 Hello and welcome. My name is Shannon Kemp and I'm the Executive Editor of Data Diversity. We'd like to thank you for joining this month's installment of the Monthly Data Diversity Webinar series, CDO Vision. This series is designed to give the year-round education on data strategy topics in addition to our annual face-to-face event from which we just returned. It was a great event this year and we're already underway for planning next year to be held in Atlanta, Georgia. This month, John Lowley and Kelly O'Neill will be discussing a telling statement to corporate leaders why you must address EIM and DG, just a couple of points to get us started. Due to a large number of people that attend these sessions, you will be muted during the webinar. For questions, we'll be collecting 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 or questions via Twitter using hashtag CDO vision. And if you'd like to chat with us or with each other, we certainly encourage you to do so. Just click the chat icon in the upper right for that feature. And as always, we will begin the 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 our speakers for today. Well-known industry analyst John Lowley is a business technology thought leader and recognized authority in all aspects of enterprise information management with 30 years experience in planning, project management, improving IT organizations, and successful implementation of information systems. He is the president and chief delivery officer at First San Francisco Partners. Also joining us is Kelly O'Neill. Kelly is the founder and CEO of First San Francisco Partners. Having worked with the software and systems providers key to the formulation of enterprise information management, Kelly has played important roles in many of the groundbreaking initiatives that confirm the value of EIM to the enterprise. Recognizing an unmet need for clear guidance and advice on the intricacies of implementing EIM solutions, she founded First San Francisco Partners in early 2007. And with that, I will turn it over to John and Kelly to get today's webinar started. Hello and welcome. And good morning, good afternoon. Good evening, wherever you may be, and thank you for joining us. Hello, Kelly, how are you? Hello, hello, everyone. I'm good. How are you? Good. This is going to be a really interesting talk today. So everyone that is on, sharpen your pencils, get your paper ready. You're going to hear a few things that you can use. And let's get started. Why we're here today is we need to address leadership today, and we need to address the messages that we get. A very common question we get, right, Kelly, which is how do I start this? How do I sell it? How do I sustain this? How do I keep people engaged? And we've been examining that on and off, and you'll see how here in a little bit. For almost a year now, kind of as a side research project, and we've got some ideas on the messaging, and we're going to talk about that. The real root problem here is we think that a lot of what we're doing and a lot of where people's heads are, are out of sync, where we're ahead of the ability to absorb some stuff. So we're going to have a practical message for leadership. Very practical. If you were looking for something that will wake them up, startle them and get you fired, you might not hear that today, but you're going to hear something that you may not have heard before. And we have a little vision to develop that message that's suitable for you that Kelly is going to go over. We are going to leave about 10 minutes or so at the end for questions. And as Shannon said, please submit your questions so we can go through those. Any questions we do not get to, we will address afterwards and answer in writing and blog those answers to. We've done that at times as well. The contributors to this are more than John and Kelly. About a year ago, this group of esteemed individuals started to talk on a phone on a regular basis about traction of data management, information management, and data governance. And you can see we have representatives there from Gartner Group, which is Doug, Danette McGilvery, who has her own firm, Kelly and I from First San Francisco. James Price is a brilliant fellow from Australia with his firm, Don Aaron, and of course Dr. Tom Redman, the data quality doctor. And he's been doing this work for a long time. And we've actually tossed a lot of things around, done some research, and we don't need to go into all that details. But a lot of what you're going to hear today comes out of some real serious thinking from a lot of really, really smart people. And we thank them for their time. So let's get moving here. This is a bunch of reasons we do data projects. And the results out of that might be MDM, it might be analytics. But these are business things that are happening to companies and are things they want to do with the data. These are the actions companies want to take from data. Now the issue arrives with what we're talking about here and I can ask Kelly to chime in on this one. Are these really one-off projects or are these part of some type of a holistic strategy? And what we'd like to see is something a little bit more strategic, right? Yeah, absolutely. And I think the other thing is sometimes even if they are a one-off project, they can be a good indicator of what that strategic viewpoint looks like. And maybe the current most urgent need that's part of a bigger issue or problem that can be addressed as more of a philosophy. Absolutely right. And so, I mean, these are the business reasons, but you can still do a one-off data project or something that's more holistic. Or you can approach these holistically and, you know, you can act global and think local. There's lots of ways to do this. But the issue is at the end of the day we have to help companies do these things with our work. But the message we deliver is not quite being received because what we hear and what we see this is when we see an organization that is truly engaged with the data management world and those types of things, what we see is a company where they want to do data right. I mean, and that's kind of a different, an easy way to say that, data-driven. But they also are talking about the value of their data. And we had great interest at Enterprise Data World on how do we actually come up with metrics to measure the contribution