 Hello and welcome. My name is Shannon Kemp. I'm the Chief Digital Officer of Data Diversity. We'd like to thank you for joining today's Data Diversity webinar, Data Strategy Best Practices. It is the latest installment in a monthly series called Data and Online with Dr. Peter Akin. Just a couple of points to get us started. Due to the large number of people that attend these sessions, you will be muted during the webinar. For questions, we will be collecting them by the Q&A section. If you're not with us or with each other, we certainly encourage you to do so. And just to note, the Zoom chat defaults to send to just the panelists, but you may absolutely switch that to network with everyone. To open the Q&A or the chat panel, you can find those icons in the bottom of your screen for those features. And to answer 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 you our speaker for today, Dr. Peter Akin. Peter is an acknowledged data management authority and associate professor at Virginia Commonwealth University, president of Damon International and associate director of the MIT International Society of Chief Data Officers. For more than 35 years, Peter has learned from working with hundreds of data management practices in 30 countries, including some of the world's most important. Among his 12 books, many are first, or many firsts, starting with Before Google, Before Data Was Big, and Before Data Science. Peter has founded several organizations that have helped more than 200 organizations leverage data-specific savings, which have been measured at more than 1.5 billion US dollars. His latest endeavor is Anything Awesome. And with that, let me turn everything over to Peter to get today's webinar started. Hello and welcome. And hello to you, Shannon, and to everybody else. Happy New Year. It's great to be here with you. And again, as Shannon notes from time to time, we've been doing this for a number of years. And we're so glad that you all are sticking with us throughout the journey on this. Yes, today our topic is data strategy best practices. And I want to start off with a favorite. Everybody has their own favorite. Gladwell quote. Well, mine is practice isn't the thing you do once you're good. It's the thing you do that makes you good. And let's keep that in mind as we sort of review this material just to give us a little bit of context on this. I contend that strategy is inherently a repeated repetitive process that can be easily improved and that the only reason a data strategy exists is to support the organizational strategy. Yes, of course, there can be complimentary of each part, but really the dependency is clearly in that direction. There's an evolution focusing on improving data capabilities within the context of the larger organizational strategy. The plans are of limited value and always discount obstacles as you'll see from the material coming up. And there's an overemphasis on technology year after year, our survey show the same results that people in process challenges are the large parts of the problem. So how does one get to Carnegie Hall? One asks, if one is a musician, the answer is practice, practice, practice. So let's talk about what we're going to look at today. A data strategy specifies how organizational data assets are to be used in support of the organizational strategy. In other words, we've got this fuel, if you will, or soil, I like to call it. And we're going to use it in support of making our garden or organizational garden better. We're going to do it in this fashion here, and that'll be the first piece we jump into. Then we'll go into a section on data strategy coordinated with data governance, noticing that there is a improvement practice that doesn't stop with just the data being improved. And that there are some prerequisites to getting yourself in a position that you're able to take advantage of this type of process here. And then we'll get to finally the last part, which is how to do the actual strategy itself. There's a very nice way of approaching this without too much difficulty. So let's jump right in. Most people think strategy is complex because we've kind of made it complex over the years and certainly at an it project level, which remember where data has typically existed up to this point is, is where strategy comes through. So if we take an organizational strategy and take one set of organizational objectives and then we look at what's happening within the typical it that message is not perceived as a significant unified message. In fact, it gets mangled and garbled all the way along the way and strategy is an essential element that should infuse architecture. So this is one place we do not want this to be messed up, if you will. And this is very much about keeping everything the organization does more or less on the same track and not changing just one aspect of it at a time so that we can follow these changes and understand how they work within all of this. Simon Sinek, if you haven't had a chance to see his wonderful Ted talk on this reminds us that human beings are pretty good at telling what we do. And right now, according a webinar, right? Well, how are we doing? That's a little bit more complicated than and then why are we doing it becomes, of course, the real important part of this and strategy is what provides the why as we're looking at organizational strategies because you see people don't motivate you because you make a webinar. They want the why you do it I like to do webinars because it helps me share some of the things I've learned with other folks here. Martin Luther King not that I'm doing any comparison here he said I have a dream speech he did not say I have a plan speech and plans quite frankly are a real aspect of strategy that are problematic so let's look at what strategy is. The use of the word strategy in literature did not really start until the 1950s or so when management consultants discovered the word from the military. And so you end up with a definition like this of a master plan or a game plan that takes us from place A to place B and the challenge with this is that this means that strategy is a thing. And it's not the way I'd prefer to think about it I like it instead to go back to the military original definition, which is a pattern in a stream of decisions. Now that makes strategy into something that is much more of a process than a thing. Let me give you three quick examples of this. This is a former Walmart business strategy every day low price Walmart did a great job year after year of convincing those four words that people should understand this is what the rationale was why Walmart exists to provide this every day low price to their community and everybody on the Walmart expresses on the way to Bentonville and talking to people at Walmart and customers and things and suppliers everybody understands this this is an excellent example of everybody understanding a simple strategy here's another strategy Wayne Gretzky. And if you're not familiar with the Canadian hockey grade his strategy is that he skates to where he thinks the puck will be, as opposed to trying to change chase around a large lump of hard plastic that is much faster than anybody can individually skate So hopefully you're seeing the theme here and simplicity and let's take it to one more example here. If I'm Napoleon in blue on this little picture here, and I'm fighting against a larger combined army that is read in the British and black in the Prussians here. How do I defeat the competition when their forces are bigger than mine the answer of course is divide and conquer and this strategy is still taught in our colleges work colleges and things like that in today's environment. Now let's take a little closer look at this strategy here. You can see that lines of supply are shown on this particular map, meaning that it was one of the major causes of analysis and the British member in red are supplied from the coast in a stand. Whereas the Prussians in supplied out of the age remember they're in black. Our strategy becomes now two part first divide so I have to hit them this is Napoleon talking to his troops we have to hit them hard enough that they fly apart on the force of our attack. In order to do that, but we're not done yet at that point we then need to encircle them and everybody needs to turn to the right and encircle the percussion to Prussians that and then go to the left and get the British so let's just review the strategy first hit both armies hard at just exactly the right spot with enough force that it's going to drive them apart. Because remember an army being driven apart is more likely to run towards their supply lines than away from their supply lines, and then everybody in the army turn right and defeat the Prussians. And then turn left and defeat the British and oh by the way please do this, all the while, somebody is shooting at you. Well, I hope you agree with me that this is really an example of a complex strategy. We're calling that soldiers are not necessarily the most able to ingrain complex strategies, the visceral level, this complex strategy is really, really difficult for a large organization to implement and this is of course why we need to think about also in terms of simplistic strategies I mean just let's think of another aspect of it how long is a strategy good for if we're the good guys over here on the left, and the bad guys are over here on the right, we're going to have one strategy if the playing field looks like this, we're going to have a different strategy. If we're up here and the bad guys are down there. Similarly, we're also going to have a completely different strategy if the bad guys are there, and we are down here so this is why it's such an important concept to get right what we mean by strategy a pattern in a stream of decisions. Also, these strategic decisions guide workgroup activities, one of the things that provides workgroup cohesion is being focused all on the common goal and so a strategy that winds up on a shelf is simply not useful or typically unused. This is not a new thing that I'm observing to you, Dwight Eisenhower had a quote that he in preparing for battle I've always found that it's useless but planning is indispensable. Similarly, Mike Tyson rewrote General Eisenhower's quote a little bit and said everybody has a plan until they get punched in the face. Well, let's go and look at our data strategy then in this context it's the highest level of data guidance that is available. It's