 Hello and welcome. My name is Shannon Kemp and I'm the Chief Digital Manager of Data Diversity. We'd like to thank you for joining the current installment of the Monthly Data Diversity Webinar Series, Real World Data Governance with Bob Siner. Today, Bob will be joined by guest speaker, Sean Rogers, to discuss improving data analytics with data governance. To dust a couple of points to get us started, due to the large number of people that attend these sessions, you will be muted during the webinar. If you'd like to chat with us or with each other, we certainly encourage you to do so. Click the chat icon on the upper right-hand corner for that feature. For questions, we will be clicking them via the Q&A in the bottom right-hand corner of your screen, or if you'd like to tweet, we encourage you to share highlights of questions via Twitter using hashtag RWDG. As always, we will send a follow-up email within two business days containing links to the slides, the recording of the session, and additional information requested throughout the webinar. Now let me introduce to you our speaker for this series, Bob Siner. Bob is the President and Principal of KIK Consulting and Educational Services and the publisher of the data administration newsletter, TDAN.com. Bob has been a recipient of the Damon Professional Award for significant and demonstrable contributions to the data management industry. Bob specializes in non-invasive data governance, data stewardship, and metadata management solutions. And with that, I will give the floor to Bob to get today's webinar started. Hello and welcome. Thank you, like always, for taking time out of your busy schedule to attend the webinar this month. As Shannon said, we'll be talking about improving data analytics with data governance. And a couple of times a year, I reach out to somebody that I think is really knowledgeable about the topic that we'll be speaking about that month, and I invite them to join me as their guest. And today I have reached into my history and data management a little bit, going back several years to a good friend of mine, Sean Rogers of TIPCO. And I'll introduce Sean a little bit more in a second. So, again, thank you very much for attending the session today. I hope you get a lot out of it. And please, ask questions, chat in the chat boxes on the right-hand side of the screen. If it's of interest to you, it's always great how we have a lot of people engaged during the sessions here. Before we get started, a couple of things I want to go over real quickly, and as all of you know, I do this data governance webinar series, Real World Data Governance, on the third Thursday of every month. And so next month we'll be talking about one of my favorite subjects, the non-invasive data governance framework. So if you're familiar with the book or the training or anything like that, I'm going to be sharing with you a framework for non-invasive data governance. So please register for that webinar as well. And there's lots of different places that you can register for that webinar. I also mentioned the book is available through your favorite bookseller, the book on non-invasive data governance. I'll be speaking at actually a couple of data diversity events coming up. One is in Chicago in October. I'll be speaking at the Data Architecture Summit. And just recently I found out that I'll be speaking at the Data Governance Winner Conference down in Delray Beach, Florida, later in the year. So also there's an online learning plan about non-invasive data governance. Shannon mentioned the data administration newsletter. If you're not familiar with it, go out and visit TDAN.com. Lots of great information for people in the data space. And last but not least, KIK Consonning and Educational Services is the name of my consulting company. And that's the place that you really can go to get a lot of information about non-invasive data governance. And with that, I'd like to introduce my guest today. My guest is Sean Rogers. And Sean and I go way back, as I said before, even into the early days of TDAN.com. Sean is the Senior Director of Analytic Strategy at TIPCO. He is known as a speaker and a thought leader and an author. He has written books on subjects that are probably pertinent to a lot of you that are on the call today. He's got 20-plus years working in the field. I'd like to introduce Sean Rogers. I think he'll be a great guest today. Sean, do you want to say hi? Bob, thanks for having me. It's great to be here. Yeah, been working in the data governance space a while. And I know that a lot of organizations are focusing on data analytics and improving what they can do with the data that they have. And I think this is really a timely topic. Like I said, a lot of the organizations I'm working with are even situating their data governance programs within an insights and analytics or a similar name group in their organization. So there's five topics that I'm going to discuss with Sean today. The first one is we're going to talk about the relationship between governance and analytics. The second one is we're going to talk about getting management to understand why data governance is necessary not only for analytics but for other purposes within your organization. We'll talk about how to focus your data governance program on analytics. We'll talk about using the focus of analytics to bolster your data governance program. And actually, as I thought more about that, the same could be held in reverse, where having a data governance program can certainly boost analytics within your organization. And the last thing we're going to do is we're going to talk about the symbiotic relationship between governance and analytics. And with that, I feel like I've been doing enough of the talking. I want to get Sean involved here in one second. The first topic, as I mentioned, was the relationship between data governance and data analytics. And Sean, what I'd like to start out by talking to you about is how do you define data governance? How do you define data analytics? I know I put a pretty strong definition behind the word data governance, what I call it, the execution and enforcement of authority over the management of data. And so that's worded with teeth, because at the end of the day, what do we need to do by putting a governance program in place? We need to execute and enforce authority. And a very simple definition of data analytics that I use is that basically using data in your organization to provide insights and make great decisions. Do you have definitions that you use for data governance and data analytics? And would you be willing to share that with the listeners today? Well, you know, I wouldn't, Bob, I wouldn't argue with you at all about defining the data governance side of this. I mean, I