 Hello and welcome, 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 stewards, defining and assigning. It is the latest installment in a monthly series called Data Ed 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. Or if you'd like to tweet, we encourage you to share how this is a question via Twitter using hashtag Data Ed. And if you'd like to chat with us or with each other, we certainly encourage you to do so. And just to note, the Zoom chat defaults ascended just the panelists, but you may absolutely switch that to network with everyone. To open the Q&A or the chat panel, you may find those icons in the bottom middle 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 to the recording of the session as well as any additional information requested throughout the webinar. Now, let me introduce to our speaker for today, Dr. Peter Akin. Peter is an internationally recognized data management thought leader. Many of you already know him or have seen him at conferences worldwide. He has more than 30 years of experience and has received many awards for his outstanding contributions to the profession. He has written dozens of articles and 11 books. The most recent is UR Data Strategy. And Peter's experience with more than 500 data management practices in 20 countries and consistently named as a top data management expert. Some of the most important and largest organizations in the world have sought out his expertise or has spent a multi-year emergence with groups as diverse as the U.S. Department of Defense, Deutsche Bank, Nokia, Wells Fargo and the Commonwealth of Virginia and Walmart. And with that, let me turn everything over to Peter to get today's webinar started. Hello and welcome. And thank you as always for inviting me into this program and to working with everybody around the world on all of these wonderful topics. Yesterday, we're going to talk about data stewards and two particular aspects of it. When we talk about defining one of the important things is that we all agree on the purposes for which they are being applied. But also it's important to make sure everybody outside of our group understands the same thing. And with those two things in mind, then the assignations to the particulars of data stewardship become relatively important. So we'll talk about in three parts. What do we need from data stewardship as a role? And what are they supposed to do? What are the data stewards supposed to do? And then the last part we'll talk about specifically assigning data stewards with an eye towards tangible improvements. About an hour from now, we'll come back for the favorite part, which is the Q&A session where we get to take your questions and find out how we should tune this going forward and what we're looking for. So let's dive in, ready to go here. What is it we need to do with data stewardship as a role? Well, first of all, we have to agree on what the definitions are. So I'm going to give you some definitions around that. We're going to talk about the role of strategy and why it's so important for stewards to be focused at a strategic level and why it's important for the strategy itself to be of actionable use. Then we'll talk about strategy as implemented through data architectures. So diving right in, if we were in person, I would ask the question, how many of you all are starting your stewardship programs as opposed to restarting your stewardship programs? And the answer is typically about half and half. So first of all, don't feel badly. It does seem as if three is the magic number instead of two, unfortunately. But hopefully the information you take from this webinar will be good for avoiding that perhaps a necessary false start around that. Because it really is about getting on the same sheet of music. And if you are a musician, you understand this implicitly. If you don't, the music defines this language. Musicians speak. I happen to play bass. One of the things that makes me as a collaborator, because I'm not good enough that you'll want me to come and play at your wedding going bump, bump, bump behind it, right? So we'll use this as a metaphor for the next couple of slides here. First of all, one of the things you'll see about data stewards is that the cipher from the same hype cycle that everything else does. When you see something like this around that says I am the one person at work, you can't possibly live without, it actually is probably a true statement, but it's not where I would start or I would lead with my first piece forward on that. In fact, we should go to the dictionary for things such as this, just as a good reference point. And let's start out with the first definition of steward. Well, as a person who looks after the ships on a plane, excuse me, the passengers on a ship or a trainer in an airport. Matt, that's not the one we want. Official who points to supervise arrangements or keep order in a large public event. No, definitely not there either. But here we go. The third definition, a person employed to manage another's property. Yes, that does sound interesting. They have specific references here to a large house or in a state, but people take care of things. Again, the second example there, a person whose responsibility is to take care of something. Farmers pride themselves on being steward to the countryside. Stewarding, therefore, we're not going to look at this definition of, you know, organize something around that. But to manage or look after, particularly a property related aspect of it. So there's a value component and an ownership component of it here. And if we look at this, manage and look after, and a person who's employed, we can see there's a job classification that we need to have called a data steward that manages the data assets on behalf of the stakeholders and in the best interests of the organization. Something very clear to be precise about. You probably don't have the resources right now to do everything. So your data steward should have a limited scope. And you should find out what the effectiveness of the data steward can be given that. Again, they have a duty to represent the interests of all the stakeholders and take this executive perspective. We'll look at it from a couple different ways today. They should have dedicated time and resources in order to be held accountable for it. So we need to introduce the concept of trust here. That is the idea that somebody is trustworthy in this case and, more importantly, in a specific subset of trust. In this case, having a relationship between a trustee and a beneficiary in this case. So we'll talk about these things in more detail. Let's go back to the word steward for just a minute, however. And again, steward of the land, great. One who actively directs is actually the dictionary definition. But if we had data steward, we can, I think, pretty easily agree that the one who actively directs the use of the organizational data assets in support of specific mission objectives is the way you should really think about this going forward. Because unfortunately, in the past, there's been confusion. IT has thought that data is a business problem. A typical response might be if they can connect to the server, my job is complete. On the other hand, business looks around, sees somebody with the title chief information officer and wonders who else would be taking care of my data. As a result, data has fallen into enormous chasm between IT and the business. And we need to work actively to repair that chasm, because what has accumulated in the meanwhile is a ton of data debt. Data debt is the concept that you're going to have to make things better before you can learn how to use them properly. And the idea is getting back to a neutral state around this, involving undoing existing stuff with some skills that you may or may not have around. And once you get to zero, at that point, you typically are going to be asked to require an annual proof of value. There are five people in your group. Everybody in your group, we're going to value it 100,000 per year. You need to show 500,000 per year in value on a regular basis. Again, a reasonable request on management's part. By the way, you get to get a good at both of these at exactly the same time today. And going forward, of course, almost all of these data challenges involve interoperability. And that's where we can assign value around it. I'll come back to that theme as well. There isn't very much guidance around these, and very little focused on getting us back to zero. So data debt is a challenge that we need to work on. It slows the progress of organizations. It decreases your quality and increases cost. These are the hidden data factories that exist around your organization. Thank you, Tom Redmond, for that excellent description of the process. Yes, Shannon? Sorry, I'm still getting reports, especially as you're transitioning, that it's super fuzzy. You can get it. Not sure what we can do there, but we can come down as about as low as I. Let me try one other thing here. It's unchanged from where we've been. Yeah. Yeah, and some are saying it's not fuzzy. So for those experiencing fuzzy, try reducing the size of your screen too. That might help, but there's something else you can do. Sorry. I want to get it right. Sometimes it's just transitioning. I think it's still some bandwidth issue, but it certainly won't be the first time bandwidth has been a challenge, right, Shannon? All right, man. Back again. Thank you so much. All right, so the key here is the analogy of the princess and the pea. Again, wonderful fable. It's interesting that the title is actually the princess on the pea. It's the original Danish transition of his thing. And the idea is that there's a discomfort somewhere way down in our system. And the princess up on the top can't figure out and is unhappy as a result. Well, data challenges doing poorly with data, failing to understand it or well, locks in these imperfections for the life of the application just the same way as the princess is stuck with the pea. It really restricts the available options that you can have in terms of additional investments and decreases your leverage overall, accounting for migration, conversion, and improvement, 20 to 40% of your IT budget, that's a lot. So bad data causes everything else to take longer, cost more, deliver less, and present greater risk. Thank you, Tom DeMarco. Let me give you an example of that just real quickly, which is the idea that the airlines had a really interesting article written on them last July. And the idea was that they were valuing American airlines at the time at about $6 billion. But the data and the advantage program was valued between $19 and $31 billion. Similar numbers for United, $9 billion for the organization, $22 billion for the data in the mileage program. It increases a lot of money, and I again attribute all of this to data debt primarily because organizations have had challenges of figuring out where strategy should play a role with respect to data. What you'll see, I think most often is a incorrect scenario which starts out with an organizational strategy of what we should do, but then in addition to that, some sort