Hello. So, you know, I probably don't need to talk about what MEV is as much given that there were, if I remember right, 17 or 18 talks about MEV. But, you know, I think in the spirit of trying to understand the incentives hidden in a lot of these systems, one question you might ask is, does there exist sort of some type of formal theory for MEV? Does there exist some type of mathematical structure to this thing that seems to be, you know, captivate people's imaginations yet no one can describe it? And so I'm going to talk a little bit about different ways of addressing this problem, including some work we've done. And I'd like to call any analysis of MEV sort of measuring the cost of feudalism. So that's sort of where the title comes from. Let's start with the operative definition of MEV. You know it when you see it, like, of course, the famous Supreme Court ruling on porn. And, you know, MEV comes in many shapes and sizes. You know, of course, we have everything from sandwich attacks, liquidations, arbitrage, NFT front-running and cross-chain. And as you can see, it's sort of quite a variable form of revenue for validators and searchers. And somehow, because we know there are many examples and we feel like we can't enumerate all of them, it might feel like the space of all MEV is super big. So how do you analyze it? You know, I know there were two talks yesterday about what is and isn't MEV, but I think we're going to focus on kind of an operative definition that can be used. We could sort of say MEV is any excess value that can be captured by validators, by reordering transactions, strategically adding or removing transactions. The idea is most of this is currently managed via off-chain auctions. So people bid on transaction priority, add imicity, you know, within bundles. And, you know, if we take a step back and look at the history of Ethereum as to why we got to the auction world, part of the reason is the blockchain was just getting spammed by people doing liquidations and back-running oracles.  On the other hand, of course, as the previous talk was focused on, there's a lot of centralization risk with off-chain auctions.  We have users who submit transactions, which are the deltas. We have a mempool. The searcher has sort of their own view of the mempool, and maybe they have some transactions that are directly given to them, which, you know, people would call private order flow. And then the searcher sends their bundle, which they're willing to pay for, to the validator, and the validator adds it to the block. The philosophical question here, though, is, how do we know value captured by MEV is excessive, or in a notion of excess? You know, in this sort of first definition, I said any excess value. Well, how excessive is excess? Describing value flows in MEV is actually quite difficult. So in Algorithmic Game Theory, you usually have two main ways of thinking about the world. One is optimizing social welfare. Social welfare means if everyone has some notion of utility, some notion of how happy they are with the world, you want to maximize the sum of everyone's utilities.  And in cryptocurrency, it's actually interesting that you have to optimize both jointly. So obviously, user welfare is important for the success of any network. I don't need to really convince anyone of that. But validator revenue being optimized is actually important for economic security. If validators are not making sufficient income from it, then they're unable to really secure the network. And in decentralized networks versus centralized networks, these are two competing goals. And I think the one reason there's a broad view of how Algorithmic Game Theory is different for crypto is that you have to sort of balance these two goals instead of trying to optimize one of them. So what pieces are sort of missing? So how would we kind of describe welfare as how would we describe revenue? You know, one thing is understanding the user's utilities. What is the user getting out? What is their individual notion of value? And that can vary over the entire network. The other thing is some notion of transaction fees and payments that, you know, the distribution of payment sizes that users are willing to handle. And the last thing is an understanding of how an auctioneer, so in flashbots or, you know, in PBS, how they allocate block spaces to users. And that, the last part is actually quite complicated and these three things are quite entangled when you want to try to write a mathematical theory for a movie. And these sort of things actually depend a lot on the application, right? The user's utility for minting an NFT is extremely different for a user's utility for borrowing against some collateral. And inevitably that means that the allocation mechanism should somehow be tied to that. Okay, so let's try to do kind of like the ELI-5 simple version of this. Which is, you know, we have a notion of block space, which is sort of a notion of max number of transactions or slots or size or gas. You can choose a unit, but for simplicity, just assume there's sort of some fixed number of units. There's sort of a notion of a binary allocation to a user of whether a particular user gets a particular slot. The users have the utility functions. I'm unspecifying the domain and range because of course it's actually quite complicated in the world of cross-chain bridging. And we see here that social welfare is defined as sort of the expected total utility of all the users given an allocation. So these X variables are effectively whether that slot is given to a certain person and this is sort of measuring their sort of net happiness, let's say. Revenue, however, is just the expected value of the payments. And notice these are quite different. In particular, the social welfare includes the utility of the auctioneer, which means that all of the payments that the user send are subtracted, or sorry, are negative terms for the bidders and it's a positive term for the auctioneer. So the payments cancel out in social welfare. So you have no dependence on payments explicitly. Whereas in revenue, of course, you depend on payments. So if we wanted to try to formulate what is an economic equilibrium for the system, you might say, okay, well, let's try to find joint maxima of the social welfare and revenue functions. However, given an allocation that optimizes social welfare, it implies a set of payments. And it can be true that the payments that you get from optimal social welfare are very far from the payments that you get from optimal revenue. There's some intuition to that. If flashbots just basically gave every bundle for free, obviously there's a lot of user gains, but the auctioneer is pretty pissed, aka the validator slash miners. And this happens all the time in MED. The idea that the optimal revenue equilibria and the optimal social welfare equilibria are sort of disjoint. So, you know, the name cost of feudalism is really a pun on the concept of the price of anarchy, which is sort of a common concept in algorithmic gain theory. Roughly speaking, you can think of the price of anarchy as if I have a set of many equilibria, I want to look at the ratio of the worst case behavior of an equilibria to the best case behavior. It's an approximation ratio. And so what this, for instance, this formula says is what's the worst case that the equilibria, the payments earned by the validator, deviate from the payments earned at optimal welfare, and what's the best case and what's the ratio of those? And you could basically think of this as if this is really large or depends on the number of transactions or users, that's really bad.  It means that even if you get to a shitty equilibria, it's not that much worse, it's not that much worse than the best equilibria. If the price of anarchy is weakly growing, so a little o of n, so like maybe it's logarithmic in the number of users or slots, maybe it's the square root of the number of user slots, that's okay, you can deal with that. And if the price of anarchy is linear in the number of users, that's horrible. That basically means that the more users join the system, the worse the equilibria is for everyone, which is like the opposite of a network effect. It's like the anti-network effect, right? No one wants that. And the interesting thing is that this quantity, if you try to write it out, depends a lot on your choice of what applications you're modeling, because to compute the prices, you need to sort of know these utilities of the user, and that of course depends. You need to keep belaboring that point, but I think that one of the reasons MEV has this, you know, both meme ability and inability for anyone to write down equations for it is because it's so dependent on the application, which is something I think the early designers of cryptocurrency networks did not divine at that time. So let's go through an example, stylized example of kind of what this looks like. How would you analyze this for AMMs? There's a very famous paradox in algorithmic game theory called Brace's Paradox, and Brace's Paradox is really about how traffic flows on a network. So we have this network on the left, and we have a source city, S, we have a destination city, D, and imagine that there's a bunch of traffic that needs to go from source to destination. Each edge that it takes is a latency, and so when you see a latency of one, that's sort of like a link that can take arbitrary capacity. It takes the same amount of time for everyone to cross that link. When you see a link of X, it's dependent on the amount of flow in that network. So you could basically imagine that, say we have 100 cars, if 100 cars all go down the route X, then it's proportional to the number of cars that are going. And so what you can do is you can say, hey, what if everyone is selfish or greedy? They're not cooperating. They don't have Google Maps. So Google Maps effectively gets you around this because it sort of implicitly coordinates people, whether you're thinking about it or not. But assume everyone's selfish and they know this map. How would they choose which path to take? And the thing you can show is basically, they would split themselves up 50-50. The traffic would go half on the upper path, half on the lower path. And so the time it would take is 3 half. We'd choose some units, such that X is from 0 to 1. On