 Hello, and welcome. My name is Shannon Kemp, and I'm the Chief Digital Officer of DataVersity. We would like to thank you for joining the current installment of the Monthly DataVersity Webinar Series, Real World Data Governance with Bob Siner. Today, Bob will discuss optimizing data governance with frameworks and maturity models. 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 would like to chat with us or with each other, we certainly encourage you to do so. And to note, Zoom defaults the chat to send to just the panelists, we may absolutely switch that to network with everyone. For questions, we'll be clicking them by the Q&A section and to find the chat and the Q&A panel, 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 introduce to you our speaker for the series, Bob Siner. Bob is the President and Principal of KIK Consulting and Educational Services. Bob specializes in non-invasive data governance, data stewardship, and metadata management solutions. And with that, I will turn it over to Bob to get his presentation started. Bob, hello, and welcome. Hi, Shannon, I just wanted to make sure that I unmuted myself before this one. Can you hear me okay? I think we've been doing this for quite some time that we would have that down to a science at this point. But thank you very much for the nice introduction. Thank you, everybody, for taking time out of your busy schedule to sit through this session, whether you're sitting through it live or you're listening to the recording of it. As I have been known to say with a lot of these webinars that these are good topics, and this is another really good topic, I've been talking a lot about data governance frameworks and maturity models in my work with the university also and work with clients recently. So I think it's a very timely topic for most organizations, and I'm getting ready to unveil a digitized version of my data governance framework. I'll tell you a little bit more about that as we go through this session. But I'm also going to be doing an online learning plan through the Data Diversity Training Center that focuses on data governance frameworks as well. So before we get started, just want to share with you a little bit of information about myself. As you can tell, I'm busy in a lot of different ways in terms of data governance in the industry. Obviously, you know about this webinar series, the third Thursday of every month. Next month we're going to be talking about a blueprint for data governance roles. So we'll be talking about an operating model and roles and responsibilities in terms of setting them up effectively for your organization. In March I will be speaking at the Data Diversity Event Enterprise Data World. It's taking place in Orlando, Florida. So I hope that you will join us there. I will also be speaking at the Data Governance and Information Quality Conference in San Diego coming up in June as well. I talk a lot about non-invasive data governance. I have two books that are out. The first called Non-Invasive Data Governance and the second one called Non-Invasive Data Governance Strikes Again. Please take a look at those if you're interested. As I mentioned, I have several learning plans available through Data Diversity. The first one was called Non-Invasive Data Governance. The second one focused on Non-Invasive Metadata Governance. The third one focused on business glossaries, data dictionaries and catalogs. But the one that I'm getting ready to record is going to focus on going into even more detail of the things I'm going to talk to you about today. I'm not going to go into detail about frameworks themselves. I'm going to talk more about how do we use the frameworks and the maturity models that we're creating to help to advance our programs within our organizations. So take a look for that learning plan. KIK Consulting is my consulting and education business. KIK stands for Knowledge is King. And as I said in my spare time, if you call with that, I'm also a faculty member at Carnegie Mellon and their Chief Data Officer certificate program. So what are we going to talk about today? I said today is a good topic. There's a bunch of topics kind of built into this topic. Not only about data governance, but how do we use these tools, the frameworks and the maturity models to help to optimize our data governance program? Some of you might be in the early phases of your program. Some of your organizations may have frameworks in place. First thing I want to talk about is how do we use the framework to establish that solid foundation for our organization's data governance program and then we're going to talk about how maturity models, how you can use the maturity models that you're developing and I can share a couple of those with you that might give you some ideas as to what your maturity model will look like. But again, coming back to it, we're really talking about how do we use that maturity model to advance our program to optimize data governance within our organization. I'm going to talk about certain techniques that you might want to consider for taking the frameworks that I'm sharing with you and the models that I'm sharing with you and the ones that you've seen from other practitioners in the industry and how do we tailor these to address the needs and goals of our specific organization. Then I'm going to talk about how to navigate the challenges because there are a lot of challenges in trying to implement frameworks and maturity models. Then last, I'm going to walk through a couple different industries, a couple of different case studies from different industries and share with you how they've used their framework and how they've used their maturity model to move their programs forward within their organization. Like I like to do in these webinars, I want to start out with just a series of definitions because if you haven't attended this webinar but series before, it's good for me to share with you the definitions that I use for these key terms, but then I'm also going