do think it is about execution and certainly about authority and process. And those, I think, things have been with data governance for a real long time. The defining of data analytics is kind of interesting, because I think it's on the move just a little bit. If I was going to expand on what you're sharing here, I definitely agree it's about the insight, you know, whereas governance is often about the data. And then I would tack on to the end of that this idea of making sure that, you know, analytics is bringing you to a point of being able to take action in the critical moments of your business or at the speed of your business. Certainly, I think even a more sophisticated or forward-looking thought around analytics is closing the loop and learning from the actions that you're taking. And I don't think we're there yet. I don't meet a lot of companies that have an architecture and an organized framework that not only addresses governance and data management issues, but also takes up to this full loop of analytics on the insight, the action, and, of course, learning from the action. So, yeah, but I think we're pretty close here. But, yeah, I've always liked the way you see data governance, so I certainly wouldn't argue with the other. Yeah, you know what, it seems to be, you know, data analytics seems to be what a lot of organizations are talking about is what types of decisions can they make? What types of actions can they take based on the information that they can find through analytical capabilities? So I've got a couple other questions to ask you about this topic as well. So really, the first one is, is it required that you have a data governance program to implement data analytics, or can you implement a program data that the analytics is? Well, I will answer that like this. Just because you can doesn't mean you should. Okay. If you have data and you have analytics tool, anybody can go off and do analytics. But I think that your journey will be a short one if you don't have the right infrastructure, the right core foundation to move forward. One of the things I think I've seen over the last couple of years in the market is that data governance and analytics have become partners. They've become much closer than they may be. We're back in the days, you know, Bob, you mentioned we've known each other a long time. And, you know, I used to, you know, the data governance space when we talked a lot about single sort of sources of information and talking about how governance affected data warehouses is a completely different conversation than we have today. And so I feel that to have maybe an expand on your question to be successful with analytics and to be able to scale analytics. And I think that's another important part here. A lot of companies started doing analytics in general, but now they're all faced with this newer challenge of, well, okay, this is good. This is good for my business. But I need to pump my brakes a little bit and figure out how I'm going to scale. And I think that the demand for scale is created at demand for closeness and a partnership between data governance and analytics. Do you get what I'm talking about there? I do, yeah, I do. And you know what, it's funny because over the years that's kind of evolved quite a bit. I mean, a lot of times with data governance was really associated with business intelligence and data warehousing, as you said. And then we've also gone through where data governance and master data management are connected at the hip. And data governance and big data were connected at the hip. I mean, so now with analytics, you know, what I'm really wondering about, is that enough to be able to take to your management to say that if we want to really improve our analytical capabilities, governance is something that we should be thinking about. Is that a strong enough message to take to the management? I guess we're going to talk a little bit about that in a little bit, but do you think that's a strong message? Yeah, I think it is, you know, because taking a message kind of up the pole on a topic like that, now you just mentioned, right, I said data warehouse, you added data governance and big data and MDM in there. Those words, those topics, don't always resonate with the C-level people within our businesses. They're thinking about things in a little bit different way. They become enamored with some of those bright shiny toys of technology and, you know, I always thought big data as a term was a bright shiny toy and it wasn't really well-defined for business decisions. But data analytics is something that the C-suite seems to understand, you know, from our friend Tom Davenport writing great books about it and others. It is where our why rests. And because return on your data investment is important to the C-suite, I think we have a really compelling story to go up and say, look, if we attach and combine or partner data governance with our data analytics and with an algorithmically driven sort of strategy, we become somewhat unstoppable in our ability to really get the monetary value from the data that we've spent so much money to curate and to bring into our businesses. You know, a lot of executives that I meet are asking me this question of, hey, I spent $3 million on my Hadoop cluster and it's turned into a hadump and I don't really know what I'm going to get from it. And the answer, I think, is analytics, right? The ability to refine the data from these structures. And so, yeah, I mean, the short answer to your question is absolutely. I think the importance or criticality of data analytics coupled with good data governance makes a compelling story that the C-suite can digest. Okay. And each of these questions for a long time, I think. But just along the lines of the other questions that are on the screen, I mean, is analytics the most important result of governing data? I can see that there's a lot of results from compliance and protection and all of those types of things, but is analytics potentially the most important result? Yeah, I mean, in some respects, I think it depends on the company and it depends on what's critical to them. I think it's extremely important. I do think that obviously smart data governance, there's lots of benefits and you just touched on that. But I do think, going back to the original question, I do think it's critical to scale. I think it's critical to move forward with. We can't just get along with mediocre governance and data management today. And Bob, you have a good view of this as well. I think we see ebbs and flows in our industry. You just talked about them. MDM was very important. Big data, BI, data warehousing. These are the ebbs and flows that you and I have written for the last couple of decades around where governance and quality