of an IT strategy and subordinated to that is a data strategy. I do believe this is wrong. Thank you, Morgan Freeman. I apologize for stealing him, but he does it so well on that. Yes, this is wrong. And the reason it is wrong is because the data strategy actually has as much interplay as not more perhaps with the IT strategy as it does going the other direction. So let's talk for a minute about what is strategy and we haven't really used the term much at all before 1950 outside of the military context. Strategy is conceived of today as a master plan, a 100 page document, 1,000 PowerPoints, whatever you're doing, but it becomes a thing, unfortunately, and that's really the wrong way to think of it because if we go back to the original military definition, it's much more of a process. And the definition that we'll use here on is a pattern in a stream of decisions. So let me give you three examples of strategy very quickly. Walmart's former business strategy was everyday low price, pretty straightforward, everybody understood it, very easy to explain. If you had to have it, everybody in the entire organization understanding one thing about Walmart, this is the thing that they wanted you to understand. A second definition of strategy then is Wayne Gretzky's definition of strategy. He skates to where he thinks the puck will be. After all, if you're chasing a hockey puck around the ice, which is slippery and the puck goes faster than you can skate, there is no hope of catching up. So you must instead anticipate and be ready to react. And that is his thought. Lots more on this particular discussion at his Wikipedia article, which is a wonderful collection. And to the third example is just a bit more simple, excuse me, a bit more complex. Otherwise it's simple. The example is how do I defeat the competition when they're bigger than I am? The answer of course is divide and conquer. Remember again, our pattern in a stream of decisions here. So I want you to imagine with me just briefly the battlefield layout. First of all, it was focused around lines of supply because an army does march on its stomach. Or more importantly, if it's being attacked, it's more likely to run back towards its stomach than it is away from its food and clothing and shelter. So the red, the British, excuse me, up here in the upper left-hand corner. And I know this slide is historic stuff, so I can't change it as blurry on this stuff. But anyway, they're running back to Ostend and the oppressions are heading for Lige, which is right here. And Napoleon observed that if he could hit them in exactly the right spot, they would both likely fall back in different directions. Plan one, hit them very hard in exactly the right spot. Plan two, or part two of plan, whatever was, you have to, everybody agree who we're gonna go after collectively next. And the idea was everybody turned to the right and defeat the oppressions. Because if we're all focused on defeating the oppressions, we're bigger than the oppressions. Remember, combine these two are bigger than us. But now we can turn our attention and defeat the British. Sounds like a great plan. Didn't work so well. And let's just take a quick look at perhaps why. Although it is still taught as an example of good strategy. First of all, again, remember, both armies had to get hit in just the right spot. Then we had to all turn right, defeat the oppressions, then all turn to the left and defeat the British. Oh, by the way, let's do this while somebody is shooting at you. This is complex and difficult. And as you already have figured out, did not work in this instance in here. Let's take one more quick look at something, which is just a relatively complex environment. If this is something that somebody might put up and say, I'm gonna build a structure around this, you would say that is more complex than alternative models. It's not that one should or should not be. But if you do have to recognize that if it's complex, it's going to be difficult to administer with a simple strategy that calls for a pattern in a stream of activities. The reason it works is because at a work group level, this works, in fact, defines the boundaries of the work group to see that they're all following the same localized strategy. If we can get them all following the same strategy, we have an opportunity to go further. Next up, we need to understand how this strategy works into this context of governance and architecture or truly architecture governance, if we want to talk about it that way. Now, we're all agree that corporate governance is, has been, should be around, et cetera, et cetera. And there's even an interesting, relatively recent development, which is to say that Jamie Diamond leading a group of others came out and said, it's not all about the money. For years and years, if you ask the question, the correct answer was a purpose of an organization is to make money. They're saying, no, we should consider the context. Now, they haven't done anything concrete, but nevertheless, at least having the discussion is a very good start. So we've got corporate governance. It's a thing we should do it. Everybody does it. If we're not doing it, we're probably in trouble around that. Therefore, there should also be, of course, IT governance, and it's focused around some very specifics, making sure that whatever we purchase in IT is focused on helping us achieve our business objectives in a way that we can really understand from a measurable perspective, because that's the thing that will provide results and motivations around this. And just focusing on some key questions, not everything, but some specific areas. There are five that are recommended here, alignment, value, resource management, risk management, and then other specific performance measures. And I show you this because I think you have a good idea now of what is corporate governance, what is IT governance. Now, here are seven definitions for data governance. And they're all good. I'm not at all going to argue with you about the merits. I just want you to imagine instead that you had decided that one of these was the right definition. And again, none of them are incorrect here. But then you have a elevator moment or have to make an elevator pitch. That is, you're getting ready to get on the elevator and the boss comes along, looks over and says, hey, Peter, you're doing this data governance. Tell me again what this stuff is. And if I start to talk to him about a very complex subject, I will not achieve success by the time I reach the top of that elevator. So I urge all of you to consider data governance with this definition. When somebody asks what is data governance, the answer should be uniformly out of the team managing data with guidance. Now, that's very straightforward. And it also sets up a next conversation. For example, if somebody were to say, would you want your data managed without guidance? Most people will generally say, you know, that doesn't sound like a great idea. The only problem that I have using this definition, and I've used it for well over two decades at this point, is the idea that as you go up the food chain and manage it becomes more detached from the operational parts, they're not sure they can see a role for themselves. And so I changed this definition just slightly for them to say it's managing data decisions with guidance. Because the individual that told you it was okay to install Salesforce, put in the bad data and that we would clean it later, probably doesn't look so prescient at this point. It may actually be possible to do that. I've seen a lot of more organizations struggle with it than I have actually seen them succeed. Yes, data decisions are oftentimes hidden in other types of decisions. Now let's take a look at how we're going to implement these data decisions in an operational context. First of all, of course, IT provides the bones, if you will, the architecture of the garden, the foundation here. You can see I've labeled that in blue. In the upper left-hand corner, of course we have leadership. Then we have business participants that play in this and kind of like to do this. Again, if I'm at the Department of Housing and Urban Development right now, my participants are very interested in housing data, as you might imagine. And it's a wonderful way to learn about a new area. Then there's everybody else. And finally, we've got, of course, our subject to today, data stewards. Notice I've written underneath it, data trustees. We'll look at that in a bit. And three products that stewards have been associated with the Glossary Data Dictionary Catalog. By the way, they're all different and yet they're all the same. And gosh, if they aren't integrated on the backside of this, we are in trouble. All right, so let's put some roles on this. First of all, many companies will draw a circle around the left-hand side of this diagram and say that some portion of leadership and stewardship will be the make up the data governance group, whatever we decide. And the role of leadership is to acquire resources and to understand data and feedback that comes in from their perspectives and to make decisions on this. And those decisions are then required to be implemented by the stewards in order to get in as if it's increased the quality of Salesforce.com. Gosh, I'm gonna sound like I give a commercial to them because I started off with it. But anyway, if Salesforce is the idea here, well, we need to change this. Maybe we should shut it down for a month and fix it and then turn it back on. Maybe we should reload it. Maybe we should clean the data first and load it the second time. Many different types of decisions. The stewards are the ones that are responsible for implementing that. They want some action to take place that affects the close-in participants as well as everybody else that this changes impact all the way around. We need to have development and feedback coming both from IT as well as from the participants that goes in so that leadership is able to make an informed guidance around that particular piece. This leads us to the definition of architecture which is quite simply the high level abstraction of dealing with things, the function of those things and how those things interact with each other. Don't worry, we're not gonna watch that video in there but it just involves some dancing. Instead, let's look at some architectures which is just silliness. This is actually a cartoon, but it's a good one because it's instructive in that it shows how Amazon is organized versus how Google was organized at one point in time. Wow, Facebook, do we really need a structure? Microsoft, again, eliminating their own products. Apple, everything revolving around one individual and Oracle with a much larger legal team than an engineering team. The reason for doing that is because you can identify with these things and understand how they come into play very, very easily. This gives everybody in the organization a common vocabulary, eliminating the express integrated requirements so that data assets are stored and used most effectively in support of organizational strategy. We don't want everybody, as they currently