data and to use it as an asset and to use it in business context and so our services help organizations to see what that means to them to right-size that investment in the sense of how much effort do we put towards this and then also how do we make sure that those tools are used that they're adopted and that they're embedded into work processes that it's not a standalone repository that never gets used >> you know we had Aaron Cal bond not too long ago to talk about trust check and I know that's one of the things that's bringing you together is this notion of a communal approach to putting to imbuing data with trust so let's talk a bit about trust check and in particular how your companies are working together to accelerate the prop the processes that you so accurately described what starts at 10 when we start with you let's trust checking and what does it mean for you yeah so trust check is a very simple it's a very simple capability although very complicated to implement the idea behind trust check is that as and when somebody's communing consuming data whether that's in a Salesforce dashboard or in a tableau report or conversely even inside of elation that immediately as they're consuming that information they're presented with context around that data talking about the appropriateness of that data for the use that they particularly have now that could be about timeliness of the data that could be about the availability of the data that could be about the quality of the data that could be about you know the privacy regulations or the security surrounding that data there are lots of reasons why one might not trust the data but often that information is off to the side right and often that information is in a place where the consumer of the data has no awareness that the policy even exists much less where to go get it that information and so what trust check is saying look this notion of governance has to actually be actionable and immediate and available in order for it to be valuable to the person that's using the information yeah and you might say that also that it might be trusted in this context but not in other context as well so how does that inform well how does that facilitate how does that accelerate implementing these processes to make sure that communities of data in an evidence-based world are better able to apply data use data and share information about that data with each other yeah absolutely so it provides a number one just automation right so fundamentally that's a value add it means that it's more available it's more shared it's faster and that can make the governance organization more relevant to the business so that the data is actually used in a more appropriate and higher-value way so first things Automation and then the second thing is that as we start to automate there's this concept of kind of learning and expanding and so being able to leverage a tool within a services practice and phonology it means that we can kind of start within one area and to leverage that learning and extend and extend and extend because fundamentally data is pervasive right it's everywhere and which makes governance really intimidating and hard so that idea of focusing learning doing something well and being agile right and growing over time a tool really helps you to do that because it is a place where people can get focused for that learning and then repeat rinse and repeat rinse and repeat so it many respects it is a reflection of manifestation of some of these good processes absolutely the you guys obviously have an enormous amount of knowledge about data Government's about the tool infer data to government's about where this all goes but ultimately a lot of your customers are still very much in the formative stages of putting this in place so how are other than just having them license elations toolkit how are you coming together to put in place services or training or something else to help diffuse your knowledge I just want to come back to one point that you mentioned is I think I think there's been a shift in the tooling market okay so I wouldn't say that the tooling has not that there's never been any tooling to deal with the problem of data governance in fact I think there's been lots of tooling that hasn't worked particularly very well so so let me put some context on that so when I say tooling as I said kind of upfront to my mind it's tooling for the resources that handle the data not tooling for the data yeah but keep going if I'm wrong I want to hear well know I think even tooling for the resources that handle the data has largely been the province of either there is a category of software that one would traditionally described in the realm of data stewardship and data governance and broadly speaking it allows you to create forms and to administer workflows with those forms right so you know there and so that is a highly unauthorized and so what a traditional you know governance implementing regime might include would be the development of policies and the enactment of those policies through a set of people who have to vary manually check the data at their disposal it is generally speaking disconnected from the data right when you have small sets of data when you have limited quantities of data that could be a perfectly fine solution when you have a very small set of policies that you need to interact or interact with because you have to have a set of goals that are maybe regulatory in nature that is an okay thing to go do when you have petabytes of data across hundreds of thousands of data sets it's an impossible thing to go do right and so I think that that sort of inundation that Kelly was referring to is is is you know born out of this massive volume of data coming up where the traditional methods just just don't work right so your tanks are you talking about such an essentially that were that we're adding that metadata directly to the data itself and creating trusted objects that the organization can use and apply as assets wherever so that is exactly a solution and the analogy that I think will then inform you know most of the people who are sort of listening us today to us today would be the sort of shift from Yahoo to Google right so if you think about Yahoo Yahoo relied upon every single webmaster tagging every single webpage to make sure that the search engine knew which webpages to go look up right that required a whole bunch of trust in your webmasters first of all some of whom were bad actors right you may not have those in the stewardship regime regime inside of a enterprise but you could right people have their own perspectives and it also required for people to have enough knowledge to tag things right so you'd have to know what to tag and that a tag would have to be right for anybody who's developed a folder system you know that those folder systems are constantly changing right and so then Google comes along and says look if we just watch what people are doing with this information and we know what people are linking to then we can say hey what what's more valuable what's more useful by watching the behaviors right and I think that's the sort of shift of a Bottoms Up approach which is different from sort of that top-down declarative approach that's come in the technology for governance and fortunately and and I think that's what people have to understand which is that the problems always been there but what's happened is the volumes and the relevance and the timely the information have just been so critical that now we have to change the way we do things and not what we're working together on it's mercury it's it's it's it has scale issues but also the annoying technology has gotten to the point where we can actually do more automated discovery about how people are using things which means you have to change the process and the people great so let's let's come back to that question what are you guys doing together to ensure that you can in fact diffuse this knowledge and diffuse these insights into organizations faster so they can pick up on some of these changes better yeah so so for San Francisco is taking some of our methodologies and ensuring that they are right size and fit for the elation suite of products especially the trust checks suite of products and so what we're starting with is the data acquisition process and that's important because the supply chain for data is what has become inordinately complex it's no longer primarily internally created data most data is actually acquired and so if we start with that ingestion process and the data acquisition process that's a huge value both to the customers that are using it as well as to the mutual organizations right so right focusing on that as a as a case and then we'll move on to the concept of information stewardship itself so stewardship across the supply chain not just the data acquisition supply chain so we are adapting our methodologies to be specific and unique to alation to help their existing customer base and obviously potentially new customers together yeah so an a great example of that I was just talking to a chief dead officer of a very large financial institution in North America you know this individual was contending with the problem of making good data available to there and you know and business audience for analytical purposes right to solve exactly this problem he said we acquire companies all the time we're acquiring companies constantly and we're getting all of this data in and I have to figure out what this data is and do I already have this data in-house do I have systems that store this sort of data do I have systems that duplicate the data but incorrectly and are there multiple of these sets of data inside the company that I'm acquiring because they've got data duplication just like we do and how do we figure all of that out right so this would be a perfect example where the data acquisition problem is critical to solve in the process of being able to create available useful government data right and so this would be a perfect example for you know the two of our companies to be able to work together because we don't speak to the implementation and the process we speak to the technical capability of simply providing the inventory so that somebody can then figure out what to do with that information but there are practices that are probably going to do better or will generate greater value out of the elation toolkit than others would absolutely yeah and so in many respects we're looking into companies like yours to help correct or define what those practices are defuse them more broadly through C package consulting and through really good partnership that you guys have been working on yeah because I mean you know you know I mean I you know I think Larry Ellison is a controversial character right right but you know I'll quietly say that I worked at Oracle at one point in time what one of the things that Larry one of the things that Larry said is you know people when they buy software are constantly asking the question of how do I figure out how to take my existing business process and fit it on top of the software that exists out there and he's like no that's exactly wrong what people should be doing is figuring out what should my business process be given the capability that I've got right and so we now have a new capability and we're we're enabling people to have more or less super powers relative to what they would have had to do by hunting and pecking through every data set and tagging it manually right and what you know Kellyanne for San Francisco are bringing to the table is the ability to have a new process that would allow them to do that at scale and faster so that's where we see per sighted excellence so in a date of first world data governance becomes more important to thought leaders helping to make that happen Saachi and sigani CEO of elation Kelly O'Neil for San Francisco partners thanks very much for being on the queue thank you Peter thank you and once again this has been a cute conversation from our Palo Alto studios thank you very much for watching until next time [Music] you