the other hand, you might think that adding more roads always decreases traffic, and this is not true. So there's this interesting thing of, if everyone's selfishly optimizing, if you add a link between the two middle cities that lets you teleport, then everyone just takes the X middle link X path and the total congestion that everyone realizes goes up. And so this sort of paradoxical behavior, I think philosophers would not call this paradox, but economists call it a paradox, is that even though you added a road, you've actually worsened congestion because people aren't routing themselves across the network well because they're not coordinating. So you might say, okay, great, what does traffic have to do with crypto? Well, first, before we even get to that, you may think MEV is always bad. There's always these articles that people are writing that are like front-running is theft. It's bribery, it's whatever. You will take your favorite negatively connotated weasel word. And you might just say, okay, yeah, we need to get rid of it at all costs instead of thinking, hey, maybe it's just endemic to such systems. We saw braces paradox, and what we're going to see is we're going to see that. For some examples of applications in crypto, you can get around braces paradox by having MEV, which sort of says MEV has some positive externality in some situations, which is the opposite of front-running is theft as a service or whatever. And again, not all applications are the same, so that's where the conundrum in defining your sort of optimization problem shows up. Another thing that's actually really important to pay attention to is if we classify users into two types of users, strategic users who are MEV searchers or people who are trying to optimize routes on chain and non-strategic user like the person who pays 80 bits for a metamask swap, if we kind of look at those two sides, routing is actually one of the hardest problems for non-strategic users. So non-strategic users actually, in fact, outsource that to third parties right now. So if I want to trade token A for token B, but there's no pool directly from A to B or there's not enough liquidity, I go to one-inch matcha gem, I'm including gem from the, you know, you could argue NFTs have the same properties.  So algorithmic game theory is studied routing. In fact, Brace's Paradox is a great example of how to study routing. And one question you might ask is, hey, does this apply to AMMs? Does this apply to one-inch? Does this apply to matcha? And the interesting thing that you'll see is that you'll imagine in this prior example, instead of teleporting in the middle link, we actually add a small amount of congestion, epsilon times the traffic. Now, you can show that equilibria basically approaches something, as epsilon gets really large, you go back to the condition where everyone routes themselves across the two paths. And when epsilon's really small, you get back to the purely selfish thing. And the idea is the MEV searchers are actually adding this extra epsilon so that people route themselves across the graph more efficiently. And that's something that's a weird positive externality that if you write out the math, I try to not add a lot of the equations to this talk, but if you write out the math, it's actually quite compelling that the social welfare somehow is improved by MEV in some scenarios. That's not the narrative you hear. The narrative you hear is like, oh, I'm just, grannies are getting robbed in MetaMask, and that's just not true always. Like I said, I'm not going to go through how you show such a thing, but I'll at least give you an idea of what the theorem statement looks like for such a thing. And the idea is we show that even when they're sandwichers, the price of anarchy is constant. Remember, price of anarchy constant, that's good. That's very surprising. You would think that, hey, as more users come to network, sandwich attackers make more of profit, they cause more of a loss, but in some liquidity conditions, they do not. The bottom chart shows you the expected profit on the y-axis, and it shows you sort of the amount of slippage inured by the user, and you'll see that it has a maxima. It's not like it keeps growing, and that's the thing that's actually quite interesting and nice about this example. So you've listened to me try to convince you that MEV's not bad, it can have these positive externalities, maybe there's something really interesting about it, and maybe you can direct it into being used for some particular applications that are just net beneficial to your network. So one question is, what do you do to harness it for good? So one thing I think that's interesting, and this goes a little bit to Alex's talk before, is suppose you did have a mechanism where it was just extremely expensive for people to bribe a validator off-chain, and in fact people were willing to basically share their MEV revenue. Imagine like a mining pool for MEV revenue. Now the reason that that's very hard to design is it's very hard to make it collusion-proof where people pay one miner ahead of time in the pool and they don't share revenue with everyone. But suppose you have a way of doing that, and there's a lot of people working on really, really interesting stuff, both on the cryptography side as well as on the sort of distributed system side for trying to do such a thing. But suppose you have such a mechanism, then you can redistribute MEV to all the validators in the system. And one very interesting thing is you can show in some sort of different types of, for different types of MEV, you actually can lower the overall inflation rate of the network if the MEV subsidy is large enough.  So let's say there's sort of opportunities outside of staking that people can earn yield on their assets with. One question you might ask is, what are the incentives that keep people staking? And the interesting thing is that MEV actually adds this feedback loop such that you can lower your inflation but also ensure that people stay staked. And so the y-axis is sort of a notion of percentage of how much of the network is staked. And you can see that at around 25 to 50% in this styles model of revenue sharing, you actually get people to continue staking, which is just, you know, it's just kind of surprising, right? Like everyone's always like, oh, we've always needed kind of hyperinflationary economics, and there's lots of reasons for that. But MEV distribution also allows you to avoid having to do that. So that's sort of like a kind of amazing blessing in disguise. And there's a lot more research that can be done in this space that I think we mainly looked at how liquidations impact staker incentives. Another interesting thing, and I think this is the type of thing that we're seeing in multiple ecosystems, whether it's in Ethereum, especially with roll-up auctions, whether it's in Cosmos. But everyone sort of is starting to realize the MEV auction of having a single MEV auction for everyone to kind of compete in may not be the best form of an auction. You may want to have an auction per roll-up, you may want to have an auction per application, so that you can tune the social welfare versus revenue trade-off. Right now, if you think about it, everyone who's making a FlashBots bundle for an NFT mint is competing with everyone who's sandwiching for the same sort of block space, and I think actually if you segment to the block space and say only this amount goes for sandwiching and this amount goes for NFTs, you might have a very different equilibria. And I think a lot of the roll-up developers are basically kind of talking about this. But an interesting thing and some current work is you can actually show that disaggregating a single auction into many auctions can actually have better social welfare for the end-user, and that's sort of the trade-off that I think. As things like PBS get developed, you have to sort of analyze these types of incentives. So, you know, I think I want to just at least impress upon people that theoretical foundations for MEV are really important. And I think we've sort of started with this thing as this like emergent, unstudied phenomena. We sort of backed our way into some mechanisms for like, you know, doing an auction, coming up with sort of rough reputation scores, which, you know, it's not totally clear exactly how that whole system can be analyzed, to a point where we actually are really able to start thinking about, okay, how do we optimize for things like redistribution? How do we optimize for social welfare? How do we optimize for validator revenue in this sort of joint manner? And formalism, I think, provides us this way of finding these truths about MEV, which show that, hey, it's not always robbing grannies in MetaMask. And the interesting thing is I think, you know, this is sort of the beginning of this kind of new branch in my mind of algorithmic game theory, where you're really focused on the joint optimization of social welfare and revenue, because both of them are extremely important to network survival in cryptocurrencies. And so I want to leave you with some open problems in case anyone kind of is interested in this type of stuff. The first thing is what's the notion of optimality for an auction, right? Right now, if you look at FlashBots, it's sort of a first-price auction with some filtering because they use a scoring function that removes some public mempool transactions. Is that optimal? Does that work when you have multiple chains or multiple sort of hierarchical system? Also, does it sort of deal with some of the problems of, hey, how do I trust the auctioneer? Right now, we have a system where we trust the auctioneer. And, you know, economists have copied cryptocurrency enthusiasts and made some trilemmas recently on proving things about when auctions, when you win and when you can't trust an auctioneer. And a lot of that work, I think if the right people were focused on it, could really determine how PBS functions as an auction and what sort of the expected properties of it are. On the other hand, there's sort of some notion of lower bounds. Like in an open permissionless system with some notion of value, there sort of has to be some lower bound to the amount that's extracted because without any extraction, it's very hard to imagine validators being able to