 Hello, and welcome my name is Shannon camp and I'm the chief digital officer 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 Siner. Today Bob will discuss one data governance for all master data included sponsored today by precisely just a couple of points to get us started due to the large number of people that attend these sessions he 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. And just to know zoom defaults the chat to send you just the panelists but you may absolutely switch that to network with everyone. For questions we'll be collecting them by the q&a section and to find the chat and the q&a panels you may click those icons in the bottom middle of your screen to activate those features. And as always we will send a follow up email within two business days, containing links to the slides the recording of the session and any 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. Hey Shannon how you doing today. Awesome, we're ready to roll. I'm the senior manager of product marketing here precisely. And we're going to dive right in. One can deny that the last couple of years have been an absolute whirlwind right. First and foremost, we know the pandemic created a seismic shift that has impacted virtually every part of daily life and business for a protracted period of time. The alliance with the changing regulatory and privacy requirements imposed by states industries business partners and customers has also demanded increased investments and unprecedented geopolitical instability has created sustained global economic uncertainty that we've all had to reckon with right. We've also seen the economic downturn impacting organizations and strongly influencing their data strategy. According to a recent study by Drexel University's Lavaux College of Business data professionals reported significant impacts from the economic downturn with 40% experiencing a reduction in staffing and resource allocation and 37% reporting a decrease in their budgets. The survey has also shown that they're taking steps in their data strategy to manage through these constraints. So responses show to focus on technologies that will help organizations manage through resource shortages, cloud adoption, artificial intelligence workflow automation and prioritizing things like data democratization and digital transformation. They also stand out as strategies that can offset the decrease of resources and skilled workers while amplifying decision making insights. Overall, the survey showed that organizations are shaping their data strategies to address those macro trends. Yet, the Drexel survey also found that 77% of respondents reported that data driven decision making is the most important goal for their organization data programs. It's even overshadowing operational efficiency, cost reduction and revenue generation, surprising. Clearly in response to the macro trends that we talked about organizations are relying on hard data to drive their decisions across all of their businesses. And how are they doing this 41% report that data governance is a top priority for their data programs this year. So what is a roadmap for data driven decisions include let's talk about some of the properties here. Number one, you should be looking at cross functional teams that can collaborate and understand the full life cycle of data in a single unified solution, delivering enterprise data literacy, and it must include robust and automated data access quality and observability qualities that are seamlessly integrated to achieve confidence around your most critical data. And metadata and master data must be easily cataloged, discovered and managed to empower teams to quickly find insights and take actions on high priority tasks and events. So with these requirements, you need what we call data integrity data integrity is data with maximum accuracy, consistency and context for confident business decision making. But data integrity is a journey, and every journey is unique to each of your companies to address your specific business initiatives. Today we're talking about the value of one data governance for all that can address those macro trends and impacts now, and in the future. The precisely dead integrity data governance service enables you to manage data policy and processes with greater insight into your data's meaning lineage and impact enterprise wide. It's flexible and extensible to adapt to multiple use cases across your organization, including master data management, and it's intuitive and user friendly, so business and it teams can collaborate and quickly interact for timely business insights with data quality you can increase the confidence and data with automated and integrated rules and alerts to deliver data that's accurate consistent and fit for purpose across different use cases operational and analytical systems, and proactively uncovered data anomalies to act before they become costly downstream issues. And depending on your use case or where you are in your data integrity journey, precisely dead integrity solutions also deliver powerful integration capabilities to break down data silos and derive real time analytics across your modern and your legacy systems to modern data pipelines and models and location intelligence services to derive and visualize spatial relationships to reveal critical context for better decisions or enrich your business data with expertly curated data sets containing thousands of attributes for faster, more competent decisions precisely dead integrity solutions work together and are powered by machine learning intelligence to optimize your teams resources and investments. Over 12,000 businesses leverage precisely technology across the globe to build value and work seamlessly with both your traditional and modern tech steps, tech stacks to help you optimize your investments now, and in the future. The leader in data integrity precisely is modular and interoperable data integrity solutions contain everything you need to deliver accurate consistent contextual data to your business, wherever and whenever it's needed. Thanks for listening. If you'd like to learn more please scan the QR code or visit our website to continue the conversation. I'm going to turn it back to Shannon so that we can hear more about one data governance for all. Thank you so much for kicking us off today and thanks to precisely for helping to make these webinars happen. If you have any questions for Sue or about precisely feel free to submit them in the Q&A panel. Thanks for joining us for the Q&A portion of the webinar at the end. And 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 tdam.