 And welcome, my name is Shannon Kemp and I'm the Executive Editor of DataVersity. We would like to thank you for joining this month's installment of the Monthly DataVersity Webinar Series, the Chief Data Officer, moderated each month by Tony Shaw. This month, Tony will be joined by Tom Redmond to discuss competing with data, strategy, and organization. He will, Tom will be joining us remotely. You won't see his name up there, but you'll definitely hear him as we move through the slides. And just 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. For questions, we will 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 DataVersity. As always, we will send a follow-up email within two business days containing links to the slides, the recorded session, and any additional information requested throughout the webinar. Now, it is my pleasure to introduce to you and turn over the webinar to the DataVersity founder and CEO, Tony Shaw. Hello, and welcome, Tony. Thank you very much. Thank you very much. Welcome, Tony, and hello, Tony. Good to have you with us today, Tom. So, DataVersity, I think, is a particularly timely topic. And so, in terms of the emergence of the Chief Data Officer role or any senior management role that has to deal with the strategic application of information today. DataVersity is really one of the first things we should talk about, but also often it's come at some point after the rain has come off the rails and we're trying to figure out what direction we're really going in with the sort of initiatives that rely on and good information today. So, you know, DataVersity should have questions like where you're going to compete with data. And if you are, then you're going to do that, whether you're planning to be an innovator or a follow-up. And there's good risk to be in on those categories. And can you execute on the plans that you have? What is your strategy for ensuring that you have the talent required to compete in the realm of DataVersity management today? So, with all that said, I'm thrilled to be introducing you today to our speaker, Dr. Thomas Redmond. Tom also knows the data doc is the president of Navasink Consulting Group. And he's helped really hundreds of organizations to understand the importance of data, to start the data programs, the data quality programs, and realize the sustained improvements that good management can bring. Tom is an internationally known lecturer. He's the author of dozens of papers, articles, books. He blogs regularly for the Harvard Business Review. And, in fact, in that particular role, he's become one of the most influential spokespeople for the data strategy movement, the decision of being data-driven. His fourth book was called Data-Driven, promoting from a most important business asset. And I would credit Tom probably being the first person to use that data-driven term to some point about the importance of data. And, of course, it's been borrowed many times since then. Prior to forming Navasink, Tom started and led the data quality labs at the labs under the authors of AT&T, and that's where he and his team were among the first to extend quality principles, data, and information on a systemic scale. So it is with great pleasure that I hand you over to Tom Redmond to talk about competing with data today. Welcome, Tom. Thank you very much, Tony. Thank you for that kind introduction. And thank you to everyone who's carved out an hour to talk about my favorite topic in the business world today. And that's data. I've been working on data a long time. And Shannon, if you would go to the second slide. And for most of that time, I felt like the little kid in the backseat of his parents' cars and mom and dad have looked up at the car and the plan is to go to grandma's. And grandma's is, you know, some way down the road. And, of course, about 10 minutes out of the driveway, you know, what did the little kid start saying? You know, are we there yet? Are we there yet? And in the data space, I feel like I've been asking, are we there yet for, you know, longer than I care to admit? And some years ago, I began to despair that we were, you know, we were going to get to data at grandma's. And, you know, but the last few years have been extremely encouraging. I'm going to explain why in just a minute. But, you know, just to go here on this slide, the data at grandma's is going to be everything. We think it's cracked up to be. There's going to, grandma's house is built on the solid foundation. It's high-quality data. The data we're not working day in and day out so we can conduct the most mundane operation or make the most mundane decision. It's going to be so good that we can actually trust it at any level from operational to day in and day out decisions to strategic planning. And we're really going to put that data to work. We're going to use those data to improve product and service. People from top to bottom are going to use them to make better decisions. Our organizations are going to be better as a result. And every now and then, one of us is going to find some real nugget data that leads to fundamental innovations and creates new industries. And the things that follow from that are, you know, the economy's going to grow. And think about how long we've been stuck with real growth. It appears to me that high-quality data is the collective of our best chance for real growth. Right? We can trust the financial system. Health care is better and less costly and we're all freer and safer as a result. I mean, that's a view of data at grandma's. And as I said, I'm now convinced we're going to get there and we're going to get there in my lifetime. And the reason I'm convinced that we're going to get there has nothing to do with anything that's going on in the traditional data space. It's what's going on in the