continue to sustain the network. And so one question is, how do you construct those lower bounds? You know, in information theory and in theoretical computer science, lower bounds are extremely hard. Upper bounds are always easy. They are really sort of some of the hardest kind of things to show. And I think the MEV space of lower bounds is still quite open. I think, you know, in spite of kind of, there being some, a camp of some people who believe with sort of fair ordering that there exists no such lower bound, I would argue, I would argue just proving that the lower bounds not zero is already a quite difficult problem that would, you know, be worth solving. And the last thing that's a very big open problem research-wise in MEV is this concept of aggregation versus disaggregation effects. And so I think a lot of people in the MEV parlance will focus on talking about things like private order flow, order flow aggregation, like what happens when a builder has all of a certain type of order flow. There are ways to talk about what the equilibria of the system are, what's the distribution of revenue to different participants based on these kind of aggregation versus disaggregation effects. But if I were to kind of give you the stylized version of what this question is asking, it's saying what is the cost theorem for MEVs? So cost theorem for those who don't know is sort of a very famous thing that I think basically says, imagine you have a company. Now suppose the company had everyone at the company an individual contractor. Why is it more efficient to actually organize as a group versus individual contractors who are giving payments to each other? And the cost theorem sort of says there's some equilibria in the middle where there's aggregation effects where it's cheaper for people to work together and share common infrastructure up to some point. And then after that point, the overhead of managing that many people starts causing you to lower your welfare. And this notion of aggregation versus disaggregation is completely unsolved yet people are always talking about it. It's kind of implied in people's language, yet not formalized. Cool. And so with that, I'll leave you for questions and also, yeah. Thank you. Do you have any questions for Tarun? If so, please raise your hand. We'll get you a mic. We got one question on this side. We'll get to you in a second. We got another question at the back. Hi. So my question is you gave an example of... You gave first an theoretical example of how removing an edge from a network can make traffic more efficient. And then you gave an analogy for how this can also happen with AMMs. And you gave a practical example of how it could happen with AMMs. But I did not understand how this practical example works. So could you explain more like how could MAV improve the efficiency of trading on this scenario with two pairs? Yeah. So think of it this way. Imagine that there's people who want to trade from token A to token B. And there's an order flow of N orders from A to B. If I look at how to optimally route the orders from A to B, if everyone gets routed on the same route, the price that everyone pays on average could be a lot higher. Right? If everyone gets routed on different routes throughout the network, you could have sort of your load balancing what the price impact is. And the point is that the MEV searchers are sort of acting in some weird capacity as almost like air traffic controllers. When there's a bunch of traffic going on the same route, it increases the MEV profit. But as the MEV profit goes up, then people start routing away from that. And you could effectively think of it as a way of sort of having a decentralized coordinator where it comes from the fact that the more traffic going on on route, the more the sandwich profit, but then once people start realizing the sandwich profit, they route elsewhere. And so this routing problem is actually the quintessential example of these cases where congestion sort of... You can add in some sort of like incentives to avoid congestion. And a lot of internet routing, traffic routing on the internet, a lot of that kind of work is like where this initially started. And this is sort of making an analog about here. But the key is that someone is getting a worse price by being sandwiched. There's no doubt. But everyone going across the graph is getting a better price on average. And that's the social welfare versus revenue trade-off. If that makes sense. Awesome. We have another question at the back. Hello. Good afternoon. I love the presentation as usual. The question is, have you made any studies around central bank digital currencies or even fiat currencies using the same theory? Yeah. I mean, I think the hard part with any of these sort of like pseudo centralized federated type of things is... Well, I mean, the validator can just always do the MEV themselves. The limit of the centralized thing is like, well, what are Facebook and Google doing? They're just doing MEV on their own auctions. They're constructing the bidding strategies for you. We're using the highest machine learning to give you optimal targeting. It actually means, actually, we're just going to front run you in our auction, but it's too complicated to explain how. So in the centralized world, these types of things are not that interesting to analyze from that perspective in the sense that the centralized coordinator will just choose the most expensive route for you all the time. But in the decentralized world, it's actually quite interesting because the user has to choose their own way through the network. So yeah, that sort of view. Great. We have one last question on that side and we'll move on to the next talk. So please go ahead. Yeah. Hi. I'm following up on the first question. In the RAS example, the conditions under which the routing would actually, or in which MEV would be beneficial is then, if you can teleport, right? These conditions are usually not met. You said under certain conditions this applies for AMMs. What are those conditions? Does it mean if I can trade, you know, print free money or trade at an unlimited or zero exchange rate? Or what are the conditions in which? No, yeah. Actually, that's a good point. So the zero is in the traffic example. In the AMM example, you can never have zero effectively in that edge. There's always some amount of liquidity price impact unless it's like, you know, a linear AMM. I think the main thing we showed is that the liquidity difference on certain edges on that graph relative to the center edge has to be large. So it's not necessarily that it's saying that, hey, you're teleporting or you have some sort of like non-balanced budget condition. It's actually just that the relative liquidity differences on different edges has to be high. And those are the conditions. And I'd say our proof is pretty pessimistic in our conditions and there are definitely ways of getting much better bounds on it. Awesome. Thank you so much. Please give a big round of applause for Tarun.


And welcome, my name is Shannon Kemp and I'm the Executive Editor of DataVersity. We'd like to thank you for joining today's installment of the monthly DataVersity Webinar Series, Real World Data Governance with Bob Siner. Today we'll be discussing Good Data Governance to Great Data Governance. This is the point to get us started due to the large number of people that attend these sessions. You will be muted during the webinar. For questions, we will be collecting them via the Q&A in the bottom right-hand corner of your screen. Or if you like to tweet, we encourage you to share highlights or questions from Twitter using RWDG. That's Real World Data Governance. As always, we will send a follow-up email within two business days containing links to the slides, the recording of the session, and additional information requested throughout the webinar. Then we'll introduce to you our speaker for today, Bob Siner. Bob is the President and Principal of KIK Consulting and Educational Services and is the publisher of the Data Administration newsletter, TN.com. Bob has been a recipient of the Damon Professional Award for significant and demonstrable contributions to the data management industry, and Bob specializes in non-invasive data governance, data stewardship, and metadata data management solutions. And with that, we'll give the floor to Bob to introduce the webinar. Hello, everyone. Thank you. Thanks, Shannon. Just doing a quick sound check. Can you hear me okay? Okay. Just making sure you didn't respond there right away. Good afternoon, everybody, or good morning to some of you, a good evening to some of you, I guess. Thank you, as always, for taking time out of your busy schedule to participate in the Real World Data Governance webinar series. I've really been looking forward to giving this webinar ever since I gave the presentation of Good Data Governance to Great Data Governance at the Dataversity Conference earlier this year at Enterprise Data World. And the reception of the subject was quite interesting, I thought. First thing I wanted to do before I get started here is just by show of hands. Everybody raise their hand if you've heard of the book Good to Great. I thought most of you would have heard of Good to Great before. Also, show of hands. How many of you heard me at Enterprise Data World in San Diego? Ah, good. At least a couple of you have seen the session before. But it's a book that's been a part of mine. It's been out for quite a long time. It's been a little bit of time talking about the book at the beginning. And then what I'm going to do is I'm going to borrow from some of the main chapters that Jim Collins talked about in the book and relate it to data governance and specifically Good Data Governance to Great Data Governance. And actually, I think that the tag that goes with the book, why some companies make a leap and some do not, is especially pertinent to this subject. Because some organizations put government in place and they feel as though they're getting a few things done, but they never really strive to be great data governance. I know if you've had an opportunity to read through some of the presentations made by some of the Data Governance Best Practice Award winners over the past several years, these are organizations that have taken a specific path to putting