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 get his presentation started. Hello and welcome. Hey, Bob, you okay? You're all good there? You're muted. I'm not sure what you're doing, Bob. I'm sorry. You know, sometimes you just have one of those days. Can you hear me now? Yes I do. Hello, my lord. Thank you very much for giving me a second. Wow. That hasn't happened in a while. Thank you, Sue. Thank you for the great presentation at the beginning. I was writing down notes of things that you were saying, talking about data driven decision making. That is the key to many organizations right now. Data integrity. There needs to be data integrity for data driven decision making. And then the idea of the macro view of data governance. And that's what I'm here to talk about today. I'm going to talk about how there really needs to be one data governance program or data governance doesn't necessarily have to be different depending on the type of data that you are that you are governing. So let's talk about that a little bit more. And I'm going to particularly call out master data, because the one of the terms that I hear quite a bit is the term master data governance. Just like I heard big data governance and, you know, AI data governance. I'm hearing a lot of different things, but let's talk about is it a good idea to create a label and put it in front of our name of our data governance program. Or should there really be one data governance program that addresses everything. So real quickly, I want to go through some of the things that I'm actively involved with you know about this webinar series. We're going to be talking about data stewardship, the data stewardship method of data governance. I'll be speaking at an EDW Enterprise Data World next month in Anaheim, California. I'll also be speaking at DGIQ East in Washington DC in December. If you're not familiar with me or my books, you've probably heard of non-invasive data governance, or at least I hope you have. The first book on the subject was written in 2014. The second book just came out in May of 2023. It's not a second edition. I hope you'll be interested in taking a look at that as well. There are some online learning plans available through Dataversity, so please check those out. My consulting business is KIK, Consulting and Educational Services, or KIK stands for Knowledge is King. And in my spare time, besides for the publication, yeah, there's the data administration newsletter. So in my spare time, I'm also a faculty member at Carnegie Mellon University here in my hometown of Pittsburgh, Pennsylvania in their chief data officer program. What am I here to speak to you about today? I want to talk about the need for consistent application of data governance across the different data types that are important in the organization. And so Sue talked about taking a macro view, and I think that's kind of what I'm implying here is that we really need to take a macro view to governing within the organization in general and specifically around data. So I'm going to talk about the need for consistency. I want to talk about the impact of placing a label in front of the name of your data governance program. It might be a good thing. It might not be a good thing. It all depends on your organization and what it is you're trying to govern. We'll talk about data governance as a multifaceted diamond. We'll talk about consolidating what it takes to consolidate multiple programs if you actually have more than one form of governance. And my guess is you probably do have more than one form of governance taking place in your organization. And then we'll wrap it up by talking about why one governance for all really makes sense. And again, I'd be curious to hear your feedback in the chat and the questions, and we could talk about it at the end of the webinar. So I always start with definitions of data governance and data stewardship, because I think it's good to kind of start with a common understanding of those terms. My definition of data governance is worded quite strongly. In fact, I have a lot of clients that don't like to use the term the execution and enforcement of authority, because it sounds like it's going to be invasive. But this is really just the definition of data governance. This is not the approach that you're taking to implementing data governance within your organization. So we'll talk a little bit more about that as well. Data stewardship is, you know, I say a lot that everybody in the organization is a data steward and that organizations need to get over that fact. The fact is that anybody who has a relationship to the data in your organization, and they're accountable for what they're doing with the data, whether they're defining or producing or using the data. They're a steward of the data. We don't have to call them data stewards, but they are basically being, if they are being held formally accountable for what they're doing, they are a steward of the data. So let's just, we wanted to start real quickly with those definitions of what data governance are and what data stewardship is, at least in terms of the noninvasive data governance approach and most approaches to data governance. Let's add in master data. What's the definition of master data. Here's a definition that I use. It's pretty generic. I don't want to read it to you but it's probably similar to your master data definition. But what's the definition of master data governance, it's just going to be my definition of data governance with the words master data at the end of it. It's going to be the execution and enforcement of authority because your data will not become master data unless somebody is executing and enforcing some level of authority over how that data is being defined, produced and used in your organization. And what is a master data steward. Well it's a person that's being held formally accountable for their relationship to the master data. And people are defining that master data. They're producing it. They're using it. We need to know who those people are. We need to help them to build into their job, the accountability of how they're governing the definition, the production and the usage of the data. Okay, so the subjects I want to talk about today. The first one is the need. Why do we need consistency and how we're applying data governance across the organization. So first I'm going to touch on what it means to govern something where we're going to where governance must be applied. We're going to walk through these five items and hopefully it'll make some sense to you. So what does it mean to govern something. And the term governance is being used quite a bit these days so, like I said in my definition of data governance it's to it's to execute and enforce authority over the management of the data. And it is to really formalize accountability for the data, the data in your daily operations of your organization. So we need to understand that if we're going to have multiple governing functions and I would again suggest that there's multiple, there's possibly multiple data governance or governing functions within your organization they may not all be focused on data, but they are governing functions. And what do they do they execute and enforce authority over whatever it is that they are governing. And they probably formalize accountability. So it really doesn't matter what it is that you're governing whether you call it data governance or finance governance, people or facility governance or even security or quality governance. The end at the end of the day we need to execute and enforce authority we need to formalize accountability, and it really all comes down to the approach that you're going to take to how you are going to implement this type of discipline around whatever it is that you're governing. So it doesn't have to be big data governance it doesn't have to be small data governance I've heard that term, I've heard the term bi data governance analytical data governance, master data governance. I don't, I don't expect to tell you that there is one that you shouldn't call