>>live from San Francisco. It's the Cube covering Informatica World 2016. Brought to you by Informatica. Now here are your hosts John Furrier and Peter Burress. Okay. Welcome back, everyone. We are here live in San Francisco for Informatica World 2016. Exclusive coverage from Silicon Angle Media is the Cube. This is our flagship programme. We go out to the events and extract the signal to noise. I'm John from my co host, Peter Burst. We have tree conflict comedy Global Head of Data Management and Steve Jones, global vice president. Big data from Capt. Jeff and I insights and data. You. Good to see you again. You sure you're welcome back. Welcome to the Cube. Thank you. And you've got my name right? It was a tongue twister, but, uh, we were talking about big data before we started rolling and kind of like where we've come to talk about over the really big data. You look back only a few years ago. Go back five years, Duke movement to where it is now. The modernisation is certainly loud and clear, but it's just not about Hadoop anymore. There's a lot of operational challenges and also the total cost of owners who want to get your thoughts. What's the trends? What do you guys see as the big trends now relative to this modernisation of taking open source the next big day to the next level? >>I think part of the pieces were actually about to publish a report we've done within the massacre on exactly that question, Uh, particular and governance and how people are making it operational. We did a report recently with our captain consulting division around Operation Analytics. Really fascinating thing that found out was the two real interesting in governance, right? The age old thing on governance has been the business doesn't engage. Well, guess what we found when you look at big data programmes is when the big data programmes start to deliver value. Guess who wants to take them over business? Guess who then actually starts leading the governance efforts, the business. So suddenly, this piece where the history of sort of data management has been, you know, going you really care about quality and the business, to be honest, going? Yeah, we don't care that much. We're still using excel, um, to the stage of which you're delivering real analytical value those pieces are going through. It's something we've been on a long journey for. I mean, we talked the other day. 2011 was the first time at camp we published a white paper on on our learnings around Big Data and governance. Um, it's amazing. Five years ago, we were talking about actually how you do governance and big data because of some of our more, uh, sort of forward looking clients. But that shift and what we're finding in that the report is the fact that people are really looking to replace this substrate. It's absolutely not about just about Hadoop, but that's the foundation, right? And unlike sort of historical pieces where there hasn't really been a data foundation, there's been lots of data silos but not a data foundation. Companies are looking to move towards actual firm data foundations across their entire business. That's a huge leap for it organisations to make and in terms of its impact on, you know, MDM and data quality and pace of delivery. Um, and those are the pieces. >>So also talk about the trends outside the US, for instance, because now you have in the UK uh, talk about that because your clients have a global footprint. The governance then crosses over the boundaries, blurring if you will virtual. But you still have physical, uh, locations. Well, I am sort of the UK and based out of London, And, uh so I see that side of the pond more often than, uh, this side. But the trends are pretty similar. And what Steve said, in fact, we were joking about it yesterday and we said, It's not for the tweet, but maybe, you know, was a little bit more big data doesn't need data quality. And my other favorite statement is MDM is dead. Long live India. Both of them are relevant. Big data doesn't need data quality in the sense that you cleanse all your data and put it into a TD WR uh, or a data lake because you can't only part of it is data owned by you. The rest comes from external sources where it needs quality is building the context on top when the end user of the analysts have a view, and there, if you build the context, then even good data could turn too bad, because in a particular context. That data is no more relevant. But bad data can turn to good because you're bringing in the context. And there was this eggs example we were talking about. You know, you you run a marketing campaign and you have all these likes and tweets and everybody loved it. Somebody then said, Okay, how about how good is this campaign? That's great. We need more. How good is it in the context of sales? Guess what? When the campaign ran, there was no difference to your sales. So then this good data that you had on the marketing campaign has turned back just to the company. That was a wasted effort that marketing. So you need contextual quality, not pure data quality. You know, if you look at e t l. You transform you do data quality before you, Lord. Now you're talking of E l t. And that's where you need quality. You need the linkages, the references, this data changes the data, and real time has been the conversation earlier so far today, the context defines the quality quality. A data swamp could be a data, you know, clean and environment. I mean, one >>of the reasons why we should presented that we present my presentation That I did on Monday was on avoiding a data swamp. So we actually think. But what we say is you've already got it. The myth is that you don't have data swamp right today, which is Oh, we've got my perfect data warehouse and it's got a perfect schemer. Really? And what does your business use Excel spreadsheets? Where do they get the data from? Well, they get from S a p. They download this and we got a macro. Somebody wrote in 1998 which means we can't upgrade that despot desktop from office 97. Right? So