 Hello and welcome. My name is Shannon Kemp and I'm the executive editor for Data Diversity. We'd like to thank you for joining today's Data Diversity Webinar, Data-Centric Strategy and Roadmap, sponsored today by Hewitt-Packard Enterprise. It's the latest installment of a monthly series called Data Ed Online with Dr. Peter Akin, brought to you in partnership with Data Blueprint. Now let me give the floor to Britt Hafner, the webinar organizer from Data Blueprint, to introduce today's speaker and webinar topic. Britt, hello and welcome. Hey, thanks Shannon. Hello everyone and welcome. Thank you for finding the time and your busy schedules to join us in today's webinar, Data Centric Strategy and Roadmap. Today we would like to extend a big thank you to our sponsor Hewitt-Packard Enterprise as well as a big thank you to Shannon and Data Diversity for hosting us. We will get started in just a few moments after I let you know about some housekeeping items and introduce your presenter. We have a one-hour presentation followed by 30 minutes of Q&A. We will try to answer as many questions as time allows, but feel free to submit questions as they come up and throughout the session. To answer the top two most commonly asked questions, yes, you will receive an email with links to download today's materials and the webinar recording so you can view it afterwards. These materials will be sent out within the next two business days. You can find us on Twitter, Facebook and LinkedIn. We've set up a hashtag data ed on Twitter so that if you're logging in or logged on, feel free to use it in your tweets and submit your questions and comment that way. We will keep an eye on the Twitter feed and we'll include answers to these questions in our post session email. Now let me introduce you to our presenter Peter Akin. Peter Akin is an internationally recognized data management thought leader. Many of you already know him or have seen him at conferences worldwide. He has more than 30 years of experience and has received many awards for his outstanding contributions to the profession. Peter is also the founding director of Data Blueprint. He has written dozens of articles and eight books. The most recent is Monetizing Data Management. Peter's experience with more than 500 data management practices in 20 countries and consistently named as a top data management expert. Some of the most important and largest organizations in the world have sought out his and Data Blueprint's expertise. Peter has spent multiple year immersions with groups as diverse as the U.S. Department of Defense, George Banks, Nokia, Wells Fargo, the Commonwealth of Virginia and Walmart. He often appears at conferences and is constantly traveling and today we are fortunate enough to have him in the office here at Data Blueprint. Always a pleasure, Brett. Happy New Year to everybody. It's good to be back online after hopefully a nice holiday break. The topic today is strategy. Good place to start the new year out. Most of you know that I do an awful lot of these particular strategy talks with my business partner more than 20 years, Lewis Broom. He couldn't make it today, so it's just going to be me, but I certainly want to acknowledge his very substantive contributions to this particular talk. Today we're going to talk about poor parts of looking at a data-centric business strategy. The first one is really understanding the business needs because whatever we do with our data should be focused on supporting the organizational data strategy. If it isn't, one might ask the question, what is the purpose of it? Second is looking at the organizational capabilities in there. Shorthand for this one is a tool in the hands of a fool. Come back to that in about 15 minutes. Then we'll look at round one data imperatives. This is really key because I call them round one or phase one with the idea of explicitly letting people know that we expect to be doing this on an iterative, ever-refining basis. I have not yet worked with an organization in 30 years that has maintained the same strategy forever. I'm sure there's some out there, but I haven't worked for them. Maybe their business models are a little bit easier and they don't need our assistance for it. Then finally, we'll finish up at the last quarter of the hour at the implementation roadmap and really talk about the need for a balance in your roadmap because if you air too much on one side, you will be perceived as more of a science project and not really implementing something that's useful for the organization. On the other hand, if you deliver too much tactical stuff, you'll never build the capabilities that you need to have in order to get the organization running. We finish up at 3 o'clock with the Q&A. I always look forward to speaking with everybody about that. Let's dive in and start talking about understanding business needs. I show the overall framework here. This is going to guide us through the entire presentation. By the end of it, you'll have this thing fairly memorized. First piece of it, as I said, understanding the business needs. What this really means is that you need to understand the business. These are the components that go into understanding the business. A lot of people don't think this applies to them, but it really is key. There is a reason that the organization that you're working for exists. It has a mission and a brand statement and some component where it's looking at all of these things in order to try and make its existence there for the shareholders, for the management, whatever it is that we're working at. Then we want to ask, in order to exist, it exists to do something. We want to know what products or services the company produces and sells. Again, this needs to market positioning and competitive advantage that are in there. Then how the company does it? How are they going to deliver? One of the funny things that we've done over the years is chuckled because Amazon has gone full circle on this one. The company originally was doing this because it had no bricks and mortar. In fact, both Britain and I live close enough that we can get the two-hour delivery directly from Amazon in there. How the company does it, they're back in the bricks and mortar business on all this. What this does is it gets us to what we're really trying to find out what does the business need in order to run and data is going to be a component of it increasingly in particular. It's important also to understand a little bit about business strategy. We reference Michael Porter in this. Porter's done a lot of work in that area. If you Google him, you'll see a lot of things that are on there. The idea on this matrix here is that you really can't be all things to all people. This is a great way of doing this. If you're not familiar with this as you're familiar with your data management environment, you might want to wander down. There's probably a group called strategy or certainly the policy levels will be able to answer these kinds of questions. For example, is your product in particular here looking at the ability to sell low cost or is it differentiation? There are different companies that fit into these. I'll throw a couple of examples at you in just a minute. Does it do this for a broad range of buyers or a narrow range of buyers? How specifically focused are your product? Are you selling rubber bands and paper clips? That's one thing. Or are you selling something that people can't get other places? Cost, are you competing on cost? Clearly Apple, for example, with their focus on premier brands is not focusing in on cost. They try to keep the cost the same every year so that you don't feel bad working over 600 bucks a year for your latest and greatest iPhones on that. Again, all this speaks to market scope. When we move now into this, let's look at a couple of examples. Of course, Walmart is going to fall into this category. All of you listening to this call know what Walmart's business strategy is. It's four simple words. Every day low price. The fact that you know the strategy and they know your strategy, everybody is okay with that. They'd much rather you know the strategy because it actually talks about the way they do it and how they deliver their business. So Walmart's going to be a category of broad range of buyers but a lower cost. When we look down at Dollar General, they're also lower cost but they're focusing on a narrower buyer segment. People that are looking for specifically dollar types of items in there. Again, trying to chip away at perhaps portion of Walmart's business is there. When we look at differentiation, Whole Foods would love everybody to shop there. Lewis actually jokes and calls them whole paycheck on a regular basis but they certainly are going to be looking at a broad differentiation. Again, a specialty cheese shop like Murray's Cheese in New York City focusing on a narrow buyer segment of people who just go shopping for cheese. Personally, I've never done that but I always like the cheese that goes into there. Then we throw another category of Trader Joe's. You can sort of straddle the middle if you try and do it really, really well. But what this gets us back to is one of the favorite questions. This is one of our favorite TED talks. I've given you a reference to it in the lower left-hand corner there. A fellow named Simon Sinak. He's made an industry out of doing this and his message is very simple. Everybody tends to be very good at describing what it is they do. That's really good because we tend to do it so we tend to be able to talk about it. But when we talk about how we do it, we don't end up being quite as good at it and it doesn't tend to be the first thing that we think about. People tend to be more concrete and focused on it so they focus on the what. How? Of course what Simon wants us to focus on is the why. That tends to be the area that people are least expressive about, least able to talk about in a regular ongoing session. So the message from this is to really focus on the why first and the other two parts will come later. Most organizations absolutely overly complicate their strategy. It ends up being things where people say, oh you need an MBA in order to understand this, but what it really amounts to is a strategy that winds up on a bookshelf. It's absolutely not