it or shouldn't call it master data governance by the end of this webinar. But I want to give you some things to think about in terms of what you are calling the discipline in your organization. So, where do we need to apply governance within the organization we need to. I know that my friend London Silverstone who is a frequent speaker at Dataversity events has said to me that we shouldn't even call it data governance we should call it people governance so we know that we need to govern people's behavior associated with the data so those people again that are not just mining producing and using the data. There needs to be standards and guidelines and things for them to follow and they need to be bought into why it's valuable to them but people must be governed so that right out of the gate. Where governance must be applied it must be applied to the people of your organization process in itself is a form of governance because it is getting the trains to run on time or so to speak. Right people involved at the right time I talk a lot about the data governance bill of rights, and that's really what processes is getting the right people involved at the right time and the right steps doing the right thing with the right data and so on and so forth. So process needs to be governed technology also needs to be governed, especially in this day and age that organizations have so many different types of technology that if they're if they're working in competition with each other, it's not good. If people aren't being taught on how to use it, it's not good. So the technology itself needs to be governed. And it really comes down to what do you need to do in your organization to formalize accountability for data. That's because accountability most likely already exists to one extent, to some extent, some people are expected to protect sensitive data, do they know what data is sensitive, do they know what proper protection techniques look like. We need to be held accountable for that. And as well, we need to have a decision making process within an organization, we need to have an escalation path that says, Okay, if we can't decide on a business unit by business unit basis we need to be able to escalate it to the appropriate within the organization so governance must be applied to people process technology accountability into escalation because at some point I'm sure there's other things that governance must be applied to, but this is fairly common and I think this is a kind of a good way to mark down the five things that we need consistency in how we're applying governance. And the one thing I say all the time is that the data will not govern itself, that the metadata will not govern itself the data governance program or whatever you're calling your program, it will not administer itself, it requires resources. So we need to apply governance to the data, we need to apply it to the master data to because it is the place that we want people to go to get to the data, the single point of truth, or the data that we want people to see. It means that master data is the place to go. And, you know, we don't really want to tell people to go to a place where the data is ungoverned. So calling it master data we, it kind of implies that that data has been governed to some extent. So what does it mean to apply governance it means to apply formalization to people's roles. It means to a fine form assigned or apply formalization to process. As I talked about before getting the right people involved at the right time, applying formalization to the process, applying, applying formalization to the enforcement. Consequences have to be necessary if you have rules if you have guidelines, if you have standards and people decide that they're not going to follow the standards. Well I guess it depends on to what level of the organizations those standards have been blessed, but there need to be consequences, if people don't follow the enforcement roles that are being defined in your organization. If you apply formalization to the rules, you need to execute and enforce authority over the thing that you're governing within your organization. So I think that's that's really what it means to apply governance within the organization is to get the people to do the right thing, build it into the process. If the idea is to be non invasive, don't make it feel like it's threatening to them, just build it into what people do. And that takes time for people to get used to doing that but then it doesn't feel as invasive as being assigned to be a data storage. I want to share with you real quickly one of my pet peeves in the industry. And if you know me, you probably know that I have a whole bunch of them, but one is just the term doing data governance. What does it even mean to do data governance okay so what I've heard in a lot of the organizations that I work with or hope to work for is that they have people in their organization that are coming to them and asking them to help them to do data governance. And I don't think unless they have a really good definition of what it means to do data governance. I saw some posts on LinkedIn this week about the same subject. They get people to change the language they're using to say, Okay, when are you going to come to help us to govern our data. Well you still need to answer what it means to govern the data but doing data governance it's not as though you do it once and it's done. So again, another pet peeve of mine is that just using the expression do data governance. So if you use the expression do data governance within your organization. I'm going to answer to what you mean by doing data governance, and I think that I'm going to outline that in a minute or two, specifically around what it means to do data governance around master data. So help people to understand that there's a difference between data that is governed and data that is ungoverned. And if you really want to make it resonate with them you've got to tie it to something that's important to them. So again, doing data governance, try to stay away from that, helping them to govern their data and explaining what the difference is between government and ungoverned data is really really important. So I talk a lot about the definition, the production and the usage of data as being really the three primary actions that people can take with data. Again, just to further the idea of what does it mean to apply governance to data. It means that you're going to apply governance to all the actions that are associated with putting definition to your data to the data modeling to the documentation to the data dictionary to the business glossary of terms of business terminology, there needs to be governance around the action of defining the data getting the right people involved at the right time and that. There needs to be governance around the action of producing data, those people that produce data. They really need need to be held accountable for how that are producing the data. The one thing I hear all the time is that there are more people born in the United States on 01 01 01 then on any other day, or maybe it was 1231 99 whatever the default is. You know, we need to get people to be held accountable for producing the data as well. And there need to be consequences. If the data is not being produced the way it needs to be produced, and then using