And here we go. Hello and welcome. My name is Shannon Kemp and I am the Chief Digital Manager of Data Diversity. We'd like to thank you for joining the current installment of the Monthly Data Diversity Webinar Series, Real World Data Governance with Bob Sinner. Today, Bob will be discussing operationalized data governance for business outcomes. 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. If you'd like to chat with us or with each other, we certainly encourage you to do so. Just click the chat icon in the bottom middle of your screen for that feature. For questions, we will be collecting them via the Q&A in the bottom right-hand corner of your screen. Or if you'd like to tweet, we encourage you to share highlights or questions via Twitter using hashtag RWDG. And if you'd like to engage more with Bob and continue the conversations after the webinar, you can go to dataversitycommunityatcommunity.dativersity.net. As always, we will send a follow-up email within two business days containing links to the slides, the recording of this session, and additional information requested throughout the webinar. Now, let me introduce to you our speaker for this series, Bob Siner. Bob is the president and principal of KIK Consulting and Educational Services and the publisher of the data administration newsletter, T-Dan.com. Bob has been a recipient of the DAMA Professional Award for significant and demonstrable contributions to the data management industry. Bob specializes in non-invasive data governance, data stewardship, and metadata management solutions. And with that, I will give the floor to Bob to get today's webinar started. Hello and welcome. Hi, Shannon. Hi, everybody. I hope everybody is staying well, staying safe, staying healthy. I'm glad to have you with us today. This is a great subject that I like to talk about a lot is how do we operationalize data governance programs. So I plan to spend the hour or most of the hour focusing on that. So good to have you all along for the ride. And if you have any questions, please let us know. Before I get started on the subject of today, I just want to share a few items that I'm actively involved in. As you know, obviously, the real-world data governance webinar series takes place on the third Thursday of every month, right about this time. And so next month, I'll be speaking about the stewardship approach to data governance. And if you're familiar with the T-Dan publication, there's a series of articles from many years ago about the stewardship approach to data governance. Also, I talk a lot about non-invasive data governance, and so there's some information for you about where you can locate the book through all the best booksellers. You can find the non-invasive data governance book. I will be speaking at the upcoming Enterprise Data World event in Chicago in October. As you probably know, that was scheduled for San Diego, but due to the stay-at-home orders, it's been rescheduled for October, so I hope to see you there. As well, I also have a couple of online learning plans that are available through Data Diversity, one on my favorite subject, non-invasive data governance, and then there's also one on non-invasive metadata governance available through the training center that Shannon spoke about. I also, in the publisher of the T-Dan, the data administration newsletter publication, published twice a month, published a new issue of that yesterday, so please go and take a look. It's free for everybody, so please go and take a look and let me know your feedback if you have a chance. And last but not least, the KIK Consulting and Educational Services, KIK stands for Knowledge is King. That is the place to go if you're interested in learning more information about non-invasive data governance. So today in this session, I plan to address the five topics that are in front of you right now. First, I'm going to start by talking about what does it mean, basically, to operationalize a data governance program. And then we'll talk about data governance and looking back to the outcomes that we're expecting from data governance. And I think most of us know that those outcomes can be good or bad depending on the value that is being added by your data governance program. We'll talk about designing your program to operationalize your program, to focus on the business outcomes that we are all striving to achieve. We'll talk about using the program purpose to demonstrate value in the program. And then we'll talk about ways to engage your stewards through their job function so that we can operationalize this discipline of data governance that we talk a lot about. But before I get started, I just wanted to cover a couple of quick definitions. And it's real important that I talk about these definitions today because they have a lot to do with the operationalization of data governance. And so what I have to define these, sometimes they're worded a little bit strongly but they're done that way on purpose because I want people to sit up and take notice of the definition of data governance and ask questions about it. But I say that data governance is the execution and enforcement of authority over the management of data. And so what does it take to operationalize a program? Well, it takes us getting to the point where we can execute and enforce authority over the management of data. You might pick a definition that is tamed down a little bit or tempered down a little bit so that it doesn't sound so aggressive. But the fact is at the end of the day, no matter where approach you take to data governance, our goal is to execute and enforce authority. We want people to do the right things at the right time in the right way when it comes to managing the definition, production and usage of data in our organizations. Data stewardship is also when my definition of data stewardship is also very closely related to the whole concept of operationalizing data governance. And that is the formalization of accountability. And so I've been known to say, and I'll say it again and I will say it, I'm sure in this webinar that everybody potentially that has a relationship to the data, people that define, produce and use data as part of their job, if they're being held formally accountable for that relationship, then they're data stewards. It's not something that people can opt into or opt out of. So we'll talk a little bit more about how the data stewards are involved in the operationalizing of data governance. The last definition that I wanna share with you is my definition of non-invasive data governance. And that really focuses on how we're applying data governance to the organization. So I talk about it as being the practice of applying that formal accountability and that formal behavior using roles and responsibilities which I'll touch upon briefly, but we're gonna apply governance to process rather than redefine every process or to call something a singular data governance process within our organization. And we do that to assure that the definition production and usage of data will do whatever that purpose is that we're focusing on for our data governance program to assure regulatory compliance, security, privacy, quality, you know, the protective data. And I just wanna restate that non-invasive really describes how we're applying governance to the organization. It's not the definition that I use for data governance, but it really talks about the application of governance. And that really is everything that we're talking about when we talk about operationalizing data governance for