and compliance and insights all kind of live. And we're at another high point where data and analytics and algorithmically driven businesses are certainly driving people to, again, look at the criticality and importance of their data governance practices. Yeah, and yet there are still a lot of organizations that are still fighting that issue every day of getting over the hurdle with management to implement governance. And in fact, if any of you out there have questions or would like to ask Sean or I questions about that, please do so, as Sharon had mentioned, through the Q&A section. It's a really important topic. And if we can tie analytics and data governance together, I think we're all going to be a lot more successful in the organizations that we're working for. So the second point I wanted to talk about is, and it kind of feeds right off what we were just talking about, is getting management to understand why data governance is necessary. And certainly analytics and analytical capabilities and where they're putting their money becomes a really important factor because they want to see good return on investment. They want to see that better decisions and actions are taking place. You know, when I define best practices for an organization, I'd be lying if I said that 99% of the time they use management support, sponsorship, and understanding as their first best practice because it's basically 100%. I mean, organizations recognize that management needs to support, sponsor, and probably most importantly understand what the heck it is we're doing with data governance. And, you know, the whole concept of non-invasive data governance is to strengthen to formalize things that are already placed in place in your organizations. I would say most organizations are governing their data, but they're not doing it as formally as they'd like to and therefore their process is associated with data are inefficient and oftentimes ineffective. Before I jump into the other questions, we've already stated that it's important for senior management to really understand this stuff. Yeah, I think that's important and I think it runs parallel to this other side of where it's getting improvement and where it's important is around this digital transformation sort of idea. Companies have been digitally transforming now for quite some time. And, you know, I mentioned scale before as to why it's sort of important to be able to put these two types of technologies together, and that's part of digitally transforming your company, but really what we're after is innovation. And when we're trying to innovate, you can never innovate without having the right, you know, you can insert your word here, ammunition or core or stable foundation. And again, we're back to, you know, how important is it to couple these technologies together to go ahead and innovate as a company? And in that innovation, I think dovetails write off your best practice, right? You know, because if you do have those things, support and sponsorship and the engagement and understanding of your stakeholders and your executive suite, they're already worried about digital transformation across the business. So I think that we play a big part here from a data analytics governance standpoint as part of that transformation and part of the strategy for innovation for companies. And I think that that's a good message to take to the C-suite. Yeah, it is. And you're right. I mean, innovation is the key. I mean, something that differentiates your company from what your competition can do. And if it's making better decisions based on the data you have, that might be a clear competitive advantage. If you know what your customers are thinking, if you know, if you can track behaviors and things like that, you can model, you can predict things through modeling. It really, that's the direction that organizations are going. And oftentimes, I will say that understanding is the most important word. But if we don't have support, sponsorship, and understanding the chances are that we're going to be at risk, and there's a lot of programs that have been at risk because of that. So can you share with me what might be some of the key messages that we can give to management to help them to improve their understanding of data governance and its impact on data analytics? I find that examples work the best. You know, if you want to be present with your customers or your business at key or critical business moments, I think it's, I do this quite a bit, where we'll show one of our customers the idea of, here's probably where you're at today, but here's what the next state looks like. Here's what the next normal looks like. And we can jump the gap from here to there, you know, in these three or four different steps or with this insertion of a technology and so on. And if you can help, I think, the stakeholders deeper in their understanding side of it by showing them where we're at today and where we can be in the close future and the impact of that, I have those conversations all the time. And the other thing is, is that I think when we look at governance and analytics, I think another message that tends to resonate is the ability to walk into that office and say, here, I understand the investments we've made in data and I'm bringing to you a way to demonstrate ROI and your investment. That one seems to get an awful lot of feedback from the executive suite. I made my little Hadump joke earlier, but it's true. You know, you and I do a lot of teaching. I've been on the faculty at TDWI for years, and I used to teach at a Duke class, and I would ask people, how many of you are here because you have a compelling and critical big data for Duke project? And I get like three hands raised in the air. How many of you are here because you're CEO, Red Forbes Magazine on the airplane last week? And every hand in the room would go up. And the problem is, is that they spent a lot of money in early BI days, early, you know, all through the data warehousing sort of generation. Now we have all of these other platforms, and I'd love to hear your thought on this, but isn't there also this issue of gravity where we're letting data live where it needs to live instead of shuffling it around as much as we used to because of cloud and IoT and other things? And so every business I meet is having a hard time getting a return on the big data, the Duke investment, the IoT investment, and so on. We're the promised land. We're the people that help them generate ROI from investment. And that's how I like to start these conversations because that's really what's important to these folks. It's not about the technology so much anymore. It's about, can I put a framework together to enhance or increase or, you know, improve the return on investment from