do, talking a tower of Babel. We want a unified understanding so that they understand as an architecture which is documented or articulated as a digital blueprint illustrating the commonality and interconnections among the actual architectural components and sharing this understanding by systems and by humans. It's a very important connection points. All organizations have architectures and the only question is if you don't have an idea what your architecture is, it cannot be understood. It cannot be understood if it's not documented well and it can't be made useful if you haven't done those things. Of course, data architectures are different. We need to have them all focused in that same area. All right, first section done now as we're looking at what data architects are supposed to do, we're gonna talk about how they resolve challenges in the organization, the framework for working within them. I like to describe stewardship as a fire station model because, again, people can relate to that and we'll talk about a specific role in the context of data governance. First piece up in terms of resolving challenges is that data stewards need to be protected from your organization, just say no to the concept of data ownership. If you have to reference something at all, absolutely reference instead the term fiduciary relationship but allowing somebody to own the data and defining data owners is one of the biggest barriers to people correctly understanding how to use that. For example, who would you define as the owners of the data to which counting plays? And we have to go from here to the concept of what is a fiduciary relationship and that's a person who is legally obligated to act in the best interest, including lawyers, trustees, doctors, accountants, corporate directors, legal guardians, there's many others. The three top duties that they have are to act in good faith to provide a duty of care and loyalty to the client in this case and that fiduciaries have specific legal and ethical standards that they must adhere to. Don't worry, we're not gonna start bringing in lawyers to do data governance, but never fear it won't take long in order to do that. So this concept of a fiduciary is well established and people understand that. We have to make people understand that the stewardship concept applies in this exact same format. The reason unfortunately we have to do that is because there's always been a big gap between how organizations get from data to information and most agree that they are dependent very much overly dependent on human beings on the stuff that's between our ears, the wet layer as we describe it, what their knowledge workers are capable of producing informal communications and described as the weakest link and especially as you're starting out on this journey make a single simplified unified mission for them to follow. Otherwise it's hard to get your critical momentum going. So one of the things to do is to look at a data governance framework and that's just a system of ideas that guides our analysis and specific things that are there in front of us organizing the project data making priorities for future decision-making and assessing means towards progress. For example, you might learn some things like don't put up the walls until the foundation has been inspected, right? Well, that seems like a really good idea. On the other hand, after you get the walls up put the roof on as fast as possible so you can work inside if you have inclement weather. Oh, by the way, we should make all of this dependent on continued funding. Interestingly that IT does not operate in that fashion neither does corporate governance which allows organizations to make investments of tens of billions of dollars without a very good hope of returning it if that money had been placed in advertising. For example, there would at least be an anticipated increase in sales around that. Let's look at another concept of framework. This is just a framework in this case for the concept of stewardship, not data stewardship but I think the elements are very nice. You can use them as you like. Again, wonderful reference here the training journal article that came from it's personal mastery, it's having a vision. It's being able to mentor as a default value defining and valuing diversity of opinion and thought and sharing that same vision with everybody else valuing risk taking and experimenting without being moved fast and break it in there that you have some vulnerability that you understand that you can get more mature that you can raise awareness around that and deliver results. Just a very brief framework of this and here is my framework for data stewardship on this. Again, you will find one or more organizational challenges that you encounter. And when you do, you'll have to make a decision. You'll have to make a strategic defense excuse me strategic decision to say I'll do these some other time and probably a lot of your decisions may have to be that kind of a process. On the other hand, you're clearly going to be in charge so you're gonna decide what to recommend given the circumstances knowing that you should provide something as a result of this. And so for some of these you'll take on and drop them into what we call a data stewardship engine which comprises regulation and policy types of issued steward activities that are both reactive as well as proactive in that area. And again, looking at it from a value perspective you may have monetary or non-monetary value that comes out. Many organizations for example, really value the reputational value that comes out of it. So we take this pile of money hopefully over time and grow it so that everybody understands that investing in these activities produces direct benefits to the organization. Those of you that are in the government don't worry you just change money into mission and this works exactly in the same fashion. So we'll move from there to this description of the fire station and you all have a picture in your mind of what a fire station is. Brave individuals ready to go if they hear there's a problem. In fact, I'll take the analogy and schedule it just a little stretch it just a little bit further. Many of you remember an old television show called MacGyver. Harmless. MacGyver was a guy who was able to solve any problem under any circumstances. And that true also is going to be a view data stewards because when you think of what's going on in data stewardship the most important aspect of it is back to our hidden data factories that Tom Redmond introduced us to. All IT challenges, all business processes disguise data problems in one form or another. And the data steward is the only person who's capable of drawing together all of those disparate strings and understanding that the root cause of these poor results is a data problem and the fixing one or more of these data problems will solve many more of these business challenges that are out there in the organization. And the only way that you can do that effectively is to have consistent analysis. You cannot depend on one off kind of analysis. First, this is new. Second, it is not as proven as other quote proven technologies at ERP. So eliminating the data debt does require a team with specialized skills that's deployed to create a repeatable process and the develops organizational skill sets that are sustainable. And we also know that the firehouse is often vacant when these individuals are out fighting fires but they also are out oftentimes doing proactive activities. And that is an important role to understand because in the proactive world, there's a ton of very interesting components that we can do with education, with preventative, with checking fire inspecting all sorts of things that can be done in this sort of proactive reactive version. Now, many organizations don't anticipate quite as active a role for these data stewards but bear with me for just a minute and let me just show you what the dimensions of that look like. When we look at what's happening in our organizations, we see the idea of course data governance is to produce systemic changes. Over time, things are going to get better. It's important to realize that, well, let's just take a metaphor. Everybody says that the data is like oil, right? Well, I don't like that particular statement because it implies that we can use a one-way production function and actually the most important component of data is reuse, not use. So a better way of thinking about it instead of oil is soil. And if you think of data as soil, there are two things that come to mind instantly. One is that nobody walks about their yard and just spread seeds randomly about the yard and expects good results. Instead, you carefully prepare a bed and you carefully plant the seeds to nurture them as they grow. And the other part of it is a time dimension. Nobody plants things on Monday and expects to eat them on Friday. But unfortunately, remember this. Everybody that you probably talked to from this point outwards in your career knows less about data governance than you do and particularly about data stewards. So you're going to be explaining to them what these things mean and perhaps changing some already existing cognitive bias that they have. Again, as a data leader, and I do include data stewards as data leaders in this leadership category because you are the ones who have the most expertise about your specific subject areas in here. They have some idea, they have some feedback and eventually organizations will start a data governance. I will give you one data point. Shannon and I participated in a wonderful event back in December where we had about 300 data governance leaders join us in San Diego for an in-person event and we're looking forward to one also in June of the same caliber around this. Just terrific to see everybody and to interact. But also we asked the question and found out that the average size of a data governance effort of the people that were at our conference was one. So that's interesting. It says the multi groups have it figured out and the ones don't, maybe I don't know or maybe the ones had an easier path to a pattern. We're seeing an awful lot of large corporations that have not yet listed their travel bans. Anyway, back to our slide here. Yes, data governance and most people think of it as we're doing some things at the policy level and that means that data will improve over time and people will generally nod their heads but I do feel it's important to actually go the next step and say, now that is kind of like sitting at the bottom, the base of Niagara Falls and expecting to influence the water quality from that position. Clearly you have to go upstream and the further you go upstream, the longer it's going to take for the water to refresh. Similarly too, you have some sort of a data refresh cycle. So data improves over time but the time might be measured in quarters and years rather than in weeks and months around that. And that, well, it's okay for some people. Some people think that that is too slow and they want to know if there's something that can be done faster. The answer is, of course, data leadership can also