 Great. Hello and welcome. My name is Shannon Kemp and I'm 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 Siner. Today, Bob will be joined by guest speaker Anthony Augman and they will be discussing achieving data quality with data governance. Just a couple of points to get us started. Due to a 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. And for questions, we will be collecting them by the Q&A in the bottom right hand corner of your screen. Or if you'd like to tweet, we encourage you to share our questions via Twitter using hashtag RWDG. And if you'd like to engage more with Bob and with Anthony and continue the conversations after the webinar, you can go to dataversitycommunity at community.dataversity.net. 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. Now, let me introduce to you our speaker for the series, Bob Siner. Bob is the president and principal of KIK Consulting and Educational Services and the publisher of the data administration newsletter, TDAN.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 Bob, shall I introduce Anthony? Are you going to take that over? I should have asked you in the beginning. Why don't you give me a chance? I have lots of good things to say about Anthony. Fair enough. All right. Well, with that, I will turn it over to you to introduce Anthony and to get us started. Okay. Thank you, Shannon, and thank you, Dataversity, and thank you, Anthony, and thank all of you for sitting in with us today on this webinar. It should be interesting. It should be fun. We've got a lot of nice interesting things to talk about as it relates to achieving data quality with data governance. Glad to have Anthony all been with me today. I will be introducing him a little bit more in a second here, but before I get started, oftentimes I'd just like to run through a couple of quick things that are kind of on my calendar and on the Dataversity calendar. As you know, the Real World Data Governance series takes place every month on the third, Thursday, and the month at exactly this time. And next month in April, we'll be talking about how to operationalize data governance for business outcomes since we're always looking for business outcomes from successful governance within our organization. And if you know me, I talk a lot about non-invasive data governance and just want to bring it to your attention again, the book Non-Invasive Data Governance that's available at your favorite booksellers. I will be speaking at an event coming up in the month of June to be speaking with Dataversity at the Data Governance and Information Quality Conference, DGIQ, in San Diego at the beginning, actually June 8th through 12th, and I hope to see you there. Also, there's a couple of online learning plans and lessons that I have provided through the Dataversity Training Center. I know Anthony has done some of the same. And this just so happens to be Data Education Month, which is a big month for Dataversity. And there's a special discount if you're interested in going online and looking to see what classes are available. Certainly, I have a few out there. Anthony has a bunch. There are a lot of quality courses that are out there for people who are interested in that. And please take advantage of the discount while it is available. Sharon mentioned the data administration newsletter tdan.com, lots of new articles and columns and blogs and features. In fact, Anthony writes a regular column on the publication as well. A new issue was just published yesterday. So please go out and take a look. And last but not least, the home of noninvasive data governance. My favorite subject is kikconsulting.com. Please go out to visit it to learn more about noninvasive data governance. And with that, I want to introduce my special guest, Anthony Aldrin, as Shannon had mentioned. Anthony is the founder of Aldrin Data Leadership. He has written the book on data leadership. And you can read everything else about him here. He has a lot of experience in the field. In fact, he was a chief data officer for, I believe, Chicago Transit Authority, and has lots of other experiences as well. Welcome, Anthony. Great to have you with us today. Thanks, Bob. I'm really excited to be here. Okay, and we're not going to sing for anybody, but we'll make this interesting as we can. And so hope everybody is safe and sound where you are. And we'll hope to entertain you and whip a little knowledge on you at the same time today. So in this webinar, we're going to be talking about, like we had mentioned, achieving data quality with data governance. And there's several subjects that we're going to touch upon. The first one being the relationship between governance and data quality. We'll talk about what I define as the three primary activities that people can take with data to begin with. And so those would be defining, producing and using data. And we'll get some feedback from Anthony on that as well. We'll talk about how we leverage and take advantage of the stewards within our environment. You know, I say everybody is a data steward, but get the stewards to help us to influence improvements in data quality. We'll talk about standardization and validation, which are two hot topics when it comes to the topic of data quality. And then we'll last we're going to talk about simplifying data governance towards a specific purpose. And perhaps that purpose of your data governance program will be to focus on data quality. There's a bunch of different purposes that are available that our organizations have taken advantage of. But today, we're specifically talking about using data governance to actually help to achieve data quality within our organizations. So the way we're going to handle this is there's very few slides that go with this webinar, we're going to do a lot of talking back and forth. But what I'm going to do is I'm going to introduce each of the subjects that I just went through in the abstract, and maybe provide a little bit of insight as to where I what I feel about these subjects. And, you know, what I think is a good way of thinking in terms of these the topics that we're bringing up here. And then I'm going to ask Anthony to either you know, he can either agree with me disagree with me add to whatever I'm saying, but we're going to start with the relationship between data governance and data quality. And so I have some very specific definitions that I use for these two terms. The definition that I use for data governance all the time is the data governance is the execution and enforcement of authority over the management of data and data related assets. And I know that's word and kind of strong. I mean, the execution and enforcement, some organizations want to temper that a little bit. But the truth is at the end of the day, we need to execute and enforce authority in order to truly govern the data. And that's specifically true as we're talking about data quality. My definition of data quality is that it's the actions and the outcomes that are related to the improvements in the data, most specifically to the definition, the production and the usage of data within our organization. Oops, go back here. And so I'll ask Anthony, you know, when you get a chance to talk here, you know, the question is, can people really do data quality without governance? And is there one that should really come before the other? If you can help us to understand better what the relationship is between these two subjects, I think that will be of great value. And actually, you know, if you can also fill us in on whether or not you believe that these two activities, or these two disciplines, can begin and belong in the same part of an organization. And then maybe also share with us, what are some