the data investment? So I think that tends to get the attention. You know what I mean? It's true. I mean, so you've got your distributed data, and with that comes distributed data governance. And so there's not necessarily any standardization of the data. And as you said, organizations are investing very heavily in this. And, you know, even with the, you know, you've talked about turning into data swaps, okay? So people even, the idea of building the data lakes is to bring it and make it available to people in some organizations to focus their analytics on the data there, but it's ungoverned and it doesn't have definition and it doesn't have, I hate to use the word ownership, I should say stewardship of that data. That becomes a real issue. And yes, the distributed nature and looking to do things on the slide and do things relatively inexpensively and going out directly to the sources when there's no assurance that there's quality of the data in those sources and it's not being owned or governed or stewarded properly is a real risk. And I think as you mentioned earlier, your program could be very short. It could have a short lifespan if the data that you're going to is not really governed. You know, so what I would like to see is kind of tap into your experience and see, you know, have you seen any, or can you talk about any examples of organizations that have basically connected data governance and analytics at the hips? And what have been some of the results? What are some of the methods? Anything that you could share with us on that topic? Yeah, you know, I see it a lot in those industries, Bob, that you and I saw adopting these other technologies earlier and faster than, say, the laggards in the space. So I see it a lot in the financial services world. And, you know, they're doing a really great job of aligning both because they understood how accuracy was critical to what they were doing and how important it was for their innovation. I just met with some customers in New York recently and that was the talk I heard. The talk I heard wasn't about technology. It was about the need to innovate. And all of these folks from FSI, whether it was banking and even the insurance companies, were all starting to see the relevance or the connection between them, and they were actually starting to bring the groups together, which was, you know, I knew we were going to talk about this and I've actually started to see organizations start to knock down the walls between, or the silos between these organizations and they're forcing them to work together because they're starting to see governance and data management and analytics through a single filter. But to answer your specific question, yeah, I think the financial services organizations are doing it. I was talking to some retail banking customers recently and of course they're talking about all these different critical business moments that they want to be part of, whether it's mortgage applications or making the next best offer or understanding what their customers are doing. These are next generation insights and actions and in order to get to the next generation insider action, retail banks understand that they need to have governance. They need to have control. And then lastly, I think over the last few months, we've seen quite a lot of... Before May anyway, we've seen an awful lot of work on any company that's global and it's existence and there's great concern about how to govern analytics and data together because of regulatory and compliance issues around GDPR. So, but yeah, I just want to recap that you should look at those industries for the innovators. Financial services is a great place to see people doing things fast. Yeah, and sorry about interrupting you. I just tried to get to the last question too, which is what have the results been when people haven't coupled governance with analytics? Have you seen any situations where they're trying to use data that's not necessarily ready to be used? Yeah, well, I think we see those examples every day. And that one's not a surprise. I do see, and you made me think of this, is that one of the financial customers I was talking to recently made a point to me that they were being more careful about sourcing the data because the analytic had become so critical that they felt that they couldn't make mistakes anymore or do good enough analytics and good enough being, well, the data's good enough. It's governed good enough. You know, it's there getting very precise and part of that precision is working backwards and saying, you know, we're not going to include this in this particular analytic or this action, this insight unless we actually can quantify and understand how good the source data is and whether or not it's going to suffice to what we want to do. So that's different. You know, I have to admit in the early days, governance was always the first thing to get kind of shoved aside. That's too hard. It takes too long. It costs too much money. And I do think that the, I think the culture around governance as it's attached to analytics is really shifting. I always talk about non-invasive data governance. I say you're already doing governance. You're just doing it inefficiently and formally and effectively and you can formalize that and it's a lot less threatening to your organization. And I love the idea of when the organizations get dependent on their analytics that they can be, say, want their data to be so good, so right, that it's going to require governance from the time that governance is brought and the data is brought into the organization. I mean, that's a great idea. And so if the people get to the point where they're so dependent on the analytics to keep their company innovating, say, if your company is even thinking about that, you really want to make sure that your data is right. So I love that idea of them becoming so dependent on it that governance becomes really a no-brainer for your organization. And so, all right, let's move on to the next topic because as I said, we can talk about this stuff all day. We're going to talk about how to focus your governance program on analytics. And so just to kind of give you a little thing about what I was thinking on this topic is that, you know, a lot of organizations have a hard time identifying the people in the organization that should be held formally accountable for data. And people who have attended my webinars in the past know that I've said that basically everybody in the organization is a data steward and they need to get over it. Well, it really holds true for the analytics field. Anybody that's involved in bringing data in or defining or massaging data has an accountability to the organization. And I think that if we can formalize that accountability, you know, we