useful and you've got to be able to have strategy that everybody can understand. Again, focusing back on Walmart, you all know Walmart's strategy. Every day low costs. That is a terrific way to do it. Everybody understands what it is they're doing and they don't need a three-ring binder or a bunch of consultants to explain it to everybody. Now strategy as a concept has actually only entered our vernacular around the beginning of the 1950s. Before that it was largely a military-focused concept of how we're going to beat the bad guys in a military action, but we've now come to accept that it has a lot of good business applicability as well. My favorite definition is Minsberg's a pattern in a stream of decisions and I'll give you an example of that right at the moment. When you're facing competition that is bigger than you, what is one of the strategies that you can use to employ in that and the answer is divide and conquer. I'm going to show you a little bit of a sort of video thing here just to explain that mainly because everybody understands how simple it is. The two larger armies, the red and the black, we're facing Napoleon and one of the things Napoleon understood even though he's facing a larger army was that when you hit an army it tends to retreat across its supply lines. So the supply lines are shown here in the red and black dots and his plan was very, very simple. If I hit them right in between the two of them, which means I have to have good intel on where each of the red and the black forces are, they will tend to retreat in two separate directions along the lines of their supplies. I can then beat up one and then turn around and beat up the other. That simple strategy won him the day there and having troops that understood that strategy obviously was key to it. You can imagine in the heat of battle you don't have a lot of time to do very complicated planning so a simple strategy worked really well there. Another example of this is Wayne Gretzky the soccer great also has a very simple strategy. He skated to where he thought the puck would be. If you're following the puck around on the ice, the puck goes very, very quickly and it becomes very, very difficult in order to catch up with it much less get in front of it or be in a position where one can score. So the essence of an organizational data strategy is that it should be absolutely simple. I say 10 pages max, one page is wonderful. The key is, again, you're looking for an easy explanation. Remember outside of data most people do not understand or appreciate how it is to do this. So you've got to have something that you can put in place with an elevator pitch. Again I'll put a couple of porter quotes up here. The essence of strategy for example is choosing what not to do. One of the things that I've looked at several organizations is that they're trying to do too much at once and it's simply not possible to do all of the things simultaneously but doing one thing well is good which means strategy often is not just trying to do just the one thing well and not able to do all the other simultaneously. Similarly, strategy at a very high level is should be able to be implemented then if it's simple enough across a multitude of IT projects. I did some work for one organization at one point where they told me at any point in time they have hundreds and hundreds of IT projects going on. I said that was terrific or were they sure that all 100 of those projects were able to be in fact focused on organizational strategy at the time and they kind of went hmm okay that's an interesting question I'm not sure. Of course the real question is if you're not focusing on strategy what are you doing? So again a high level of abstraction that you're able to do this and really if it doesn't support the organizational strategy where people look at it and say oh yeah I got that then it's going to be very very problematic. So another porter quote here the essence of strategy is choosing to perform activities differently than rivals tend to do. You've got to be able to explain it why data is helping your organization create a competitive advantage. Maybe it's adding value to products and services maybe it's enhancing the customer experience. We hear the word transparency a lot certainly government organizations want to do transparency data is all about transparency if you don't have the data you have no transparency. High quality data enables organizations to do more with less and can help us creatively disrupt excuse me disrupt how we end up doing work on this. Finally everybody gets the data is increasing the internet of things all sorts of competitive things are out there to help increase the flow of data all around this and I'm going to give you a little bit of consulting advice here you can sit in any meeting that you want and say the following and you will look really really smart. There will never be less data than right now so let's move now from our understanding the business needs to getting the current state of organizational maturity and why data strategy has been kind of hard. Now I'm back to our framework we've talked about business needs and out of business needs everybody says okay great then what I should do next is come up with a solution and I'm sorry to tell you that that is absolutely wrong. Most organizations try this and they fail absolutely miserably. What we'd really like to do is to look specifically at the current state of readiness not just your data management group not just the IT group but the organization as a whole because if we don't understand what the organization is capable of we have no ability to hand it the right set of tools techniques people process and technology. So I put a big X there to make sure we just got a placeholder and we'll come back and look at this in just a second. One of my favorite sayings these days is a good technology in the hands of an inexperienced user rarely produces positive results. I had somewhat many cases like handing a key to a Tesla I don't know how many of you have had a chance to ride in a Tesla much less dry one but they are phenomenal pieces of engineering but they can end up with some very poor results. As a result of that as well that is actually a Tesla stuck between two buildings have no idea what the story was there but ouch. And I say that because in many cases organizations go out and purchase technologies. This is a piece from my good friend website on this Mr. Ferguson here and it's a great explanation of how to build an integrated master data management system on this. The challenge however by focusing only on the technology is that most people are not really aware that master data management rarely succeeds by itself. In our experience we found most often that you need to add at least two other components in the case of master data management you really need also to have good data governance and good quality data in order to make it succeed. Because if you don't have these other two components in place to do the kinds of things that you need to have the MDM technology works absolutely terrifically. However if people don't understand that you can't just use the MDM as another place to store data and I've heard this many many times when I'm working for organizations and they will say where are you going to stick that data well I didn't know where else to stick it so I stuck it in the MDM. That's not what we want to do and of course the other part of it is that we understand from an architectural perspective master data should be higher quality data than your typical organizational data and governance is the only way in fact you're going to do that. Again these interdependencies are largely unknown so governance makes the case for and is responsible for implementing data quality which is a necessary but insufficient prerequisite to success again of the master data initiative and the MDM capabilities help to constrain the governance effectiveness around that process. Again not knowing these pieces here what we're trying to do is to say to the organization you shouldn't buy expensive complex technology unless you have the stomach for learning how to do it. Again crawl walk and run is the way it's usually articulated in that process. Similarly from an understanding the organizational capability perspective most people don't realize there's a absolute correlation between Maslow's hierarchy of needs and organizational data management capabilities. Most people remember Maslow from high school it's the idea that in an oversimplified session if you have food clothing and shelter needs at the bottom of that pyramid of needs you're never going to get to self-actualization. Each of these levels needs to be present and solid before you can move to the next level so getting to self-actualization one has to have a steam if one doesn't have a steam one will never get to self-actualization. A piece we use in technology in today's environment we call it flow. We'll understand it in exactly the same way. Our data management practices are similar everybody's focused on the top half of this golden pyramid that I like to call the golden pyramid of data management practices may are a technology-based focus but that is of course just the tip of the iceberg and if we don't understand the foundational practices that have to go along with it which really are organizational capabilities we can do these other things but they are problematic in fact it's also important to understand the linking the arrangement of the architecture that these practices have and what I'm showing you in this example here is that governance strategy quality and operations are strong practices but the data platform and architecture in this instance is considered weak. Now we did a blueprint get questions all the time and people will say to me well Peter I understand you need to do these foundational pieces but my boss says it's got to be done by Friday. The problem is without these foundational practices everything takes longer costs more delivers less and presents greater risk to the organization that if instead you crawl walk and run your way up to the top of that pyramid. Similarly it's important to understand the DMM structure of these integrated practice areas so these five that I'm showing here governance data management strategy data