great data governance within their organization. So if you're not familiar with the book, I'll tell you a little bit more about it. If you are familiar with it, I'd be curious as to what your thoughts are as to how you relate the subject data governance to the book, Good to Great. So before I get started, I just want to again run through what some of the webinars that are coming up in the next couple of months. The next one should be quite interesting as well. There's been a lot of call for a webinar like this one, Governing Customer Data and Is There a Difference? And I think some people will say yes, there's a difference between governing customer data and others, and some people will say no, it's just data, and then we just need to govern it. So I won't tell you what my thoughts are until next month. Then managing governance metadata from mass consumption, governing data, big data and small data, come one, come all, and then talk about managing data governance expectations in the December webinar in this series. So I'll just spend a moment here talking about the book before I jump into it a dozen years ago. Okay, so back in 2001, she put out a book called Good to Great through Harper Business, and it was, as they said here, is the first book that really stretched far beyond the traditional business book, Audience and Market. It stretched into technical people and non-technical people alike. There was this up on the internet the other day, and the book, Good to Great by Jim Collins, even though it was written in 2001, is still the 20th bestseller on the business bestseller list. So you can go check that out. But the book is readily available, and I can't tell you how many times I've seen it my clients organizations have had this book. So I highly recommend it. It's fine to light read, but also very relevant to what we're going to talk about today. So in this book, in this webinar, I'm going to borrow from main points of the book. I'm going to demonstrate how organizations have hit that glass ceiling, where they've really been able to go beyond being a good program to a great program. If you're not familiar with what the cover of the book looks like, it's on this slide right here. All of the pertinent information that you need to go and track it down. Again, I find it to be a great book and something that you might be interested in reading if you haven't had a chance to look at it yet. So he also wrote the book, Bill Plast, The Management Studies of the 90s. Boy, the 90s seems so long ago, doesn't it? Disapprove the current management hypers and superhuman CEOs. You'll be able to tell that through some of the things that I talk about here. Do you know how to enable mediocrity to become competent? Enable competence to become excellence. And those are the things that are just written on the outside of the cover of the book. So I think there's a lot of good stuff on the inside of the book, which we're going to talk to. I mean, in fact, what I did for this webinar, since it's kind of an abbreviated presentation of the good to great presentation I gave back at EDW, I had almost three hours for that and I have about an hour for this or maybe a little bit less so that we can take questions. I'm going to focus on these five chapters of the book. And I think they're early chapters, but they're not the first five. They're five of the first chapters of the book. We're going to talk about the good coming the enemy of great and I think that's one of Jim Collins' favorite sayings and he says it throughout the book. We'll talk about level five, leadership, first the who, then the what. Confirm the brutal facts yet never losing the faith. And I know a lot of organizations, when it comes to putting governance programs in place, have to keep that faith for some time just to make sure that it's understood and that people get the idea that governance is not this huge challenge that everybody makes it out to be. And we'll talk a little bit more about that as we go through this as well. And then we'll talk about a culture of discipline. If you remember, I may have talked about this before. There's an article that I wrote back in the TDAN publication many moons ago called the 3Ds of data stewardship and it was database, discipline, and de facto. So talking about discipline is being one of the key pieces of putting the governance program into place and formalizing people's behavior. Again, we'll talk more about that as we go forward in the slide deck. So with all the webinars, since I don't know how many of you are brand new to this webinar series and I'm assuming that some are but some may have been here sometimes in the past or hopefully many times in the past, I want to share my definitions that I use for data governance. So just pardon me a minute here just to kind of walk through those real quickly with you. Data governance to me is the execution and enforcement of authority over the management of data and data-related resources. And I know you've probably heard me say that before but when I talk about things in terms of non-invasive data governance but then the line of governance as being about execution and enforcement of authority, well the fact is non-invasive kind of describes the way that we can put governance into place. But the definition of data governance is that we need to be able to execute and enforce on authority management and management of data in our organizations in order to govern the data. So we try to do it in a non-invasive way if we can do that and if it fits the culture of our organization. Otherwise, we do not execute and enforce authority. We need to have the right people making the right decisions at the right time and all of those things that people talk about the different bills of rights in regards to data governance. Stewardship, if you've heard me speak before, you know that I've talked about the fact that almost anybody in the organization could be a steward of data. All we're really doing is formalizing the accountability over the management of data and data-related resources. So I'm certainly one to tell you that you don't need to go out and tag each person specifically as a data steward and tell them to do data steward stuff. The fact that they have a relationship with the data, whether it's defining the data or producing data or using data as part of their job, there's really a level of formal accountability around the way that they manage the data, and that's really what non-invasive data governance is all about. So the definition of non-invasive data governance before we jump into the book here is it is the practice of applying formal ability and behavior through non-invasive roles and responsibilities, and I'll share some of those with you through this one as well. To exist the end or new processes to assure that the clinician production and the usage assures those things that you see, regulatory compliance, security, privacy, protection, quality, all of the great things that we want to expect out of our data. We want to do it in such a way where we don't make people feel as though it's over and above what they're presently doing. It's just a more formal way of getting things done and thus becoming more efficient and more effective in what we do around the management of the data assets in our organization. I don't want you to take from this session. Well, it's kind of a lighthearted session, but I do plan to address those things that I talked about in relationship to the book. I want you to get an idea that this book may be a good read for you, but also some ideas as to what it might take to take your organization from being good in data governance to becoming great in data governance. I want you to think of some of the things that you might need to put in place so you can plan to be great, and then take some pragmatic disciplines and share with you some tools and templates that I've shown some of them in the past in previous webinars, but I want to show them again in the context of what we're talking about here in this specific webinar. All right, so we're talking about good data to great data. We want you to understand, well, whose definition of good are we using? And in the definition of great, are we going to use? Well, I think in my opinion, when we're talking about traits of great data, great data governance, they basically follow these specific four traits, and I think they make sense from most organizations that it's important when it satisfies the business need. And one of the things that I'll talk about a little bit later in this webinar is if we can get the business to tell us the need that data governance is going to address rather than us tell them the need that data governance is going to address, that's going to win for us. If we can get the business people to tell us we're going to add value for them, and then we can take that to the management. It's not us. Again, just evangelizing on the need for data governance, it's basically saying we're going to address these specific needs that are coming from the business areas of our organization. Another trait of good versus great is view it as an investment versus an expense. A lot of organizations think about, well, how much is it going to cost for us to put a governance program in place? And I've spoken about it before where I don't think you're going to buy a software solution and implement it and therefore have a data governance program. Typically, we're looking at it as an investment in time and resources, because typically data governance costs really what we've put into it as far as time and effort. We're not going to buy a software solution and implement it. It may help us to be to enable a data governance program, but by implementing the software it does not put a governance in place, especially if we're looking at executing and enforcing authority over the management of data, which typically can't be done through a piece of software. Formalizing on formality, efficiency, and effectiveness, focusing on quality, those are the traits of things that are great in our organization. If we buy the business, if we view it as an investment, we become more formal and we focus on improving quality in whatever we do. Those are the four traits that I want to share with you in regards to what would make a great data governance versus just a good data governance