focused on data activities that bring about business goal achievement. It's the key linkage between the two and providing guidance when faced when a stream of decisions or uncertainties and unfortunately most of the data decisions that are made at operation level are problematic but we also have to keep in mind that a lot of our data decisions are made at the top of executive level where they make data decisions without realizing it will come to that in just a little bit. So the data strategy most usefully articulate how data can be best used to support the organizational strategy, and it's got to involve a balance of remediation and proactive activities. No organization I've been in over the last 35 years has zero amount of data debt. Some have more or less that is clogging their data pipelines with bad data debt scenarios that are in existence here. Data strategy measures over time we should be able to see some effectiveness that people are seeing some good effects from it that the volume should be not a whole lot longer than the organizational strategy in fact I'm going to quote John Lathley here who says that the data strategy should really be the data chapter of your organizational strategy great way to think about it from in there. You should immediately think of this as having versions because if I hand you version one you're going to not be surprised when I hand you either version one point one or version two point in there but if I don't set that expectation up in front of you can be lots of confusion and finally understanding here the goal is to share agreement among technical business and systems references that are there. I'm going to show you this next picture this is from my friend Chris Bradley thank you Chris of course on this this is really aspirational after you've done this several cycles of what we're going to talk about today this is the goal that you should try to get to but trying to do that at first is generally not a good good focus on this so let's look at our planning options. We can one plan the entire process get one attempt and say, we're going to get there in this series of x number of steps that we're going to get there. This requires numerous upfront assumptions, because plans have to be detailed and specific and objectives have to be reached if instead I think about strategy as this cycle. I can use these cycles to focus in on and again use corrective feedback to make sure that I continue to stay focused in on things that are important to the organization, because over time as I get good with this. What we're going to discover is that the organization will have increased capacity and its ability to improve operations, but the focus also evolves from reactive largely to proactive in general and that's really important in order to keep things moving forward. Over the years I've had these and other things be proposed as strategies. These are obviously are good technologies but they are not in and of themselves strategy so let's take a quick stop here and just sort of say a data strategy specifies how it's going to be used. I understand now strategy should be a pattern in the stream of decisions, and this would be a pattern in the stream of decisions about data that help us achieve a certain aspect flavor improve a subset of data, etc, etc, that they have to work together as complimentary strategy and that itself is going to evolve periodically on this again just to show an example here as if we were playing video games in the process and strategic focused at first might be on space and I can go up there and work on it. Get rid of space and solve that space problem. And then when I go to focus on cost, I can work over there and eliminate the cost of cost on this. Hopefully this makes sense to you because the next thing we need to dive into is the understanding of how data governance and strategy are so closely. Here are just to start with seven definitions of data governance, including one from my own organization to DIMBOK the name of DIMBOK. All of these are fine definitions but I just want you to imagine having an elevator speech and the boss looks over at you and says, Hey Peter. What do you think about data governance? I've just heard about this before and I understand it's important strategy. So talking to them about any of those definitions out there as good as they are is not going to produce a happy result of the boss saying, come on, tell me some more about this. It sounds intriguing. Instead, it's much better to start off simply and describe data governance as managing data with guidance. I actually got 20 seconds on the elevator right left and I can experience and answer questions and things like that. In fact, one of the questions that somebody might ask is why would you want your soul non depletable, non degrading, durable strategic asset managed without guidance? Of course, you wouldn't want that to be the case. And the implication is of course if you don't have data governance you are in fact not managing your soul non depletable non degrading durable strategic asset. And I find that this definition is insufficient once we get to a certain level of management and that you have to alter it slightly because people don't realize that they're making data decisions. So we have to educate them about these decisions being strategic in nature. Again, I'll give you a very simple one, Salesforce implemented by January 1, right? Well, aside from ruining everybody's holiday, it's probably not the best way to go on that. We'll circle back to that in just a little bit. Most people understand there's some data stuff in there and they might have gotten that there's data management and data governance. But typically if you try to explain to them all of this, this is what you get. So stop trying. Instead, just talk about your data program in general. It'll make all the conversations easier. And all they have to think of is it's part of the data stuff that they do over there. Because everybody's getting the picture that data as your soul non depletable non degrading durable strategic asset beats out the other forms of assets that we have. Again, financial assets can be used up real estate inventory they can degrade over time. And if we look at it as an asset it really has some unique characteristics. And people want to say that data is the new oil I'm quite opposed to that because data thought of as oil is a production only function and data actually does better on a reuse component there. So I like to add a letter to the word oil and call it soil. There's two things that are important here one, if we prepare the soil, we don't just randomly flick seeds about the yard and hope that it's going to work. And seconds, we don't plant things on Monday and expect to eat them on Friday. So it's a much better metaphor for the whole thing. On the other hand, if you need some sizzle to sell everybody does any bacon's great as a great buzzword these days, etc, etc. Key here is that data as that form of a strategy just like your HR group has a strategy, your data group has a strategy and it should receive attention on par with other strategic initiatives that the organization is doing. And you will need professional administration to make up for the past neglect. I'm going to show you what I consider to be an egregious example of this just to see how real it is in 2020, both American and United Airlines were valued by Forbes marketplace article at $6 billion and $9 billion US respectively in there. But they were valued at the data in their frequent flyer program as being tens of billion dollars over this. Do you not believe that the head of American Airlines and United and they're all the same. If they could figure out the way to unlock the value that they have in that data. It could be just an absolutely astounding thing you could buy American Airlines for $6 billion in the marketplace and sell it to somebody else for just slightly less than that $6 billion or maybe even a little bit more if you can improve it slightly and then just keep the data as that $10 billion thing off the top and you've got a But nobody can figure out how to do this. And part of the reason for this is that people are unsure how to deal with data debt. It's not easy to visualize. It doesn't look like anything when you look at it, but it slows progress. It decreases quality. Increasing increases costs and it presents greater risk to organizations. So, one of the things we have to think about from a data strategy perspective is our focus on this. Of course, you have a lot of and chaff in your organizations data we chaff we're going to talk about precisely how much but let's just start with a basic question is well organized data worth more dollars. The answer to that has been long evident but sometimes we forget and we need to be reminded our friend here Abbie covert put together a great example and said, look, if I just handed you a random piece of paper pieces of pages pages in a book and didn't give them page numbering or alphabetic contents, all these things that are in there, it would absolutely not make as much sense as if I arranged them in order and write to them as if you're going to read it from start to back. Abbie has some great additional examples. I just want to make sure she gets credit for that but if you took the page the binding off of those things the paper disappears quickly knowledge becomes ephemeral. So, better organized data increases in value I think that's pretty easy to do. Now you have an excellent example of that if you need to make it in a board meeting because, believe it or not, that comes up as a topic from time to time, not so much of the board meetings but the board's actually seen this and understands it. But here's another thing that's critically important for understanding 80% of all of your organizational data is wrought. Wrought standing for data that is redundant obsolete or trivial the question is of course, which data to eliminate most enterprise data is never analyzed at all. So who is most qualified to do this kind of analysis work well. And that would be the people who you have involved in this you want to get them involved in your data governance effort. So remember data strategy exists to support the organizational strategy and no other reason on this. Sure, you may come up with some answer when when wins those are great but the principle reason is supportive organizational strategy. The next question is data strategy also tells the data governance group, what data