(futuristic music) >> Welcome back everybody, Jeff Frick here with theCUBE. We're having a CUBE conversation in our Palo Alto studios, we're waiting for the crazy madness of the second half conference season to begin but before that it's nice to get a little bit of a break in the action and we can have people into our studio in Palo Alto. We're really excited to have our next guest really adding to this journey that we've been kind of watching over a course of many years with Informatica, she's Tracey Newell she's the newly announced President, global field operations from Informatica, Tracey great to meet you. >> Yeah nice to meet you. >> Absolutely. So we've following Informatica for a long time, I think our first visit to Informatica world was 2015 back when it was still a public company, I think it was Info which still has this legacy, that's the hashtag for this show. >> It certainly does. >> Which is kind of funny cause it's not really a stock ticker anymore. So it's been quite a journey and really well timed with kind of the big data revolution. You joined the board a couple years ago. >> I did in 2016. >> But you just decided to leave Mahogany Row and take off the board outfit and jump in and get on the field and get dirty. So why did you decide to get into the nitty gritty? >> Yeah so I joined the board because I really believed in the mission so. Digital transformation is something that's real, it's a boardroom discussion. Every enterprise and government around the world's trying to figure this out and so I wanted to be part of that and I've had a front row seat for a couple of years. >> Right right. >> I'm not one to sit on the sidelines for very long and I thought this is just too much fun and I want to get in the game so I asked to step down and I've recently joined as a president of Global Field ops. >> Great so your background is a little bit of confusion due to history, a lot of sales, you've been running sales for lot different companies, been in the valley for a while. But sales is really under you so you haven't really left your sales hat, that's just part of now a bigger role that you're going to be doing with Informatica. >> Yeah that's right, it's a bigger and broader role, but my favorite thing is running sales organizations. So I've done other things too, I've run operations, and customer success, but I was thrilled to join and also run professional services as well cause that's so important to the delivery and for our customers. >> So you'll write the digital transformation, it's the hop topic, it's what everybody is talking about, and it's true and as Informatica is in the middle of it, data is such a big piece of the digital transformation as everybody, we used to joke, there are no companies except software companies. I think we're taking it to the next step, now there are no software companies, really everybody should be a data company, and Informatica is sitting right in the middle of that world. No that's right, yeah data is the new currency, it's become of the most important assets for enterprises, everyone's trying to transform, they're trying to disrupt, they're trying to take on the leader or they're trying to keep their lead. And they need all their information throughout their organization in order to do that and so you know one of the stories hat I really like, Graham Thompson's our CIO and he talks to lots of CIOs and he'll use this analogy in that you know he'll say does your CFO have good containment strategy around their most important asset, and that's revenue. Does your CFO, does he or she know what the data is and inevitably the CIO will say of course. Well that's great does he know or she know how they're spending the money and who's spending the money? Do they have controls and compliance and security around that and of course the answer is yes yes yes and yes. And it inevitably turns to the CIO to say well if data is your most important asset, if that truly is the currency in your organization, do you know where all of your data is? And the answers always no. And there's lots of reasons for that, it's most enterprises have hundreds if not thousands of databases and shadow IT projects everywhere. But if the answers no then how do take advantage and leverage that information to the companies advantage? How do you control it, how do you have compliance and that's where we come in. >> So what's the Informatica special sauce? What's the secret sauce that you guys can bring to the party that nobody else can? >> Yeah so I think inevitably that it would be the platform so our intelligent data platform is really important to the enterprise. The CIOs that I've been meeting with for the last decade have said you know I can't have ten widgets that are all solving a similar problem cause it's just too expensive. I need the bet with the leader in the space and so what we're doing to provide that for enterprises is really important and yet at the same time, you've got to be the best at what you do, you can't just be comprehensive but you have to have best debris technology. We're spending 17 cents of every dollar in r&d and we're so focused on just this one thing, our mission is to lead in digital transformation for the large enterprise and we've been doing this for 25 years so we've spent billions of dollars at making sure our customers are invested in us and that we protect that investment. >> Right. So what is your charge is as you're starting your new role I think the press release just came out a couple days ago. You know what does O'Neil say to you, you know we want you, here's where we want you to go take down that next mountain, what are some of your short term priorities, what are some of your longer term priorities? >> Yeah so we have a great opportunity in front of us. So stating the obvious I'm here to drive growth and expansion both in market share opportunities, we have over nine thousand customers globally and yet we all know that there's a tremendous opportunity to continue direct market shares. This is a global phenomena and yet our largest customers we have 85 of the Fortune 100, they certainly need a lot of support and we're here to help provide that leadership. And we do a lot of best practice sharing, we do a lot around helping customers on their journeys cause we see these themes given that we do work with the largest companies around the world. >> And I'm sure you're going to be getting on a plane and meeting with a whole bunch of customers over the next, over the next several weeks and months but was there something from your board position that you could see was a consistent pattern that you really see an opportunity for growth, kind of an unexploited opportunity as people are going through this digital transformation cause we talk all the time, it's how do I get started and you know I have small projects to give me early success and kind of those types of conversations but clearly we're kind of beyond the beginning and we should be starting to move down the field a little bit. >> Yeah certainly. So we work with all the global SIs and we won't ever try to take their place you know Insentrum, Delite, Capgemeni, Cottonsmith, they're tremendous at what they do and we partner with them very well. But we've absolutely seen consistent themes as we work with these big enterprises, I mean we've seen Coca Cola work on delivering new packaging for the World Cup where they drove exponential sales and they wanted to use the power of all of their data. The data in the Cloud, the data that they have on premise, the data in all the SAS applications and that's where we come in and really help them, helping them to leverage all of their information and to do that in an intelligent way and so we've seen several patterns emerge how customers can get started and we've created a series of workshops and summits and specialists that we we can sell on a pro forma basis in helping customers figure out where those quick fixes are. There's a couple of key big buckets, we see most large enterprise moving from on premise to Cloud and they're trying to figure out a a migration strategy so we help a lot there. Most customers are trying to figure out how to get closer to their customers so we do a lot of work around customer intimacy. Intimacy could be driving the top line, cross sale, up sale, or even customer retention. B&P Paraboss did a lot of work with us there around getting closer to you know in their wealth practice. And then we do quite a bit around governance as you would expect. That's a hot topic with GDPR again if you can't say you know where all your data is well then how can you be compliant? >> Right how can you delete me? >> How can you delete me if you don't know where your data is. There's a number of practices that we've set up and we'll do some not for fee consulting work to help customers try and figure this out. >> Yeah clearly when we first met Informatica in 2015, you know the Cloud was moving, the public Cloud, but it wasn't near what it is today. And I guess you guys just had a recent announcement, Google Cloud Next is coming up in a couple of weeks and so you guys are now doing some stuff with Google Cloud? We are yeah so we're pretty good listeners I think that's important if you're going to be a business partner to your clients you got to know what they want and one of the things that clients have said to us is we need you to partner with our partners. You know the days of proprietary and sole source, you know we're going to be everything to you without working with anyone you know those days are over. And so the key Cloud partners our customers have asked us to work with include Amazon, Google, Microsoft, Azure, so you're right last week we did make an announcement that we've done deep integration and we're spending our r&d dollars for customers that are investing with Google to make those investments more valuable and we announced API management and integration with Google make that easy for customers so. Informatica world we announced native integration in our Ipass platform for Microsoft so over and over again you'll hear us continue to do more with the the partners that our customers want us to and that's a win win for everybody. >> Its just so funny too because when people talk about a company like say Coca Cola which you brought up they talk about it like it's a company. No it's like not a company, it's many many companies, many many projects, many many challenges you know it's not just one entity that has a relationship with one other entity. >> That's right. >> But the other thing I think is interesting times and Coke's a good example or Ford or pick many old line industrial companies that used to have distribution right and what was the purpose of distribution is to break bulk is to communicate information and to get the product close to the customer. But the manufacturer never knew what happened once they shipped that stuff off into their distribution. Now it's a whole different world, they have a direct connection with their in customer, they're collecting data from their in customer, and so they have a relationship and an opportunity and a challenge with that they never had before. They just sent it off to the distributor and off it went and hopefully it doesn't come back for repair. (laughing) >> No that's right but you're exactly right, and that's the challenge that customers are facing. I don't care if it's a customer in the mid market or it's a customer in large enterprise or if it's a government organization. They need to know all aspects of their customer partner supplier information and how to communicate globally if they're going to drive disruption. And one of the CIOs of a Fortune 500 made a comment that we decided that we were going to disrupt ourselves before someone else disrupted us. And that's, that's my comment on why this is a board level discussion, it's super important, and we can help solve those problems. >> It's funny Dave Potrick one of my favorite executives used to be the number two guy at Charles Schwab and I remember him speaking when they went to fix price trading back in the day, I'm aging myself unfortunately but you know he said the same thing, we have to disrupt ourselves before somebody else disrupts us. And if you're not thinking that way you're going to get disrupted so better it be you than someone that you don't even see and usually it's not your side competitor, it's the one coming from a completely different direction that you weren't even paying attention to. >> That's right. And we see that over and over again and you made the right comment in that it's not always easy, some of these Fortune 500s through consolidation, even the Global 2000. They've done all these acquisitions and so you've got hundreds of BUs that don't have any systems tied together and how do you start to create a common connection in so that you can build your brand and you can try differentiation and that's the key, that's back to the intelligent data platform. >> Right and as you said and there's not single systems and now we got API economy, things are all connected so you don't necessarily even have that much direct control over a lot of these opportunities and you said that first I think it's just like okay where's your data? Can you start with the very simple question and a lot of people aren't really sure and can't even start from there. >> That's right. >> So good opportunities. >> Absolutely, there's no question. >> Alright Tracey, well thank you for stopping by, congratulations on your, on your new position and moving from Mahogany Row down into, down into the trenches. >> Down on the field. >> I'm sure they're going to be happy to have you down there on the field. >> Yeah no thanks Jeff I'm happy to be here and thanks for the time today. >> Thank you and we'll see you in Informatica world if not sooner. >> That's right. >> Alright she's Tracey Newell I'm Jeff Frick, you're watching theCube from Palo Alto, thanks for watching. (futuristic music)

