 Welcome and happy New Year everyone. My name is Shannon Kemp and I'm the Chief Digital Manager for Data Diversity. We would like to thank you for joining today's Data Diversity webinar data strategy best practices. It is the latest installment in the monthly series called Data Ed online with Dr. Peter Akin. And what a great topic to kick off our 12th year of the series. Can you believe it Peter, it's our 12th year of doing this. I love it. Yeah, 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'll be collecting them via the Q&A in the bottom right hand corner of your screen. Or if you'd like to tweet we encourage you to share highlights or questions via Twitter using hashtag data ed. And to open the end if you'd like to chat with us or with each other we certainly encourage you to do so just click the chat icon in the bottom middle of your screen for that feature. And so the most commonly asked questions as always we will send a follow up email to all registrants within two business days containing links to the slides and yes we are recording and will likewise send a link of the recording of this session, as well as any additional information requested throughout the webinar. Now let me introduce to our speaker for today Dr. Peter Akin. Peter has been internationally recognized manager 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. He has written dozens of articles and 12 books. And Peter has experienced with more than 500 data management practices in 20 countries and is constantly named as a top data management expert. One of the most important and largest organizations in the world have sought out his expertise. Peter spent multi year immersions with groups as diverse as US Department of Defense, Deutsche Bank, Nokia, Wells Fargo, the Commonwealth of Virginia and Walmart. With that, let me turn everything over to Peter to get today's webinar started. Hello, and welcome. Welcome to you Shannon and again thank you for hosting this for 12 years straight in through it's just been wonderful to work with you and your team. And I'll do another bit of praise on this as well while I throw up the fact that books are on sale right at the moment, blah, blah, blah, but we're really talking about different topics that are of interest to you all. And Shannon is the primary focus of that so joining into this community. Yeah, sorry. We were good for a moment but now it went, there it is it's just taking a moment to resolve. So, the flames were too much for it right. Again, jump in if we get stuck. But yeah, it's really been wonderful and of course that's our goal is to get as much of this stuff out there for you all as possible so as you have suggestions how we can improve the process and everything else. By all means, please do funnel them in through Shannon or just reach out directly. She's the boss, I am the talent as they say on this thing is not a great way to think about it. I like being talented in some capacity I suppose. Yeah, so the other is to start out here in the slide as long as it's holding still. This is the Malcolm Gladwell quote in the upper right hand corner practice isn't the thing you do once you're good. It's the thing that you do that makes you good. And that really is the sense of what we're talking about so I'm also going to give you something that they do in the military called a bluff, which is the bottom line up front it's kind of like when the flight attendant. He's at the front of the plane and says if Charlotte, North Carolina wasn't in your travel plans you're on the wrong plane. Tough luck staying your seats at this point. What we're talking about here is just the observation over the past 20 years and coming to a little live on the video here is we've got the cameras sometimes fooled into thinking they can do something but again Shannon let me know if it cuts out or anything. It is cutting out so I would kill the video. We'll just kill that sucker then. We're gonna, we'll make it work one way or another and y'all will get you make sure you have a copy of the slides as well to make sure you can can see all that. Well and we had a, we're still recovering from that storm. We were without power for the whole of last week so it's probably not a lot of capacity in the session anyway all right anyway. There's a little person in the corner so start out and say that trying to put together a complex strategy at the beginning of your data journey is generally not the best way to do it it's it's more along the lines of thinking instead that what you really should be doing is pulling a team together and improving your team capacity, all around data topics and things like that. Because and trying to figure out what that's going to be at first is very difficult because the first thing that you're going to do is to try to figure out what you need to fix eliminate in your data debt. And when you eliminate that data debt, then you can build on top of it until you know what the debt looks like it's really not a great way to do this so instead of putting your plan and following it to the end which you know we can think back to when you were 18 how much did you know about what you were going to spend the rest of your life doing. At that perspective is just a very difficult process and instead, put that same effort and passion and everything that you're doing into the process of building a team, practicing what I call strategic application of data concepts and we'll see how that works out in here, and we're at that process, and that's really the main message on this so let's get a little bit of context going. First of all, strategy is inherently a repetitive process that can be easily reproved if you look at it from that perspective. Many of the branches of military work with for example revise this strategy, at least every once a decade in some cases. The idea is that they still have a group that does that and it gets good at it, but there are some dependencies that exist and primarily the data strategy itself exists to support the organizational strategy. The basis most crude level, it's, what can we use data to better help the organization succeed in its strategy to do on that, that your process should evolve as it goes through, saying, in particular that you're going to start out and be rough but you have a couple of cycles through the process and there really is a nicely defined cycle that gets you there is much more important than the actual product so when when organizations come to me and say hey, will you help us write a data strategy I just want to say, do you really know what it is you're going to do, what is that output going to be because in most cases with good data questions, the best outcome of many of these investigations is going to be another question and that's always a challenge around that. The 95% of the problems in our data space and therefore in data strategy as well are people and process challenges and this is born out year after year in research that continues to illustrate this, and yet we over emphasize the technology component all of this. So, how bottom line do we get to Carnegie Hall the answer is practice practice practice. So let's go through the program. Again, over the remaining 42 minutes sorry 52 minutes we have will talk about data strategy applied to data assets in that context. And then we will look at why data strategy and data governance are so interdependent. And then we'll move on and look at the prerequisites for implementing a data strategy and that is the idea that there is this data debt that we need to eliminate or at least reduce substantially. Before we can really start to do the things we want to do which is the exploitation side of the data equation. And then what is that cyclical process look like in specific is the focus of the last portion on this and of course we get to the Q amp a session so where we're going to start here is looking specifically at what is a strategy, and many people are oftentimes put off by this strategy because it seems complicated. And when it gets right down to the basics, your organization is going to do the same things your organization has done. But some things are going to be different. And that's really what we need to do is to provide the motivation, the motivation, and the parameters for the change that's going to occur on this. There's a wonderful TED talk from Simon Sinek, called how great leaders inspire action and I'll just summarize it here for you in a minute. Human beings we're pretty good at describing what it is that we do. We're less good at describing how we actually do that process and we're quite frankly not very good at describing why we do that process. And of course, strategy is the thing that provides that why it says this is the reason we're going after it, and another component of his talk involves the motivation and we said this motivation, people don't care what to do. It's why you do it. Again, there's lots and lots of different components that you can throw around this but his example was, whoever Martin Luther King didn't give the I have a very sorry he gave the I have a dream speech. I have a plan speech. It's two completely different components, you look at it that way. So let's look at what is strategy and started out about 1950. You would not have heard this word used in the business context and certainly not in technology context prior to that it was exclusively the domain of the military province but the Peter Drucker's world got a hold of the concept of strategy made it a very good study and have done a lot of really interesting work around it, but it's come to me in this sort of master plan as long as we're working at this or this grand design that we're doing. And that unfortunately becomes a thing. And that thing is is really not what we value I think we're fine able to process. So I go back to the military concept here which is that a strategy is a pattern. It's a stream of decisions. And that's a much more useful decision description because the process here that we're describing now is how are we going to go through our process of operating. And we'll just very briefly at three strategies that give you an idea on this. Walmart's former business strategy which they've widely published is every day low price. It's brilliant. It's, it's understood. It's achievable in many people's minds. If you were on the Walmart Express the regional just to take you into and out of Bentonville, somebody along that try playing right is guaranteed to say to you, you know, every day low price, every associate knows it every resident of Bentonville knows it. It's a very successful application of strategy and in Walmart. They expect their employees all the way up and down technology and otherwise to be guided by the strategies. If you make a