 Hello and welcome. My name is Shannon Kempen. I'm the Chief Digital Manager of Data Diversity. We would 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 joined by guest speaker Anthony Alvin to discuss data management is data governance sponsored today by Precisely. Just a couple of points to get us started. We've been very excited for this webinar and we'll start with our questions. Great. I'll just click on the Q&A panel. It's important for you to join the chat with us and the panelists if you're wanted to. 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 and with each other, we certainly encourage you to do so and to note zoom defaults the chat to send to just the panelists. But you may absolutely switch that to network with everyone. to activate those features and as always we will send a follow-up email within two business days containing links to the slides, to the recording of this session, and additional information requested throughout the webinar. Now let me turn it over to Sue for a brief word from our sponsor precisely Sue, hello and welcome. Hello and thank you Shannon. My name is Susan Pollock and I'm the product marketing manager here at Precisely. Thank you for joining us today at this DataVersity event, we're going to be talking about data integrity in your data management journey. Just wanted to start out by saying with the pandemic and other unpredictable market conditions data leaders such as yourself are accelerating innovation in your data strategies. Key business initiatives in 2022 revolve around transforming customer experiences, applying AI to proven business cases to derive new insights, increase operational efficiencies, encourage the power of location to solve new problems, all while ensuring that the business is secure and compliant. Some of the statistics that we're seeing out there is 83% of CEOs want their organization to be more data driven. Digital transformation investments this year are going to top $6.8 trillion by 2023. Global data infrastructure this year should increase $200 billion and 68% of the Fortune One Hot Thousand companies now have CDOs that's up tremendously over the last decade. Each of these initiatives is heavily dependent upon integrated, clean, accurate, contextualized and enriched data in order to deliver the maximum benefit to the organization. These all support the fact that data is the fuel for decision making today and the conclusion is that data integrity must be a key component of your data management journey. There's been no consistent definition of data integrity that the market uses and the definitions out there often focus on collections of technical capabilities related to just data quality. That is not good enough today though. Today's businesses need a definition of data integrity based on their need for data that is not just accurate and consistent but also rich in context. Here at Precisely, we have defined data integrity as data with maximum accuracy, consistency and context for confident business decision making. Let's break this down a bit. Data integrity is actually a journey. That's one thing that we hear very loudly and clearly when we talk to our customers. Everyone is on a journey to continuously improve the integrity of their data, better understand their business and ultimately better serve their customers. What we've learned from our customers is that there isn't a standard linear journey to data integrity that works for everyone and the days of large corporate initiatives are dead. Customers told us that their business and their IT teams are working more closely together than ever jointly identifying the specific scope that delivers meaningful business impact and as a result, they tackle data integrity through distinct projects that give them business value no matter where those steps fit into this journey and then they plan their next move. And not surprisingly, that means they want solutions that give them the freedom to make those choices. With data integrity as a journey, it's continuous and it requires best in class solutions working together to deliver value to the business. There are many different steps along the path as you can see in the wheel here on the path to data integrity like integrating silo data, measuring its quality, adding location intelligence and enriching it with third party information just to name a few. But one of the hottest topics in data strategy today is data governance, which is what we're going to talk about, but let me just say something. The point of data governance is not data governance. We find that overwhelmingly, the drivers for data governance fall into three general categories, manage risk around ever increasing complex regulations and new environments, make timely decision making for more profitable growth and increasing operational efficiencies to improve performance and reduce costs. Data governance for data governance sake, or even the perception of that makes no sense. The true point of data governance is delivering business outcomes and connecting business goals, objectives and value with measured impacts and risks. The value of data governance is to deliver the right data to make the right decisions at the right time and with the right context. Let's look at a few examples of each of these drivers. In the reporting and compliance area, we're looking at internal reporting requirements, privacy regulations, industry specific regulations like HIPAA, Basel, Sovensy-2, financial requirements, stocks, NAICMR, and actually we're seeing a rise in the ESG reporting and operationalizing of that. In the analytics and insight area, we're needing to get a complete 360 view of a customer, being able to leverage artificial intelligence and machine learning to get more exacting insights, bringing in Internet of Things, IoT information, having more of a global view of your analytics, being able to expand your outlook at things, and being able to do this in real time to make split second, very timely decisions. As far as operational excellence, we're looking at enhanced customer experiences, reducing operational costs, making sure that your systems, migrations and consolidations are moving faster and more effectively, improving your working capital and strategic sourcing. In order to achieve these business goals and outcomes though, what should a successful data governance program include? First and foremost, data governance should be designed with a business value-centric approach. Data governance should link governed data to the KPIs, goals and objectives that impact data users up and down the organization, and that includes not only strategic leaders but operational teams and technical teams. Business goal tracking provides real-time views into how governed data supports business processes, compliance events, reports and metrics. Precisely describes this as a top-down, bottom-up, middle-out approach based on proven expertise with hundreds of companies across all industries. Successful data governance