 Live from San Francisco, it's theCUBE, covering Informatica World 2016. Brought to you by Informatica. Now, here's your host, John Furrier. Okay, welcome back everyone. We are here live in San Francisco, California for Informatica World 2016. It's the SiliconANGLE Media's theCUBE. It's our flagship program. We go out to the events and extract the signal from the noise. I'm John Furrier, the co-founder of SiliconANGLE Media. Our next guest is Stuart Bond, the director of data integration software research at IDC. Welcome to theCUBE. Thanks for having me, John. So you're an analyst studying market share, the horses on the track, horses for horses, as we always say, but the landscape's changing, right? Cloud is certainly changing the game up here. Informatica, pretty clear, I mean, very clear in their focus is about the data. Well, they're really agnostic on cloud dogma, if you will, they're not really tied to anyone cloud. Love the Amazon integration. Love the fact that they're looking at the master data management piece that's now holistic across environments. Makes a lot of sense to me. So the question is, how real is that for these guys? Is it vaporware? Are they delivering? Does it match some of the trends that you're seeing? Yeah, absolutely. So Informatica's been providing data integration services in the cloud for a while now. They were one of the first that got there. So yes, it's real, it's happening. They were initially there with Salesforce doing an integration between Salesforce and on-premise applications. There's absolutely still a lot of opportunity for vendors in the cloud for data integration. As data moves, as applications move to the cloud, data moves to the cloud. And the cloud applications are recreating the silos that we tried to get away from so long ago. Give me an example, is this a good point? Yeah, so I mean, Salesforce, your CRM data is held in Salesforce. Your ERP data is held in NetSuite. Your HR data held in Workday. So now you've got this data all over the place. And the master data about the people and the places and the things that you care about as an organization is in multiple places. You have to gain control over that. You have to understand where it is and you have to make sure you know where to go to find the most recent version of the truth. And the problems and the consequences of not getting that is impact the latency of data moving to an app at real time. Whether it's, hey, here's someone coming into a retail outlet. Oh, there are previous customer. I got to go to another system. Is that the kind of thing that? So latency is certainly an issue, but also when you pull that data together to do any kind of analytics or reporting on it, you want to make sure you get the most recent version of the truth for that data. You want to make sure you're looking at the right customers, bought the right product. You want to make sure you understand all of that behavior through the data that's distributed among the clouds. What's the biggest trend that you're seeing? Because you have an architect background we were talking before we went on live here. Not just an analyst, but you've been a practitioner. And you mentioned the silos. I mean, people want to get away from the silos. But now in the research business, you try to study the impact of who's winning, who's losing, impact the customers. The cloud and the blurring of the lines between data and cloud is a horizontal disruption because the workflows and the workloads themselves and the workflows are now horizontal. They're technically either abstracted above the silos or breaking down the silos. So it's not as easy to say, oh, this vertical, this sector, those notions of measurement are changing. How do you look at that? Because that's something that we're trying to squint through and say, okay, what does winning mean? What does market share mean? What does all this mean? So how do you try to squint through that to look at impact share value? So yeah, you're right. The lines between the different types of integration, where the data is, how the data is used, definitely those are getting very blurry. For years, I did application integration as an architect and data integration. And there was always this conversation, but well, what's the difference between the two? And when you move to the cloud, it just becomes integration. At the end of the day, you're still moving data around. You're still moving data either between applications or among a process or into some sort of reporting solution data warehouse or Hadoop or something. So at the end of the day, it's really all the same thing. And it's just the integration. Cloud is really a deployment option. It's where do you want to put your applications? Where do you want to put your data? It's more than that at times when you think about all the business value that cloud brings. But we're really, at IDC, we're actually tracking vendors, cloud shares, the revenue that they're making in the cloud versus the revenue that they're making on-premise. And we're actually able to track the vendors and back on the horse track again. Who's leading the race there? Well, certainly when we talk to Oracle, they're trying to shift to the cloud. So it's hard to bundle when you have software license moving to the cloud. Are they just bundled in deals? Is it true cloud? So as people start to figure that out, that's one thing that we're questioning. But at the end of the day, it is the new normal, cloud is happening. What is the biggest challenge that you see for companies as they move to the cloud? And now Informatica has a good story. They have the Informatica cloud that they announced, love the subtraction layer between