quality data operations platform and architecture are all the same that we're in the basement of the basement foundation of that pyramid on the previous page that's here and what it means is from a data strategy perspective we're going to manage data coherently as opposed to at the workgroup level try to elevate that practice up to the organizational level there is a professional category now of data managers we call them data governance professionals in this process and that data needs to be maintained so it is fit for purpose it's effective and efficient we're never going to get to a hundred percent correct data so let's not try for it but let's find out what works and what is useful in that process. Platform architecture implementation data operations have been understood in the right life cycle are all critical pieces in order to do this and of course you need some level of organizational support now I've just given you in one minute here the subject of one of the webinars we're going to come up with Melanie Mech and I are going to do this one in May where we'll spend an entire day on this topic working into it but the bottom line from a strategy perspective and from understanding organizational perspectives is that these five areas are linked by a weak chain method which means that each able each k excuse me each area is able to be rated from a maturity perspective and if we understand that the weakest link nature of the reporting is key to this so that this entire foundation is only as strong as the weakest link in this foundation this gives us a very good set of targets to do now that the scoring on this is kind of harsh it's kind of like getting a D in high school and being called a D student for the rest of your life and here we're looking at a sub area of data governance for example and we're seeing that this one area means that their entire data governance practice can only be as strong as the weakest area that's in there this ability to granularize these pieces gives us improved guidance on each of the processes and our scheme for rating them is actually very very scientific with the exception of cmm and itel in this case process improvement frameworks rub cobit pmi actually show a decrease in on budget and on time project performance and of course cmm cmm i gives us the best improvements on budget and on time in order to do this now what this means is that we're going to rate each of those areas in our organization from a one you have a pulse to two somehow our processes are managed are repeatable three they're actually defined which means we can start employing standardization to them for we measure them because if we don't define them we can never standardize them and then for measure them again measuring them gives us the ability to say more or less and finally if we look at them over time we can say are they in fact able to be optimized should we do a little more of this a little less of that this process of looking across things is the basis for tqm iphone nine thousand many other process improvement frameworks again i showed you on the previous slide this is the best way to do this is also the simplest and most importantly your management already understands it so we take these two pieces from each side of the equation the assessment components which are there's five areas that i told you about before and rate them on that one to five scale for the cmm scale we can now do things like look and see how the organization is now some of you may be from the insurance industry this and analysis we did a couple of years ago for the insurance industry and you can see here the average insurance company that was included in the survey did not have repeatable data management practices that's horrific and it's not the way it should be similarly we can look across organizations and come up with little specific pieces and say well here's one for example that i did for an airline and when i was working with this airline i showed them that they were one one two twos and one and they kind of went what's that and i said well it's maybe not important if you don't care but here's your competition and they all went oh okay we're the ones in there the twos that's bad to make it so simplistic but sometimes you need to have that sort of a process the white lines on here by the way are the overall respondents so showing them how they compared at the time to everybody else that was in there what this says is clearly this organization should not be putting more time and effort into making the twos into threes they need to put more effort into bringing the ones up to the twos also is a bit that can be helpful internally because you'll notice i didn't tell you which airline or which insurance companies that are on here but the world bank group told us we could use these numbers as they were going through it may have done an internal survey where they compared their treasury group their information systems group and their business group on their practices and this is specific to the data governance area but clearly what you saw here is that at the time their governance practices were absolutely world class and so it wasn't necessary for them to go out and hire external consultants in order to find out how to do the things they want to do improve they just had to walk down the hall and walk with their colleagues in order to come up with this so this assessment process gives you the ability to understand what's happening in your organization and whether you're capable of using these pretty advanced technologies that come in which is where we see most of the mismatches that are on there the other thing I put up here the industry benchmarks and the overall benchmarks but we won't look it up but when we look at overall organizations what you're seeing here is again that in this era of big data which I call 2007 to 2012 most organizations didn't improve their practices and that's really a problem when you consider the amount of volume that is increasing for all of their data now one of the other reasons that it's real important to do this is because data strategy does not tend to exist well at a lower level of the organization it's like the game of telephone or somebody says a phrase in one year and as they go through and pass it all the way around they eventually come up with something on the other side so the kid at the end of this particular telephone said it's a pie tray and no no the answer was it was 1492 Columbus sailed the ocean blue by the way if you have a chance to do this at your own meetings it's actually quite an effective exercise just take 10 people sitting around a table and have them pass a phrase around like that and see if the phrase goes from one place to another correctly the problem with strategy and particularly difficult strategy is that most organizations have large and complex it operations and so if there is a coherent strategy or a single set of organizational goals and objectives it's communicated to the division at it but you have the same game of telephone that occurs which means at the project level at the it project level strategy is not well perceived and what does get down there is often confused inaccurate and incomplete resulting in it companies that do not well understand organizational strategy i'll give you a very specific example from the groups that we worked with it's a logistics company fortune 450 company that had four divisions you can see them up here and they'd experience significant growth over the past decade and they were going on an enterprise wide modernization program and understood that they needed to do more with their data they had a revenue goal growth of achieving 10 billion in revenue by the year 2020 and as we were working with them we started to go through this exercise and so what are your brand promises what are the things that you do and interestingly enough they were kind of going on I know I guess I'm somebody that can tell us this now as you imagine we fly around a lot and we're at the airport and on the carousel advertiser came up unmatched capacity unrivaled service undeniable flexibility undisputed experts and unprecedented control and we took these back and said hey what do you think about these and the it person we were working with on the other side was writing them down and say gosh these are great can I take them over to marketing and see if they like them and of course the absurdity of it was these were what marketing had put up there but they didn't even know their own strategy now if you look at them in this case as a strategic and strategic perspective they add four different businesses brokerage services were low cost broad range of buyers intermodal was a little bit more expensive outsourced services gave them a differentiation play and the truckload brokering capability gave them a lot of differentiation what they needed to do was act as a single company because what would happen is that somebody would call them up and say can you move something from place a to place b and they'd say well no we're at capacity today and they say but I'm looking out on a lot and seeing some trucks that are sitting there it's just an awful sort of a situation they didn't have visibility so they needed to go back in and change the buyer power that was currently moderate to week and set these other variables up so they could look at this now I'm giving you a fair amount here in this current state inventory of the data management practices and understanding this we also need to get a set on sense of what the data assets are that's an inventory problem the businesses are they good at understanding business process architecture or not what sort of technologies do they have currently in their organization and finally we need to understand organizational readiness we found that organizational readiness is actually one of the biggest challenges and we've used this chart absolutely want to give credit to Mary Levitt that's an adaption of her managing complex change model but the idea here is of course if you have confusion you probably have a great action plan wonderful resources great incentive skills that you're missing the vision so you've got the vision incentive resources and action plan you've probably got an anxiety because somebody doesn't have the right set