business outcomes. So let's start out by talking about what does it mean to operationalize your data governance program? So the first thing I'm gonna do is focus on the meaning of the word operationalize. We'll look at the different phases of a data governance program. And I've kind of simplified those a little bit so that there's just some quick hit ideas of what the different phases of a data governance program will be. We'll talk about what an operationalized program looks like. And then we'll talk about what really provides for or prevents you from operationalizing data governance within your organization. So the first thing I wanna address here is what does it mean to operationalize something? And if that something is specifically data governance, what does it mean to operationalize data governance? And so I've used the dictionary, I've used some quick sources to help me to really get a better understanding as to what the definition of to operationalize is something. But these bullet points that I'm providing to you on the screen right now really reflect what it means to operationalize something, to put it into use, to prove that it's demonstrating value to the organization, that if you're activating it or getting it started in a lot of the webinars, I talk about some of the different components that are necessary to start a program. But now we're talking about taking those components and activating them or starting them. So it's really putting data governance into action and an old Yiddish term of getting off the Schneid. That's what we're all trying to do with our data governance programs once we've defined those core components that I'm gonna talk about briefly. So we wanna get basically move past the definition and the development phase of our program. And you can finish this sentence for us. Now data governance program that has not been operationalized, well, I guess it's good on paper. It's good as a book definition of what data governance is, but at some point in time, those folks that are standing behind you in your need to put data governance into place, they're gonna wanna see some results. And that's gonna require that we operationalize the program based on all these terms. And what I wanna do is I wanna underline the key words in each of those bullet points. So really to operationalize means to put into use, to determine or prove that this is working, to prove or to demonstrate, activate, start, initiate, all of these things, getting off the Schneid. That's really what we're trying to do when we're operationalizing our data governance program. So that's really all I have for you in regards to the definition of what it means to operationalize. We really want to make sure that we have our data governance program prove its value. And the way we're gonna do that is we're gonna activate it and we're gonna demonstrate value from what we're doing with our program. So one of the first things that we need to do is figure out, well, what are we putting our data governance program in place for? We need to define a purpose. And what I'm gonna do on the next slide, I believe it is, I'm gonna share with you some examples of some purpose statements. In fact, I added a new one that just came from a client that I'm working with presently. So if they're on the call, they'll recognize it, but there's a lot of different reasons why organizations put data governance programs into place. So the first thing that we wanna do is we wanna define in some very brief terms, well, what is the purpose of our data governance program? And then we wanna make sure that we suit our program to fulfill that purpose. And one of the things that I share a lot and I've done webinars on in the past is what I call a data governance framework. And it really goes through the core components of the data governance program, which are the roles, process, communications, tools, metrics. And we wanna make sure that as we're defining and as we're developing our program, that we're making certain that each of those core components align with what our purpose is. Now, I'll also speak in a little bit about what it means to kind of take our program beyond its initial purpose, but it's really, really important. And I'm gonna come back to this a couple times in the webinar that talks about why is it so important for us to have a defined purpose statement for our program. And I find that a lot of organizations focus on that purpose statement first and they get it worded appropriately and they share it and vet it through the appropriate people in the organization. But that purpose statement becomes a really important piece of getting started with your program and really being able to tell whether or not or we've operationalized our data governance program for those business outcomes that we're talking about today. So one of the things that I do, actually it wasn't the next slide, but one of the things that I've shared a lot in the past and you've probably seen it, there is a specific webinar that goes into the details of what the data governance framework is all about. But if you look at this data governance framework, basically it takes those core components that I spoke about. You know, I added a column, the data column, but the roles, processes, communications and such all across the top of the framework matrix. And it looks at it from different perspectives of the organization, from the executive strategic all the way down to the operational and support levels of the organization. But what I wanna focus on really here is the top part of the framework, the data, the roles, the processes, all of those things. And we wanna make sure that when we're defining our program and how we're designing our program for our organization, that we make certain that we're aligning these roles and processes with the purpose. What are we trying to achieve? What are we trying to prove with our data governance program? How are we intending to demonstrate value to the organization? And so one step that I take with the data governance framework to move it one step further is to now fill in the framework. From the perspective of each of those different levels that are along the left-hand side, I fill in the pieces of the framework. And these are just examples of the framework in terms of it being non-invasive, if that's the approach to data governance that you're taking. And it's just some ideas as to some nouns and some verbs that you can fill into the framework that will help you to make certain that whatever you're doing with your program, that it is aligning with whatever that purpose is that you've defined. And the end result is basically, you're looking at each of these different components from each of the different levels. It's kind of like the Zachman framework, if you're familiar with Zachman framework for enterprise architecture, but it's focused more on data governance. So again, those core components that I'm sharing across the top of the screen and viewed from each of the different levels that you have within your organization. And I find that the ones that I use, the executive, strategic, and all the way down, they pretty much align with the way most organizations are set up. Some organizations will take a certain level and combine it with another level, but you wanna make certain that all of these different levels are being addressed, especially when you are looking for business outcomes, for positive business outcomes to come from your data governance program. So here are the examples of the data governance purpose statements. And these are