program. I want to share with you a line that I may have shared in the past, which came from a client who said, don't let perfection get in the way of good enough. When we're putting a governance program in place, let's make certain that we can evolve over time and that we don't think that it has to be perfect right out of the gate. The idea is that we learn by doing and if we can learn by doing it and we can improve things, make the little things that are good, turn those little things into great things rather than trying to think that we can be great right out of the gate. Because in most organizations, it is exactly what I said. It's an evolution. It's not a revolution. And it's something that is done incrementally in most organizations. So that's what we're planning to be great. And I just thought I would share this slide with you real quickly. This little guy here in the picture is certainly planning to be great with the help. We want to talk a little bit about planning to be great within our organizations. What are some of the things that we need to do to emphasize planning for data governance in our organizations? Well, one of the things that organizations do is they set up an information policy or a data governance policy or a data quality policy or something to that extent that basically gets to sign off from the people at the level of the organization that say, yes, this is important. We realize that we need to be certain that we follow the rules associated with these guidelines associated with our data. So, for example, data can be managed as a corporate asset. We need to formalize accountability for the management of that data, that we need to be consistent and that we manage data across the organization. A lot of other things are typically written into an information policy. Stewardship has applied accountability. I talked about that a little bit before. You know, the whole idea of not necessarily having to go out and assign people to be data stewards, we should be able to recognize who those people are or are working with the data and, again, formalize their accountability, apply their accountability to existing processes and things that we have to deal with our organization rather than trying to tell this as being a governance process which is to change what we do. Now, we don't really need to do that. I mean, there are situations in your organization where that becomes important, but for most of the time, what we want to do is we want to apply governance to the existing processes, get the right people involved in the right time to do the right thing, to make the right decision, at least most of the time. So, we want all enterprise data. In most organizations, they view governance as something that's going to touch on all of their enterprise data, but then oftentimes they focus initially on that core data that is associated with key indicators, performance metrics and those types of things within an organization. So, we really need to put a program in place. We need to plan a program that will catch on enterprise data, but we need to specifically focus on specific core data. Again, if we're going to do this incrementally within our organization, we want to concentrate on quality and how we're going to measure the success of governing the data and we want to make sure that metadata becomes something that's front and center to governance. I talked about in a previous webinar, the relationship between data governance and metadata, which is data data is certainly a byproduct of your data governance program. We need to have a plan at how we're going to manage that metadata that comes out through some of these tools and templates I'm going to get in a minute. But we also want to make sure that we govern the metadata as we are identifying the specific data that's going to be valuable to our organization. So, the other thing to think about is money solution versus practice and discipline solutions. My suggestion typically is to go with the practice and the discipline solution rather than trying to throw money at putting your governance program over the needle from good to being a great data governance company. All right, we're going to talk about these five different chapters. As I mentioned before, good is going to be a great level five first two than what? Confidential facts and a culture of discipline. Before that, I want to share with you a data management. Just take a quick minute. We don't throw some comic relief into these things. They become somewhat dry. But I just thought this was a great example of a Dilburn on data management. Here are some accurate numbers so he just made up a number for a report. Studies show that numbers aren't more useful when you make them up. And then he made up a number 87 study. So, just thought that was kind of a number for you. Let's talk about how good became the enemy of great. Basically, one of the things that Jim Collins talks about significantly in the book is that there are so few organizations that become great because they're all on being good. So, they're always looking for ways to improve. And I know in years gone by, continuous improvement was always a big thing in a lot of manufacturing companies. A lot of other organizations have also started to launch onto that again where they're looking for continuous improvement. Well, you want to take your program. You don't want to be satisfied when you know how to win or two or three. The idea is to build governance into what people do in their daily jobs so they don't even recognize it as being governance. So, we need to settle on being good. We need to aspire for becoming great. And so, things that we need to ask ourselves are a couple of things that we need to ask ourselves are, do we have a good program? Is it fine that the business need? Or is it that we can be doing maybe even reaching out to the business part of the community and asking them, what can we be doing for you? Okay, we've put things in place. Are there things that we could be doing right now that go over and above the things that we're presently doing for you? To truly meet your business need greater than we're meeting it right now. Are you satisfied with the good? Or is it important to get to great? In some organizations, the people that have responsibility for their daily governance programs seem to be able to make a name for themselves within their organizations because they are having that level of impact on the organization. So if you're looking at data governance to be something that's going to help to drive your career and I know there's a lot of you out there that that's probably the case is that governance could become your ticket to moving up within the organization. Well, you don't really want to settle on just being good. You want to keep looking for ways to improve. So what's the difference between a good program and a great program? The great program is the one that's always looking for a program that was used many years ago. Again, one of the suggestions is go to the business people within your organization and get them to tell you where's the value from your existing governance, but then if they can also share with you what can take you to the next level that's worth its weight and gold to your organization and what it takes to move from good to great sometimes it takes additional resources sometimes it takes as much as we need to look at these things we need to recognize what comes back from becoming even greater than what we're presently doing and then start to address some of those issues head on as we move from being a good in data governance to becoming great in data governance. So in this chapter of how the good became the enemy of great talk about four phases of going from good to great the search comparing it to what I want to do is kind of walk through each of those real quickly with you so phase one is the search well what we're searching for putting governance in place within our organization what we're looking for is level of targeted behavior whether it's formalized behavior or just getting the right people at the right time to become involved in initiatives what we're looking for is typically that target behavior in an organization when you put a governance program in place there's no ultimate end game you know in the end where you've won and everybody cheers and throws their arms around you the fact is when you think about the fact I think I shared the story before where a CFO asked me how many stewards are we going to need and how long are we going to need them for well if you can kind of reverse that question back to them and say how long do you have to be compliant how long do you have to listen to the regulators the truth is that there is an end to that so we always want to improve the quality of data we always want to have formal accountability for the data so the target behavior is to build into what we want in our organizations and if you get to the point where data governance becomes second nature to your organization looking for as an organization looking for that targeted behavior and we're looking to make it part of what people do on a day-to-day basis so we're going to begin by defining best practices for our organization making it practical and dual make sure that the organization so when you define a best practice the two criteria that I use is it practical and doable and will it add value and will we be talking about that in some more detail but when you define your best practices around data governance for your organization you've got to make sure that they're practical and doable and will it add value and that they get risk if you don't achieve that best practice so what I'm saying is that Jim Collins talking about was comparing to what so once you identify these best practices the first thing that we need to do as an organization is say where do we stand in comparison to those best practices what is the leverage that we're making sure there are opportunities to improve as well as you move forward with your your governance program you want to make sure that you're addressing those opportunities those gaps between what you're presently doing and what you say you want to do with your best practice and then come to some recommendations when you get done with it and then develop an action plan that becomes so important to most