of the skills that are required for each of these categories? And do they complement each other? Or are they different? So please, can you start us out by telling us what you feel is the really the relationship between data governance and data quality? Great. So first of all, Bob, I want to thank you for putting those questions on the screen, because I would not have remembered them all by the time we got through that. So thank you for doing that. I'm gonna go through the screen. And I want to highlight something, because you and I have had many conversations before. And you know how much I love your definition of data governance. So totally agree on that. I want to highlight what you said about data quality, because I think it's an important distinction to make. And that is, I would agree with your date, your definition pretty much in its entirety, in terms of a definition for data quality management, as far as the world of data management goes, and data quality management is really about saying, how do we take these data assets and improve them to a point of sufficiency for what we want to do with them. And I'm paraphrasing, obviously, from from your definition. But I think there's an action component, which is actually the data quality management. And I think data quality without the management piece is really about saying, is this data in its current state and whatever it is, is it suitable or fit for the use that I want to use it for. And that's the kind of the underlying core of data quality. So I want to draw that distinction between the two things, because I think it's, it's something where a lot of times people think about data quality as that kind of, you know, assessment of the data's goodness or badness. And that's, that's only part of the equation, because in the data governance data management world, data quality management is what we care about most, because we're about taking action and making data as as suitable for as many uses as we would like to have for it. And so before I go to the other questions, what is your response to that? You know, I agree with you. I mean, I think it really goes before it goes beyond just the actions and be beyond, as you stated, the goodness and the badness of the data. I mean, that's a good way of describing the way I think a lot of people think about data quality. But if we think about the, you know, again, the actions that our people are taking and the outcomes that we're expecting to get from them, that's really where the focus needs to be, because, you know, quality definition of data can lead to improved quality production of the data and improve, improve quality of the production can lead to improved quality usage. And they're all very closely related. So, yeah, I think the actions are extremely important and people need to just start thinking a little bit beyond whether the data is correct or not correct. Right. And so now as we get into that second question, the can you do data quality without data governance? Now, given what we were just talking about that, that question makes a lot of sense, right? Because if we're going to do data quality, if we're going to do data quality management in the technical sense, you OK, if we're going to assess data suitability for use, and we're going to improve it where it doesn't meet the uses that we have in mind, what do we think? Do we think data governance is probably an important part of that? I would say yes. And I'd also say, you know, people try to do data quality all the time without coordinated data governance. And I'll give you credit like you always say, right, that that whether or not you're actively managing it, you have data governance of some sort, right? You have something that's governing your data and data quality really benefits from coordinated data governance. Because otherwise you won't have a clear understanding of what suitability for use even means. Like people kind of do it ad hoc independently. They mean well, but without coordination, they lead it leads them down very strange and different paths. So I think that's that's where I would land on that question is, well, no, you can't do data quality without data governance. But then you also always have data governance of some sort. And it's still probably not a good idea to try to do data quality without coordinated data governance. And what I always say is it's there is some level of governance there. It might be informal. But it's there's still some level of definition production and usage. And oftentimes the informality of it leads to inefficiency and ineffectiveness. So I'm with you 100%. Yeah, and there was just a comment in the chat. You know, the you can't really govern much with data unless you have some minimal level of data quality. And so they are kind of codependent. They're kind of reliant on one another to enhance one another. And so I think that, you know, as we get further down the page, you know, in terms of what comes first, probably data governance of some sort, I would say you have to understand a little bit of what you have. But that is kind of it implies a certain amount of usefulness. I mean, that's really data is only use and only value comes from applying it from doing things with it. Data that just sits somewhere isn't going to provide a whole lot of good for anybody. So that's where I think it's maybe less important to say which one is the one that comes first, you're not going to do a whole bunch of data governance without data quality, you're not going to do a whole bunch of data quality without data governance. You they really need each other that they're very, very much dependent. And I think it in reality, it's because there's a lot of overlap between them. And I think that, you know, data quality, I mean, we've seen organizations out there, they don't even bother with the data governance terminology at all. They just focus on data quality as the entirety of their efforts that to us looks a lot like data governance. I think, you know, if folks that are watching this are relatively new to the space and we're throwing around terms like data governance and you'll hear things like metadata management and other terms that aren't familiar to you naturally. As you get into this and understand what we're really talking about, realize that the terms are fungible. You can change the words to resonate inside your organization. That's okay. That's actually encouraged because what really matters is saying, how do we take those data assets just like Bob talked about? How do we take those data assets and enforce authority? How do we make them useful to our organizations through the business processes and ultimately the business outcomes that data needs to drive? That's why we do all of this stuff. The labels don't matter. Focus on how do we make our organizations more successful through the use of data. That's really what we want to be going for. Yep. And so what do you think about where it belongs in the organization to get that question all the time and does it belong in the same place or these separate functions and keep in mind that we got to move quickly. We got lots of questions to address today. Yes. So the very short answer is I am going to say they actually both belong in the same department and all the departments. So I think this isn't about one of the things that I think has steered us wrong is when we talk about things like we need to start doing data governance and that's created as like this bolt-on to everything else. This stuff is fundamental now. Like our organizations are data driven. Like we can't really be successful organizations without using data. Data governance, data quality, they need to exist everywhere. Now you can have a coordinating