conflicts that regular people are seeing with data in their day-in and day-out personal lives and work lives. So Shannon, if you could go to slide three, I prepared a couple of vignettes to illustrate. Slide three involves a junior executive. She happens to be from Columbus, Ohio. She's won some contest at her company and is in New York City for the annual sales conference. It's her first time in New York City and she's managed to bring her husband along. They're all excited about it. And it's a Broadway show. And it's the first time they've seen a Broadway show. All is great. And they work out of the Broadway show and her phone jingles and a message comes up that says, Christ Mojito's wearing a hole right around the block. And so here's the interesting thing. What do you think about that, right? Is that the most terrific thing ever, something that extends a glorious evening for this woman and her husband? Or is it just plain creepy that people know where she is and what she's doing to such an extent that they can make a great movie. They can make an offer like that. I don't know the answer to that. But that's a question that people in all walks are facing again and again. My convenient involves a family practitioner. And once the patient said, let's just say it's a guy my age, he's trying to do too much on the weekend. And he strained his knee and he comes in on Monday morning and the family practitioner's gotta decide what to do. And then in the past, just months ago, he, in this case with family practitioners and male, would have sent the patient off for an MRI. But lately he's gotten a directive from the insurance company and the insurance company has analyzed the results of thousands of MRIs for injuries similar to this one and concluded they don't lead to better treatments and they're not paying for them anymore. So what do we have here? Is this just the best thing ever that big data has allowed us to gain insights that make the family practitioner more effective? Or is it an encroachment on his domain? And again, reasonable people can be on either side of that. Many people may even think it's both. Shannon, if you go on to the next slide now, slide five, the third one yet I'll give is the rising middle manager and she's in the process of preparing her first presentation for the board. And in the process of practicing, we're addressing the day before, she notices this number that looks strange and so she calls her assistant in and her assistant looks at it and agrees it looks strange and so she sends him off to track it down. And he finds out that indeed the sales number for the widgets department is incorrect. He corrects it in her presentation and she's all ready to go. Goes off, gives her presentation, is a big success, right? And it turns out the correction they made was the linchpin of the discussion that follows. And she gets back to her office, she's so excited. She awards her assistant on the spot award, dinner for two, $200 at any place. She wants to go to eat and then she says to him last, you know, we better check those numbers from the widget department every day, right? Every time we need them, we'd better check them. Notice what she didn't do. She didn't call the widget department up and say, hey, we found a problem in your numbers. She didn't have the courtesy of doing that. She left others in her company to be victimized by the same data that she would. And in particular, the widget department, she didn't give them an opportunity to get to the bottom of the issue. What do you think about this one? Is this just great because our rising executive has been successful in her first big try at the board or is this just complete managerial irresponsibility by leaving others victimized? And not making it possible for a department to get to the root cause of the problem. Again, reasonable people can disagree on that. And so, you know, the next slide, Shannon presents another vignette. I'd like to go on to slide five. I mean, I've presented, I've talked about three of these things. I hope everybody listening says, if he can do three, he can probably do 30. And if I can do 30, the collective us can do any number we want. You know, things that are tough issues, the data is right in the middle of that everyone in their personal life or their work life is smack in the middle of them. I have a client who points out that, you know, no matter what discussion they're having inside his business data come up, they come up in every single conversation. So, what's going on here? And I've sort of, slide seven is set up as a point counterpoint slide. I mean, individually, this is just work, right? This is the tough social and organizational issues that managers get to deal with every day. You know, similarly on point, I mean, the successful, those who've won the war and developed the best product, they've always taken advantage of superior data, right? There's plenty of big data successes all over, right? And so, you know, so there's a way of looking at this where things are just swimming. But the counterpoint to individually is collectively these issues suggest something deeper, right? And yes, while there's good historical roots for data and the advantages of superior data, the advantages that stem from superior data, they're just exploding everywhere, right? And finally, as impressive as the big data successes are, if you really take a hard look at the financial crisis, what you see is a colossal failure of data. We see things that ended their way into mortgage applications that three steps later were bundled with something where an organization or an individual couldn't evaluate the risk. And the risk was far, far higher and turned sells. So now, on slide, I mean, here's my opinion, I think reasonable opinion people can be all over the map, but what