sponsor what we call a data improvement project. And then we can say that data improves as a result of focus. Let's go back a couple of slides and pretend that one of your data stewards had come up with a wonderful description of a problem that if they identified this one thing, it would solve a hundred things. Great multiplier and attach some very good value to it so there was no question it was going to be done. You might ask data management to do that in the organization but I found that organizations working together as a team produce better results in here. So we think about this as sort of this nail getting on the wheels and having some specific focus and achieving some sort of tangible result in that action. Well, obviously the sooner we can get this feedback look improved, the sooner we can get our structure of stewards, community participants and again, just every data user involved, the easier it will be for us to make data things happen. Now, unfortunately, this is where most of the stewardship groups that I've worked with over the years have stopped is that they've been very excited about data things happening but we have to get excited about not just data things happening but also about organizational things happening. You'll notice I've drawn the approximately equals sign in there, which means this is an area that we can continue to improve on. While it's good to satisfy and be happy, celebrate, excuse me that we've got these data things happening, it was wonderful. It's better still for us to translate them into those dollars. And if we can do that, we are going to be able to value our own results very, very well. For example, if I told you it was gonna cost you a hundred dollars to record in Salesforce to change every record or $10 of record in Salesforce, you wouldn't make different decisions depending on what you were looking at over there. But if we can tie it to the dollars, people don't really have concepts on this. So we've got to practice this and we've got to get better at it and we've got to combine things that actually show up where one and one equal 11 around all how we are trying to get these things in play. So again, many people do not see that active data governance and this data stewards having an active role is appropriate for them. I have found that organizations that share this responsibility within their data management group as well as their data governance group really do strategically a lot better than groups that try to separate all of this. In fact, one of the things I would urge everybody to look at is to start off, especially while you're small as a team on this. Again, this gets us into the next area. So let's just go ahead and move forward again. Let's start simply. Let's understand that the differing cadence of data requires a different structural approach. We can't just apply traditional approaches. Excuse me to all around all this. There are some foundational prerequisites that we want to get. But what we're really looking for is the ability to do this gracefully and help people understand the value as well as the scale up composition around each and every one of these. All right, so first of all, here's just one article that just says, look at this. If you're a whatever, this is what your steward is and you have to learn all this stuff. Yes, it's complex and yes, it's difficult. In fact, I would actually start off with a very simple approach. And I do this out of great love for David Plotkin's book. He gave a great definition of these and he's not at all suggesting that you start with these. But one has gotten the impression when one looks at this many stewards to figure out that they have to figure out the differences between each and every steward upfront at the beginning of their journey where they know the least about it. It's generally not a recommended idea. By the way, also, if we're gonna put this many stewards in play, we also need a data steward auditor as well as a data steward manager. Again, David did a wonderful job with his book, Recommended Hardly. Let's go back to our original definition. Very, very simple. One who directs data assets in support of specific organizational missions. And then say the important question here is actually not the one, how should we manage this data every time we come to a piece of data? But instead look at it from the perspective of should we include this data item within the scope of our stewardship practices? The important thing there is you're specifying some kind of objective rule, excuse me, subjective rule, I'm so sorry. Objective rule where you can specify precisely what's actually happening here and saying this is within my scope, this is not within my scope. Again, remember you're claiming fiduciary relationship here, not ownership around that. And the idea is most importantly, document your reason. It's not that you're going to make the right decision, although you will get better at it. Certainly make decisions to document that reason in an ongoing blog or whatever it is that you do to a lab notebook is a rule that I use in many instances that gives you the ability to understand what's going on here. So the problem is when people hear what you're doing, oh, I hear you got something going on here. They'll get, yeah, there's something going on data and you're trying to tell them that there's this data governance program that you're part of and guess what they hear? Thank you, Lucy. Sorry, Lucy's teacher around all, Charlie Brown's teacher around all that. Yeah, they just don't get it. Most people that I talk to that are not in data don't get the difference between data management, governance program, the big data and everything else that they just lose it at data. And that's important. So I would suggest a starting place is to start off and say, we have a data program. And that's what we're going to talk about. And once you've got the idea to have a data program, we can talk about who makes it up. By the way, data stewards are part of the data program. And that will give everybody an idea of where they fit in. What do the stewards do? This was, I wish I could find where that came from, but it came from, I think it was something at Capitol London, I'm not positive. Again, nice description about it tangibly within an organization. What do they do to improve our organization's value and what do they use to achieve the organization's objectives in there? So we've got two things. We've got better quality data and we've got better use of the better quality data. That's a wonderful multiplier for most organizations. Similarly, you need to also advocate or evangelize for increasing this scope and rigor of these data-centered practices and ensure the effective and efficient delivery of data management perspective. But unfortunately, most organizations really don't pay attention to do, they change those last three and leave them off. But here's what the perspective of the steward really is. The perspective of the steward is that there is this thing called an organizational data machine and all inputs going into the organizational data machine and all outputs are data. In fact, everything going into the organization, everything coming back out of it is also. And the question becomes, how much do we manage formally? And a steward is best able to determine that because they're familiar with both the business context and the uses of the data within that business context. If they apply too much governance, it will become too expensive and it will run slowly. If they apply too little, they will miss opportunities. Interoperability is where you determine the value that helps you to understand where that specific piece should come from. So keep that in mind for a second here because unfortunately what happens with most groups is that when we have this idea of what we've got, we really don't know what we don't know. And when they start to survey, we end up, and I like to use not that all data should be of perfect quality, that's nirvana. And I'm very happy with that as an end result in anybody's mind, but it takes a long time and a lot of money to get there. And I wouldn't recommend it as a direct approach. In fact, when I see these data strategies and say we're going to deliver the best environments, it's like, oh my goodness, that may or may not be worth what you're trying to do. Everything that you do is subject to price quality and speed functions within that context. And what you discover very quickly is not that data should be of good quality, but data should be of known quality. And if you don't have that known quality, it's going to be even more important that you become that. And as you push this known percentage upwards and upwards in the organization so that you can continue to excel within there, but it's not the idea that we're going to make all of our data of perfect quality. We're going to carve out a specific piece that applies to my specific stewardship role, and I'm going to try to get to know the data within that particular piece and decide what level of governance is appropriate depending on the data's use. Next topic here, do you get full-time or part-time stewardship? I will always take full-time. The answer is, of course, you get one-tenth of 10 individuals or some other fraction, two-fifths of five individuals. Given the general literacy rates that are there, we're going to have to educate these individuals. And so having the full-time is a really good argument for that and saying, we're going to need sustainability in this area. We need to train some people that will eventually become our train the trainers, whatever it is that you want to think about in here. Remember, these individuals are the ones we're asking to charge here. They're taking the organizational data strategy, which, recall, only exists to support the organizational strategy. And they're taking that data strategy and they're translating it into things that they can do to better support the organizational strategy, data things that they can do to better support the organizational strategy. And of course, going back on that side, we need to say, how well is it working? Give us some feedback, et cetera, et cetera. We also have, of course, in Peter's world, IT, they're having a coordinating role within that. And of course, IT ends up closer to the business. We get some feedback loops in there. We get everything all nicely diagrammed, but I wouldn't show people that. I did give them a simpler version here and also extend out the role of where the stewards fit in. So the question then becomes, from a data governance calculation, I have a limited number of stewardship resources. What should I do with them? What is the most effective use of those stewardship investments? And the most effective use is pretty easily determined when you have a series of specified business goals. Those specified business goals can be coupled with the language of metadata. That translation needs to occur. The stewards, of course, should be involved in that, but this is where everybody has to speak the same language because if we focus the stewards on metadata-specific business goals, they will be able to achieve