of our data. Those organizations that have chief data officers are a top data job. They really take deliberate alignment of their business. And they put culture and business alignment before the technology. And there are many, many examples of success. And we're going to talk about when someone is successful what's going on versus unsuccessful in a little bit. The ones that aren't engaged, they're doing data projects. But what we notice is they apply it directly to a project. If we start to talk holistically, they go, we can't handle that. It's not us. We're not ready for it. Or you hear it's too abstract. We're seeing places that are hiring chief data officers and top data jobs and then getting rid of them within a year or turning them over because they've given them impossible tasks to do. You get to just do it, give me access to all the data type stuff. And the big symptom here is technology before culture. People are buying the tools and buying the technology before they've even addressed any of the organizational aspects. And as Drucker said, culture will have strategy for lunch. And a lot of organizations are learning this the hard way. But the message isn't quite being received because we've got a real dichotomy between successful organizations and organizations that are struggle. And this is a summary of what we see in those two things. So then you, of course, you want to do a root cause analysis here, right? If you want to, what is at the core of things? Well, it's a lack that this stuff is really important. But what creates that? Well, leadership and management doesn't feel they've got justification for it because they're not going through the right steps. They're not doing things for the business. They're doing the technology projects. Things aren't being enabled to support this. You do a big MDM project and you're supposed to have a good stable set of standards and some governance to make it successful. And we find that the data governance is pushed to the side or it takes a local project level flavor versus an enterprise flavor. And lastly, an operating framework or an engagement model. You're a steward. Go off and be a steward. Here's a book. Be a steward. And that's not how to do data governance, all right? You've got to engineer some type of communications model. There's a lot of root causes within those, but just kind of summarize it. Kelly, anything to add to that before we start to dive into some of our data here? I think maybe just to call out specifically data quality is not a root cause or a barrier. And so I think that that might be just something to explicitly call out here is that the challenges that we're facing aren't rooted in data. They're rooted in organizational issues. They're rooted in justification and business cases and business sponsorship and all of that. So just wanted to make that obvious. That is a really good point. What is missing from this slide? What is missing is data quality. What is missing is easy access to data. Those are requirements that we have to meet to be successful. Absolutely. Those aren't the root causes. And for those of you who've said keep your pencil sharp and your paper handy, that's something to write down, all right? Those are requirements. What most people have driving them as a goal or an objective is a requirement. And this messaging we're going to talk about has to address these root causes. So let's go into some of the research behind this. And there's a concept called diffusion. And we're all familiar with it, even if we don't know the name. And we'll get to that in a second. But first, Kelly's going to talk about how you get an organization to act different. Because that's kind of what we're talking about here. And I'm going to let Kelly have the floor here for a slide and a half or so. And so take it away, Kelly. Great. Thanks, John. Yeah, so what we're trying to think about is truly how do we act differently in what's driving this behavior or lack of behavior, right? That we have, that is impacting our ability to progress these data programs. And as John talked about, both within First San Francisco and with the group of other thought leaders that we participated with, we always come back to the why. Why is this hard? Why isn't it happening? Why isn't this so difficult? And so I wanted to pull in a thought process from a man named Dan Barnett. And Dan Barnett has a business consulting organization that helps companies to achieve their most optimum results. And I thought that this concept was really pertinent to the way that we think about data, because one of the biggest blockers is our culture. So how do we drive the culture? Well, ultimately, culture is based on the way that people believe and behave within their organization. So culture is that conglomeration of beliefs, behaviors, activities, unspoken norms that show how an organization both responds to their employees as well as they respond to their clients. And ultimately, beliefs are really what drive everything. And the way that people believe about their company is how leads to how they behave. And there's a lot of research behind this. And one of the things that gets, you know, John and I excited is it starts to get a little bit more interesting when we look into these root causes. So this research is based on a series of experiments that were done by a variety of universities, but they were all published in a Harvard Business Review article called Decisions in Desire. And essentially, they applied electrodes to these people's brains that are part of the experiments to see what parts of the brain become active when they make decisions. And they found that the limbic system, which is your kind of your animal brain, it's the most primitive part of your brain. It is where all of your fight-and-flight activities and how your, I guess, unplanned responses occur. So your decisions are made both in your limbic system and in your neocortex. And your neocortex is that human part of your brain that we all think about as what makes us different. This is our planned behavior. It is our predictive movement and proactive movement, not automatic movement. It is where we think that we make all of our important strategic and analytical decisions. But what they found is that it's actually between your limbic system and your neocortex. So that means when we make a decision, we're actually calling