the data I think that one's the no brainer. We need to hold people accountable for keeping data private for keeping data secure for following the rules. And that means any type of data, if you're going to govern. If you're going to implement information governance, and you're going to focus on unstructured data, that data, the usage of that data needs to be governed. The same as the data that is in bits and bytes in the databases and the tables in your organization. So, when we talk about the governance of data. The same thing holds true for the governance of metadata, because the metadata in your organization is not going to govern itself either. We need people to be responsible for the definition of what metadata is going to be governed, but producing that metadata and using that metadata. And so that when it comes to metadata and that's a really good example do we have to have a separate governance program do we have to have something called metadata governance. We use that term a lot but I typically allude to it as being the governance of the metadata. It's the executing and enforcing authority over the metadata it's the formalizing of accountability for the metadata. All right, so now let's talk a little bit about what is the impact of labeling your data governance program and these are some subjects I want to touch on what it means to label your data governance program. What's the appropriate use of labeling, you know, the benefits of doing it the challenges that might result from it. And then, do we call it master data governance or is it really master data governance I'm going to touch on that real briefly as well so the first thing is why do we label things in the first place well we make we label things to make them easier to understand in most situations, and there's lots of things that get labeled like data there's big data small data and analytics data, every type of data, you know, metadata. There's a lot of different approaches there's a non invasive data governance approach there's a traditional or a command and control approach to data governance. There's different methodologies that are labeled. There's different intelligence. You know, there's central intelligence, which is a great movie by the way, there's people intelligence human intelligence, there's artificial intelligence. So things get labeled. The funny thing that is within our organizations often we don't label the data though, we don't give the data we don't apply the same role to the data. And we might have five different versions of customer or five different versions of student. But if we add applied a context at the beginning of what we're calling something. Instead of saying how many students do we have it would be how many active students do we have how many, how many graduate students do we have, there is a need to add context so data is data but really is it or is it the reason why we have so many types of data and organizations cannot get away from having the same data defined multiple times is that we don't apply context to it. And if we applied context to it I think people would understand it a little bit better, and would recognize that we're not comparing apples to apples or oranges to oranges when we're when we're comparing that these results are not the same as other results. And again there's the examples are there's data governance metadata governance information data governance has really taken off in terms of it being focused on unstructured data. So that's what I've seen within the industry. So let's talk about the appropriate use of labeling when we really need to differentiate something like we need to differentiate specific pieces of critical data in our organization. The best idea is to apply some context to it is to add a label in front of what it is called just so people don't say, Oh well we have different numbers of students. Well yeah we have different numbers of students because you want to look at graduate students I want to look at active students you know we want to look at potential students at a context. And so we appropriately use labeling when things are different we don't want to call them the same thing. When things are the same, typically there's no need for labeling. So the question is going to become do you need a separate governance program for data that you're calling a different name, or is governing master data really different than any other type of governance. What I want to share is a couple things and I'm not going to go through these next couple slides in a whole lot of detail, because I know we'll run out of time but I want to talk about what are some of the benefits of adding a label in front of the name of your data governance program. And so here's just some that I came up with, you know that if you're really taking a different approach to the different type of data, then you might want to call it something different. But to be honest with you when you're governing metadata and you're governing data, you're still governing the definition the production and the usage of the data. And I think that's the same one, whether we're talking about structure data, or unstructured data. It's just that we might have different names for the different roles, it might be authors and content approvers and things like that and in an information governance program. But you know it may help you to tailor your approach so that might be a good benefit of labeling your program. It might lead to increased efficiency. When you know having a one size fits all approach for your organization isn't going to be cost effective. It could enhance security because you're focusing on specific things that are within those different types of data that are important compliance and regulations. And there's another page you're focusing on specific quality and accuracy of that specific data type within your organization. You're focusing on ownership. So you can see there are a lot of benefits of labeling your governance governance program. But I also want to talk about some of the challenges that you might have might run into as you are labeling your program. And you know you might have multiple programs within your organization so multi multiplying, or having multiple programs can have complexity. It can help you to build silos of governance. Now, typically, I say we're not going to go around assigning people to be data stewards we're going to recognize them for what they do so we want to kind of stay away from the approach so having multiple data governance programs with different labels might might instigate that as a problem. It might have inconsistent policies, difficulty in mapping the relationships between people and data. There might be redundant efforts these are all things that might be challenges that are associated with labeling your program compliance issues if you're not working together scalability concerns, all of these things and I hope you'll go back if this makes sense to you again refer to these slides when you're thinking about, you know, do we need one program, or do we need multiple programs. So I talked about how sometimes a comma misplaced or a space misplaced, or even a quotation mark this place can cause problems. So we are trying to master data governance which I think a lot of organizations are looking to do, or is it truly master data governance, where we have to call it