almost word for word taken from real life. This is real world data governance. So they're taken from real life examples of organizations that are presently putting governance programs into place. So the first one's really important. And I found a lot of organizations like that and kind of gravitate towards that one. And that is to use the strategic data of the organization with confidence. And when we use that as a purpose statement, we wanna focus on, well, what do we mean by strategic data? Or don't we really wanna govern all the data in the organization? But the fact is you're not gonna put the lead switch and have governance come on to the entire organization at once. So you're going to at least initially start with that data that's meaningful to people. And that is the strategic data, the latter part of that purpose statement with confidence. Well, that confidence can focus on the protection of the data, it can focus on the quality of the data, all of those purposes that I listed in the definition of data governance and the explanation of what non-invasive data governance is. So protecting classified information or even better, protecting sensitive information in your organization may be at least the initial purpose of why you're operationalizing data governance for your organization. I had a client recently that said they want data governance, the purpose of data governance to provide great data to the organization. But they took it one step further and they said, well, these are what those letters what G, R, E, A and T mean, governed, repetitive, engaged, accessible, trusted. So we want to provide governance data and we want to provide data that's repeatable and to be used by multiple parts of the organization, certainly data that's accessible, certainly data that's trusted. So that was their purpose, was to provide great data. And I think in that organization, they took their purpose statement and they added a second line to it to say, you know, what was the purpose beyond just great data because that could have a different interpretation depending on who you ask. The one that I just added recently is the purpose of the data governance program is to strategically manage data assets to have accurate, trusted, secure, business ready data to help this organization to protect and serve, which is what their focus of that organization is. And so I think it's great. It's a little bit longer than the others, but it really hits on the key points. You want accurate, trusted, secure and business ready data. And the one that's used a lot and the one that people will talk about quite a bit is how to improve how data is managed as an asset. If we're gonna get the most value out of our data, we need to recognize that the data is an asset. So let's look at the term operationalizing in terms of some simplified phases that organizations go through when they're developing data governance programs. And the first one is to define and sell the value of data governance to leadership. We need to get our management to understand the value. I always say to support, sponsor and understand what data governance programs are all about. That's the very first best practice. To gain resources. We know that I always say that the data is not gonna govern itself. The metadata is not going to govern itself.  We need to have resources because it's not gonna happen on its own. Specifically the design and development of the program is not gonna happen on its own. But you need to recognize that's the next phase. Once you've defined and sold the value of the program, you need to have resources to make the program happen, to get to the point where you're even remotely close to operationalizing your program. Then you wanna select an approach to data governance. I talk about the approach in terms of the command and control you will do this approach or the traditional approach that is the field of dreams. So if you build it, they will come. Or the non-invasive approach, which really starts with the idea of you're already doing this. We're gonna help you to formalize that. We're gonna help build efficiency and effectiveness into what you do. The next phase of the simplified phases of data governance is to design and develop the program. And then we get to the point where we wanna get off the slide. We wanna get the program to start actively governing data in the organization. And that is the operationalize the program. And then the last phase, and I just added that at the very end here, is to measure the program. Is how are we knowing whether or not the operationalization of the program is actually doing what we're asking of the program to do. So let's talk about, well, how do we link data governance to the business outcomes? And as I mentioned before, the business outcomes can be good or they can be bad. And I'll talk about both of those here real briefly in a couple of seconds here. But then I also wanna focus on those definitions that I shared with you up front in the webinar. I talked about the execution and enforcement of authority, the formalization of accountability. Those are all action verbs. Those are all things that we can do to provide actually to link data governance and the definitions that we're using for these terms to the business outcomes in the organization. We're gonna do it through improved understanding of the data, through improved efficiency and effectiveness that's related to now taking what is informal and inefficient and potentially ineffective and making it formalized and making it efficient and making it effective for our organization. And all that happens through the formality of the relationship that people have to data. That's the whole stewardship aspect of it that I mentioned at the beginning of the webinar. So let's look at the good business outcomes. These are things that most organizations can expect that they'll receive if they've set up a program in the appropriate manner for their organization. And those good results can include these things. So it's the results that we get from improving the understanding that people have in the data and the confidence that people have in the data. Again, confidence may be that they trust that data, that they know that they can get the understanding and the definition of the data when they're looking at it, when they're looking for it, and they can get that in their business glossaries and their data catalogs and their data dictionaries, so which we talk about quite a bit. Another result that we expect from the good business outcomes that we can expect from operationalizing data governance is to protect sensitive data. We know that certain data, we know that PII, personally identifiable information, personal health information, intellectual property of the organization, that it needs to be protected. And that might be the goal, that might be the purpose of your data governance program. So that's a good result that we could get from our program is to have an auditable way to be able to demonstrate that we're protecting sensitive data, that people know how the data is classified, they understand the rules associated with how they can define, produce and use data within the organization. Lots of organizations focus their data governance programs on the results that we can get from people's access to the data. So some organizations have focused on that as their entire purpose of their program to improve the accessibility to the data. Others state that they wanna improve the quality of the data and that could be the quality of the definition of the data, quality