assets do to support strategy and the governance group feeds back and says how well is strategy working. Obviously we may need to change one or both the approach to data governance and the data strategy in and of itself on that. If we also look a little further in Peter's world at least it projects are influenced by the data governance initiatives. data delivered by it. And how does it support the strategy we have some operational feedback. Just make sure we've got a nice complicated diagram up there never show this to management. Let's make it simpler. Here we go. All right. Organizational strategy data strategy data governance and now I've got this concept of data stewards. These are your most effective investment in the data governance context is to get your data stewards working on something what should they be working on. They help things that are definable in explicit business goals. None of this silly things are going to get better. Right 1000 points of light or anything that's get focused and get specific on it. And the language of data governance has to be metadata so we infuse those servants they become two way corridors going back and forth. We're implementing strategy and also telling us how well strategy is working so that people can have a good feedback session around all of these. Okay, let's move on a little further. Let's talk about effective data strategy prerequisites are challenging enough around all of this. First of all, we've got a data strategy framework I'd like to present to you and show you the way that most people do it incorrectly and how it should be corrected. Most people say I've got some business needs and we'll figure out what the business solutions are in order to do that. This is wrong. Thank you Morgan Freeman. So I'm going to have just a touchy bit in there. Yes, why is wrong with this. Well, it leaves out a very important component. What is the state of your existing organization. If your organization is attempting to do something is beyond its organizational capabilities, you're simply wasting money and that happens over and over and over in organizations that are really unfamiliar with the process. So let's just take an example. Would you take the fob to your brand new Tesla and hand it to a 16 year old that just got their driving less license and ask them to go out and drive in the big storm that is currently blanket in the United States right now. Probably not the best idea for a good outcome in that. The current state of the organizational readiness is a absolutely key piece in here in figuring out your data strategy framework. We're going to come back to this and look at the second half of that after I go through these particular phases. The first one is what the data assets do to support strategy and there are some prerequisites as I mentioned before, there's prepare for dramatic change and determine how the work is going to be done recruit a qualified knowledgeable enterprise data executive staff and other talent, and then eliminate the seven deadly data sins will take them in order. Of course, dramatic change. Why is this well, my first book in the subject was going to be called CIOs aren't not a very nice thing to describe now a CDO job description here. I ended up just so that you can have this and see the role strategy plays but understand there are more chief digital officers than there are chief data officers right now so we're definitely in a minority. Let's make it a little bit less complicated senior leadership should be part of this. The portfolio is every knowledge worker in the organization focused on productivity improvements and organization wide conversations and focuses on data and innovation. The CDO establishes fiduciary responsibilities. This is done through the organizational data governance program. Most organizations fail by trying to do too much at once. Instead you should focus on specific cyclical things that can be attacked prove that they've worked and move on to the next one. Again, focusing in on getting rid of that data debt reducing its value that's there and driving to understanding of the data value chain in the process. As I mentioned my first book in the subject I originally wanted to call it CIOs aren't ended up calling it the case for the chief data officer it still ended up causing a little bit of controversy. When it was translated into Chinese the title came back chief data officer combat, not that there's a bad thing there but you are putting in place two different individuals both of which have either information or data in their title, and that can lead to confusion and certainty doubt resentment. Hey Mario looks like I'm going to see you this summer at the CDO event. There is a class of professionals that can help us with this, and we should not ignore them we should ignore them at our peril. It's the idea of change management and leadership. And this is a concept. I actually have to explain the young people because all the locks will be electronic, but right at the moment when I look at an organization, and I see vision skills incentive action plan and frustration. I know that they're missing the resources when I see anxiety as a symptom. I knew they're missing skills, only when all of these things line up correctly does one achieve change and that's the main reason. Organizational data programs, including data strategy objectives. Do not succeed change is the biggest culture is the biggest impediment to shifting about organizational thinking about data. If you're really passionate about this. I have a free no registration case study download that you can use to go a little bit further into that topic. Let's move now from dramatic change to recruit a qualified knowledgeable enterprise data staff. Now the first question that comes about is how does one recruit a qualified staff. If one is not familiar with the process of hiring data people explicitly you've gotten good at hiring IT people, but not data people. Take us on a quick side deviation here and talk about the Enron Corporation may or may not be something that you remember, but in the summer of 2001, the price for Enron fell from $90 a share which was astronomical at the time to which is still pennies even in today's environment. A wonderful book by Kurt Eichenwald here called the conspiracy of fools makes for a great summer reading book I know it's the middle of winter right now but if you do have books that you're thinking about for the summer or somebody likes to read it forget about the Enron stuff. It's just a great story but back to our Enron story here. They had some problems obviously if the shares were selling for a 26th sense of peace, they had a company called Diner G come in, come in and offer them several billion dollars for an attempted rescue. Now, I don't know about you all but if you're having conversations with somebody and you think you're going to have a longer term relationship, develop out of that you should probably have a money conversation before you get married or whatever other type of engagement you decide to sign on the dotted line for. The reason for that is very simple. Diner G gave Enron several billion dollars at the beginning of the week and Enron spent the entire amount of money in one week. Oh, my goodness. That takes actually a lot of effort to do that. I'm not going to talk about that. That's in the book but time to have this conversation. Remember, it's not after you get married as before because you would have found out that any person at Enron can write a check for any amount of money for any purchase at any point in time. Enron went back to Diner G at the end of the week and said, can I have some more money and Diner G said, what happened to the several billion dollars I gave you last week and Enron said, no. Now, this is painting to you of course a financial picture that is simply not up to speed and we have in the time since Enron come up with objective criteria for somebody getting the designation of CFO, Chief Financial Officer in an organization. There are just some of the designations that may become important to this. I have to brag here just a quick bit. Again, I'm at VCU Virginia Commonwealth University and we're in Richmond, Virginia, our competition across the street is our good friends at the University of Richmond, from which the Enron financial officer Jeffery Skilling graduated at one point in time and they thought they had a great alumnus until they discovered he was going to jail. I believe he's gotten out of jail now. So what point here is to tell you though that we have now objective criteria for describing a CFO. These are necessary but insufficient prerequisites for describing a CFO. We're just developing good sets of prerequisites for CEOs, much less CDOs. There are challenges and the challenge is bigger than just that because what have we taught to most knowledge workers about data? The answer is of course nothing at all and what percentage of them deal with it on a daily basis. The answer is of course 100% of them. This means that most people who deal with knowledge data know very little about it, know much less than they should know about it. So I hope that the knowledge workers in the IT profession are going to get this because for 30 years we've been teaching one course how to build a new database. We've got a little better in recent years. We're sometimes teaching more data courses and that's wonderful with the requirement for this is still one core database, which means that IT professionals get the sense that data is a technical skill that is only needed when developing new databases. I'm going to move two databases together. I'm not creating a new one and I don't need these IT professionals. If I'm going to move from one ERP to another ERP, I don't need a data professional here. Is the thinking, yeah, I think it's incorrect thinking, but it's what we've trained them and people do tell me. I'm surprised you think a data person would be useful in there. And of course, the other part of this is we can teaching people for 30 years that the answer to every data related question is a brand new relational database. I'm going to quote Abraham Maslow here if the only tool you know is a hammer, you tend to see every problem as a nail and gosh we're surprised that the problem today is that we have too many databases that are out there without integration that nobody keeping track of them that things are going on that are way way beyond where we need to be. No, this is crazy stuff in order to do