decision, and you make a decision that focuses in supportive strategy. It's very unlikely that you'll receive any sort of admonishment from Walmart you may get coaching, but that's a little bit different on that. So that's example strategy. Number one strategy number two is when Gretzky and some of you may be familiar with him. His strategy is quite simple he skates to where he thinks the puck will be. And if you step back from what's going on in an ice hockey match and say yeah the chances of a large person chasing around this small piece of plastic that's moving much much faster than we chasing will not be effective. So going to where the puck will be seems to be the most effective and he's got a wonderful conversation that he lists between he and his father on his Wikipedia entry out there to take a look at it. Our third example of strategy is Napoleon at Waterloo and the question basically is, how do I defeat the competition when their forces are bigger than mine are the answer is divide and conquer. And again we'll go back to our decisions. So I'm showing a little map here I'm going to blow it up just to give you a little bit more focus on this. This is an analysis that Napoleon did that they still teach in strategy school in the US military, and what his observation of the battlefield was that the British who are in red were being supplied for troops beans and butter right out of Austin which is on the coast of Belgium. So, if there is a questions where was the food it's there. It's just a sort of basic question that people go after. And the British in black right there, we're being surprised to mess with the British the Prussians being supplied out of leash in black there. And so Napoleon said my my chances this, if I hit that army that combined force of red and black, very hard at exactly the right place. It's very likely that I can make them fall apart. And what Napoleon understood as as many strategists understand is that an army when it's turning around and running is much more likely to run towards its food, clothing and shelter, away from its food, clothing and shelter so this is part one of Napoleon strategy a waterloo here. Part two was to go about and make sure that everybody in his army understood first, we're going to turn to the right and get the Prussians. And then only after the Prussians are defeated will we turn to the left and get all the British. You can see this is a fairly calm. You can see at it. First of all, both armies have to be hit in exactly the right spot to make them spring apart for you to them to retreat back to their supply. Secondly, everybody, everybody all soldiers had to turn to the right and defeat the Prussians then turn to the left and defeat the British and oh by the way, please do this, while somebody is trying to kill you. This is a non trivial exercise, perhaps unsurprisingly, it was unsuccessful, you know it is still taught as an excellent example strategy well what I'm showing you is the value and the most critical necessity of making strategy simple, such that everybody can follow these pattern in a stream of decisions let me give you a more operational context perhaps if we're the good guys on the left here. We're going to have a different strategy. If we're at the top of the mountain, then if the bad guys are at the top of the mountain. So there's an example, all the way around actually three quick examples one flat one left one right. In order to do that and this is why this pattern in a stream of decisions is such an important component to adopt definition to adopt as part of our strategy in this. And that's why a strategy that winds up on a shelf, just isn't useful because there's no muscle memory there's no sense that we've applied it or that it might be useful in fact, the most cogent comment I found on that this comes from General Eisenhower, who said in preparing for battle, I've always found that plan, but planning is indispensable and I certainly agree with him. It's also been restated slightly more vividly by Mike Tyson who said, everybody has a plan until they get punched in the face. So look at what is a data strategy then given that component it's the highest level of data guidance available. It is the idea that we're going to focus on a few key things instead of trying to fix everything at once focusing on activities that happen to result in business goal achievement, things that are tangible for the business, things that management will understand and appreciate as you're focusing them through that provides guidance when faced with this stream of decisions or uncertainties. Going here again this is the idea of general versus specific in order to do this and empowering our associates to do what is they need to do understanding the situation best as they do. Data strategy, most usually articulates how data can be used to support organizational strategy. One of the things that's very key and we'll come back to this in just a second but we're no longer going to filter these data strategies through it. It's not that it is bad or anything like that. It's that it needs a different set of perspectives on it, what my colleague Kathy Doss has termed data here so we're just going to adopt that terminology right now. And this data strategy is going to involve some balance of remediative and proactive measures in order to do this now if you're looking at a data strategy one of the things that you can measure eventually is its effectiveness but the timeframe that you're going to use to measure this is very important for people to understand will be a longer timeframe than most people are used to years is the typical measurement that the volume of the data strategy probably shouldn't be longer than the organizational strategy that just doesn't seem like a good idea. Most importantly, perhaps that put versions in place. If you put version one out of your strategy. When you go to put out version to nobody will say, hey, I thought we already had a data strategy. Instead they'll say, yes, version two comes after version one and measuring understanding all the way around is this common agreement and that's what we're pulling is this now this slide I'm going to show you next year number 32 is from my friend Chris Bradley who we've worked together and some really interesting activities and it's just his summarization of how you could put all this information on one page. I'm going to urge you not to start off doing this but maybe maintain it informally. And when you feel like you've really got it then bring it out. And he's got all sorts of talks where he'll talk about this particular process instead let's look at what we're faced with in terms of options somehow before we have good information about the entire process we're supposed to come up with a plan that's going to get us from here to there, which requires upfront assumptions because the plans have to be detailed and obviously always underestimate the cost What I'm urging is to adopt the use of iterative strategic cycles that will move us steps closer to the goal because over time, this process of showing you these strategies helps us in two areas one, as we do this more we come better at it. And when we come better at it we're able to take and be more effective at cleaning up which means we have more time to devote to the proactivity of this I'm going to show you this slide one more time before we finish today and just want to give you a heads up. And the most common strategies really this is wordal again, you know, it's people are just saying, I'm going to put my faith in these technologies and that's what these things are they're not bad. But they in and of themselves can only form a component of your strategy as opposed to your entire strategy. So we're at the end of the first section here data strategy specifies how data assets are to be used to support strategy we've covered what is a strategy what's a data strategy and how do they work together and focus on that pattern in a stream of decisions and again think of it. Some ways if we need to gamify this my strategic focus here for the first iteration is going to be on space so I'm going to go up and zap it like a pac man or something like that and then for my second time I'm going to focus on costs. Again you'll see what this iteration cycle is as we get a little further around this process. So let's move on. Again, the data strategy is necessary for data governance and this is key because there needs to be three objectives around your data that you need to make progress for, which is first of all improving your organization's data and unfortunately, that is where most people stop. We also need to improve the way people use their data. The definition of a knowledge worker is somebody who uses data for a living, we can make them more efficient and effective with that process. That's only going to help our organizations and only when your people and your data are better with data you can use them effectively to support that strategy. Let me just start out by saying there are competing definitions in our industry there's nothing wrong with any of these definitions they are all fine but I would ask you to imagine getting on an elevator with a native who looks over and says, hey Peter, I understand you're going to be doing something here to help us with data governance can you tell us what that is. And those definitions I put on the other pages would not make sense to that individual before we finish that particular ride. So, while they are all good definitions I have a shorter definition that I think I like to use in these instances which is quite simply that in this case data governance is managing data with guidance. And of course the appropriate question comes up almost immediately. Would you want your soul non depletable non degrading durable strategic asset managed without guidance. Do you manage any of your other assets without guidance. The answer to both those questions is typically no. If I'm talking to executives I change data governance to just slightly insert the word decisions, because one of the things that that happens to data is that people make decisions and I'll show you this in a few slides up, but they don't realize our data decisions again is that process of putting your data years on and sort of looking at things from a data perspective as opposed to perhaps the more comfortable perspective where you have been looking at it before because data is our most powerful underutilized asset is the only thing as I said that is not depletable degrading or durable in our organizations and we compare them against other strategic level assets, they compare quite favorably, they win, if you will, in the Google term data is the new oil you will see 5 million hits out there at Google on that and I'm