can really improve the data that's feeding our analytical capabilities. And, you know, oftentimes, as we mentioned before, governance must really focus on those enterprise missions and directives. And so take a look at folks at your mission and directives and see what, from your data, what of them, which ones of those are related to improvements in information and data. And say that if we really want to do these things, how can we not even be thinking about data governance? Any thoughts on that? I mean, anything that you can share about how to focus your program on analytics itself? You know, it's interesting. I think I've mentioned once or twice while we were talking that there's sort of this new process that I see around sort of being algorithmically driven. You know, back in the 90s, Don Tapscott wrote this great book about, you know, the digital economy. And at the heart of Tapscott's book, it was, hey, you better get prepared, you better be ready because this whole digital thing is going to happen with the Internet and we're going to be all connected. And this connectedness is going to give us all this data and it's going to be a whole brand new world and frankly, a whole brand new economy. And I think Tapscott was right when he wrote that book. But I think what's also happened since he wrote the book in, like, 96, what we've also seen is we've gone through sort of a shift around the data economy. And we got way late again about, you know, how are we managing it? How are we governing it? Let's just go get all of it. And that took place for quite some time. And I think now we're coming out of this data ideal and we're heading towards this algorithm economy. And an algorithm or an analytic economy, whichever word you prefer, is really where companies are starting to entrench themselves in all of the things that they've been doing. And so I find that that's a conversation or a premise for projects or an ideal that will resonate with a larger, broader team in any environment. And so from how do you get the attention of folks about why to couple this is because you can't participate in the algorithm economy without pure data, without governed data, and without systems at the analytic edge that will help you do those things. But the bottom line is these now go very much hand in hand, much more so, I think, than some of the other things. And it's partially driven, Bob, by precision. Because in our BI days, if I gave you a report that was lightly governed, that, you know, was pretty close and overall revenue sold in my western region, that was generally good enough. But if you're going to interact in a one-to-one relationship with somebody or some service or some process, you have to be precise. And that precision, I think, is driving the new need. And I think that that's something that I would spend time talking to upper management and enablers of these projects. I love that statement. I wrote down algorithms next to it and I might have to borrow that from you. Of course, tell the people where I heard it first, but especially if it gives them an opportunity and oftentimes it'll get them to ask questions and say, what do you mean by algorithm economy? And that's an open door right there to explain to them, you know, why this is so critical to make certain that we're successful in all the ways that we want to be successful. So that's a great term. I had never heard, had done before, I'd never heard algorithm economy. I really like those ideas. But, you know, we talked about this a little bit earlier and it's with business intelligence and data warehousing and metadata and MDM. The governance is different for analytics than it would be for any of your other types of data integration projects. Are there things that you need to include that you wouldn't include for, say, a traditional implementation? And now this is becoming traditional at this point, but one of the older traditional ways of leveraging your data. Well, you know, I think that that tends to happen around, I guess the word that comes to mind for me that might be different is this focus on having a more diverse sort of set of practices and capabilities and people. You know, I'm thinking back to your earlier question about who's innovating, where am I seeing these use cases? And I mentioned FSI and retail banking as a couple of them. Almost everyone that's doing that work is bringing together the teams and ways that I haven't seen in the past. And it's not just the teams, but it's also the technology. So when you touch on things like MDM or if I added, say, catalogs to that or, you know, or data virtualization and governance, most companies now are sort of bringing all guns to bear on the battle. But in order to do that, they are actually bringing different teams together. Early on in my career, especially in the BI space, we need a report that tells us X and someone would say, great, well, let's spec out what we want to know and then we'll throw it over the wall to the data guy. And it was siloed. And it wasn't super efficient because we all know the joke, you know, the data guys would help them build what they need and they'd show them X and of course business would come back and say, that's great, can I also have Y please? And it was a great example for the need for collaborative sort of interfaces here. And I guess that's where I'll end, Mike. My answer is diversity is the key to success in people and in technology. And the finishing point here is the ability to collaborate towards what's truly valuable, what drives growth, and a more sophisticated approach to things. So that's what the leaders in the market are doing right now. They're knocking down the silos. They're not throwing it to the data guy anymore. They're partnering with data experts, governance experts, MDM people, and the business in a way that you and I used to write articles about years ago that Cash wouldn't be nice if we could get to this. And I think just because of the need to innovate and the ability to do things we couldn't do before from a technology standpoint, I really do believe that the walls are getting knocked down and I think that that's a big difference of what we see going forward. You know, I know both of ours, David Lotion, wrote a book on the Savvy Business Intelligence Manager and you know what, they're becoming more and more savvy and technology aware and able to use the technology. So it is becoming really important, more important to the organization as these folks, as it's no longer, like you said, tossing it over the wall and hoping that you get something back. Because the problem is if you toss it over the wall to two different people, you're probably going to get two different answers and that's not really acceptable to a lot of organizations either. So we're