we believe is that a full strength data revolution is brewing and it's not just big data. Now, the big data is what's making the news and companies are all excited about data scientists and so forth, but there's a whole lot more for every two big data things that are going on, there's a hundred little data things going on, playing out in departments and individual vignettes and so forth. Well, historian, but I can read and it does appear to me that all revolutions are chaotic, messy, and inherently unpredictable. Hang around for a while and then move at dizzying speed. Certainly that was the case to the quality revolution in manufacturing. I think anybody's gonna be in touch, it is gonna remain untouched. I don't think anybody has any job now that doesn't involve, it will involve enormous amounts of data and it will involve much, much more. And roadmaps, by the time people have figured their way through the data revolution and you can write those roadmaps, it'll be too late. The final point and really on point, especially here is the technology challenges to pull off what we need to pull off on data are tall, but they pale in comparison to the organizational challenges. So slide nine, I've noted the fourth most common things I hear is as I wander around and talk to companies. And the first is, we're data rich and we're information poor and you hear this in lots and lots of guys. What it betrays is that the organization has not really thought through how it's gonna use its data to never mind compete with the data. Another thing I hear all the time is, I've been in this industry 25 years, these data couldn't possibly be any better. And it really betrays for me an institutionalization of just accepting mediocre quality data. Accepting the workarounds and the high costs that come with them. Tom, you gotta keep in mind that we're much more siloed in other industries that I work with. It's one of these amazing statements that I have because people don't know what's going on in those other industries or with those other companies, but they've concluded that they're much more siloed. And of course, silos are the enemy of data here. And finally, some version often not even spoken, but so deep in our psyche that if it's in the computer, like data's in the computer, it must be IT's responsibility. And so these observations have led me to conclude at the top of slide 12 that these organizations are unfit for data and remarkably so. I started making this claim in public two or three years ago and I thought there would be an enormous backlash. I thought that people would really wish back hard to this agreement, but so far I've not seen that at all. People by and large, once this is stated, understand that it's capturing an essential truth about where we are with our organizations and data today. And you can see on the remainder of the slide, I've called out some specifics. I've translated the UI here into some more generic specifics, but the first, and I think this is really, really important, is we don't know how to compete with data, nor have many companies gained enough experience to actually even decide how they're gonna do that. We do not have the experience to know how to compete with data. By the way, when I hear the word strategy, to me is always in a marketplace connotation. When somebody says, well, what's your data strategy? That is the same as how are you going to compete with data. Strategy, in that sense, to me, does not mean high level plan, but how are you going to achieve advantage in your marketplace? We have the talent up and down on the organization. I think the McKinsey guys got it directly correct in their big data reports some years ago when noted that the country, the United States, and not including the rest of the world, I believe, but the United States was probably about 100,000 data scientists short, and a million switched on managers short of what we're gonna need. We've already mentioned that Tilo's getting away of data sharing, and quality is really an interesting one because there's lots and lots of examples and lots and lots of industries of quality done right, and the managers that companies gain for themselves when they manage data quality, but in too many, it's essentially unmanaged, and then the last bullet is, you know, those who've gotten responsibility for data outside of IT have simply done better, particularly in the quality sphere, and step one is getting it out. Now, I don't know, I look around and I read all the management stuff and stay current and so forth. My conclusion is working through these issues is the management challenge of our generation, and I want that capital T, capital H, capital E to be fully understood. This is not an important issue or one of the important issues. It is the most important issue, and part of the reason it's the most important issue is it creates such high leverage for the other ones. So let's move on to slide 11. I want to just summarize this up. I mean, and here's the challenge for leadership. By leaders, I don't mean just people in senior positions inside organizations, but those at any level, in any spot in their company who are willing and have the courage to step in front. And the challenge in a nutshell is that a full-strength data revolution is brewing, but today's organizations aren't ready. And the solution's not gonna wait for your organization to get ready. You're gonna have to start doing that on the fly. At high level, there's two specific pieces of advice. The first is sort of how you're gonna compete with data. And the second is build organizational capabilities. So let's talk a little bit about these things. Tony mentioned my, so go to slide 12, please, Shannon. Tony mentioned my book, Data Driven. And one of the things that I did there that I was very proud of and has