to define what a data governance framework or what a maturity model is as well. We'll do that throughout the webinar today. My definition of data governance is worded quite strongly. In fact, it makes some of my clients cringe because it's worded so strongly. Using the words of execution and enforcement of authority, that's worded strongly, but the truth is at the end of the day, in order for governance to do what you're really trying to do, improve quality, improve risk management, improve confidence people have in data, you're going to need to execute and enforce authority. So you can tame that to something else, but some organizations that work would say, you know what, we need to use that as a definition because it's very forceful. So again, the execution and enforcement authority, when I talk about data stewards and data stewardship, you may have heard me say that everybody in the organization is potentially a data steward. Well, if people have a relationship to the data and they're being held formally accountable for whatever that relationship is, as a definer, producer or user of that data, they're a steward. It's not something that they can opt out of, especially if they're being held formally accountable for how they're defining, producing and using data across the organization. So like I said, a steward based on that definition of stewardship is a person that's held formally accountable for what they're doing with data across the organization. I've provided a pretty general definition of data management. Again, I don't want to go through it, but there's a lot of different aspects, a lot of different disciplines, knowledge areas that are associated with data management. Data governance is one of those. If you look at the demo wheel, the DMBock, data governance resides right in the middle of it. And so all of those other disciplines, all those other dimensions of quality, data governance sits right in the middle of it. So the last two definitions I want to share with you and then I'm going to jump into a couple of maturity models and frameworks just to share with you what you might want to be thinking about or even love to hear in the chat whether or not you're creating a framework already. A framework is basically a set of guidelines. So a framework itself just kind of shows that you're putting your thoughts together in an organized manner around all the core components that make up your data governance program. And I'll share some of those with you in a minute. But then we need to be looking at each of those core components from each of the different perspectives of the organization. By perspectives, I mean the executive level of your organization all the way down to your operational and your support levels of your organization. We need to look at these core components from those perspectives in order to make certain that we're covering everything that we need to build a successful program. A maturity model, on the other hand, is used to gauge where you are and where you'd like to be in terms of maturity around each of the different disciplines that are associated with data management. And so a data governance maturity model is a maturity model that is basically talking about how mature your organization is, how set your organization is to, how prepared your organization is to be able to move your governance program forward in your organization. So I'm going to share with you a couple of the maturity models. I'm going to share with you the framework, and hopefully you'll get a little bit better idea as to what I'm talking about. So this diagram right here, and I'm going to make it bigger in one second if you can't read it because it's too small. This is an empty framework. This does not have the information filled into it that is going to help your organization help you to set forth a plan to optimize your data governance program. So the first thing that you're going to need to do with the framework like this is work your way across the top and determine what the core components of your program are. And I've done that. I've already done that. It's going to be on the next slide that I share with you. But you might want to think about as you're developing a framework to optimize within your organization, are these the appropriate components for you? Do we need to add other components that I'll, like I said, I'll talk about that a little bit once I've laid those out for you. But then we also need to look at these different components from each of the different levels. So the levels in your organization may be different. But I typically talk about an executive level, strategic, tactical, operational, and support level of your organization. Now, depending on the size and complexity of your organization, your levels may be a little bit different. But the framework has to be set up so that you can customize it. You can articulate it specifically for your organization. So here's a larger version of the blank framework. I've used this framework in several of the tutorials that I've taught at data governance, at the university events where we start with a blank framework and by the end of the tutorial, we've started to fill in that framework as to what we need to know and what we're going to need to address as we move our program forward. So here's just an example of what one of those frameworks would look like filled in. And so I've highlighted the six core components. And again, I'm not going to go into too much detail about them because I want to talk about how do we use the framework to optimize our program. But I've done other webinars in this series about the specifics of the data governance framework. And so you can certainly go look for that information there. But I define the core components as data roles, processes, communications, metrics, and tools. I've had clients, I've had organizations, I've had people at conferences give me lists of things to add to those core components, like business outcomes. I think the business outcomes and