that desktop is office 97 because it's the only one that has a supply chain spreadsheet on. So the reality is you have the spread. Have it today. I think to the point you said about the country difference. One of the things we've seen, I think from a sort of a culture difference between Europe and here in the U. S. Is the U. S. Has been very much the technology pioneer, right is well, you know, the Hadoop stuff. The sparks of all that technology push European companies are seeing a lot of have taken quite a while to get into the, uh the Hadoop marketplace, but particularly the larger manufacturers, Um and sort of I'd say the more robust, like pharmaceuticals and these large scale organisations are now going all in. But after thinking about it. So what I mean is is that we've seen sort of lots of POC is used to be, like, four or five years ago. People doing PhDs here in North America. They're very technically centric. And then people like Okay, >>Exactly. Whereas >>over in now, in Europe, we're seeing more people going. Okay, We know where we want to get, too, because we've seen all the technology. Now it works. We're gonna start with thinking about the governance and thinking about that. What's the right way to go about this? So I think from a timing perspective, the thing that was interesting we felt beginning of last year that we begin to see some earlier states. Larger programmes in Europe, Maybe towards the end of the reality was by the middle of the year we were seeing very, very large pieces. There was almost a switch that happened, but we've our return, this notion of governance because it's really important. And you've said it here today about 20 times the rules of data Governments have been written piecemeal over the past few decades. Uh, started off by saying, uh is which application owns what data? And is the data quality enough so that the application runs or not? Uh, then compliance kind of kicked in, and we utilised compliance related rules to write the new rules of data governance. What is data governance in the context of big data? And the reason I ask questions specifically and maybe put some bounds on it is we're trying to get to a point where the business puts a value on data trade data as an asset that has a value. And the only way we're gonna be able to do that is through governance rules to support it. So what does data governance mean in a big data context, I >>think, Yeah. So the value is really the impact, and I go back to a very simple analogy people, When you didn't have computers, you had your ledges. You locked it up in a safe and took the key home. So you protected who had access to your data? You then put it on PCs. But then you give them access with Loggins. Then you said, Well, I'll tell you what you can do with my data. That was the era of B I. Because you had reports all they could do was print a report. Now you've given them access to do whatever they want with data. Now, how do you know? First thing on the governance aspect is what are they doing with the data? Where did they get the data for which they used to come up with that? What is the exposure to your organisation if somebody has, you know, uh, traded around, they traded around with labour rates or, uh, you know, fix them or done something you're talking about. And then you work backwards, Arlene. Age. So now I need to know first thing what? Not just who accesses my data. And I need to know. What are they doing that I need to know where they got the data with it. >>Well, I think this is >>You don't know what they're when they're going to access it and what they're going to do with at any given time. But I >>think that's the thing is where we have the This is where the sort of contention comes in. Right. To be honest between the areas back to the value is from a data management data governance that those things are all true, right? We need to know those pieces. The other reality is that today how do you show the business, Actually that they value the pieces, which is ultimately the outcome. So the piece we're finding on the research and the research we're about to publish soon with Informatica is one of things it's really finding. Is that where when do you get the business to care about governance? And the answer is when you demonstrate an outcome which relies on having good governance. So if you do a set of analytics and you prove that this is going to improve the effectiveness, the bottom line, the top line or whatever, the firm and particularly Operational analytics customer analytics, where they're real measurable numbers, we can save you 6% on your global supply chain costs. But in order to do that, you need a single view of product and parts, which means you need to do a product. MDM Well, that's a very easy way to get the business engaging government, as opposed to we need to do product MDM What? >>We're going to 3 60 view of the customer. >>So you So we're still pricing the value of data based on the outcome? Absolutely. And then presumably at some point, there is some across all those different utilisation and that will become the true value of the data. Is that I think the piece, I'd say in terms of that, if we sum it up, it's sort of it becomes a challenge because ultimately the business pays. Right? So one of the things I like about the big data stuff and the programmes are doing these large scale companies is the ability to deliver value to an area. So what we call insight at the point of action, and that's the bit where I pay. So, yes, I could sum it up in Theoretically and the C I can say, Well, I'm delivering this much value, but it's at those points of action. And if you say to something right, I deliver you $2 million. It costs you $100,000. That's much better than we have to say in totality. This delivers you, you know, $2 billion and it costs you $20 million or $200 million. That's an abstract piece, whereas except when I'm thinking about investment BAC, because I need to be able to appropriate the right set of resources, financial and