 Hello and welcome, my name is Shannon Kent and I'm the Chief Digital Manager of Data Diversity. Hope everyone is staying well and safe out there and we'd like to thank you for joining the current installment of the Monthly Data Diversity Webinar Series, Real-World Data Governance with Bob Sinner. Today, Bob will be discussing master data governance, or excuse me, master data governance in action. Just a couple of points to get us started due to the large number of people that attend these sessions. You will be muted during the webinar. If you'd like to chat with us or with each other, we certainly encourage you to do so. Click that icon in the bottom right hand corner of your screen for that future. For questions, we will be collecting them by the Q&A section or if you'd like to tweet, we encourage you to share highlights or questions by Twitter using hashtag RWDG. 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 Sinner. 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 Deema Professional Award for a significant and demonstrable contribution to the data management industry. Bob specializes in non-invasive data governance, data stewardship, and metadata management solutions. And with that, I will give the floor to Bob to get today's webinar started. Hello and welcome. Hi, Shannon. Hi, everybody. Thank you very much for taking time out of your busy schedules to sit in with us today on the webinar. I just wanted to echo what Shannon said. I hope you're staying safe. I hope you're staying healthy. And for those of you that celebrate, I hope you have a fantastic Thanksgiving holiday coming up here in the next week. So good to have you with us. Great subject for today. We're going to talk about master data governance or master data and data governance and the relationship between the two and how to make it more actionable within your organization. So before I get started, I just wanted to share a couple pieces of information with you, as you most definitely know. This real-world data governance series has been going on for a while. We're on the third Thursday of every month, right at this time. And next month, we've got a great subject, a great year-end subject where we're going to talk about the future of data governance as it relates to the Internet of Things, artificial intelligence, information governance, and the cloud. So please join us and put this on your calendar for the September webinar. Also, as Shannon mentioned, I wrote a book years ago called Non-Invasive Data Governance. So if you're more interested or interested in learning about non-invasive data governance, please look up the book and certainly let me know if you have comments in regards to staying non-invasive with your data governance program. I'll be speaking at a couple of events coming up, DataVersity virtual events for this year. The DGIQ conference will be taking place next month. I'll be giving a couple of presentations at that. So if you're not registered for those, there's opportunity for some more information regarding data governance and information quality. And I'll also be speaking at the Enterprise Data Governance online event in January. So please go to dataversity.net to learn more about those events. Also, I have provided a couple of, or actually now three, different online learning plans that are available through the DataVersity Training Center. One specifically on non-invasive data governance, one of my favorite subjects. One on non-invasive metadata governance, because we all know that the metadata is not going to govern itself. And then the newest one is on business glossaries, data dictionaries, and data catalogs. So if you're looking for online learning, please go out to dataversity and check out those courses that are available. As Shannon mentioned, I'm also the publisher of the Data Administration newsletter at tdan.com. Please go and visit it. There's lots of great information from a lot of different people around the world actually. So there's lots of information on tdan.com. Please go and visit it if you're not familiar with it. And last but not least, KIK Consulting and Educational Services can be found at kikconsulting.com. KIK, by the way, stands for Knowledge as King. And that is the home of non-invasive data governance. So please go check that out. The site is brand new, or it has been updated recently. So there's a lot of new information that's available on the site. So today, the subject, as we mentioned, is called Master Data Governance in Action. And there are several topics that I want to hit on during the next hour. The first one that I'm going to talk about is, well, what really makes it Master Data Governance? Or is there really a difference between Master Data Governance and just what we call Data Governance? And do we need to include the word governed when we're talking about Master Data? Because that is one of the most important attributes of Master Data, at least in most organizations as you're creating your Master Data Management solutions. There is a lot of governance that takes place around the Master Data. So we want to talk about aligning that with how we do data governance for all other types of data within the organization. And the way we're going to talk about that today is looking specifically at the roles and responsibilities that are associated with MDM and comparing those to the roles and responsibilities that are part of implementing a data governance program. We'll talk about the qualities of governed data. We'll talk about governing to a master version of the truth. So we want people to understand that this is the version that we want you to use. So we're going to talk about governing to that version of the truth. And then we'll talk about incrementally implementing master data and doing that by domain, by domain, or subject area, by subject area within your organization. Because we know that we typically can't do it all at once. We need to do it incrementally and learn from what we're doing as we're implementing our programs. So first thing we're going to do is I'm going to define data governance for you. And I do that a lot in the webinars, but I haven't done it in the last few. I want to provide my version of my definition for data governance. And then I want to compare that to the definition of master data. We'll talk about different types of data governance that might exist within your organization. And if there really is even such a thing as master data governance and whether that should matter or not matter to your organization. So let's start with the definition of data governance. And there are lots of definitions of data governance that are out there. And I wanted to share with you the one that I use and I've been using for many years. And to be honest with you, it makes some organizations cringe a little bit when they see that when I use the words execution and enforcement of authority, it sounds like it's worded very strongly. And it needs to be worded very strongly in order to get people to sit forward and pay attention to what we're doing with data governance. There's several different approaches to data governance. There's the command and control approach. There's the traditional field of dreams approach where we define our