function and I get that's probably really the intent of this question. But that coordinating function of data governance and coordinating data quality probably needs to happen through a multifunctional cross department type of group because the impact of it is across the entire organization. So I think they should be everywhere in some form. Okay. That sounds good. I think we addressed a lot of those questions really well. I agree with you. I believe that data governance is an enterprise function. It can start within business units within parts of the organization. But overall, if you're looking for to help our leadership, a topic near and dear to you, the leadership to really get the same answer to the same question depending on not really getting a different answer depending on who they ask. It really, really needs to become more of an enterprise function. And whether we start small and work from the ground up or start from the top down, it depends on the organization. But as long as it starts somewhere and you can demonstrate value, that's really kind of the most important thing. So I talk a lot about things in terms of definition, production and usage of data. In fact, I say that every discipline associated with data and data management falls under one of those. So data modeling and data dictionaries and business glossaries would fall under definition. And the production would be ETL and bringing data in from the outside and big data and artificial intelligence. And then the usage of data can be the protection of sensitive information. It can be the improvement of the quality, the improvement of the understanding of the data. So oftentimes, I talk about there being three actions. So when I give you a chance here in a second, please let us know, are there any other additional actions that people should be thinking about? And really, what does data governance have to do with these actions? And then also, what does data quality have to do with these actions? And really, the bottom line is, are these improvements in these three things, the definition, production and usage of data, dependent on formal governance? As you said, we're already governing our data. We could be doing it informally. How does formalized governance help us to improve the quality of all three of these actions? Right. So I will, I struggle a little bit with the three actions. And it's not that I disagree with them. It's that I prefer to use five categories or five groupings. And so in my data leadership framework, I have access, refinement, adoption, impact and alignment. And I can pretty much promise you everything you would put in those three I put in my five and neither of them really matter that much. Like in the end, it's how does it resonate and how do you take action with that grouping? What added benefit does that grouping add? Now, I think in this case, and what I do like about these three actions is that from a governance perspective, they are very well aligned to crafting and kind of structuring the appropriate roles and relationships with data because that's really what that grouping is all about. It's really about functionally, you know, how do we as people and as as as business processes, people doing things, how do those relate to the data assets that we have? And here's where like these, I think these three actions are a great model to use for evaluating that and then extending that through our governance activities to the people that need to learn that because frankly, it doesn't really matter what you and I think it doesn't really matter what the folks watching this things. It's what is the story that we can tell to the massive number of people who will never sit through a Data Diversity webinar on data quality with data governance and have them know what to do with the data. And so in this case, if we talk about these three actions, defining, producing and using data, I would like to ask you a question before I offer my responses is help me understand the difference between defining and producing data, not because I don't know but because I want to hear your words on how you explain the difference between those two. So I think using data is pretty straightforward, but where does defining and producing really, how do you break those apart? So actually, I can see how there is some foggy line between the two. But I don't know if I really have a definition for each of those, but data definition is where we are putting meaning to the data, where we are, as I hate to use the word definition in the definition of the word definition. That makes sense. But the idea is that we want to make certain that we can improve the business community understanding of the data and the outcomes that come from the data. So to do that, we've really got to start with definition and that could be through data modeling. It could be through the use of a spreadsheet or any other tools that you have available to you to put clear definition to the data. People talk about data architecture and data architecture is a very important subject and a lot of the data architecture has to do with the definition of the data. Now, the production of the data oftentimes is dependent on good definition of the data, especially in old legacy systems where there aren't the types of edit checks and the things that need to be in place to assure that only at least valid options are being taken when people are producing the data. People need to understand the data. They need to understand how it's going to be used within the organization. So when I'm talking about production, I'm talking about people that are entering data in, data that's being brought from the outside, the internet of things which is bringing us data from all sources. So there's a relationship, certainly there's a close relationship between the two, but that's how I would differentiate definition from production. That's perfect. And it made me think, so I recently read this fantastic business book. It was this analysis of business process. It was called The Little Red Hen. And the Little Red Hen, the book is all about this hen, and she needs to make some bread. And she's going to make some bread, and she's looking for people to help her make the bread. And it goes through, it's a children's book if you're not familiar with it. But it's a perfect story here because if you think of the role of the definition, it's determining that it's bread that we're going to make. It's the recipe that we're going to need to find. And it may even be figuring out where we're going to get the raw ingredients and things like that. It's all about the planning side of things. Then the producing side is where we're actually growing the wheat and mixing the dough and putting it in the oven. And then the using of the data, using the bread, is the people actually producing the reports or eating the bread, depending on which of your context. And that I think is what matters is to recognize what is that process? What's that life cycle? And what's important to talk about in the context that you need to talk about it in. And so I think that the thing I like, and please, if you're listening to this and the little red hen is a story that you can resonate with or that your people might remember or understand, it's recognizing that everybody wants to eat the bread. Everybody wants to use the data, put it in place, all of that stuff. But sometimes we need help. We need help to understand what that data is. We need to understand how that data can be used. And that's the role of data governance. That's the role of data quality is to make those things clear to people so that we can then use the data and we can all enjoy the benefits from it. And so I think that, to me, both of these, the