the skills we've used this template dozens and dozens of times to help diagnose what's wrong with your organization and believe me when you do the same thing with your organization in terms of trying to determine organizational readiness you may have an idea that again just to pick on the MDM thing from Mike Ferguson it fits right there in the vision thing you can do the incentive the resources in the action plan but if you haven't got the skills to know how to implement that you're probably seeing anxiety in the organization and people will look at this and really kind of get that there's a saying that Peter Drucker made years ago culture eat strategy for breakfast and the idea is of course the culture can be the biggest impediment in most cases to understanding what's going on you can have everything working really really well but if the culture ain't ready for it it's probably not going to work well at all so that's what we mean by data strategy is kind of hard understanding that current state of organizational maturity and whether you're in fact ready to take off on this wild adventure with your Tesla or whatever it is you're going to get to let's move now to data imperatives and again I stress the key to this so remember we've already done this business needs we come down here for the solution no that's not right only when we understand the business needs and understand the current state of the organization doesn't make sense to then start to work with business imperative and these business imperatives are the things that you need in order to satisfy the business needs now many people like to try and figure out how to sell this and I like to do sort of a series of thought-provoking questions this is an old joke but the CFO says what if we train our people and they leave and HR comes back and says what if we don't train them and they stay ooh that's an interesting conundrum people like that they actually do this is an old joke I put the credit for it up there strategic thinker says what if we can just train the top people to leave and frankly have been to a number of organizations where that might be a better idea so question people come up with is why do I need a data strategy and the answer is because you want to manage your data with some guidance because if you're not managing the data with guidance what are you doing these data strategy components these delivery pieces the organizational needs should be sequenced and I'm going to give you just a macro perspective on it here but it's important to understand at least this so again we could look at a dichotomy here that for to put to us for improving operations or doing innovation and most organizations don't have a formalized data strategy at all but if they do have a formal data strategy it may be focused on the right long kinds of things for example here walmart is well known for being experts and increasing operational efficiencies and effectiveness they are terrific you've heard stories about them again they are just an absolute phenomenal group at learning how to do things because everybody in that company understands every day low cost and that's a really good thing from both an employee perspective but also from a consumer perspective the company like apple however uses data to create strategic opportunities and they look at this and they go oh my goodness you know what can we use data for and what they did is they said we need to get into the move is extremely business because the download business is going away same way that the vinyl business went away the cd business went away the lazy business never really got there in the first place but anyway we come up with this idea here and I'm saying okay we're going to use data to create strategic opportunities the question is are the people in version three of that strategy going to be good at increasing organizational efficiency and effectiveness and the answer is that's a very different type of person a very different type of thinking very different type of data science if you will if you want to think of it that way and think the converse increasing the organizational efficiency and effectiveness is also not necessarily the kind of thinking that you need to have to do innovation so there's a pathway that makes sense go from without your strategy use the increasing efficiencies and effectiveness in v2 and use that money to invest in creating opportunities to get you up to v3 and of course v4 means that you're getting good at both v3 and v2 I would estimate I've met a handful of companies over the years that can actually do this anyway at all again only one in ten organizations actually has a board approved data strategy so that becomes really problematic now let me give you a quote here and this is a quote from bill gates but this is the quote most everybody sees and problem is it's almost always taken out of context so let's look at the context here application design and businesses are now irrevocably linked virtually everything in business today is an undifferentiated commodity except how a company manages its information how you manage the information determines whether you win or lose how you use information may be the one factor that determines its runway success or failure from a business model perspective what this means is we have to get people to understand the same things that we do here at data blueprint which is the data is our most powerful underutilized and poorly managed organizational asset it is the only non depletable non degrading durable strategic asset that is possessed by any organization and as an asset people understand that they need to treat it better so we see this on the boards an awful lot of the time where people say data is the new oil problem is we don't think about oil after we use it and so it's really the wrong model for it a better model is data is the new soil plant stuff in it and good things will grow that's great we actually saw this it's a t-shirt we're thinking about making I don't know Brit will get into this or not data is the new bacon now that probably is not great but whatever you need to do to sell it is really great because if we can help to unlock data management value here this helps organizations strengthen their capabilities provide solutions that are going to work for the place that the organization is at that point in time and reestablish the partnerships that we've long lost between it the business and in this case the data groups that are there now Gartner's done a real interesting survey here just last year and what they did is they said how are CEOs notice not CIOs but CEOs recognizing data is an asset we're actually seeing a lot of work in this area so 33% of the CEOs say they were measured the benefits of each type of information asset 24% said they quantify the value of this I'll show you an example of that in just a bit 22% of them said our information assets are well catalogued none of these are good by the way 11% said they don't regard information as a kind of an asset that may be true for about 10% of the companies okay we'll let them have that and 10% so they're directly monetizing assets by bartering or selling them outright put in a quick little plug there for my book on monetizing data management which can help you all to start figuring that out now I tell a little quick story here data blueprint one of the things that we get a lot of time is that people come to us and say can you develop a data strategy for us we said we'd be glad to help you out with that organizational strategy is a wonderful thing we need to look at and and they go okay great and we also could use the it strategy that goes into there as well there's a problem with this though thinking in traditional fashion and that is that if we look at it project specific or application-centric development what it means is that organizations start out with strategy and then they think they should go and implement it project that's a pretty reasonable way of thinking about it but what that means is that the data is only considered within the scope of the it project which means that the data is informed around the applications and not around the organization wide requirements around the shared requirements it means that the processes are narrowly formed around the apps and very little data reuse is possible the reason for this is because we've taught people wrong for all of these years it projects follow a waterfall or an agile model in order to create this and they create more data silos they say we can develop the software and we can develop the data within the confines of a project the problem is that evolving data is different than creating systems again if we consider data as our sole non-depletable non-degrading durable strategic asset this means that we have to understand that data systems evolve as opposed to making them create and that there has to be a natural sequencing here where data evolution is separated from external to and precedes these system development life cycles so whatever you're calling as a data strategy is to come in and take a look at your data strategy and say how are we using data in the organization and make the fundamental changes that you need to have in order to do this I've been trying to articulate this concept for a while and I apologize this is not the best I've been able to do but we've got some shared data out there at the top in the upper left-hand corner in the gray we have an individual IT project they may ask for some metadata assets or some reusable data as part of an individual IT project and those results go back into the individual IT project at the end of the implementation we should extend the original shared data to include the new data that was developed as part of that IT project so it's our metadata layer data architecture layer at the very top that's there and the second IT project that comes along to do this again may get some data from it may use the data hopefully we'll share its data back with the rest of the organization the metadata catalog the architecture that's up there and it's increasingly important because over time the number of requests increase the utility the results increase and most importantly the contribution that data is making to this process increases as well but what this really means is that