data they want to promote freedom of the data, the sharing of the data, bringing them silos. What are some other vendors? What's their competition doing, for instance? I mean, what are other vendors doing relative to the cloud? You see, I may ask it differently. Do you see a winning formula versus a non-winning formula? We don't have to get specific on the vendors now. We don't have to pick on somebody, but what's the winning formula? Not sure if there's a winning formula yet. There's certainly different models in terms of are you running on another vendor's cloud or you're running on your own cloud, being able to manage that, operate that. It's a whole new model for software vendors to actually run an data operations center and provide those SLAs to their customers. So they have to make the choice of is it our data center that the software is running on? Is it Amazon? Is it Azure? Where is it? So I think there's, over time, it will probably see more of a combination of different cloud services, delivering these solutions to customers rather than standing up the software or standing up their own data centers. So I want to get your perspective on something. So take your IDC hat off for a minute and put on your architect hat and pretend that you and I are going in and we've been hired by a big company to come and clean up their mess or help them transform to the modern era of the digital transformation. And as an architect, what do we do? What's the first step? What do we look at when we assess and make recommendations as an architect on how to really be positioned to leverage the expansion of cloud, cloud native, IoT. It's right around the horizon, knowing kind of what you know. You see a lot of vendors, you go out and talk to a lot of people, but now as an architect, what would we do? What would your advice be to your peers on how to proceed? Well, certainly, we always spoke about what is the business problem? What's the business problem that you're trying to solve? That all comes back to that. You've got to be able to trace your requirements for your solution from the business problem you're trying to solve, back to the technology and the solutions that you're designing and you're implementing. So starting there, working backwards, get your requirements, understand what they are, talk to the people in the business that want the solution. Make sure you understand that and bring it back to the most common denominator you can to come up with your solution and put something forward to solve the problem that you're looking at. What's the biggest barrier as you see for companies in progressing to a cloud era, to a new modern infrastructure cloud on-premise IoT? Is it organizational, is it technologies at the silos, all of the above? Yeah, it's probably all of the above. I'm going to come back to data because data itself is critical. I'm on record somewhere saying that data is core to digital transformation and data without integrity isn't going to be able to support those digital transformation initiatives. Some people have talked about it being the new currency or the currency of digital transformation. That currency has to be available and it has to have some integrity in order for it to be used correctly. So when we talk about data integrity, we talk about data that can be trusted, data that's available when and where it's needed, secured, available only to those people they need to see and compliant under the situation which it's going to be used. So coming back to that and looking at that and you think about digital transformation and all that it involves, at the core of that to the end of the day, you get down to the data. But if you've got dirty data and you're not able to figure out where your problems are, where your issues are, you've got conflicting data all over the place, it's more difficult to come up with good results. And we heard the word context being used, how data can be looked polluted, it'll look like a swamp but actually could be relevant if accessed properly by the right context, context to where or whatever term we're looking about. So this is where we see those solutions like live data map coming into play where metadata is driving the information, the context around the data. It's answering the five W's of data. Where to come from? How has it changed over its life? Who's using it? Where is it being used? When is it being used? And it starts to bring a whole new context. The addition to that now is relationship. How is this data related? Not parent-child relationships and a relational database, but how is the information about this customer? How is this customer related to this customer, related to the products that I sell and the services that I sell? The services I deliver as perhaps a government agency. So it really comes back to being able to expand on that metadata and know how that data is being used. And for organizations they have to be smarter about their data and how it's being used. So it's also not only for the apps, it's for the management, right? What's going on? Yeah, we've heard people talk about, well, master data management is no longer finding the golden record or the source of truth for a particular customer or a particular product. It's finding the source of truth for that customer in the context in which that data is going to be used. And you know, there's a lot of dogma around old way. If you look at, depends how you look at it. If you look at it from the old school, old way, yeah, those things could be there, but now this new way, new things are emerging. So I want to slay some myths with you. So let's, let's slay some myths. What is out there? What would you say folks watching is, I don't