otherwise, to the data based not just on individual exploitations but across an entire range of applications. Tyre range of utilisation, right? I think I think so. But again, in terms of the ability to bill and charges that if I can, my total is the summation of the individuals. So that's why I worked with the CFO once you have the CIA was in the room, said the business case for their for one of their programmes, and CFO said, Well, if I had, it took all your business cases and adding together this company twice the size and cost nothing to run. So there's been a history of theoretical use cases. So what we're seeing, I think on the data and the outcome side is the fact that particular Operation Analytics they're absolutely quantifiable outcomes. So while then you can say? Well, yes, If you then add this up. We need to make an investment on based platform. The two things we're finding are because you can use these much more agile technologies. These projects don't take 12 months to deliver first value, so you can. And because the incremental cost of working in a lake environment is so much less, you know, I don't have a 12 month schema change problem. So that's one of the things we're seeing is the ability to say yes as a strategy. We're going to spend 20 million or whatever over the next five years on this. But every three months, I'm going to prove to you that I've delivered value back because one thing I've seen on data governance, sort of strategic programmes historically is 18 months in. What have you delivered? What have you done for me? Proves that it has value right that >>you've forgotten. And I think also what we're seeing with big data initiatives is the failed fast methodology like the drug trials and farmers. So what's your project? It's actually the sum of all the all the programmes you've run. And we were talking about apportioning uh the budget, whose budget? Because it's now being done by the individual businesses in their own areas. So there's no CF or sitting there and saying, Well, this is the budget I give I t. And this is how you apportion it. It's all at the point of the business and they find we'll do all these fail fast programmes and I've then hit one, which makes me big bucks. And I love this concept because essentially talking about the horizontal disruption, which is what cloud and data does just fantastic. And I'm sure this is driving a lot of client engagements for you guys. So I got to ask a question on that thread Jerry Held talked about earlier today. I want to ask the question. He made a comment, but alternative questions. You guys, he said. Most CFOs know where their assets are. When you ask him to go down, the legend they go, Oh, yeah, they asked. What's about data? Where the data assets. The question is, when you go talk to your clients, uh, what do they look at when they say data assets? Because you're bringing up in the notion of not inventory of data I'm sitting around whether it's dirty, clean, you can argue and things will happen. But when it gets put to use for a purpose, Peter says, data with a purpose that's this would keep on narrative. What is there a chief data officer like a CFO role that actually knows what's going on? And probably no. But how do you have the clients? They're just share some colour because this is now a new concept of who's tracking the asset value. >>And I think there's two bits and I'll start without it. And then if you talk specifically post an L, which I think is a great example of what happens with data when it becomes an asset, is the ability to understand the totality of data within any nontrivial organisation is basically zero because it's not just inside your firewalls. I'd also question the idea that CFOs know where all the assets are. I'm working with a very large manufacturer, and after they've sold it, they need to service it, and they can't tell you where every asset is because that information now lives within a client. So actually knowing where all of the assets they need to service are, they might know their physical plants and factories are. But some of these assets a pretty big things they don't know necessarily where they are on planet Earth. So the piece on data is really to the stage of because it's also external data, right? So really the piece for me about government and other ones Do I understand the relationships of these pieces in terms of the do I value data as an individual pieces because of what I can do with it? Sometimes the data itself is the value, But most of the time we're finding in terms of when people describe value, it's to the outcome that it's based upon. And that's something that's much easier to define than how much is my, uh, product master worth. Well, I can't really say that, but you know what? I can absolutely say that 6% reduction in my supply chain costs because I have a product master. But I think post and l is a great example of what happens when you go the next step on data >>because you're looking at addressed it. And actually, it's not just posting now. We were talking to another uh, male company. A postal company. Where? Data asset. Okay, my address is our data assets, but I have multiple addresses for one person, and what they wanted to offer was based on the value of the packages that you get delivered. They wanted to give you a priority or a qualification of the addresses. They said this is a more trustworthy address because anything about £50 this person gets it delivered there. This is a lot of mail. So do you consider the insurance or the value of the packages that you get delivered to be a data asset? Most people wouldn't. They would say, Yeah, the addresses a asset. That's the data asset. But there's a second part to it, which you don't even know. So the answer really is yes and no. And it all is contextual because in a particular context, you can see if I know where everybody lives. I know where everybody is and I have all the address. You almost got to look back after the outcome and kind of reverse track the data