engagement was another suggestion as an additional column, but you may be able to address those by these component names that I've given to each of these. And then down the left-hand side, as I've said before, you've got the executive all the way down to the support level. If you can't read that, I'm going to make it a little bit larger for you as well. And just to give you as an example of the framework, let's just specifically look at the roles column. Well, we recognize that at the executive level there needs to be some type of steering committee. There needs to be a council or something at the strategic level. We've got subject matter experts, data domain stewards, data owners, if you call them that, at the tactical level all the way down to your operational stewards and the people that administer your program at the support level. And IT is also in the support level because IT and data governance need to work hand in hand. I'm not going to talk too much about that, but that's the idea as to how you fill out the framework. You start with those core components. You say, okay, what do we need to know about each component at each level? And like I said, I'm not going to go through this in detail, but the one thing I did want to share with you is that I'm creating a digitized version of this framework. And so keep an eye out on LinkedIn, keep an eye out through Dataversity. I'm going to be sharing that with people relatively soon. I would hope it's getting to the point where I will share it. And the nice thing about this framework is that, again, it talks about those levels of the organization. Every organization has levels. Every organization can identify what the core components are across the top of this framework, but the real benefit to the framework is in the bridges themselves. It is when we need to look at the role specifically at the strategic level. And again, that's what the digitized framework is going to provide. But what I want to talk about is how do frameworks like this, how do we use things like this to optimize governance within our organization? So we're also talking about maturity models. And I wanted to share something with you. This came from a team of students that I am mentoring in the Carnegie Mellon program that I mentioned earlier. The only thing that I saw is that there's a misspelling in the upper line of the gardener's building blocks. This is an example of a maturity model that this team is using to produce the things that they need to produce as part of that CMU program. So they decided to pick the Gartner maturity model. And you can see there's five levels of maturity within the organization. This is pretty much a standard maturity model that a lot of organizations are using based on five levels and things like that. What I want to do is I want to share with you how I took the Carnegie Mellon, the CMM capability maturity model that was developed at the Software Engineering Institute at CMU and developed it into this level of a maturity model. Again, I could spend almost the whole webinar just describing what's on this slide, but you can see that the different columns within the graph are different disciplines that are part of data management, that are part of the DMBOT. In fact, this assessment was based on the maturity in the different levels of the knowledge areas defined by the DMBOT. And again, it defines the different levels, initial defined, managed. Again, there are different levels. I have adjusted the capability maturity model and kind of flip-flopped levels two and three because I'm of a strong opinion that you have to be defined before you can get to the point that you're managed. Again, not the point of this presentation, but this is a maturity model. So why do organizations put together maturity models? Because they need to know where they are. They need to know where they are trying to get to. And they also need to define the steps that they're going to take to get from where they are now to where they want to be. And again, like I said, this is, I'm going to give you a larger version of this as well in case that's more readable to you. But as you can see, it's based on the CMU Software Engineering Institute, CMM. And this assessment was based on something called an MRA, a matter requiring attention. If you're in the financial industry and maybe some other industries, if you receive an MRA, it's something that you need to address. This organization was told to address their level of maturity in different aspects, different knowledge areas of data management. So now I want to get into the meat of this session, which is really talking about optimizing these maturity models and optimizing these frameworks and what role they play in kind of building that solid foundation. And in fact, later in the webinar, I'm going to go back to that framework and I'm going to show you how, just highlighting certain blocks of the framework, most organizations can't address everything all at once. So with some of the things I'm going to share with you today, I want to also share with you where this might fit in to using the framework and how you might use that to communicate to people across the organization. So what is the pivotal role of the frameworks in establishing this foundation that we're talking about? Well, we know we want to align it with our strategic initiatives across the organization, strategic data initiatives across the organization. For most organizations that are implementing data governance, data stewardship, there's a big risk mitigation and compliance factor. Quality and integrity is always something that is being addressed by these programs, maybe not directly out of the gate, but at some point in the future. Operational efficiency, I hear that all the time from organizations, they need to improve, they need to reduce costs and they need to become more efficient and effective as an organization. I talk about stewardship as really being the, going from informal accountability to going to formal