happy they're looking for it that way but I don't think it's the way to think about it, because unfortunately, it only focuses on the exploitation side of data how do I take that data, move it into a one way product that is never reused a better way to think about it is soil data is the new soil there's two reasons this is important one you don't just rock about the yard and fling seeds here there and they and expect good things to happen know you carefully prepare the soil that you're going to put the seeds in and the other part of it is a time related you don't start your seeds on Monday and expect that you're going to harvest them on Friday it takes planning, it takes time, but it also needs some sizzle, if it is going to sell and would rather have people talking about data and anyway shape or form because it does deserve its own strategy it deserves attention on par with similar organizational assets and it requires professional ministration to make up for past neglect I'm going to show you a very brief example of perhaps where this might be this was last year in fall of 2020, there was a Forbes article that I will put up here in just a second when this finishes building that said that American Airlines was currently valued at that time at $6 billion US, and they're interestingly advantage program the data that they have on us American Airlines flyers was valued between 19 and a half and 31 and a half billion dollars similarly over at United, they were valued at $9 billion whereas their mileage plus program was valued at at least twice what the company was valued at you will say that this is in process because they haven't managed their data in a way that they know how to monetize it fully at this point in time, and partly because they are still dealing as all organizations are with lots of data debt, the time and effort it takes to return your data from its current state to whatever you'd like it to be I call it getting back to zero because then you can really do the fun stuff, and it generally involves undoing existing stuff that is required and you probably don't have skills to undo and redo those efforts. Again, when you start from scratch. It's also typically going to require an annual proof of value and now you luckily get to get good at both of these things have exactly the same time. There's not a lot of guidance and there get data debt is something that we need to do because it slows progress decreases quality and increases our costs all the way around. At in this case data overall it becomes a process very much that I call separating the wheat from the chaff. And the idea here is first of all to understand the question the answer to the question excuse me is well organized data worth more than less well organized data. And in order to help people understand that I asked them to go back and look at some information came from a wonderful book here I'll show you in just a second. And this is the idea that before the information age we still did things like this, we're putting this from a book here wonderfully how to make sense of any mess I think I sold a number of abby's books for her and I think she's done an excellent job of how to explain information architecture to people who may not understand this, or maybe their first introduction. Imagine making the spine off of abby's book and distributing the pages without page numbers on them. We all understand that that data becomes a semeral very very quickly so okay I've convinced you that better organize data increases in value. How do we go about applying that to our organizations well the next rule is the rule of rot. That is that 80% of your organization's data minimally is redundant obsolete or trivial and the question becomes then which data do I eliminate. Nobody wants to answer that question but truthfully most enterprise data is never analyzed so the question comes up for your organizations who is qualified to perform this process of eliminating these. Let's take a look at how it works out in the practice data strategy and data governance are going to be working closely to support data strategy, which supports the organizational strategy. So what's the organization doing and how can data governance better support organizational strategic implementation. There's no question about what to do now many of you organizationally are driven by regulation that's another process to get good at. For all of you in terms of that but that's never going to be an organizational strategy unless the organization decides that it might want to become good at complying with regulation which is something I've seen some people expand this a little bit further in Peter's world data governance would have an input into it projects as well there's lots of synergy that could go on between there. Lots of cost savings as well that are very interesting as they work into organizational operation if you can imagine that 80% of organizational data is redundant obsolete or trivial that can have a tremendous impact on business operations. If we put in some feedback loops in there we can have a fairly complex picture that I probably wouldn't share with others outside of this I keep it simple like this, and also add in here the role of data stewards because this is how you're going to be effective in doing this now. I put in the stewards here in particular because stewards need to understand the data strategy expressed in terms of business goals. They can't be complete geeks and not care about this they have to have a business mindset. Also, data governance must speak the language metadata in order to do this if you're using anything other than metadata, you are making a mistake in risking precision around all of this and taking those business goals and metadata and putting them down here in operational world where the stewards can implement it. This is your test of my being relevant. Of course the feedback along here is plans progress, etc, etc. So, we've covered here again that we need to improve your organization's data but we also need to improve the way your people use your data. And if you say I don't have people all people that need to be smart with data the answer is, then you don't have any knowledge workers, of course you have knowledge workers and of course they will benefit from improved data literacy because only those types of knowledge and skills can be useful for having people support, use data to support their organizational strategy. Now we're going to talk about some prerequisites this is the elimination of data debt and data into 15 minutes. Most organizations when they start start out pretty straightforward it's a process where I look at my business needs and I've been working in this area for years and years you say to yourself, of course I would be working at specifying the business needs and we'll come up with a solution. The problem is the reason that doesn't work. Thank you Morgan Freeman this is wrong yes. The reason is because we're leaving an important dimension out here and that is what is the current state of organizational readiness. If we have the good understanding of the organization being able to implement. This is a good place to take a look at it. For example, I have seen organizations implement. Master data management another one of the topics that we'll talk about in this series of data at webinars here and master data management where they implemented well technically, but the organization doesn't quite get it so I will be wandering the halls and hear somebody say, I couldn't figure out where to put the data so I stuck it in the MDM right there you cringe and say oh my goodness I know that's not going to work. I'm going to babble that you for a minute here so that you get that right way to do this is to match your business need improvements with the current state of organizational readiness so that they are ready to move out in the right direction. We'll come to part two of this framework in just a little bit I'm going to stop here to talk about how getting ready for data strategy is really important. There are five excuse me three steps that you go through here prepare for dramatic change and determine how you're going to do the work there is a change management dimension here. Recruit a knowledgeable enterprise data executive and other talent in here realize perhaps that your other executives will be a good input to the discussion but will certainly not be the ones who would be only making the decision around eliminate what I call seven deadly data sins in order to do this all of these are necessary prerequisites to being able to come back and do then a call call walk one strategy let's talk about each of these very briefly. First of all again dramatic change and determine how to do the work. I've said already in here that much of the challenges that organizations are reporting in these areas is non technology challenges, and then you've got people like me who wants to go out and write books that say things like CIOs aren't, which is a terrible thing to say and I'd like, of course to be provocative not rude and so luckily somebody talks me out of that title returned it into this particular one here the case for the chief data side, of course, is that we need a new focus of leadership because CIOs are slammed and what I do here is, if we want CIOs to do more with data, we will have to ask them to do less does anybody want them to give up this this or this and I named some specific people said, you know, I like CIOs handling that sure. And also about 50% of CIOs say to me, yep, I'll be glad to give the data piece to somebody else in this. When this book got translated into Chinese the title came out interestingly enough, chief data officer combat did capture what was going on in terms of trying to make the case for it. We look at CIOs versus CDOs there's actually a rather synergistic relationship that can be developed between the two. But as I said before most CIOs at this point are saying yep I realize that there's a lot that's going to be done with data, and I've got plenty on my plate so here you go CDO good luck. Call me if you need me of course most of our more gracious than that in terms of working together, and it does represent an element of destruction that is going to occur in our organizations, because when we do say somebody whose title is chief information officer, and then we say but we need a chief data officer as well. It becomes confusing to people, and we need them to very carefully articulate what it is that we're attempting to do, and probably the best place to start with that is to realize that separating data and information are going to be bad ideas in most organizations. What I observe is that most spend time arguing about data versus information, and instead, if they