this and therefore it's no surprise that many, many organizations make inaccurate decisions based on bad or outdated data. And that's because they're truck, excuse me truck, they're stuck in a bad data decision spiral. I'll describe it this way. And this technique makers are not data knowledgeable. Therefore, organizations keep making bad data decisions those bad data decisions results in poor treatment of organizational data assets and poor quality of data, which result then in poor organizational outcomes. We get into the cycle here of, oh my gosh, how do we get out of this bad data cycle. Thank you again Morgan Freeman. Yes, absolutely. This is wrong. And more to the point I mentioned earlier installing Salesforce by January 1 as a potential goal that may make sense from an IT perspective, but I have many, many case studies that prove over and over again that taking the extra time to not just achieve an IT goal of implementing Salesforce.com by January 1, 2024, but instead taking an extra month or so to make sure that the data in Salesforce is of sufficiently known quality that you feel good that you can release it. It's a good decision to do that. And the reason is quite simple. The population out there, remember the wow, wow, wow, wow, cannot tell the difference between Salesforce filled with bad data and Salesforce. They will turn it on. If the data in Salesforce is of poor quality, then we will have a very significant problem in here because the workforce, the knowledge workers of the organization are not able to differentiate between Salesforce. A good piece of software filled with bad quality data and Salesforce not working the way they want it to. Again, very, very important to make that distinction here and I'm talking about this in the context of finding good people out here. So we have discovered over time that the more you're able to focus on various aspects of things, the better the outcomes will be. And we've discovered that having chiefs like a CFO, a chief risk officer, chief medical officer are important for organizations as they go through various stages in their organizational development. And we've also noticed that the singular singular focus in there is extremely helpful that CFO does not balance the books that chief risk officer does not test this software and the chief medical officer does not perform surgery. These are critical things to be able to put in place. And yet you're supposed to go out and hire a chief data officer that's going to do your data strategy. And you don't know what those things are. Well, first of all, ask your board. Your board is much more likely to be able to come up with things that are going to be helpful and areas that they can find other pieces. There are organizations such as the data management organization that we have. There's a data leadership conference that we do out of MIT on a regular basis. The data diversity itself has tons and tons of events and we're going to all gather out at enterprise data world coming up to look at some of these topics here. But you can't assume that your internal organization is going to be able to do the hiring correctly. It generally is challenged around that topic. We've decided that we need to have a top data job. I prefer to call it that. Some people call it enterprise data executives. Chief data officer seems to be a title. But as I said before, they're more digital officers than there are data officers and they're going to set up a data governance organization and they need three things in order to do this successfully. They need to be focused 100% on leveraging data assets. They need to be unconstrained by an IT mindset and they need to report to the business. If you can't change these three things in your organization, then your success rate for your CDO is going to be problematic. The enterprise data executive will take one for the team and get crushed and the average tenure of the CDO is lasting about 18 months in today's world on this. So to recruit this to get to your strategy, you're going to have to have somebody who's able to put in place a search that's going to go beyond the levels of the organization and find. Yes, the internal existing expertise that your organization certainly possesses, but also the leadership capabilities that have presented prevented your. Achieving the success so far. Finally, there's the seven deadly data sins that you need to eliminate. Again, I mentioned the case study on them earlier. They are, you don't have the qualified data leadership. You don't have a pro a robust programmatic means of sharing data. You don't have your data program aligned with your IT projects. You are not managing expectations. You're sequencing data strategy implementation. You're failing to address culture and data management change management organizations and you're not understanding what it means to be data centric. So I'm going to give you a quick lesson in that number one category. The others are a different talk in here, but I'll take this if it looks familiar. It's from the agile manifesto. Of course, we are uncovering better ways of developing IT systems by doing it and by helping others do it through this work. We value the value and of course the key here is there's value with the items on the right hand side, but on the left we value those items more we value the idea of making sure that data programs drive IT programs and not the other way around that we value informed information investing over technology acquisition that we value stable shared organizational data over IT component evolution. And that we value data reuse over the acquisition of new data sources that are in there. So now we get to the last phase of this, which is really the idea of where are we going to find out specifically what does it mean to go through these strategic cycles. I call it Lava rinse and repeat you've eliminated all of the things that you need to poor prerequisites, including the seven deadly sins have qualified data leadership and implemented project. Excuse me. Change management program management professionals to help you out with this process. You're right here you want to find out and we're going to go through this strategic cycle a couple different times in the remaining 20 minutes that we have here in the program, but these are the five steps identify the primary constraint exploit organizational efforts to remove it subordinate everything else to the exploitation decision alleviate the data constraint and excuse me elevate the data constraint and then repeat the above steps to introduce a new constraint to the process. If that sounds familiar it should I will show you where I get all that from in just a second here. So recall the first part of the data strategy framework again don't just go out there and look at a solution look at a solution that is targeted to match your organization's ability to implement that same thing has to be true in your data strategy, and only when you have a data strategy that matches the existing set of or state of organizational readiness, can you develop road maps and execution plans that are going to help you in this process that said, you must however pay significant attention to balancing in here the concept of creating business value with new capabilities. And nothing but new capabilities management will eventually figure that you are a science project. That is not the message that you want them to get. If you focus on nothing but business value, you will not be able to bring the culture changes needed, wide enough and far enough into your organization in order to do this so this need for balance is very very critical. There are a number of other books out there on data strategy money are very good. But they really come down to this sort of a message, your data strategy should be a combination of your data governance strategy or metadata strategy your data quality strategy I'm not going to read the rest of these. I also in 35 years have only encountered a fraction of companies that have even some of these documents prepared once again thank you Morgan Freeman for being so vociferous about this yes. Until you get all of these individual pieces done on an ideal world this is what should happen, but this is implementing towards that data strategy that winds up on the shelf, as opposed to the one that is cyclical in nature. So I showed you this picture before we're going to eliminate space as the first thing. Well, again in our process of doing this, we're going to go through a what's called a strategy cycle. This is the first strategy cycle and at the end of strategy cycle I have fixed the space problem whatever the space problem was. Now I go through strategy cycle number two. A strategy cycle number two allows me to go in and work on the cost cycle. Again here, focus on it, get it set up and eliminate it by also going around that strategy cycle that, like I said if it sounds familiar should come from a book. The book is called the goal. Interesting little side story about this but my spouse who is a what called a rescue accountant told me that she would not have any conversations with me about business, unless I read this book first. I've always been thankful for that and we've had some very good business conversations ever since then. Of course, this is a management paradigm that talks about the adventures of Alex Rogan working for a company called Unico, and he goes in there and finds out that he's going to lose his whole factory in a week or so if he's not careful. Sorry, it's months actually, in order to do this, and he goes to his old professor who is a golden rat in disguise there and learns about the theory of constraints. Theory of constraints is what I'm using for data strategy the chain is no law no stronger than its weakest link so it's great to say that I'm going to take and use as a strategy. AI to help exploit my data well. Informatica has a saying right now that is everything's ready for a I accept your data. Love it Informatica they certainly get credit for that. The key here is the data is often the weak link but there are other things that are programmatic about it as well. And again those are the deadly sins that we talked briefly about on this. Each cycle in this case goes through a specific focus and the key to getting this right is to understand older ops theory, which is to identify the current constraint in there and make decisions about how best to exploit it. Subordinate all of the non constraints to the new piece that can be exploited