all one thing so is it the mastery of data governance, or is it truly the execution enforcement of authority over the master data and the data the question is, you know, formalization of accountability is going to need to take place either way. It's the way that we describe it. If it's really data governance focused on master data or master data governance, it might have some impact on the way that people perceive what you're talking about. And I just want to give you an example I don't know if you've seen this example before of a misplaced comment, a comma. So instead of saying you know let's eat grandma. It's saying let's eat grandma. So, you know, just, it means something different, depending on where you are putting your emphasis. You know, is, are we really mastering data governance or are we talking about something that's data, data governance that is specific to only master data. So let's talk about data governance as a discipline against all types of data let's take that macro view that suit talked about earlier, and let's start with a data governance framework let's look at the components of a successful governance data governance program. Let's look at the organizational levels, take it even a deeper dive into something that I've been looking at recently which is the multi dimensional facets of data governance, and then talk about what a data governance framework for master data might look like. So if you've attended these webinars before, or you've attended some of my presentations at the data diversity events. And sometimes it's called a data governance and information quality framework a lot. And you can see the way it's broken out is across the top there's those core components of a successful data governance program. And if you look down the side. There's the different perspectives of the organization that need to be considered. For example, we need to know about the data at the executive level we need to know what processes are important at the tactical level. What are the tools of the support levels of the organization. So just like the Zachman framework if you're familiar with that, you know, john kind of inspired what this looks like it's taking a look at the really the core components from all the different perspectives of the organization. Well that might have a lot might have to do how you label your data governance program might have a lot to do with how people are going to perceive your program. So those six core components that I talked about the first one is the data, the data column. It is truly the asset that is being governed is that your master data is that your information is that your data is that your metadata is that your analytical data. Is it your AI data it is what data is important at each level of the organization. And it's the role that different people that people in the organization play and the processes that are important at that level or the communications that needs to take place around data at that level or the metrics or the tools that are going to be used by those levels. Again if you've attended my webinars in the past you know that I've shared my operating model of roles and responsibilities quite a bit where it really aligns with the left hand side of that framework where there's an executive level. That's made up of a leadership team there's a council level at the strategic level. There's subject matter experts and stewards and somebody to administer the program in your organization. One of the things that I said I was taking a little bit more of a deep dive in is that every one of those places where those components met one of those perspectives. We could start looking at it in different dimensions. There may be an impact on the different industry that your organization is in to determine what is the best way for you to implement your program. The organization size might have something to do with the organization type. Again I hope you'll refer back to this and consider that you know it's not a one size fits all data governance for everybody. It's more one day to governance for all within your specific organization. And so I know a lot of people are using things like chat GPT and large language models and I just thought I would ask a large language model, what it believes are the data, what would go into a data governance framework for master data. So I wanted to share that with you and be transparent with you. And these are all the things that were listed as being necessary within a data governance framework for master data. Data quality stewardship ownership classification all of these things. And I'm guessing that you're having the same reaction that I'm having is that, you know, basically, aren't these just parts of regular old data governance that we need to apply to the master data to the metadata to the, to the information to all the different types of data within the organization. Think about it. I mean, if there's distinct things that need to be done for master data, then for certain they need to be governed. But just asking some of the artificial intelligence engines. These days what should go into a master data governance framework. It is the same thing that goes into a standard data governance framework. So let's talk about, if you have multiple programs, you know, what will it take for you to consolidate existing labeled governance efforts within your organization. So we'll talk about governance by any name by any other name, or should I say by another name consolidating efforts keeping them apart. Let's walk through these subjects. Before I turn it back to Shannon at 10 to and see if there's any questions about what I'm talking about, or what Sue talked about. So governance by another name some organizations do not like the term data governance. They think it sounds too imposing they think it sounds too threatening. And even if you include the term noninvasive in front of it. Data governance so they still think it's going to be threatening and controlling on the organization. What we talked about earlier is that I define data governance as the execution and enforcement of authority. And that sounds quite invasive but it's not really that that is invasive it is that is the goal that no matter what approach you're going to follow the data governance. I firmly believe that at the end of the day you're going to want to execute and enforce authority over that data. So some people don't like to call it data governance some people are moving towards calling it data enablement has a lot more positive view of what the output is but my wife pointed out to me last night that enablement doesn't always have a positive connotation to it. You know you think about it in an intervention that the that type of enablement doesn't always I guess it's pointing in a good direction, but do we want to call it data governance we want to call it something else. I'm not a fan of data enablement but I think it makes sense so I think it needs to be included in there somewhere. And there's lots of other names for data governance. Some of them aren't very clean and I don't really want to share those with you. When it comes to consolidating efforts I wanted to share with you a couple steps that you can follow that if you have multiple programs that you might want to consider following as you're bringing those programs together. So the first thing is to assess