into some of these things around fight-or-flight. Anyway, the idea is that this link between your limbic system and your neocortex shows that our most inherent behavioral responses are also where we keep our beliefs. And our core beliefs drive our behavior in a very, very real way, not just from an external way, but the way that our brain processes these transmissions in our brain. Therefore, if we want to impact culture, we need to impact our beliefs. And this is kind of where we got back to, okay, so how do we impact the beliefs around data? And so this is a lot of what has led up to this conversation, is trying to impact the belief system that occurs within a company around data, as well as just within the industry as a whole. So the takeaway from this slide is that your beliefs drive your behavior, and your beliefs and your behavior together create the culture. And if we don't address the beliefs, the behavior and the culture around data, we will never get to the results that we want, and focusing just on the results is going to be a non-productive exercise. So what we're going to talk about now is kind of diffusion of beliefs, behaviors, and culture. Yeah, exactly. Yes, and so now I'm going to turn it back to you, John. Oh, okay, okay. Are you done? I'm done just because I think that you're much more knowledgeable about the Gartner hype cycle, and it's another application of how diffusion occurs and the way that people believe around technology and how they adopt it. Yeah, and something to tell our audience here is these two things are connected, because now someone's gone, wow, this is this really cool thing about limbic and neocortex, and now I'm looking at the Gartner hype cycle. And someone's elbow just slipped off the table with that, but they are connected, and here is why. First of all, if we take a topic like analytics, where everyone says we're going to be data-driven, we're going to do analytics, and then you understand that even with all of that perfect analytics, most business decisions are a factor of a very different process than what we saw here. You go, okay, then maybe we have to learn how to be data-driven. The other thing we have to learn is in terms of adopting data management and data governance, which is our topic here. What's that message? We've tried to measure this before. So we have these, what in hindsight seem to be limbic, because they're measures of behavior. There is no engagement here. These are great tools. We're not disparaging the tools at all here. There are terrific ways, these are terrific metrics. One great adoption thing is the Gartner height curve, and we all love the trough of disillusionment. I wish I'd invented that. That's just really cool. But it talks about how things ebb and flow until a new idea gets stable. But we're measuring the reactions to this, and the reactions to this, remember, as we talked about, are a function of two parts of the way people do things. We also have the famous adoption curve, which was set out by Everett Rogers. You have your innovator, early majority, late majority laggers. And then that came out in the late 70s, 80s, and then Jeffrey Moore in his classic business book, Crossing the Chasm, took that. And when you go from early adoption to the early majority, that's when something really takes off. So people say, oh, that's great. That's when we've adopted the technology, because we've gone from early adopters to early maturity. The thing is when you look at the Gartner height cycle, and you look at these, you're looking at a historical thing. It doesn't tell you how to do that. It just tells you what you've done and how events might pan out. So what we've got to do is move ourselves to a process of what's going on at these various phases. Because there's one thing that all of these both leave out. When you have an innovator and an early adopter, or you're at the beginning of a height cycle, somebody always has a success. And somebody takes that success and then becomes an innovator. And then an early adopter says, I can do that, and they do that. And then the early majority says, well, we can do that, and they do that, and et cetera, et cetera. What makes within each one of these steps, whether it's the height cycle or the diffusion curve from Rogers, what makes success? Where do people engage in the idea? And our esteemed group started to kick that around, and we did some research and found another set of ideas. And I hope I'm not making anyone sleepy here, but we're going to touch this really quick and then move on to the juicy stuff. And there's another group of research called the Technology Acceptance Model. And it is based on shifting your idea from feelings to action and moving your thinking into a more methodical way. So you perceive that things are useful, but then you actually engage in a new technology or a new system or a new approach. And this is more what we would think a rational type measurement of how people do things. So in terms of adoption, it's not an adoption curve. That's a look back. What we need is to master the diffusion of an idea in your organization no matter what maturity or part you are. If you're a laggard, fine. If you're an early adopter, fine. But it doesn't matter. You still have to go through a process to think more analytically and think more about data. You just don't install a bunch of rows and columns or structures and have answers stick to your face and expect yourself to be able to deal with them. There's some things that have to occur. Some things have to change. All innovators go through an acceptance process as do all others on the adoption curve. The intent is to go from idea to practice no matter where you are. At the bottom of this little matrix here, we have our innovator through Laggard, which is the diffusion model from Rogers. But we also have the technology acceptance model up the left where something triggers you and then you actually use it. And I can put an X in every box on that matrix and find somebody out there in data governance and data management land that's done something in that box at that level of maturity and that intent and adoption of an idea. So you need to address acceptance of the idea no matter where you are. Here's the next thing to write down with your sharp pencil to Mr. Message. It doesn't matter that your organization is a Laggard. It doesn't matter that