program. And we hope that people will gravitate toward it. So if you build it, they will come. That's why I call it the field of dream approach. And then there's the non-invasive approach. And that is recognizing the existing levels of accountability within your organization. So it really doesn't matter which of these approaches that you take. Of course, I wish that you would take the non-invasive approach. It seems to make sense. But the truth is no matter what approach you take, at the end of the day, we need to execute and enforce authority over to the management of the data and over the definition and the production and usage of the data and the data-related assets like the stewards and the metadata and those types of things. So I prefer to word my definition of data governance very strongly. And again, people may ask questions about it. People might push back. We can always temper the definitions. But the fact is we need to be able to execute and enforce authority over the management of the data. And that's specifically true when it comes to the master data within your organization. We need to make certain that this data is accurate, that it's timely, that it's going to give value or return value to the people that are going to make use of that data. So if we look at some of the other definitions of data governance that are out there, and I share with you the DAMA definition, I share with you the Data Governance Institute's definition, and you see they use a lot of the same words. They talk about exercising authority and control. They talk about executing according to agreed upon models. And I'm seeing that a lot of organizations are now starting to adopt those stronger definitions of the term data governance. And now in the years gone by, some definitions included harmonization or the orchestration of people and process and data. And that kind of gives perhaps the wrong idea. It makes it sound like people are going to get together and put their arms around each other's shoulders or under back when we could do that. And they can harmonize and they can orchestrate data governance. Now actually we need to execute and enforce authority. And that is the goal of data governance programs. And that's also very important when it comes to the master data within our organization. So now let's define what master data is. And a lot of you might have your definitions for master data, but I've taken this one also from Dama, that it is master data is data about business entities that provide context for business transactions. And a book that I read and something that you can refer to and find on the internet, another definition for master data is that it's business objects that contain the most valuable and agreed upon information that's shared across the organization. And to be honest with you, to get to a breed upon information requires governance. While we're finding those people within the organization that we need to agree upon the information, why don't we recognize them as being stewards of the data or being subject matter experts of the data? I'll talk a little bit more about that in some of the slides that are coming up. I tend to hear the terms master data and reference data being used interchangeably. And in fact, they mean different things to a lot of people. So we ought to think about distinguishing master data from reference data. And oftentimes, when I hear reference data, it is data that provides context for business transactions. It's concerned with classification and categorization, but oftentimes the reference data is data that's coded data. Data that might be coded either internally like different hierarchies or different categories or it might be coded data that you're acquiring from outside the organization. So let's make certain that we help people to understand what we mean by master data. And if they ask the questions as to how is this compared to reference data, we're able to distinguish the differences between master data and reference data. And master data, typically by its nature, nature is almost always non-transactional. And it's often kind of the key objects, the key subjects, the key domains as we'll talk about in a little bit, the domains or subject areas of data across the organization, things like parties and products and financial structures and location concepts and things like that. Those are some of the common master data subject areas that organizations look like, like customer and product are often at the top of the heap as people start to address master data across the organization. So it seems to have become a common occurrence over time that people refer to it as master data governance. And so there's different types of data governance that we might think about. There's information governance, there's big data governance, that was a term for a while and metadata governance is even a term or an expression that I use to talk about the governance that takes place around metadata. But the fact is there's really only one type of governance and it actually causes confusion for the organization if you have a master data governance program and you have a data governance program or you have a big data governance program, in my mind they should all be the same. And you can call it what you want and you can refer to it by different names but in the end result is we need to execute and enforce authority over that data. So we use the types of data that are being governed in the name and I would suggest to your organization, again if you're trying to eliminate some confusion that you just refer to it as data governance and it's the data governance of the metadata. It's the data governance of the master data or the big data or the information or even records within your organization. And people talk about data governance 2.0 and 3.0 and I'm sure we'll be hearing about data governance 4.0. And people refer to next generation data governance. I believe there's an online learning plan through the Data Diversity Training Center specifically on next generation data governance. And that's kind of interesting because the topics that I shared with you regarding next month's webinar, the internet of things in the cloud, things like that, that's all things that we as data governance practitioners need to be thinking about in terms of what is the next generation of data governance. Is governing data in the cloud any different than governing on-premise data? And so we just need to keep that in mind. What we call it might either raise questions or answer questions for people across the organization. So is there really such a thing as master data governance? And I have shared with you before and as I noted down in the bottom right-hand corner of this slide, this was a topic of the real-world data governance webinar back in May of last year where I talked about specifically a data governance framework and I used this model. And so the things that we need to focus on when it comes to data governance, they're gonna be the same. They're gonna be the definition of the data itself. We're gonna have roles and responsibilities associated with both data governance and master data management. We're gonna need to have people responsible for stewarding that data. As we all