what each of the programs are doing assess what each of the activities are doing when you know just give us an example. If your organization has a data security function. That's a governance function. It's not a data governance function but it's a governance function. If your organization already has a project management office. That's a governance function. It's not a data governance it's a process governance it's a project governance function. So if you're going to try to consolidate any of these efforts you need to do an assessment of what are they doing to understand what their present scope and objectives and where the overlap is identify the commonalities create a unified vision. You know establish a framework that takes into consideration. You know both of those things that you're considering trying to combine into a single thing. So look at the roles and responsibilities and how they've been defined and find a way to at least consider how do we merge these things together if you're going to consolidate governance efforts. And again the biggest point that I really want to make from this slide is, there are a lot of governing functions in your organization that don't go don't go by the name of governance. I mean there's a lot of confusion in fact there's data management and data governance and confusion around who's responsible for what you know even if you're trying to consolidate those efforts. You need to do an assessment you need to identify commonalities, create a unified vision all of these things are going to be important whenever you're trying to consolidate efforts within your organization. So what are some of the initial steps that you should follow to keep the efforts apart. So if you're going to have multiple quote unquote data governance programs within your organization. And they're governing different types of data then you really need to clarify the objectives, what are the objectives of data of customer data governance versus master data governance versus metadata governance or, or any other types of governance within your organization so clarify the boundaries, establish the, or clarify the objectives, establish the boundaries, recognize who's going to be responsible for these efforts, that there may be unique policies there may be unique efforts unique metrics, or that they might need to be combined metrics between those things that you're trying to pull together. Now build that partnership between the functions, and just quick by a quick definition of what is a partnership between efforts like between your data governance program and your information governance program looks like. Well it starts with building a level of trust between the two functions. And then to, there needs to be connected communications instead of communicating separately, there needs to be a partnership in the communications around the governance there needs to be shared goals mutual respect all of these things. They're pretty obvious, but I mean if you're looking for what do we need to know that we have in terms of ducks in a row and we're trying to pull these functions together. You want to build trust you want to build communications you want to build these things. Business values of building those partnerships well one of the things that I've learned in several organizations that I've worked with recently, who have set data management and say and set data governance functions within their organization that they that you should be looking to to make the value or get the most value out of those partnerships by leveraging expertise sharing best practices, reducing redundancy. I can't read through all these things but the business value of the partnership is going to be really important. For all the reasons I described on the previous page. Here's some things that we need to do here's some of the benefits that will come from merging together governance efforts, or at least partnering them and bringing them together. The last thing that I want to talk about before I flip it back to Shannon is why one governance for all organization say one data governance for all makes sense. Because you know, I think you're probably experiencing this in your organization, but even having one data governance initiative is hard enough by itself. And having multiple efforts maybe significantly harder and it may raise questions as to why do we do this. Why do we have multiple efforts kind of doing the same thing. I think it's time to call it master data governance. And I'm going to share with you a role that I may have talked about in previous webinars that doesn't exist right now that could very potentially very potentially exist in near future. And then I'm going to talk about what to do in the meantime while we're while we still have one or two or no programs in place. So I grabbed several quotes off of the internet. Of course, that's where everybody gets about governing. And you can see the definitions I like the last one the best but I'll just start at the top, writing laws is easy but governing is difficult. I think that people will agree with that it by writing policy guidelines standards that's the easy part, the hard part and it's not always that easy, but then governing to those standards. That's more difficult. I'm going to jump to the very last one as well it says the government is best which governs the least, because it's people discipline themselves. That's the whole concept of non invasive data governance that's really the should be the concept of data governance for everybody in the organization is if we can get the people to do the right thing in the right way at the right time. Without being told every step of what they need to do, your program is going to be more successful, most likely because now it's become built into what people do instead of being instead of it feeling like it's an add on to people. So multiple governance efforts is significantly harder for the reasons that I kind of mentioned earlier but there's oftentimes lack of alignment and there's duplication, or there's inconsistency or silos. So think well and hard about whether or not you need to have two programs or having one that is working that falls under the auspices of the same label is going to be better than confusing the heck out of people in your organization by potentially having multiple labels around your data governance program. And so I am not calling for people to call it master data governance but perhaps there are some situations where it might be appropriate within an organization is certainly when you have large and complex data sets when you have multiple data domains that need to be governed and that it in the domains might not even just be subject matter such subject matters, they may be totally different domains being different types of data within your organization. You know, and so if master data requires that there needs to be governance around master data so don't get me wrong I'm not saying that it doesn't need to be governed. I don't know if we need to call it master data governance. And so we look for enterprise wide data integration cross functional data usage, compliance and regulatory requirements. You know those might be some ideas as to what you might want to consider if you're going to continue to call it master data governance. So now I'm going to share with you. If I wish I had a drum set or