accountability. And I talk a lot about going from informal to formal, will take you from being inefficient to being efficient and being ineffective to being effective. And then the last pivotal role is the innovation and the competitive advantage that you can provide to your organization when establishing a framework as your foundation for your program. So let's go through each of these. And again, I want to keep bringing it back to the framework that I shared with you. And one of the first things, one of the first pivotal roles of the data governance framework is to align with what's going on within your organization. So making certain that the business objectives of your organization, I have a client right now that focuses a lot on the mission and the vision for data governance within their organization, defining those objectives and making certain that those things that we include within our framework, align with the business objectives is really important as well. So if we're looking for ways to optimize our data governance program using artifacts like the framework and the maturity model, we need to make certain that the things that we're doing with data governance align with the business objectives, they align with data projects that are taking place. I could tell you stories about an, well, I'll tell you a quick story about an organization that is looking to start a data governance program, but they have a huge project that's left the train station already that's already in progress, and they cannot stand in front of that project, so we need to make certain that that project is delivered with effective governance as part of it. So we need to align with the data projects within our organization. We need to prioritize wherever the organization is making data investments, and that includes people's time. We need to facilitate cross-department data collaboration. We can also use the framework to kind of identify where that type of communication is taking place. I've seen organizations take the framework and put together two of the bridges that I spoke about earlier and say, okay, we're going to focus on these things together. So again, the goal of this webinar is to optimize your program by using these tools and using these frameworks and those types of things that I'm talking about today. Risk mitigation and compliance. Again, I don't, I can't think of any organizations that I'm working with now or going to be working with in the future or working with in the past that mitigating risk and affecting compliance within the organization hasn't been a big part of their program. Again, we can use the framework to start to fill in what data is important to what level of the organization, what role has responsibility for that risk mitigation. We can use the framework to address all of these things. And again, that's what this webinar is about. It's about taking what we were collecting and making it an item to put on your shelf somewhere. It can be an active part of the work that is taking place as you're implementing your program and moving it forward. Quality and integrity. Again, these are things that are often at the forefront of people's minds as they're building out their data governance programs, figuring out again what data is important, what the standards are that apply to the data at each different level of the organization. You can use the framework again to communicate to people across your organization but also to define the specifics around data quality and integrity for the data within your organization. You can use the process column and the communications column. The metrics are certainly oftentimes associated with quality and integrity and even the tools component that's in my framework. Again, if you're focusing on quality you may want to take that framework and you may want to customize it so it focuses on specifically what your organization is trying to accomplish with your data governance program. Operational efficiency and cost reduction. These are again all things that organizations are trying to do by implementing formal governance frameworks. The interesting thing is in the industry I'm not even sure there's a single definition for what the term framework means because I've seen organizations talk about their operating model their roles and responsibilities as being their framework. To me the roles again is just one of those four components that make up the entire program and the other being data and processes, communications, metrics and tools. The roles and responsibilities are really important but that's not going to be the only thing that you need to be concerned about when you're developing your program with operational efficiency and reduced costs using the framework to help to communicate to people as to those things that need to be addressed is really important. Again I wish I had more time for this webinar because there's so much that you could go into about different ways that organizations have built frameworks and used their frameworks to get that competitive advantage in terms of their data governance program. So innovating innovation and competitive advantage using the framework to identify what items within the culture need to be impacted, what items within the literacy of the organization need to be impacted at each level you need somewhere to store that information. I'm just suggesting that that empty framework or even the one that I've started to fill in might be a good place to start. So again we want to foster innovation, we want to have a competitive advantage, we need to support all these things that are coming down the pike AI, machine learning, I mean these are things that are talked about in every single webinar I believe at least in almost every single session that took place in the EDGO event a couple weeks ago. I mean we need to be able to stay ahead of the curve and the curve is forever curving. Let's just leave it at that. So innovation and competitive advantage. Again we can use our data governance