must measure the value of data governance to document the continuing return on your investment, but also to continue to inform and validate to all of these data users that the work effort that they're putting in to their data governance program is paying off to them, for them personally, with real business value around their own objectives and goals. We all know that data is exploding and growing at exponential rate, yet data that directly impacts the business goals of a company is estimated to be as little as five percent of the data that existed in an organization. We've heard more than one unfortunate story where a company felt that cataloging all of their data was the first and foremost important step, but for what purpose, and is that really sustainable? The second key component around successful data governance programs is focusing on what matters, strategically determining what data sets will directly impact your defined business goals from the top-down, bottom-up or middle-out, and that will set you up for a sustainable and manageable data governance initiative. In this chart, you'll see that from the volumes of data that exists, it's usually a small sliver that is truly critical data. Still, that critical data can come from everywhere in the organization, from tables and fields that exist at the system level to master data, the foundational data that's required to run your business and conduct daily operations, through the data that feeds KPIs, performance measures and analytics, and the data that delivers actionable insights around defined business objectives and business drivers. We all use information to gain intelligence and insight, but it's only useful if the data is accurate. The third premise for successful data governance programs is closely integrated data governance and data quality processes. Data quality processes and tools are needed to not only clean the raw data, but to illustrate errors, anomalies and issues in order to help compile the best standards, metrics, and monitor data quality over time. Data quality needs appropriate data governance processes and practices to ensure the data is cleaned and maintained within an appropriate data framework that's relevant and pertinent to the business needs to the users and the company at large. The symbiotic relationship of data governance integrated with data quality is imperative in successful data governance programs to build trust with visibility into metrics and rules, to measure value accurately around tangible business outcomes, and to create and inspire confidence and accountability by operationalizing data governance and data quality contributions from the data community at large. As further proof of the importance of these relationships, research from Drexel University's LeBole College of Business shines a light on how data governance programs have a direct impact on data quality. With 75% of data governance respondents in this report, they acknowledge that data quality is their organization's number one top data concern. And 2 thirds of those respondents, 66%, have reported that data quality is the leading benefit of their data governance program. This report delivers a compelling message for businesses who are looking to understand and trust their data. I wanted to come back to my original challenging statement. Data governance is not the goal. The goal is delivering better business outcomes, and the keys to delivering better business outcomes include the ability to connect business value to data governance, focus on what matters, and remember that data governance and data quality are intimately intertwined on your journey to data integrity. As we mentioned in the beginning, data integrity is a journey. Today we talked about how data governance and data quality is critical to your data management strategy, but the precisely data integrity suite is modular and delivers value at every step of the data integrity journey. From integrating all of your critical data to connect today's infrastructure with tomorrow's technology to verifying that data governance and data quality are built into your data-centric processes to ensure accuracy and consistency. By leveraging location intelligence for enhanced and actionable insights and enriching your data for decision-making with expertly curated up-to-date data, business location, and consumer data. Precisely is proud to be the leader in data integrity where our software, data enrichment products, and strategic services deliver accuracy, consistency, and context in your data powering confident business decisions. You would welcome the opportunity to help you wherever you are on your data integrity journey. Thank you so much for listening today and enjoy the webinar. Back to you, Shannon. Thank you Sue so much, and thanks to Precisely for sponsoring today's webinar and helping to make these webinars happen. And if you have any questions for Sue or Precisely, feel free to submit them in the Q&A panel, and she will be likewise be joining us for the Q&A portion at the end of the webinar today. 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 specializes in non-invasive data governance, data stewardship, and metadata management solutions. And with that, I will give the floor to Bob to start his discussion with Anthony. Hello and welcome. I think I should remember to take myself off of mute because I was ready to talk with myself being muted. Hi, everybody. Thank you, Shannon, for the introduction. Thank you, Sue, for the sponsorship and for the great presentation. Appreciate having you all with us today. I wanted to start out by saying a happy St. Patrick's Day to everybody who celebrates. And today's topic is particularly interesting, especially I know with a lot of the organizations that I'm working with right now, there is some level of confusion between what's data management, what's data governance. Sue talked about data governance and data quality being integral to data management. I think there would probably be some people that said data management and data governance are integral to data quality. I think it's not that there's a lot of confusion, but there are a lot of organizations that are trying to differentiate or trying to determine whether or not data management is the same thing as data governance or if really they are entirely different things. So happy to have everybody with us today. Before I get started, I also wanted to welcome Anthony Ogman as my special guest, Anthony and I have done several real world data governance webinars in the series. We've talked about data architecture being data governance and data modeling being data governance. I think it's really relevant today for us to talk about how data management is data governance. So I'm going to introduce myself real quickly and then I'm going to turn it over to Anthony to introduce himself real quickly. And then we're going to jump into the topic of data management. Is data governance or isn't it? I guess we'll try to figure