your organization is an early adopter or an early majority. It doesn't matter. You won't be successful unless you work diligently to get people to use the material that they've got. And it isn't a matter of turning it on and telling them Monday morning it's ready to go. It takes a little bit of work. We actually did a little bit of a survey amongst our peers. We picked out our contributors, the six people we talked about, a few other folks we know, and then some people that we just said, you know, take this and tell us what you think. This is not an exotic survey. Actually, at the end of the presentation, there are questions. I will show an example of the actual survey. But what it is, it's measured the acceptance of an idea. And to make it easy on people, we made it in an urban planning model. So if you're going to adapt data governance, all right, and your organization is a pioneer, all right, that would be an intensity of embracing the idea of like a one or something like that. If you're a more mature organization, how would you adapt it? So we asked a series of questions, and look how this is skewed. This is skewed way to the pioneer refugee settler. People who think that they're embarking on this is going to be a struggle, that it's going to take an extraordinary amount of work. Now, how do you sell that idea to management if you say, we've got this great idea, we're going to build a civilization, but it's going to take 100 years and have enormous costs and use an enormous amount of resources. We have to get people to think more as this is more of a community building or an urban metaphor. So you've got to change people's thinking. And this is from people who know this business, answered it this way. So we've got some strong evidence here that we just got to kind of change the way we think. We have to move to data-driven. We have to think data-driven. We have to think using data, all right? We just can't think about projects. The projects might even be getting in the way. Someone told me that the secret to a really good golf swing was ignore the golf ball, then put the golf ball down and let the golf ball get in the way. We're probably thinking about something like that along here, too. So what is the process here? Kelly's going to walk us through our five-step process and then we're going to dive back in for the next 15 minutes or so. She's going to go over the process and then we'll have the messages and then we're going to dive into your questions as to how we might be able to help you here. So Kelly, take it away. Great. Okay. So we're going to go back to this thought process of changing beliefs and behavior starts with changing culture. And the way that we want to think about this is creating a vision of how data will move your company forward. So I know that we've all done vision statements in the past. We've all done mission statements in the past. So this is just a way to consider the importance of doing a vision process and creating a vision statement to really abide by it and help drive your belief and your culture. And the idea is you want to have a vision statement that is crisp and it is resounding. It has a business purpose and it is already aligned with some of your other cultural norms and that vision should start to appeal to the belief system and to the limbic system and help people to respond. So this is just an example of a vision statement here in which the organization plans to be leveraging data for competitive advantage. And this is a vision statement that fit nicely within their company culture because they had a competitive culture. They were very externally market focused. They wanted to be the best of the best. And so bringing in this concept of competition and competitive advantage pulled in the limbic system and helped to drive this belief that data actually does help with their competitive advantage along with other things. But data has a role in the way that they compete just like some of their other behaviors and beliefs. So starting with a vision that is compelling and plays to the belief system is a great way to start. The vision then starts to be more further articulated in, as John introduced, kind of a five step process. The vision is then turned into a clear picture of what the future looks like and a picture of what it means once that vision is starting to be accomplished. That picture helps to engage people and help them to understand where they're headed. That picture, of course, also pulls in things like principles and other sorts of behavior aspects that we've all done before creating those guiding principles. Through the vision and the picture, the picture also informs the plan. That plan might be your roadmap. It might be your strategy. But the plan then helps people understand what are the steps that we're going to take in order to get to the picture and to accomplish our vision. And then the participation articulates what the individual roles are within the delivery of the plan. Now, one of the things that, on this kind of infographic, we had down at the bottom is the purpose. The purpose and the vision do, I guess, align tightly together because the vision is the statement of that aspirational goal and the purpose is the why. Why are we doing this? So together the vision and the purpose have that sort of limbic response that helps people to believe that this is a very compelling program within the organization. And then the picture, plan, and participation spells it out in a bit more detail. So this is a thought process of how we start to address diffusion, adoption. However, we want to think about it, but we're addressing it from its very base nature or the belief system, not just addressing it down at the end result, but really the plan and the participation. We're taking a step back and looking at it from the vision and the purpose and the belief system. So if we go to the next slide, this kind of links this process of division, purpose, picture, plan, et cetera into exactly where it hits the beliefs and the behaviors. So your vision and your purpose, what is your big, you know, I don't know if anybody uses the term be hags or your big, hairy, audacious goal or another organization. I've heard the wig, the wild, inspirational goal or any sort of high-level strategic statement of a goal that helps to inspire people is your vision statement. And then the