data governance and data quality are kind of intertwined with all of those different kind of core activities. But again, I think you'll find a way to draw that path to where data really becomes valuable for what you're trying to do as an organization. And then that'll help you pull out those steps where you're going to need help making that bread. You're going to need help defining that project. You need help answering those questions that maybe you don't yourself have all of the knowledge to answer yourself. You know, when you mentioned the book, The Little Red Hand, I was thinking, really, Anthony, there's a business book called The Little Red Hand. I think you've got kids on your mind since they're probably in your house with you. And I can kind of vaguely remember reading that book to my 20-year-old and my children in their 20s at this point. But that's pretty funny. But it's a great analogy, I think. And it's one that I might have to steal from you and use at a later point. The idea of The Little Red Hand, we needed to find what it is. We need to produce it according to plan. And that's really what governance is all about, too. And then we want to use it. Everybody wants to eat the bread. Everybody wants to consume the data. So on those subjects, I think that's a fantastic analogy. And I expect that I would hear people start to talk about it in terms that way. I mean, everybody knows the book, that business book. The little golden book is now being redefined as a business book. So one of the things that we talk about a lot, you and I and everybody that we talk to, is data stewards and how data stewards are related to data governance and how the stewards of the organization can influence how we're making improvements in data quality. So first thing I'd like to do is start out with, do you have a definition of what a data steward is? I'll share with you mine and then let you tell us what yours is. But I say that a data steward is basically anybody in the organization that has a relationship to the data. And that relationship is as a definer, a producer, and or as a user of the data if they're being held formally accountable for that relationship. So I think they all understand that people that use data that's sensitive, that has to be protected, that has to be held private, you know, they can't opt in or opt out of protecting that data. So as a user of that data, they are automatically a steward. And organizations are starting to realize that. I say everybody is a data steward and the people should get over it. And then really what I'd like to hear from you is, you know, what are stewards like within organizations that you have worked with? You know, how many are there? Or if you can even state that there would be, should be a certain number or the different levels of stewards. And also, how can those stewards provide some level of influence over the improvement of data quality? So let's start with, you know, how do you define data steward? I gave you my definition and I say that, you know, everybody can be a data steward if they're being held accountable for their relationship to the data. Yeah, well, my wheels have been turned and ever since I heard you say that again, it's that accountability for how they interact with and the relationship for the data. That's the thing that's sorely missing in so many organizations is that nobody seems to be held accountable for how they use data in a lot of places. And that's led us to a point where data is not trusted or other challenges have happened. And so I want to say that your definition, I think is the definition that I would love to have in place. I just don't want to diminish the importance of data stewards because of how few organizations actually hold their data stewards accountable. And maybe I might argue that we could change that definition to say who should be accountable? But, you know, it's not dependent on that accountability actually existing. I think I really, you know, overall, I really love your definition of it. I think it hits it really home. And I think that the debatable point on it is more of an academic exercise. So I won't belabor that. But I think it's really about saying, you know, we're all, anybody who's working with data in any organization is a data steward and needs to be a data steward, needs to be held accountable. It's not about calling them a data steward or not. It's making sure that the people that use data are accountable. And that's the thing. Like people think sometimes that if they read the data, that if you consume some data in a report or something like that, that you're not responsible for anything that you do with it because you're not changing the data. But you are, you're changing what the data means in the organization. Even if it's very slight, by sharing or using information in any way, you are making an impact on the data itself. It's kind of like, you know, like, you know, forces. There's always that, you know, opposite force. And when you touch data in any way, there is some sort of counter results to that action. And you know what? So you really are focused in on the usage of the data. But I would say from a data definition perspective, people that are defining data for these big initiatives, for these initiatives that your organizations are investing lots of money in, they're putting, as I refer to them, cheeseburger definitions. You know, what is a cheeseburger? A burger with cheese. What is a patient account number? It's an account number for a patient. You're using the same words to describe the subject. So producers, or I'm sorry, definers have a job, have a formal responsibility for making certain that we're providing the documentation and the metadata that's going to help people to truly understand that data better. And then the producers, for a long time, where people were doing key pudging information and data in, you know, they would want to be as efficient and effective as they could be. But you know, at one point in time, I think I read a statistic that more people were born on 010101 than any other date in the history of the world. And why might that be? Because the people that are entering the data, that might be the default date. You know, people are using the defaults instead of putting some thought into the data that they're actually producing. So I feel like the accountability for people has to be through all three of those actions. Yes, the usage is probably one of the easiest ones for us to understand. And probably the ones that the government is forcing us, forcing our hand to take more, pay more attention to. But the definition of the production, the same, you know, if we can hold people, the stewards of the data, formally accountable for the definition of the data, formally accountable for the production, aren't we going to lead, aren't these stewards going to be influencing how we're improving data quality in the organization? Oh man, Bob, you're absolutely right. Now, it shows like my technical background because I immediately jumped to the border condition to solve that. And you're like, what about all the other really important stuff? So yeah, that's exactly correct. I think, you know, I honed in on a point that I think was more subtle because it's so important. Like those folks that you just described are absolutely the kind of core aspects of data stewardship and absolutely they need to be, you know, held accountable. They absolutely need to be effective in their job. I am so with you on that whole notion of cheeseburger definitions. It's so frustrating in organizations because they'll put work into this and then they realize they've just used a bunch of words, reshuffled them and acted like this is going to be sufficient. And