of the production, quality of the usage of the data. Again, these are all good outcomes that we can get from having formal data governance in place. We also can look for results from improved decision-making. Okay, we give the data to the people that are making decisions on how we're operationalizing our organization. And if we can give them better data and they can improve the way they're making decisions and you can demonstrate that through the value of those decisions, that's another way to be able to link your data governance program to positive or to good business outcomes. And also the last one is to improve results, to improve the efficiency and the effectiveness of people getting the data. How long does it take for them to get the data the way they need it to be in order to provide their job function? So we look at the good business outcomes and I'll look at the bad business outcomes. If you notice, they line up with each of the good business outcomes. So what are the results that we're gonna get from our organization when it comes to our inability to understand or our lack of confidence in our data? What happens when people lack the confidence in the data that they're being told that they should go to to make decisions, to operationalize or to get their function doing what their function needs to do? So some of the bad results can be that they don't understand the data. They don't have the confidence in the data. So therefore they create their own data resources and they go after those data resources rather than the ones that are the quote unquote golden record or the system of record for that type of data. And what are the results that come from our inability to protect our data? So what if the people that use sensitive data don't know that the data that they're using is classified in such a way that it dictates how that data can be handled in the organization. So you can get bad business outcomes from an inability to protect your data, inability to access data. People don't have access to the data they need, poor quality data, all of these things. The inability to make decisions, the inefficiency and the inefficient and the ineffective use of the data. These are all bad business outcomes. And we wanna make certain that we're focusing on those things that are meaningful to the organization. And if you also notice, each of these types of outcomes are based on the different purpose statements that I provided a couple slides ago. So if we have the ability to do these things those are the good outcomes. If we don't have the ability and what becomes of that, those are the bad outcomes that we could get from really not having data governance or not operationalizing data governance to its full extent across the organization. So in the definitions that I shared with you at the beginning of the webinar, I talked about the definition of data governance being the execution and enforcement authority. Well, those words are worded strongly and their word is strongly for a purpose. Again, I state that it's to get people to sit up and take notice of what we're doing or to question, now, is that really how we want to define data governance? Well, no matter what approach you take, you want to execute and enforce authority. That's only the only way that you can get the organization to follow the rules to make decisions in the appropriate way using the data. And the word execution basically is operationalized performance. That's how we're executing something to enforce something is to operationalize administration, to help the rules. And the authority is the operationalized power. Who has the authority to make the decisions? Who has the authority to, or who is the one that we go to in order to execute authority and to enforce authority from the data across the organization? So that's another reason why I wanted to show those definitions to you at the beginning of the webinar because they're so important. Again, execution enforcement and authority, these are all terms, they're all action verbs that can really closely be related and help us to link data governance to the business outcomes that we're getting in our organization. I define data stewardship as the formalization of accountability. Again, those two terms, formalization and accountability, they're action words. So we need to operationalize foundation across the organization, operationalize responsibility. And as I said, formalizing the responsibility that people have based on their relationship to the data is a key component of operationalizing the outcome of business, of data governance to provide those business outcomes that we are so looking to achieve with our program. Through the improved understanding of data and data related assets. When I talk about data related assets, a lot of the times I'm talking about metadata. And where does that metadata reside? Well, we all understand the terms business, glossary, dictionary, catalog, repository. I've done webinars on that. In fact, one of the more popular webinars that I've done focuses on specifically those subjects, the glossary, the dictionary and the catalog. So we're gonna improve people's understanding of the data and then improve their ability to get to that data in a more efficient and more effective manner. Another way to improve understanding of data and data related assets or even to improve the understanding of your data governance program is to build where I call a home for data governance, basically a home page where people can go and learn about the different things that data governance are doing, how the program is set up, but having a web page or a SharePoint site or a OneNote site or something that focuses on what data governance is, that's really critical to your organization as well. People, your organization needs a face for the program and instead of them calling you and asking you questions all the time, the idea may be to build that into a home page that people can use to improve their understanding of the data. Another way to link data governance to the business outcomes is through the improved efficiency and effectiveness that's related to that formality that I talked about, taking some behaviors and responsibilities that are informal and making them more formal. A lot of times people talk about the 80-20 rule where people that are using data, they go into, they're spending 80% of their time just understanding the data and finding the data and crafting the data in such a way that they can use the data to do the other 20% which is analyze the data and use the decision to make decisions. Accessing the appropriate information, people want to know where does the data reside? A lot of those answers are in your data catalog. So when we're operationalizing our data governance program, we need to operationalize how we're collecting the metadata about the data in our organization. So if we're gonna improve efficiency and effectiveness by improving people's access to the appropriate data, that's certainly a way to demonstrate positive results or the good results that I spoke about earlier for your data governance program. Researching the data, making decisions, knowing who to ask. You know, one of the things that data governance is always looking to achieve is to improve people's efficiency and effectiveness. If they know who to go to to ask a question or to address an issue and having that information recorded somewhere goes a great distance in helping people to understand what data governance is all about and helping you to achieve, to operationally achieve success with data