>> Announcer: Live from New York City it's The Cube, covering IBM data science for all. Brought to you by IBM. (light digital music) >> Welcome back to data science for all. It's a whole new game. And it is a whole new game. >> Dave Vellante, John Walls here. We've got quite a distinguished panel. So it is a new game-- >> Well we're in the game, I'm just happy to be-- (both laugh) Have a swing at the pitch. >> Well let's what we have here. Five distinguished members of our panel. It'll take me a minute to get through the introductions, but believe me they're worth it. Jennifer Shin joins us. Jennifer's the founder of 8 Path Solutions, the director of the data science of Comcast and part of the faculty at UC Berkeley and NYU. Jennifer, nice to have you with us, we appreciate the time. Joe McKendrick an analyst and contributor of Forbes and ZDNet, Joe, thank you for being here at well. Another ZDNetter next to him, Dion Hinchcliffe, who is a vice president and principal analyst of Constellation Research and also contributes to ZDNet. Good to see you, sir. To the back row, but that doesn't mean anything about the quality of the participation here. Bob Hayes with a killer Batman shirt on by the way, which we'll get to explain in just a little bit. He runs the Business over Broadway. And Joe Caserta, who the founder of Caserta Concepts. Welcome to all of you. Thanks for taking the time to be with us. Jennifer, let me just begin with you. Obviously as a practitioner you're very involved in the industry, you're on the academic side as well. We mentioned Berkeley, NYU, steep experience. So I want you to kind of take your foot in both worlds and tell me about data science. I mean where do we stand now from those two perspectives? How have we evolved to where we are? And how would you describe, I guess the state of data science? >> Yeah so I think that's a really interesting question. There's a lot of changes happening. In part because data science has now become much more established, both in the academic side as well as in industry. So now you see some of the bigger problems coming out. People have managed to have data pipelines set up. But now there are these questions about models and accuracy and data integration. So the really cool stuff from the data science standpoint. We get to get really into the details of the data. And I think on the academic side you now see undergraduate programs, not just graduate programs, but undergraduate programs being involved. UC Berkeley just did a big initiative that they're going to offer data science to undergrads. So that's a huge news for the university. So I think there's a lot of interest from the academic side to continue data science as a major, as a field. But I think in industry one of the difficulties you're now having is businesses are now asking that question of ROI, right? What do I actually get in return in the initial years? So I think there's a lot of work to be done and just a lot of opportunity. It's great because people now understand better with data sciences, but I think data sciences have to really think about that seriously and take it seriously and really think about how am I actually getting a return, or adding a value to the business? >> And there's lot to be said is there not, just in terms of increasing the workforce, the acumen, the training that's required now. It's a still relatively new discipline. So is there a shortage issue? Or is there just a great need? Is the opportunity there? I mean how would you look at that? >> Well I always think there's opportunity to be smart. If you can be smarter, you know it's always better. It gives you advantages in the workplace, it gets you an advantage in academia. The question is, can you actually do the work? The work's really hard, right? You have to learn all these different disciplines, you have to be able to technically understand data. Then you have to understand it conceptually. You have to be able to model with it, you have to be able to explain it. There's a lot of aspects that you're not going to pick up overnight. So I think part of it is endurance. Like are people going to feel motivated enough and dedicate enough time to it to get very good at that skill set. And also of course, you know in terms of industry, will there be enough interest in the long term that there will be a financial motivation. For people to keep staying in the field, right? So I think it's definitely a lot of opportunity. But that's always been there. Like I tell people I think of myself as a scientist and data science happens to be my day job. That's just the job title. But if you are a scientist and you work with data you'll always want to work with data. I think that's just an inherent need. It's kind of a compulsion, you just kind of can't help yourself, but dig a little bit deeper, ask the questions, you can't not think about it. So I think that will always exist. Whether or not it's an industry job in the way that we see it today, and like five years from now, or 10 years from now. I think that's something that's up for debate. >> So all of you have watched the evolution of data and how it effects organizations for a number of years now. If you go back to the days when data warehouse was king, we had a lot of promises about 360 degree views of the customer and how we were going to be more anticipatory in terms and more responsive. In many ways the decision support systems and the data warehousing world didn't live up to those promises. They solved other problems for sure. And so everybody was looking for big data to solve those problems. And they've begun to attack many of them. We talked earlier in The Cube today about fraud detection, it's gotten much, much better. Certainly retargeting of advertising has gotten better. But I wonder if you could comment, you know maybe start with Joe. As to the effect that data and data sciences had on organizations in terms of fulfilling that vision of a 360 degree view of customers and anticipating customer needs. >> So. Data warehousing, I wouldn't say failed. But I think it was unfinished in order to achieve what we need done today. At the time I think it did a pretty good job. I think it was the only place where we were able to collect data from all these different systems, have it in a single place for analytics. The big difference between what I think, between data warehousing and data science is data warehouses were primarily made for the consumer to human beings. To be able to have people look through some tool and be able to analyze data manually. That really doesn't work anymore, there's just too much data to do that. So that's why we need to build a science around it so that we can actually have machines actually doing the analytics for us. And I think that's the biggest stride in the evolution over the past couple of years, that now we're actually able to do that, right? It used to be very, you know you go back to when data warehouses started, you had to be a deep technologist in order to be able to collect the data, write the programs to clean the data. But now you're average causal IT person can do that. Right now I think we're back in data science where you have to be a fairly sophisticated programmer, analyst, scientist, statistician, engineer, in order to do what we need to do, in order to make machines actually understand the data. But I think part of the evolution, we're just in the forefront. We're going to see over the next, not even years, within the next year I think a lot of new innovation where the average person within business and definitely the average person within IT will be able to do as easily say, "What are my sales going to be next year?" As easy as it is to say, "What were my sales last year." Where now it's a big deal. Right now in order to do that you have to build some algorithms, you have to be a specialist on predictive analytics. And I think, you know as the tools mature, as people using data matures, and as the technology ecosystem for data matures, it's going to be easier and more accessible. >> So it's still too hard. (laughs) That's something-- >> Joe C.: Today it is yes. >> You've written about and talked about. >> Yeah no question about it. We see this citizen data scientist. You know we talked about the democratization of data science but the way we talk about analytics and warehousing and all the tools we had before, they generated a lot of insights and views on the information, but they didn't really give us the science part. And that's, I think that what's missing is the forming of the hypothesis, the closing of the loop of. We now have use of this data, but are are changing, are we thinking about it strategically? Are we learning from it and then feeding that back into the process. I think that's the big difference between data science and the analytics side. But, you know just like Google made search available to everyone, not just people who had highly specialized indexers or crawlers. Now we can have tools that make these capabilities available to anyone. You know going back to what Joe said I think the key thing is we now have tools that can look at all the data and ask all the questions. 'Cause we can't possibly do it all ourselves. Our organizations are increasingly awash in data. Which is the life blood of our organizations, but we're not using it, you know this a whole concept of dark data. And so I think the concept, or the promise of opening these tools up for everyone to be able to access those insights and activate them, I think that, you know, that's where it's headed. >> This is kind of where the T shirt comes in right? So Bob if you would, so you've got this Batman shirt on. We talked a little bit about it earlier, but it plays right into what Dion's talking about. About tools and, I don't want to spoil it, but you go ahead (laughs) and tell me about it. >> Right, so. Batman is a super hero, but he doesn't have any supernatural powers, right? He can't fly on his own, he can't become invisible on his own. But the thing is he has the utility belt and he has these tools he can use to help him solve problems. For example he as the bat ring when he's confronted with a building that he wants to get over, right? So he pulls it out and uses that. So as data professionals we have all these tools now that these vendors are making. We have IBM SPSS, we have data science experience. IMB Watson that these data pros can now use it as part of their utility belt and solve problems that they're confronted with. So if you''re ever confronted with like a Churn problem and you have somebody who has access to that data they can put that into IBM Watson, ask a question and it'll tell you what's the key driver of Churn. So it's not that you have to be a superhuman to be a data scientist, but these tools will help you solve certain problems and help your business go forward. >> Joe McKendrick, do you have a comment? >> Does that make the Batmobile the Watson? (everyone laughs) Analogy? >> I was just going to add that, you know all of the billionaires in the world today and none of them decided to become Batman yet. It's very disappointing. >> Yeah. (Joe laughs) >> Go ahead Joe. >> And I just want to add some thoughts to our discussion about what happened with data warehousing. I think it's important to point out as well that data warehousing, as it existed, was fairly successful but for larger companies. Data warehousing is a very expensive proposition it remains a expensive proposition. Something that's in the domain of the Fortune 500. But today's economy is based on a very entrepreneurial model. The Fortune 500s are out there of course it's ever shifting. But you have a lot of smaller companies a lot of people with start ups. You have people within divisions of larger companies that want to innovate and not be tied to