that out throughout the webinar today. So just to let you know, a couple of things that I'm involved in in the data management and the data governance industry. It's this webinar series. Next month, I'll be talking about the role of data governance in a data strategy and there are several places where you can go to register for that webinar and all of the webinars in the series. I wrote a book several years ago called Non-Invasive Data Governance, The Path of Least Resistance and Greatest Success. It is available at your favorite booksellers. I'll be speaking at a couple of diversity events that are coming up. The Enterprise Data World, the EDW event has gone virtual and I will be speaking at that event next month. And I'll also be speaking in person in San Diego at the DGIQ West Conference, the Data Governance and Information Quality Conference. I have a couple of learning plans, lesson plans that are available through the Data Diversity Training Center, one on Non-Invasive Data Governance, one on Non-Invasive Metadata Governance. And then the most recent one has to do with business glossaries, data dictionaries, and data catalogs. I am the publisher of the online publication, the Data Administration Newsletter, a brand new issue was just published yesterday. So please go check it out. My consulting business is KIK Consulting and Educational Services, where KIK stands for Knowledge is King and the focus is on knowledge transfer and in my spare time, I have also an adjunct faculty member at Carnegie Mellon University right here in Pittsburgh, Pennsylvania, in their Chief Data Officer and Data-Driven Leadership programs. And with that, I will turn it over to Anthony to introduce himself. And I must say, you look great in green, Anthony. It's so fortunate that I sent you that picture with the hat already on it, not even realizing that this was going to be on St. Patrick's Day. So I'm very excited that that picture worked out for it. Bob, thanks for having me on. Yeah, a little background on me. I've been a Chief Data Officer. I've done technology development. I've done consulting. I currently work in pharma, but I've also done a lot of thought leadership work, especially with the training center. Like Bob mentioned, I have a data architecture course and a learning plan and a data leadership learning plan, as well as I did the what is series on data management through the training center at Data-Diversity. I'm also the author of Data-Diversity's first published book on data leadership. Stop talking about data and start making an impact. Where I'm particularly excited to be spending some of my time these days is on the podcast, which Bob's been a frequent guest. So we go way back and you'll probably notice that during the context of today's conversation. But if you enjoy the conversation today that Bob and I have, certainly check out the podcast, dataleadershiplessons.com. We do live video on the YouTube channel, which has been growing nicely. And then the podcast is available anywhere you get your podcasts. So search for data leadership lessons on those podcast apps. And yeah, we hope to see you over there if you enjoy what we have today. And if you don't, give us another try and go subscribe anyway. Well, Anthony, it's always great to have you. We always have a lot of fun when we do these webinars, especially when we talk about subjects that might be near and dear to a lot of people and the first, the topic that we're going to talk about today. First, we're going to talk about the similarities between data management and data governance. We'll talk about some of the differences between the two and how you can differentiate between them, how you can use one to sell the other and the other way around. Basically how you can use data management to sell data governance within your organization and how you can use data governance to sell data management within your organization. And then ultimately we're going to decide if the two disciplines are, in fact, the same thing or if, in fact, they are something different. So let's start by asking you the question, Anthony, what's the difference? I mean, how do you define data management and how do you define data governance and how can we really tell whether they're the same or whether they're different? Right. So if we're starting with the similarities, right? I think you have and I'm going to put you back on the spot real quick, Bob, because your definition of data governance is one that I really like myself. So can you start with offering? What is that definition of data governance that you have? Well, I define it pretty strongly. I define data governance as being the execution and enforcement of authority over the management of data and data related assets. Right. And what I like about that is that it does have some strength to it. And it and it connotes both a kind of decision making framework or a values based framework as well as an action component. OK, Anthony, can I just interrupt you for one quick second? If you would take a couple of minutes to to answer this question, I literally need three minutes away from my computer. So just take a couple of minutes and start with this. And then I'll come back and rebut it. I was made for this. I was made for the opportunity to talk without end for three to four hours, three minutes to four hours. So, yes, so data governance, to me, is really about having that decision, having that policy, having that alignment with organizational strategy, whereas data management, to me, is about implementing those ideas, implementing those decisions, implementing those values through the course of the activities you take on with data organizationally, because with data management, it's there's a component to it around technology and around process that I think starts to pick up where data governance is kind of decision framework and enforcement of authority starts to trail off. I think with data governance, there's only so much you can do until you are fully implement data management. What I mean by that is if you think about how is data really existent in an organization, right? It comes from external sources through subscriptions or through connections, wherever you get that external data. And then a lot of an organization's most valuable data are byproducts of its operations. So how do we look at customer records and the transactions and the orders and the services and all of the things that we do as a business that spins off data? And I think about when we work with data and we do anything with data, frankly, we have the kind of core outputs, which are the intentional artifacts of the work that we're doing with data. So for creating reports or we're driving sale strategies or we're creating manufacturing processors or whatever, whatever those may be, we have a targeted mind of what we're trying to do. That creates more data. It creates more things that spin off of those activities, which then need to be governed and managed as well and have the