say biggest BS out there, but like you hear things like, oh, put it in a data lake, use this tool or we're good. Or what would you say to practitioners that are out there building architects out there and BI and data guys trying to figure out the future? What's, what's a myth? Like what's a myth and what's real? How should they think about things? What, what anecdotes, what buzzwords do you think are like way overblown that might not be as hot or pan out as they might think? So one of the myths we, I've dealt with is, well, just put all the data in the lake or put all the data into the big data repository. We'll figure out what, what we're going to do with it later. I've talked to organizations. I said, well, why are you collecting all that data? Why are you putting it there? They don't really know. We're not sure yet. We're not sure yet. So we're going to collect, maybe figure out what your business problem is first. Coming back to that business problem as business drivers. What answers, what questions do you want answers to? What are the business problems you're trying to solve? And focus on that. And light bulbs are going on in these people that I'm talking to that, oh, yeah, I guess we should have it tied to a business problem, a business issue, a business driver that we're looking at. So that's certainly one of the things that I hear about a lot. One of the myths that I try to dispel. So the major reaction is just collect it. We don't know yet. Yeah. Usually, some will argue, hey, it's cheap. So I'll do it. Is it really cheap or inexpensive? Depends, right? Got people working on it. Depends, yeah. Depends on how much data you're putting in there and what kind of technology you're using. How are you using data? How are you pulling the data out? What are you doing with it? Okay, how about AI and cognitive? Yes. Cognitive, certainly IBM's all over that. Good marketing. But AI, certainly, you see chatbots seeing all this new innovation for automation at the edge. Is that a myth? Is that something that's real? That there's levels of realness to it. Some of the solutions we see with people put the cognitive or machine learning tag on it. Well, it's really just a rules engine that's looking at the data and making a decision based on a rule that was previously put in there. But there are those solutions there. They have the deep learning. They have the capability. The machines are learning. They're understanding. And there are real solutions that are coming out in play. At this point, we're seeing a lot of recommendation engines coming out. And so people working alongside the cognitive technologies rather than the cognitive technologies taking over what they do. Great, final question for the folks watching. What's this show about this year? What's exciting here for Informatica World? What's the hot notable trend that's or product technology coming out of the show? Well, the thing that really excited me was the realization of the Live Datamap technology. We heard about it a couple of years ago. I wrote about it then. I was really excited about metadata becoming the foundation for all things data, whether you're doing data quality, data security, data integration, whether it's cloud to cloud, cloud to on-premise. That metadata is so core and unifying that is so critical to being able to find that integrity in your data. So I was really excited to see that Live Datamap is now a product that's being applied in the big data management addition that's being applied to their master data management solutions. It's demonstrating that metadata really is something that will help your organization move forward with data. I love it. I mean, Dave Vellante, my co who's not here and Peter Burris on our analyst team as well. They love, I mean, totally agree, metadata is the future. And actually making it more intelligent too. As the data grows, you need to have this new concept of making the data about the data intelligent. So we have data about the data, metadata about the metadata, right? So it's very meta. Stuart, thanks so much for sharing your insights on theCUBE and real quick, I just want to plug a survey you've done. If you can just quickly share the survey you did about some of the movement of the cloud, you mentioned it before we came on. Just quickly share the data on the survey you just did. IDC did a survey of 650 data integration users back in the fall of 2015. Some interesting things came out of that survey, including there's more respondents, there are more people that responded to that survey that are integrating data that lives in cloud and hybrid environments than those that have data and are integrating data in on-premises only. So the shift to the cloud is certainly happening. So shift and more agile activities happening. So boring, slow molasses on-premise, that's what it's my take. I mean, which is kind of slower innovation, less innovation, does that tease out? Innovation equation at all? The survey data didn't really tease out the innovation piece, but we certainly teased out where the data is, how the data's being integrated. We teased out, for example, how does the data quality, how does the integrity of the data change when you have more data in the cloud? And it turns out that the more data you have in the cloud, the less people trust it because it's more distributed. Stuart Bond, sharing the data, we're getting all the metadata about the data here inside theCUBE and sharing the data live to you. Thanks for joining us. Thanks for your insight. This is theCUBE, sharing the data, extracting the signal of noise, sharing with you. We're here live at Informatica World 2016. You're watching theCUBE. It's always fun to come back.