it's not easy. Like, it can be difficult sometimes to find the other words to describe the thing. And I get it, it's hard, but it's still important to put that into context while also recognizing that even the actions of doing definitions, that those actions of, you know, creating policies and all the things we do in data governance that influence the data and are part in my opinion of data stewardship, even all of those, we have to recognize if those don't actually serve a greater purpose, then they become detrimental to the overall health of your data and in your organization because if we don't do this and if our work, if our definition, whether it's a cheeseburger definition or any other definition, if nobody ever finds it, nobody ever uses it, nobody ever improves something because of it, then we've done nothing of impact, nothing of value other than add cost and complexity to an already, you know, expensive and complex world around us. And so Anthony, I'd like you to address an age-old question or at least as long as I've been saying a question because some people push back and have reasons for why they push back, but is it so if you understand that pretty much anybody in the organization either defines data or produces data or uses data or two of the above or three of the above, that everybody pretty much has a relationship to data, how is it possible that we can make or is it even possible that everybody in the organization can be considered a data steward? Well, I think you just answered the question. I think that because everybody's impacting data and everybody's reliant on data for what they're doing, we all have a role in this. And that's how, you know, to me when it comes to data stewardship, that's the message. I think is that everybody has a role to play here and part of that role becomes a data user, part of that role is a data definer or producer. It's every single person in an organization is impacting the organization's data in some way. And a lot of the folks that would potentially like without context say, oh, I'm not a data steward. I don't know what that is. They may be the ones doing more data entry than anybody. And so they may not need to know like all the ins and outs, but they may be impacting far more data, just like I love your example on how more people are born on January 1st than any other date because that was easy for somebody and had no idea of the impact of their data entry decisions on others in the organization and downstream systems. Oh, and so some people have told me, I've been told this several times that if everybody is a data steward, then nobody is a data steward. Now, please understand everybody that there's different levels of stewardship. There's operational stewards that define, produce and use data. Then there's tactical stewards that are looking at the data as a cross organizational function. So the fact is that potentially everybody could be a data steward. In fact, I would venture to say that without having everybody being considered a data steward, we're only covering a percentage of our organization. Now, do they have to know their data stewards? Do they have to call them self data stewards? No, for sure, they do not. But you know what? If they're being held formally accountable, then they are stewards. They don't have an opportunity to opt in and opt out. The last question around this specific subject is how can these stewards, and I can guess what your answer will be, but I want to hear from you, how can these stewards influence the improvement in data quality across the organization? I think there needs to be a better bi-directional flow of insights and even just metadata around the data that people are trying to use. I think it becomes, you know, we as the people producing data for use need to be much more clear about what data is going to be suitable for and that people consuming data need to be as clear as possible and what they're trying to do with it. And if we can start to make that communication more clear in our organizations, I mean, right now in most organizations, it's completely hidden from view. So if we can make that a little bit clearer between the two, we can start to provide insights into both sides of that. And that'll help us improve the quality of data simply from being more clear about how we're trying to use it or what we want to get from it. The other thing I would mention too is I think that there's a case to be made, especially we're hearing a lot around data literacy as being this very interesting topic to a lot of organizations. I would argue that that in some ways may be supplanting data stewardship in terms of popular nomenclature. Because if we agree that everyone's a data steward, I think it's ridiculous to say, well, no one's a data steward. That's just people playing with words and not realizing the impact of what they're saying, in my opinion. But if we say data literacy is about teaching folks their role and responsibilities and building capabilities to use data in a responsible way and produce data in a responsible way, I think that might hit home at what's most important versus debating whether or not data stewardship is a thing or is not a thing in a particular context. Just something I wanted to throw out there because I find that an interesting parallel to the data stewardship conversation. When I drove by a church the other day and they had a sign out as they're announcing services and since a lot of people are kind of hunkering down, they're not holding the services, but the sign said, everybody is a steward. And I think, wow, there's somebody with me that everybody is a steward. And the truth is that if we can engage these people in the appropriate manner, we can use that to improve the quality and the value of the data in the organization. So let's kind of jump into the next topic real quickly and talk about the usage of the stewards to improve standardization and validation of the data. So the first question I'll have for you is are these things the same things? Are it standardization and validation actually the same thing? And what do these things have to do with data quality? So why don't we just kind of start with that? Yeah, so I've been thinking about this question because it's not one that I've thought about a lot, but I would generally say they're not the same thing. And it's because when I think of validation, I'm usually comparing one new thing to a preexisting thing. So I may be validating that a new set of data is the same as another set of data that you're trying to match up to. Whereas standardization is around saying, all the data that kind of looks something like this should look this way. And so it's about, standardization feels to me like we're trying to get things together in a clear pattern for what's best for that thing. Whereas validation is saying it's more transactional. Is this thing B the same as thing A? So I think it's standardization would be more do we want thing A, B or C or something like that versus validation is just are we doing one versus the other? Do you have a different definition or is that live with how would you like? I thought you put a lot of great context to that because the way I had it written in my notes here to talk about is that kind of you need a standard in order to validate anything, right? How do you know if something is right or wrong unless you know what is right? And so the standard is in the one word I think that you left out and when you were talking about standards is consistency. If we want to be consistent, if we want the leadership to get the same answer to their question no matter who they talk to. A lot of that has to do with the definition production and the usage of data, but we