governance in your organization. So let's talk about those different core components here quickly about how do we design these components to provide for the business outcomes that we're all looking to achieve? So we'll talk about the data governance core components. I'll spend a little bit more time talking about the need for senior leadership to support sponsor and understand what we're doing. Like I said, if I said that that was 95% of the organizations that I work with, 95% of their very first best practice I'd most likely be lying to you. It's closer to 100% because without the senior leadership support sponsorship and understanding our programs at risk. I don't know if any of your organizations have experienced that I've experienced it where all of a sudden something else comes up that's more important and senior leadership says, okay, well we need to focus our attention there. They need to support you. They need to sponsor you certainly because like I said it requires resources. They need to understand what the heck it is you're doing what the heck we're doing when we're putting a data governance program into place through the detailed roles and responsibilities through taking those stewards who have relationships to the data and formalizing those relationships to the data and by doing repeatable processes or having processes that, hey, we did this for somebody else we can do this for you. We've got a process defined for that that really helps us to take our program get it off the snide, get it moving demonstrate to prove the value of the program. So as I mentioned before the data governance framework those core components of the program across the top we wanna make sure that while we're designing our program that we're focusing on those core components and making certain that they align with the purpose that we spoke about earlier. So we know how the roles of process and communication I don't have the time in this webinar to go through each of these in detail but please go back to the data diversity site look for the webinar that has been done on the framework and we'll go into a lot more detail in that webinar on what these different components are and why they're important to the operationalization of data governance. So it's been a minute here talking about the senior leadership. As I said, I'd say that it's best practice numerous it's the one right at the top it's the first one that people should look at and the criteria that I use for determining whether something is best practice is it's gotta be practical and doable within your organization where you should make it a best practice. And certainly the operationalization of your program is gonna be at risk if you don't have senior leadership support sponsorship and again most importantly the understanding of what it is that you're doing. So there's really there's four questions that need to be answered for every best practice you define number one is why is this a best practice for our organization? Why do we select that best practice? What are we doing that we can leverage in support of this practice? Where's their opportunity to improve associated with getting our organization closer to that best practice? And then what are the gaps between what we're presently doing and what are the risks associated with those gaps that we need to address when we go about and operationalize our program. So operations will always depend on this as the very first best practice which is we need to get our senior management or senior leadership, whatever they're calling in your organization to support sponsor and understand what the heck it is we're doing when we're developing our data governance program. So we need to do it through detailed roles and responsibilities. And at EDW in Chicago in October, I'm doing a tutorial on a complete roles and responsibilities that align with each of these different levels. And you can look in the framework to the things that I've highlighted. There's the roles column of the core component of the program. And when we fill out the framework and we can go back and look at the framework that I had provided earlier in the webinar. What are we calling the executive role? What are we calling the strategic role? That's typically the data governance council or the tactical role which is the subject matter experts or the data domain stewards as I refer to them often. Even down to the operational level with data stewards and the support levels which may be your data governance office or your administrator or certainly IT and project management and HR and legal and audit. All of these are potential partners that exist at the support level. So that's just one example as to how you can go through the framework and you can start to fill in the things that are going to be important as you set up your program to operationalize your program. I talk about formalized stewardship quite a bit. And so really what we're doing is formalizing accountability for the management of data. If people define producer and use data as part of their job and if they're being held formally accountable for that relationship, they're a steward. And I use the example, okay, let's take everybody who's on this webinar right now. Okay, you folks on the left, you use sensitive data and you need to protect that data but you folks on the right that use that sensitive data you don't need to protect that data. No, that's never gonna fly in our organization. So if you use data that is sensitive and that needs to be protected, you are a steward because now you're being held formally accountable for that usage relationship that you have to the data. So data stewards are what are necessary to operationalize data governance. And like I mentioned earlier, my next webinar next Thursday, next, I'm sorry, the next third Thursday of the day, I'll be talking about using stewardship approach to data governance. And I've mentioned before and I assume again that everybody is a data steward. And that's really, or should I say anybody is a data steward that defines producers and or uses data as part of their job and that pretty much covers everybody in the organization. And to be honest with you, the only way that you're gonna completely cover your organization to operationalize your data governance program is if you think about the idea that potentially everybody in the organization is a data steward. No, we don't need to embrace them all at once. No, we don't need to engage them all at once. But if they use data that needs to be protected then we need to share with them how the data is classified and what the handling rules. How can they print that data? How can they transmit that data? How can they store it on their desk if you're familiar with clean desk policies and things like that? Surely the only way to completely cover your organization is if you at least consider the idea that everybody in your organization is a data steward and I add for kind of an extra measure there that people need to get over that fact. Yes, it may make your program a little bit more complex because we got to deal with everybody in the organization but we got to get over it. If we want complete coverage of data governance in our organization then potentially everybody in the organization is a data steward. Looking at another column of the framework, the process column, what are the processes that are key to operationalizing data governance? And those processes could be proactive. That means that we're getting ahead of the curve in terms of making things proactive to our organization or they