going to jump on to our next class topic. Let's talk about using the focus on analytics. And I think we touched on this a little bit earlier. Using the focus on analytics to improve the value and improve data governance within the organization. So I mean a couple things I'd like to do is really draw that connection. I think we've done it partially. We've got a connection between governance and the improved analytical capabilities of organizations that have tried to do analytics and their data has been ungoverned. I mean do you find that they need to improve the data so they go back and implement governance or is it just a failure and they just move on? I can't see the latter happening. No, not at all. And to touch on your first piece, those dotted lines between governance and analytics, I think if I, again, I've been giving you words to me that mean a lot to me when I have these conversations and I think maybe I'm back to the real dotted line, there could be innovation but that's a little ambiguous. There can be use cases. There can all be all these different things but doesn't it ultimately get down to our capability to be precise? And part of that precision is what's really driving kind of what's, I don't know if it's broken but it's certainly something that a lot of companies are trying to figure out. I got an interesting story when I was early in my career from a mutual friend of ours, I suspect, Dr. Richard Hackathorn. And Hackathorn told me this great story about, he said Mary sent her son to the market on the corner in New York and when the boy ran into the market there was an order, a basket of bread and some other things that Mary usually bought each week and then the boy was there to pick it up. But in the same instance, the person who ran the store also had the capability of saying, hey, you know that mustard your mother likes just came in, I put a bottle of it in the bag and I'll put it down to her account. And also just let her know that the dresses that she'd like to purchase are going to be here next week. And on the surface it's not like, okay, what's the point of the story? And the point is that you had a one-to-one shopping experience with a customer, he extended credit on the fly and actually discussed with them in a real useful way his inventory supply chain. So Dick Hackathorn made the point, Dick made the point of how do we scale that to a billion people or a million people or to other critical experiences? And so it's about precision and it's about moving at the speed of whatever the business is. You can't do either if you don't have governance and the right analytics and opposing sides. So the short answer to your piece there is I think precision is something that a lot of executives can understand. One-to-one is something executives understand and they know it's the holy grail of interaction, whether it's with customers or supply chains or making credit decisions, it doesn't matter. It's that one-to-one part that is the new hurdle to jump and you can't jump it the old way. And you know what, we live it every day. Yeah, right? You do practice every day, right? Yeah, and we're starting to expect it, right, Bob? I mean, I'd be a little put off if Netflix didn't tell me what to watch next. I'd be stunned. I'd expect that level of service. I'd be mad at Amazon if they didn't say, hey, we saw you bought a blue golf shirt of Perikakis, we'd go nice with that. I expect it. And that's my, I think your second question was, well, what happens if we fail or what happens if that doesn't work? And I think what we end up affecting terribly is in the area of context and trust with the people that were, or the processes or the vendors that we're working with. So if I can't be persistent, I can't keep my analytics in the pocket of being contextually good, right? It would be silly of Amazon to offer me a draft. It would be silly of Netflix to offer me a type of movie I've never watched before. So the context part becomes really important, which is completely 100% dedicated to the governance of the data that's feeding that process. And if you screw that up, you screw up trust. And the more you screw up trust with your customers or your processes or what have you, the more likely you are not to achieve the innovation that you're working for. So for me, the dotted line is precision. For me, the downside of failure is going to be ruining context, ruining trust, and eventually not being able to innovate. You know what I mean? I think that we've all become so used to living that way and having things brought to our attention through the analytics that are done with our data. Right. That, again, if we don't trust what it is that they're telling us, or if it's not accurate, we're not going to, but if we see value to it, then we're going to continue to do it. I had one more question on this subject too before we jump into the last subject. And that is really has to do, and I know you're pretty active in the focus on the technologies, but can you share with the attendees today, the participants today, what are some of the new technologies that people should be looking for when it comes to analytics for their organization? Well, yeah, there's a few. You've heard everybody, I think we've talked about real-time or right-time analytics for a very long time or streaming analytics, and I always felt like, especially a few years ago, we were still talking future tests, but I do think that the time has arrived. I find that a large propensity of the customers and users that I interact with are looking to solve a variable sort of group of problems that are pursuant to the data. And so they have data that's at ref, they have data that's on the move, and the on-the-move data has become a lot more interesting to customers in general because it allows you to fuel critical business moments at the speed of a business. And so if that's important to a customer, they're starting to look at that as a trend. You know, Bob, there's a ton of talk about where ML and AI fit, and that's a very sophisticated approach to advanced analytics, this idea of being able to predict what's going to happen in your business instead of looking at it historically. And I would like to make the point that the math behind all of this is not new. What is new is that a handful of drivers have come home, and I think everyone on today's call probably feel these drivers. And if you think about it, these are probably true. You're probably trying to serve a much larger and more diverse community of users through governance and data towards analytics. They're more demanding and they're unafraid of the applications. There's this Google generation that's showing up in the workplace with an expectation of using insights to take action. So the community has gotten bigger. At