the corporate balance sheet. They want to be able to go through, they want to innovate and experiment without having to go through finance and the finance department. So there's all these open source tools available. There's cloud resources as well as open source tools. Hadoop of course being a prime example where you can work with the data and experiment with the data and practice data science at a very low cost. >> Dion mentioned the C word, citizen data scientist last year at the panel. We had a conversation about that. And the data scientists on the panel generally were like, "Stop." Okay, we're not all of a sudden going to turn everybody into data scientists however, what we want to do is get people thinking about data, more focused on data, becoming a data driven organization. I mean as a data scientist I wonder if you could comment on that. >> Well I think so the other side of that is, you know there are also many people who maybe didn't, you know follow through with science, 'cause it's also expensive. A PhD takes a lot of time. And you know if you don't get funding it's a lot of money. And for very little security if you think about how hard it is to get a teaching job that's going to give you enough of a pay off to pay that back. Right, the time that you took off, the investment that you made. So I think the other side of that is by making data more accessible, you allow people who could have been great in science, have an opportunity to be great data scientists. And so I think for me the idea of citizen data scientist, that's where the opportunity is. I think in terms of democratizing data and making it available for everyone, I feel as though it's something similar to the way we didn't really know what KPIs were, maybe 20 years ago. People didn't use it as readily, didn't teach it in schools. I think maybe 10, 20 years from now, some of the things that we're building today from data science, hopefully more people will understand how to use these tools. They'll have a better understanding of working with data and what that means, and just data literacy right? Just being able to use these tools and be able to understand what data's saying and actually what it's not saying. Which is the thing that most people don't think about. But you can also say that data doesn't say anything. There's a lot of noise in it. There's too much noise to be able to say that there is a result. So I think that's the other side of it. So yeah I guess in terms for me, in terms of data a serious data scientist, I think it's a great idea to have that, right? But at the same time of course everyone kind of emphasized you don't want everyone out there going, "I can be a data scientist without education, "without statistics, without math," without understanding of how to implement the process. I've seen a lot of companies implement the same sort of process from 10, 20 years ago just on Hadoop instead of SQL. Right and it's very inefficient. And the only difference is that you can build more tables wrong than they could before. (everyone laughs) Which is I guess >> For less. it's an accomplishment and for less, it's cheaper, yeah. >> It is cheaper. >> Otherwise we're like I'm not a data scientist but I did stay at a Holiday Inn Express last night, right? >> Yeah. (panelists laugh) And there's like a little bit of pride that like they used 2,000, you know they used 2,000 computers to do it. Like a little bit of pride about that, but you know of course maybe not a great way to go. I think 20 years we couldn't do that, right? One computer was already an accomplishment to have that resource. So I think you have to think about the fact that if you're doing it wrong, you're going to just make that mistake bigger, which his also the other side of working with data. >> Sure, Bob. >> Yeah I have a comment about that. I've never liked the term citizen data scientist or citizen scientist. I get the point of it and I think employees within companies can help in the data analytics problem by maybe being a data collector or something. I mean I would never have just somebody become a scientist based on a few classes here she takes. It's like saying like, "Oh I'm going to be a citizen lawyer." And so you come to me with your legal problems, or a citizen surgeon. Like you need training to be good at something. You can't just be good at something just 'cause you want to be. >> John: Joe you wanted to say something too on that. >> Since we're in New York City I'd like to use the analogy of a real scientist versus a data scientist. So real scientist requires tools, right? And the tools are not new, like microscopes and a laboratory and a clean room. And these tools have evolved over years and years, and since we're in New York we could walk within a 10 block radius and buy any of those tools. It doesn't make us a scientist because we use those tools. I think with data, you know making, making the tools evolve and become easier to use, you know like Bob was saying, it doesn't make you a better data scientist, it just makes the data more accessible. You know we can go buy a microscope, we can go buy Hadoop, we can buy any kind of tool in a data ecosystem, but it doesn't really make you a scientist. I'm very involved in the NYU data science program and the Columbia data science program, like these kids are brilliant. You know these kids are not someone who is, you know just trying to run a day to day job, you know in corporate America. I think the people who are running the day to day job in corporate America are going to be the recipients of data science. Just like people who take drugs, right? As a result of a smart data scientist coming up with a formula that can help people, I think we're going to make it easier to distribute the data that can help people with all the new tools. But it doesn't really make it, you know the access to the data and tools available doesn't really make you a better data scientist. Without, like Bob was saying, without better training and education. >> So how-- I'm sorry, how do you then, if it's not for everybody, but yet I'm the user at the end of the day at my company and I've got these reams of data before me, how do you make it make better sense to me then? So that's where machine learning comes in or artificial intelligence and all this stuff. So how at the end of the day, Dion? How do you make it relevant and usable, actionable to somebody who might not be as practiced as you would like? >> I agree with Joe that many of us will be the recipients of data science. Just like you had to be a computer science at one point to develop programs for a computer, now we can get the programs. You don't need to be a computer scientist to get a lot of value out of our IT systems. The same thing's going to happen with data science. There's far more demand for data science than there ever could be produced by, you know having an ivory tower filled with data scientists. Which we need those guys, too, don't get me wrong. But we need to have, productize it and make it available in packages such that it can be consumed. The outputs and even some of the inputs can be provided by mere mortals, whether that's machine learning or artificial intelligence or bots that go off and run the hypotheses and select the algorithms maybe with some human help. We have to productize it. This is a constant of data scientist of service, which is becoming a thing now. It's, "I need this, I need this capability at scale. "I need it fast and I need it cheap." The commoditization of data science is going to happen. >> That goes back to what I was saying about, the recipient also of data science is also machines, right? Because I think the other thing that's happening now in the evolution of data is that, you know the data is, it's so tightly coupled. Back when you were talking about data warehousing you have all the business transactions then you take the data out of those systems, you put them in a warehouse for analysis, right? Maybe they'll make a decision to change that system at some point. Now the analytics platform and the business application is very tightly coupled. They become dependent upon one another. So you know people who are using the applications are now be able to take advantage of the insights of data analytics and data science, just through the app. Which never really existed before. >> I have one comment on that. You were talking about how do you get the end user more involved, well like we said earlier data science is not easy, right? As an end user, I encourage you to take a stats course, just a basic stats course, understanding what a mean is, variability, regression analysis, just basic stuff. So you as an end user can get more, or glean more insight from the reports that you're given, right? If you go to France and don't know French, then people can speak really slowly to you in French, you're not going to get it. You need to understand the language of data to get value from the technology we have available to us. >> Incidentally French is one of the languages that you have the option of learning if you're a mathematicians. So math PhDs are required to learn a second language. France being the country of algebra, that's one of the languages you could actually learn. Anyway tangent. But going back to the point. So statistics courses, definitely encourage it. I teach statistics. And one of the things that I'm finding as I go through the process of teaching it I'm actually bringing in my experience. And by bringing in my experience I'm actually kind of making the students think about the data differently. So the other thing people don't think about is the fact that like statisticians typically were expected to do, you know, just basic sort of tasks. In a sense that they're knowledge is specialized, right? But the day to day operations was they ran some data, you know they ran a test on some data, looked at the results, interpret the results based on what they were taught in school. They didn't develop that model a lot of times they just understand what the tests were saying, especially in the medical field. So when you when think about things like, we have words like population, census. Which is when you take data from every single, you have every single data point versus a sample, which is a subset. It's a very different story now that we're collecting faster than it used to be. It used to be the idea that you could collect information from everyone. Like it happens once every 10 years, we built that in. But nowadays you know, you know here about Facebook, for instance, I think they claimed earlier this year that their data was more accurate than the census data. So now there are these claims being made about which data source is more accurate. And I think the other side of this is now statisticians are expected to know data in a different way than they were before. So it's not just changing as a field in data science, but I think the sciences that are using data are also changing their fields as well. >> Dave: So is sampling dead? >> Well no, because-- >> Should it be? (laughs) >> Well if you're sampling wrong, yes. That's really the question. >> Okay. You know it's been said that the data doesn't lie, people do. Organizations are very political. Oftentimes you know, lies, damned lies and statistics, Benjamin Israeli. Are you seeing a change in the way in which organizations are using data in the context of the politics. So, some strong P&L manager say gets data and crafts it in a way that he or she can advance their agenda. Or they'll maybe attack a data set that is, probably should drive them in a different direction, but might be antithetical to their agenda. Are you seeing data, you know we talked about democratizing data, are you seeing that reduce the politics inside of organizations? >> So you know we've always used data to tell stories at the top level of an organization that's what it's all about. And I still see very much that no matter how much data science or, the access to the truth through looking at the numbers that story telling is still the political filter through which all that data still passes, right? But it's the advent of things like Block Chain, more and more corporate records and corporate information is going to end up in these open and shared repositories where there is not alternate truth. It'll come back to whoever tells the best stories at the end of the day. So I still see the organizations are very political. We are seeing now more open data though. Open data initiatives are a big thing, both in government and in the private sector. It is having an effect, but it's slow and steady. So that's what I