held up pretty well was I tried to understand the different ways there are to put data to work. And so I brought that slightly up to date since Data Driven was published. And so you can see I think there's about 18 distinct ways to get at it and what's right for organizations. There are gonna be a lot of different ways to compete with data. And as best I can tell, here is the best list that we have now. My fervent hope is that in about 10 years somebody writes a paper and they go, well, now, Redmond made a big beginning, but he missed this, he missed this, he missed this, he missed this, he missed about 27 new ways to have developed data to work, to compete with data. So on slide 12, now 18 different ways, I think these boil down to four basic strategies. And the reason I think this is because three of these strategies are basic strategies in anything. They're sort of the innovation strategy, the stay closest to customer strategy and the be the low cost provider strategy. And those translate as creating advantage through innovation through the big data, through the advanced analytics and doing that and continuing to do it. It's not about finding one nugget. It's about building an organization such that you can find big nuggets and little nuggets and so forth and get those nuggets out into the world and create an advantage for yourself. The equivalent of the customer strategy is a content strategy. The low cost provider is the simplest way to take cost out of most businesses right now. It's simply to improve data quality. There is so much hidden rework, so many hidden data factories in companies today that high quality can be a money to be saved. It's just enormous. And then the fourth one is building a data-driven culture and data-driven's been around and talked a little bit about it in my book. I mean, I think the short way of understanding this is we use data. We bring data together into the decision-making equation in a smart way. We combine it with our intuitions and our tacit knowledge and so forth to make better decisions, top to bottom, bottom to top, left to right to left, individual settings and group settings. And I don't really see anything equivalent to that in the classic strategies, but I'm very encouraged that that strategy will be available to some. I also think it's going to be the most difficult to pursue. I don't want to spend too much time on this. I think the basic organizations are going to have to do in the next several years is they're going to have to gain some experience with all of these strategies. And I have to understand what their competitors might do and what the startup down the road is likely to do and pick horse that makes the best sense for them. Before we kind of dive into what you can do organizationally about this, to mention one content strategy because people may not be too familiar with it. The others have sort of been around. There's more on them. But the basic concept of informationalization is to make existing products and services more valuable by building more data into them. The obvious example is the GPS, which first got built into cars and obviated the need for the map. But a car is more valuable because you can put into it where you want to go and it will know where you are and sort out turn-by-turn directions for you. It's a big-time strategy. Michael Eisner, one of the top half-dozen executives of our era has pointed out that content is king. And informationalization is not the only content strategy but a big one. And as you work with companies, so far we haven't come up with anything that couldn't be informationalized. And you know what's my favorite example of my mentor, Blaine Godfrey suggested, which was the hospital gown. And there's no more mundane product than a hospital gown. But the folks that in the state are working at informationalizing it. The building sensors in it is continually monitoring the pain of the blood pressure and various other devices. And there's a little RFID device in there that's transmitting on a continuous basis the results of those measurements to the nurses station and sending alarms when needed. Information is available to all. And this is one of the reasons that they like it. Everybody ought to be considering this strategy. It's not require... You don't need data scientists, right? You don't need massive changes to your organization. And everybody should be thinking about an informationalization strategy. It may not be right for them, but they should be thinking about it. One of the reasons they may not be right for is they don't do it right. You're just going to know the customers. They're already in information overload. And one of the things that's not an example of informationalization is when it adds to this, to learn more or visit our website at... Well, people know they can visit your website by now. And you're just dumping a lot of stuff on them that they don't need. So point one is going to be to have to sift through your strategy and figure out what's right for you. And by the way, this isn't just on the company level. This can be in the department level. It can be in your work group level. It can be at any level. What are some of the specific things we're learning organizationally? And so I have a half a dozen slides on that. Shannon, if you're on slide 15 now, first is as far as I can tell, high-quality data is prerequisite. I mean, it is both a legitimate strategy on its own, but for any of the other strategies, you may need more quality data than we have. You may not need super-quality data. I like to take every last nickel out of your operations. But you do need higher-quality data. And of course, the reason is in professionalization, you expose data. And when you expose the data, if it's wrong, gang or customers, we all know examples of where that's