framework to help us to establish that solid foundation around innovation and competitive advantage. So now let's switch gears a little bit and let's talk about how the maturity models because I've spent a bunch of time talking about the data governance frameworks. How can we leverage the maturity models to assess where we are, to advance where we're going, to be able to measure what progress we've made according to the appropriate people in the organization, the attention of what do we need to be doing? What should we be focusing on? So the topics that I like to talk about in terms of taking the maturity model and leveraging it is how do we benchmark it against current data governance capabilities? How do we tailor the roadmap? How do we enhance stakeholders? Let me walk through each of these real quickly with you and see if they make sense to you. So we need to use the maturity model as I mentioned before to kind of benchmark where we are right now. What do we have the capabilities of doing? What type of capacity do we as an organization need to build in order to excel in these capabilities that we're talking about for our data governance program? So we can use the maturity models to assess the existing practices against industry standards. We can identify strengths in areas where we know that we can improve. We can also highlight the areas that we're going to focus on first and second and third and those types of things. And I'm going to do the same thing with the framework, which is there's a lot of blocks. There's 30 bridges in the framework that I shared with you can't be expected to start with them all at once. So you need to identify which ones. In fact I've had organizations take the framework and just build out, you know, what level of maturity are they at for each of those bridges and then set targets for where they want to get to for each of those bridges. There's other things that we can do. We can tailor the roadmap to evolve as the organization is evolving. So you can use the maturity model, as I said before, to highlight those things that you're focusing on first, prioritizing those initiatives that are going to help you to achieve the level of maturity that you're trying to achieve in the outcomes in your organization. We can use the model to set realistic and incremental milestones in the example that I shared with you. It not only showed and I know that one was a little bit old, but it showed where that organization was in 2019 where they wanted to be in 2021, 2022 and again, the key of these maturity models and the things that people do with these maturity models is set the the steps of things that we need to do to get from one level of maturity to another level of maturity. So you can tailor your roadmap for however you see your data governance program evolving in your organization and engage the stakeholders through the roadmap to help them to help you to define where you are and where you need to be specifically focusing on the business outcomes that they are trying to achieve. And again, you want to align your maturity model with the investments that you're making in the organization and use those in combination to achieve the maturity levels that you're talking about within your organization. You can use the maturity model to enhance stakeholder engagement as I know I keep feeling like I'm repeating myself, but you can get these folks involved in the conversations about how mature is an organization you are and what level you want to be able to get to. So you can demonstrate you can use the maturity model to demonstrate progress to foster getting people in different parts of the organization to collaborate and to work together. There's just a lot of ways to use you're collecting a lot of information during your assessment. The goal is to use that information to set a path for you to move forward. Recognizing what your maturity level is now and where you want to get to is an important part of that path. Driving policies and standards every organization I work with is starting with policies and standards and guidelines and procedures and best practices and those types of things. We can use those if you remember the tools column of the framework that I shared with you. These policies and these standards they're all tools of your organization. Again you can use the framework to optimize your program by focusing on the tools column but you could also evaluate what level of maturity are you at in terms of the tools that you're using within your environment. One of the things to look at would be how well are you taking advantage of the tools that you already have and developing a plan to use those existing tools to build on your organization's maturity. Measuring and communicating the value of governance again when you're showing where you are, where you want to be and the steps that you're taking to get there that becomes a really important piece of moving your program forward. So now let's talk about taking these frameworks and taking these models and using them to address the specific needs and goals within your organization. So the first thing I want to talk about is customizing the framework to align with your goals, adapting it to the different culture in your organization and there's a lot and governing culture and governing techniques and governing philosophies in organizations are different. We need to take that into account when we're focusing not only our maturity models but our frameworks on the culture of your existing organization. That's one of the reasons why you can't take somebody else's framework and implement it where you are because the culture of the organization is different. We need to apply it appropriately to the culture within your organization. So let's jump through each of these real quickly. Talking about customizing the framework, well I talked about already as shared with you a blank framework at the beginning that you could put in the most important core components to your organization. I've put the six