results much faster than if we give them something that still is subjective in nature around that. Of course, they're going to report back on problems. What kinds of individuals are we going to be looking for for these stewards? Somebody who can think in systems perspective. If you haven't been introduced to the concept, think of the parable of the forest and the trees. And it's the idea that the parts of the system can be best understood in the context of the relationships rather than in isolation. So understanding the whole is important here. It is an example of lots of specific parts that are moving. This is, again, some numbers that I've picked up over the years here that just describe a very well-functioning organization. You can't run almost 30 billion queries a day without having really strong support for your automation pieces. And this leads us to a conundrum that I call the data sandwich challenge that we have around this. The idea here is, of course, that we have a leverage point in this high performance automation depending on a combination of three things. The data literacy of the organization, the data supply that the organization has, and the standards that they're using, if at all, in the organization. And these are generally to varying degrees. Again, the idea is not a binary thing that everything should be standard data, but judicious use of standards within the organization can be a tremendous assets, particularly after you have a chance to study it and understand it. Because what we're trying to do, of course, is smooth out all of these various aspects and the smoother we can get, the better these components will be able to run together. And the idea is, of course, you can't make something that works this well without investments in engineering and architecture. And those investments can be actually articulated very, very nicely. I had to go to a tree farm in India to see behind the cash register the following statement here. It's the Deming Code, of course. Quality architecture and engineering and architecture work products do not happen accidentally. Just thought that was so wonderful to discover that there. And of course, we enhance this, of course, with our data stuff here, put our data ears on and we get this data engineering there. So we can't, without a significant investment in data engineering and architecture through the use of our stewards, create products that will buy the performance that we need to have in order to be able to achieve what we're attempting to achieve organizationally. Data is not a project. And thinking about it from a classification perspective, it's closer to a durable asset, an asset that we invest in and expect more out of at a future date, an asset that has a usable life of much more than one year. And while it may be reasonable to expect project deliverables in 90 days or sprints if you're in the agile software development world, no problem, data evolution is, like I said before, more correctly measured towards years than it is towards 90-day increments because data does evolve. One of the gratifying things about being as old as I am is that I've gone back to organizations I worked with 40 years ago and found that they are working with primarily exactly the same data that they were working with over the years. It is a significantly more stable component than anything else. And this is, if anything, one of the excuses that the CIO community has is that if we had had the stability and data that they've had in the, excuse me, that they had in their IT evolution that we've had in data, they would be a lot further and faster and in their own maturity curve as well. Again, these architectural components need to be ready and a prerequisite to agile development because otherwise you'll just develop more data silos. And the only way to avoid doing this is to commit to understanding one, the difference between a project and a program. And if I were in live mode, I would ask you to raise your hands and tell me if you're a PMP certified, which is a wonderful designation and quite useful around this. I'm not gonna read the ideas on this, but just very definitely one of the easy things to differentiate between a project is that a project has a start and a finish and since data tends not to have starts and finishes, it tends to be long in the tooth. It's much closer to a program and you need to make management understand this that the idea of your data program has to be long in the tooth there as well in order to apply correctly around this. The idea here is that your data program, it needs to be as long and as well funded and as steady as your HR program. After all, nobody's going to go to your organization and say, hey, I think we're gonna get along. We don't need lawyers anymore and let's just lay off the HR department. Next time things get tough, right? Sorry, HR departments. I could just as easily have said finance. That would be just as incorrect in each case, but the key here of course is the most organizations have functioned with a semi-functioning data program, an underfunded data program and unable to function correctly data program because they're blocked from certain key technologies and aspects here. So this is why you need a data program in there and why stewards are one of the most important elements of that program that is there. See, a stewardship perspective on this is very different than probably most. The stewardship perspective is going to look at an organization and have plans and those plans are going to translate into things that the organization does and sometimes the things the organization does actually turn into products that they make, although you're seeing less and less manufacturing in corporations and more and more outsourcing around all of that. Again, the Apple model has proven successful in other industries here as well. And so it's reasonable to say your organization is all about data and some cases, in some cases, it's not about just data in that idea. So the question is, of course, from the stewardship perspective, what business are you in fact in? And one of the ways to describe this, again, reading the goal, you'll enjoy the book if you haven't already. It's a fun process that covers Alex Rogo the exploits of Yunco company in here. And it teaches the system that management, excuse me, that system performance is an organization that is purposely oriented, but has limiting constraints. And there's always one constraint that's in the way, find it, fix it and move on to the next in order to do that. Because of course an organization is only as strong as its weakest link in the critical processes and data of course ends up being on the critical processes very quickly, which requires a special skill on your part in order to be able to identify these areas. Let me give you an example of just what that might look like. This is a somewhat typical organizational set of practices that I see. There's a group called data management that operates in a somewhat black box sense. Not that they are not being transparent, it's just that nobody tends to give them any love what's going on in there. They are producing data that is then consumed by whatever the ETL sources or the warehouse source or the cloud source or whatever it is that the organization is using to consume and then produce a series of data marks and dashboards and visualizations in the process. And that part on the right hand side there uncovers errors as we would hope it would. The challenge for most organizations is they only feedback into this first part here whereas the feedback from the stewardship perspective should be to direct the learning and feedback back to that black box area in order to make it work properly in there. Another focus of stewards is the sequencing of activities here. Many organizations don't have stewards will classify them as incorrect in just a second here but the idea of stewards are gonna be focused on two types of activities, improving operations or innovating and not having one of course is not a good option on that. So our next level over there would be the stewardship next the quadrant there that are focused on efficiencies and effectiveness. And I think everybody would agree that Walmart has demonstrated over the years unbelievable skills and abilities in that area. They're held up as models and should be around that. Apple on the other hand has been pretty good about strategic opportunities up there but here's the part I want you to think of here. First of all, asking people to do both at once is very difficult. It's kind of like being in a product company and a services company at the same time. Most organizations choose to treat them separately rather than together in that. So we'll get rid of the both component there as well on this, but let's think about just for a minute if we took the T3 people and I'm gonna use Johnny Ive who's the wonderful Erudite British designer that was with Apple for many, many years now is still supplying really wonderful designs around this. He would come on with the Apple iPhone video and it would float through the air and he would tell you how wonderful it was and he was so well spoken and things that just made you wanna go out and buy them. I know I did in there. And I want you to imagine him being told to be cheap. It's just not gonna work. And I similarly want you to imagine that the data stewards in Walmart too who might be very focused on being effective and efficient are now told to be imaginative. And that's just, I'm not in their character. It's a fundamental difference. It's not that it's impossible but you don't expect good results instantly in the process. So what is the approach that one should take here? Well, the idea would be very absolutely take some money from an effectiveness and efficiency perspective and use that money to justify your own positions but also to create the strategic opportunities going forward. And I love to stop here and just point out two things. One that only one in 10 organizations actually has a strategy involving their stewards in any way that's meaningful around us. But two, that the organizations that I've worked with over the years have credited me with more than $1.5 billion in savings. So there is money to be made in them their hills. And we're not quite sure how to articulate it in less crude fashion but I think 1.5 billion actually does add up on this. So I get upset when people say it's hard to put a dollar. Yes, it is difficult. Let's talk about what's going on here. Well, first of all, we've talked about stewardship as a role here. Again, given some very solid and hopefully grounded definitions of stewards and data debt in particular, understanding that stewards are the implementers of strategy after all, who else is going to do it? They're going to help shape that strategy from the business perspective and they're going to do it through uses of architectural components as they work. What are they supposed to do? Well, they're going to resolve challenges that stem from data debt. They're going to be stuck with fixing problems. They're also going to be charged with making improvements proactive and reactive. You're