happened with GPS and people providing the GPS. Don't send themselves any friends when they send the drivers into the middle of the ocean. Now, the other good thing about data quality is we know how to do it. There's lots and lots of examples in company and lots of industries that have really made substantial improvements. And I want to cut through everything on slide 16 and sort of look at it in a way that would explain the last couple of years. And it goes like this. From a perspective of a piece of data, only two moments in its lifetime really matter. One of them is the moments it's used. And the moments it's used, it's either fit for purpose or helps complete the operation, make the decision or make the plan or whatever is going on at the time at the moment of use. It either does so in an appropriate way and to be a little bit simplistic or it doesn't. It has to be real work. People have to go get confirmatory evidence, whatever it is, right? So that moment of use is very important. It's when the data either adds value or not. But when that data is going to meet those needs at the moment of use is by and large determined at the moment the data is created. And there may be a tangled path that's illustrated by that greenish arrow from the moment of creation to the moment of use. But whether the data is of high quality, fit for purpose, et cetera, et cetera, is determined at the moment of use and more or less set at the moment of creation. So what do we have to do? We have to have those two moments talking to each other. We have to have the moment of use saying, hey, this is what I need there. And if that doesn't happen, then the moment of creation, whether it meets the needs or not, is just pure dumb luck. And so the whole reason we have data quality management is to connect those two moments in time, right? Now, our organizations are complex. I mean, if we just had the man on the left and the woman on the right and say, hey, you guys talked to each other, right? Well, making that happen for the sale and the different organizations and so forth across space and time is the reason we need data quality management. Now, one final point on this slide, and it gets to where should management for data quality reside. And I'd simply like to point out that neither of the two moments that matter occur on IT, right? It does not make sense to have lead responsibility for data quality reside at some point other than those two most important points. Shannon, if you go to slide 16, when it's done right, this is the reason that data quality is such a strategy on its own for providing a low cost, being a low cost provider. And also, you know, just accelerates the other strategies. And you can see the picture is what happens when it goes right. There's a lot going on in this slide from setting requirements to getting some measurements and there's four improvement projects buried in there. But you can see that, you know, from the time this started until about 18 months later, theirs have gone from sort of, you know, now 50% of the data is okay to 95% plus percent of full order of magnitude improvement. In particular example that I'm giving average savings of $500. I mean, it's just that those who are leading your data quality programs and responsible for it make pictures that look like this one. This is what we're trying to achieve and time and time again we're finding that it's fully reasonable to expect. She would like to slip to slide 19 now if you would. The next thing we're beginning to learn from an organizational question is how to answer where do we put our analysts. And what you can see is a spectrum of possibilities on the top scale is the sophistication required in the analytics. Basic process improvement requires a bit of analytics. And so that's over on the far left and then if you're trying to really compete through innovation, you know, make the discovery of your industry as equivalent of the discoveries of the Higgs boson, then you've got to be over on the right. And our experience is teaching us that for things closer to the left, the analytics team and the analysis ought to be closer and closer to the line and from things further and further to the right, which require greater specialization, greater time going down more blindly and so forth. The organization, the analytics organization ought to be set up more or less in a permanent lab without day in and day out responsibilities like those in the line. And of course, there's things in between on the spectrum and part of the challenge is to figure out where you lie and get yourself put your channel list in the right spot based on where you lie. The next thing we're beginning to learn is expressed on slide 20 and the top of the slide says think end to end. And my view, I'm sort of calling this end to end thing the D to the fourth process, right? Data, discovery, delivery and dollars. And the basic idea is in terms of your success at competing with data, it's more or less going to be dictated by how you do on the worst of these things, right? So we've already talked about you need some high quality data, right? You need to be able to find something truly interesting in it. You need to get the results to a decision maker into a process into a new product or services or whatever it is. And you need to figure out how to make money from it. And individually none of those are easy and selectively they're really, really hard and so you're going to get in front of that then from the very beginning it's really important to begin thinking end in. I find the experiences and what was learned in the good old fashioned industrial labs really, really pertinent here. So slide 21 is a slide that I've spent the last seven or eight years really trying to understand. And I think it's the most important point in competing