can be reactive. What do we do when there's an opportunity? What do we do when there's a problem? So those are the reactive processes and we don't just really want to focus on the proactive and the reactive processes with a lot of organizations that I'm focusing on and I'm focusing with right now, they focus on, well, how do we take in additional opportunities? And so I think I initially started talking about these things in terms of issues but they're not always issues. They may be opportunity for us to improve how people are using the data. So those are opportunities rather than issues. Certainly issues are opportunities too but there's positive opportunities, things that we can do better to be better serving our organization. That also includes how do we get things approved within the organization? Perhaps we want to set up a repeatable process for how we go about approving the artifacts associated with the program. And the repeatable processes, well, those are like I said, those are the keys to operationalizing data governance but the fact is that operationalizing your program also, if you look at the last three columns of the framework, we need to operationalize communications.  Well, we need to operationalize the communication. We need to operationalize the metrics. That's not gonna happen on their own. We need to operationalize the tools. It's one thing to have a tool. It's another thing to get it into the hands of the people that are going to use it and get them to understand how can they get the most value out of the tools? So that's again, one of the reasons why I shared the framework with you because when you're focusing on operationalizing your program, you wanna focus on all aspects of the framework because if you do address those components from each of those levels and then you go and you activate that and get off the snide, so to speak, that's gonna take you a long way towards operationalizing your program for business outcomes in your organization. So now once we've done these things, once we've defined the components of the framework, how do we use the program or the purpose of the program to demonstrate the value to the organization? So we need to start by focusing on the appropriate purpose. We need to design the program so it's fit to serve that purpose. We need to extend it beyond that initial purpose. If that's what the goal of your organization is, just not to do one thing, but to be multifaceted in your approach. We wanna extend beyond that. We wanna demonstrate value. And by all means, I've talked to a lot of organizations that say communications, communications, communications. Those are the three C's of data governance because communications is critical. We need to help people to recognize that they are stewards and that there may be some activities that are associated with their relationship to the data that we need to help them to do. We need to improve their efficiency and effectiveness in relationship to those activities. So this is a repeat from an earlier slide. So we need to select the appropriate purpose and a vision for the organization. And I shared these with you earlier. I don't wanna go through them again, but there's one thing that I really want you to take away from this webinar is the importance of defining a purpose for your program. And these are some great examples, no pun intended. Maybe that one is truly a great example. But you wanna define a purpose that's meaningful to your organization and make sure that you're focusing your program on that purpose that you've defined for your program. And you wanna design your program, as I mentioned before, so it's fit to cover that purpose, to build people's confidence in the data that they use. And that may be by creating a business glossary of the terminology, what language do we use as a business? What's the data that resides in a specific resource like a data warehouse or in your data lake or within a specific application or database? We wanna build confidence in that data so that people will use that data more or use that data better. You know, we wanna protect sensitive information, even though that might not be the primary purpose of your program. And when I say that there's already governance taking place in your organization, I would venture to guess that you're already doing things to protect sensitive information. We don't need to call that data governance. Data governance doesn't need to take that over. That's certainly one of the purposes is to protect that sense of information, to improve quality, to engage people, to provide that business ready data to all of your constituents. And that could be your customers, it could be your internal customers or your external customers. And to provide data that we're actually managing. And that's not just a data swamp rather than a data lake. Let's provide definition for that data so that in understanding as to where and how to get to that data and how to use that data to make sure that the data is managed as an asset of the organization. So what I mentioned earlier that we wanna view beyond just what our initial purpose is, we want to take it to infinity and beyond. We want to use the operating model, that roles column. And you might have seen a pyramid that I talk about quite a bit. I talk about the operating model and look for different ways to be able to use it. If you're focusing on quality, well, maybe we can extend that to focus on the protection of data too. We can focus that on making the data great for our organization. We can extend ways that we communicate with people in the organization. Because we know again, the communication is critical to our success and develop new processes as different opportunities are presented to your data governance team or your data governance office. We wanna discover new ways to measure the success of the program or to utilize the tools that we may already have in our environment or that we're entering into our environment. So these are all ways that you could take your data governance program and extend it beyond just that singular purpose that we talked about earlier in the webinar. And then demonstrating the value, we wanna demonstrate there's several ways that we can demonstrate value of data governance to our organization. That is through measuring how it's being accepted in the organization and how satisfied the customers of data governance are with what data governance is doing for them. There's ways to measure business value through improvements in efficiency and effectiveness and return on investment. I mean, organizations are putting lots of money into different resources that they have in their organization like the data lake, like their analytical platforms. And the true value out of those assets that we're creating is the return on the investment that we're getting from investing the time, the money, the effort into those things and the business value can come from improved decision making as well. Then there's their involvement. We can measure the involvement of people, incremental expansion across the organization. We may focus on a slice of a slice of the pie before we even get to the first slice of the pie and then take it to all the other slices of the pie. So we're incrementally expanding data governance through the organization. And we can demonstrate how many people are we involved, how many different parts of the organization are now embracing data governance as a regular activity in that. And one thing that we need to think about when it comes to demonstrating value is being able to audit