potential of creating value. So where I see the data management coming into it is getting into more of the details of the artifacts themselves, whereas governance is dealing with the patterns and the tie into the values of the organization, the organizational strategy around the decisions and activities that are being driven by or are creating more data assets that the enterprise cares about. So are you saying then, at least the way it sounded was that you were saying that data governance is a part of data management or is it the other way around? I don't know either is a part of either. I think there's a Venn diagram where they're they're closely related. There's some overlap, but they're still distinctly different. And I think it's really about saying data is the. Noun in question or the the the entity in question, data governance and data management are you can almost think of them as like spotlights that are exerting some sort of force on data and are trying to manage what has happening with data. But they're coming at it coming at it from slightly different directions. I think data management is thinking through an implementation, a technology, a artifact lens, whereas data governance is thinking through a strategic and acceptable use and compliance lens. They both contribute to ultimately doing the thing that matters with data. And Sue mentioned this in her talk around precisely, it's creating data value. It's creating organizational outcomes. That's what matters. Everything else is us slicing up terms, trying to figure out which is which and whether the same thing are different at the end of the day. All I really care about is can I use these data assets to drive something meaningful in the organization? We can have a lot of fun slicing these things up and having semantic arguments, but I don't care about those nearly as much as how would we create real business value in the end? And I understand that, but I know that many organizations today have had data management groups and now they have data governance groups as well. And there's a clear question or should I say an unclear question in a lot of these organizations as to where do the boundaries of one begin in the boundaries of the other and where do they overlap? So data governance to me is very people specific. It's very behavioral specific. When I say the execution and enforcement of authority, it comes down to getting the people to do and it's going to I'm going to steal from what Sue talked about earlier. I oftentimes refer to it as bill as the bill of rights with the word rights within quotes of getting the right people involved at the right time with the right understanding of the right data to to come to the right decision and then data management basically is so the data governance is the people aspect of data management. Data management is that practice of collecting the data, organizing the data, protecting the data, storing the data. So I actually see kind of I kind of align with what Dama says. And they put data governance right smack in the middle of their demo wheel saying that it's going to be an important component. It's going to be the people component of all of the different knowledge areas that are associated with data management. So I see them as really that, yes, they're both driving towards the same end. But they're different aspects of how we're getting there. So I'm glad you brought up what how Dama defines it. And if you picture the Dama wheel and the data management body of knowledge behind it, right, like they call it a data management body of knowledge for a reason. And in that you could argue that data governance is a component of data management. I think that you can imply that from from the Dama wheel and the DMBAC itself. But what I'm not sure about is whether or not those are really important distinctions versus are they just clever ways of plugging in the words in a way that seem to make sense? Like, do I take the fact like if we if we say, OK, well, the DMBAC is the golden, you know, authority of this. What does that actually translate to into the behaviors and activities I'm going to manage in my organization? Because this is really an organizational change and organizational structure conversation, because what where I'm going with this is that you don't do data management. You don't do data governance without being something else. Data management and data governance are both collections of sub components. Because like what is what is going and doing data management unless you define it with other actions? Same thing with data governance. So you can group those how you want to. And I think most organizations should consider the marketability of those groups, the organizational strengths, the political landscape in their organization, those are going to be more important considerations than whether or not what we call our groups aligns with what certain standard and generalized bodies of knowledge would have us be. And I would always advocate for that. It's like throw out the rule book if your organization will get it done better when it comes to business value in the end. And that's that like let's let's argue like from an academic standpoint today, love doing it, but take this under advisement and then go break all the rules to do what will get the most impact made in the end. That's that's what ultimately my argument is. I don't disagree with you about anything that you said, but I do know that within organizations that have enterprise data groups and then also have data governance groups. This is a constant struggle. So of again, defining the boundaries and where I always solve the problem by talking about there being a partnership. There really if you're going to have two separate groups, exactly what you were just saying, Anthony, is that you're both pushing in the same direction, you're pushing towards the same business outcomes that you want from your data. Just do everything that you can to to clearly define who's doing what. And, you know, there are similarities, but at the same time that there are similarities between data management and data governance, and you can almost in a lot of organizations kind of use those interchangeably. There's a lot of differences, too. So maybe spend a little bit of time talking about how these are really two different things. Yeah, well, I think a lot of times, too. You're going to see data governance will grow out of some pain points that are related to either compliance and legal or other kind of more business centric aspects of your organization, especially large organizations, whereas data management functions will often grow out of your technology organization or some of the related project management and coordination activities and security and access controls and things like that where that kind of butts up against what you're seeing from like governance and compliance on the business side. So it really can depend on where those are growing out of. But I think that it comes back to