the same time, the economics have shifted. And the economics shift that I'm just pointing out is that there's things like open source and there's ways to do things now at a price point that literally we couldn't do years ago. The first big data project I ever witnessed was one on the East Coast where they were doing the genome and it was government funded. And it was the only way to crunch that much data. Now I can crunch that much data on my laptop. So there's an economic advantage. Then there's this technology advantage when we look at things like Hadoop and big data and open source languages like R and others, they're helping customers innovate and they're disrupting from a technology standpoint. And then the last part, which is very close to our conversation is we now have all the data. Not just most of the data, we can have it all. It just depends. Different companies, different goals. But bottom line is whatever data you need, you can have. I remember writing articles in DM Review Magazine years ago about isn't it peculiar that we make all of these critical business decisions on 20% of the data? And the other 80% is highly unstructured, uncurated, ungoverned. And it's not something that we can bring into our systems. Well, here we are today. Companies can have all their data. So if you have all your data, you have technology disrupting your workplace, you have a bigger community of users and an economic advantage, things are going to shift. And I think that's part of this conversation. Right now, this is what's helping us make changes. Yep. Yeah. And it's just becoming more and more important. And not only is it becoming cheaper for organizations to be able to do that, and it's more readily available, just the whole idea of just-in-time analytics and making the right decisions. So like, when somebody enters a specific store and they've got specials that are customized specific to the person, and those are sent out to them because they know the history and they can predict what somebody's behavior is. I remember years ago, working for a large supermarket chain here in Pittsburgh that was doing some of these things. It was noticing when people bought X, they also bought Y, so they could increase the price on one and decrease the price on the other and advertise the decreased price and they would have the same or better profit margin. I mean, people have been using data to do all these things, and it's just becoming more and more of a differentiating factor for organizations. So again, all you folks that are attending today, if data analytics is really important to you, then this is really the justification to make sure that we get the data right and that we have that sense of precision that Sean talked about. The last topic that we want to talk about is kind of a summary topic. I talked about it being a symbiotic relationship between governance and analytics, and I want to make sure I understood what symbiotic meant. It sounded good at the time, but the interdependent relationship between these subjects, you really need to successfully govern your data. I think we've made a very big point of that in this webinar, and you get a lot of great examples of how organizations can really maximize the value of their data and of their analytics, but it comes down to getting the data right. And if we think about how much time people spend, you use the 80-20 rule in a different way, but I always say that we spend 80% of our time doing what we need to do to get the data ready so that we can do 20% of what our job tells us to do. Well, wouldn't it be better if we had the data and that we trusted the data and it was governed and that we had the metadata? All these things that date back to the beginning of, at least the beginning of data management time from the time that I was involved, which seems to be a whole lot of years. So anything else in the relationship between these two topics? You know, I will say that I feel, you know, I'm going to join you on the old end of the pool, Bob. I feel like I've witnessed a transition around the conversation about data governance and even just data management in general, but data governance was often a nice to have, not through my eyes, but through the eyes of a lot of customers I knew and I met them. And it was always, I always felt early on that we were always trying to convince people how important this is, that it wasn't a nice to have, it was a have to have. And that's why I brought up ebbs and flows earlier today because I think there's been times when pressure has been applied where a lot of, you know, managerial or executive level folks have gone, okay, okay, I'll give you a little bit more headcount. Let's hire a steward, let's do this, let's do that. But they never liked it because it was always hard to explain in the board room. They could never tell anybody why they were investing there instead of the newest bright shiny toy. Now the bright shiny toy is actually coupled to and dependent upon great data governance. So great data governance and analytics go hand in hand. And I think for the first time, I mean, I think you could admit, Bob, that big data and data governance did not go hand in hand, right? There's certainly an application there, but it certainly, it wasn't when in the fight in the board room, you know, they weren't saying I want to build a data lake and I also want to enable it with great data governance. You know, it just wasn't happening. So now we're on to this. Yeah, you know, as a discipline, it was never really that well mailed down. It didn't mean a lot of different things to a lot of different people. And now that we're looking at analytics decisions and take better actions around the data, just like compliance and protection of data is a no-brainer. Well, you know, you need to make sure that people are following the rules associated with the data. The same thing holds true for getting the data in the way that it needs to be for you to be able to do successful analytics. So if there's one message that you want the attendees to take back with them after this webinar, can you summarize it down to one message and then we'll get into my last question here in a second. I would say the message would be, is the next time an initiative is put for data governance, the first question, the first demand that should come from a data governance team is, how will we interact with analytics? Are they on the team too? Who do I talk to? Who's my partner here? And if someone says, well, we just, you know, we haven't figured that part out, I would slam on the brakes and get as hard as I can. I mentioned diversity before. I mentioned teaming and collaboration. I do think that this is the new way that we're going to do things going forward. And so if your company is dragging