need to standardize. And I found that with many organizations that I work with that their only way of really achieving data quality is to start by defining standards because if you're comparing two different types of data you don't know if they're the same thing or not. Yeah, you can compare the values of them but what is it that you're trying to accomplish? And I kind of get this kind of I keep coming back to the little red hand example, right? What are we doing? Why are we doing it? How are we doing it? And a lot of these things have to do with the standardization. So if we're going to standardize things then once we've done that we can validate it. That's my thought is that they are that there is like kind of, I hate to go back to say it this way because of the little red hand example but it's the chicken and the egg type of thing, right? I mean, we're not going to be able to validate unless we have something to validate against which is the standard. So, and I think that, you know, we both understand that these activities have a lot to do with data quality because how are we going to know what's right and what's wrong unless we have some type of a standard to be able to validate against. So where does data gardens fit into the equation of the standards and the validation? Maybe you can address, you know, what's the impact that data governance has on these things? Right, well, and I think that, you know, the data governance and data quality kind of go hand in hand in terms of its relationship to standardization and validation because I really like that you mentioned consistency. And because if you think about data quality and I'll get to the data governance in a second but in the data quality, you know, piece of it, we're talking about suitability for use, right? And so if we're talking about suitability for use and improving things, well, that connotes that we have a use in mind and that use could be something and I like to use the needs and data quality, it's about perspective, it's about kind of a point of view. So like the needs of a marketing group to determine between, you know, marketing approach A and marketing approach B may require less underlying data quality than the finance group who's trying to balance the books. Like that's probably a safe assumption under most circumstances, right? So if we think about that overlay in terms of suitability for use, data governance says, okay, how do we evaluate that? How do we determine what to do to satisfy the needs of the finance organization for what is arguably probably a pretty important activity of balancing books as well as serve the needs which may not even require the same quality or even the same data as what the marketing group needs to accomplish and so the data governance says if data quality is about a subjective use case about a point of view, data governance is about saying, I'm looking at this more broadly and saying, okay, well, what do we need to do and support and enforce to make those other things happen? And I think that's more of a governance activity than the data quality management itself. Data quality management can execute what the data governance group decides what the data governance function decides but it becomes more of a acting on the data here's how you use it, here's how you validate it, that kind of thing. Data governance kind of gets to the point of, okay, given what those perspectives are and those needs are, here's how we actually put people to work. Here's where we tell them, hey, run in that direction because we need to accomplish this and that. There's inevitably a limit to the resources we have to apply to these kinds of efforts and the data governance organization and this is something that we don't hear enough about I think Bob, is that the data governance organization has to be absolutely central to the prioritization of those different options and that's I think so important especially when we talk about data quality we're never gonna get all of the data perfect that would require infinite resources it's just not gonna happen. I agree with you and so what I'm thinking a lot of people might be asking when it comes to data quality is what do we need to standardize and then once they're standardized what do we need to validate and can these two things really be done if you don't have some level of formal governance taking place? Yeah, I mean, I think that what makes me sad is when people equate data quality to things like is all of our names properly capitalized and is our addresses correctly formatted? Like that's fine but that's just kind of that's just look and feel stuff that's not real quality. I think it hits more like what's more important when you have say you have a bunch of customers is it going to be more important to fix that January 1st birthday or is it gonna be more important to fix their email address or phone number? And compared to email address and phone numbers their billing address or delivery address which are more important there and so recognizing that there's different levels of importance of different pieces of data and your business should determine which ones we put energy in towards fixing because I will say and one thing we haven't really talked too much about is improving the quality of your data is really hard and really expensive a lot of the time. The best way, and I recently had a conversation with Tom Redmond that just continues to flash a neon in my head is how important the creation, that initial recording of data is to the data quality story. If you get that wrong, it's gonna be two, three, four, five times more expensive to correct that data later than if you had just gotten it right in the first place. Governance is a huge role in that. You just made a big point as to why the definition is so important and there's quality in definition too. So if we have standards for definitions and we can validate that we're not using cheeseburger definitions or we're not leaving definitions blank or using one word or just using the name of the term as the definition, those are extremely important. So I agree with Tom too that it really starts with good definition and good production in order for the data to be used the way that it needs to be used. So we've got one more topic that we're gonna kind of fly through here a little bit quickly so we can get to a bunch of the questions that have been coming up and I've been popping up in the chat and in the Q&A section. But one of the things that I find is really important for organizations is they define a purpose for data governance. And I'll give you some examples of some common purposes that I've seen. One would be to improve the quality of the data or to improve the quality of the strategic data or to provide strategic data with confidence. Another one would be to protect sensitive information. Another one would be to improve data understanding so it will improve shareability and use around the organization. So what are some of the most common purposes that you've seen for data governance and does data quality rank high up on that list of the purposes if you're gonna say, okay, here's a purpose statement. Why are we doing data governance? It's for this reason. Is that data quality? Yeah, so I have like a devil and an angel on my shoulder right now arguing because on one hand, I think that you can make a strong argument that data quality is a great reason to use to do data governance. On the other hand, I don't know that either of them is a reason to do anything. Like by themselves, they're intermediate steps to what really matters. And what really matters is a better data-driven business, like a business that is more successful. And I think the most common purpose for data governance