realizing that this pain gets felt in different ways and different parts of an organization. And because of that, the efforts that need to be taken take on slightly different forms, even though there should be a high degree of coordination there. When I when I build, you know, I spend a fair amount of my time building up massive data and knowledge platforms. And one of the key criteria around integrating all these moving parts in an enterprise is saying, OK, how can we allow the independence of movement and the speed of progress while staying well aligned in our different lanes so that we're all kind of pushed in the same direction? So we create these highly aligned, loosely coupled architectures that facilitate coordination without adding a lot of additional friction, which is where I think if we think about data governance and data management, data governance really is going to kind of start from that top down perspective, at least theoretically, where we're going to have to have policy, we're going to have to understand, hey, where is our organization going to drive revenue increases or cost decreases or risk management, which is often the driver of a lot of early stage data governance efforts, insurance and banking, especially. You see compliance and regulatory issues driving that risk management function that data governance needs to perform and that comes through policy. And then you kind of implement from top down. Data management, on the other hand, I would argue, oftentimes comes from saying, we got all this data, we're not sure what we can do with it. And that's where a lot of times you don't have what you said is the key piece of data governance, the folks that are doing data management, getting the data on that granular level, this kind of bottom's upside. They don't have the authority to enforce anything. They're not even sure what to do with it. And if you can connect those two kind of polar ends, now you can create those high aligned kind of connection points and allow the functional needs of technology groups, business groups, the people that care about solving this for the different problems that they are contending with, you can do that in high alignment without causing this big slowdown of everything, which is a big danger. Every time we start talking data governance is because we try to do too much and we lose that focus on driving value and driving these kind of tangible things. I also think it's because we don't do a terribly good job of measuring data management and data governance in our organizations because we're talking about really we're talking about metadata or we're using metadata to talk about data. And we don't do a terribly good job of measuring metadata and quantifying progress across how these things evolve. And then we look back on it and we're like, well, we did a bunch of stuff. Hopefully that added value. But if we say, hey, the value to the organization is the thing that matters most. We've got to think about how do we measure these as well? You know, I am so glad that you brought up metadata because I was that's exactly where I was going. You know, you know, great minds think alike, right? But there's the whole idea of the organization needing information about their data, the data documentation, the metadata, whatever you want to call it. I've seen struggles between like factions within an organization that say, OK, the data management team is responsible for metadata management. But then oftentimes you'll see data governance teams having the responsibility of activating their programs through the use of metadata and the data catalog. So even just using something as simple as metadata to talk about where, you know, how is data management different from data governance? Where does metadata fit in? And where does, you know, I have one of the questions on the screen here is where does the discipline of data governance reside in the organization? I mean, in data management oftentimes does not fall under a business unit. But you know, I hear organizations talking about data governance. It's a necessity for it to fall under a business unit. And then that that immediately, you know, you can point to something like metadata saying, well, then who's going to be responsible for the metadata? How does that fit into the question of how are they the same and how are they different? I'm going to see if I can dodge this question creatively. So what it's it's I say it jokingly. But I'm kind of honest about that because I think about this in that data is everywhere in our organizations, right? Data is everywhere and we're talking specifically today about data management and data governance, but it's awfully hard to not talk about metadata like we just started doing or things like data stewardship, which I think really can start to blur the lines between data management and data governance as well. Because if we go back to where, what do they really mean? And we're thinking about data management is like, OK, where am I storing my data and where are those repositories? And how are we doing, you know, ETL jobs and data transformations and pipelines and all that stuff that happens to do data things and governance is more focused on the rules and the compliance and that stuff and, you know, values and things. You know, all of those start to blur. There's an overlap in those Venn diagrams. But then you also think about, well, what about stewardship? And what about what happened? Now, we can have the discipline leadership or the the center of excellence for these to exist, probably I think the right answer we're supposed to say is that it should exist somewhere on the business side, because that's where it's most relevant. But then you can look at the technology science. Hey, business side, you can't get a whole lot done with data without some technology excellence as well. And so if your technology organization is not very effective, your data organization is going to have some trouble, right? And so, like, I don't really care that much about where it exists. I think that's more of an organization specific challenge in terms of where it can be most effective to drive data of value. I think that arguments can be made that the data leadership should exist more closely aligned with business leadership for some obvious, you know, strategic and values and compliance reasons, but that can make the implementation of data technologies and some of the data management day to day kind of bombs up stuff more difficult to achieve, especially if those folks aren't well represented on the technology side of the organization as well or the technology side isn't well represented in the data governance side. And vice versa, if you if you grow up, I've seen plenty of data governance organizations grow up through a technology organization and they're really kind of data management organizations that are calling themselves data governance, and that's fine. But they struggle on getting the alignment to the business. They may