those things that we're measuring and that we're reporting. And I've been known to say and I've written articles about this in GDN that there are no facts without the data. So to get people to understand the metrics and to trust the metrics, they need to have the data. You need to make your metrics auditable for your organization. And really delivering those metrics become part of that continuous communication that's so vital in making certain that we can operationalize our program. When I talk about communicating the value to the people that matter, well, first thing we need to ask are who are the people that matter? Maybe it goes beyond your senior leadership and the like other things that you may call them. The senior management or your standing committee, we certainly wanna communicate that effectiveness to the business leaders. And oftentimes the business leaders, or should I say the leaders of each of the different business or parts of the business in your organization, they may make up a data governance council or something similar to that. The subject matter experts, the people that are the authorities on certain subject matters data, we wanna get to them and wanna make sure that they understand the value that data governance has to them. And then all those supporting functions that I talked about, well, first of the data stewards, that's basically everybody. We wanna orient everybody to the need for data governance, how we're doing data governance in our organization. So then there's the supporting functions such as IT, security, HR, all those ones that I mentioned earlier, they need to support, they need to understand what we're doing. And one way we're gonna get them to support what we're doing is to get them to communicate with them and to get them to understand the value that data governance brings to them. And then the last one here is the external people as well, the customers, the prospects, the business partners, auditors, if we can demonstrate that we've recorded information about the data and about the stewards and how we're formalizing and how we're operationalizing data governance, those are all things that need to be communicated to people. So the last subject I wanna share with you today before I turn it over to Shannon to see if there's any questions is how are we going to, what are some of the ways that we can engage our stewards through their job function? And the first one is the recognizing of who the stewards are in the organization. I'll share with you what I've been known to say are the rules of becoming a data steward. Everybody is a data steward, I talked about that. And a steward basically is a steward if they are healthfully accountable for what their relationship is to the data. So we're gonna engage those stewards through formalized process and have repeatable ways to do things that are demonstrating value to the organization. So the first thing is recognizing definers, producers and users of data. There's already people in your organization that are defining, producing and using data as part of the job. We wanna recognize who those people are. We don't wanna assign them. That sounds a lot more invasive. Assigning immediately the term of assigning leads people to believe that this is now over and above what I'm presently doing. And we don't wanna identify them. Okay, it's great if we identify them but there's a positive connotation that comes along with recognizing something, recognizing somebody for what it is that they do. So assigning goes with command and control approach. The identifying goes with the traditional approach. And the data definition steward is a person that's healthfully accountable for how they define the data. How they model the data, creating definitions that aren't what I called cheeseburger definitions. Again, the definition of a cheeseburger is a burger with cheese. The definition of a student account is the account of a student. Now we gotta go a little bit further than that with the definition. So we're gonna hold people formally accountable for how they're defining the data. And they're data production stewards if they are being held only accountable for how they produce data. And usage stewards, and as I mentioned before, if they're being held formally accountable for how they use and consume the data. Basically, everybody who defines, produces and uses data is a data steward. And we need to just realize that fact. So my eight rules of becoming a data steward are that first of all, a data steward can be anybody that it really describes a relationship and it's not a position. We don't need to hire stewards. They don't need to have the title of steward. They don't need to be told how to do their job, but they do need to recognize that they have a responsibility of being a steward. I always say that public or industry data steward certification is a load of bunk because these people do what they do. They use the data, they define it, they produce the data. So it's very difficult to certify something if we don't really, if it's not focused on specifically what their actions are. And the truth is that more than one data steward exists for each type of data. And we really need to focus on formalizing people's accountability, formalizing that relationship that people have with data. And I said, everybody is a data steward, get over it. There's an article on TDAN that talks about how it's really the only way to absolutely and completely cover the data, cover all the people in your organization that you're holding formally accountable for the data. So organizations need to inventory and know who those people are and give access to who the data stewards are. And I talk about that a lot in the common data matrix that I tend to share a lot. So a steward is a steward if they're being held formally accountable and being held formally accountable is a huge part of operationalizing anything in your organization. Management must agree to holding the people accountable and being accountable means that there must be consequences. So if you use data in a way it's not supposed to be used or you don't protect it, there needs to be a consequence for that. Now, that sounds more invasive than non-invasive, but the fact is if we're going to execute and enforce authority over the management of data, there needs to be consequences for people not following the appropriate behavior that's being laid out to them. People don't have the ability to say, well, I use that data, but I'm not a data steward of that data. No, basically everybody is a data steward. They use sensitive data. They need to be educated on how to protect that sensitive information as one example. We engage the steward to respond last processes and that's the processes associated with defining, producing and using the data. We use racy matrices oftentimes to formalize that process. And oftentimes it's the data governance administrator or the data governance lead that has the responsibility for operationalizing those processes that I spoke about earlier. So in this webinar, I talked about the five things. What it means to operationalize anything, operationalize data governance, how to link data governance to the business outcomes, how we design our program and looking at that framework that I provided and making certain that the roles, the processes, the communications are all aligned with that purpose that we spoke about. Talk about using the purpose of the program to demonstrate value.  