when we look at it from a data perspective again. What's the one thing that we can do that will help in the most efficient way specify in terms of those very specific business goals that are there. Subordinate all non constraints to that piece and alleviate that current constraint or elevated depending on which way your analysis works out. And of course you're not done in order to do that. This process of cycling through organizational strategic initiatives is a way that the organization can go through all of these pieces to understand and get better at this. And if this sounds surprisingly like plan do check act. I hope it does because I in 35 years have seen literally hundreds of different methods. They're called methodologies when they're brought in because consultants like the 25 cent words instead of the nickel words. I remember the nickel is on this. And every method I see a cruise to plan do check act and this theory of constraints is different from that. Let me show you how this might actually play out in the real world here and let's just assume that an organization has, you know, sort of got part of the message they see some passage Michelin Casey did and the data strategy books and so forth. Which is really nice. And the idea is that the CEO is trying to figure out why he can't get what he wants to out of his organization people keep telling him. The data is not right and we've got to get the data right so he quote buys a data warehouse and wants to know what's wrong why doesn't that actually work. Well, the data warehouse was purchased by, you know, they bought it from company X, and they put in some data that somebody told them to put into it, but nobody really knows whether that data is used by the decision makers in order to do this. So, hopefully this isn't the first time you're seeing the ups there is a dama wheel the dama dimmock wheel. Again, the idea here is to say what data management consists of and these are practice areas we call the pie wedges that are out there. And what happens unfortunately when most people are doing this is that they try to do it all from one perspective. I'm going to tell you that you really need a three legged stool to get structural integrity correct and in the sense of doing your data strategy you're also going to need it here as well so the strategy for finding out what's going wrong with this warehousing operation that this CEO has paid for, but apparently not gotten value out of it may proceed by going in this kind of a fashion. So if you look at the dimmock and get some guidance around data quality around the data warehouse and business intelligence. Oh yes and around data governance as well so it's a combination of those three things and that's going to be our focus for our first strategic cycle we're going to do this for a certain time we're going to see what we learn from the process and we'll use that as input to iteration number two iteration number two looks a lot like iteration number one, but notice now we've not focused so much on data quality but we've actually focused on metadata management management now notice the other two areas get to experience points each, but we have one for data quality and now one additional one for metadata management. Again, our third attempt at this is probably going to be better. Still, let's look at this as reference and master data data governance and data warehousing pieces, all three of these efforts are probably needed in order to do this. You can see that doing trying to do all of this from a data strategy would have been absolutely crazy nuts to do this but confining it to three areas though it is much more likely that you will get people understanding what it is there's trying to do and be be able to come up with some very tangible results from this one of the most tangible is to figure out what are your various data value chains around the organization. Now, the people that know this might be your data professionals but in addition to that you have a bunch of knowledge workers in your organization who have a 10x multiplier on them they know so much about what's going on there. They know the sequence of tasks by which they build competitive advantages and try to also get social and economic benefit on this and it's a very good way of doing the analysis they Harvard link is right there in the chain in order to take a look at this. So, strategy helps your data program over time increase capacity by cycling through the theory of constraints plan to check act and doing little small things that chip away but achieve specific goals. It means that eventually as your organization builds up its data governance organization and its data strategy you can graduate to a multi pronged approach. You can have one strategic initiative but you can also set up numbers two and numbers three if that becomes something that could be very, very useful for your organization. All of these pieces require the organization to achieve very, very well with what it's trying to do and you're going to get that better result. If you're able to take the full time efforts of some people. So I'll give you an extreme example I've been offered as much as 10% of 10 people and I would always say I'll return all 10 people to you and take just one person FTE. Similarly, one third one half no matter what it is you're going to get better results. If you build up skills around this data stuff. So take your data strategy exploit it try to work your way through it in fact I'll have got a guide that which turns out to be the program overview. A data strategy specifies how data assets are to be used to support the organizational strategy. We've talked about what is a strategy a strategy is a pattern in a stream of decisions. We should absolutely see them continue to work in that area and the data strategy is how do I take my data assets and make the strategy by itself more effective and they have to work well together, or we will be unable to show business value from this in a way that will help everybody else. Data strategy is necessary for your effective data governance. You can improve your organization's data, but just improving your organization's data isn't helpful. There's another talk that Shannon and I do that is focused around the idea that many in your organization will avoid tasks entirely. And it turns out that number grows to two thirds in your organizational hierarchy will try to do another way of solving the problem without data boy that's a scary thought, and at your, at your chief level, just me at your sea level. 80% of the folks there would pay me under the table to help them become more data literate like a bunch of them do, but it's actually easier for everybody to get on the same page. We've got to take not just the data being better but improve the way people use that data and that has to be part of your data strategy in order to work effectively on this because only by improving your data and improving the way people use that data. Can you have people using the data to support the organizational strategy. The first two are necessary prerequisites to the third. The data strategy prerequisites also then indicate a lot of organizations are unable to take advantage of this because they cannot overcome the data, the lack of data competencies that they have they don't have the organization ready they have many of the seven deadly data sins still operational within their organization. And then they have to go through a process leather rinse and repeat I showed you three cycles of leather rinse and repeat, but you can also understand the balanced approach is required in order to do this that you can't simply show one or the other of the two extremes there's got to be equal amounts of business value, and at the same time organizational learning, or this stuff doesn't stick. I can't tell you the number of organizations who used to be really good at data management. Then they got Potter sold and the new new management didn't seem to have a priority on them that the others did it's just a very very sad piece. Now let's go back to our little example here the CEO comes in and says all right so I bought a data warehouse and it didn't work. You have to say so what is the purpose of the data warehouse well our strategy is to get more money from our existing customers I'm not saying that's a good strategy but it let's just say that that's what their strategy is. So what should the data strategy be. Well it should help us uncover customers that we haven't drained their pockets dry all together, and those two should work well together we find out whether it's easier to train them by email, or to drain them by virus. No I'm just kidding it's not that bad a company. So the data strategy is necessary data governance efforts then instead of being focused on all of the things that are going on in the organization are focused on that subset of data initiatives that are going to help this data processing effort, produce results on this last process through. Again, improve the data improve the way people use the data which means you're going to be putting in some significant dollars in there, and then you can get people using data to support the strategy, by the time you are very clear there, you need to at this point in time particularly if you're fairly new to the process and somebody says I'm going to invest a million dollars in the process, you say to them great 200,000 can be for technology and 800,000 for people and process support. If you have a million dollars to invest in technology in order to make that work well for the organization you're going to need $4 million invested in the people and process side of the equation in order to get it to work. Again, the idea here with the CDO is they've come in they've got a data warehouse, and they're looking around and saying wow we need to get ready for this we can't just go out and drive just the same way as the 16 year old is not going to be able to go out and drive the brand new Tesla in the snow. As their first driving experience again we want to make sure that we do that we want to compensate for the lack of data competencies so we're going to practice and practice again and practice again I bring you through through cycles there may be a lot more. Each of those cycles should be devoted to eliminating one or more but not all seven deadly data sins again we're trying to reduce them. We can't snap our fingers and change things in data. It takes time it evolves. Most organizations agree that five