go and implement a tool who out there has had a tool implemented in your enterprise that they then announce with great fanfare. They're like, you get to use this now and you're like, I didn't ask for this. And so you can lose the relevancy to the business, even though you're doing a bunch of things to enable data management ostensibly, the business doesn't care. And so, like, there's a mist on that side, too. So it really does come back to good leadership and understanding change management, understanding organizational strategy and understanding how ultimately all of these activities are going to contribute to creating something of real value to the business. It always comes back to real value to the business. I should have timed that dodge to the answer the question. That was that was the longest dodging an answer to a question. I answered the question at the end. Did I? I think you did. You know, what I'm trying to do here is I'm trying to really kind of clearly draw a differentiation between data management and data governance. And it almost sounds to me as though you're saying that nobody really cares. Nobody really cares that we, you know, maybe it's just the data people that are trying to differentiate between data management and data governance and the data and the business people and even the IT people that don't care as much that there's a differentiation. So am I hearing you correctly? Because I try to say, you know, it needs to be it's really a distinct thing. And you're saying, well, you know, who really cares if we make that decision, if it's distinct or not? Absolutely. I would call this data hopscotch jubilee if I thought it would get our organization working with data better. You know, like I don't care what we call it. What I care about is do we put the right kinds of processes and technologies and people and strategic tie-ins and organizational strategy in place to capitalize on our assets and data is a good source of assets, which are so powerful because they're not finite, they don't reduce when you use them. They get more powerful when you use data. That's where there's so much potential in that. So call it whatever you want, but look at what you have sitting there waiting to drive better business and then use it. And if you can focus on that, figure out then, hey, what's going to resonate with people? I've seen terms that go great in one organization be absolutely absolutely poison pillow terms at another. Don't fight it, work with it and find a way to just start driving activities that matter and then support those and find how you can leverage those data assets that you are lucky enough to have to drive a business that you're lucky enough to have and then figure out your semantics on the back end. And that's to me the most important thing, but these can both be used very effectively if you're careful. So I don't want to say I don't care at all because there is good like theory around this. There is good or is good. There is good thought behind what data management is, what data governance is or what have you, what we're trying to draw forward on this webinar is that, hey, understand those, learn what really matters in terms of the functions behind those like I talked about their collections of other things, but then translate it into the language that will resonate with the people around you because if it does, then you're going to accomplish the goals you have in mind and I will absolutely throw away any terminology that's not constructive towards those end goals. And I think we have a bunch of just really questionable terms in terms of what we use in the data management and data governance spaces to communicate things that don't necessarily resonate with people outside of that data area. Those the data people out there. So you just introduced data hopscotch jubilee. I wrote that one down. So if I refer to that, everybody knows where we got that from first. But, you know, and I agreed to using the terms that are going to resonate within your organization when I talk about noninvasive data governance, I only suggest that people actually use that terminology when it's going to have some impact when it's going to get people to ask questions about what the heck do you mean by being noninvasive and doing something such as governance? So can we can we leverage the the differences in the similarities of one to sell the other? I mean, do we need to frame it in such a way that one of these is really a part of one or the other? How can we how can we, you know, enable successful governance through data management or the other way around? That's that's a multifaceted question, but it got me thinking about like my current day job. When we say data governance, we really could know. Generally speaking, we really can know to a compliance mindset or a legal mindset or policy mindset, which is a relatively limited view of what data governance is in data circles in the broader data industry. So what I would do in that case is that I would say, OK, well, I'm not going to try to convince a bunch of people to use the term data governance differently. What I would do is introduce a term like data management or try to find a term that didn't already have a loaded meeting so that I could fill in some of the gaps of saying, hey, we got to do some more data quality or really master data management is the thing that we need to do. Could I create something like a responsible data citizen program or whatever? Like you can figure out a way to to market that. And I think if you think of the words themselves as vehicles for communication first and vehicles for being, you know, semantically correct second, then you can start to be more creative with some of that language while recognizing and creating some framework of translation because what won't work is if people are trying to learn something like data citizenry or something like that isn't a common term out in the broader data circles, we got to give them a chance to understand, hey, how do I go outside of the walls of this business and learn more because we need to expand that understanding to a much wider audience than what we often have in our organizations. And so we need to give them that kind of decoder ring to understand, OK, how do I take the way we talk about the stuff here in every organization is a little weird, right? Like they all use some weird words in weird ways. And that's true kind of in any organization. But how do we translate that if I want to go more broadly in my knowledge and reach out and do a CDMP certification or do something like that, where we now need to know the quote unquote right terms for these things. And I think that's really where you have to be precise in your semantics is understanding, hey, here's how we talk about it here. Here's how they talk about it everywhere else. Make sure you use those words if you're going elsewhere, but we're doing this so it's easier for people inside our organizations to know what we're talking about. And, you