 Live from Las Vegas, Nevada. Extracting the signal from the noise. It's theCUBE covering Informatica World 2015. Brought to you by Informatica World. Okay, welcome back everyone. We are live in Las Vegas for Informatica World 2015. This is theCUBE SiliconANGLE's flagship program. We go out to the events and extract the signal noise. I'm John Furrier, the founder of SiliconANGLE. And I'm joined by two guests from EMC Barbara Latulip, Chief Data Governance Officer and Anod Singh, Enterprise Data Management Architect at EMC, welcome to theCUBE. Thank you. Good morning. Back in Vegas. Back in Vegas after EMC World. At EMC World last week. Biggest, one of the biggest shows ever. It was incredible. Great success. Congratulations to EMC. You guys doing great. Thank you. We had 72 interviews there. But we're here at Informatica World where it's the age of engagement. Talk about moving from productivity to engagement, which I love that by the way. I think that's really relevant. I think the engagement data play is the holy grail. And I think it's just the beginning of that battle. Dave Laugh and I talk about this all the time on theCUBE. But you guys are involved in managing a lot of the data architecture and also on the business side. So Barbara, talk about your role at EMC and we'll get into the architecture because you move from IT to the business, which is very transformational in your role. But that's where the business is going. So talk about your role and the movie. So to your point, it's been transformational and it's focused on engagement. So about a year ago, I've moved from the Enterprise Architecture Group and now I'm working in the Total Customer Experience Group, which is about engaging with our customers. And a key part of that is going to make sure that the data is accurate, that we can enable our data science teams to have that single source of truth. So with the business ownership, we've gotten a lot more buy-in and we have some key initiatives such as the Business Data Lake. So it really brings data governance to the forefront. Explain the Business Data Lake you just mentioned. What is that? Right, so a lot of companies have a lot of data warehouses. What we like to call, they have a lot of ponds. So the benefit of the Data Lake is to have that one. And we call it, I know Bill Schmarzo, my colleagues call it the Data Universe because you can only have one. But the Data Lake is really focused on bringing all that information together so that can be leveraged by your analytics community to enable better decisions, right? So we want to really focus on that customer experience so that we can get some insight and perspective as how do customers interact, what are their kind of tendencies and what are their propensities in the future. So having that good quality data in the Lake is really key as an enabler for good decision-making. And the thesis there, as you mentioned, ponds, with the smaller pockets of data, that could be siloed, right? You bring it together so everyone can act on it. Is that what you're doing? Again, we are kind of decommissioning the ponds and we will be in one large Data Lake, right? Because you can't govern all these ponds and that's just going to lead to my nightmare, which is a data swamp, right? So how do you avoid the data swamp? And by getting to a Data Lake where you have that one repository of different types of data sets and information, we now can govern it. It's different types of assets because we actually govern analytical models. It goes beyond their traditional master data management capabilities. And now, talk about the architecture now. Under the hood, I mean, the business logic is sound. I mean, that's where everyone's going. You see that's, if they're not either on that path, they know they have to be on that path to transform with the Data Lakes and more importantly, using data actively and acting on it. But making it work is not that trivial. So what are you guys doing in complaints of what's happening under the hood? So you spoke about last week at the EMC world, right? So a lot of keynotes were focused on, especially the David Goulden's keynote, was focused around the era of digitization. So while it's the era of digitization, it's the era of big data of the analytics, there's only one thing that's common across all of this. It's the data, right? We call data as the new king, data as the new natural resource that's going to help us drive value for our businesses. And we shouldn't forget that the quality of the data science programs, the outcome of the data science programs, the data analytics is only going to be as good as the data within our systems. And that's where the enterprise architecture plays its role. We're trying to drive the value and the focus on the data itself so that we know the data is available, it's accurate, it's consistent, it's secure. So we can drive these data science and the analytics program while driving the data architecture in the background to drive value for our businesses. And that's the key thing now. What is going to make that happen? Is it the hardware, is it the software? Is it the master data? What's the tooling and what's the platform? It's a combination of multiple things, right? You start from a core data architecture practices, molding it into the master data management tool sets, data quality, data framework, data security. All these things have to come together to provide this one single view of the complete data architecture which will be given to run our business data lakes on. And that's where the value is provided back to the business. And I think also we have kind of four major initiatives we're looking at. And one is getting the value for our data lake focuses on what's the business case. So we have approximately eight different business cases with our data science team right now. And those do go through