(techy music) >> Announcer: Live from Las Vegas, it's theCUBE, covering Informatica World 2018. Brought to you by Informatica. >> Hello, and welcome back to theCUBE. We are broadcasting from Informatica World 2018, The Venetian in Las Vegas. I'm Peter Burris, once again, my cohost is Jim Kobielus, Wikibon/SiliconANGLE. And at this segment, we're joined by Matthew Cox, who's the director of Data & Technology Services in McAfee. Welcome to theCUBE, Matthew. >> Thank you very much. Glad to be here. >> So, you're a user, so you're on the practitioner side. Tell us a little bit about what you're doing in McAfee then. >> So, from a technology standpoint, my role, per se, is to create and deliver an end-to-end vision and strategy for data, data platforms and services around those, but always identifying a line to measurable business outcomes. So my goal is to leverage data and bring meaning of data to the business and help them leverage more data-driven decisions, more toward business outcomes and business goals. >> So you're working both with the people who are managing the data or administering the data, but also the consumers of the data, and trying to arbitrate and match. >> Absolutely, absolutely. So, the first part of my career, I was in IT for many years, and then I moved into the business. So for probably the last 10 years, I've been in sales and marketing in various roles, so it gives me kind of a unique perspective in that I've lived their life and, probably more importantly, I understand the language of business, and I think too often, with our IT roles, we get into an IT-speak, and we aren't translating that into the world of the business, and I have been able to do that. So I'm really acting as a liaison, kind of bringing what I've seen of the business to IT, and helping us deliver solutions that drive business outcomes and goals. >> What strategic initiatives are you working on at McAfee that involve data? >> Well, we have a handful. Number one, I would say that our first goal is to build out our hub-and-spoke model with MDM, and really delivering our-- >> Jim: Master data management? >> Our master data management, that's correct. And really delivering our, because at MDM, that is where we define our accounts, our contacts, we build our upward-linking parents and our account hierarchies, and we create that customer master. That's the one lens that we want to see, our customers across all of our ecosystem. So we're finishing out that hub-and-spoke model, which is kind of an industry best practice, but for both realtime and batch-type integrations. But on top of that, MDM is a great platform, and it gives you that, but the end-to-end data flow is another area that we've really put a priority on, and making sure that as we move data throughout the ecosystem, we are looking at the transformations, we are looking at the data quality, we're looking at governance, to make sure that what started on one end of the spectrum look the same, or, appropriately, it was transformed by the time it gets to the other side as well. I'll say data quality three times: Data quality, data quality, data quality. For us, it's really about mastering the domain of data quality, and then looking at other areas of compliance, and the GDPR just being one. There's a number of areas of compliance areas around data, but GDPR's the most relevant one at this time. >> There's compliance, there's data quality, but also, there must be operational analytical insights to be gained from using MDM. Can you describe how McAfee, what kind of insights you're gaining from utilization of that technology in your organization? >> Sure, well, and MDM's a piece part of that, so I can talk how the account hierarchy gives us a full view. Now you've got other products, like data quality, that bolt on, that allow us to filter through and make sure that that data looks correct, and is augmented and appended correctly, but MDM gives us that wonderful foundation of understanding the lens of an account, no matter what landscape or platform we're leveraging. So if I'm looking at reporting, if I'm looking at my CRM system, if I'm looking at my marketing automation platform, I can see Account A consistently. What that allows me to do is not only have analytics built that I can have the same answers, because if I get a different number for Company A at every platform, we've got problem. What I should be seeing, the same information across the landscape, but importantly, it also drives the conversation between the different business units, so I can have marketing talk to sales, talk to operations, about Company A, and they all know who we're talking about. Historically, that's been a problem for a lot of companies because a source system would have Company A a little bit differently, or would have the data around it differently, or see it differently from one spectrum to the next. And we're trying to make that one lens consistent. >> So MDM allows you to have one consistent lens, based on the customer, but McAfee, I'm sure, is also in the midst of finding new ways, sources of data and new ways of using data, like product information, how it's being used, improving products, improving service quality. How is it, how is that hub-and-spoke approach able to accommodate some of the evolving challenges or evolving definitions and needs of data, since so much of that data often is localized to specific activities after they're performed? >> In business, there is a lot of data that happens very specific to that silo. So I have certain data within, say, marketing, that really is only marketing data, so one of the things that we do is we differentiate data. This kind of goes to governance, even saying there's some data as an organization is kind of our treasure that we want to make sure we manage consistently across the landscape of the ecosystem. There's some data that's very specific to a business function, that doesn't need to proliferate around. So we don't necessarily have the type of governance that would necessitate the level of governance that an ecosystem level data attribute would. So MDM provides, in that hub-and-spoke, what's really powerful for that as it relates to that account domain, because you're talking about product. Products is another area we may go look at at some point, adding a product domain into MDM, but today with our customer domain, and kind of our partners as well, it gives us the ability to, with this hub-and-spoke topology, to do realtime and batch, whereas before, it may have been a latency as we moved information around, and things could get either out of sync or there'd be a delay. With that hub-and-spoke, we're able to now have a realtime integration, a realtime interaction, so I can see changes