and say, OK, that stream. I >>would say that people who start with we've had 30 years of trying to say it's the data object that has the value, and it's never ever happened. As soon as we're starting talking about the outcome and then backtracking and going in order to this outcome, we needed addresses which historically issues that would have been the value. But actually it was It was that plus the analytics of prioritising them for risk that suddenly that's a lot more valuable. That outcome of you know, what this person tends to be here, this area people seem to see as lower risk. This is where I can therefore look at the work office for those people. It gives you more information about the >>notion of the data swamp turning into data quality because the context, Sri says, is really key. Because now, if you can move data to context in real time data in motion where people call these days the buzzword. But that's the value. When you when you when you stumble upon that, that's where you say, Well, I thought I had bad data. No, Actually, it's hanging around waiting to be used as potential energy. As you know, it's the same thing with questionable. They're moving from being a postal supplier to delivering packages. Now, you know they have a very short window to deliver packages. So just how do you get to a building? Do you have to go through the backyard? Do you have to call somebody to get it? Now that data becomes valuable because otherwise you know all their deliveries go off the radar screen, right? Because they just shot to schedule >>was going to say about the quality. Want a great example of qualities that we spend a lot of times say process data and manufacturing will clean it up before it goes in the reporting structure, which is great, and that gives you a really great operational reports. There's now an entire business of people doing the digital discovery of processes so they can use the bad data to discover what your processes are and where your operational processes are currently breaking down process. If I cleaned up the data, they wouldn't be able to do their jobs. And it's this fascinating stuff we're finding a lot with. The data science piece is its ability to get different value out of data, >>chemical reactions, alchemy. It's all the interactions of the data. This is interesting. And I want to ask you guys, I know we have a minute left, and I want to have you guys take a minute to explain to the audience Cap Gemini and how people how you engage with the customer, uh, and context to their progress. Where are your customers? On the progress bar of these kinds of Congress? Because we have a nice conversation. I'd love to do an hour for this. Go up. We can geek out. But reality is day to run a business, right? So and in the tier one system integrators like captain and I all have kind of different differentiation. What do you guys do differently with this area of your practise? How are you engaging with your customers? And where are they on the progress bar of Are they like while you're talking gibberish to me, are they on board? Where are they? >>I think I think we've got a bit of a man. We've been on this journey a lot longer than most. Like I say, 2011. We're talking actual data governance and big data. You don't talk about that if you haven't been doing it for a while. we were the first systems integrated and as we Cloudera pivotal with massive partner with homework. So most of what's interesting is when people talk about data lakes and some people are thinking that stuff new. We're talking about the problem of most of our clients are now looking at the problem of having We will have multiple data lakes for P. I reasons for operational efficiency reasons from budget reasons. Whatever it may be we're looking at, how do you collaborate beyond the firewall? So I'd say, Obviously, we've got a continuity of customers. But a lot of our customers are going beyond the stage at which they're worrying about big data within their four walls to the stage of how do I collaborate beyond my four walls? And this, for us, is the switch on governance and data, and what we do is is the difference between sort of capture announcement other ones is. So when's recess is the global MGM guy and Gold Data Management guy? He actually his team is in all of the countries, so he has P and l responsibility for that. When I have it for big data in the >>country, you're out implementing the value extraction >>were in multi. I mean, it's really at the stage of kicking tyres. We're at the stage >>behind the kicking tyres a long way back in 2000, 11 >>1,002,011. By now, sort >>of driving the Ferrari on the autobahn. You know, 90 miles an hour straight, narrow. It's a lot more work to do, right. There's always a lot more things keep changing and that's that's the best part >>of what we do next. And that's the point for us is the reason we're in this is that it's what's next and I think that people, the reason governments are changing fundamentally is this move towards global collaboration. So the more you look at health exchanges and all of these things, the more people collaborate outside the four walls. That for us, is the problem we want to solve next, which is why we're working on industrialising what we now consider the boring stuff which is building a data lake and doing the internals and ingestion in those pieces that were not interested in putting bodies on that. It's about how you solve the next problem. >>Stephen Pre, thank you so much for joining the Cuba because you're good to see you again. And welcome to the Cuban love nightclub. You made it, um, great to have you love to do it. Do this again and again. I love the context. I love that you guys are on this, you know, data quality at the right time. Really? Right message? Certainly we think certainly relevant. So thanks for sharing your insights on here. And And the data on the Cube live streaming from San Francisco. You're watching the Cuba right back. It's always fun to come back to the cube because