organizations now the interesting thing is with some organizations they can kind of take this to define the program define the roles on board people provide orientation of the program to these individuals while at the same time let's start tackling some important that we have within our organization so there's kind of two prongs going on simultaneously there's the let's act now let's start to govern data now and the other prong is let's get our ducks in a row let's make sure that we have best practices defined that we have an action plan defined all communications plan that we talked about often which is vital to the success of a governance program the third phase of the chapter how good organizations become how good becomes the enemy of great basically is that black box you know what does it take to make the leap to go from being a good company to a great company what are the messages that we need to share with our management and one of the things that you probably knew before is the government that the organization management person of your governance program is going to be whatever you give to them so you give them the perception that governance is a great challenge that it's huge it's complicated it's going to cost lots of money that it's going to get in a way of initiatives and things like that that's what they're going to believe but it's not your message that you should give to your management you say it's a certain aspect we both in the organization that have lots of abilities associated with the data and we need to just formalize what they do when they do it how they do it and those other things it doesn't necessarily need to sound easy but we can tell them that if we just put some time into this and we formalize accountabilities rather than throwing a whole lot of dollars at it that we can be very successful and we can move our program from just good to being able to exceed the expectations that are set out for us by the business inside that black box is also there's best practices and critical analysis and then the communication plan all those things really need to be built as part of your program as you start to move forward people want to know why you're addressing certain things the way that you are well a best practice indicates that this is our behavior but here's where our actual behavior is it basically demonstrates to individuals a road map of what you need to do to move your program from being good or being not even there at all around governance to having a governance program first before we take that program from being a good program to being a great program so the black box I think it's a it's a great pick this is the fact that we know that we need to create some things to make this program successful in our organization but we're not really sure what those things are we're going to start with best practices and you do that critical analysis you start with an action plan and a communications plan and you develop some of the tools and templates and things that I'm going to demonstrate for you in the next couple of slides I mean those are some of the things that are kind of going into that black box when you're creating your governance initiative but the one is that I love this picture as well I don't know where that picture was actually taken place moving from K to concept I don't expect that you're going to be able to flip a switch and have your governance come on for your organization that's going to need to be incremental we're going to need to learn by our mistakes so we're going to move from what might be presently described as K-Lots within our organization to some concept and some sense of what governance looks like within the organization and again I mentioned earlier the last bullet on this slide is extremely important getting the technical resources to tell you rather than you telling them the value that governance is going to bring to your organization again if you're going to go from good to great instead of addressing what you think their needs are if you can get them to articulate what their needs are and where governance will address those you will have much more likelihood of moving from being a good data governance program to a great data governance program so then there's real quickly here we've got about a half hour left to go Jim Collins talked about level 5 leadership and his book good to great let's talk about that for a second so the way he described it within the book was that there were five levels of leadership there was the executive the effective leader the competent manager the contributing team member and the highly capable and I think if you would probably immediately be able to identify people in your organization that fit into each of these five categories and so we're going to go up a level 5 in one second but what I want to do real quickly was compare that to the pyramid program that I use so often in these webinars where you've got the executive strategic tactical and operational levels they're going to line up a little bit with what Jim Collins talked about within these five levels so again you realize that we need it to build roles that are associated with the executive strategic tactical operational support and you can have your understanding as to what kind of attitude is needed by each of these different levels how much do they need to understand about governance how they're going to participate in governance and what their activities are going to be so typically when I talk about the five levels I talk about the fifth level being that executive level with good interaction with the data governance program the data governance council at the strategic level the domain stewards the operational data stewards the data governance team and the IT professionals within your organization so that's what Mr. Collins talked about his five levels those are the levels that I typically talk about and in some organizations they've said well let's try to remove one of those levels of producing and using data as part of their daily job you've got people at least to some degree that are subject matters of data across the enterprise they're more at your tactical level you've got strategic decision makers you've got executive level people within your organization there's a lot of understanding and support data governance may not be where it needs to be but you've got all those five levels within your organization and it's very important that you record that information and make it available to us as we move our program from being a good program to a great program so in the next chapter I'm going to share with you a couple of the different tools and templates that you may be able to use to do that so Mr. Collins talked about level five leadership he talked about humility and will a compelling modesty and unwavering resolve to do what must be done so if you can get your senior most management to understand what governance is how is it going to be effective what roles people are going to play the fact that it's going to be not invasive in nature in your organization is a big win for your organization if you can get somebody at the senior leadership level to understand these things and to have unwavering resolve behind what you're trying to do with your governance perspective for most organizations the level of support that you get from any individual business in your organization it's really going to be directly corresponding to the level of support that your senior most management has for governance it's almost important to across the board get senior management to your executive director level to understand the value of governance so that you can get into that within the people who work under them within their organization so you can get unwavering resolve to do what needs to be level five leadership displaying a workman like diligence so being that plow horse rather than being a show horse because they're not in it for show they have some level of commitment to improve the third chapter of the book and in this chapter I'll share with you a couple different templates that hopefully you can take and apply to governance in your organization so we always talk about addressing who first then what I thought this was a great quote to come from the electric cool cool cool cool cool cool cool cool cool cool cool cool cool cool cool cool cool cool cool cool cool cool cool cool cool cool cool cool cool cool cool cool cool cool cool cool cool cool I know that just from experience, I can tell you that if you try to bite off too big of a piece of the elephant, you're going to have a really difficult time in being successful with your governance program. And I can tell you time and time again that there's been organizations that have taken small wins and kind of clung to that low hanging fruit that we describe as being the opportunities that are right out there for your taking. You know, in organizations it makes a lot of sense to address those first and try to address the big issues that you have within your organization. Some of you guys talked about when in doubt, don't hire people, just keep looking for the right person to play a certain role. When you need to make a people change, then act. Don't be afraid to act. It talks about putting, this is very important, this third discipline of putting your best people on your biggest opportunity and not your biggest problem. And that's the same way of thinking for most organizations. They typically will take their best people and put them on their biggest problems rather than viewing what's the biggest opportunity that we have within our organization and applying those people to those opportunities. You're not a genius with a thousand helpers. You've got the level five leadership who has the right people to get on the bus, kind of builds that superior executive team. Once you have people in place, start figuring out what the best path is to greatness. And again, that's something that Jim Collins talked a great deal about in the book Good to Great. So how does this relate to governance? Well, one of the things that we need to do and one of the very first best practices that I've found with a lot of organizations is that we need to have senior management sponsorships, support, and understanding of the Data Governance Initiative. One thing to sponsor is one thing to support. And I can tell you time and time again and again that there are people at different levels of the organization that hear about Data Governance and say, I need a program like that when they don't really understand what that really means. So the people that have the responsibility for putting governance in place or the ones that want to have the responsibility for putting governance in place need to basically focus on that third aspect of what senior management needs. Focus on understanding. They understand that there's issues. And they understand there's already governance. And they understand that we can kind of formalize accountability rather than going around with a 2x4 and hitting people over the head. Very often they'll sit forward in their chairs and ask you to explain to them how this is going to work within their organization. So a couple of things. First the who and then the what. So the first is the who. And I know I've shared this with you folks before.  