I'll help you to operationalize your data governance program to provide business outcomes, positive business outcomes for your organization. And with that, I know I went through a lot. I'm gonna turn it over to Shannon to see if we have any questions today. Bob, thank you so much for a great presentation as always. And if you have questions for Bob, feel free to submit them in the bottom right-hand corner in the Q&A section. And just to answer the most commonly asked questions, just a reminder, I will send a follow-up email by end of day Thursday, or excuse me, end of day Monday with links to the slides and the recording of this session along with anything else requested. So diving in here, Bob, there's a lot of great questions coming in. Trying to discern which of the following carries the most recognition or if there's any certifications you recommend for CDMP, CIMP or GGSP, is there something that people can specialize in for data governance? Yeah, they can. I mean, DAMA International, if you're familiar with the DAMA, you can go to damac.org. They have the CDMP, the Certified Data Management Professional. And certainly one aspect of that is data governance. I'm not familiar with any organization that right now certifies people in data governance. But if you're well versed in what you're hearing through data versity, through the community of data versity, I'm not sure, I've never literally never been asked if I have been certified in data governance. I don't even know if that is a thing. But I've gone to DAMA first, I'd go to the Data Governance Professionals Organization which is dgpo.org to find out if they know of any ways to certify individuals. But I'd say start with DAMA International, go to the DGPO and ask them and you'll probably find some ways that you can get some validation from the education that you've received in the subject to get quote unquote certified in data governance. So Bob, do you have any suggestions on how to demonstrate value in a Skunk Works way, kind of under the radar of management to help make the case to upper management beyond the obvious of doing something small, any topics that resonate with certain kinds of businesses? Well, if we can get them to understand that they're investing heavily in these platforms and they investing heavily in the technology like the analytical platforms and the data lakes and data warehouses, help them to understand that it's great to have that technology set up. But if the data isn't good, it's not going to really help. I'll be making decisions based on data that they don't have confidence in, that they don't trust, that they don't even know exists or they can't go to. So I would certainly start with, to what level are the resources that we're investing so heavily in? To what level are they documented so that we can improve people's confidence in the data? Certainly we can look at the efficiency of people right now, how does it take for some access to data? Improve that by operationalizing how people ask for access and how it goes through the appropriate escalation process to determine whether or not that person should have access to the data. Yeah, there's a whole lot of ways to be able to demonstrate the value to senior management, but if you're going to kind of do it in a skunk works, I think is what you said, a skunk works manner, you know, start with the day-to-day activities of people. Ask them where they're spending their time, where they feel that time would be better served and use those types of things to measure and then you're really starting at that grassroots at the skunk work position of the organization and demonstrating and getting people to tell or to give you testimonials that you can take to your senior leadership that says, because we formalized this, now it's so much easier for us to get access to data. It's so much easier to understand the data. I'm spending less of my time manipulating the data and more of my time doing what I'm being paid to do, which is analyze the data. So any and all of those things can be used kind of from the grassroots level to take up to your senior leadership to get them to understand why data governance is important and the value it can bring to the organization. So Bob, could you provide some details on typically who should be taking on these different roles in the data governance ecosystem, like qualifies and sets and a set of skills needed for different roles and also, you know, one of the things that we've been struggling with on our end is who should be vetting on existing business rules or different metrics and who should be putting up a new set of rules or methodology for new metrics? Well, there are a lot of questions in there. So first, I'd like to lead you to an article that I wrote on tdan.com, called a complete set of data governance roles and responsibilities. It seems to be one of the most red pieces. Like I said, I'll be talking about that subject at EW in October in Chicago. So those are certainly ways to get those responsibilities and to understand the skill sets that people have. First of all, if we're gonna formalize accountability based on people's jobs, things what people do presently, then we don't need them to take on a lot more responsibilities or skills. What we need for them to do is follow the rules, follow the standards when it comes to protecting sensible data, follow the format that we're providing to them to record the definition of the data. So we don't need a whole lot of additional skill sets as to the different levels of the organization. Well, typically senior leadership, and that could be your C level, it could be above the C level if there is such a thing in your organization that would play that executive role. Certainly the leaders in the business areas, they could be at the strategic level or make up your data governance council. The subject matter experts, the data domain stewards as I call them at the tactical level, we need to help them to use what they do best, what they know best to answer questions in an efficient and effective manner and get to kind of operationalize the use of these subject matters so we can answer questions. And we can get people the data that they need in order to perform their job function and in order to operationalize data governance for those outcomes that they're looking for. When it comes to the operational level, everybody's a data steward. So, yeah, they just need to be able to follow the rules. They need to understand where this is going to add value to them and how we're not just doing this for, because we like to put data governance, provisions into place, I personally like to put them in place, but we're doing it because we need to formalize accountability for the data. We need to execute it in force authority. So that was a great question, but it was multi-faceted for sure. And we are coming right at the top of the hour here. I'm afraid, but if you have more questions for Bob, please feel free to continue to submit them in the bottom right-hand corner of your screen in the Q&A section. I will send all the additional questions to Bob and he will write up the answers, which will be provided in the follow-up email, which I will send by end of day Monday with links to the slides, links to the recording and all the other things he's presented. Bob, thank you so much for another great presentation and thanks to all of our attendees for being so engaged in everything we do. I just love it. And I hope you and everybody stay safe out there. Hope you have a great day. Thanks, everybody. I look forward to seeing you in the data in the university community. Take care and stay safe. Thanks, Bob. Thanks, David.