going to have to develop a framework. You're welcome to use mine for a starting place. I've had good results with it so far. People also like the idea of explaining a fire station to other people because people relate to it well in there. It is important though for the organization to understand what the role of stewards are going to be in the context of data governance. And the guidance has to be start simply. One of the most important things that I hadn't said it prior to now, but I'll say it now, is that when you appoint your data stewards always announce that you're appointing your first role of data stewards, the first round of data stewards, the first group, the first tranche, whatever role, whatever you want to give them as far as an adjective describing them there. Because I've seen so many organizations where they've appointed the stewards and left somebody out. And I know that sounds silly, but it makes a difference when you appoint the first round and expect the second round to be appointed. And that makes sense that you're going to do it as well. I have similar guidance for organizations that are doing data strategies, obviously. Do your data strategy and label it as the first version. And then we can talk about version two, version three and version four. And they're expected and in some cases even anticipated and welcomed. We're moving towards our Q and A and just to get us there, I'm gonna do a couple of quick takeaways. Well, perhaps you prepare some questions for Shannon and I to jump in with in here. So stewards are going to be the experts for the short term as well as the long term. Organizational literacy rates are not where they should be. I believe personally that our focus in data literacy should actually be on knowledge workers, not on data professionals, but that is a different, excuse me, webcast that we'll do at some point in the future. But stewards do own the results. Therefore they should control the remediation process around that. The need for stewards is of course increasing. Organizations are just getting handles on realizing how productive their stewards can be, realizing what level of support that they're going to have. And of course they're doing this in the face of an increasing data volume as well, which is going to be challenging. We haven't demonstrated a lot of practice improvement. I think that's one of the reasons we've seen so many false starts and imprecise definitions that we've been using in here. So I do think it's imperative on us to get better at getting better, but also in addition to that, start to look specifically at bodies of knowledge around this like and pledge to you that DAMA International is going to be working hard in that space to try and help out. We're always looking for volunteers and helps all put a pitch in there as well. Data stewardship is, as I've mentioned a couple of times relative to new, and it's going to have to conform to organizations, not the other way around. We talk about data governance as being a bespoke, a custom for that organization grouping. And that seems to be the case. It's not that you must have a unique one in order to do it, but that the organizational culture and technology and process combinations do end up dictating something that works on a unitary basis, as opposed to a one model seems to fit all around that. So we don't have a best way of saying how to be a data steward. I think I've given you some guidance here today on how to define what the data stewards should do, how to use them, and also how to think about appointing them in that context, because if we don't have the appointments in the right context, we are absolutely going to put the right wrong people in the right jobs and that's not going to be a healthy success under any success, any set of circumstances. Stewards have to be driven by a data strategy. And if you have anything else that nothing else that you take away from the webinar today, please think about this data as a pattern in a stream of decisions. Anybody within Walmart is not disciplined if they make an error and they make an error on the side of organizational strategy. It's a comforting, it's a good, it's a well understood people have understood this over the years. Can you imagine trying to take a hundred page strategy and have everybody follow it? It's just not going to work. So we need to be a compelling strategic vision for the stewards to do this. And there's got to be some sort of application of data management. So I use the word direct data management application, but it's a clearly a governed function and also a co-joined function in there that has to be coordinated with business and IT, otherwise will not be successful. And finally, at the bottom line, we need to speak the language of metadata. If we don't speak metadata, we will not be able to work in this organization. Again, I don't want to make that sound like I'm whining, but when I was working for the Defense Department at one time, I got sent to city XYZ and it turned out somebody had actually wanted to send me to the city ZYX and a little bit of dyslexia had caught in and cost the government extra travel stuff. Don't worry, that's probably the least amount of money that the government's ever wasted on something like that. And we have actually helped the government in many, many ways save some of these things here. So let's move towards the top of the hour which we're getting close to here. We've got some events coming up again next month. We're going to talk about reference and master data management as a strategy, not as a technology, that's very important for that. And Shannon, we're back up at the top of the hour here for your for our questions and answer session. Thank you so much for another fantastic presentation as always. And just to answer the most commonly asked questions, just a reminder, I will send a follow-up email by end of day Thursday for this presentation with links to the slides, links to the recording and anything else requested. Lots of questions coming in, Peter. So timing in here, we are rebooting our stewardship program. We're having a challenge with where the business thinks IS owns the data. I think that may be supposed to be IT, but this has made it difficult to find data stakeholders and stewards. Do you have any advice on how we can sell the need for stewards and stakeholders from the business? Absolutely, one of my favorite things to do with organizations and you don't need me to do it, but is to hold a data horror show around Halloween or figure out a data Easter show if Easter's what's coming up. But you'll be amazed at what sort of people will come up. I have a wonderful office at the university and we don't use it very much these days but we are down there. And one of my favorite data people I never even met until I held an event and this individual came out of the back office and I was like, oh, you're a data person too. So be visible about it. Absolutely, look to attract. And interesting, I'll go back to Shannon, your confusion about IT and IS. We label them both. It's terrible, very bad practice. If we were being graded, we would flunk. Unfortunately, we're in charge. So we have to put up with the confusion around whether it's an IT or IS or whatever it is. But I'm glad it's me that's just confused. Yeah, the only Shannon. Peter, you say that data storage should be both accountable and responsible for data assets. Is it a good idea to partition this role? For example, add more senior data owner or data sponsor to advocate for data at a senior level or add a data custodian who is a technical specialist who understands the data model constraints, et cetera, and can support the data steward in implementing standards and processes. Or is it better to try and put everything one person to ensure clear responsibility and accountability? Goodness, let me pick A, all of the above, right? Or something like that to make it easier on myself. No, no, it's a good question. And unfortunately, the answer is, of course, that it depends. And I'm not dodging your issue. What I would suggest is that you start simply and have somebody in charge of acting as a custodian or a steward or a fiduciary. And that fiduciary role is explicitly called out. Having that kind of a person is going to be a different place for the organization. You do not need to appoint 10 of them. You can do one and see if one helps. In fact, I would urge small rather than large. And the next question was, do we appoint a custodian or do we put somebody in over top of them? Your organization will evolve. I've seen sometimes where the addition of a specialist here, and I'm just going to add the auditor in pretending that it's a specialist on there, that that was able to materially help. Again, what is the bottom line of this? If we can make something faster, better, cheaper for the organization from a data perspective and that that data perspective tangibly improves some of the strategic key factors that the organization is attempting to achieve, that's a win. And we want to be able to push that forward. On the other hand, if you try to say, I'm going to have to have an Uber steward that's going to be over top of the things. Again, be careful on that because people, when they're over top, they tend to try to say, well, I should know about what's managing it. And there's a difference between somebody who's responsible for managing it and somebody who's responsible for making sure it is managed correctly. So this is an important aspect. I also want to go to the use of own. I know that the questioner probably had written that down, so I'm not going to ask the questioner specifically, but I want to really hit that hard. This is the one area that I have found that has caused organizations the most problems of any is that they've defined a concept called a data owner. And this leads to, of course, the data mining that you're seeing around that process. Data flows through the organization. So saying that somebody owns it is not correct. Somebody is responsible for it while it is going through these set of organizational business processes. Those are definable objective. That is the scope of a steward's role. If a steward can't keep up with the work in that role, then you need to add more resources. If the steward has idle time, then you need to increase the scope of the steward's role and perhaps take on as Alex Rogo did at the very end, four factories instead of just one. If you haven't read the goal, you won't know that reference, but I think I gave a good precise answer to that question. Shannon, yay me for remembering it all. That's all. Absolutely, that was a good one. Great. So as the stewardship is done by a data catalog tool, should the steward be trained on the data catalog tool? If so, what is the time period? Two weeks, three weeks a month. Everybody can be perfectly trained on all the tools that they want in two weeks, three weeks a month. Very good question, and we should address it at a high level. So let me get the right slide up there. Correctly understanding, not just that the only thing the stewards do, but one of the most common things that they're understood to do is to maintain