purpose is why that big, audacious goal is going to be compelling for the organization. The two together help to address the limbic components in terms of those core beliefs of why we're doing this. Behavior is then addressed in a bit more of the why. So the picture of the future state, the principles that articulate how people are expected to behave, the plan in terms of exactly what are those steps within the roadmap, et cetera, and then who participates in the plan? How does it involve the rest of the organization? By addressing from this perspective, then we can start to look at the results. Those results are, of course, how we impact the business as a result of the EIM program. But again, when we're trying to think about adoption and diffusion, rather than starting with the results, which we have all done, by the way, including John and myself, we do want to take a step back and consider how that vision purpose system can better help us drive to the results, not just looking at that end state or the results. So this is kind of a five-step process to get to that core limbic decision-making process. Any thoughts on that, John? Are you on mute? Yes. First one must unmute to articulate one's beliefs and behaviors. That's right. There's... So just to tie this kind of our topic of messaging here today and again, something for the sharp pencil and the paper. There's a couple of really key things here that have been bandied about as optional, but the more and more we get into this and talk about it in research and, again, this group of six people that we've been working with, we're going to be on mute here. We've got some more things to come up with and all of that. So we're kind of mid-course on this, but there is definitely things we're sharing right now. A couple of things that are definitely we're sharing, things like principles. If you're going to get people to think differently in an organization, whether it's on the one project, go back to our little thought bubble slide, slide two or three. And I'm sorry, that was slide four. And either think holistically or not holistically, but still do things intelligently or on data. You've got to shift their thinking from reacting to a requirements perception and coming up with a data behavior or a data practice, sometimes the word that is used. What we've seen, when you have something about beliefs, then what goes along with any belief system, even outside the realm of data, any other type of belief system, you have principles and you have vision. So the emphasis here is to weigh in on this slide with Kelly. I cannot stress the importance of principles enough here. I cannot stress the importance of vision. Even if you are doing a data-centric initiative and you don't have the luxury of an enterprise oversight, you still want to use this as a laboratory to better manage your data, do principles. Without principles, you have no philosophical anchor for a change in belief. And you're changing beliefs that data is something out there that we deal with to something that's really, really central to how we do business. You've got to have those principles. I can't emphasize that enough. Who does what? The vision. It's all extremely important. We have lots of methodologies, right? We have maturity models and all of that, but if you really want to boil it down to the strong message, which is what our presentation is for here today, it's this simple shift from an incorporation of beliefs and behavior into being data and being data-driven on that. Ready to go on to the next one there, Kelly? Yes, and I just want to, I guess I said this already, but I just want to have everybody on the phone reconsider how they're using their vision statements, how you're using your principles, because you've probably all done an aspect of this already. So how would you align what you've currently done back to this concept of driving adoption via beliefs? And just consider should we take an opportunity to update these? If the written is correct and action-oriented, we just need to be using them in a different way. So just as you're thinking about it, consider that perspective as well. Okay. Now, let's see. So we have kind of the two ways to drive the messages, two different outlooks here, and Kelly and I talked earlier, we're just going to talk with this one together. There's the top down way, to engage leaders with a passion for data. So let's talk about that. I think the word passions is important, don't you, Kelly? Exactly. Again, going back to our beliefs and our limbic system, if we're passionate about it, it's important, right? It starts to drive our behavior. Absolutely. And James has several wonderful case studies of clients he's worked with. He's had fabulous success through information management. We don't see them a lot because he does work in another hemisphere, but some of his work is fabulous. Now, Dr. Redmond, another one, he introduced this concept at EDW, and it went over very well. More of a bottom-up type thing, the provocateur. I like that. I mean, it sounds mysterious provocateur. I am a data provocateur. I don't know if you put that on a resume or not, but it's, you know, the provocateur is someone who instigates change, right? And we do see organizations that do accomplish some bottom-up type of things. Or they take a project and do something really cool data-wise with it and then attract some attention and do that. But now the provocateur has to be a risk taker, too. They have to push things a little bit. That's why they're a provocateur. But that is another way to look at this kind of thing. Now, I will tell you right now that our group hasn't determined if one or the other should dominate or it should be both or it should be 60, 40, or 70, 30. We're still working on that. Or there's some middle-out option here, too, that there might be as well. But here's kind of two ways to look at how these messages can be delivered and how these messages can flow. Anything else on that one, Kelly, or move on? Yeah, and I would just say, think about what would work within your organization. Maybe you have a bottom-up program that's happening right now. And maybe you're trying to determine, is this valuable? Can we get this done as a bottom-up? Well, this is a great perspective. Think about this organization as provocateurs to help drive this. Or maybe you've got a top-down approach where your