when we look at data development we really need to think about it in a completely different fashion fundamentally in support of strategy we should be developing database goals and objectives but that these goals and objectives should not drive the IT projects but that they should drive the information products and the information products then drive the IT projects because this way we can develop these data assets from an organization wide perspective and also make sure that we copy the system perspective and finally the last part maximize our data reuse so this process here of organizational strategy IT strategy and data strategy is all wrong and what it should be is something like this the data strategy should come directly off of the organizational strategy and be developed in conjunction with the IT strategy however we would like the data strategy to have more of an influence on the IT strategy than the IT strategy has in the data strategy after all you show me your data IT assets that are your sole non-depleting non-degrading durable strategic assets and I'd like to see them because they don't exist now the other part of this too saying round one is important to make them understand that there will be a round two and that is absolutely critical to make sure they understand that this change in the way you do things then allows the organization to start unlocking the value of its data as it goes forward get into our last section here then implementing data strategy road map on this again we've gotten to hear business needs current state now gives us combined our ability to create strategic data imperatives and now through execution of those imperatives we come up with a roadmap however the roadmap is a really really key part of this because it's your articulation of what's going to be happening and it's got to be a balanced approach if you deliver no business value on the left hand side of the diagram there you will not be able to sustain your effort on the other hand if you focus all of your efforts on infrastructure capabilities you will similarly not be able to derive the business value that you need to have it it's very much of an art it has to be done as a compilation and a balancing action between business value delivered and new capabilities delivered to the organization if you go too much on one way or too much on the other way you will have zero bits of success in implementing your data strategy let's dive in and take a look at a couple of quick examples the query that we found running in one organization and this query might look actually kind of complicated however when you consider the fact that it actually ran literally hundreds of thousands of times a day it makes a difference on whether it should be in fact optimized or not so when the query came back optimized you can see it was a lot simpler in order to do this now the problem is when you repeat bad data practices thousands we've even seen millions of times a day it means that your organization is suffering from what we call death by a thousand cuts and I'll give you a very specific example on this it's one of our customers we've worked with over the years it's a multi-billion dollar chemical company it's kind of fun because we went in the other room one day and told the guys that we're in the other room that they needed to wear lab coats for this environment they're thinking oh cool healthcare we said nope engine oil they went what now this company goes in and it develops additives for engine performance and what they're trying to do is help the fuels burn cleaner the engines run smoother and the machines last longer and when they need to do this they take it downstairs they run these tests and they run literally tens of thousands of tests annually when you consider that the tests cost up to a quarter of a million dollars a piece and they're keeping no metadata on those tests you can see there's an upside potential there for this group now let me explain that the research group here these are a group of about a hundred phd's in chemical engineering very smart individuals again each of them we're just going to put a price tag on it of a hundred thousand dollars a year you know those are not correct examples but it's roughly a ten million dollar a year investment in this particular piece and the question is how many tests can they perform each year can they increase the customer satisfaction with the products and can they come up with improved productivity themselves as they're trying to do better things into this now I have to tell you we actually got to this stage here which was mapping out their process and the organization said whoa stop you're done and we went no no we haven't delivered any business value yet and they said yeah but we never knew what it was that they did in the first place just seeing here's a lot of activities that are designed to get data into something called FAS it's their version of analytics that were there what we did this for of course was to try and look though at what they were doing and the idea was I've circled it in red there they had a phd in chemistry who had some thing called an exploded formulation that would take it from one computing system and turn around and digitally type it into another digital computing system I guarantee you anybody that's listening in on this webinar today could find a better way of doing that particular process the second thing that we found that was problematic was that they were using manual file manipulation moving things around by hand using usb and in some cases even floppy drives in order to do this means the data can only exist in one place and it's dependent on schedules in order to increase the productivity of the group third component of this is the ability to duplicate or manipulate the data so you can see I've circled three things in red where we found there are places where we had tribal knowledge set up so that people just had to know that they had to either cut and paste certain things over here in order to get it from this cell into this cell of course cutting and pasting by hand could be problematic but if nothing else it could represent the possibility of introducing errors into the process then we also had some synonym reconciliation that we had to resolve the idea here was that people were again doing their work doing a pretty good job on it but coming up with little problems that they weren't able to resolve because they didn't understand the ability of everybody else to speak the same language now that gives us sorry gives us the ability to set that and fix it standardize on one particular language and finally we had another set of tribal knowledge requirements where you had to know where people were going to be setting up for doing different things at different parts again and maybe that somebody knew this came from the UK and they had to transfer the units from English Dometric units etc etc in order to do that and finally does anybody know what that technology is right the idea is that set of databases were never even made y2k compliant so we have a lot of organizations that are out there and again these guys were y2k compliant but they were chemical engineers they didn't know what y2k was they didn't have any concept as far as how things were going to play out in that process so they didn't know that they didn't know that they were using technology that should have been retired years ago now I bring all this up because this made an excellent example of something on their data quality roadmap we helped improve their business practices we helped them do all the lines of architectural improvements quality things integrated some of their systems together which meant we could reduce the number of tests that they had increase the number of tests they were able to accomplish per researcher and reduce the time to market for new products and finally at the very bottom of that of course the question is what was the business value to the organization and the answer was their $10 million group saw a $25 million gain in productivity each year thanks to these additional improvements that we had now doing that is good we've also got to make sure that people understand that now this is a bounce I said in that sense this is one way of delivering very concrete pieces if you concentrate on doing just that type of solution what you'll end up with is in fact lots of those little solutions but you haven't made anybody better and this is what you need to concentrate on the other side of the equation getting better within your organization on practicing how to be good at data management I often ask audiences what is the world's oldest profession and the answer of course is accounting if we look back at data management we actually have about 100 years of focused activity in that area so that only in 2009 did the data management body of knowledge get produced by data excuse me DEMA international I was president at the time we were very happy to get the first version out there and it gives everybody an ability to understand what's happening so one of the things that you need to do on the other side of that equation is to take a look at data management in general and say what are these things are going to be important for our organizations and what are the knowledge skills and abilities that we need to have both within our group and across organizations in order to come up with this on the other side of that we want to balance out the dimbok with the DMM and the DMM is structure that Carnegie Mellon has put forth giving us the ability that they showed you before to measure how well we're doing in each of those areas and to give us a nice pathway of getting better about it so these are the two infrastructure pieces that we'd like you to concentrate on at the same time you're delivering specific tangible dollar oriented results in order to come up with the balanced equation that's there so we're going to finish out here as we head towards the Q and A and I'm going to walk you through the framework one more time again our data strategy framework starts out by understanding the business needs understanding the organizational mission the strategy and objectives the organizational structures that are going to be helpful and the performance measures that you're going to use in that