its feet a little bit and doesn't quite see the synergy yet, I think you have to help them by demanding that you bring the right team together, not just a bunch of people that understand data, but people that understand the business challenge and understand what they want to do in the critical business moment and have all of those people convene to do things. And I think that that's one of the most important, I urge companies to do that all the time. Don't do it like you've always done it. Do it this new way. Put a diverse team together, stakeholders, so everybody understands what they need to get. And, you know, what it means, a lot more work for us folks that are focused on data governance because we need to get the data right. We're just not there at this point. So, you know, getting from people, you know, in regards to the importance of this subject and how they've enjoyed the conversation and how they've enjoyed the conversation, but I kind of threw it out there for you and said, you know, can you share some lyrics of a song that best represent the relationship between data governance and data analytics? Just something for people to remember as they're leaving the session today. I thought I had a good one, but I'm watching the chat and I think Ray made a big bridge over troubled water. It's also one of my favorite songs, Ray, so thanks. But, you know, when you and I were talking a week ago about some of the slides, Lean on Me jumped into my head immediately when I realized that, because I do think we're leaning on each other to be successful there. And it was kind of funny, I actually looked up Lean on Me this morning, you know, and it's by Bill Withers and I'm sure we've all heard the song, but, you know, the first couple of lines in our lives will have pain, will have sorrow, but if we are wise, we know that there's always a tomorrow and you guys can go read the rest of the lyrics on your own, but I thought that the first four lines were pretty appropriate to our conversation. There's work to be done, there's challenges, but I like it. If we lean on each other, I think there's value to be had. I want to get to the questions that people have. Without the other, they really need to be together. So these are the things that we talked about today. And with that, what I'd like to do is I'd like to turn it back to Shannon because I think we've got a few questions today. Bob, thank you and thanks, Sean, for joining us for this great presentation. Just to answer the most commonly asked questions, just a reminder to all registrants, I will send a follow-up email by end of day Monday with links to the slides, the recording, and anything else requested throughout. So diving right in here, data governance structurally should belong to business teams or IT teams. I don't do enough research around where data governance functions at its highest level. And I'm sure there's people on the call today that might have opinions there, Bob. You may as well. I don't know which org it belongs to, but I do know that there has to be a bridge. I do know that there has to be a partnership. I guess off the top of my head, I wonder if there's an advantage to it being closer to business than to IT these days, because it's so enabling to the goals of business. And that might be an interesting thing to take a deeper look at. I don't know, Bob, what do you think? When people ask me if it should reside in business or in IT, I answer the question, yes. I say it needs to reside somewhere. It needs to be a partnership between business and IT. I think the business is the place that most people will say that data governance should live, but I have seen it successful running out of IT as long as it wasn't viewed as being an IT project for IT's sake, that it was really a partnership with the business. So I really think that it is that bridge, as somebody just stated, a bridge between business and IT, sometimes in shared services, but it can reside somewhere, but it needs to be somewhere. All right. Well, to you just expand on that, who should be the leader within that? Do you want to take a shot? Sure. I see, well, the leader of a governance practice is going to be somebody that has the skill set and understanding exactly how that works and how to move forward with it. What I am seeing, though, is a lot of co-leading around this idea of these analytic projects. We're seeing a lot of customers that are doing centers of excellence for analytics, and part of the center of excellence then includes people from data governance, people from data management, and there's a lot of co-leading going on there. So I think that's kind of the new thing. I don't think that, you know, I would say there's one place or another. I think the governance function is always best driven by people who understand that challenge, but they have to be able to bridge the gap, they have to be able to work with the other teams, and I think the companies that are embarking on centers of excellence are showing great examples of how combining diversity in a team is getting them the best thing. There's interesting reading out there for you folks around the systems of insight that's published by Forrester, you know, one of the analyst firms, and we at HIPPO follow that quite a bit. We think that there's an awful lot of good things in their infrastructure and framework, and when you go off and look at the system of insight, you'll notice that they have analytics engines right next to data governance and those types of tactics from the very beginning in their framework. So a great reading by Brian Hoskins, their analyst who writes on this. I recommend that you take a peek at it when you get a chance. Okay. Strategic level of your governance program, of a data governance council, and analytics is involved in that, that oftentimes I see analytics kind of take things more and more in within those types of groups within an organization. And, you know, there was some comments earlier from the previous question, additional song select suggestions where I fell into a burning ring of fire. I love that. I can't get my eyes off you. There's no more questions. Everybody's got it. You guys explained it so well. There's just no more questions coming in. It's okay. We're just quiet today. But you guys, thank you so much for this great presentation. It has been fun. I just love the dialogue. Sean, thank you again for joining us. And just a reminder, again, to everyone who's sent a follow-up email by end of day Monday to all registrants with links to the slides and links to the recording and, of course, to many of the templates that Bob offers up. So I hope everyone has a great day. And thanks so much. Thanks, Bob. Thanks, Sean. You're welcome. It was great to be here. Thanks for having me. Thanks, Sean. Bye.