something to do a drum roll, the role that doesn't exist in a lot of organizations and one that I've talked about now a couple times that I think might need to exist within an organization. When we think of terms in terms of all the different types of governing that are taking place within organizations. There's nobody overseeing the connection between all the governance functions. Like I said there's they're not necessarily called governance functions, but to have a CEO achieve governance officer, having a C level position that oversees all the forms of governance and I just listed two handfuls of types of governance within the organization. You don't typically have security governance but you have information security you don't typically have risk governance you have risk management. So those might not be the names of those divisions. But at some point I think it's going to get to the point where people will recognize that there needs to be some type of central leadership around all the governing functions within an organization and there are a lot of governing functions within an organization. So what do we do in the meantime. Well in the meantime, we govern effectively even if it is independently even if you have different types of governance going on. Think about and maybe plan to consolidate the governing functions if you see that that's going to be necessary. Now we know that in this day and age I think Sue talked about this earlier as well, organizations are cutting back in their resources. They might not be interested in having multiple governing functions so you might want to have one data governance for all. And that would include your master data to develop the partnerships data governance plays a role in all levels of governance whether it's security governance or risk governance. There is a huge data component. And as I say often I said that everything will not govern itself the data the people the process, the technology the program will not govern itself. You know my my idea in the meantime, make certain that you're focusing on on doing the best job that you can in those areas. So what did I talk about today I talked about well why do we need to be consistent in how we're applying data governance. What's the impact of adding a label to your program we looked at the different facets or the different components in the levels of a data governance program talked about it, you know, how do we consolidate or do we keep them apart. And why one governance for all makes sense. And with that Shannon I'm going to turn it back to you to see if we have any questions. Bob thank you so much for another great presentation. If you have questions for Bob or so feel free to submit them in the Q&A section, and just to answer the most commonly asked questions just a reminder I will send a follow up email with the links to the slides and links to the recordings to all registrations on Monday for this webinar. So diving in here so what are your thoughts on the idea of using an MDM program as a pilot or catalyst to facilitate data governance in a global org that has no overarching data governance at all at this point. Well, I'll start out with this and answer this and then Sue I'd love to get your feedback on it as well. I think it makes a lot of sense, because you're not governance has to be an incremental progression within an organization you're not going to flip a switch and go from ungoverned to governed in one day. So if you're going to if you select and I know a lot of organizations have actually started their data governance efforts in master data. They don't always call them master data governance but they call them data governance and they're focusing on master data so thank you for that question I think that question is really spot on that data governance or that master data is a really good place to initiate your data governance program. I'm going to jump in and say I agree 100% right that's exactly what we were talking about before as far as the data integrity journey. It depends on where you are at. If you're working with master data, you've got a good program there, certainly expanding that into other domain areas or into other other operational areas make sense now. The pros and cons of that is sometimes a master data management solution doesn't have the extent it's not extensibility they don't have the they can't always translate to other needs. It's truly a master data management solution right, but in general agree 100% Yeah, I agree that if it's really only honed in on the master data then there may be some additions that don't need to be made to the program but yeah it's a great place to pilot your program. Perfect. So specific to healthcare data. If there's no real need for a business to have master data when should be a metadata management to build into the governance framework. Can we start the journey with the metadata management. Good question but I would maybe contest because I'm not a master data expert but I would contest the idea that there's no master data within healthcare. I would think that patient data and record data and and every transaction data would potentially or the data that's associated with the transaction would be master data. Can we start with metadata to I mean you could define the discipline around the governance of data, specifically focused on metadata, recognizing that somebody needs to define what data is going to be necessary in the organization. You know, somebody needs to produce the metadata there needs to be governance around that, or the metadata will not be produced like I said it will not produce itself. So, yeah, I think that metadata could be a candidate place to start but again I'm not sure about there not being any master data in the health care industry. I'm going to jump on that too that's a sidebar right, I would I would say there's definitely master data but hey, I think most people think of it more operational right so okay it's not master data, but does it need to be governed 100% as a matter of fact health I mean think of all that the sensitive data that you're talking about there, certainly it needs to be governed. You know is met is metadata the place to start. I'm going to go back to, you know, things that we always look at you know people don't buy data governance or put data governance in for data governance sake. They do it because there's a specific pain point or a problem that they're trying to solve. And so, depending on what the pain point or problem is you're trying to solve around that health care question. It may be 100% the appropriate place to start, depending on what what it is that you know is your number one priority or number one focus area that you think you can get some high value returns. Perfect. So, a lot of if this than that, or before that so it shouldn't there be reference data managing government before master data government. Oh boy. I've done webinars on that. In the past where we talked about the differences between reference data and master data. Again, now I'm only going to provide my perspective but reference data is a type of master data for certain, at least in the way that I understand the term being used. So, yeah, I think that if you start with the coded information, especially just kind of to allude back to the health care industry. You know the diagnosis codes the procedure codes the, the, all