the collection of metadata about the organizational data from a business perspective. And if possible, integrate it with the technical perspective that is there as well. Yes, they need to know what's going on. Again, I'll make a very simple example. This is not because Kaliber has paid me any money or anything, but just I know that the US Army is in the process of implementing Kaliber in parts of the US Army. So somebody in the US Army who is a data steward would absolutely use Kaliber and is anticipated to be using Kaliber as a major component. It depends on what technology you're purchasing and what technology the stewards will have access to at the moment that they have need of it. This is not something that you can expect the steward to go back to their desk and look at it later. They're most often asked for an answer right there on the spot in order to do this. A very brief example of this is one of my favorites, the Nokia Term Bank. Now Nokia is an organization that in addition to knowing Finnish and Swedish also required all of their employees to know English which made it very easy for us non-second language speakers to interact with them. And we would be in a room speaking in English and somebody would use a term and they were, I don't wanna say trained, they were conditioned, they were socially, they understood they would benefit by all immediately diving to their laptops, accessing the Nokia Term Bank and looking up that term when they didn't know what it meant. That often helped them learn English very rapidly but also helped them literally get on the same sheet of paper. And by building out their glossary data dictionary catalog whatever it is, we're going to call these things in a very nice fashion like this over a period of 10 years it became part of everybody who was a Nokian to immediately look for that Nokia Term Bank first before they would start to look for any other sorts of expertise in order to find and define that particular term. So yes, absolutely stewards need to use the tools they need to be trained on the tools and it depends on what tool you buy, how long you're gonna have to spend on your training but if you're telling somebody to learn it off the side of their desk they're probably not gonna do a very good job of learning it. So I would suggest a priority would be helpful in that context. Thank you, Shannon, great question. Thank you. So I'm continuing on here what skills and personality traits should you look for in a data steward? Data nerds or people, people strong business knowledge or does that not matter? Do you find someone who is implicitly doing it already and familiarize their role or do you create new appointment? Great question. And I think you will have a mixture of what you just described in here. Let's look at what we're asking stewards to do. Again, if we put all the arrows on the chart here just to pull them all up they make decisions, they implement, they understand. The idea is that a steward should really be able to interpret what the effect of the organization is going to have. And one of the conditions that I require from stewards that I'm working with is that they understand the sources, the uses and destinations of the data. So in other words, where is it coming from? What are the issues with it as it's coming to my area of focus? What does my area of focus do with it? Are there three main processes? Are there 20 main processes that process it? And who uses my data downstream and what is the criticality factor if we're going to use my data to declare something that involves law enforcement then we're going to pay a little bit more attention to it than if we're just putting pins on a map on a social media site. And I don't mean to be glib about that. The idea for who should be in that pool. First of all, look for ones that want to become part of this. People are frustrated. I talk to business people all the time who say, oh, I'm so glad they're finally getting around to doing this. I've been trying to get them to do it for years but they won't listen to me because I'm an insider and we need to have somebody from out of town with a briefcase explain it to these guys in order to do this. The second trait that I would look for is somebody who is more of a people person and able to think in the systems thinking role that I described a little while ago. The idea of understanding things from a systems perspective is important because it really does talk to the organization's ability to go up and down levels of abstraction very, very rapidly or at least very concretely. Again, if you think in the Zachman framework, I can talk to a row one person and a row three person and two completely different vocabularies but they will both understand me in the context of what we're talking about. So this systems thinking is a very important part of that. Another component also is their own data literacy. And while we're not talking about data literacy in this webinar specifically, there are some objective tests that we're starting to develop to determine early screen for data literacy. So I would say, for example, if the overall focus in the idea of getting a data steward in place was that we wanted to have two candidates that were in front of us and both were equally qualified in all respects but I could determine that one candidate was more data or the other one I would solve the equation for the more data literate person. The challenge, of course, is how do we test for that? And sometimes it's a difficult test. Sometimes it's an easier test but I believe that within five years major HR departments worldwide will include screening for data literacy as well as other things that they're screening for or they'll build it into their AIs who knows where that's gonna go. Anyway, great question. Thank you for submitting. That's a wonderful topic. We have so many great questions coming in. QC performance standards tied to a stewardship responsibilities made formal or is it other duties as assigned? Yes, I would not like to be other duties as assigned. So very difficult to describe in terms of performance but some organizations, for example, will measure the number of communications or the number of decisions that are made. I don't like to do anything that looks fuzzy. I think it's incumbent if you have a group of five data stewards and each data steward is getting paid 100,000 to be able to show at least 500,000 in savings, faster, better, cheaper at the end of the year in that. And that if you're unable to do that and unwilling to do that, you better have a big corporation to hide and because I don't see a good future with that kind of an attitude. So when data governance produces something through what the stewards are doing, I'm sorry, I'm on the wrong slide here but in the way of achievable pieces, it's important to crow about it to write it down on a website, a blog, whatever it is that you're doing and say this use of metadata allowed us to achieve or help achieve this business goal and this business goal enabled my organization to deliver more in accordance with the mission or enable saving money or earn additional revenue. Again, it comes down to a simple series of choices and we need to be focused on those choices to the degree possible or we get lost, confused and lose focus around it. So performance standards, I think that there would certainly be a 360 component evaluating how that individual got along with everybody else because we can all learn from each other. I've always found that type of feedback to be wonderfully supportive and valuable in there and that the organization should work much more as a team as opposed to a series of departments. You go over here, I go over here, this person helps with this and this person helps with this but instead that there's somebody they can trust to get answers. It's funny, I can remember a story from back in my youth where we had what we were doing in those days I made several million dollars around the world by helping organizations figure out what their ERPs had been customized to become because there was no documentation on the changes that had been made. We got known as the people with the facts and they would just come to our cubicle and say, I've got a question about implementing training and I said, okay, how can I help? And they said, well, I understand you've got some models or something that can show me where things are. Well, if I organized the metadata in a certain fashion I can say that these modules are more complex than these modules. Therefore I'd plan on investing more time here instead of here. Simple things like that actually help quite a bit. So the innovation component is an important one somebody that can think outside of the box and come up with this. Again, I believe that as a component of everything that's in this diagram here right now it is absolutely best positioned to initiate what used to be called business process re-engineering exercises. The old hammer and champions stuff for those of you that remember the ancient days. But it's the idea that data focus remember six segment didn't work because most organizations can't make a commitment to it. But something along these lines is something that most organizations can commit to it. And again, if your group that costs the organization half a million clearly shows that it's saving 5 million the organization is going to say what additional resources can I give you such that you will be able to help me even better? Again, great question as to this performance standard let's not put it into memo writing and policy development. Although those things are important the business value is going to be, I believe long run much more important than the others. Thanks for the question. Great. And another great question coming up here you know which we probably could turn into an entire webinar. How do you make data stores accountable in a very decentralized organization with very different data needs and data terms? So the terms are a starting place if you can't agree on the terms you will never get to any of the rest of the pieces on it. Let's go to the other map that I was showing about where the data stewards play a role in there. Just take me a quick second to get to that. There we go. So again remember our components here start out with little feedback we've got some leadership and I said the data stewards are part of that leadership in there because they've got both the knowledge of the data but also knowledge of if we're going to call it a subject area or whatever their domain happens to specify within there. So I think everybody agrees on this side of things that that's particularly important. Again, if I am investing 500,000 a year in this data governance structure with five full-time people in those roles each earning 100,000 in there. And I invest that for five years I've now invested two and a half million. And if somebody doesn't perceive that the data has improved in some fuzzy sense of two and a half million dollars worthwhile I don't predict the future that group is going to be a long-term future. I've seen this come and go too many times in my existence or something pops up as a fad doesn't fund