CEO has hired a chief data officer and you're trying to figure out how to drive a top-down. So based on your existing program, where are you and how can you leverage either one of these outlooks to help get the end result that you're looking for? Okay, all right. So actual delivering the message in just really just two or three more slides here, but these are going to... We're going to be talking about these a little bit here. First, reality. Kelly, this is the reality of what's going on right now, right? Yes. Whether we like it or not, it is. Yes. You want me to care? Okay, no problem. So we've got internal pressures. We've got external pressures. And a lot of times we think about those external pressures as negative, but in fact, catalysts can move the data program forward, such as regulatory compliance and increasing regulatory compliance, right? That is, you know, the greatest provocateur that we have, right? The secret is, of course, moving beyond regulatory compliance, but we do have a great external driver from that experience. We have great external drivers in terms of our demanding customers. I think that on that infamous slide four or whichever one with the thought bubbles, one of the big drivers of doing a data program are experience across multiple channels, right? So our clients are getting more demanding, our internal employees are getting more demanding, and the technology, like it or not, is giving us new opportunities to either push our data external to our firewall, keep it in-house, update some old legacy systems, et cetera. And all of this is being managed while you still have to keep the lights on internally, and you've got other sorts of internal pressures as well, both from a technology perspective and as well as the way that you're looking at how you manage your products and your customers. So it is actually kind of a nice time to start thinking about this because we've got a lot going on that will force the organization, whether they like to or not, to start to address data as one of those key assets that informs a lot of the decisions around how you manage your internal operations and your external go-to-market. So I have a story to kind of show what's happening here. We have several clients that are or organizations we speak and communicate with as well that are addressing the Internet of Things and we all probably know that the Internet of Things is if we think we have a lot of data now, oh my goodness, right? Just look what's coming. And this organization wants to address and has already set up its team for the Internet of Things and are starting to see the overwhelming technology challenges that they're going to have. But what they really haven't talked about and we've got to work with them about is what do you do with it? It's not just one thing to say, well, the Internet of Things is the next really cool tech that's coming down the road and we can do cool things with it. What can we do with it? Big data kind of started out as a third generation data mining analytics and we had an idea what to do with it. The Internet of Things is one of those is a classic laboratory for what we're talking about here today. Whether you're an early doctor or you're going to be a laggard, or a creator of that data, or you're forced to deal with it for competitive pressures, you are going to be in the data business pretty much end of discussion if you're a big company here pretty soon. And it's going to be a real challenge and if you don't accept that reality, wow, you're not going to get anything out of it. You're just going to spend an awful lot of money, I think, but that's it. So let's talk about the message that we want to give. And Kelly and I are going to kind of tag team on this because of that prior slide. First of all, it is all data now, isn't it? That prior slide, Kelly, it's all data. What really is there right now that isn't data? It's hard pressed to think of something. Yeah, and I think you may think that your company produces widgets, well, guess what? To understand your most operationally efficient way to produce the widgets to understand your most effective way to sell your widgets, all of that is data. So regardless of whether you produce like hammers, it's all going to be about data in terms of optimizing that process. And by the way, there is such a thing manufactured that is truly a widget. And I think we should probably check to see if there is some Internet of Think technology associated with widgets. There are a couple of different kinds of a certain popular draft beer to create the bubbles and it's called a widget. That's my next research project is we're going to have to examine a lot of widgets. The key here is your problems are solved now and your opportunities are grabbed with data. There's no such thing. So for the sharp pencil in the paper, there are a lot of indicators. Show me something without data and show me something that we can be successful if data is in everything we're doing and then we contrast how we treat our data now, then tell me how we're going to be successful here. And that's compelling. The intensity and the sincerity of this idea of data. Is it thought out? What's the business value? Do you have the stamina? We changed the word on that when we were doing this presentation. I used another word. I started to use willpower but it's more than just will. It's like training for the marathon. You might have the willpower to do it but you still have to build yourself up to be able to execute. Absolutely. And I think that part of what we want to address with this word because I do agree it's an important word is in the same way that we think about that adoption curve and where you have the trough of disillusionment. Well, that trough of disillusionment occurs within your own EIM program or data governance program or even like master data management or data quality program. You're doing you have a high likelihood of hitting a trough of disillusionment and so rather than abandoning the program do you have the stamina to continue to keep going based on the belief that this is going to be a competitive differentiator for your company, for example. So it is truly the stamina to get beyond some of these obstacles and not throw up your hands and say it's all too hard. We have better ways to spend our money and spend faster and all of that. I wonder how many of our