years is a good range to measure how your data is improving on this. And then you can see, you want to start to get good at this strategic methodology, excuse me method. Lather rinse and repeat and knowing that you've got this balanced approach. After you do the analysis you will start to see that the data value change start to come out and this is where you start to catalog these and make sure that the rest of the organization understands them because those data value chains can become wonderful ways of describing the cost the architectural tradeoffs that need to be made between various organizations. So we're headed for our Q&A session again let's just go back to the bottom line up front here. Multi-page data strategies are less useful than the process of creating them, especially at first. Put a time box on it give yourself two hours or three runs at chat GPT or whatever measure you want to put on there in order to come up with this. But do not spend a huge amount of time creating especially your first one. Remember to make sure that everybody understands these are versioned. So the first one is simply the first one and we will have it once we've achieved it replaced with the second one. There's way too much time spent writing the perfect plan I get lots and lots of people that say please please look at this and read it tell me whether this is good or not. Well, again, I'm happy to but if it takes longer than a page to really talk about this, you know, we're really looking here at some significant overhead that comes into all of this. And by cycling through this, you have a much better way of understanding how to use data to support the overall organizational strategy. It's just not possible to do this. Once and get it done, we've got to consider and make sure that everybody understands we're going to do it. And then we're going to do it again and then we're going to do it a third time and again get it into the process you will achieve much more success. If your repetition is done with a full time staff, or at least people who are largely dedicated to the process looking at data strategy in order to do this. My publisher would be remiss to me if I didn't put out there that he's absolutely willing to give you guys 20% off if you want to go out and type in the codes to the website so that you can get them and we're getting close to the end here. Again, we've got a couple of upcoming events data modeling fundamentals next the role of data stewards after that. And I think that's when we go into edw and then reference and master data. So hopefully this has been helpful looking through the overall process of understanding organizational data strategy. The goal is not to create a lot of paper but instead to practice things and to get things done, and to make sure that everybody understands that you have gotten those things done. Because if you don't, all the people are going to get is. And that's happened way too many times. My colleagues, John, loudly and Tom Redmond have an article at Dataversity that will drop you a link into here as well that talks about how this kind of focus is going to be more helpful to organizations than trying to get the big picture done, and to get everything focused in one specific bite. Shannon we're back to three o'clock it's up to you and let's see what sort of Q&As are wonderful audience has for us. Thank you so much for another fantastic webinar kicking off the year so great. And just answer the most commonly asked questions just a reminder I will send a follow up email by end of day Thursday to all registrants with links to the slides the recording and anything else requested throughout. So diving in here Peter. I often confuse strategy with strategic plan, for example like Walmart's every day low prices is a great example of strategy, which of courses operationalize and implemented through a detailed strategic plan I see a lot of thick documents labeled data strategy that are are really multi year implementation plans, and often do not even include a concise statement of the strategy. There's a wonderful observation there yes many organizations. The plan is what is it we want to achieve, excuse me, the strategy is what is it we want to achieve the plan is how are we going to achieve it. And absolutely you need to be able to actually achieve the plan that you say, again one of the things I've observed over the years is a lot of CDOs going into organizations. Big data was popular Shannon. We had a couple of webinars on this and they'll look at something like this and say oh big data is going to take care of all that and a year later management looks around and says well, where are we going to do with all these Hadoop clubs clusters that we've got and how is that going to help our business I don't see anything changing out there. Sorry CDO you are out of it so a great great piece I could have certainly been more articulate in the book and I'll certainly take that under advisement but I think that's a wonderful distinction the strategy is everyday low price the strategic plan is, and let's just take Walmart's as an example I'm going to line up everybody that sells carrots and individually I'm going to meet with them and give them all of the last 20 years of carrot sales, and they're going to come back to me and tell me how together, we're going to sell more carrots at an everyday low cost to our consumers. Wonderful, wonderful way to describe the distinction there. Thank you. Thank you. Yes, and yeah, I was laughing when you mentioned big data. So, how do you measure, how do you measure strategy how you may have to change the strategy based on the outcome therefore is it necessary to measure the outcome of the strategy. Absolutely. And again, if you'll recall the key is to, let me see if I can get to that slide. Look at this from the perspective of strategy has to incorporate specific business goals. Our strategy is going to help us achieve x, y and z whatever those happen to be. And they've got to be expressed in terms where people can understand that and I say people it's the rest of the knowledge workers in the organization. So if they look at this and say okay strategy. Yeah, we're trying to achieve greater sales. Are we achieving greater sales. Well, that's something everybody can take a look at and see. I did want to hit the measure though because it is a good question. Again, the key is that over time. If I'm doing something I should have some sort of outcome. Again, I've seen a lot of corporate initiatives go green and we're going to become green. And that's going to be helpful of the organization. Well, what is it attracting young people to come see us are we getting known as a green organization or in fact just sort of, you know, putting the words and putting lipstick on the pig here, if you will. Similarly, the volume should be very easy to understand it should be shorter than the organizational strategy again it should be the data chapter of the organizational strategy. If it's going to be that way. People should be preset to understand versions and they're going to be versions to this that when I achieve this goal with this particular strategic cycle, I may need to recalibrate and the question was specifically asked do I need to recalibrate goals. I may find out for example that by doing a 90 day data quality exercise on the data that's in the CRM. I can achieve more quality that quality certainly comes at expense somebody spending 90 days worth of initiative in order to do this. Was it worth that cost and can we go forward from there and say should we put another 90 days into it, or will we be able to in fact solve this problem by just simply looking at 45 days or am I looking at 90 years to fix this. Again, good question in terms of what's going on and then we've got to have common agreement make sure that people understand so it's got to be simple enough. But you're not an 18 year old trying to remember to turn right and get the bad guys and then turn left after you've hit them really really hard so that you can get all of that together. That's a very, very good, good question on the effectiveness of us and it's got to be the kind of thing that doesn't help. I'll give you one more example of this where we go to the next question. There were several organizations that had integration goals they were going to grow by acquisition and when they had grown by acquisition. They were saying, yep, and the reason we're acquiring all these systems so we'll have a million customers. But we'll only have one set of systems, and every executive that had signed off onto this vision of strategy had left the company well before the deadline was up for when the integration would be accomplished and this these organizations are probably even today, trying to get over the data debt that has been caused by these badly thought out organizational integrations. That may be beyond where the question was asking, but it was certainly a great question. Thank you for that. Thank you, Jenna. So, this next question has already started some a lot of discussion. In a business that has neither should a data strategy come before data governance or should they be synchronous. If there's a resource that could be referenced and how to go about presenting the need for both. I agree with the synchronous approach to it. Again, I think the best thing that you can do is to get people to buy into this kind of a diagram as I'm showing here on the screen. The idea is that data strategy helps focus, but data governance is going to be doing excuse me, I had a hiccup. So, when you have a group that's dedicated to data governance one way or the other, whether it's part time or full time, but they're given some responsibilities for it. They are not going to have the resources that they need to have to do the entire set of things that need to be done. That's why a strategy comes in and says these things are more important than those things. Even better still it's better. If you instead of saying we're going to start with the A's and work our way through all of the variables that are out there all of the data tables all of the data attributes. No, no, no. We need to find out which ones are the important ones and focus on them. Remember all that talk about rot. There's no point in cleaning up rot. It just doesn't effective in order to do that. So, key is to say what are the architectural pictures that you have here and the idea is, we've got organizational strategy supported by data strategy organizational strategy