know, you and I have had conversations about buzzwords and maybe it's not even, you know, adopting buzzwords. It's adopting the words that are going to resonate the best with people within your organization. I mean, I, you know, when people talk about things like data literacy and they I've seen some organizations partner data literacy up with data governance and I've seen other organizations partner data literacy up with data management and, you know, I think that they need to be presented cohesively. So letting people know how they are the same, how they are different, but how they work together to get you to the outcome that you're expecting. And, you know, organizations, I've found that organizations that are very successful in data management and have had a good data management history are more able to implement effective data governance programs just because they've got people thinking down that path. And so chances are they've already started to use terminology that resonates. You know, we could hold another whole webinar on, you know, making it interesting to the organization using the words that are going to resonate well with the organization, gamifying data governance and making it such that, you know, again, people are are willing to invest some time and thought process into data governance. We're down to the last few minutes before we get to the point where we're going to take some questions. What are we deciding here? What's the answer to the question? Are these two disciplines the same? Are they different? Does it matter? How should these be presented to organizations? Yeah, I mean, I think they're siblings, you know, and I think that they're siblings with their own families, right, that they each have collections of things underneath them. And I really do like just kind of to what you were just talking about. Want to emphasize like, even though I'm trying to make some jokes here, like the words are important. Don't go and just make up a bunch of stuff to make up a bunch of stuff. Like if there's if there's a missing link, try to figure, you know, think deeply about what you might do creatively, but like use the right terms to start with and just overlay a recognition that we're trying to do a marketing effort and a change management effort as much as, you know, trying to be accurate and good stewards of the data. So, you know, think through all of that, but don't just go and make up a bunch of weird terms like, don't don't walk in and say, oh, we heard, you know, Data Hopscotch Jubilee is the next thing to do. Don't do that. That's ridiculous. Be thoughtful about that. But I do think like we're casting slightly different vantage points on basically the same topic with data management and data governance and, you know, think through what do you need? What are the areas that you're specifically deficient if, you know, you're really good at creating policy, but not so good at creating implementation, create more emphasis on data management so that you can raise some of the awareness of some of the implementation side. Vice versa, if you're good at building systems, but you're bad at making decisions and creating policies, start with some data governance efforts that are a little bit more focused. And that really should be, I think that the answer to it is that these are tools just like the disciplines themselves, the words are tools to use to complete your overall ecosystem of data capabilities. And I think that's really the thing to kind of take away. In my opinion, I'm curious, Bob, what your thoughts are. Well, again, I, as we started out the session, the webinar today, and I was talking about data governance being really being the people related aspect of data management, if you want to put it kind of just put it quickly. And in fact, data stewardship is another term that really has to do with formalizing accountability for the management of data. And that also that also relates very closely to the the people aspect, the behavior, the policy, the guidelines, even the standards and things like that. So in my mind, it's pretty clear. There's a pretty clear separation between what data governance is and what data management is. But I guess what you've brought to the table here and made food for thought is, you know, use the terms and present this away within your organization that's going to resonate with people within your organization. It's not as least what I'm hearing is it's not as important to differentiate. It's more to kind of bring them together and show how they're similar and show how they can work together to achieve the same level of business outcomes. That's what that's what I think. But to me, you know, somebody who's doing putting a data management program in place, it's a little bit more wider scope than or it's a it's significantly more widely scoped than a data governance initiative, which really focuses on people and formalizing accountability. Yeah, I think that makes a lot of sense. That's the way that I view it. So what did we talk about today? We talked about the similarities and the between data management, data governance, what are some of the differences are between the two? You know, should we sell them separately? Should we use one to sell the other or does it really matter how these things are viewed as being related within the organization? And I guess, you know, Anthony, I'm not surprised. We haven't come to a single answer as to whether the disciplines are the same or different. You know, I worded the name of this webinar this way. Data management is data governance because I wanted people to attend who said, heck, no, they're not the same thing. They're very different. And then there's other people thinking that, you know what? They're very closely related. So I hope that this conversation really just got you thinking that I'll get all the people who are attending thinking about how do these two disciplines or one discipline relate to each other? And with that, I'm going to turn it back over to Shannon to see if we have any questions for today. I love it. Thank you so much. So much great chat going on in the chat portion amongst the attendees and just to answer the most commonly asked questions. Just a reminder, I will send a follow up email for this webinar by end of day Monday with links to the slides, the recording and anything else requested throughout and diving in here and to like to invite you back in. This question came in particularly during your presentation. What is your definition of data integrity? Are you using it in terms of the conformed dimensions of data quality? Actually, in light of the conversation that we've had about mindfully choosing the words that you're using about data governance, data management, or at the new trending title, Data Hopscotch Jubilee, integrity is also something that we wanted to be mindful about. Yes, you're absolutely correct. That as far as the conformed dimensions of data quality, that talks about data integrity, but the way that