our governance pyramid, right? So we have an executive steering committee as well as working in more tactical steering committees. The second one is really about focusing on, I'd say enabling the data scientists to have access to the data a lot quicker than they do today. So I think they spend about 70% of their time searching for data and trying to reconcile all those disparate sources. It's our goal. A lot of wasted time. Oh, a lot of wasted time and redundant effort, right? So our goal with our projects at EMC is to reduce that effort so they can really get the ROI on their projects. And I'd say really it's about the collaboration, right? And some collaboration between business users who are always siloed as well as bringing IT. So it truly is, I'd say a business and IT partnership at this point right now as well. We got to break down the styles both on IT and on the business side, there's two kind of silo busting mentalities you have to go on. And then ultimately that gets you to being customer centric which is a word that is kicked around. Hey, let's be customer centric. Let's find the personas and let's market to the individuals. So that's the buzz. That's the way everyone wants to do that. That's the holy grail. How do you guys view that? I mean, that's the destination people want to get to is, hey, I'd love to know, you know, in real time, my prospect, what they're interested in, who they're talking to, what's happening in the moment, how can I add value and learn and or sell and or create a happy customer? And it's been a journey for us. So to your point, you know, we started with a customer hub about three years ago and that's matured all the way from an analytical hub up to an enterprise hub where we do master and have one single universal ID that we push out to all of our receiving systems. So we finally get that complete view and now we're moving into both a contact hub and a vendor hub. So it's been a journey between analytical and operational as well as going multi-domain. Especially with the big data, the analytics that we talk about, Bill Schmarzer from EMC, right? We talk about this- He's a CUBE alumni too, he's been on many times. Okay, so we talk about the data universe, right? So look at the data universe as a world of unknown and as part of the master data management, if you have a responsibility in driving that ship towards something that we know and we trust and that's where the master data management plays its role. And as Barbara mentioned, it's not that something you can achieve over a day. It's a continuous journey of taking one piece at a time, one piece of the puzzle at a time and taking it through its journey to actually achieve that enterprise view of the master data that we have. There's no big bang theory in master data management. You can't just blink your eyes and they're there. You've got to chip away at it. Like you said, one piece at a time, that means you have to set up a system for it, right? So how does a customer operationalize something like this? So take us through some of the kind of the details. Like, I mean, is it complicated? What did they do? If someone wants to get to that point, how do they operationalize master data? And I think our journey for more business case started with sales and marketing wanting to really drive more opportunity management. And once that analytical hub was set up, then we said, really we want to be able to manage and create all customer data in one central location. So I think you have to look at really what's your burning platform. We also had a very large ERK migration. We moved off of Oracle onto SAP. And they were counting on us to deliver good data quality as part of the migration as well. So I think that's slightly different for every company. So you have to look at your particular place within your company and where you want to grow your data quality. Okay, so I guess I'd like to ask next set of questions is lessons learned. Barbara, what have you learned in your role? Because you've had an interesting perspective, data science, I mean that's cutting edge, you're in the data, you probably can see the good bad and the ugly on that as well. And then IT, you guys first have a good IT department, I'll say, you can see guys, but IT can be a hornest nest. A lot of stuff going on, policies, a lot of governance challenges. What lessons have you guys learned and have been magnified to get to the point now that you could share with folks out there? And feel free, good bad and the ugly, whatever you can share, experience would be great. At least from an IT perspective, the main focus should be at the data level, right? We are moving into this era where we talk about digitization, we talk about internet of things, information generation, and everything relates back to the data. So the real win is only when you start focusing on the data from an enterprise architecture view, right? You can only drive value from your analytics and science programs when you have the focus on data. And it's all about building the foundation, right? As long as you have a very strong foundation with our mass data management systems, our data quality and the data governance organizations, the one that Barbara is driving, is only when you'll be able to stand up the IT architecture on top of that, which will help you drive those values for the businesses, right? You want to talk about from a business perspective? Yeah, and again, I think, you know, you have to demonstrate business value. The company doesn't want to wait for a year and then all of a sudden understand the business value. So I think one of our successes is we try to deliver new functionality, typically every two months, if not every quarter. So we have a roadmap where they can see some incremental value in that. And it's really key to spend the time upfront to build that business value