manage them together they would have a far easier solution to what they're working on. What around all of this though is that these are socio problems and we have a class of professionals who can help us called change management and leadership professionals. You may even have them and part of your organization several of the local organizations that I work with have them as well, including Virginia Commonwealth University, where I am a faculty member. The idea here is to take a look and understand that just like and I know we're going to have to explain to kids eventually what is a key and what is a lock this is an old physical lock that you're looking at those of you that don't recognize it and you can see that it only works when you line things up correctly well in order to get organizational change. And she delivered to a wonderful model here where she expressed the same kind of thing I can walk into an organization and I can see alignment on vision. I can see necessary skills I can see incentive. And I can see an action plan but I also observe frustration, and what I observe, not what I'm missing are resources. Same thing if I look at vision incentive resources action plan and I get anxiety, but nobody has the skills. So, as Mary said, all of these need to line up perfectly in order to get organizational change. And that's a non trivial task it is one of the primary reasons that organizations and organizational strategy have failed, because data is the biggest and culture is the biggest impediment to shifting about data. I'm having trouble reading today goodness. My cue cards I'd be better at at this point. By the way, if you're interested in learning more about these cultural issues you can see I'm harping on them quite a bit, because they're important. So there's a free case study that you can download here courtesy of the Association of the computing machinery on that so let's go to part two of our remediation here. Let's prepare for dramatic change and determine how to do the work and that is a big piece. I actually introduce in the data literacy book, the concept of AA. Now a lot of people are a little hesitant when you start talking about this but when I sit around a table with a group of executives and I introduced the concept of alcoholics anonymous or any self help program. Many around the table will nod their heads because they've had experience with it not that they know but they know somebody who knows or they've been exposed to it. It's the best way we have of making changes in behavior where behavior needs to be changed. So I don't preach at you or anything but I do actually use it as a way of determining whether people are serious about it. If I can talk to your leadership and describe them to me in terms of changing like an AA type of change with the executives go yeah I know what change you're talking about. We're more likely to have a productive concept around that productive conversation. The second part of this is how do you get people. Now, the interesting part of the people discussion is that we don't really have it well defined even within our own data industry much less as we the data industry interface with the business and the it communities that are on here. But before I go into that I'm going to deviate slightly just to give you a sense of why. And the answer is and Ron, I know that's a terrible answer but it's a really good one. So, and Ron you have to remember was named America's most innovative company for six years in the row. And on the seventh year it suffered this largest chapter 11 bankruptcy in history is one of those oh my god. The sky is falling out by the wonderful conspiracy of fools by Kurt Eichenwald who's been in the news recently as well unfortunately, but it's a great the actual beach book that you can take with you so we're at August of 2001 and the reason nobody remembers this was because it obviously occurred right before 911, but right before 911 and Ron was going from $90 a share to 26 cents a share. The diner G a company came to the rescue and it's going to be a white mate rescue we're going to give you several billion dollars to help you get through a temporary cash crunch and and Ron spent the entire amount of money in one week. After you marry somebody is a bad time to discover that your partner has a business practices the person that any amount of money for any purchase at any time of course you agree with me or bad physical controls, and I'm nervous to go back to diner G at the end of the week and say can I have some more money and diner G said, wait a minute, what happened to the several billion I gave you last week and and Ron said, I don't know. Now, I'm giving you this as a very bad example of clearly terrible practices around the process of being responsible financially, we've since implemented and of always in the accounting industry, looked at objective qualifications that say to people who must have certain degrees or pass certain certifications, if you're going to be in that type of a position. But in it it's been very different first of all again we start with knowledge workers, what do we teach them about data and general absolutely nothing and of course what percentage of the needed. That would be 100% last time I looked on this, what do we teach it professionals about data though is even more shameful we give them one course on how to build a brand new database if there is one skill on planet Earth we need less of. It is how to build a plant a new database on this we have plenty of people who are good in that, but we give them no concept of data as an organizational resource and the it professionals and others who go through these programs. The idea that data is a technical skill that's only needed when developing new databases. Of course you've all heard the Maslow adage if the only tool you know as a hammer you tend to see every problem is a nail thing you shouldn't be surprised as well when we've tried to solve every data problem by creating more and more databases. And the surprise also that many companies also indicate that they have made bad distance students based on bad data. And that's not surprising because business decision makers and technical decision makers are not data knowledgeable similarly. They make bad decisions then or collectively perhaps that result in poor treatment of organizational data assets poor organizational quality and bad organizational outcomes. So we're in the lather rinse and repeat cycle here we want to break out of this particular cycle. Thank you again. Morgan Freeman, the most typical example of this is implementing sales force calm, and then deciding to clean the data in sales force calm, they should obviously occur in the other way around, primarily not because of anything good or bad about sales force it's fine not because of what we're looking for, but because sales force gets perceived. And it's hard for users to tell the difference between sales force loaded with good data and sales force loaded with bad data. The reason we had to break data out was a focus reason. There are lots of things we can focus on but we clearly need to put more focus or a chief focus around a single thing and we've seen this happen in the financial area where there's a chief medical officer a chief risk officer perhaps a chief medical officer, and also the chief financial officer does not balance the books the chief risk officer does not test software, and the chief medical officer does not perform surgery around this. So, bluntly, this lack of focus has been challenging and we need to bring in a person, if you will one throat to choke to be responsible for data, but are we going to ask the people currently in our organization to be the only input into that, and I would say we need to, and we have it in the federal government already sharing hiring panels and informal information behind the scenes, so that people can understand what types of chief data officers they are mandated to have by the way as a part of the law we won't go into that here but it's a wonderful development list in the federal government area so we are saying in fact we need some help recruiting somebody for the top data job that would be my title for it because that can exist at any level of the organization. And unfortunately, as soon as you say chief, many chiefs band together and say, do we have enough chiefs do we need to add another one to that and that's of course the wrong conversation to have on that. Many organizations have adopted an enterprise data executive that's perfectly fine as well I don't mind what you call it it's obviously known as the chief data officer and is seen as being responsible for data governance and other types of things that are primarily focusing on leveraging the data assets in support of strategy being unconstrained by an it project mindset because it's hard to do strategy project by project and reporting to the business so there's a good deal of synergy in that context. In order to do that. And it's very likely that the first enterprise executive is going to use up all of their political capital so we're seeing a lot of organizations rent a first enterprise data executive to make changes to do things that they can easily do and put in place where they don't need to worry so much about being nice and recouping later on, and that somebody else can come in and take over later on around that again not a happy prospect around that but it isn't all roses that's for sure in there. Let's talk very briefly as well about the seven deadly data sends these are the large piles of data debt that organizations faced in order to do this luckily they were seven of them so it worked out really really nicely and I'm going to start off with number two, which we just finished talking about lacking data leadership again many organizations will come in and say well because I have an advanced degree or able to really understand data well. I should be the data lead in the organization and that may be true, but that individual is also going to have to understand an ability to put a problem in a business context and to work with their peers in that same context around that as well. Organizations have not developed a robust programmatic means of developing shared data, and consequently they have a tougher time sharing data costs for it takes too long, and it doesn't deliver full results around all of that that their data is still run as a by product of an it project. Data pieces for it. That's good but again you can't assemble a program from a bunch of disparately connected data projects. Also we have to manage expectations properly, again gets to the political skinny process and people parts of things. Wonderful stories I can tell around this won't at this point in time but just to say