know, the metadata, that information about the data that helps to make it more useful is going to be important. The process, the metrics, the tools, and the communications. If you look across the top of the framework diagram that I'm sharing with you on the screen, you see those are the core components of a successful data governance program. And oftentimes we need to look at these components in terms of different perspectives in the organization. How do the executives see this? How does the strategic level of the organization tactical operational support area? So data governance is data governance. Do we need to call it master data governance? I'm not gonna tell you not to within your organization. And the question becomes, does it really even matter? You know, I say that the most importantly, you name it in a way that makes sense for your organization and gets your organization that feel comfortable about data governance. So you can refer to the governance of master data as being master data governance. But one thing you should remember is that the core components of governance, no matter what type of data you're governing, they need to be the same. They need to be the data and the roles, the processes, the communications, the metrics and the tools are also necessary in implementing master data and implementing data governance. So just be aware that if you have more than one data governance in your, something labeled in your organization as data governance, it's gonna raise more questions than it's going to answer. People are going to be forever asking you, what's the difference between master data governance and the data governance program and our enterprise data governance program that we're putting into place? So just keep that in mind as you're defining master data and you're defining data governance, it doesn't make sense for your organization to have multiple names for different types of data that you've governed within your organization. So what I wanna do is I wanna focus on kind of aligning those roles and responsibilities that are associated with data governance. I wanna align those with master data management, at least the best that we can. And I'm gonna share with you examples of the names of the roles that have been used within data governance and also the roles that I've seen being used often in master data management as well. We'll kind of compare and contrast those. We'll look at the similarities and the differences between the two and then I'll spend a minute talking about, well, why does it matter? Why should we look to align the roles of master data management with the roles of data governance? So typically when I talk about roles and responsibilities associated with data governance, I look at it from those levels that I shared with you within the framework. There's the executive, strategic, tactical, operational support and I've had organizations also add an administrative level and that is who has the responsibility for administering the program. So oftentimes you'll have a steering committee at the top level of your organization. You'll have a council, whether it's a data council or a data governance council or a data strategy council. Those are some different names that are given to that. A set of roles or a set of responsibilities. So pick the one that makes no sense to you. The tactical level is really most important because that's where we stop, we start breaking down silos and we start looking at data as a shared asset across the organization. And I refer to them as data domains, subject areas of data, had a client actually say, well, you're really describing subject matter experts associated with the data and I couldn't disagree with them and they said, okay, well, that's what we're gonna call them because people recognize what the term SME or subject matter expert means within the organization. And there's the data stewards at the operational level, the people that define, produce and use data as part of their daily job. I mean, those people I've been known to say that everybody in the organization who either defines and or produces and or uses data as part of their job. And that could be virtually everybody in the organization if they're being held formally accountable for their relationship to the data that is how they define, produce and use data. They're a data steward. So we need to think about that in terms of master data as well. The people that are defining the data that's going to be the master data and putting master data definitions to that data, those are stewards of the data. The people that are producing that data or taking that data from other sources within the organization and producing the metadata certainly have responsibility for verifying and validating and certifying the data that becomes master data. And certainly the people that use the data need to understand the rules associated with how they can use the data. So basically anybody who defines, produces and uses data is a data steward. And we need to kind of get over that fact and understand that if we want to cover the entire organization, that everybody in the organization that has a relationship to the data is a data steward if they're being held formally accountable for that relationship. And certainly Paul has the opportunity to base that if you define data, if you produce or use data that you're gonna be held accountable for what you do with that data. And then at the support level there's the data governance partners and the working teams at the administrative level is that the person that has the responsibility for the program, whether it's an administrator, a lead, a data governance manager. And back in the August of this year I did a complete webinar on roles and responsibilities associated with data governance. And this is the model that I use quite often the pyramid diagram. It kind of goes through each of those different levels that I just defined. And I'll also be doing an interactive live streaming or live training with the diversity coming up in March specifically that goes into a lot of details associated with these different roles and responsibilities that are parts of the data governance program. But now when we look at the roles that are typically associated with master data, yeah, we might have an executive team that's telling us that it's important for us to have master data and have that single point of truth or the place for people to go to get the data that they can truly trust. And we've got councils, we've got people, I don't find too many organizations that actually have master data governance councils or strategic levels that are associated with their master data programs. But the role that I wanna focus on most is that tactical level. And if you're working on customer master data or product master data, the people that have the authority to make decisions associated with that data, they're typically at that tactical level of the data governance model that I just shared. And we can consider them to be master data domain stewards. They're certainly subject matter experts of the data. And then the people that define, produce, and use the data are the stewards. So oftentimes the supporting groups to a master data management initiative are information