case. And it really, you have to show the IROI. So they want to see the dollars, right? You can't just stand up a hub and not be able to really demonstrate the value, whether it's, you know, I think we had a huge improvement when our resources going to an automated MDM hub. I think we can create a customer in less than, I think it is about less than eight to 10 seconds now. If it passes data quality checks, we've gone real time. So we've been able to come up with a very strong business value case. So focus on what- It's about the ROI, that's a good, that really comes down, the rubber meets the road on ROI. How do you get to that point? What are some of your KPIs? What are your key metrics for that, that piece of the puzzle? Who that's key? Yeah, so in our customer hub in, you know, we were having teams going in dual of not triple data maintenance and multiple systems. So you can look at it as a productivity. We also wanted to make sure that, you know, we could improve data quality metrics. We worked with several departments like accounts receivable. Tax jurisdiction was another huge implication. So we were driving down the actual dollar cost of someone either having to take a phone call due to customer service or changing an invoice all the way to actual touch points with hands-on. So it was the goal was how many times did you have to redo the same information because it might be incorrect or inaccurate? It's all about delivering that ROI time over time, right? You cannot have a big bang and deliver the ROI at the end of the day. But the approach that we took as Barbara mentioned here as well is to do a 30, 60, 90 day deliverables and show that ROI back to the business. We can build your enterprise services in 30 days, but we need those enterprise services to actually play their part with the analytics and everything else that's going on in a 60 to 90 day period. And that way we're able to showcase this really on investment. Were they happy with the results? Very happy. And again, now we're embarking on our contact hub journey, right? So we were able to demonstrate a lot of value. And I think what's even more key is we delivered services. So now we have orchestrated services for real-time address validation, geocoding, right? We're embarking on email validations. Twitter handles. Yeah, Twitter handles. That's a hard one. Yeah. Getting the email addresses for the Twitter handles that don't provide email addresses. So we're delivering services. So our services are incrementally being delivered and then they can be reused. So they can be reused for some of the other hubs we're building as well. So again, I think the reuse, delivering the value and having a strong ROI. And you're happy with the Informatica piece of it. I mean, that's going okay. Excellent, right. Yes, we've been really able, I think we brought up data quality for our large SAP implementation less than three months with the address doctor as well. So we really were instrumental in... Data quality is really an important piece of it. And that was quick to turn around for you guys. Yes. I do with that. Yes. How hard is data quality? Tell us. Oh, absolutely. It's not only hard, but it's equally challenging to implement it as an enterprise asset, right? We try to govern data as an asset and to bring that asset-style culture into the data quality framework, it's absolutely critical to the organization. And the ERP implementation that Barbara was talking about, we brought this data quality framework in less than three months and we're actually able to show the return on investment that we were building with the data quality. And it actually translates from a data quality into master data management into the optimized and predictive modeling with the analytics and the data science programs. And it's seamless for you guys. You feel good about this? It is. And I think the challenge with data cause, not the technology, it's the business, right? So when you have a governance council and you're trying to get everyone to agree to what business rules do you want to measure, right? And what feels are important to the business and how they are synchronized across the enterprise, the bigger challenge is really working with the business on the business rules and then implementing them in IDQ and data quality, right? And I think the other key part is we're actually exposing a lot of these dashboards now externally. So I think BI used to be very internally focused with the reports now with our dashboards they are being exposed externally. So that really drives the imperative for good data quality. Awesome, and just to give you guys the final word for this segment, what's the show like here for the folks that aren't here? Describe what's happening in Informatica world here at the Cosmopolitan Hotel. Oh, I find it absolutely interesting. There are new products on displays. There's Informatica's roadmap of what's going to come and Informatica's lead on driving these MDM data quality and the area into the big data is absolutely interesting to learn about. And it's relevant. It is. You feel good about this company. Yes, yes. It's been excellent listening to Tohei this morning, right? I think we're going to focus. We're at a MDM conference a bit this morning listening to some of the case studies, but you're always interesting to see how your colleagues and peers are implementing the products and solutions as well. And now I think we're going to be focusing a bit on the big data portion this afternoon as well. Excellent conference. All right, great. Barbara Imran, thanks for coming on theCUBE. Appreciate it. Informatica customer EMC, which we know very well from EMC world. Congratulations on all your success, by the way. It's been great to see EMC doing so well. And thank you for your new role. Thank you. We'll be right back. Thank you. Right after this short break.