that it's very very key to make sure that you make everybody on board and understand what it is we're trying to do that we are measuring time in years as opposed to weeks and months. We're certainly not sprints in the same context here. There's a sequencing that needs to be followed during data strategy implementation I think I've got a part of a chapter on that in the book, and then failing to address the major cultural and change management issues. And one deadly sin though is not understanding data centric thinking. And so we're going to explore that just a little bit to the detriment of the others and just say, we've seen a lot of this over the past 1015 point of years data driven data center data focus data provocateur etc. Great titles but what does it all really mean and that's a big challenge for us. We've got to develop some objective characteristics around it or nobody can look at it objectively and say you're doing it or not. This is an inspiration from the actual software manifesto which is very straightforward. Over time, their goal was for postulates individuals and interactions are more valuable than process and tools working software is more valuable than comprehensive documentation customer collaboration is more valuable than contract and responding to change is more valuable than following a plan. Similarly, on our side I've put them up as well. And again tried to do it in just the exact same very simplistic fashion to say that, well, there is value of the things on the right we value things on the left more data programs over it programs and again I'll just drive through them real quick, which is just to say that if we really understand what happens in their data is the foundation the heart and soul if you will have it and everything else, and and business and that you've got to build your data program as a real solid foundation, and then build it on top of that, but you need to understand the idea of making sure that you're investing in improved information improved data over technology because technology will not deliver you any improvement in the data, it is really straight through that you need to have a shared stable organizational data over it components again value both sides of the equation but one side should win out and receive investment around data reuse, again just the fact that most organizational data isn't reused as a prize use good dollars problem so again just some things out there if those sound interesting come on engage in a dialogue about it and we'll talk more around this this may not be the final version of it it's version two already. We're moving right on which so that's what I'm talking about in terms of things that are preventing you from really jumping forward and doing this in a way that will get you also to Carnegie Hall, which is what we're going to talk about next. Again, we're assuming we're right here we've eliminated the prerequisites and we're ready to now start off and lather rinse and repeat and there are five steps in this process. I'll just pop them all up there and then we'll go through them in order to do this but it hopefully will look very familiar to some of you because it is actually the theory of constraints. And let's go back to our strategy framework where we were here we blew this up and said no only when we have a perfect match between these two, or at least a close match between these two would you actually invest in something put together a roadmap and the other part of this diagram that's crucial. So we've got to have a balance in our results between producing business value and creating new capabilities if we do only business value we won't get the muscle memory of what we need to do as an organization. And if we only do the muscle memory part the right hand side of that, we won't create business value. Again, management will think we're a science project and that's just not the way we want to be seen we need to have it all pulled together. So balancing business value as well as development capabilities is a challenging challenging process, but that can be done and it actually means we are able to do more with less as we go around it. When you talk to others about data strategy they tend to say, yeah it's a combination of all these things and while these things are great I've yet to meet any organization that has all of them, ready to go and ready to integrate in here again we're going to go to Truman. Thank you again sir I just love that he agrees with me on this. Again, yes, it just doesn't work. There's no way instead. Remember our data strategy is more like a video game we're trying to eliminate but we're going to do is go around this particular cycle and watch at how to approach that and it makes a lot of sense because then once I build the first one. I've got that one taken care of I can go off to the next one reduce the data depth that's out there. And again try after the cost one now we go after that. And again, is that my way around the whole process and several times the goal. It's a wonderful book by LSU gold rat, when you're literally just talking to people as well and they will very often reference this as well it's a wonderful book describing the theory of constraints, which says that if we view our system our data production system in this case is being limited by achieving one or more of its goals with a small number of constraints, then we focus on that constraint restructure refractor whatever it is we need to do get rid of that because the constraint in data is only as strong as the weakest link. And so consequently, we need to address it in exactly that manner by making sure that each data cycle has a specific purpose that we use where we identify a constraint, we exploit the constraint quickly and easily if that doesn't work with the restructure to bring all the power to bear on the constraint to focus it, repeat the other process until it's complete, and then go back and start over again if we talk in data terms it's a little bit more noticeable perhaps. How can your organization best support organizational strategy by using data in order to do this what is the one thing or top three things that are blocking you the most. What are things that we can do to correct it operationally what can we do to rapidly restructure and fix this. If we can't do it that way, we need to then subordinate all non constraints other projects need to slow down we need to improve these activities to the expense of the others by addressing that particular constraint and again if that doesn't work repeat the process. This looks a lot like plan do check act plan do check act you know the Deming cycle. It's the same thing yes exactly continue to go around it, and that's the part that you want to get good at. It's a team that understands how to go through this process, they will get this down very quickly to a leather rinse and repeat process and be able to put time and attention into the specifics of the projects that you're looking at, in a way that produces valuable results to the organization almost immediately let me dive into a specific example here. This example, this is the dimbock if those of you are seeing it just first I apologize. That's the data management body of knowledge 10 places surrounding a center of data governance on this. Just to observe you guys if you look at this symbol, it doesn't tell you one's more important than the others although clearly putting the governance of the center is making some sort of a judgment around that process. I know that it's a governing process. In order to do that. So let's just take an organization that says I'm going to use best practices as I'm told to do in the federal government law now that you do this. And I'm going to make a collection of data that's going to in order to do this. Most organizations will say which of the pie slices do I start with thinking it's one to the exclusion of the in more likelihood your strategic implementation to this will be better because it's perfecting operations in three data management practice areas three pie slices, or two pie slices and the centerpiece. I think of it like a three legged stool, where again you're trying to get structural sound this. You're not going to sit comfortably on a two or one legged stool in order to do this it's likely you're also going to need three areas of the dimbock in order to do this and the first one might be let's do some data bear data warehousing, but also do some data maintenance and data quality management around this will do one cycle at trying to implement X number of months of work or sessions of work or cycles of work however you manage around that. And each of you get a one participation trophy for being part of that and I don't mean to done a great participation trophies, but it's the you've done it once that's good that's what we're looking at because most organizations have done it. So now we're going to go to our second iteration on this. The data warehouse but now I'm going to look at it from a metadata and it should perfect and gain some experience there but now I've got two X points in data governance data warehousing one X in metadata around this, our third strategic implementation here might be very similar but now changing and that are understanding the metadata to a more refined component of metadata which is reference and master data on this and really finishing up the project here with three X's of data warehousing experience three X's of data governance experience but one X of reference and master data experience and this is precisely what I want you to understand take away as we look at what's happening in this I told you twice. Again, doing these strategic cycles over time increases the capacity and improve your internal processes and then changes your focus from reactive to proactive about this. And if you get really good at it, you can parallelize your operations. So now you can spin up another team, multiple teams in order to get started it's really just up to the organization to understand, achieve, provide, demonstrate value around all these and this is really the key because organizations don't start out. When I look at data strategies they say yeah we're going to do all these great things of data. Wonderful. What's that going to do for the business. That's the real question that you want to ask. So by keeping it focused in this type cycle and looking at it as getting a team to do better with what you're doing. You will eventually get to the point where you're good enough to spin out a second team and a third team. And then you have a good capacity as you're implementing all of the rest of these alright so I've got just a couple minutes to summarize here let's go back through the very beginning on this. We'll try the camera I've got it