the other codes are extremely important and there needs to be, and that's all reference data. There needs to be governed to and because those change over time and they need to be mapped and they need to be converted so I would say reference data. There may be pockets of reference data that might be the ideal place to start your data governance program, because you have problems with consistency in the use of that data. I don't know Sue what do you think. I'm actually going to almost mirror my answer that I just had before it depends on what is the problem that you're trying to solve what is the use case that we're talking about. Sometimes you're right reference data is the precursor. Sometimes it is, and sometimes it is the problem that you're trying to solve and so it needs to be you know so I would look at each individual circumstance and say that's where the argument comes right is what is the use case that you're trying to that you're addressing, and is referenced that are a precursor. Is it a sidecar, or is it something that is, you know, not necessarily part of the use case that you're looking at. Right. It is funny that that question came up. I was on another webinar earlier this week and that exact same question came up. So it was the same person. No, it's a great question, you know, and again, you know, there's a lot of questions of what should I do first in a lot of our webinars, you know, for sure. So Bob this question is very specifically for you. Is there a second edition available in digital format or not a second edition but it's the, the latest version of your, or your newest book I should say data governance, non invasive data governance strikes again. Is there a digital version. There is a PDF version and go to techniques pub.com and that is my my publisher, and there's lots of different versions, actually there's lots of different versions in the second book hasn't been translated into a lot of languages yet. But the first one has been translated into many. So all the different versions all the different types. There's an audio version as well of the first book so you may want to look at that as well. I see folders from business community here the words data governance and intuitively they don't know what that means so when data management is said they understand what the management that it is the management of data. Is it okay to use words like data management or does semantics matter. We're finding in a lot of organizations that data management and data governance actually don't mean the same thing. And so they can't be necessarily used interchangeably data governance is really mostly associated with the people and the behavior of people associated with the data it's the execution and enforcement of authority over how you're managing that data, but the management of the data is again from my experience it's much more operational in nature. And it's also in a lot of a lot of ways a lot more technical in nature because data management functions oftentimes own the repository they own the catalog they own the data quality tools, but data governance should be a partner and if you go back to some of the things we talked about, there needs to be a partnership between data management and data governance, or there needs to be some clear differentiation, so that they're not, they're not battling each other. There's some clear understanding as to what the bot the boundaries are, who's responsible for what that they should be working together. And so no I don't agree that just if I know governance is a scary word. I've had organizations that have not used the term and have called it information asset management, instead of data governance. I don't know if that really helps things at all, you know, even using the term like enablement. I mean it doesn't even really say what governance is what governance is is just like government. It is making certain that the rules that are being defined are being followed. And so that's I'm sorry my little rant here but you know governance is the is the right word to use because at the end of the day we need to execute and enforce authority, we need to formalize accountability. We don't have to do it in a smash mouth way, a command and control way there are other ways to do it. So that's my thoughts on that. And the funny thing about what you're just we're talking about is that what we need is data governance around the word data governance right. Every company is going to call it something different and I am surprised that we're seeing new things come up because data governance has a bad taste in some people's mouths. We can call it alphabet soup right, but company has to say, this is what we mean by it, and these are the parameters we need to know what those are what those parameters are so this is going to be one of those esoteric questions as far as data governance and what it means, although I agree in theory I do agree with you Bob, you know there are certain things that as you look at them. There's indisputable properties that need to be a part of data governance but what what one company calls it and what another company calls it is is kind of where where the things are changing in the market right. And I can picture you banging on the table as you were saying that there has to be you know I can hear you doing that. And you know it. It all comes down to communications and with people and helping them to understand that governance doesn't have to be a scary word that there's there are alternative approaches to implementing governance. That's not going to be threatening to your organization so I think that's a lot on us as the practitioners is to share that word, and whether or not you have one day to governance program or you multiple data. There are lots of organizations that would give anything to have one day to governance program, but if you have multiple programs. Get that or you have a data governance and a data management program, bring them together. Get them to work together because that's really going to be where the benefits going to come from agree 100%. And we're so close to the top they are but you know there's lots of questions, you know, keep them coming for a minute Bob will type of some answers and we'll get those out in the fall up email so. And again just to remind her I will send a follow up email by end of day Monday for this webinar with links to slides links to the recording and Bob you know it's interesting there's a question there's a couple questions in here about how do you explore some of the consequences to enforce it and we'll maybe we can do a webinar on that next year. I think that would be really good and if there's some positive and good ways to incentivize good data be good data behaviors, rather than negative infractions. So, so that's a great subject, but you know what if there's no consequences, then people, they're going to feel less there. Yeah, it's a great subject. That's why I, you know, there's not a lot of time to go into unfortunately but well and Sue, so great to have you here as always just thank you for joining us again and thanks to precisely for sponsoring today's webinar and help me, these webinars happen. No, thank you for having us here we thoroughly enjoyed it. Thank you all. Thanks all our attendees. You all are awesome. We'll hope you all have a great day. Thanks Bob and Sue. Thank you.