is also going to be supported by other activities. I would encourage others that are contributing into that area to make clear what those those things are again from our perspective data strategy supports the organizational strategy what do I need to do with our existing data assets to make it easier to achieve the organizational strategy. If it's not perfect, then data governance is generally the process where we go in and address the people and process types of issues which is so sad to see that most data governance initiatives are focusing in on technology. And the idea is data governance is focused in on the things that are preventing us from achieving full data strategy which will help us to achieve the organizational strategy and how do we achieve the things that come out of data governance through our data stewards. So hopefully this this picture will be helpful getting folks to understand how these roles are in place and the real question to ask is, what other capabilities do we have in our organization at this point in time that are capable of delivering results such as I need to have. How do I make data more effectively used in our strategic operations. How do I improve the way data is strategically used in those areas well that's a data governance function, and what types of data stewards. Do I have in place what types of knowledge and skills do I have for them that can help me to actually achieve the goals and objectives that I find was there. Great question. Thank you for your chance to go back over that and hit it a little bit more carefully. Perfect. Thank you. So, Peter, taking one step ahead from this program, how does enterprise content management fit in with data strategy. This question is because I start with the assumption that content influences culture. I agree with that as well. Let me go back to the dama DM Bach. We were talking about a little while ago, and you'll notice that content management is about 7pm. So, excuse me, seven o'clock on the clock hands there at the bottom corner. Real quick. First of all, we, when we did this, this was an unintended use of our model. But the first group that put out this model did a great job on it. And what we were trying to show was that these are the areas that comprise that make up data management. Instead, people read this and said, these are the things you have to do to do data management, which meant a lot of people said you have to do document and college content management. And there's no, if these are things that you can do your goal as a data leader should be to figure out which of these are more important to your organization and focus in on getting better at those activities as opposed to simply doing those activities in there. So, the content follows form and function and I always like to see a space on the org chart, because that's another way to make sure that form follows function around that and content. Like I said, we are not seeing as many organizations go off and do document and content management. As these other initiatives they are tending to approach those separately, mainly because there are a lot of discovery technologies that work really really well to some degree and for some types of operations. And so it's not as integrated as we have perhaps envisioned that it would be going around this process, but nevertheless we are still seeing this and certainly would like to see it certainly in the pharmaceuticals industry there's a tremendous amount of need for this kind of integration, particularly when you get down to things like prescriptions or free form initiatives, it's all sorts of requirements that help out around that. Anyway, I think I got to the question Shannon content management and part of it. It's, it's something that can be extremely helpful and if content management is higher on your priority list and some of these other areas it's as good as spot as to get started and include around the cycle that was the whole point of telling you the three cycles was that that worked for that organization, but it's probably not right for you, and you are best qualified to figure out what is correct for your organization. Yeah, so, you know, I'm going to skip around a bit in the line of questions here because I think this but says a nice follow up. You know, where does enterprise architecture fit into all of this. Absolutely so if you see enterprise architecture is right there just past 12 o'clock data architecture management. The key of course is that architecture. One of the key elements of architecture is in fact strategy. Let me give you a very specific example. I had a number of colleagues that participated in rewriting the back office trading system for one of the major banks on Wall Street. A couple of decades ago. And the interesting part was that that that particular technology essentially shut down when it got to 10,000 errors. That seems like a crazy number and a crazy thing you might ask how often did it come to 10,000 errors. Well, it was enough such that it featured on the board of directors, how many days they had managed to go under 10,000 errors in their trading operations. You can imagine this is an absolutely silly type of requirement from their perspective, but the rewrite of their system had to understand that focusing in on errors, their focus here was on an error centric architecture. And that was how they focused on so it was just data centric but it was one specific type of data in this case errors. Technology implements architecture. This is another problem with package software. When you go and walk through the airports of the world as I've done recently and see company X uses are all the best companies use software X or whatever it is. These are not allowing architecture to support the strategic objectives. It's very critical that architecture have strategy imbued in there as I think Shannon we do a talk on this around may where we might get this topic in a little bit more detail. But certainly the, the, the idea of strategy and architecture, they are interdependent. And if you have a play a bland vanilla architecture without any sense of strategy in it. It's probably not going to meet the business objectives of the organization. Great question. Thank you. And then we've got a couple questions asking about the public sector here. So, what do you have a great case study for data strategy you've seen for the public sector or nonprofit sector. Interestingly, I think if there's anything written from a strategy perspective, I haven't seen as much success. The, the public sector has tended to like large planning documents. As if they get paid by the page or the PowerPoint slide. And so there's been a tendency to slip into that and to stay in that sort of conversions. You know, organizational speaker. I'm not saying what what is very interesting is that the federal government because of the FIPA law that was put into effect a couple of years ago. Yeah, now I'm seeing parts of the US federal government that are getting eight, nine figure allocations that are coming out of the office of management and budget to work on data specific initiatives, which was an intended outcome of this and then hopefully we'll show some very good plans but in order to do that they have to go through the traditional procurement cycle and there's a large amount of RFP work and that RFP turns into the requirements and you can get the sense here of how it all works. It's not a way in which the federal government works I would say more, we're likely to see this, not at the top level but more at the grassroots level where they don't have the, the allocations that I was describing earlier on that. There is, I think an article on the maturity of these organizations that I might be able to send as part of the PDF on this. So, probably just one person interested in it, but I'll, I'll hack an academic article on to the very end of it that has some public sector specific information on there for you. Great question. Thanks for asking. Along that same line Peter in the public sector in the public service we have a different type of data data that we produce from the programs and services we deliver data we receive and share with other public partners and data about ourselves. How do you think and suggest about data about ourselves in terms of strategy to enhance the CDO strategy and department organizational strategy. Again, great, great question. Most federal agencies when you look at them have some aspect of information processing within them. Again, agencies tend to collect data whether it's by survey or sensor or any number of other different things and they take that data and they do some value added processing again very clear data value that comes out of this and they exchange that information with partners and other public agencies as well as other for profit organizations. One of the interesting plays recently in all this area was that IBM had bought the weather channel. Now remember the weather channel just takes data that the federal government comes up with and somehow monetize it with a data government. Excuse me, the federal government hasn't figured out how to do just yet, or maybe has decided not to do is perfectly good choices on on both of those options. But that the weather data was supposed to be a big hit with IBM just like Watson was supposed to be a big hit with it. Unfortunately, they sold off the weather channel the other day and don't don't have it anymore. They haven't figured out how to make it a good profitable now maybe somebody else will maybe it's the thinking and IBM is different than other places who knows people who usually buy these things are the people who think they can compete. But all all of these, you know, see the organization see the federal government organizations as an information processing role and as you look at it as sort of the dominant role in there you'll start to see how these organizations have already come up with their data value options, which means they may not be paying as much attention to some of the strategy options in here if they've already gotten to it because I contend that the data value stream is an outcome of a strategy and if you already know the outcome. Then your strategy is focused on some other aspect of it other than identifying the data value change that are there. But again, I will add that article of the end here makes a little note. We had some time left. So, hey, so if data strategy is developed to support the business strategy. What if data does