technology and different IT partners or groups that partner with IT in the first place. So we need to call them out and we need to define what is their responsibility in terms of governance and governance specifically of the master data within our organization. So at least from what I have seen, it's not considered an industry best practice to have a separate set of roles and responsibilities for master data. We can take advantage of those roles and responsibilities that we've defined for governance or let's say your master data initiative takes place first before data governance is even taking place. Let's look for the governing roles in that initiative and let's build out our data governance program based on what's working for us in the master data management realm within our organization. So what are the similarities between master data roles and data governance roles? At the executive level, it's pretty much the same. We've got management, senior management, we've got steering committees. At the strategic level, it's pretty much the same. We could have one data council or one data governance council that handles both data governance and master data, but at the tactical level, it starts to make a difference and at the support level, it makes a difference. And I kind of call that out with this slide right here is the real differences between the roles that we need to align or bring together are at the tactical level and at the support level. So if it's necessary within your organization, you might need to draw out a distinction between a master data domain steward and other data domain stewards. But the fact is that it typically wouldn't be a separate subject matter expert for your master data as it would be for all of the other, for example, customer data within your organization. You know, they might be different people, but oftentimes they're the same people that we need to get engaged. So I'm not recommending that you draw a distinction between your master data domain stewards and your other domain stewards, but it's just something, again, it needs to be customized to fit your organization and what makes sense to you. So, you know, why does this matter? Why do we need to align these roles? Well, the first thing that we want to do is we want to make certain that our data governance program and our master data management initiatives are answering more questions than they're raising. So oftentimes organizations get started around data management and data governance and master data by focusing on creating that single set of roles that is necessary for both master data and for data governance. And so oftentimes we start by defining master data subject areas and oftentimes those are defined already for us. You could very quickly jot down what are the main buckets or subject areas of data that we're concerned with, especially when it comes to the master data management and we can determine who the owners of that master data are or we may already know who they are. Now, I have the word blast there next to owners because if you know me, you know that I kind of shy away from using the term owner, but I also recognize that a lot of organizations use the term owner. But it typically, it implies probably exactly the wrong thing for your organization. They don't really own the data. The organization owns the data. A steward is somebody who takes care of something for somebody else. And so we oftentimes call them owners of the data but really they're subject matter experts or data domain stewards as I called out in the previous slides. So we need to determine who the authorities are for that data and we need to make certain that we record and we share information about who those authorities are. So typically we're gonna go to those people anyway. So we know who the people are that are the decision makers around domains or subdomains or even critical pieces of data in the organization. But it doesn't become as helpful to the organization until we start to record that information somewhere. So people can go to a resource to see who has the responsibility for what subject areas of data within the organization. So now let's spend a few minutes talking about the differences between governed data and ungoverned data. And you probably have a laundry list of things that you can list out but I wanna share out some of the key distinctions between what makes data governed and ungoverned. We'll spend a minute talking about the dimensions of the data quality and you could have six dimensions, you could have eight dimensions, 10. Depending on who you ask, there's multiple different numbers of dimensions of data quality that organizations can focus on. We'll talk about measuring the impact of governed data and ungoverned data and how they're different. And then we'll spend a minute talking about effectively communicating the impact. So on this slide, what I'd really like to do is kinda call out what the differences are between governed and ungoverned data. And as I mentioned earlier in this webinar, I think that we need to include the term governed into master data because it really doesn't become master data or the data that we want people to refer to until we've had some level of governance around it. And in fact, when I talk about non-invasive data governance, if you already have a master data initiative in place, my guess is that there's already some levels of governance that are taking place around that data. And if we can formalize that, instead of creating it as something new, that that makes sense to a lot of organizations. So look to your master data initiative to help your data governance initiative and look to your data governance initiative to help through your master data initiative. It kinda goes both directions. So what are the differences between governed and ungoverned data? Well, some of the difference could be that they're formally owned or they're formally authorities or people that are recognized as being the subject matter experts of the data or in ungoverned data, perhaps that data is not owned by anybody. Nobody has the accountability for that data. So we don't know who the data domains do it is or the subject matter expert is or it's not recorded or it's not being shared anywhere. And the same thing holds true for data that's formally defined. Oftentimes governance focuses a lot on the glossaries and the dictionaries and the data catalogs that we're becoming dependent on within our organizations. So we need to make certain that we're creating great definitions when it comes to all levels, when it comes to the information and the data and the metadata that all needs to be formally defined. And another difference between governed and ungoverned data is oftentimes for governed data, there's metadata available about that data. And most organizations that implement master data management solutions, they have metadata, they have information about the data to build people's confidence in the data so that they trust the data and they can use that data. And oftentimes data that's not governed there's no metadata available. People are going to work from quick hit definitions that you may provide but under formally governed data, metadata becomes such a huge backbone of supporting