that I see is somebody told them they had to do it. And the thing is I like that because at an organizational level, that's true with regulatory compliance or other legal mandates or what have you. And at the individual level because a lot of people get thrust into a position where they're like, hey, we got told by these other people that we have to do it, that's now your job. And so that it kind of goes down, you know? And so I think that's maybe the most common reason I've seen data governance start, but I also think that that's not a good reason to do data governance. Like it's one thing that data governance needs to achieve, but the real reason to do data governance is to help your organization, your business make the most of the data it has. So it's not over-complicate that. Data quality is a surely important function in that journey, but data quality by itself is only important as it drives the kinds of activities you wanna use your data for. By itself, we can spend a whole lot of time on data quality, make data better and better and better that nobody ever uses and has no positive impact on your business whatsoever. So to me, I may be, you know, breaking it down more semantically than I really intended to, but I really think that that's, you know, from my perspective, these are data quality data governance. They're important, important pieces of our data journey, but they are not the entire data journey. They are intermediate steps, important ones, yes, but they are not ends into themselves. So, and so I just to add to the idea of the purpose is, you know, even coming back to the analogy that you spoke about earlier, why are we doing this? What is, what are we expecting as the business outcome? So when I even talk about protecting sensitive information, a lot of that depends on how well is the data classified? How well do people know what the classification is and what the classification means? How does the classification impact how the data is handled? And that's from printing to emailing, to putting on a jump drive, to printing, to all of those things. I mean, that's a quality aspect of the usage. And so again, kind of going back to the definition production and usage, those purposes all relate to data quality. So I like your idea of, well, what are we in this for? And so since we only have a couple of minutes left before we switch it back to Shannon, can you share any last words that you have about how organizations can use governance to achieve quality, can you achieve quality through the engagement of their stewards and data governance? Any last words you wanna share with people before we take some questions? Absolutely, to just put a bow on it. And I've spent a lot of time during this webinar talking about, you know, kind of this notion of applied data governance, applied data quality where it only matters when you do things in the business. Well, the fact of the matter is is that this happens asynchronously. Like we don't just do governance and then all of a sudden the business is better and there's not always direct line of sight to it. So it's important to recognize that, yes, that piece is important, but we may not control that piece. We don't necessarily control when the data gets picked up. That's why it becomes so important for us to know these are the kinds of things that we need the data to support. And this is the kind of a communication about data quality that our governance groups need to define around this data so that people, whoever's picking it up, whenever they pick it up, they have a clear understanding of this is what it can be used for, this is what it can't be used for. And if there's a problem with that, go to these folks, those are the kinds of things that we and the folks on this call can do to help our organizations be more successful. So recognize it's not pure line of sight, it's not just do this. And then immediately that other thing happens. It's that we need to recognize those things are important, but we have our sphere of influence, our sphere of control, and we want to be able to do the best we can with that so that the people picking it up, whenever they do, are able to make the most of it. So, and my final thought is, and I know I share this with you a lot, Anthony, and I've probably shared this on my webinars a lot, is that the data is not going to govern itself. It requires a plan. It requires a purpose and be prepared for folks that are on the call, on the webinar, that if you're asked, what is the purpose of data governance? What is the purpose of data governance for your organization? That's really critical to be able to answer that and to answer it consistently. So you're not getting different answers from different people in the different parts of the organization. Try to get your organizations on the same page. Try to define a purpose that makes sense that is aligned with perhaps even your organization's mission and your goals. So the purpose is extremely important, whether or not that purpose actually has the words data quality in it or has the protection of data or has understanding in it. Make sure you define a purpose for what you are trying to achieve with data governance. It's like a little redhead saying, what are we producing this data or this bread for and who's gonna eat the bread when we're done with it? And that analogy is the top takeaway from this conversation. So with that, thank you, Anthony. Please stay with us and join in on the Q&A. I'd like to turn it back to Shannon and see, Shannon, did we, I told you first of all that we could take hours in the webinar. But- Oh, I didn't doubt that. You didn't get any questions today. It's very good. No, it's great presentation. And Anthony, always good to have you with us. If you do have questions for Bob and Anthony, feel free to submit them in the Q&A portion of your screen in the bottom right-hand corner of your question, or of your, right-hand bottom. Yeah, I can speak today. Just to answer the most commonly asked question, I will send a follow-up email to all registrants by end of day Monday with links to the slides, links to the recording, and anything else requested throughout. There's been a lot of comments in such throughout the presentation. I don't see any questions currently in the Q&As. I'll give people a moment here. But let me scroll up here through some of the chat. There's been a lot of chat going on as always, which we absolutely loved. So, data quality ownership should be with business functions or organizations need to define a line with business objectives. Heather, you want to speak to that? Yeah, I'll touch it. I'll take it first and Anthony, I'll let you chime in. But the term ownership is something that just kind of rubs me the wrong way. I know a lot of organizations use the term ownership to define people's relationship to the data. But the truth, at least from my perspective, is that the organization owns the data, that people actually steward the data, the definition of steward in the dictionary, as somebody that takes care of something for somebody else. Well, if you think of yourself as somebody who is taking care of the quality of the data for your organization, you're potentially a steward. So, I try to stay away from the term ownership because it implies it's my data and I can make the decisions and I can do what I want in regards to it. So, your organization might use that term, but my comment on that question or that comment is really that the term steward and getting people to understand that they may be a steward of the data just basically because they have a relationship to the data is really what I would consider more than kind of where does the ownership fit into this? I think ownership or