see. >> Um, um, go ahead. >> I was just going to say as well. Ultimately I think data driven decision making is a great thing. And it's especially useful at the lower tiers of the organization where you have the routine day to day's decisions that could be automated through machine learning and deep learning. The algorithms can be improved on a constant basis. On the upper levels, you know that's why you pay executives the big bucks in the upper levels to make the strategic decisions. And data can help them, but ultimately, data, IT, technology alone will not create new markets, it will not drive new businesses, it's up to human beings to do that. The technology is the tool to help them make those decisions. But creating businesses, growing businesses, is very much a human activity. And that's something I don't see ever getting replaced. Technology might replace many other parts of the organization, but not that part. >> I tend to be a foolish optimist when it comes to this stuff. >> You do. (laughs) >> I do believe that data will make the world better. I do believe that data doesn't lie people lie. You know I think as we start, I'm already seeing trends in industries, all different industries where, you know conventional wisdom is starting to get trumped by analytics. You know I think it's still up to the human being today to ignore the facts and go with what they think in their gut and sometimes they win, sometimes they lose. But generally if they lose the data will tell them that they should have gone the other way. I think as we start relying more on data and trusting data through artificial intelligence, as we start making our lives a little bit easier, as we start using smart cars for safety, before replacement of humans. AS we start, you know, using data really and analytics and data science really as the bumpers, instead of the vehicle, eventually we're going to start to trust it as the vehicle itself. And then it's going to make lying a little bit harder. >> Okay, so great, excellent. Optimism, I love it. (John laughs) So I'm going to play devil's advocate here a little bit. There's a couple elephant in the room topics that I want to, to explore a little bit. >> Here it comes. >> There was an article today in Wired. And it was called, Why AI is Still Waiting for It's Ethics Transplant. And, I will just read a little segment from there. It says, new ethical frameworks for AI need to move beyond individual responsibility to hold powerful industrial, government and military interests accountable as they design and employ AI. When tech giants build AI products, too often user consent, privacy and transparency are overlooked in favor of frictionless functionality that supports profit driven business models based on aggregate data profiles. This is from Kate Crawford and Meredith Whittaker who founded AI Now. And they're calling for sort of, almost clinical trials on AI, if I could use that analogy. Before you go to market you've got to test the human impact, the social impact. Thoughts. >> And also have the ability for a human to intervene at some point in the process. This goes way back. Is everybody familiar with the name Stanislav Petrov? He's the Soviet officer who back in 1983, it was in the control room, I guess somewhere outside of Moscow in the control room, which detected a nuclear missile attack against the Soviet Union coming out of the United States. Ordinarily I think if this was an entirely AI driven process we wouldn't be sitting here right now talking about it. But this gentlemen looked at what was going on on the screen and, I'm sure he's accountable to his authorities in the Soviet Union. He probably got in a lot of trouble for this, but he decided to ignore the signals, ignore the data coming out of, from the Soviet satellites. And as it turned out, of course he was right. The Soviet satellites were seeing glints of the sun and they were interpreting those glints as missile launches. And I think that's a great example why, you know every situation of course doesn't mean the end of the world, (laughs) it was in this case. But it's a great example why there needs to be a human component, a human ability for human intervention at some point in the process. >> So other thoughts. I mean organizations are driving AI hard for profit. Best minds of our generation are trying to figure out how to get people to click on ads. Jeff Hammerbacher is famous for saying it. >> You can use data for a lot of things, data analytics, you can solve, you can cure cancer. You can make customers click on more ads. It depends on what you're goal is. But, there are ethical considerations we need to think about. When we have data that will have a racial bias against blacks and have them have higher prison sentences or so forth or worse credit scores, so forth. That has an impact on a broad group of people. And as a society we need to address that. And as scientists we need to consider how are we going to fix that problem? Cathy O'Neil in her book, Weapons of Math Destruction, excellent book, I highly recommend that your listeners read that book. And she talks about these issues about if AI, if algorithms have a widespread impact, if they adversely impact protected group. And I forget the last criteria, but like we need to really think about these things as a people, as a country. >> So always think the idea of ethics is interesting. So I had this conversation come up a lot of times when I talk to data scientists. I think as a concept, right as an idea, yes you want things to be ethical. The question I always pose to them is, "Well in the business setting "how are you actually going to do this?" 'Cause I find the most difficult thing working as a data scientist, is to be able to make the day to day decision of when someone says, "I don't like that number," how do you actually get around that. If that's the right data to be showing someone or if that's accurate. And say the business decides, "Well we don't like that number." Many people feel pressured to then change the data, change, or change what the data shows. So I think being able to educate people to be able to find ways to say what the data is saying, but not going past some line where it's a lie, where it's unethical. 'Cause you can also say what data doesn't say. You don't always have to say what the data does say. You can leave it as, "Here's what we do know, "but here's what we don't know." There's a don't know part that many people will omit when they talk about data. So I think, you know especially when it comes to things like AI it's tricky, right? Because I always tell people I don't know everyone thinks AI's going to be so amazing. I started an industry by fixing problems with computers that people didn't realize computers had. For instance when you have a system, a lot of bugs, we all have bug reports that we've probably submitted. I mean really it's no where near the point where it's going to start dominating our lives and taking over all the jobs. Because frankly it's not that advanced. It's still run by people, still fixed by people, still managed by people. I think with ethics, you know a lot of it has to do with the regulations, what the laws say. That's really going to be what's involved in terms of what people are willing to do. A lot of businesses, they want to make money. If there's no rules that says they can't do certain things to make money, then there's no restriction. I think the other thing to think about is we as consumers, like everyday in our lives, we shouldn't separate the idea of data as a business. We think of it as a business person, from our day to day consumer lives. Meaning, yes I work with data. Incidentally I also always opt out of my credit card, you know when they send you that information, they make you actually mail them, like old school mail, snail mail like a document that says, okay I don't want to be part of this data collection process. Which I always do. It's a little bit more work, but I go through that step of doing that. Now if more people did that, perhaps companies would feel more incentivized to pay you for your data, or give you more control of your data. Or at least you know, if a company's going to collect information, I'd want you to be certain processes in place to ensure that it doesn't just get sold, right? For instance if a start up gets acquired what happens with that data they have on you? You agree to give it to start up. But I mean what are the rules on that? So I think we have to really think about the ethics from not just, you know, someone who's going to implement something but as consumers what control we have for our own data. 'Cause that's going to directly impact what businesses can do with our data. >> You know you mentioned data collection. So slightly on that subject. All these great new capabilities we have coming. We talked about what's going to happen with media in the future and what 5G technology's going to do to mobile and these great bandwidth opportunities. The internet of things and the internet of everywhere. And all these great inputs, right? Do we have an arms race like are we keeping up with the capabilities to make sense of all the new data that's going to be coming in? And how do those things square up in this? Because the potential is fantastic, right? But are we keeping up with the ability to make it make sense and to put it to use, Joe? >> So I think data ingestion and data integration is probably one of the biggest challenges. I think, especially as the world is starting to become more dependent on data. I think you know, just because we're dependent on numbers we've come up with GAAP, which is generally accepted accounting principles that can be audited and proven whether it's true or false. I think in our lifetime we will see something similar to that we will we have formal checks and balances of data that we use that can be audited. Getting back to you know what Dave was saying earlier about, I personally would trust a machine that was programmed to do the right thing, than to trust a politician or some leader that may have their own agenda. And I think the other thing about machines is that they are auditable. You know you can look at the code and see exactly what it's doing and how it's doing it. Human beings not so much. So I think getting to the truth, even if the truth isn't the answer that we want, I think is a positive thing. It's something that we can't do today that once we start relying on machines to do we'll be able to get there. >> Yeah I was just going to add that we live in exponential times. And the challenge is that the way that we're structured traditionally as organizations is not allowing us to absorb advances exponentially, it's linear at best. Everyone talks about change management and how are we going to do digital transformation. Evidence shows that technology's forcing the leaders and the laggards apart. There's a few leading organizations that are eating the world and they seem to be somehow rolling out new things. I don't know how Amazon rolls out all this stuff. There's all this artificial intelligence and the IOT devices, Alexa, natural language processing and that's just a fraction, it's just a tip of what they're releasing. So it just shows that there are some organizations that have path found the way. Most of the Fortune 500 from the year 2000 are gone already, right? The disruption is happening. And so we are trying, have to find someway to adopt these new capabilities and deploy them effectively or the writing is on the wall. I spent a lot of time exploring this topic, how are we going to get there and all of us have a lot of hard work is the short answer. >> I read that there's going to be more data, or it was predicted, more data created in this year than in the past, I think it was five, 5,000 years. >> Forever. (laughs) >> And that to mix the statistics that we're analyzing currently less than 1% of the data. To taking those numbers and hear what you're all saying it's like, we're not keeping up, it seems like we're, it's not even linear. I mean that gap is just going to grow and grow and grow. How do we close that? >> There's a guy out there named Chris Dancy, he's known as the human cyborg. He has 700 hundred sensors all over his body. And his theory is that data's not new, having access to the data is new. You know we've always had a blood pressure, we've always had a sugar level. But we were never able to actually capture it in real time before. So now that we can capture and harness it, now we can be smarter about it. So I think that being able to use this information is really incredible like, this is something that over our