effective governance programs and supporting effective master data management programs in business intelligence and data analytics platforms, anything that you're investing on in your organization, people want to understand the data. So the metadata needs to be available. In governed data, the quality is assured. In ungoverned data, well, the quality of the data is what it is. And oftentimes governed data, the data is certified or it's validated or verified so that people can trust that data. In ungoverned data, it's kind of uncertified. People can make of it what they want but it hasn't been through that rigor that you identified the owners and the stewards and created that metadata. And one of the big difference between governed and ungoverned data is the level of confidence that people can have in the data. And if we're focusing on a master data management initiative within our organization, we need to have confidence versus having levels of uncertainty around the data. If people are uncertain about the data, I would find that they typically wouldn't call that data master data because calling it master data in the first place implies that it's been through some process to make certain that this data is validated and that it's there to be used effectively across the organization. You know, when we look at qualities of governed data, we also look to the different dimensions of data quality. And I wanted to take, it borrows something from an article or a piece of content that was published on Dataversity back in November a year ago as to what the specific dimensions of data quality are. And oftentimes there are completeness, validity, accuracy, consistency, all of these things are different dimensions of data, the dimensions of data quality. And so around completeness is, do we have all the data that we need? Are all the pertinent fields filled? Or can we have standards for data if the data is only populated 50% of the time or 20% of the time? Or even with some types of data, 90% of the time might not even be enough. So being able to measure the completeness of the data, the validity, the accuracy of the data based on standards, based on real world situations within your organization, is the data consistent? Is it unique? Or are there multiple occurrences of the data? Is the data being kept up to date? So when Amber wrote about the different dimensions of data quality, they're very similar to other dimensions of data quality that I've come across in my research and in the work that I've done. And I just wanted to share with you another example where they use pretty much the same ones that Amber had defined back in November of last year. So if we're gonna look for qualities of governed data, that governed data needs to be complete. You wouldn't think about providing master data that was incomplete or inconsistent or that didn't conform or that wasn't accurate or that didn't have data integrity or timeliness. Now, we need to focus on the different dimensions of data quality to be able to say that okay, the data that we are providing in our master data management solution, it's being governed. So there's different ways to measure the impact of governed data using those quality dimensions that I just talked about, talking about the time that it takes to prepare data for analysis. Oftentimes people talk about the 80-20 rule and spend 80% of the time manipulating and formulating the data the way that it needs to. Well, we're looking to cut that out when we start with master data management. And certainly the governance of that data can reduce that amount of time that it takes to prepare the data. The data should be suited for purpose if it's considered to be master data. How much time does it take to request to gain access to the data? How can we use this data to address different data opportunities? And those really need further definition within your organization. What is an opportunity to improve? What is the data quality issue that needs to be addressed? And even the reduction in data incidents are ways for us to be able to measure the impact of governed data. And since so many organizations are focusing on data analytics, I just wanted to share with you some examples of the usage of data analytics to measure the quality or measure the impact of governed data across the organization. And a couple of these come from real-life examples of organizations that I've worked with where salespeople say it's not their job to enter all the information about the sale, or the engineers say it's not my job to I'm not a data person, I'm an engineer. Well, I wrote an article on TDAN recently that data is everybody's responsibility. So if the salespeople or the engineers of your organization refuse or push back on becoming data people, we need to figure out ways to make certain that that data is effectively collected and that we can use it for data analytics. And so data collected to improve performance, even looking at how well people are integrating data, or maybe they don't know what data can be integrated or they're looking for innovative ways to integrate data to make business decisions across the organization. So when we're measuring the impact of un-governed data, it's the inability to standardize the data because we don't have a standard defined, an inability to measure data quality because we haven't done a benchmark of what the quality of that data is at the beginning, or people are still spending too much time manipulating the data for analysis, or we don't have the ability to improve on our data or address those opportunities or to log incidents. So measuring the impact of un-governed data, again, we have the inability to do some of these things that I just talked to you on a couple of slides ago. We need to get the salespeople engaged. We need to help them to recognize that they are stewards of the data and that they play a key role in the organization. And we need to effectively communicate the impact of the fact that the data is governed, especially when it comes to master data and the investment that we're making in that data. So my suggestion is, and I have several clients that do this already, is to work with people from your corporate communications department if you have resources like that available, they're perfect partners for data governance because they're the ones who know best how to shape the messaging to maximize how these things are being ingested or being consumed across the organization. And utilize the different tools that you have in the organization. You know, dare I even say gamify data governance to make it interesting or to gamify master data to make it interesting to the organization. You know, I oftentimes share two questions that I suggest that organizations ask when it comes to convincing people that data governance is necessary or even that master data management is necessary. The first question, and they're actually kind of two sides for the same question, really. But what can't you do because you don't have the data or don't have the confidence in the data enough to be able to do it? And then the flip side of that is, well, what would you be able to do or what could you do if you had confidence in the data? And oftentimes building that confidence