And the common data matrix is the first who part of what Jim Collins is talking about. We need to identify who the organization knows what data and how they use that data across the organization. So I'm going to try to figure this out here. So the first thing we want to do is we want to identify what are the domains of data or the subject areas of data. In this example here it's customer data. It's customer address data. It's customer demographic data, financial data. It's product data, service data, accounts receivable data for the different parts of the organization which you can define across the top. And so you know who uses what data across the organization and there's a change to a business rule associated with the domain of data. It takes all the guesswork out of who we need to talk to and when we need to talk to them in relationship to the change to the data. So I just had this conversation with a client earlier today where we talk about doing this inventory of who does what with data. So the first thing we need to know is the who. We need to inventory what data we have, where it resides, who in the organization touches that data whether they define it or produce it or they use it as we mentioned earlier. There's a level of accountability that goes along with that relationship to the data. So we need to document that information somewhere and the customer data matrix becomes a great place. I know it's probably not the ideal place to manage that information, but at least you're putting your arms around governance in your organization. We can identify, again, what are the domains of data and then how they're being used across the organization and that kind of addresses the first two part of what Jim Collins was talking about. So once we've identified the who and we know who is defining who's producing and who's using the data across the organization, the next thing that we need to address is, you know, what we're going to do basically. So here's one of the things I wanted to share with you and I've shared with you something similar to this before which is it's a level of these are some of the different activities that we will associate with data governance and we can click on one of those and bring up a list of what the different activities are associated with that one repeat action. And in this, let's call it data governance activity matrix, we can take the different levels of stewards that we have across our organization and associate them to the different steps of whether it's research and information quality issue, identifying and monitoring risk and compliance needs, monitoring information quality, all those things we all recognize are very relevant to govern our organization. So once we've identified who does what with data across the organization, the next step may be that we want to associate them with the what. What do they do? So they get a better sense of make them from again being good at what they do to being great at what they do. If we can formalize accountability and get people involved in the appropriate steps of a certain activity like resolving or researching information quality issues, that's what governance is all about. Again, if we're just trying to go from being good to being great, we need to formalize people's accountability interactions that they take. Here's an example of that, master data certification process where we have the what's of the process and the who's of the organization and different roles and responsibilities and we identify who's responsible, who's accountable, who needs to be consulted, who needs to be informed, and so on and so forth. So what we're doing is we're taking the who's that we identified in the common data matrix and we're documenting and we're formalizing accountability for the different steps of the different things that we want to do within our organization. The fourth step that I'm going to talk about real quickly here is confronting the brutal facts. I just want to go through them real quickly and give you some messages that I suggest that we share with our management. We're already governing data. We can summarize how we govern data by putting structure around it. We can improve how we manage risk, how we improve quality, how we coordinate and cooperate around data. We don't have to spend a ton of money talking to the head with an organization that I'm working with and we don't want to add additional overhead to what they do. We don't interfere with the existing activities within the organization. We don't have to spend a lot of money but at the same time we know we recognize that we need to put time into it. Somebody has to have the responsibility for putting the program in place and last but not least is we need structure and we should consider a noninvasive approach because in my experience noninvasive seems to fit to the cultures of organizations these days. Here are some messages for management and again it goes back to our management believing what we tell them. So if we're dealing with data governance it's going to be a huge challenge that it's a technical solution that we're really governing data then we're sending them down the wrong path. So we're not going to have to be a huge challenge. It's not a technical solution. It's more of a people solution and we're not even governing the data itself we're governing people's behavior associated with it and we do this in an evolutionary way not a revolutionary way where we can build things into our organization and improve them step by step. We can take certain aspects of our program from good and focus on them and make them great rather than trying to make the entire program great. So let's talk about one of the things Jim Collins talked about is a climate where the truth is heard. There are some questions and not with answers so if you go into the business people in your organization and you tell them that governance is the greatest thing since sliced bread and this is what it's going to do for them. Yes, that makes sense to them. Maybe it'll resonate with them and they'll believe everything you tell them. If you look at it from the other way around if you look at them telling you or getting a good enough understanding so that they can tell you where governance is going to add value to them then you need to lead with the questions don't lead with the answers. Engage in dialogue and debate conduct with autopsies and recognize that previous projects took longer than they should. Issues took longer than they should to resolve. Identify where does the things fall down and that took the extra time. Did we not know the appropriate people and gave the appropriate people at the appropriate time? I had a client many years ago who told me that when they were working on their customer relationship management initiative they started out with a team of five people and then it went from five to 10 to 15 to 20 as more and more people caught wind of the fact that they were working on customer relationship management and everybody said, you can't do that without my being involved. If we can identify what took us so long in getting the appropriate people involved at the appropriate time we can address that through things like the common data matrix that I just showed earlier where we know who does what with data across the organization and there's a red flag mechanism. I have a client now who is building the name of their company or data governance at thenameoftheircompany.com and they're making people on the fact that if they see a red flag there's somebody that they can reach out to and let people know that not only do they have grapes more on the lives of where there are things that need to be addressed within the organization. In fact, they are better than dreams. So, pack your words with business and technical people's words, as I said before. Learn and then preach to them. Ask them the questions like what is the quality of the data preventing you from doing? What do you do with the data that you presently can't do? Sometimes when you go into a meeting with business people and you ask these types of questions it's like somebody turns a stick and all of a sudden it starts pouring out. Oh, you know, if we had the ability to compare costs across the organization or compare costs across plants you know, we weren't able to do this or if we knew where our raw materials were or we knew who needed to be made aware of certain changes to a business rule that we could get right 100% of the time. You know, they're going to be the ones that can tell you where governance is going to add value to them rather than the other way around. So you have to ask by getting facts from them using their words rather than your words when you're pushing your governance program forward. People have the tremendous opportunity to be heard. Again, give them that feedback loop. It's always really important to your organization to put feedback where people can tell you where governance is adding value where it's not adding value where they need it to add value and those types of things. So give them those types of feedback loops into what you do. And I'll talk about that here real quickly before we take some questions is the culture of discipline. And that's the sixth chapter of Jim Collins book Good to Great. So Jim Collins talked about developing a culture of formal discipline. That is what data