we looked about data integrity, very mindfully, is that just data quality is very limited. Data integrity really is a much broader concept about accessing all of the data that you need, make sure that it's transformed and you're able to leverage it consistently, that you can make sure that it's trusted, it's accurate, that it's that it's enabled with the right data sets so that you can make those data decisions because that's really why we're doing data governance, right, is to make sure that we're realizing our business outcomes. So our definition is that integrity is data that is accurate, consistent and has context. And again, very mindful because as as we've seen in the discussion today, choosing the words that make sense to your culture is very, very important as far as relating what you're trying to get across. Hopefully that helps. And what I would add one thing to that as well, because a lot of people think that data integrity and access and accuracy and those types of things are different dimensions of data quality. So data integrity is really in my mind is also getting people to have confidence in the data that they're using and whether that is through improvements of their understanding of the data of the documentation of the data, the context that you just talked about. You know, again, use the word data integrity if that if you feel like that's going to resonate successfully within your organization, use the word data quality if that's the word that is going to resonate with people. So I think that was a great example of how words matter. Yep. I love it. So which role would respectively play data management and data governance in a data quality assessment framework? Can you ask that again? Yeah. So which role would respectively play data management and data governance in a data quality assessment framework? I'm not sure I understand what will understand the question. But the if you're doing a data quality assessment, it's oftentimes it's going I believe it's going to tie back to data management and to data governance. So I'm not really certain. I don't know, Anthony, you have a different take on what that person is trying to ask. No, I'm a little confused by the the question, though. I would I would emphasize that, you know, as driving change, the value that we're trying to create that we talk about, it's all about creating a differential, right? So you have what do you have as your baseline? What do you do? What's to do nothing? Like if we did nothing, this would be the result. What we want to do is maximize the beneficial differential. So you think about what all of data quality is keeping us from the business outcomes that we're trying to drive is something that we are using as a component piece insufficient to drive the activities or decisions we're trying to to drive with data. And so depending on what that assessment is, your your resulting action could be that you need to take a clearer policy stance on certain data so that it can be created more consistently or that the storage mechanisms or the data lifecycle are going to be managed differently. But but that's where it would track back to a quality deficiency that you could then say, well, if we can't get this data to be at a level where we can then do these activities and decisions that we're going to be operating suboptimally in driving that value differential. So is it realistic to associate data management and data governance schools to business schools having the right information on the right time and warrant business success? I would think it's very important to tie governance and to tie data management to the business goals. In fact, I see when organizations publish mission statements and vision statements for the organization, a lot of them tied directly back to data. And so I suggest to look and see, you know, what is being stated on your organization, on your organization's website as to what some of the goals and the mission of the organization is and certainly certain to tie everything that you do with data management and data governance to the data aspect of whatever those items are within the vision statement for the organization. That's that's what I've seen as being successful. Yeah, you need to connect it to the things that are most purposeful within your organization, and it is certainly one way to get your senior leadership more in line with why are we doing the data management? Why are we doing the data governance? Because we're help we want to assist you to achieve these goals that we have for our organization. I don't know, Anthony, have you seen anything different from that? Or how do you tie it to that? I would agree with that. What I would add is I often call data the closest thing to truth we have in our organization, and it disturbs me how often business strategy and business goals or whatever business thing that they're trying to create aren't rooted in that reality of data, like they don't realize what's actually possible. And so business strategy that's not tied to data reality is fantasy. And the other direction is if you're operating data without a connection to those business goals, then you're similarly lying. Like you're basically just disconnected. And if those two items of your business strategy and your data governance and data management practices are not linked just at the hip in both directions, then you are missing out on tremendous opportunity, most likely, because there's no way your data is going to contribute to the organization as much as it should, if you don't have that. I agree. Actually, I concur with both of those, because that really is one of our top three, right? That's one of the top three things about a successful data governance or data management program. If you are not connecting it to something that makes sense, data governance for data governance sake doesn't mean anything. Right. It has to have a business value to it. And I think what maybe some people are missing with that question is that there's all different types of measurements that you can have around your your data management and data governance. It's not just a quality measure. There's many, many different things that you can see will be tying to reaching your business goals with better data governance and data governance measurements. I love it. Well, that is perfect timing that does bring us to the top of the hour. Thank you all for these great presentations. And thanks to our attendees for being so engaged in everything we do. I love that again, the chat that's been going on today. And just a reminder, I will send a follow up email by end of day Monday with links to the slides, links to the recording and the additional information requested throughout and thanks to precisely for sponsoring today's webinar and helping make these webinars happen. So it's been a pleasure having you on. And Anthony, always great to have you on and joining us with another webinar. Thanks, everybody. Thanks all. Thanks, everybody. Thanks, everyone.