in the data is building up in governance of the metadata. And we all know that that directly relates to master data. We need to convince people that the data is accurate, that it will solve real life business problems for these folks. So let's talk in a couple of minutes about governing to a master version of the truth. So we'll talk about why master version is important and we'll talk about getting to the single point of truth I'll share with you just a series of quick steps for doing that. What are the problems caused by having more than one version of the truth and the impact of governance and then measuring the investment in these things. So there's a lot of different names that are given to master data management or master data. And oftentimes organizations will refer to it as the single point of truth or the golden record or data that's fit for a specific purpose or data that is certified. So master data, and I don't need to tell all of you practitioners this, that master data becomes extremely critical to the organization whether you're implementing a new CRM package or a new ERP package where you're bringing in data from different types of sources to get to that single point of truth, that one place that we want people to go to understand and to utilize the data across the organization. So going back to the question that I asked at the very beginning of this webinar is can data become master data if it goes ungoverned? I think most people would say that in their governing initiatives that there are master data initiatives that there are levels of governance taking place. It might be informal and because it's informal it might lead to inefficiency and effectiveness. So if we can formalize how we're governing the master data and leverage that to build out the governance of other types of data across the organization, then the master data truly becomes governed. And again, I say that we should include the term governed in our definition of what master data is. So organizations may ask, what's the difference between the master data and transactional data within the organization? Well, the fact is that the transactional data or data in operational sources might not be as governed as the data in our master data resource. So I wanted to share with you a couple steps or a handful of steps or maybe a few more that we can take to get to that single point of truth of data within the organization. And one of the first places we can focus on is defining those different domains or those different subject areas of data. And so for a lot of our organizations or our companies we know that product data is important or service data or customer data or employee data. Those are all different domains or subject areas of data that organizations consider when they're building out their master data initiatives. And we can certainly get to that single point of truth by leveraging the roles and responsibilities, leveraging the ownership or the responsibility or the accountability of that data across the organization. We can create working teams and you may have done this already. If you're focused on master data management you might have a product working team or a customer working team. Again, they have a specific role within data governance. They certainly have a role that they play within master data. Let's work to align those roles together and get them working together as we move forward. So you may look at your master data initiative and say we go to Joe when we've got a question about product data. Well, maybe it makes sense to recognize that person as being the master data domain steward or just the data domain steward, the subject matter expert or the authority for that data across the organization. Defining processes for standardizing data within those different subject matters or the different data domains and we wanna make that repeatable. So the next topic I'm gonna talk about is how do we do this incrementally? How do we incrementally implement data governance but how do we also incrementally make master data management stable within our organization is to define repeatable processes and governance often helps with the definition of those standard processes for standardizing the data within the different domains. So there's the definable processes and we want those to be repeatable but then there's the definable standards for the data itself. And I would think that in a master data solution we wanna have that data well defined. We wanna record that metadata. We wanna make that data documentation available to the users of the master data. And I'll be darned if that isn't one of the same goals that we have for data governance. So master data, data governance, they're oftentimes connected at the hip and they need to be because they are both pushing in the same direction and we need to leverage them in conjunction with each other instead of as separate initiatives. So if we have more than one version of the truth we can run into problems. And one of the problems is when our senior leadership asks a question they might get a different answer depending on who they ask or who performs the analysis on that data and there's several reasons for that. So the answer that they get might depend on who they ask and it might depend on what data that they go to to get their answer or what data they have access to or that's available to that person or certainly their understanding of the data. It's not uncommon for people who are using the same data resource to get different answers to questions on that data resource based on their understanding of that data. Again, that just emphasizes or reemphasizes why metadata is so important to both master data initiatives and to data governance initiative. So oftentimes it really depends on people's confidence in the data the access that they have to the data the understanding of the data. And to be honest with you data governance often is the discipline in the organization that provides that information to help you to improve your outcomes associated with the data in your organization. So I've used a slide similar to this one before where I talk about the impact of data governance just on data in general and it makes perfect sense to add the word master in front of each of these are included in each of these things that it's going to improve because the impact of data governance making data governance actionable as part of your master data initiative certainly it can improve and master data accountability improve the definition of that data improve the production of that data to the standards that are defined for us improves the usage because people have better confidence in the data and that's when I list that separately as it improves the confidence that people have in the data that's one of the impacts that data governance can have on master data and it certainly improves the value of that master data. And we know that I listed as one of those core components in the framework at the beginning of the webinar that metrics are really important. So we need to measure the investment in master data and the investment in data governance so that we can demonstrate to the organization the value that is coming from those investments. And oftentimes organizations