governance is all about. The fact is that he mentioned the culture of formal discipline. The fact is things are done very informally now. Organizations are done inefficiently and inefficiently. And if we could just identify the appropriate people, get them engaged at the appropriate time, it's really formal. It's not different. We're not picking up the organization and dropping it on its ear and picking up all the pieces of what we're doing and taking advantage of those things that exist within our organization and we're formalizing them. I spoke real briefly about that article many years ago. You know, data stewardship in 3D, where it's about the fact, though, the fact that there's already people in the organization that are stewards. We need to identify who they are. There is a discipline aspect that we're talking about here. There's the database or the common data matrix or the steward repository or whatever tool you use to collect the information about who the stewards of data are and engaging the appropriate steward at the appropriate time. And then there's an article that will be coming out in TDAN in the next month called the Data Governance Bill of Rights. It talks all about getting the right people to do the right thing at the right time, giving the right information with the right decision making which leads to the right results often. Isn't that, again, what governance is all about? It's getting the right people involved at the right time to do the right thing. And if you can do that, you can go from being just a good organization when it comes to governance to being that higher end organization, that great organization around data governance. So, again, the Bill of Rights and the organizations that go with them, they're just getting the right person by using the common data matrix at the right time using that activity matrix that I shared with you to give them the right information. That's the data governance metadata, the decision flows, policy, all of those types of things. That's really what governance is all about. And if you build that culture of discipline into your organization and people start recognizing it as secondary to what they do, then you can truly say that you've won this data governance game if you consider data governance to be a game. So, again, when you call in the United States and build a culture, don't be a tyrant.   Take a different approach with people. Tell them, you know, you're already doing governance. There's ways that we can govern better. Stay in those results depending on building a culture of self-discipline where people themselves take the initiative that they talked about in the level five executive. They say, this is something that's very important. I'm taking my job very seriously. When I see a data governance issue, I contact the data governance team or I personally take some initiatives to get this thing recorded so that we can start to address it. A culture of self-discipline involves duality. People adhere to a consistent system. People are given responsibility within that framework to be very valuable to the organization, and I think that's what you'll find by putting non-invasive data governance in place. What it will do is it will get the right people involved, the right people to understand that we don't need to be tyrants about this, that we can be incrementally within our organization and that we can move from being good around data governance to being great around data governance. To summarize real quickly for you, we talked about what's good and what is great, and it really depends on how you define these things within your organization. When we talk about good being, we have great and how the organization has just set all up being good and don't ever focus on that continuous improvement from yesteryear that they used to talk about all the time. Level five leadership, first the who then the what. I shared with you some templates that made a value to you when you take them back to your organization. Connect with brutal facts and letting your organization know that there's more than one way to do data governance. We don't need to have the command and control, hit people over the stick and tell them what to do. We can recognize that there's a lot already taking place in our organization and we can just take advantage of that as we're building our program and then building that culture of discipline when you take all the guesswork out of who does what and when. It's all documented, it's all formal. So that basically wraps up what I'm going to talk about today. I just again wanted to share with you really quickly before we take questions. Again, the upcoming webinars, the next one should be really interesting, governing customer data. Is there a difference between customer data and other data? Managing governance metadata for mass consumption. We'll talk a little bit more detail about some of the things that we talked about today and how to gather that governance metadata and how to make it available to people so that it adds value to what they do. We'll talk about big data and small data. We'll talk about building governance expectations and with that, I want to turn it back to Shannon to see if we have any questions regarding the webinar. If I unmute myself. My apologies. I feel afraid we have no questions coming in yet but everyone's really quiet. It must be the heat of the summer. Go ahead and enter your questions in the bottom right-hand corner of the screen if you have any. As Bob says, we have. One question from James Benedict says, one of the roadblocks to go from good data governance to great data governance is vendor lock-in with many organizations. I know my comment about that. And I guess that's true but it's kind of a hole to fall into. It's a question of whether you are so bought into what the vendor tells you or the people and the highest part of the organization are so bought into what the vendor tells you that there's really no opportunity for creativity within your organization to devise what you need to. So I would say it could be a roadblock but at the same time there's a lot of darn good consultants out there. So even just having these conversations with them about what it would take to move your organization from being a good program to being a good program I think that's one of the things that we can take as a value add out of that situation. On your non-invasive process so when I talk about non-invasive rather than non-evasive I have non-invasive is taking the opinion that there's people in your organization already that have some levels of responsibility around the data and then which formalize those. So rather than again I can compare it to the 2x4 approach where a 2x4 approach is you're going to get a club and you're going to hit people over the head you're going to give them a new title you're going to tell them what their role has changed you're going to let them feel as though you're adding to what they're presently doing rather than formalizing what they're presently doing. So a lot of it really comes from the words that we use when we go into an organization that can be more invasive and about command and control or can be more focused on getting the right person involved at the right time for the right reason and all those things that I mentioned before. And so non-invasive is really the approach the definition of governance again going back to what I mentioned earlier is all about the execution and enforcement of already over the management of data but we don't want to do it in a way that we threaten people within the organization it doesn't have to be over and above the existing work culture there's ways that we can formalize at least to a certain extent what we're doing now rather than defining it also as being brand new. Next question is have you used the software-based data governance tool is one you did at all? Kind of a two-edged sword so I have I've worked with several tools one being a tool by the name of Calibra it's a great data governance tool ASG has fantastic tools that can be used for governance oftentimes if you're not talking about somebody like Calibra it's kind of repurposing other tools metadata tools workflows different tools and those are tools to help to enable your governance program but the ones most organizations looking to and I'm not trying to push one organization or another would be Calibra ASG would be organizations like that even the companies that use Erwin as a tool or Informatica as a tool these are all excellent tools for enabling any sort of activity within your organization and certainly for capturing the metadata associated with who what the data in the organization For note if we add data guidance to someone's responsibilities such as data quality monitoring and remediation should we discuss their job description? It just depends on your organization whether or not you need to change their job description I mean if that person's responsibilities were to monitor data quality to remediate data quality issues and now you're just formalizing it then no I don't think it's absolutely necessary to go out and change their job descriptions as I said before one of my pet peeves is people thinking that they need to go out and change people's titles from whatever their titles are the fact is that they are accountable for what they do with the data whether or not they're identified as a steward or not so if they're just using data then they have some accountability for how they use that data if they're producing data they have some level of accountability for how they're producing data so my suggestion is that we don't change people's job titles we don't necessarily have to change their job descriptions either we just need to educate them what it means to do things in a best practice manner rather than that's why I say we can do it in a non-invasive way rather than an invasive way that's all the questions we have coming in just thank you again for the another presentation just remind everyone we will be posting their regular webinar and slides to dataversed.net within two business days and also send a follow-up email to let you know the links and other requested things that were presented so again Bob thank you for this great presentation and thanks everyone for attending I hope everyone has a great day thank you thank you Sharon