with data. It's the one that I frankly at least understand even though I've been thinking about it so long. But the best I understand, when you really want to compete it helps to have something the other guy doesn't. And you know in just good old fashioned industrial, industrial in the industrial world it helped to have a process that others didn't. It helped to have a core of knowledge that others didn't. It may help to have a patent like a patented drug that others didn't. You could protect and you could make and sustain competitive advantage for some time. And it appears to me the same is going to be true for data. It's going to be helpful to have some data that others don't. And so part of the trick is to sort out which data of yours are uniquely your own. And which you want to keep from becoming standard. Now by the way, I mean you get to define your data any way you want. And your business processes create more every day. And so you get to build as much subtly and nuances as you can in your data. I mean that's not universally true. You need to standardize some data so you can communicate and so you can keep operations inexpensive as you deal with others. But having something and a small fraction of your data that others haven't is in my view going to be key to long term success. And by the way, I mean there's deep historical roots for that. So if you look at companies like S&P, right, they have something called a Q-SIP. Nobody else has a Q-SIP. And there's plenty of other examples like that. I think this is one of these things that organizations have to really start thinking a lot about. Access to data that everyone else has, you may be able to get an advantage for a few minutes, but others will copy you and you won't sustain it. The next thing that we're really learning is, this really is about talent. It's talent up and down the organization charts. It's about making everybody smarter. In my view, I did a little look at the people that I've known that have considered data scientists over my experience at Bell Labs and since then. And I've known a number of good ones, but the truly great ones are in short supply, the truly great ones who just ask different kinds of questions and think about things in new ways. And it's really helpful to have a few of those that we've already talked about for every manager, every good analyst you need, you need dozens of good plus managers. And just to support that, I think a clever analysis that I've ever known about in my time working with data that actually bore fruit. The analyst got a lot of recognition, but there's some manager, some owner who's the real unsung hero who took a chance and put his or her career on the line and said, let's take this forward. And of course, leadership is essential in revolution. And sooner or later, the stone cold sober evaluation of what we can pull off is just absolutely essential. We're covering a lot of ground and that's how I design. The last thing I really want to suggest we're really beginning to learn, and I think this is essential, is this is going to take a lot of people and it's going to take a new structure. Five or six years ago, I began to ask myself, what are some of the other things that companies use as assets? And the obvious answer is people and financial resources. And so you look at how they're managed and then say, okay, what does that imply for data? And I found thinking about the way people are managed most useful in thinking about where we're going with data. And so in people management, you sort of deserve these traits. I mean, the thing that really is a corporate HR department, it really does have some line work like secession planning and setting the pay scales and picking out the insurance we're going to use next year and so forth. And that's one level. And then there's departmental HR which may help people conduct their searches for candidates and deal with problems and set up specific training programs for the departments and so forth. But the thing then that really strikes you is that every single manager is responsible for the day in and day out people management. Corporate HR and departmental HR are doing high-level and policy things. And real management is going on every day in line with your managers. And so if you sort of take those traits and say maybe you can take more and then go over and say, okay, what is it going to take to manage the data assets? And you get these parallel answers to that. And you can see I've written them down. By and large I mean it is people and organizations that create data and use data day in and day out. And so that's where the responsibility to create high-quality data is going to reside. And that's where the responsibility to put data to work in novel ways is going to arrive. And that's the essence of what this is all about. We spend a lot of time I think thinking about, well, you know, what's the Chief Data Office going to look like and where we really ought to be spending our time. We're just going to go on in regular departments and how are we going to create the Chief Data Office to make them more effective. So Shannon, if you could go on to the last slide, which is slide 24, I mean, I hope I've excited you. You know, we're going to get the grandmas. And we're going to get the grandmas in my lifetime. Organizations are going to be there in the next half, a dozen years. Others it's going to take longer. And don't get the grandmas. We're really not going to have to worry about it. I mean, it just simply will not exist. And I hope that's one way that that scared you. I mean, the leadership challenges is really, really clear. These things that are going on in these conflicts that everyone is facing are real. They're all encompassing, right? People in the dual organization are the least of it. And we're not