focus on kind of those three staples of both master data and data governance which are the people, the process and the technology. And if we can demonstrate improvements in efficiency and effectiveness if we can improve on the way we do things and become more consistent in the way we do things or make use of the technologies that are available to us or that we can acquire in the marketplace and we wanna focus on the ROI and being able to measure what the return is from our investments that we're making in master data and data governance. And I thought I'd add into this slide that ROI, if we can put this in term of business outcomes we're gonna be able to demonstrate the value of our master data management initiatives and our data governance initiatives. So the last subject that I wanna talk about before I turn it over to Shannon to see if we have any questions for today from the webinar is how do we implement master data governance domain by domain or subject area by subject area? And so the first thing I'm gonna do is define for you what a data domain is at least in my mind and provide some other names that your organization might use associated with the data domains. We'll talk about the incremental delivery of both master data and data governance. We'll talk about the upside and the downside of doing it incrementally rather than doing thinking a big bang approach to implementing either one of these disciplines within our organization. So one of the first things we wanna do is we wanna define what a data domain is. And so if you're gonna use that terminology make certain that you, it might not be some common language, excuse me, it's being used within the organization. And you may not wanna use the term data domain at all but you want to be very clear as to what you mean by domain what are the different domains of data within the organization. And oftentimes the term subject matter or subject area is used to define what a data domain is. And so I'm referring to domains as subject areas of data and oftentimes organizations will start with high level domains, break those into subdomains or even some subdomains all the way down to the critical pieces of data that we have within our organization. And some of the common data domains that are used within organizations are customer and product as I mentioned earlier, vendor or part or employee or location or even the use of the term party. Party seems to be a pretty high level. What different types of parties do we have within our organization? They can be customers, they can be consumers which might be different from customers. They could be employees. You know, there's a lot of different parties. So I'm not certain that party is an advisable master data management area to use but some of the other names that you might be using for domains within your organization might be subject area, might be categories of data. You might align the subject areas to the different lines of business or business areas or business functions or themes or dominions. You know, certainly master data is typically broken down by different domains or different subject areas that we're focusing on within our organization. And when we talk about the delivery of master data or the implementation of data governance, we recognize that we ought to do this incrementally. You know, I oftentimes talk about starting with a slice of the pie and or even starting with a slice of a slice of the pie and making certain that we build a reusable, repeatable practice, you know, focusing on whatever that slice is. And then we reuse that. We learn from our experience. We improve with every single iteration of different subdomains or subdomains or even critical data elements. So the expression that's used a lot is eating the elephant one bite at a time. And that's what we're doing when we're implementing master data governance because I've yet to see an organization that has been successful in just turning on master data for everything that's considered master data within the organization. So you're going to learn from experience of doing this. You're going to improve with every iteration and you're truly going to develop that principle version, that controlled or that governed version, or we can even call it a guru version of that specific data, which is really what we're looking for when it comes to delivering master data within our organization. So when we're talking about the incremental delivery of data governance, we're looking at validating the roles that we've defined for other data governance through master data management. Who are the subject matter expert? Who are the stewards of that data? You know, recognizing who are the people that we've already engaged as part of our master data initiatives and actually slotting them into specific roles that we have defined for our data governance program. And I talked about validating and vetting the data and creating repeatable processes and creating improvements in the confidence in the data through our MDM initiative. Well, those roles and processes and data, they're parts of that, those core components that I have listed across the top of the data governance framework that I shared in the webinar last year. I'll be glad to talk to anybody about if you're interested in learning more about this framework. But the communications, the metrics and the tools, those are all critical components of a successful data governance initiative. And I would even venture to say that, you know, the data, the roles, the processes, the communications, the metrics and the tools are all critical components of master data as well. So the last thing that I wanna talk about is I wanna talk about the upsides and the downside of doing incremental delivery rather than trying to take a big bang approach and implementing it all at once. And I'm gonna start with the downside and then I'm gonna flip to the upside as we do wanna end the webinar today on a more positive note. But, you know, what are some of the downsides of doing incremental delivery? Well, the first one is that, you know, proof of value requires patience from people. You know, once we've created a sound master data environment, you know, for one area that people might start saying, well, we also wanna address that for other subject areas of data. And the downside is that it can take an extended period of time, that it requires management's commitment to sticking with the program to make certain that we are delivering different additional levels of master data across the organization and that we wanna take a very specific focused approach and that, you know, being agile and delivering it quickly. And yeah, that makes sense to a lot of organizations, but, you know, I haven't talked to too many organizations that have used the agile approach to delivering master data because master data is, you know, they expect it to be validated, they expect it to be certified, they expect to have confidence in that data and governance is required to achieve all of those things. So now to end on a positive note here, you know, the upside of incremental delivery is that we can improve efficiency over time from learning from our mistakes or learning from things that we could make more efficient and make