not support the, what if it doesn't support the business strategy. Well, of course, that should be a real obvious component right away. Great question. And then this has happened to many organizations where they'll say, you know, this is what our plan is. But golly, you know, if we can't support the plan here, we've got to do one of our changes, right? We've got to change our strategy to do something that we can support use data to support it. We've got a plan on data not making that big a difference in our organizational strategy. There's no magic here. Just like if we've stopped teaching data flow diagramming techniques for some reason, I don't think they fit into object orientation in a way that was easily understandable but that's really where our data value components come from is to look at how data flows through parts of the organization and where we have value that's added to it in different ways. And each of those different places that data is added value becomes a place where we can start to improve the overall operations. And those should be the focus of strategic operations. And I can't be as broad as strategy is going to do it. I'm sorry, as broad as quality is going to do it, but maybe quality and then the first place to look at quality is around our reference data because that has much more leverage potential than does our master data. And then once we've got our reference data and our master data, then we can start to look at other types of data that we have in the organization that need to have quality approach to them. And maybe that's three or four strategic data cycles that we've gone through, gotten better at, understood the technology, understood how to use it, what is useful for, I could go on and on and on. But these are great ways of looking about how all of these things are going to be useful in creating strategies that are meaningful to the organization so that it understands better the role data plays in organizational strategic objectives. So, can you give more information about the seven data sins I'm trying to help clean up our data management, even though I don't feel qualified. Yes, that's where that case study comes in so if you just download that case study given a read and that'll be glad to chat with you on it but the interesting thing about the case study and I take. I think some pride in doing this the way it should be done. I've had well over a dozen companies come up to me and say, how did you get inside our company and understand our problems so well these are so. And of course they're not there are problems that are really universal which is why they became the seven deadly sins. The question was, you know, can we dive more into them and it's just not time today in order to do it but I can pop it up I think we do have a. We do the seven deadly on these are not Shannon I forget. Yeah, I'll put it in the chat. Yeah. Again, like a data leadership, you got to have a robust means of sharing, developing that shared data, making sure your program are aligned between data and it, managing expectations correctly sequencing the strategy address the culture and management perspectives, and then what is data centric thinking. So, lots of lots of good pieces there yeah that's a much longer topic to get into and hit that case study. You don't even have to register for it but you can always get some more details around that and have to have a conversation. Thank you next question. Yeah, these are great. And I did put the link to the chat of the webinar recording we did a couple of years ago so three years ago now, which is crazy. So, Peter, we get this question a lot. So this question is certainly not alone. Unfortunately, I don't have the ear of our CEO. And even if I did their understanding of the need to have data strategy is shaky. So what can I do to help elevate this conversation and make progress towards having a data strategy. Really good question and it is tough because it is a case of not knowing that they don't know, but you're faced with the bad data decision spiral that's here which you can do is to try and socialize this diagram internally again you guys are looking at all of these PowerPoints and if they, you know, you to put different language on and to make them work for your organization by all means do, but you've got a situation here where nobody is looking out at what's going on from the data perspective you've got somebody and again I'm going to pretend that the questioner is asking me from a logistics thing so you've got people that pay attention to trucks and people that pay attention to warehouses. You've got to pay attention to the data and if you don't have anybody paying attention to the data that is everybody's responsibility and if it's everybody's responsibility, it ain't getting done. So, so getting attention at this level requires them to look at something like this and say, okay, we aren't making data decisions and the technical people aren't making them so we're making some bad data decisions, which results in poor treatment of our data assets as poor quality data and poor organizational outcomes and if you go back to it, the root of all organizational problems are a data problem at some level. So you can reverse engineer if you will that that value chain all the way back to find out where we have to address it from a data perspective in order to make things change. That's a great question I understand the frustration that's there and you're not alone in order to do this when you come to events like EDW we actually have people that fall down on their hands and knees and say oh my God I found my peeps, but we also have some solutions to and that's again there's lots and lots of opportunities to hear not just from me but and Shannon's other stable people that she's got here but also you guys bringing your own stories and to tell how this goes and oh there is a good data story it was in. I think IRS had it I'll have to see if I can find that one too because I know they will. Oh there was a panel that something written on it sorry. I'm battling Shannon anyway great great question and try to get them to understand this diagram and if they understand where they are in this diagram. The next question becomes. How do we break out of it and if they're not willing to have that conversation and polish your resume and go find another job. I say that glibly but they're not going to get it. There's a request Peter free to share the share that presentation so the link. I'm sorry which one channel. There's a request that to share the PowerPoint also in your suggested by you the PowerPoint. You got you got yeah Shannon's going to send you copies of all of these things that are here to have the source docs you got to reach out directly to me because they're in keynote. Yeah I'm converted for you but. Perfect yeah so yes it's source docs if, and I do include your email in there so you have access to that so you can request that directly from Peter. Perfect. Okay. All right so we've got about four minutes left Peter I'm going to try and slip in as many as I can hear we got lots of great questions. Yeah, instead of saying data is the weak link why not use and why not the use and handling of data. That's what I think getting to the question that I was trying to hit out through the outline here trying to deliver multiple messages at the same time here yes. Data strategy is necessary for effective data governance because you can't just improve your organization's data you also have to improve the way people use their data. And when we've done measurements. Now this is not just me but the data literacy project IBM's got a brand new study on a Deloitte Scott went out and keep coming up with the same results. We have to get better about people becoming more literate. I've been doing a lot of executive data training around this it's a really good exercise for boards to get up to speed in order to to learn how to do this. I guess the data literacy books probably a good way to take a look at as well. In order to do that I've got a 12 step program in there that helps organizations get better at this. It's partly tongue in cheek but it actually works as well so. Again good good question yes you can't just increase improve the data you've got to show people how to use data and then how to use data in supportive strategy which is a three step process. It is not easy and certainly not just as simple as doing data quality exercise agree 100%. Alright she minutes left I'm going to slip in another question here we've weighed full data democratization at 1000 plus employee company and I wonder if that's too lofty for a data governance program thoughts on that. It's particularly at the beginning I mean just think how at the beginning of any initiative where you know the least about it you're supposed to tell everybody how long it's going to take. What benefits you're going to get and what sort of investment you're gonna have to achieve in order to achieve those benefits. So it's much better to start off with a small series of cycles and to do one portion one slice one aspect. What's going to deliver the most value to the organization fastest that's the thing that should drive you because you never have all the time and resources. And if you're going to try to do it even if you think you have all the resources you're still going to do it poorly. Let's get back to where I was at the beginning what gets you to Carnegie Hall practice practice practice is the thing that you do that makes you good as opposed to. Let me get back to Malcolm make sure I quote him correctly there we go. It is the. There we go. Practice isn't the thing you do once you're good is the thing you do that makes you good. That's really the key that's where you can't satisfy all of the organizations things remember we've already talked about it takes years in order to change these things around. Yeah, it's, it's definitely find a small slice make an example of it and get people to demand say, come do to me what you did to those guys. Right, that's really what we're looking for. Anyway, hope this has been helpful and a pleasure as always. Indeed Peter thank you so much and thanks to all of our attendees for being so engaged in everything we do just appreciate the great questions, but it is all the time that we have for today. Just again, I will send a follow up email by end of day Thursday with links to the slides and links to the recording of this presentation. Thanks everybody. Thanks Peter. Thank you everybody.