lifetime we've never had and now we can do it. Which hence the big explosion in data. But I think how we use it and have it governed I think is the challenge right now. It's kind of cowboys and indians out there right now. And without proper governance and without rigorous regulation I think we are going to have some bumps in the road along the way. >> The data's in the oil is the question how are we actually going to operationalize around it? >> Or find it. Go ahead. >> I will say the other side of it is, so if you think about information, we always have the same amount of information right? What we choose to record however, is a different story. Now if you want wanted to know things about the Olympics, but you decide to collect information every day for years instead of just the Olympic year, yes you have a lot of data, but did you need all of that data? For that question about the Olympics, you don't need to collect data during years there are no Olympics, right? Unless of course you're comparing it relative. But I think that's another thing to think about. Just 'cause you collect more data does not mean that data will produce more statistically significant results, it does not mean it'll improve your model. You can be collecting data about your shoe size trying to get information about your hair. I mean it really does depend on what you're trying to measure, what your goals are, and what the data's going to be used for. If you don't factor the real world context into it, then yeah you can collect data, you know an infinite amount of data, but you'll never process it. Because you have no question to ask you're not looking to model anything. There is no universal truth about everything, that just doesn't exist out there. >> I think she's spot on. It comes down to what kind of questions are you trying to ask of your data? You can have one given database that has 100 variables in it, right? And you can ask it five different questions, all valid questions and that data may have those variables that'll tell you what's the best predictor of Churn, what's the best predictor of cancer treatment outcome. And if you can ask the right question of the data you have then that'll give you some insight. Just data for data's sake, that's just hype. We have a lot of data but it may not lead to anything if we don't ask it the right questions. >> Joe. >> I agree but I just want to add one thing. This is where the science in data science comes in. Scientists often will look at data that's already been in existence for years, weather forecasts, weather data, climate change data for example that go back to data charts and so forth going back centuries if that data is available. And they reformat, they reconfigure it, they get new uses out of it. And the potential I see with the data we're collecting is it may not be of use to us today, because we haven't thought of ways to use it, but maybe 10, 20, even 100 years from now someone's going to think of a way to leverage the data, to look at it in new ways and to come up with new ideas. That's just my thought on the science aspect. >> Knowing what you know about data science, why did Facebook miss Russia and the fake news trend? They came out and admitted it. You know, we miss it, why? Could they have, is it because they were focused elsewhere? Could they have solved that problem? (crosstalk) >> It's what you said which is are you asking the right questions and if you're not looking for that problem in exactly the way that it occurred you might not be able to find it. >> I thought the ads were paid in rubles. Shouldn't that be your first clue (panelists laugh) that something's amiss? >> You know red flag, so to speak. >> Yes. >> I mean Bitcoin maybe it could have hidden it. >> Bob: Right, exactly. >> I would think too that what happened last year is actually was the end of an age of optimism. I'll bring up the Soviet Union again, (chuckles). It collapsed back in 1991, 1990, 1991, Russia was reborn in. And think there was a general feeling of optimism in the '90s through the 2000s that Russia is now being well integrated into the world economy as other nations all over the globe, all continents are being integrated into the global economy thanks to technology. And technology is lifting entire continents out of poverty and ensuring more connectedness for people. Across Africa, India, Asia, we're seeing those economies that very different countries than 20 years ago and that extended into Russia as well. Russia is part of the global economy. We're able to communicate as a global, a global network. I think as a result we kind of overlook the dark side that occurred. >> John: Joe? >> Again, the foolish optimist here. But I think that... It shouldn't be the question like how did we miss it? It's do we have the ability now to catch it? And I think without data science without machine learning, without being able to train machines to look for patterns that involve corruption or result in corruption, I think we'd be out of luck. But now we have those tools. And now hopefully, optimistically, by the next election we'll be able to detect these things before they become public. >> It's a loaded question because my premise was Facebook had the ability and the tools and the knowledge and the data science expertise if in fact they wanted to solve that problem, but they were focused on other problems, which is how do I get people to click on ads? >> Right they had the ability to train the machines, but they were giving the machines the wrong training. >> Looking under the wrong rock. >> (laughs) That's right. >> It is easy to play armchair quarterback. Another topic I wanted to ask the panel about is, IBM Watson. You guys spend time in the Valley, I spend time in the Valley. People in the Valley poo-poo Watson. Ah, Google, Facebook, Amazon they've got the best AI. Watson, and some of that's fair criticism. Watson's a heavy lift, very services oriented, you just got to apply it in a very focused. At the same time Google's trying to get you to click on Ads, as is Facebook, Amazon's trying to get you to buy stuff. IBM's trying to solve cancer. Your thoughts on that sort of juxtaposition of the different AI suppliers and there may be others. Oh, nobody wants to touch this one, come on. I told you elephant in the room questions. >> Well I mean you're looking at two different, very different types of organizations. One which is really spent decades in applying technology to business and these other companies are ones that are primarily into the consumer, right? When we talk about things like IBM Watson you're looking at a very different type of solution. You used to be able to buy IT and once you installed it you pretty much could get it to work and store your records or you know, do whatever it is you needed it to do. But these types of tools, like Watson actually tries to learn your business. And it needs to spend time doing that watching the data and having its models tuned. And so you don't get the results right away. And I think that's been kind of the challenge that organizations like IBM has had. Like this is a different type of technology solution, one that has to actually learn first before it can provide value. And so I think you know you have organizations like IBM that are much better at applying technology to business, and then they have the further hurdle of having to try to apply these tools that work in very different ways. There's education too on the side of the buyer. >> I'd have to say that you know I think there's plenty of businesses out there also trying to solve very significant, meaningful problems. You know with Microsoft AI and Google AI and IBM Watson, I think it's not really the tool that matters, like we were saying earlier. A fool with a tool is still a fool. And regardless of who the manufacturer of that tool is. And I think you know having, a thoughtful, intelligent, trained, educated data scientist using any of these tools can be equally effective. >> So do you not see core AI competence and I left out Microsoft, as a strategic advantage for these companies? Is it going to be so ubiquitous and available that virtually anybody can apply it? Or is all the investment in R&D and AI going to pay off for these guys? >> Yeah, so I think there's different levels of AI, right? So there's AI where you can actually improve the model. I remember when I was invited when Watson was kind of first out by IBM to a private, sort of presentation. And my question was, "Okay, so when do I get "to access the corpus?" The corpus being sort of the foundation of NLP, which is natural language processing. So it's what you use as almost like a dictionary. Like how you're actually going to measure things, or things up. And they said, "Oh you can't." "What do you mean I can't?" It's like, "We do that." "So you're telling me as a data scientist "you're expecting me to rely on the fact "that you did it better than me and I should rely on that." I think over the years after that IBM started opening it up and offering different ways of being able to access the corpus and work with that data. But I remember at the first Watson hackathon there was only two corpus available. It was either the travel or medicine. There was no other foundational data available. So I think one of the difficulties was, you know IBM being a little bit more on the forefront of it they kind of had that burden of having to develop these systems and learning kind of the hard way that if you don't have the right models and you don't have the right data and you don't have the right access, that's going to be a huge limiter. I think with things like medical, medical information that's an extremely difficult data to start with. Partly because you know anything that you do find or don't find, the impact is significant. If I'm looking at things like what people clicked on the impact of using that data wrong, it's minimal. You might lose some money. If you do that with healthcare data, if you do that with medical data, people may die, like this is a much more difficult data set to start with. So I think from a scientific standpoint it's great to have any information about a new technology, new process. That's the nice that is that IBM's obviously invested in it and collected information. I think the difficulty there though is just 'cause you have it you can't solve everything. And if feel like from someone who works in technology, I think in general when you appeal to developers you try not to market. And with Watson it's very heavily marketed, which tends to turn off people who are more from the technical side. Because I think they don't like it when it's gimmicky in part because they do the opposite of that. They're always trying to build up the technical components of it. They don't like it when you're trying to convince them that you're selling them something when you could just give them the specs and look at it. So it could be something as simple as communication. But I do think it is valuable to have had a company who leads on the forefront of that and try to do so we can actually learn from what IBM has learned from this process. >> But you're an optimist. (John laughs) All right, good. >> Just one more thought. >> Joe go ahead first. >> Joe: I want to see how Alexa or Siri do on Jeopardy. (panelists laugh) >> All right. Going to go around a final thought, give you a second. Let's just think about like your 12 month crystal ball. In terms of either challenges that need to be met in the near term or opportunities you think will be realized. 