unplugged but again, do you see what I mean when I say a data strategy says, how are we going to take the data assets that we have in our organization, and make sure that data strategy that strategy for the organization is better supported. There are a number of ways to do you as leaders are most qualified to actually do that work in there, but in order to do it you have to understand strategy is not a 100 page document it is a stream pattern in a stream of decisions and a data strategy then is a data strategy focused on, which is what are we going to do from data's perspective for the next calendar cycle with the efforts of my team in order to try and improve these things because only by working together, will we have them work in the way that a data strategy does support the organizational strategy and if the does it correctly, the organization will be running around the data leadership and saying more. Similarly, data strategy is necessary for effective data governance, the biggest challenge that we have in data governance and I'll just give you a data point Shannon and I were both at a conference in early December. Excuse me that was wonderful but the average data governance office of the attendees of that conference was one, and that was pretty astounding it's great that people are out trying to learn more about it and do this but it's also very challenging as an organization to come up with resources to do this or to demonstrate effectiveness so the data strategy is what tells your data governance what's most important. And again it's going to be some combination of improving your organization's data, but also improving the way people use their data. This can be accomplished by skilling up your existing knowledge workers, but it can also be put in place right now at HR by screening additional data literacy, as you're bringing new people on board particularly in the knowledge workers, because only once you've improved their data and improve the way people use their data. Can you improve the way people use data to support the strategy. It's a little bit complex and it needs a little bit of work in order to do that. This also then leads us to the problem that most organizations run head into, which is the lack of strategic preparation lack of organizational readiness for something like this, because the organizations are very challenged in order to pull all of this together they don't compensate for the lack of data competencies, and they don't eliminate the seven deadly sins to this. Again we just finished off with the iteration description, which is pretty straightforward leather rinse and repeat just like this. It's like the shampoo bottle boy. Better watch that I got one more thing is we're getting ready for questions and answers on this that I want to run through again just reiterate the multi page document strategy is less useful especially at first too much time is accomplished at the expense of becoming proficient at the cycling which is the better way to think about all of these things. Now, let me just take this one last minute here and indulge myself with a trip back to 1977 the year I graduated high school. And if there was one thing I hated more than anything else. It was this song by the Bee Gees, which is a stay in alive. You can see I don't hate this song of it anymore but man in 1977. It was a disaster I couldn't stand this song, because it didn't have any live musicians it was all done by computers with the career I ended up with boy that's ironic isn't it. So, Bruce Springsteen starts playing this song. And better still, it's to understand here not the song was at fault but obviously the way in which the song was instrumented or produced or however you'd like to do it. And I'm going to cut the sound back off on that. I think it'll cut up your song as well around but stay in live as a song is a fabulous piece of music, but to look at this specific performance which has only been played three times according to the Bruce Springsteen database of all songs in the world. That's out there he took this on his, his band just literally the night before which means the element of practice is critical around this as well. And only because he had a crack band that could do this could he spring this song on the plane on the way in, and they pull off a performance to this. Now I don't have time here to play you the whole performance around this. Instead, we'll tell you that we've got some upcoming events that we'd love you to pay attention to and hopefully see you out at some of these things again there's some event pricing on the books, but now it's time to turn it back over to Shannon for our question and answer. And I'm in the black circle there somewhere guys. Well, Peter, thank you so much and thanks to our attendees for being so patient is things started coming through a little bit better. I know we'll work on that bandwidth issue and make sure things are a little bit more clear in the future. So, and just to answer the most commonly answered questions, we will be sending I will be sending a follow up email for this webinar by Thursday to all registrants with links to the slides the recording and anything else requested throughout this been some great comments and resources put in the chat as well. So diving in here, Peter, what skill set a person must have to be a data strategy maker at an organization. Tough question. I think that the great to say I know that when I see them which sounds terribly subjective. What I see in people that I recognize and there are people who've been frustrated, perhaps in it perhaps in the business who for years and years have been afraid to say more because they know that nobody else either agrees or supports the challenges because so many of the challenges that organizations have and I say this over and over again that the root of all business challenges is the data problem and one form or another these people recognize that this is nothing new, but it's not recognized more broadly so there's an element of frustration pent up frustration in fact we see this in Dama as well. Those of you that didn't get the intro on this. I'm the president of the data management association, and we see that our members come to us after about 10 to 15 years of frustration in it. And basically what happens is they say data three times and somebody says to them, Peter you said data three times now you're the data person right so the question is to what skills, I think the skills that come in is certainly systems thinking and understand that data is the root of literally everything that happens out there in the business world. Good people and process explanation skills. I didn't know that I had a nice gift to be able to explain things easily to people and I apparently do have that. When I was working for the US military I would get an order that said get Peter and his slides to Detroit, or Denver or whatever city he was needed in because they needed something explained to somebody. It's a good skill to have and if you understand that ability to be able to do it that would be really good. A lot of patients to just that you're going to be listening and learning and doing all sorts of things around that. Great question though and I don't know that anybody's really put a list like that together so maybe that would be a wonderful thing for a future webinar. Reach out and maybe we can toss his ideas around somewhere. Thanks so much for the question. So Peter, do you have a source for the 95% of data space problems being attributed to human error I'm familiar with an IBM study from a decade ago, attributing 90% of data breaches to human error but not data problems, all data problems, any results out there. So, Randy Bean and Tom Davenport right the forward for Randy beans seminar that he releases every year and he literally this weekend released the latest version of it so the latest version is the 2020 data survey or whatever it is, and he asks a specific question in Are the problems in your organization process. People or technology and overwhelmingly over the past four years it's been 8020 at the minimum on that rising to as low as 5% technology problems and 95% people in process. It's a very, very consistent result supported by wonderful academic research that is entirely supportable, and just no question at this point in time that we are crying out for another component of data leadership in here that focuses in on these people and process and tries to do something by the way let's just think of something else too. If we have people and process problems that are problems that clearly organizations are reporting repeatedly a very five year period, the same proportionality. No improvements around that that also says what is going to address those data governance is the tool to address those exact type of problems. There's nothing else in our data toolkit that has the equipment in order to do something like that so again great question thank you for asking. And you have on hand or off the top of your head provide a real business world example of a data strategy statement. So this is the pattern in a stream of decisions around this and rather than organize something around somebody else's let's go to the Walmart statement which is one of the more clear ones for words. In order to do that and when, and I observed this this was the wonderful thing about working for a really interesting organization such as this one. They let this guy there every day behavior, and more important than just that I didn't call it out on the slide. It also governs the behavior around work groups. So the work groups focus on strategy. It's a component they understand so if I'm looking at a specific data instance of this, and the organizational strategy is every day low price. I do in data probably are not going to be useful if I start with the a variables and cleanse them and work my way through the Z variables right that's just not a very good way of responding to the business but if I find a way where I can impact lots of prices in a measurable way that helps both the consumers and the corporation in this case. So it's a very demonstrable way of data strategy helping out in that context so what would happen in the data group is that they would say, if this is the organizational strategy our data strategy to support this strategy needs to be X. And we're going to focus on trying to eliminate three things in order one, two and three, by the way to and three can change depending on what you learn from one but that's what your essence of your plan is, which is a lot better than having to read through 100 pages of something that probably, and most of the time does not turn out to occur. Perfect. So, um, Peter, are we using the term quote unquote strategy from a military