stewardship of the data, stewardship of the data should be more of a business function than an IT function. But in some organizations, the business is IT and we need to take that into consideration as well. Anthony, any thoughts on that? Yeah, I mean, I basically plus one to everything you just said and I think that it does get a little bit muddy sometimes around technical ownership versus business ownership. I'm like, why even have that debate? Like to me, we're all stewards. We all have roles with the data, but to assign one person who's ultimately responsible, it feels like we're trying to put too much in that one bucket, into that one person. And that's why in a lot of the organizations that I've worked with, I see a resistance. People don't wanna be named donors because that just sounds like a recipe for disaster for that. Like too much effort, too many people call it others. Let's maybe spread it out a little bit, provide some people who have knowledge of this, but I don't think in many organizations too, things are complex. Maybe one person doesn't have that whole complete knowledge and decision-making authority over every aspect of this data. Let's not pretend that they do. And let's not point to one person and say that the quality of the data is just dependent upon the owner of the data. Because as we've talked about, if everybody is a data steward and there's people that define and produce and use data, let's count on them. Let's get them engaged. If we're gonna improve the quality of those three actions, governance is all about formalizing their behaviors and getting them together. Somebody, Lynn Silverston, a good friend of ours and also has been a guest on my webinars that said, we could call it people governance because the data is gonna do what we tell it to do. It really should be people governance and governing people's behavior associated with the data. And that kind of comes back to the definition of steward and use the term owner if it makes sense to your organization but make sure that you have a caveat to it and that you share with people, well, what does that mean? What aspect of the data do they truly own? You know, I wanna add one thing and it's not completely related to this but so important that I think people should hear it is that when you're creating, some of us are gonna be creating data governance organizations and we're gonna have to find those key people, people that you would normally be inclined to call data owners, you're gonna ask them to be part of your data governance council or whatever. And it's usually, these are the high performers, these are the ones that really, you know, get a lot of stuff done. There are also the people that tend to be in a million meetings all the time because they're good at what they do and they're already over committed and you're gonna ask them to do that much more. It can be a recipe for disaster if we put too much reliance on those folks that would love to help but are just never gonna have enough time for what we need because this is time consuming stuff. So be very thoughtful on that and maybe try to get some proxies or get some people that may not be quite as high level or what have you, get them involved, get them working on the details of this because you may get more cycles and end up with a better overall outcome. I agree, I agree. All right, well, we've got a few minutes here. Let me see if I can get another question in. Should data governance and data quality be second line of defense functions alongside risk and compliance or should data governance be a first line of defense function? Anthony, you wanna hit that first? Wow, I think they're all first line of defense functions. I don't know how you can break those apart. Like I think I get the need, like I like the notion of data security especially being very important, regulatory compliance being really important but like governance and quality, they're like we talked about during the webinar, it's they're pervasive, they're everywhere. They need to be like, I like to say woven into the fabric of the organizational business processes. So it's not about this is a separate barrier that they need to pass through. It's more this is the business. And to me, I can't really distinguish one or the other because they're all very important. And I think if we start to chunk them up in that way, we run a very big risk of fragmenting them in a way that they shouldn't be fragmented. And to add to that, the protection of the data, the regulatory and the compliance and the risk management, those are extremely important. And your organization is going to focus on those probably more than they're going to focus on data governance. But the fact is that even if you don't call it data governance, risk management, any form of risk management, IT security, data security, any aspect of any of those disciplines that have to do with risk management and compliance and regulatory control, they are governance. So for an organization to say, we're not doing any governance, oh, but we do risk management and we do compliance and we address regulatory concerns. No, the fact is that your organization is not allowed to say, no, we don't want to do those things. They're being forced to do those things. And those in themselves are actually aspects of governance. And so I don't know if you put governance above those, beside those, underneath those, it really doesn't matter. But as long as people understand that really the only way to get to data protection, the way to get to compliance and regulatory control is through making certain people do the right people, do the right thing at the right time in order to make certain that we are achieving those goals and all the other goals of our organization. Well said, Bob. All right. Well, we have just a minute. I'm gonna try and flip it. This one last question, and if you guys have additional questions, I'll get them to Bob to answer it, to be included in the follow-up email. How can a well-defined purpose or the why's behind the data governance and quality? How can we well-defined the purpose or the why's behind data governance and data quality initiatives? I would first look to the mission and the vision of your organization and look to things that are the most pressing issues within your organization and make certain that governance is not addressing something that's out in the left field. I don't have nothing wrong with left field. I actually love the left field. I played left field in Little League. But the idea is that governance really needs to address those things that are most important to your organization. So look to your mission, look to your vision, look to the things that your organization is investing most of its money in and focus your purpose of governance on those things because that's gonna be what makes the people happy and satisfied with what governance is doing. And I would just add to that and remember, we're talking about data. So measure, quantify, create numbers. We need to lead by example in that. And I think that's a great way of trying to get at prioritization and what's most important is by looking at numbers of here's where are the biggest impacts gonna be and here are the numbers behind it. Agree? See, we agree. Well, again, thank you both so much. Fantastic discussion and a presentation and thanks to all of our attendees for being so engaged in everything we do. Again, just a reminder I was gonna follow up email by end of day Monday to all registrants with links to the slides, links to the recording and all the additional information. Stay safe out there, everybody. Hope y'all have a great day. Thanks guys. Thank you. Thanks, everyone. Thank you. Thanks, Anthony. Thank you, Bob. Bye-bye.