itself. But on the other hand, it just went through the example if this group that's cost the organization a half a million each year then has the ability to generate 10 million, 50 million in response to this the organization is not going to have any idea of saying, oh, that's not good. We shouldn't get, we should get rid of it. By the way, you can't just do it and not tell anybody as well. We need to do the celebration piece. So the question was about accountability and the idea is that you're going to build a structure that allows the organization to understand specifically what's happening. I'm working, for example, right now with one federal US federal agency that has been given permission to use data under a certain set of circumstances and by another agency. And they want to make sure that they absolutely do not violate that letter or spirit of that. Cause of course, no one federal agency is fighting amongst each other. So they feel themselves specifically accountable for the use of that particular data. It's a wonderful thing. I know too many organizations that would just go, I'll just do it anyway. Nobody will ever find out again, not a good way to do it. So we're looking at this accountability. If I've got data things happening over time and my feedback loop goes from data improves over time back into this instead of going through the organizational things happen, I don't think we can be successful. And I think that people saying, I cleaned 16 data elements last week, or I wrote a policy that's going to prevent anybody from entering in the finite particulates of cold dust color incorrectly ever again. I don't know that that's going to be seen. But if we can turn these X's into dollar signs and make things really happen on that other side, people become accountable and people will say, look, I don't pay you all half a million a year to watch TikTok videos all day. I want some specific things to happen in there in order to make this organization move forward. So great question with accountability. It's a tough challenge in order to do that. And I think it goes back to the bespoke nature of this. I wouldn't try to be comprehensive in making them accountable, but find a couple of things that you really want them to do really well and encourage them to do that. And the right kind of person will turn that easily into a job description and go, cool, that looks like fun. Where's the wrong type of person will go, seems a little fuzzy to me. I don't think I want to get involved in that. I was invited to become part of the federal government because I understood the difference between information engineering and nuclear engineering. I don't know anything about nuclear engineering. So it was hopefully a successful interview. Anyway, thanks for a good question. It's an area we need to grow into. Data stores looked at to understand the lineage of the data. A leader has advised that the business doesn't understand the IT side, meaning database tables, et cetera, of the data. Is this the scope of the steward role? I would argue the scope of the steward role is understanding as completely as possible where the data is coming from. What are the sources? What are the types of things that can go wrong with that data as it's approaching my scope of operations? I would argue very strongly for an objective measure of that scope of operations because otherwise you'll find data stewards talking all about what are the parameters rather than actually getting anything substantive done. I would make sure that the data steward understands how each of the business processes in my area of domain are able specifically to be delineated and to understand if there's any cross-pollination between them or whether they're all using data in silos. And what are the things that can go wrong in those areas? What are the most common areas that cause organizations problems? And finally, where does that data go when it's done? Where do I do? How does it go away? Let me go to the organizational data stewardship view of things because that I think illustrates it well. The idea again is that if we don't get the inputs and outputs of where these things are coming from, you don't have proper context for where all of these bits and pieces are going. So there's a slide I was trying to get to and talk at the same time, never a good idea. But the goal is to know as much as you can about your area and I'm gonna make this one data steward. So this is one data stewards area. It may be that all the data attributes that start with an A belong to that data steward. I would question whether that was a good strategy or not in order to do that. And the idea of the data stewards knowing where things come from, what we do with them internally and where they go from here, that is what the definition of data lineage is all about. Part of that knowledge is that some of this data goes into a massive set of, again, I'm gonna make up some numbers here, massive set of teradata processes where it's replicated and chopped and anonymized and de-anonymized and comes out in a format which you would barely recognize is going in. That may be legitimate business process or it may be unfortunate accidents that are occurring because you don't take good enough care of your data internally around that. All of this relates to provenance and what we're really looking for is governed by the word either material or essential. Material is something that would show up as a financial reporting statement. Is it material to say that your CEO had a heart attack and was incapacitated for half a year? Probably, that would be an important material standing. Essential is part that needs to be made up of the model. That's what you're determining as you're going through in your area. What are the pieces that you need to manage? What are the pieces that you need to manage with guidance? If you do too much of this, it'll be too much work and nobody'll get anything done because you'll have a lot of bureaucracy. If you do too little, you're missing opportunities and you need to be able to catalog those opportunities to articulate them and to make them known in a wider capacity so that you can gain support for these types of initiatives. Again, probably more than you wanted but I think that was a pretty comprehensive answer. I might've got to be on that one if I was creating myself. Thank you and we can certainly inquire for additional details if needed. But moving on to the next question here. Do records managers have a role in data governance? Should they also advise data stores about retention and destruction of data? 100% and thank you for bringing that up. It's unfortunate that we don't pay as much attention to the rich tradition of good solid literature and research that exists in the record management community. There are a number of these parallel organizations that we absolutely need to take advantage of. I'll give you an example of how dumb some of us can be where I was in Belfast, Northern Ireland and I like to travel to various cities. You guys know I used to do a lot of that before the pandemic and in the process of doing that I was hinting around that I wanted to get down to Dublin and somebody finally said, Dr. Akin that would be Dublin, Ohio that you're trying to hint that you want to get to. And I've sure enough I was at Dublin, Ohio within a year because that's a place where they have done some phenomenally good work around metadata standards and ignore it and reinvent the wheel at our own peril. So thank you for pointing that out and I absolutely agree. It's an essential component of what we're talking about from data management and more importantly it has a rich and well-researched tradition in there. I think we got time for one more question here. You know, stewardship is a big shift to turn an organization. How important is a communication plan? Huge. I forget, I think I used to say it's 30% of whatever it was that you're doing but I'm not sure it's not shifting the other direction. It really is. I may have gone through this part of it too rapidly but people just don't get what's going on. They've got a lot of things that they're worried about. For example, one of the things, I think I was probably 30 before I realized this, the higher you get up into management the more what they do looks like lists. Now this was inside the John Zachman head 50 years ago but takes the rest of us a little bit to understand that John of course is always correct on this. So the first thing most managers look at is, is this on my list or is this on somebody else's list? And while that's an important distinction it's not necessarily the most helpful but if we're running around and I see this happen a lot organizations are trying to talk about their data governance programs and the roles of stewards in it. And again, what comes out is just this. I'm eventually not going to be able to use that because everybody who recognizes that will be my agent dead. But what they discern is that you have a data program and let's keep it at that level. There's an HR program, there's a finance program and you can make the data program understand and everybody to understand in the organization that the data programs and your accompanying data stewards are something that you need to have you would not get rid of your HR and your lawyers. And this is exactly in that same category in order to do this. Make one more additional comment on that too. And that is that we did a, we're on our third start with data stewardship in the Commonwealth of Virginia and I wish I could tell you it was, it was different. But one of the things that we did early on that actually was quite a bit of a motivator was we gave them quote badges and they were Commonwealth data stewards and that was a real motivator and we had a lot of really good traction around that. And of course then life came by and the key person who was a key motivator left the program and they were back to start over again doing well at the moment. And certainly of course I want my own state to do as well as it possibly can. So Shannon you said we were at our last so we'll remind everybody of our upcoming events. And also we have an event in person in June as I mentioned too. So we're hoping to see everybody back then. Yes, and we are celebrating and diversity. We celebrate data education months. So data governance and information quality. You can use hashtag or use discount code data EDU to save money off your registration there. So hopefully we can see many people in person. Peter, thank you so much for another great presentation and thanks to our community for just being the best. I love all the network that's been going on throughout the webinar today. Really amazing and awesome as you also celebrate and support each other. And if there's anything we can do to help out with that let us know. Also a big shout out to all the women out there for International Women's Day Celebration. All right, well thank you all again. Just a reminder I will send a follow up email by end of day Thursday with links to the slides, links to the recording and all that groovy stuff. Hope to see you next month. Thanks all. Cool, cheers, bye Shannon.