that I consider to be most generic and most general but we need to make certain that we're capturing information about each of those four components specifically as it relates to the objectives. I mentioned earlier making certain that we align our program and our framework with the business strategy the mission, the vision. This is a good place to do it. Customize your framework, again use the ones that are being provided to you like the one that I'm providing to you but customize it specifically to your organization. So that little guy on the right hand side somebody told me that that looks like me. I'm sorry I don't know if it really looks like me. I don't know if I have knobby elbows and knobby knees like that and I certainly am not that skinny but that's me telling people all the time that having a data governance framework is a necessity within your organization just to put your arms around what you as an organization are doing to move your governance program forward. We need to adapt our maturity model for different cultures and these are things that we should be considering. We should be assessing the organization's culture and how ready is the organization to start governing the data. I can tell you have a lot of organizations that have started with their frameworks and their maturity models but the organizations aren't really ready. They don't have this sponsorship, they don't have the support, they don't have the understanding of the executives in order to move their programs forward. So use the adapt your maturity model based on what the culture is what's going to be acceptable, how ready your organization is to get started with moving your program forward. I can't tell you how many basically every organization I talk to is talking in terms of digital transformation. Even if they don't use that specific language if you looked around within your organization you will probably find somebody at a relatively high level of the organization that has this as their responsibility. It's a huge responsibility and we need to make certain that data governance is there to support the transformation to support the transformation in however terms that's defined within your organization. So since a lot of organizations are taking on digital transformation and it's this huge engagement a huge initiative within the organization they're following agile methodologies. They want to get this delivered quickly they're building up technical debt data debt as they're doing it but they have these frameworks in place and they're using the agile approach because they know that as part of digital transformation is not going to wait for them they need to be active with it as it's moving forward. So they're doing it in an agile way consider using a data governance framework as a tool to start capturing that information that's important about your data governance program and digital transformation. I had a client that used the framework to just start to fill it in with specifics that focused on their digital transformation. Again I would guess that I could ask for show of hands but since this is a webinar it's going to be hard to see your hands most of your organizations are addressing digital transformation. We need to make certain that our data governance programs are there to support that transformation but then these specific artifacts these frameworks and these tools if we can start to flush out how mature we are around areas that we need to improve in in order to do the digital transformation to also to identify what are the core components at what's levels that need to be addressed having a framework having a model to address digital transformation it's got to be part of your future I hope you're thinking about it and I hope you find a framework that works really effectively for your organization. We need to another way to make sure that your model is to make certain that it scales to your organization. I mentioned earlier about that organization that has this big data digital transformation project taking place that the data governance program cannot stand in front of. They realized that's what this way they cannot put up a roadblock for that project but they need to be there to support them. The idea is that they're going to develop a governance framework that's going to do a maturity model for their organization based on what they're working on now what's most important to the organization now but always keeping in mind the future of the organization. We need to make certain that whatever we define with the initial parts of the organization that we're addressing that it's scalable across a larger part of the organization so we need to make certain that as we're tailoring our framework as we're tailoring our maturity model the ability to scale to the organizational size and the added complexities that come with scaling to a larger sized organization. I would say every organization I've ever worked with starts small, starts in a specific area and incrementally expands this across the organization. You can't have all your eyes dotted and teased crossed for your initial initial use of governance but you want to keep in mind what will start moving into this part of the organization or that part of the organization. We need this to scale to something that's larger than what we're focusing on just initially. Integrating it, if your organization has a continuous improvement process as many do, how do we how do we build data governance maturity into that continuous improvement process. Again, these are just ideas to share with you as to techniques that you can take the framework that you've decided to use and the model that you're using to assess where you are and to integrate that into other initiatives within your organization that may be like continuous improvement processes even data literacy processes and projects and initiatives that are taking place within your organization. I've got two more things to talk to you about before I turn this back over to Shannon. The