definition, does it make sense to use orient observe decide act loops as a framework for iterating our organization's data strategy. And that was in that kind of context so the the first piece that I brought up here was that strategy as a business term only started becoming popular in the 1950s, when the management consulting industry discovered the word. So without that particular insight it's unlikely we would have come across it under any context and that the original definition for it was much more of a process rather than a thing. So focusing on a process we can improve it whereas when you have a thing, you only observe whether you correctly or not achieved it. Unless that thing is a living thing in which case you have to worry about the update problem so the framework that was suggested there is absolutely reasonable to incorporate underneath this context of pattern in a stream of decisions. And that's really what the concept of strategy in the military originated with and the concept of business should not differ far from that or they should use a different word. So in the military strategy you're out there in the world and the world has different things there are forests and trees and lines and tigers and bears, and your unit needs to be able to respond needs to be equipped to respond to all of those things that are happening. In some ways this is very complimentary of agile enterprise management, and it's a very useful component of the yes absolutely I'm going back to the military definition explicitly saying that the component of strategy and here I'll give you one more example is too hard to figure I had a colleague who was working in one of the big manufacturing companies and their CEO decided that they needed a strategy which is a wonderful thing to have, and the data governance lead was reporting up to the CEO at that point in time. So in that process of, you know, having the CEOs here literally, it was very easy to convince the CEO that a strategy made sense in that part of things and, while that was okay. The implementation then was then to go out and hire some 10 big firm consulting people to a tune of about 10 million bucks to develop a hundred PowerPoint slide deck, but literally has been sitting on the shelf and hasn't done a thing so again hopefully your experiences are better than that but it is nevertheless a big big challenge with all of these data strategies trying to get something that is operational and useful. And that I wouldn't invest too much but you don't want to invest too little. It's quite a balancing act. Good question. I have a lot of great questions here coming in so Peter, you know we suppose we have an enterprise data governance program in place with the various practice areas people metadata quality life cycle, etc. But a data strategy for specific data initiatives be informed by the data governance program or will it shape governance to be more effective in other words is it a chicken and egg problem in practice or does strategy envelope governance. It absolutely should be a chicken and egg problem and of course the wonderful thing about the chicken the egg problem like it real technical is that they actually figured out it was the chicken in first in that context but I like to this diagram here. So you're not going to spend your time thinking about data strategy for a week exercise, once a year and figure with things, although that's not a bad way to do it it should be more of a continuous process but it clearly is not going to be the dominant set of cycles set of topics that are occupying those data governance cycles, then easy to get behind something that says not only are we doing this because we think it's the thing that right thing for the data to group to do, but it's also the right thing for the data group to do to make the organization achieve its strategy after all that is what we are here for we are part of this larger organization that we want to support in order to do this so it's a very nice people about strategy and I started talking to people about data strategy more than 30 years ago and they would look at me go what we need a strategy for our data for. And generally what I had to say at that point in time was well sir the Russians are doing it all the time, and that would get their attention, very, very quickly in that. So, very much that these two should influence each other as I'm showing them a diagram here, but at the same time it's not necessarily a continuous process the data governance office should be running. There are simply multiple cycles of work that is designed to reduce the data debt, improve the way the organization uses its data in support of strategy again great question thank you for asking. So, band data decisions slide, this appears to be a knowledge capacity and hiring talent acquisition perspective that organizations need to be aware of their organization. So the bait bad decision slide yeah. Oh that thing yes okay good thank you. And would you repeat the question channel. Sure yeah this appears to be so on that bad data decision slide this appears to be a knowledge capability and hiring talent question perspective that organizations need to be aware of their organization. Any of you all were any of you screened for your data, your way in. My guess is the people who were doing the screening either didn't know what questions to ask, or perhaps just assume that your reputation was good enough in order to do that yeah this happens all the time I'll go back and elaborate a little bit on the Salesforce.com example because it is so ubiquitous in that I have 12 of these things within the past 18 months. So this is a situation where an arbitrary deadline, a technology driven driven deadline of Salesforce calm will be installed by again I'm just going to make up a day in March 1. So we all know that only one third of it project succeed with full functionality within the schedule that they provided and again, delivering for the cost that they originally contracted for, which means if I dentist had that bad a record I would find myself another dentist. I would have good records around it track. So consequently, these bad decisions are often bad data decisions and when I educate executives about executive data literacy. They then realized that that decision, even though they just wanted Salesforce to be in because they thought it would help improve sales faster, which generally is what sales forces claim to fame is. It wouldn't help if Salesforce calm got implemented with bad quality data. I have one example, one organization was very courageous and actually put their name behind the 10s of millions of dollars. They were able to save by taking what would have been a bad data decision right at this part of the decision making process where it says bad data decisions. They came to a wonderful decision maker who said. Well, how much of the data do we actually have converted in order to do this. The answer was 40% and the individual said go back and come back when that number is 80% I'll authorize the overruns on this project so instead of finishing for an arbitrary deadline. They measured the effectiveness of the process made a better data decision and claimed specifically the $40 million a year advantage. The first year alone. Now $40 million may or may not be peanuts to your organization. Most people say, yeah, it's more my salary so I'm probably going to report it and say I helped to bring that about. Again, great question in order to do that. I know that's making that together example. And here since you've given me opening it in order to do this is the decision to your programs I see so many organizations that come along. And what they do is they say okay we're going to have data governance program we're explaining the data governance we're going to explain data quality of people. We're going to explain data profiling the people going to explain data meta and all of the things that go into making up these components in there and I say no. Everybody outside of data will hear blah blah blah blah blah just like Charlie Brown hearing the teacher, but the one word they will get is data so give yourself a data program and make it very simple managing data with guidance but more importantly, make management that that's a data decision. So it's not terribly offensive I think to say put your data ears on and rethink about that data if you force them to implement that they won't have had time to cleanse the data that's going into Salesforce. If the data and Salesforce isn't cleansed a Salesforce becomes upset because they want their customers to get good experience with Salesforce on this and users are generally unsophisticated in unable to tell the difference between Salesforce with good data and Salesforce with bad data they just see the force, and then they look at it and they say Salesforce sucks now please don't take that out of context I'm sure somebody will make it into a wrap video where Peter's running around saying Salesforce sucks Salesforce sucks Salesforce sucks right now. It's not the case somebody's made bad decisions, I think Salesforce should be reaching out to me and saying, who are those people we'd like to correct that customer and anybody tweeted Salesforce these days anyway, I think I've babbled enough on that Shannon back over to you what a great question that was. Indeed so another question you know while we have an overall organizations organizational strategy. Can we have multiple data strategies for various data initiatives but all informed by an informing the overall enterprise data governance. Yeah, because I find data strategies are devised every time a data initiative is undertaken or proposed. The question I would ask in return on that would be, are they achieved after their proposed. I would be afraid of being associated with lots of promise and under delivery around that. But the, the question is, can there be separate pieces to it. Yes, and you use the word coordinated I think that's all that you have when you look at where most organizations are and again I've worked with some organizations that this is not true. But in most organizations they are really not the pinnacle of success in terms of how they manage their data they're challenged all the way around they're trying really really hard. But it's just not an easy task in order to come up with something. And so consequently what they need to do is focus in just a couple of areas. They have limited resources again I mentioned to you the conference the Shannon and I were back in December. The average data governance office was one. Right, so that's not a lot of people in order to do that. So the question of breaking something into. So, one of the things might be I'm going to use my elevator slide just because