more effective. You know, we can build those repeatable processes, which mean we don't need to redefine them if we find that they're working for the first or second or third categories or subject areas of master data we're focusing on and we can build on the subject matter expertise as we do. And the bottom line is that by implementing, by putting data governance into action for master data, the real goal is to build that confidence in the master data so that people trust it and you get the return on investment that your organization is making when it comes to building out master data. So in this webinar I talked about, well, what makes it master data governance and is there really such a thing? We talked about aligning the roles and responsibilities of data governance with master data management to make master data actionable within your organization. Talked about the qualities of governed data and mastering to that single version or that master version of the truth. And then we talked about some of the downside and the upside of doing this incrementally and recognizing we're not gonna flip a switch and have governance come on to the entire organization. And with that, I am going to throw it back to Shannon to see if we have any questions today. Lots of questions coming in, Bob. Thank you so much for another great presentation. If you have questions for Bob, feel free to submit in the bottom right-hand corner in the Q&A section of your screen and just answer the most commonly asked questions. Just a reminder, I will send a follow-up email to all registrants by end of day Monday for this webinar with links to the slides, recording and anything else requested throughout. So diving in here, Bob, I see management of data assets being used in definitions of data governance. How would you define data management then? That's a great question. I oftentimes, I have many clients that actually ask to differentiate between data management and data governance. And data management, a lot of things fall under data management. If you look at the DAMA wheel, for example, there's a lot of different aspects of data management and data governance is really the people aspect of data management. Data management could be data modeling. It could be metadata management. It could be business intelligence and data warehousing. So data governance is really focused on executing and enforcing authority over that data, getting people to behave in the right way. So there is a data governance component to all of the different knowledge areas that DAMA talks about in the DAMA wheel. But data management, at least the way I view it, is the umbrella term that goes over data governance just being one of those disciplines that make up data management. Oftentimes the data management projects like building a data warehouse, building an analytical platform, they have a budget within themselves. They need to be delivered on time and within budget. Data governance is a program. Data governance is something that we put into place and it needs to be there forever. Like I've said before, you won the data governance game if it's just built into what you do within your organization. Data management initiatives tend to be much more focused. I just would make certain that I do differentiate between what data management is and what data governance as a component of data management is. Does it make sense to distinguish between data governance and IT governance? Wow, that's a great question. So IT governance might be something completely different. And the reason I say that is that IT governance could have to do with everything that is related to IT. So for example, the governance of the security of who gets access to what data and what process takes to get to that data. IT governance is much more technology focused and data governance is much more people and their behavior associated with the data focused. So I haven't seen any organizations yet who use the terms IT governance and data governance interchangeably. IT governance is usually around prioritization of projects, funding of projects, getting the right resources focused on those projects when data governance is then more focused on people's behavior associated with the data. Your data governance should be integrated with generic data governance. What about metadata governance and the governance of data models? You know what, that's really, that's a good question too. And you know, one thing that I say a lot and I don't think I said this in this webinar is that the metadata is not going to magically appear. It is going to take that there's time and there's resources and that there's a resolute effort focused on the metadata. So metadata governance, as I mentioned earlier in the webinar, is just the governance of the metadata. The metadata will not create itself. It's not gonna become available by itself. So we need to have people in the organization that are defining what metadata is most meaningful to the organization. You know, we need to have people that are producing the metadata. And that includes the data models and making certain that we have standards for how we're entering definitions into the data models. So, you know, metadata governance and data governance are the same type of discipline. The only real difference is on the data that we're focusing on. So a metadata governance initiative is applying data governance to the metadata of your organization. Just like master data governance is the implementation of the data governance discipline for the master data in your organization. They're the same thing, but what we're governing is different. And the same question for data science and analytics governance. Yeah, I guess you do have struck a chord with those first couple of slides of all these different types of governance. Well, you know, what data are we using for data science? And what data are we using for analytics? And organizations are investing heavily in data science and data analytics and building out platforms, you know, focused on being able to provide those capabilities. So, you know, I would say that again, they're, you know, they're basically the same type of governance needs to be applied. We need to know the data, the roles, the processes, the communications, the metrics, the tools associated with data science and data analytics. But we need to know those around data, just any other type of data within the organization. So how I'd answer that question as you've heard me say it before is that it is the data governance of the data that feeds our analytics and feeds our data science. Well, Bob, thank you so much for this great presentation. And as always, but I'm afraid that is all the time we have for this presentation. If you have questions, feel free to keep submitting them and I will get them all over to Bob to include the answers in the follow-up email, which I will send to all registrants by end of Monday with links to the slides and the recording as well. Thanks to all of our attendees for being so engaged in everything we do. We just loved it. Thanks for all the great questions and hope everybody has a great day and stay safe out there. Thanks, Bob. Thanks, Shannon. Thanks, everybody. Have a great holiday if you celebrate. Take care.