12, 18 month horizon. Bob you've got the microphone headed up, so I'll let you lead off and let's just go around. >> I think a big challenge for business, for society is getting people educated on data and analytics. There's a study that was just released I think last month by Service Now, I think, or some vendor, or Click. They found that only 17% of the employees in Europe have the ability to use data in their job. Think about that. >> 17. >> 17. Less than 20%. So these people don't have the ability to understand or use data intelligently to improve their work performance. That says a lot about the state we're in today. And that's Europe. It's probably a lot worse in the United States. So that's a big challenge I think. To educate the masses. >> John: Joe. >> I think we probably have a better chance of improving technology over training people. I think using data needs to be iPhone easy. And I think, you know which means that a lot of innovation is in the years to come. I do think that a keyboard is going to be a thing of the past for the average user. We are going to start using voice a lot more. I think augmented reality is going to be things that becomes a real reality. Where we can hold our phone in front of an object and it will have an overlay of prices where it's available, if it's a person. I think that we will see within an organization holding a camera up to someone and being able to see what is their salary, what sales did they do last year, some key performance indicators. I hope that we are beyond the days of everyone around the world walking around like this and we start actually becoming more social as human beings through augmented reality. I think, it has to happen. I think we're going through kind of foolish times at the moment in order to get to the greater good. And I think the greater good is using technology in a very, very smart way. Which means that you shouldn't have to be, sorry to contradict, but maybe it's good to counterpoint. I don't think you need to have a PhD in SQL to use data. Like I think that's 1990. I think as we evolve it's going to become easier for the average person. Which means people like the brain trust here needs to get smarter and start innovating. I think the innovation around data is really at the tip of the iceberg, we're going to see a lot more of it in the years to come. >> Dion why don't you go ahead, then we'll come down the line here. >> Yeah so I think over that time frame two things are likely to happen. One is somebody's going to crack the consumerization of machine learning and AI, such that it really is available to the masses and we can do much more advanced things than we could. We see the industries tend to reach an inflection point and then there's an explosion. No one's quite cracked the code on how to really bring this to everyone, but somebody will. And that could happen in that time frame. And then the other thing that I think that almost has to happen is that the forces for openness, open data, data sharing, open data initiatives things like Block Chain are going to run headlong into data protection, data privacy, customer privacy laws and regulations that have to come down and protect us. Because the industry's not doing it, the government is stepping in and it's going to re-silo a lot of our data. It's going to make it recede and make it less accessible, making data science harder for a lot of the most meaningful types of activities. Patient data for example is already all locked down. We could do so much more with it, but health start ups are really constrained about what they can do. 'Cause they can't access the data. We can't even access our own health care records, right? So I think that's the challenge is we have to have that battle next to be able to go and take the next step. >> Well I see, with the growth of data a lot of it's coming through IOT, internet of things. I think that's a big source. And we're going to see a lot of innovation. A new types of Ubers or Air BnBs. Uber's so 2013 though, right? We're going to see new companies with new ideas, new innovations, they're going to be looking at the ways this data can be leveraged all this big data. Or data coming in from the IOT can be leveraged. You know there's some examples out there. There's a company for example that is outfitting tools, putting sensors in the tools. Industrial sites can therefore track where the tools are at any given time. This is an expensive, time consuming process, constantly loosing tools, trying to locate tools. Assessing whether the tool's being applied to the production line or the right tool is at the right torque and so forth. With the sensors implanted in these tools, it's now possible to be more efficient. And there's going to be innovations like that. Maybe small start up type things or smaller innovations. We're going to see a lot of new ideas and new types of approaches to handling all this data. There's going to be new business ideas. The next Uber, we may be hearing about it a year from now whatever that may be. And that Uber is going to be applying data, probably IOT type data in some, new innovative way. >> Jennifer, final word. >> Yeah so I think with data, you know it's interesting, right, for one thing I think on of the things that's made data more available and just people we open to the idea, has been start ups. But what's interesting about this is a lot of start ups have been acquired. And a lot of people at start ups that got acquired now these people work at bigger corporations. Which was the way it was maybe 10 years ago, data wasn't available and open, companies kept it very proprietary, you had to sign NDAs. It was like within the last 10 years that open source all of that initiatives became much more popular, much more open, a acceptable sort of way to look at data. I think that what I'm kind of interested in seeing is what people do within the corporate environment. Right, 'cause they have resources. They have funding that start ups don't have. And they have backing, right? Presumably if you're acquired you went in at a higher title in the corporate structure whereas if you had started there you probably wouldn't be at that title at that point. So I think you have an opportunity where people who have done innovative things and have proven that they can build really cool stuff, can now be in that corporate environment. I think part of it's going to be whether or not they can really adjust to sort of the corporate, you know the corporate landscape, the politics of it or the bureaucracy. I think every organization has that. Being able to navigate that is a difficult thing in part 'cause it's a human skill set, it's a people skill, it's a soft skill. It's not the same thing as just being able to code something and sell it. So you know it's going to really come down to people. I think if people can figure out for instance, what people want to buy, what people think, in general that's where the money comes from. You know you make money 'cause someone gave you money. So if you can find a way to look at a data or even look at technology and understand what people are doing, aren't doing, what they're happy about, unhappy about, there's always opportunity in collecting the data in that way and being able to leverage that. So you build cooler things, and offer things that haven't been thought of yet. So it's a very interesting time I think with the corporate resources available if you can do that. You know who knows what we'll have in like a year. >> I'll add one. >> Please. >> The majority of companies in the S&P 500 have a market cap that's greater than their revenue. The reason is 'cause they have IP related to data that's of value. But most of those companies, most companies, the vast majority of companies don't have any way to measure the value of that data. There's no GAAP accounting standard. So they don't understand the value contribution of their data in terms of how it helps them monetize. Not the data itself necessarily, but how it contributes to the monetization of the company. And I think that's a big gap. If you don't understand the value of the data that means you don't understand how to refine it, if data is the new oil and how to protect it and so forth and secure it. So that to me is a big gap that needs to get closed before we can actually say we live in a data driven world. >> So you're saying I've got an asset, I don't know if it's worth this or this. And they're missing that great opportunity. >> So devolve to what I know best. >> Great discussion. Really, really enjoyed the, the time as flown by. Joe if you get that augmented reality thing to work on the salary, point it toward that guy not this guy, okay? (everyone laughs) It's much more impressive if you point it over there. But Joe thank you, Dion, Joe and Jennifer and Batman. We appreciate and Bob Hayes, thanks for being with us. >> Thanks you guys. >> Really enjoyed >> Great stuff. >> the conversation. >> And a reminder coming up a the top of the hour, six o'clock Eastern time, IBMgo.com featuring the live keynote which is being set up just about 50 feet from us right now. Nick Silver is one of the headliners there, John Thomas is well, or rather Rob Thomas. John Thomas we had on earlier on The Cube. But a panel discussion as well coming up at six o'clock on IBMgo.com, six to 7:15. Be sure to join that live stream. That's it from The Cube. We certainly appreciate the time. Glad to have you along here in New York. And until the next time, take care. (bright digital music)

>> Narrator: Live, from San Francisco, it's the Cube, covering Informatica World 2017. Brought to you by Informatica. (techno music) >> Hey, welcome back, everyone. Live here in San Francisco, this is the Cube's exclusive coverage of Informatica World 2017, our third year covering Informatica, and more to come. I'm John Furrier with Silicon Angle, the Cube. My co-host, Peter Burris, Head of Research for Silicon Angle Media, as well as General Manager of Wikibon.com, check out the great research at Wikibon. Some great stuff there on IOT, cloud ping data, great stuff. Of course, go to SiliconAngle.com for all the coverage YouTube.com/SiliconAngle for all the Cube videos. Our next guest is Bruce Chizen, board member of a lot of private companies, also Special Advisor at Informatica. You're on the board of Informatica, no? >> Executive Chair. >> John: Executive Chair of Informatica. Not only as Special Advisor, Executive Chair. Welcome back, good to see you. >> Great to be here. >> You were on last year, great to have you back. What a popular video. Jerry Held was on yesterday. Let's get some Board insights, so first question, when are you going public? (laughing) >> Good one. >> John: Warmed you up, and then, no. I mean the performance is doing well. Give us a quick update. >> Company's doing well. Q4 was a good quarter, Q1 was a good quarter. I think we will be positioned to do something late 2018, early 2019. A lot depends on how the company continues to do. A lot depends on the market. The private equity investors are in no hurry. >> John: Yeah. >> But it's always nice to have that option. >> So it's one of the things we, yeah, great option. Doing well. We heard that also from some of the management. We got O'Neil coming on, we'll press him on some of the performance side, but always had good products out, we talked about it last year. But the industry's going through a massive transformation. You've seen many waves over the years. The waves are hitting. What's your perspective right now? I mean, it's a pretty big wave. You got to get the surfboard out there, there's a set coming in. What's the big wave right now? >> So, data is driving every transformation within every organization. Any company that is not using and taking advantage of data will be left behind. You look at how companies like Amazon and Google and now a lot of our customers like Schwab and Tesla and others, the way they're using data, that will allow them to continue to either be successful in the case of a Schwab, or be a disruptor, like somebody like Tesla. Fortunately for us at Informatica, we are helping to drive that digital transformation. >> One of the things that I always observe, younger than you are, I've only seen a few waves in my day, but in the waves that were the most impactful in terms of creating wealth, and opportunity, and innovation, has had a cool and relevant factor. Meaning, if you go back to the PC days, it was cool and relevant. If you go back to mini computer, cool and relevant. And it goes on and on and on. And certainly internet, cool and relevant. But now, the, you mention Tesla. I'm testing driving one on Friday. My kids are like "Don't buy the Audi, buy the Tesla." This is my kids. So it's a cooler, it's a spaceship, it's cooler than the other cars. >> Bruce: Or an iPhone on wheels. >> Peter: (laughs) Exactly. A computer on wheels. >> So cool and relevant, talk about what is the cool and relevant thing right now. You talk about user experience, that's one. Data's changing it. So how is data being the cool and relevant trend? Point to some things that... >> If you look at what's happening from the chip on up, everything, everything will be intelligent. And I hate to use the term "internet of things," but the reality is everything will have intelligence. And that intelligent information will be able to be taken advantage of because of the scale of the cloud. Which means that any company will be able to take information, data, analyze it on the cloud, and then use it to do something with. And it's happening now. Fortunately, Informatica sits right in the middle of that, because they're the ones who could rationalize that data on behalf of their customers. 