it has three parts to it. Your strategy might have three parts to it one, make sure that you get on the elevator when the bosses on the elevator right then have a good speech prepared so that you can deliver something to the boss that makes you look good as opposed to foolish on that and then hopefully follow up meeting by the way, what I've just given you there is a really good pitch for what you should be doing in data, almost every one of your data practice areas we call them the pi wedges around the data, the data governance, you should have absolutely a 32nd, a three minute, a 30 minute, and a three hour version of that ready to go. The idea is that everybody on your team needs to have the same response to what's going on there so for example when they say when is that data strategy going to be ready I heard you're going to have that ready by Friday. We'll have some things ready for you by Friday but really are taking a different direction than perhaps the typical organization here we're not going to have a plan that we're going to try to guess what's going to happen in the future, and instead come up with something that is useful. So the question was, would all three of those elements be part of that if I was going to call my strategy elevator pitch, then clearly part one part two and part three need to occur, although there's clearly a dependency in there and I'm sure that's what the question is looking for when they said coordination back and forth with all of these pieces, making sure that they all work back and forth. So short answer yes, but key is to make sure that you have that coordination, able to go back and forth on that. Peter, is it mandatory to have to for a business strategy to have a data strategy, since there are many middle sized companies that don't have defined business strategy how should this be addressed to define the for the to address the definition of data strategy. Yeah, a great question I hadn't actually dived into that but I have seen it happen enough times. I think I'm fairly safe making the statement that in a small size company, I think a data strategy is probably more useful than an IT strategy. And I don't mean that to disparage it. I just mean I think that the data strategy can be more effective in that context remember what we're doing here is we're looking up for guidance in many decisions that we make a pattern in a stream of decisions. And people in these organizations if they adopt the data strategy, for example, that we're going to make sure that we are not at risk for GDPR, or something like that and we're going to make sure that somebody qualified handles all that we're going to transfer all that risk to somebody then that will become the philosophy around that and people won't all just collect the spreadsheets worth of stuff here or there and, you know, do something that's perhaps I'm not authorized officially, you know, you see the air quotes come out from time to time. And in all of that, I think that the concept around understanding what's going on from a data perspective is different from it in particular, because of one factor and the factor is simply this growth. Now, in most small companies, you're not necessarily concerned about what we call surveillance capitalism, but you should be at this point. So basically, if you're not capturing data on somebody else somebody is capturing data on you it's eat or be eaten out there. Hunter be hunted as far as what's going on. And the idea of needing to have some sort of an approach to that is critical, because your it isn't going to rate your data is growing your it staff isn't growing at the rate data is growing data is growing at a rate that is just almost incomprehensible incomprehensible to imagine. I'm going to give you a URL here domo domo.com and look for what's called the year and data or something like that, where they do every year this visualization of what's happening in the data world. There is nothing else that's expanding out there because we can't see it feel it and touch it. It's very difficult for people to get interested in it again just going back to the elevator picture would be very difficult in most cases to get executives interested in data had not. We've been talking about data for the past decade in terms of big data and all the rest of the data science these things that have gone on around this. But it's just absolutely critical to understand that when management is trying to approach this subject if we can do a good job ourselves of saying hey, here's what needs to happen here's an articulation on this here's a short first speech that we can we can deliver over and over again, and I'm sorry Shannon my cat just jumped in front of me and completely distracted me so now I'm even lost the track of the sentence give me give me a word so I can close this one up and actually respond to the question just question. You're, you're my dad right. You were almost there that help for that maybe not so helpful that. You probably hit the question already to right. I did it well I know I haven't here you know that you know, you know how should be addressed the definition of data strategy is the ultimate goal for it for especially for middle sized companies to. Yeah, so what, because it is thank you for doing it because it is changing so dramatically in there. The way my cats would multiply if I let them right, I will just say that in there. It's, it's something that needs to be something needs to be done about it and if you the longer you wait the longer you delay the more catch up work there's going to be the more data debt that you will incur. And the more you'll have to undo before you're ready to start doing. And that's very tough all the way around thank you Shannon I appreciate that. Yeah, absolutely. Yeah, we've got time for at least for about one more question here you know and what is the optimum data strategy iteration duration. So in other words what should those cycles be based on in order to do this, what a good question, the process of going through the theory of constraints is actually written up in the goal and I make my students read it. I don't know if you say this before but my wife, when she and I got together 20 years ago and started you know what do you do what do you do kind of thing. She said all right well look here if we're going to talk about business you must read this book called the goal. Before we try to have any business conversations. I was like what I've read tons of business books why do I have to you know, if you're not going to read the book, we're not going to have conversations so of course I read the book and understood why it was she wanted me to read this. It's a way of viewing problems to say that we'd like to achieve a certain level of performance, but something is impeding our performance achievement. Something is getting in the way. If we don't explicitly try to get that thing out of the way, it will continue to be in the way. Now another colleague Tom Redmond calls these hidden data factories although that's a more of a disparate view but a constraint as something that causes one or more types of hidden data factories in your organization. And to exploit them. Are you going to need something as simple as Oh, I'll change from allowing users to enter data free form to making them pick off of the pick list. Maybe or is there something more fundamental than this in that in order for somebody to actually understand the information that they're supposed to be helping the customer with to improve the customer experience. They're trying to integrate information across 23 data screens in our ERP. Maybe that's what we ought to work on instead of trying to make it faster better cheaper around all that and again give you a good trade off there but it was it was certainly leading to that. That's a really cool question and I think I hit that one out of the park says thanks for asking a good question on that. I love it and that does leave us time I think to slip in just even one more. So, um, oh, I love this one because I know right off the top of my head, the book what book resources would you recommend about data strategy. So and of course, I'm going to tout it for you Peter your own book. I should never say my book, my colleague and co author Todd and I have been three books together on this and really enjoying the process of going through all of this but that's a start but there's some other books out there and if you haven't actually seen sun zoo. I would absolutely recommend that as well because that is the essence of the strategy by itself, regardless of data, I tear otherwise sorry I went to the wrong slide there, because I know we're getting ready to clean back up by the way I didn't answer the last question I forgot that that Peter they are asking, you know, is it a week or is it a year and the answer somewhere in between but it is nice that you have oftentimes an opportunity to align it with budget cycles in some sense. So let's just say that you have five of you in your it, excuse me in your data group, and you're each being paid 100,000. And so you cost the organization 500,000 a year. So probably you ought to find some way of demonstrating at least 50000 in value annually, I would work backwards from that. And you'll find it's actually not hard at all to do I have had some very successful groups, and that is the focus. Gosh channel we just leading right back into this again of the next book that Todd not working on which will be sort of a monetizing version to there's so many of these good stories out there. So we want to try and share them with you and actually if you've got one you'd like to contribute reach out to us as well. But that's going to be our focus because so many of you have done some really clever things to get us there. Perfect well Peter, thank you so much for taking off the year or 12th year was such a great topic really appreciate it thanks to all of our attendees and community for being so engaged and just other great questions and patients as always really appreciate it. And, and again so just a reminder, I will send a follow up email by end of day Thursday for this webinar of links to the slides links to the recording. Anything else in me email if you don't receive it by in your inbox the time you get to your desk on Friday morning. Peter, thank you so much thanks everybody. Thanks for 12 years of really fun doing this but I think also valuable it's it's very nice to meet you all when I go out to attend Shannon's events and things and you all walk up and say hey I found your webinar useful because all I see is a blank screen and I talked Shannon. Yeah we do have a great community so thank you all for sticking with us for this amazing Peter and thank you appreciate it. Thanks everybody.