organization I worked with one organization at one point where they had over 1400 individual performance measures that's a lot to manage but that's okay if it works for the organization then we need to track them most organizations take those business needs as a whole and try to go directly to a solution again this is wrong trying to put that solution in place without understanding the organization's current state and again I don't just mean the state of IT or the state of data management practices in there it's the whole state of the organization what is their organizational readiness are they able to manage and understand business process architecture management data management practices do they have a good handle on what data assets they have and what technology assets that they have and are they able to use them we have both of those capabilities both of them are necessary neither one isn't sufficient then and only then does it make sense to make our strategic data imperatives what sort of a vision are we going to come up with for these round one imperatives again absolutely key to making everybody understand these are the first there will be second fourth iterations as you go through I'd recommend annual cycles on this however some organizations make you report more quickly on this which leads us to one final quick point on all of this a strategic data imperative is that you cannot manage data as a project data has no beginning and no end and consequently cannot be managed as a project instead it must be managed as a program if you have trouble figuring out the differences that are articulating the two Google program versus project on the web and you will absolutely find lots of examples on how to do that once you understand your first set of data imperatives things that you can do that will impact the things that the people who run your organization need in order to do this again in a chart the logistics company example there it was balancing out their capacity there then and only then does it make sense to make a roadmap and that roadmap needs to be a balance between business value that you're delivering on a regular basis because no matter what you do sooner or later somebody's going to turn around and say well that was great but what have you done for me lately and balancing that capability between new capabilities so again part of your effort is delivering business value part of your effort is delivering the new capabilities to the organization so that it will be able to do this in the future if you get a data strategy then what we'd like to do is help everybody to understand the importance of data so don't take that data strategy just within the organization try to make that an explicit component of the organizational strategy and again remember do not subordinate it to the IT strategy it cannot succeed in an IT project mindset it will create then a vision for the entire organization that everybody can identify with identifying specific strategic imperatives which show people how it will help them to increase their ability to execute strategy it defines the measures and I say defined it really formalizes the measures and benefits that are there to tell them what the future is going to look like again imagine a world where people didn't check out of the hospital and check back in the next day if you're in healthcare and it then specifically describes the data management improvements that are needed finally then we need to understand when it can happen gives the outline of the approach is this estimated levels of investment that are required in order to do this so we are just about at the top of the hour and I will turn it back over to Britt so we can start to run our way through some questions hope that's helpful all right now it's time for a question answer round time for you guys to ask the questions so just click on the Q&A window feature at the top of your screen and you should be able to submit your questions through that window currently we have a few questions in the queue so I'll get right to them and pass them on to Peter here the first question is how does the strategy flow from business architecture capabilities and then business process management excellent question so if we go back to our overall framework and let me get to the very top here we're looking at this from a data-centric perspective and saying that we can't do things without understanding the holistic perspective on all of this and the business process architecture is going to be one of those perspectives so when we talk about the business mission and how the organization succeeds in order to do go back to another slide as well it is absolutely critical that this be done in context so again we're analyzing the business here and trying to figure out how it actually does what it does what the company does why it exists and how the company does it are the things that define the business needs some of those business needs are going to be process oriented some of those business needs are going to be data oriented if we understand one without understanding the other we only understand half of the picture so this is why we talk about it here in the current state as I mentioned a couple of times in the presentation doing the business needs without understanding the current state of the organization leads an awful lot to people get buying into big expensive hardware and software solutions but that they don't actually work in the organization again I said that I actually heard people saying I didn't know where else to stick the data so I stuck it in the MDM you can clearly see that this organization did not get what they were supposed to out of implementing a master data management strategy as one of their strategic data imperative imperatives the roadmap delivering to whatever they're resulting in their business needs I think I answered that one Britt finishing capturing very quick the next question that I have was actually a couple parts to it the first question was and actually you have to bear with me because I'll have to put the two together from the DMM data management strategy is one of six disciplines it is there in overarching and then the next she kind of redacted and said oops data strategy that includes all six or should they have an independent strategy yeah so good question let's get to the DMM and just make sure everybody else can track with us on this where's it there we go so what you're seeing here on the DMM is that there's no point in having governance there's no point in doing architecture pieces there's no point in doing data operations and there's no point in attempting to improve the quality of the data in the organization if you don't have some guidance that says what is it that's going to pull it all together so the data management strategy is key in the DMM structure to being one of the five components that we put in there we also put in their additional support practices from the organization I had that on a different slide but I can pull that one up to take a look at it as well but the idea is that if you don't understand how that works organizationally you will not be able to in fact help people understand why certain things have to be done first, second, third, fourth the strategy is all about what's important remember back to the Porter quote that I had up there earlier strategy is oftentimes deciding what to do and what not to do and what things you should do differently from other organizations who are doing the same thing for example now that Amazon actually has concrete and mortar implementations right the places where they have to go where they're concrete and mortar they're not actually inviting customers into most of those places they're really focused on a one-way we'll pick it online and deliver it to you as opposed to going shopping going shopping nowadays means going for your iPad so again if I understand your question correctly you're looking at these six processes the supporting processes in the five integrated data management practice areas that we have and saying strategy is an important component of those it's not subordinate or overarching to them it's an integrated piece because data manager strategy tells us what should be governed quality then says which pieces are important to the organization achieving its objective what platform and platforms can be evaluated as more or less supportive of the organizational mission in order to do that and finally the data operations the life cycle management says we can actually do better or worse in order to do this most organizational data by the way 80% of it is wrought which means it's redundant obsolete or trivial and that's another thing that you may need to have as a strategy is to say we've actually had many organizations that'll hire us to come in and improve their data quality and we'll say which ones and they'll say well no all of it and I say well that's a guaranteed employment contract for life we'd love to work with you on that but it wouldn't be giving you business value as a result so consequently let's concentrate on the ones that are really important to you the ones that make a difference in terms of your ability read the question for me one more time but let me make sure I've got all of the pieces because that was a fairly nuanced piece from the DMM data management strategy is one of six disciplines is there overarching and then there was a redaction data strategy that includes all six or should they have an independent strategy yeah so the only independent piece in this model here is the external organizational strategy it comes down actually give me a second I think I can show you another version of the same thing we will include this in the slides to bring you guys are looking at works in progress here but hey what the heck right so here you can see a little better articulation of how that works out the external variable that comes in data management practices and the infrastructure organizational strategy however is at the top and you can see how it works is way through eventually evolving at the bottom set to business value they call this my pachinko model because you drop it in because bing bing bing bing and all the way through and tries to come up with it anyway I hope that answers your question we'll certainly give