'Cause there's going to be a lot of it and somebody needs to govern it, secure it, homogenize it. >> John: You consider them an enabling platform? >> Absolutely, absolutely. I was joking, we just went through a rebranding exercise. And it's kind of cute, new logo, and it's kind of bold and sleek and it shows we'll have a leader, but it's a logo. But there's really around the messaging, we are finally getting across that we are the ones unleashing the power of data. That's what Informatica does. We'd just never really told anybody about it. We're very product focused, not really helping customers understand how uniquely positioned the company was. >> And it's also, you guys have done some things. Let's just go back and look at going private. Brought a new management team, have product chops again, we've talked about that in previous years. Last year in particular. So, okay, you have the wind at your back. Now you got Sally as a CMO, now you got to start being a humble braggart about the cool stuff you're doing. So which is marketing, basically. >> That's correct. >> John: But now, it's digital. >> Yeah. >> So, what's the Board conversation like, you say "Go, go build the brand!" >> So first of all, being private is great. (laughing) Because we get to do things you couldn't do as a public company. We're, a lot of our customers what to buy the products and solutions via subscription, that has huge impact to the P&L, especially in the short term. Cash flow's fine. So the PE guys are going okay, it's great, because we'll come out of this as a better company, and our customers like it because that's the way they want to buy products. So, that helps a lot. The conversation at the Board level has been, "Wow, we're number one in every category in which "we participate in. "Everything from big data to cloud integration "to traditional on-premise, to real-time streaming, "and, and, and data security." >> You're only one of three vendors in the Google general availabilities banner which went out yesterday. We covered that on Silicon Angle. >> We're number one there, we had AWS speak at our conference, we had Azure speak at our conference. All of the cloud guys love Informatica because we are the ones who are uniquely positioned to deal with all this data on behalf of their customers. As a private company, we're able to take advantage of that, spend some extra money on marketing. You know a lot of our customers know about us, but a lot more should know about us. So, part of coming out, having a new logo, having a new digital campaign, changing the website, that costs money. But as a private company, we get to do that. Because the fruits of those efforts will end up occurring a couple of years down the road, which is fine. >> So let me see if I can weave those two thoughts together in what I thought was an interesting way. Given that increasingly a lot of data's going to be in the cloud, and that's where the longer analysis is going to be required, that means a lot of the tools are going to have to be in the cloud. Amazon Marketplace is going to be a place where a lot of tools are going to be chosen. People are going to go into the Amazon Marketplace and see a lot of different options, including some that are free. They may not work as well, but they're free. You guys, what happens with marketing, and what's happening with that kind of a trend, is you need to buy, as customers, to choose tools that are actually going to work to serve or to solve the problem, to do the work that you need them to perform. And so what Sally Jenkins, the CMO, has done, with this new branding, is introduce the process of how do you buy us more customers to choose the right tool to do the right job? Does that make sense to you? >> It makes absolute sense, free is good. But be careful what you ask for. Sometimes you get what you pay for. You're talking about enterprise data. You want it to be governed, you want it to be secure. You want it to be accurate. >> John: Now there's laws coming out where you have to do it. >> You look at GTB... >> Peter: GDBPR. >> GDBPR in Europe, the privacy issues. You look at what's happening with Facebook, or what was reported today with France and how they're not happy with Facebook's privacy behaviors. It's an issue. It's an issue for anybody who does business anywhere, especially if you're a global company and you do business in Europe. You have to worry about corporate governance. Data security, data governance, data security. That's Informatica. The other thing is, while there will be some customers who will say "I'm going to AWS," there will be more customers who will either say "I have some legacy "systems that I'm going to leave on-premise, "and new projects will be in the cloud." Or they're going to say "I'm moving everything to "the cloud, but I don't want to be held hostage "by one cloud provider." And they're going to go with Amazon and Azure and Google and maybe Oracle, and, and, and. And again, because Informatica is Swiss, we're able to provide them with a solution that allows them to accomplish their data needs. >> Well, congratulations on the performance, I want to get that out of the way. But I want to ask a specific question on the historical, holistic picture of Informatica. Going back, what were the key bets that you guys made? 'Cause you guys sit around, and you got the private equity now coming to the table, they have expectations, but at the end of the day you've got to build a business. What were the key bets that is yielding the fruit that we're seeing? >> The number one bet was that the company had great products and a great R&D organization. We believed that, and fortunately, we got it right. Because if you don't have great products and passionate R&D organizations around the world, you can't make up for that. It doesn't make a difference how much you spend on marketing. At least not in the business that we're in. So that was number one bet, and that proved to play out well. The second thing was, this was a company that had done so well for so long that they never needed to change their business processes to behave like a billion, two billion, three billion, four billion dollar company. Many of their business processes were like that of a 200 million dollar company. And that's easier to fix. So things around back end, IT, legal, finance, go-to-market, marketing, sales. >> John: Less of a risk from an investment standpoint. >> That's correct. So that's what we believed, we were right And where we've been spending most of our energy and effort is helping the company, through the new management team, improve their business processes and their go-to-market. >> So we had a critical analysis yesterday during our wrap up session, and one of the comments I made, I want to get your reaction to this, was although impressive, your number one and all these Gartner Magic Quadrant categories, but that's an old scoreboard. If we're really living in digital transformation, those shouldn't really be a tell sign for what the performance of the new KBIs or the new metrics are. And so we were pontificating and analyzing what that would be, still unknown, we're going to see it. But Peter had a good point, he said "At the end "of the day, customer wins." >> Yeah, that was my reaction. It's like at the end of the day, all that matters do the customers.... >> What's the scoreboard look for customer wins? I know you were at the executive summit they had yesterday at the Intercontinental right around the corner. I had a chance to meet some of them at that dinner, some conversation. But I want to get your perspective. What is the vibe of the customers, what are those customer wins, and how does that translate into future growth for Informatica? >> Any customer who is looking at data, data management, strategically, is going with Informatica. >> Mmm hmm. >> There are a number of competitors that we have who try to compete with Informatica at the product level, and they end up doing okay through pricing, through better sales tactics, but when we have the opportunity to speak to the Chief Data Officer, the CIO, the CEO, they go with Informatica. It's the reason why Tesla went with Informatica on their project where they're trying to tie together the auto business with the solar business. Because if they get to know both sets of customers and are able to sync that up, one plus one will be greater than two for them, and that's why they did that deal. Or it's why Amazon has chosen our MDM solution for their sales operations. So you look at leading companies who are able to look at the enterprise level, at the strategic level, they are going with Informatica. That's why we know we're winning. >> So Bruce, give us three sentences, what is strategic data management? >> Strategic data management is being able to take reams and reams of data from all different platforms, traditional legacy, big data, real-time solutions, and data from the cloud and be able to look at it intelligently. Use artificial intelligence and machine learning to be able to analyze that data in a more intelligent way, and then act on it. >> So two questions on that point, I was going to ask about the AI washing going on in the industry. Every event now is like, "Oh my god, AI, we've got AI," but that's not really AI. What is AI, we call it augmented intelligence because you're really augmenting with the data, but even Google IO's got a little neural net throwback to the 80s, but what's your thoughts on how customers should look through the lens of b.s. to say, "Wow, that's the real AI, or the real "augmented intelligence." >> Does it do anything? That's ultimately the question that a Chief Data Officer or CIO or CEO...is something changing because of the artificial intelligence being applied? In the case of Informatica, we announced an AI platform called Clair, "clairvoyant," so artificial intelligence. What is Clair? It allows you to develop solutions like our enterprise information catalog, where an organization has thousands and thousands of databases, it's able to look at the metadata within those databases and then over time keep disclosing more and more data appropriate to the information that you're looking for. So then, if I'm an analyst or a businessperson, a marketing person, a sales person, I can take action on the right set of data. That's true artificial intelligence. >> Bruce, I want to get to one final point as we are winding down here. Again, you've seen many waves. But I want to talk about the companies that are trying to get through the transition of this transformation, Informatica certainly cleared the runway, they've got some things to work on, certainly brand-building. I see that as their air cover in many rising tide will float a lot of boats in the ecosystem. But there are companies where they have been in the infrastructure business and the cloud is one big infrastructure, selling boxes and whatnot. Other companies have traditional software models, download, whatever you want to call it, on-prem licenses, not subscriptions. They're working hard. Your advice to them if you are on their Board, or as a friend, what do you say to them, what do they got to do to get through this? And how should customers look at who's winning and who's losing, in terms of progress? >> The world of enterprise computing is moving to the cloud. Legacy systems will remain for a while. They need to figure out how to take their legacy solutions and make them relevant to the world of cloud computing. And if they can't do that, they should sell their company or get out of business. (laughing) >> And certainly data is the oil, it's the gold, it's the lifeblood of an organization. >> Of any organization. Even at Informatica, internally, we're using our own intelligent data platform to do our own marketing. Sally Jenkins is working closely with our CIO Graeme Thompson on working on solutions where we could help better understand what our customers want and need, so we can provide them with the right solution, leveraging our intelligent data leg. >> Bruce, thanks for coming on the Cube. Really appreciate your insight. Again, you've seen a lot of waves, you've been in the industry a long time, you have great Board presence, as well as other companies. Thanks for sharing the insight, and the data here on the Cube. A lot of insights and analytics being extracted here and sharing it with you. Certainly we're not legacy, we don't need to sell our business, we're doing great. If you haven't, make the transition. Good advice, thanks so much. >> Bruce: Great to be here. >> Bruce Chizen inside the Cube here. I'm John Furrier with Peter Burris. Stay with us for more coverage after this short break. (techno music)