ready. And we have to get ready. The revolution's not going to get away. For most, it's too soon to set a strategy. You just don't have the experience. But it is time to start moving on that level and thinking about what's the way for us to compete with data. Quality's prerequisite. And step one, you know, move responsibility for data quality out of IT. I don't know of any organization that did that that then moved it back. See, impairment, think and then, right? Start out which data are strategic, which you can protect, which will give you, which is uniquely your own. And then begin to build the organizational capability we're going to talk about. I mean, the, you know, if I was going to say one thing, is the people, you know, and particularly people with data in their title is being courageous. You know, if you make a decision and go off in a particular direction, there is a likelihood you're going to be wrong and you're going to get hammered for it. But if you stay where you are now and don't make a move, it is a rock-dead certainty that you're going to get hammered and be wrong. So if you want to increase the likelihood that you're going to stay alive, be smart, pick something, and then move and move fast and aggressive. So thank you for your kind attention. Shannon, I believe we have time for questions. And, you know, pick out what is important now. Well, thanks, Tom. We have about 15 minutes and we can deal with numerous questions. So I'm going to invite everybody to submit your questions in the little Q&A window in the bottom right-hand corner. It's the easiest place for us to pick those up. I'm going to ask a question to get us started though, Tom, because you do a lot of civil consulting, boardroom consulting, and I'm just guessing that you've seen a change in understanding and a change in the sort of questions that women at C-Suite are asking over the past five years. Can you tell me how it's changing and is the level of understanding appropriate for the sort of issues that are being faced or is there still a lot of misconceptions? Well, my experience is I think that there's a few organizations where boards and C-Suiters are really asking the question. You know, the true tops of corporations. I mean, that's pretty rare. In terms of, you know, like the top people I'm dealing with, I mean, it's certainly true that the level of people who are asking the questions has gone up and the organizational level of the questions has gone up. And then the questions that I'm asking have shifted a lot more from the how do we to, you know, what's the organization we have to build? Where are we going to put that? Who are we looking for to lead that organization? Right? How much money are we going to have to spend? How fast can we possibly go? The questions are, you know, they're more difficult to answer. They're more strategic. They're very little, you know, of how to and more of the who to kind of variety. Does that make sense? Sure. Ashley and I segue to a couple of the questions that Richard has asked. I'm going to jump to the second one here and ask you, and I know you have some opinions, and I think Richard does too from what I recall of some of his LinkedIn posts. But what's your feeling about the role of the chief data officer? And what is its role or not? And how do you compare that role to, say, the CIO or CTO role? Well, I do have strong opinions on this. I attended a chief data officer's conference this summer, and I don't want to say which one it was, but I was so mad after it. I had to hold back before writing my blog because I was angry. I wanted to really think it through. In all companies and lots of things, the C thing has lost a little bit of its meaning. I think you need a C-band when you are truly committed to the idea that we're going to figure out how we're going to compete with data. That it is a business priority to do so, that the bullet is recognizing that this is either a real issue now or it's an emerging issue now, and that you have the stomach to at least proceed. I think it's the same as anytime something's a C. If a C is a C for human resources, you're effectively saying, look, we need people to compete. If it's a C for finance, we need to manage our finance as well to compete. And frankly, most CIOs in the last few years don't meet that standard anymore. They've been pushed down, and maybe not pushed down a name, but pushed down in terms of level of responsibility. So this is my view. I don't think you should hire a C until you're ready to take the plunge. It's different if you're just going to stick your toe in the water, and I do think you ought to be sticking your toe in the water, but you don't need a C to stick your toe in the water. Okay. As you can hear, it's recently, Nainthi, and I think I understand the question. So I can read it out if we need to interpret it. We can backtrack a little. So, to give you a question, today, many companies are re-stressizing on the basis of technology, innovation, and from an operational standpoint. Is it possible for a company not to compete on data and only focus on technology, innovation, and operations because it observes that that is what appears to be happening in the majority of industry? So, I mean, is the question, is it possible to ignore the data? In part, my interpretation here is that you had said at one point in your presentation about the strategic advantages of owning data or having access to data that nobody else did. And I think the question is prompted by the notion that strategic advantages is often gained by operational requirements or just being able to execute better than the next person whether you're using the same data or not. Yeah, so, look, I do think that if you want to take cost out, if you want to compete in being the low-cost provider, then quality will help