you guys a copy of this as well in order to set it apart of the package on that then I have a few more here and actually a couple just rolled in so keep them coming is data governance a component of data strategy or is the data strategy governed by data governor so a great question let me get back to the presentation here so the answer is they are both components on this but you're not likely to be doing data strategy all the time and continuously hopefully that makes sense on the other hand if you're just sitting around at the data governance meeting and wondering why the heck you're at that meeting somebody's clearly not doing things the way we want them to be done so data strategy is a component of the DMM but you're not going to revise your data strategy all the time in fact how often are you going to revise your data strategy only as often as it needs to be revised given how the data management group is providing value to the organization as a whole again let's just take an example of a CRM customer relationship management example if a customer relationship management improvement is key to an organization success and the data management strategy says buy package x and you buy package x and put it in and it doesn't give you the results that you want then clearly you're going to have to revise your data management strategy and say maybe we selected the wrong CRM package or maybe we're taking the wrong approach to our CRM I'm oversimplifying and not giving you guys any context on this but hopefully that makes sense so the strategy may need to be revised maybe the organization let's take the opposite direction let's say the organization says well we can get everything we need to from our existing customer data by integrating it internally and making our own CRM system so the governance would then be focused on making that data higher priority perhaps than other types of data or other types of data initiatives within the organization as you do that you want to evaluate and say are we in fact giving better information about our customers to the people who service our customers if the answer is no then we probably are not working correctly we need to go back and revise our data management strategy by the way never good idea to revise your data management strategy unless you go back to the organization and say oh by the way are we still operating under the same organizational strategy or do we expect to be in the next couple of years my mother worked in the purchasing agency of a large company for a while and she would say she always knew when they were going to change the strategy because they tell her to stop ordering any more paper any more litter head and stationery and things like that and because they knew there was a new you know slogan and all sorts of other things coming along in order to do that and that would really give her a problem she'd come to what some insider information anyway I think I answered the question governance is an integrated component and strategy should be done periodically but by golly they both got to be done because if we don't have one or the other it makes it hard to figure where we're going or why we're doing it okay the next question is can you go back to the data centric strategy slide IT project centric data strategies also have the problem of IT project centric funding can you share insights last question tips for moving to a data centric funding strategy so great question great insight as well yes this is the real fundamental problem when we look at this model this is how the organization do things they say I have some strategy and they have some other strategic components I certainly don't mean to imply that strategy is always implemented solely by IT projects but IT projects are always implemented that way and as the questioner asks correctly that's where the funding is so they ask you the question how long is it going to take you to do a data architecture for that IT project and the answer is I can't do a data architecture for that IT project because it operates at a different cadence a different rhythm a different set of funding goals what we have to look at again IT projects are in a project method data is a program and it has to be funded differently so your first action as a data strategy may be to enable some portion of the organization's funding to fund you as a program instead of a project it's like asking you how far are you going to be able to drive on this car and saying by the way I have a gas tank of one gallon so no matter how far one gallon takes you you're going to have to stop and refuel all the time which means you're going to spend more time refueling than you are actually driving and that's going to be a problem and that's kind of like what we're doing here so again great question keep this in mind and if you come up with a better articulation share it with us because these are things tools for you guys to use data is different than IT data must proceed data must be separated from and it must be external to the only way you can justify that now you do a little bit of that justification and show that you save the organization $25 million or brought the organization $25 million a year additional productivity in the chemical company example that I gave you the third section or the fourth section of this you won't have trouble getting that funding again it was interesting I actually had a business lunch today with a fellow who's working for one of the local banks and he said our entire group is focused on that we've been told to demonstrate next year that the investment in our group will produce a 10 fold improvement in the operations to others if our group's costing a million dollars they want us to show 10 million dollars worth of savings next year in order to do that and I said well or else what happens they said or else we all get fired boy that's a tough one great question next question is a two parter somewhere around slide 71 there was the idea that the data strategy based on business strategy produces information product please define and provide examples of what you call an information product sure let me go back actually to hear before I go to slide 71 the idea here is that only a data management program can produce products that are recognized by the organization as being useful and only the ability to have those products can actually mean that the IT project world will look to you for some particular pieces so I'm gonna switch too far sorry hang on I'm gonna go back there so the idea is within this model of operating what we're really looking for is the ability to programmatically develop things at the data and information layer that proceed or external to and are separate from IT projects and that those data projects can be then used by IT projects to improve things now this is another area I haven't gone into this much today but those of you that have heard me speak know that I claim to have investigated many more than 100 IT failures over the years and in 100% of the cases data has been the root cause so improving your data will actually help your IT systems as well in terms of the implementation that goes in there now the products should be used in the following fashion then we start out with the idea that the data management program is nascent and it's gonna be used on an individual project and that's what I was showing on this series of slides here where they get some particular pieces now the products here which is what the the questioner was asking what is it? well this is our first definition of person in the organization and again let's presume you're following a fairly worn path like implementing a master data management solution either with technology or simply as a discipline by the way MDM is defined by Gartner as a strategy not an actual technology so we share views on that this end result here might implement the first version of person on the first IT project but then we put that person back and that person then goes back in and extends the next version of that to be the second version of person hopefully it doesn't require major changes only some minor pieces because if you do a good job architecting in the first place you don't have real problems with that again that depends on having the necessary knowledge skills and abilities and right set of people in order to do this and it's the third version of it will become increasingly valuable and meet more and more organizational needs as far as going over all of that so again the idea is here within that process try to figure out how you can get the organization to understand the value of having a common definition of all pieces by the way if you want to take that level up to a higher level of abstraction this is a dangerous place to get into but Lynn Silverstone and I've had many many discussions about abstracting persons up to parties and the problem is if you sit around talking about parties they don't think you're actually doing any work but parties are the next level of abstraction up since then so if you go up to the party level of abstraction there's probably another word that you can use for it instead of party but it is something that the organization can make use of so that way if a person becomes an employee an employee then does business with the organization and buys things from it you can handle all those with the same data structure which at the moment most organizations throw up their hands and go oh my I just give up great question hopefully I answered it let's not let's get some clarification on it the next question is and I have I have four more as of now and there may be a few coming up oh we're going to make Shannon do overtime today yeah man just a little bit we'll try to get through a little quicker how valuable is the CMMI DMM model for determining the DMM and is it something that can be done by the organization without hiring a vendor and he's wondering if there's a way to bypass the model license for scoring their data maturity I don't think you can get away with bypassing the model license for their maturity but you can certainly do a self assessment in this again the five areas are and we encourage organizations to do this themselves yes eventually if you decide you need some professional guidance it makes sense to go out but you can certainly do