listeners have been in an organization or participated in an effort where data management or data governance hasn't been quite successful. There's disillusionment around it and then something else happens and they have to do another initiative that requires similar activity but you get the word and I am pretty sure if we had a poll facility set up we could take a poll right now and that is how many of you are not allowed to use a word like data governance anymore or data management or data warehouse because you had a project that failed and instead of stamina to get through it you just don't use the word anymore, right? We're not going to do it anymore. Think of another way to use it, use a different word which of course confuses people and things like that. That's what we're talking about here. And again, those diffusion curves the Gartner Group, HypeCycle and the Jeff, the Rogers diffusion curve they're all valuable. You will have these things happening in markets and happening in technology and what is your will to get through those particular phases here? So here's the message and I'm just going to deliver it and allow Kelly to expand on it and then we'll kick it back to me for the questions and then I can show the survey while people are asking the questions. I have a couple of questions here already. I'll start with the first one and then we'll take it from there. Doing nothing about data management or data governance is not an option. Simple as that. Just sitting there is an option. Kelly, I think you have to either make a conscious decision and accept the risk that you're not going to mess with it or you're going to mess with it at a project level or you're going to embrace it. Thoughts on that? Absolutely. And I think that when the audience, when you're coming up with this, oh, we need budget to do this, we need budget to do that, consider the cost of doing nothing because there will be a cost to your organization of not addressing your data, whether it's addressing data quality, whether you're addressing your governance and protection, whether you're addressing your data management processes. Doing nothing ultimately has a cost to it and that cost actually grows exponentially over time. Whereas the cost of actually addressing a problem early on, that actually reduces the cost over time of managing that data. And so it truly is whether you accept the risk to do nothing or you accept the cost or the process to establish a discipline. And in most industries, doing nothing is truly not an option and will cause the organization to become non-competitive or to have some sort of regulatory compliance issue that then obviously creates other problems within the organization. And the other thing is this last bullet point I think is really, really important and it was one that John came up with, this concept of waffling sometimes is just as dangerous as doing nothing because waffling also creates some cost and going back and forth of, well, maybe we should, maybe we shouldn't, that sort of thing, that also creates a cost. Now, I'm not saying that you have to invest, you know, millions of dollars from the outset, but just doing nothing is a cost. And if you're trying to calculate why we should do something, calculating a cost associated with that lack of action is just as relevant. And waffling has a finer point on it. When an organization says, well, you know, we're considering our options about this data management thing, but in the meantime, continue doing things. The organization put in a very precarious position of making assumptions. And those assumptions are invariably going to cost you more money. So, you know, the message here is pretty simple. Make a call, Mr. Executive, Mrs. Executive, make a call, all right? Either make a conscious decision and accept what we are now documenting as profile risk. You know, no matter what kind of organization you are, then make a call to try to embrace this idea and get people to function and engage at a higher level with data-centric type work, governance management, BI analytics, whatever it is. Make the call to make it a more holistic cultural thing or keep it at a local level and take a look at it again and accept the interim risks and document those interim risks. And we're right at our time for our questions. So, we'll just do it. Our next CDO vision series is June 9th. It's a CDO interview. Kelly, we have the person yet. Have we confirmed? I think we have. Absolutely. And so, you know, one of the reasons that I'm excited, and I didn't want to interrupt in the beginning, but when we were talking about some of the opportunities and challenges and the fact that there's been a lot of turnover from a CDO perspective, we have the concessions of a previous CDO on our next webinar. And John Batega, who was one of the very first chief data officers ever identified, promoted, given the ultimate opportunity, is going to be with us, talking about how to make the role of the chief data officer successful and what he's learned over the past in terms of successes and failures. So, I'm really excited to have him participate and to address this issue of, you know, I think we called it the top data job turnover. Why is there a lot of turnover, and what can you do to prevent it? Cool. So, John Batega, next one. And it's also June 2nd is the first Thursday of the month. That is a typo on my part. It's really June 2nd at 9 p.m. No, it's actually June 2nd at 2 p.m. Eastern Time on this Webified Broadcast Channel. Off to the questions now. Kelly and I will both take this one. It might take us a few minutes. By the way, if you have a question, please, shoot it in. We've only got a few here. I know there's more than that out there. And send them in. But I have a question right now here from Regu. And it is, we are doing the standalone type governance and data management with many projects and many initiatives. What is some advice to start to bring those together? Kelly, I'll kick that to you, and then I can follow up. So, it's always hard to answer questions if I can't ask a question about the question. But anyway, so what I'm assuming this means is that the standalone governance is project-based governance, where you're looking at data issues on a project-by-project basis. And so to me, this sounds like one of these bottom-up sort of grassroots sort of processes, which is okay, by the