made-- >> At the spoke? >> Peter: At the spoke, right. So the spoke pops back to the hub, hub delivers that back out again, so I can have something happening in marketing, translate that to sales, very quickly, translate that out to service and support, and that gives me the ability to have clarity, consistency, and timeliness across my ecosystem. And the hub-and-spoke helps drive that. >> Tell us about, you just alluded to it, sales and marketing, how is customer data, as an asset that you manage through your MDM environment, how is that driving better engagement with your customers? >> Well, it drives better engagement, first of all, you said an important thing, which is asset. We are very keen on doing data as an asset. I mean, systems come and go, platforms come and go. It's CRM tool today, CRM tool number two tomorrow, but data always is. Some of the things we've done is try to house and put a label on data as an asset, something that needs to be managed, that needs to be maintained, that needs to-- >> Governed. >> have an investment to. Right, governed, because if you don't, then it's going to decline in value over time, just like a physical asset, like a building. If you don't maintain and invest, it deteriorates. It's the same with data. What's really important about getting data from a customer's standpoint is the more we can align quality data, again, looking at that, not all data. Trying to govern all data is very difficult, but there's a treasure of data that helps us make decisions about our customers, but having that data align consistently to a lens of an account that's driven by MDM proliferate across your ecosystem so that everyone knows how to act and react accordingly, regardless of their function, gives us a very powerful process that we can gauge our customers, so that customer experience becomes consistent as well. If I'm talking to someone in sales and they understand me differently, then I'm talking to someone in support, versus talking to someone in marketing or another organization, it creates a differentiating customer experience. So if I can house that customer data, aligned to one lens of the customer, that provides that ubiquity and a consistency from a view in dealing with our customers. >> Talk to us about governance and stewardship with the data. Who owns the customer data? Is it sales, is it marketing, or is there another specified data steward who manages that data? >> Well, there's several different roles that you've going to hit through. Stewardship, we have, within my data technology services organization, we have a stewardship function. So, we steward data, act on data, but there's processes that we put in place, that's you're default process, and that's how we steward data and augment data over time. We do take very specific requests from sales and marketing. More likely, when it comes to an account from marketing, sorry, from sales, whose sales will guide, you know, move this, change this, alter that. So from a domain perspective, one of the things we're working through right now is data domains, and who has, I don't know if you're familiar with racing models, but who is responsible, who is accountable, who is consulted, who just receives an interest or gets information about it. But understanding how those data domains play against data is very, very important. We're working through some of that now, but typically, from a customer data, we align more toward sales, because they have that direct engagement. Part of it, also, is that differentiated view. Who has the most authority, the most knowledge about the top 500, top 1,000, top 2,000 customers is different than the people you had customer 10,000. So you usually have different audiences that play, who helps us govern and steward that data. >> So, one of the tensions that's been in place for years as we tried to codify and capture information about engagement, was who put the data in, what was the level of quality that got in there, and in many respects, the whole CRM thing, took a long time to work, precisely, because what we did is we moved data entry jobs from administrators into sales people, and they rebelled. So as you think about the role that quality plays and how you guide your organization to become active participants in data quality, what types of challenges do you face in communicating with the business, how to do about doing that, and then having your systems reflect what is practical and real in the rest of your organization? >> Well, it's a number of things. First of all, you have to make data relevant. If the data that that these people are entering is not relevant and isn't meaningful to them, the quality isn't going to be there, because they haven't had a purpose or a reason to engage. So, first thing is help make the data be relevant to the people who are you're data creators, right? And that's also to your business leaders. You also want the business leaders coming to you and talking about data, not just systems, and that's one of the things we're working toward as well. But as part of that, though, is giving them tools to ease the process of data-create. If I can go to my CRM tool instead of having to type in a new account, if I can then click on a tool and say, Hey, send to CRM, or add to CRM. So it's really more of a click and action that moves data, so I ensure that I have a good quality source that moves into my data store. That removes that person from being in the middle, and making those typing mistakes, those error mistakes. So it's really about the data-create process and putting a standard there, which is very important, but also then having your cleansing tools and capabilities in your back end, like the MDM or a data stewardship function. >> So by making the activity valuable, you create incentive for them to stay very close to quality consideration? >> Absolutely, because at the end of the day, they use that old term, garbage in, garbage out, and we try to be very clear with them, listen, someday you're going to want to see this data, and you probably should take the time to put quality effort in to begin with. >> Got it, one last quick question. If you think about five years, how is your role going to change? 30 seconds. >> I think the role's going to change in going from an IT-centric view, where I'm looking at tools and systems, to driving business outcomes and addressing business goals, and really, talking to business about how do they leverage data as a meaningful asset to move their business forward, versus just how am I deploying stewardship governance and systems and tools. >> Excellent. Matthew Cox, McAffee, data quality and utilization. >> Absolutely. >> Once again, you're watching theCUBE. We'll be back in a second. (techy music)