you do that. It is clear, but that's a day, I mean, that is a particular day to focus. Now, whether you drive that, you're thinking about that through the technology or aligning with the technology. Man, I don't have a feel for that. I do know that almost everyone who's made the quality improvements that showed on that in the middle of the day there, those are magnitude improvements, they did that outside of their tech organizations. I'm not sure I understand the question. I think everything is possible, but I think it's risky to ignore data. And I'm just kind of thinking about the industries I've worked in and ever ignoring data. Okay. We probably have more questions than we're going to be able to deal with right now, so I'm going to have to select a couple of last sequence. This one, though, I think is probably typical in many organizations. Steve is asked here, how senior management is ILO driven, gives examples of finance, HR, et cetera. There's no overall data champion. What kind of information in terms of your data is valuable and can be leveraged better would be useful for senior management to hear? Let's suppose this means senior corporate management, which does manage this ILO. I believe that's the intention of the question, yes. Across those different ILOs, what does senior managers need to understand in order to get out of this ILO driven mentality? Here's the way I'd think through that. Most data exists in ILOs and it really does never leave. And managing that data inside the ILO is perfectly fine. We set up ILOs to create some efficiencies and build some expertise, and the data that's just used inside those ILOs, I mean, it can stay there. The problem is that most organizations, in a lot of the value they create, or in some cases the understanding of what the organization is doing, the management of across the ILOs depends on data that needs to cross ILOs. You know, I ran into an organization just the other day and in order for them to deliver something to a customer, Department A has to do something, Department B has to do something, C has to do something, and D has to do something. And senior management needed to understand the end-in time to get started A and get through C, and so they wanted to get a cycle time. And what they found was that A knew how long it took to get through A, B knew how long it took to get through B, and so forth, but there was no understanding of how long it took to get from A to B and from B to C. And so the way I think about answering the question is, is find out that there's something that senior management can't do because it doesn't have a window into the connectivity of the ILOs. And that's the data that needs to be managed on a corporate level, right, to manage the corporation. And in most organizations find there's tons of that stuff. Okay. This is front-layer. The data that needs to be managed on a corporate level is corporate data, meaning it's used for corporate purposes. You point strongly to that it should get out of the data business. So the data should be managed by the business rather than by IT. You know, as a practical matter, I think C inevitably gets thrown into the technology questions and the news that the business doesn't yet know about how to manage its data. My question to you is how do you sort of structure things in that context so that IT can be useful but not take on the responsibility that the message that you're delivering there. Yeah, so let me first point this at IT. I have a director of marketing now and she's been insisting, Tom, everything you say comes across as your anti-IT, and I really do not. I think when it comes to data, IT has been asked to do a job that it is just not in a position to do. From a data quality perspective, it's neither a creator or a customer, so it's like trying to correct all these errors made by others. And I think that IT organizations, the smart ones anyway, what they should really do is recognize that. We can't really do this well and take lead responsibility for educating their business partners on why they can't do it well and on the silo level and on the corporate level, helping their business counterparts figure out a better place to manage the data and take the lead for getting management of data out of the IT organization and where it belongs. And so they can concentrate on what they can do well, which is the technology. Tech departments really fall down with this data thing. So Tech departments got to realize they got a lot of skin in the game, and they can show, they're probably in the best position in many companies, to show leadership and where management for the data belongs. We're looking to wrap things up here, Tom. Before we go, I'd like to mention to everybody that Tom's fact will be joining us amongst a couple dozen really top world-class experts on data strategy at the CDO Vision Conference taking place in Austin, April 29 through 30. It takes place alongside the annual Enterprise Data World Show. So I hope to see many of you there. Just hop online and request an invitation. And Shannon, I'm going to turn it over to you again, Tom. Shannon, you're going to tell everybody about how we'll follow up with things and they can get copies of your slides, et cetera. Absolutely. And thank you, Tony. And thank you, Tom, for this great presentation. And as always, thank you to our attendees who are so engaged and so interactive and everything that we do. We just love it. And just for everyone, we'll be posting the recording of the webinar on Dativersity.net within two business days. And I will send a follow-up email to everyone by end of day Thursday with the links to the recording and a link to the slide. And again, thanks for attending today's webinar and I hope everyone has a great day. Thank you. Thank you, Tom. Bye-bye. Everybody else.