an awful lot of this yourself apologies Melanie if you're listening in but I know that you'd rather have people thinking about it than do it Melanie Mech and I as I mentioned are going to do a full talk on this in May but the idea here is that if you look at say how we're managing our data as an asset coherently across the organization are we looking at fit for purpose for our most important data items are we growing a set of individuals who know how to manage data assets as a profession understand how to fit architectural perspectives into our business needs and make sure data operations match yes you can do that and the way you do it is by matching them up on this particular scale so I'm not going to build it for you here but you remember that was the one through five scale you can look at that internally and perform your own self assessment and that is a great place for you to start I would be clear and say this doesn't mean that this is the way it is the organization but this is the best that our judgment gives it there's actually a paper a reference paper out there that I wrote back in 2007 where we took about a couple hundred organizations that had done this themselves and given their own ratings to this and the paper was accepted for academic publication so clearly they felt it was worthwhile in this case and you can show the organization and say look we're ones in these areas and twos in these areas and we ought to get better at the ones before we take the twos and try to make them into threes so it's a great question hopefully that answered it for your short answer yes but I want to make sure that you get the full detail on it okay next question can you explain the linkage or correlation between the slices of the dembach wheel and the cmmi pentagon well that's a great question and the short answer is we're working on making it more formalized both of these two pieces were done by separate organizations and again dembach did this one so this is the slices if you will the piece of pie was the slices and then how we get the slices to work within this context here the chart I showed you a little while ago that we'll include in here is sort of a first attempt to try and get that into place but the actual formalization of it has not been completed and it's one of those actions in progress if you'd like to help out with that process we are both I think volunteer larger volunteer organizations so we depend on the good efforts of y'all in the concerned and caring public to help out so send me your name and address if you'd like to volunteer for it otherwise wait a little bit and we will absolutely get it done because it is as the questioner asks a very necessary piece to do okay what is the role of a metadata repository in the data architecture strategy name the top three things to focus on in implementing a metadata repository gee we're going to do a topic on metadata coming up somewhere soon I don't know which that's probably that piece of paper the top three things from a data strategy okay sorry what's that the importance of MDM no that's actually master data I'm sorry that's one of the sort of them things I know I hear I had a case of oh metadata strategy just not to log it but if you need more give us give you a preview on that okay Lexia there so top three things great question this would be like an exam question for somebody if you're looking at metadata from a strategy perspective first thing I would be storing in the metadata repository is elements of the strategy so this is something that the vendors haven't done all that much work with but it's pretty easy to implement let's just presume that again I'll go back to my CRM customer relationship management example CRM is high on the priority I would be coding things in the metadata repository and saying these are related to CRM or they're not you could actually grade them as a one two or three real important somewhat important or we're going to ignore that for this year kind of a thing so it's another piece of metadata that lets you know whether you're looking at something that is data related is actually important in there now that's number one number two I think I would do actually whether the thing is of known quality now this is something that's very scary to organizations remember I gave you that wonderful piece of consulting advice where you can say at no point in the future will there ever be less data than there is right now well similarly you can also say to most organizations do you know that we can say right now all of our data is of unknown quality man that'll get management's attention in a second if they don't have any idea of that all of our data is of unknown quality it's going to scare that you know what off of them so in your metadata repository if you have even an inkling of whether that data is of good or bad or really sucky quality that's another piece that I would put on there as well finally the other thing that I would add to that is something that most chief data officers are charged with as well which is the idea that the most organizations when they're trying to do are told to start by going off and doing a data inventory and if they do a data asset inventory they will be completing their first role as a chief data officer now I think that's an absolutely preposterous solution because first of all when they tell you to go off and do the data asset inventory they ask the next question how long is that going to take you to do and I've never known anybody that's actually come up with a data asset complete inventory of everything that's in their system so three weeks now three months now three years from now you still may not have the answer to the complete data assets but if you put on their known and hypothesized assets into your metadata repository you can incorporate them with the quality and priority codes that I gave you before and that will certainly help out as well that's actually pretty good off the top of my head I wouldn't bank on any of that advice but watch for it it will certainly be the next book as we work our way through it thanks for a great question I have another one here and there's a couple that are kind of borderline that we may be able to address really quickly here as time kind of expires can data governance be successful without the other pillars of DMM? It's really difficult I mean when you say the other pillars I'm assuming you're talking about the five pieces here if what is data government is going to do if it's not going to implement a data strategy improve your data quality improve your operations and make sure that your platform and architecture are harmonized to support your business objectives I don't see how that could be the case it seems to me that would be doing governance for governance sake which is busy work which nobody likes to do if you look out there on the web there's a video that somebody did to the Fong of Hotel California about being in a data governance meeting that was really really boring if you can't find it let me know and I'll send you a copy of it but no I don't think the answer to that is you do not want to do data governance without these other things it is reasonable to ask what you're doing in a data governance meeting if people don't seem to have a purpose they should get you a bigger screen right now if I see you as a for some reason the turning is a little weird here there's actually two questions here that are kind of buried and well this one I think is kind of fun what comes first data strategy or data governance or is this a chicken and an egg scenario if you're doing governance without a strategy you shouldn't be so I would absolutely put strategy first because your strategy has to be derived again from the organizational strategy if you don't have that good luck to you on anything that you're trying to do so I would absolutely not put it a chicken and egg situation and say that if you don't have a data management strategy a data strategy that is relative to your organizational strategy there's just not much point in doing what you're doing and then I have one more really quick one that was also hidden in there what advice do you have for improving organizational readiness yeah well that's a great question unfortunately I would start out by keeping your expectations small I know that's a terrible thing to say but Rome wasn't built in a day and changing the way organizations have neglected data for years and years and years isn't going to happen with a new CEO a new strategy a new other thing so this organizational readiness chart diagnosis is is really it goes to really the heart of everything we do as data professionals of course we want to solve problems so we can have an organizational readiness problem it's usually not all of these things but it's usually some subset of them so use this as a template to figure out exactly what's going wrong in your organization point it out to leadership and see if they can help you to address it otherwise you can similarly point out to them and say there's not a whole lot point in putting any more money into this thing if we don't know which direction we're going right again journey of a thousand miles begins with a single step but if you don't know where you're going any road will take you there so with that I guess we're out of time Britt I believe so let me go through my little end of discussion field here thank you everyone for participating in today's event we hope you have enjoyed it thanks again to our sponsor Hewlett Packard Enterprise the Data Diversity and Shannon for hosting us once again you will receive today's material within the next two business days our webinar next month will be the importance of MDM on which now stands for master data there we go details details I read it right this time yeah we're good hopefully you will be able to join us for that as well as always feel free to contact us if you have any questions thanks everyone and have an awesome day thanks Peter thanks Britt and that wow that's a huge picture of me thanks as always to our attendees for being so engaged in everything we do and taking the time to ask so many great questions and just to reiterate thanks to our sponsor Hewlett Packard Enterprise who enable us to do all these great webinars for everybody hope everyone has a great day