way. But maybe you can start to see some synergies across those different standalone initiatives, and you can identify economies of scale and how those standalone initiatives can start working together more coherently. And through taking a look at those consistent activities, consistent practices, and how they are addressing consistent data issues, you can start identifying where you want to create this concept of a vision and a purpose, and that sort of thing. And it might be very fundamental that your vision for data governance is to optimize efficiencies in your organization. And your purpose is to reduce costs by 30% of all new project initiation because of data. So you can still have a vision that is compelling, and it doesn't have to be grandiose if you are starting from the bottom-up. So that would be my thought. John, I don't know if you had some other thoughts as well. I think that's fine. I could expound, but I'd probably just be rephrasing some of what you said, and we have two more questions here, which I hope we can get to them. Here's the question. These are bold messages. What kind of opportunities should we look for to deliver these messages? And that's a good question because we've said, go say some pretty strong things to leadership. So maybe we should wax on the practical. I'll start with it, and Kelly, you can chime in. From the practical standpoint, you're going to have update meetings on large projects. You're going to have trip reports from conferences. You're going to have diagnostics after projects are finished that went well or didn't go well. You're also going to have situations to address, as Kelly remarked, external factors that are driving your need to manage data compliance, et cetera. So when you have those opportunities, is when that message gets delivered, and it is. Now we have to make a conscious decision at this point. Do we just do a one-off focused application of data governance, data management best practices, and document the risks? Or do we start to build out, whether it's bottom-up, be provocateurs, or be evangelical from the top down? Do we continue to build something of an enterprise perspective? But these opportunities will present themselves, and I think you need to deliver repeated small messages. Kelly, any other thoughts on that? Yeah, no, I would absolutely add to that. And some of this is also your internal stamina to do that. So identify those existing communication channels and communicate your message in that way. Identify your provocateurs that you can use to be your evangelist to communicate the same message. And so sometimes having this short put sweep vision statement means that you're all saying the same thing, and you can tie back to and reference that vision statement over and over and over. And I'm going to use another example. So I have three kids, two of which. One is six, and one is 12. And I swear they are the same person, but in a male and female body six years apart. And they, when they want something, they repeat it over and over and over and over. And I swear by the end, I ultimately agree, right? And I'm sure that you either have kids, brothers, sisters, anybody. The idea is, you know, repetition and helping to create some awareness by saying a consistent thing over and over and over, regardless of the communication channel, right? What we're about, we're leveraging data for competitive advantage. And this is how we did that today. This is how we did it in this meeting. This is how we did it with this project, et cetera. So that consistency and repetition is very valuable as well. I like that. Are we there yet? Are we there yet? Are we there yet? Absolutely, right? Can I have it? Can I have it? Can I have it? We'll call that the Bart Simpson technique. That's right. Okay. All right. One other here I've got, and then if nothing else flows in here, we'll be able to do a wrap-up here. Our organization has a training and organization change department. Is this something they should be involved in? And I think if the organization has that, you've already got a lot of stamina or potential there to do that. We've interacted with a few of those over time. Kelly, any examples come to mind? One recent example here in my town here in the Midwest where they had a lovely group of people that could facilitate anything and they were very, very, very helpful in building out a data governance program. So yeah, there are people whose whole specialty is to get people to think differently. And I would certainly leverage those. Any thoughts on those? Yeah, and I think it goes back to what you just said, John. So some of this needs to be practical. And if you've got existing mechanisms within your company, do everything you can to leverage them, whether it's one individual who has a change management title, whether it is someone in marketing that does your internal communication. So whatever you have internally that is tasked with a similar sort of activity, leverage them. Get them on side. Use them also as your provocateurs, right? So I think that that's a great opportunity that you have. And getting a wider net of people that you can both go to for help as well as for dispersion and diffusion of the message, it's absolutely a great opportunity to take advantage of it. Cool. All right. I don't see anything that we haven't kind of maybe touched on a little bit already and we are getting near the end of our time. Once again, June 2nd, 2 p.m. Easter time, true confessions from a chief data officer. Ooh, I like that. Anyway, I'm going to kick it back to Shannon for the wrap up. And I thank everybody for giving us an hour of their very busy time to listen to us. We are always grateful for that and we hope you learned something. Shannon? Absolutely. Thank you. John and Kelly, thank you so much for this great presentation. As always, really appreciate it. And thanks to our attendees for being so engaged in everything we do and submitting such great questions throughout. Just a reminder, I will send a follow-up email within two business days. So for this particular webinar by end of day Monday with links to the slides, the recording of this session, and anything else requested, that was requested throughout. And, yes, as John mentioned, June 2nd is the next webinar and hope to see you all there. I hope everyone has a great day and thank you very much. Thanks, John and Kelly.