first one is what are some of the challenges what are some of the pitfalls that you might run into when you're trying to take your framework and your maturity model and really use it to elevate your data governance program moving forward. So I'm going to talk about overcoming resistance to change one of those things, one of those challenges that a lot of organizations have is getting executive buy-in, getting executive support. I'd even add to that line gaining executive understanding because I know that it comes from a lot of organizations at the highest part of the organization that we need to put a governance program in place. We need to govern our data better. Getting them to buy in and support is really important but getting them to understand what it's going to take to be successful. I mean realistically getting them to understand what it's going to take to be successful is going to be extremely important as well. Managing quality aligning with things that are taking place in the organization. Let me walk through some of these real quickly with you as well. So overcoming resistance to change one of the ways that you can navigate that is to identify what's causing this resistance in the organization. Is there a competing effort? Is nobody being held accountable now but we're introducing accountability as an important factor for the organization. We need to understand what's causing the resistance if there is resistance for change. I honestly I haven't seen an organization yet that if you can share with them the maturity model you can share with them a framework that shows that you have a plan for what you're moving forward. That's going to help to decrease their resistance for the effort itself. So engaging data champions utilizing training and education. These are all different ways that we as organizations can overcome this resistance that we have to why do we need to do this thing called data governance? Well share a framework with them. Share a maturity model. Share where you are. Share with the people what you can't do because people don't have the confidence in the data to be able to do it. I've talked about that in other webinars that I've done through dataversy as well. Ensuring executive buy-in and support. So this is an example of where I'm going to take the framework and I'm going to kind of highlight those areas within the framework that you might want to consider focusing on first. And we want to use it in order to ensure that executive buy-in and support there are certain things within the framework that you may want to focus on first. When I do a data governance best practice assessment for an organization the number one best practice 100% of the time is that senior leadership support, sponsor and understand what the heck it is that we're doing. Support and sponsorship is easy. Getting them to understand and really articulating to them not only the value of governance but articulating to them what it's going to take to be successful is going to be extremely important as well. So you can use the framework and here I'm going to think I did this on this slide. I highlighted just the specific bridges within the framework that you might want to focus on first. There's still a whole bunch of those that you might want to group some of those together but you can use the framework to focus on how are we going to communicate? How are we going to know what data is important? What the role at the executive level is? How are we going to articulate these things to them? We can use a framework to do that. We can use a framework to optimize our program within our organization. And not only that we can use the maturity model again just to share where we are where we want to get to and the steps that we're going to take to get from A to B. So another one of these common challenges, how do we manage quality and consistency? And again, I wanted to do the same thing and highlight and if you notice the different bridges within the framework that I'm highlighting here, they're different than the previous circles that we're showing up on the framework itself. But again, if we're going to navigate the common challenges that we're having, we need to manage quality, we need to manage consistency, we need to consider these steps. We need to consider establishing quality standards and metrics, having robust processes to maintain and to affect data quality. These are all things that we need to be considering. And if we can consider these things, it will certainly help us to navigate the challenges and the pitfalls that come from trying to implement data governance frameworks and data governance maturity models. The question always seems to come up where does IT play a role in data governance? Should data governance reside in IT? Should it reside in the business? The answer to that question is yes, it needs to reside somewhere but it's not necessarily specifically the business. Saying that data governance resides in the business is almost a cop out because there's a lot of different areas of the business. We need to be more specific as to what parts of the business are going to run the program. We're going to support the program moving forward. Data governance, if you've seen sessions that I've given on the operating model of roles and responsibilities, IT plays a big role. There's even stewards of data in IT. There's certainly custodians and system owners and those types of things that we need to recognize. One of the challenges that organizations have in implementing frameworks and maturity models is making certain that we're engaging with IT, understanding what makes them tick, acknowledging them and the role that they play in the management of data because if you try to do data governance without IT, it's going to be as though you have one hand or two hands tied behind your back. They have to be a partner of yours and everything that I've seen in those organizations where they partner with their IT groups, they're much more successful than those that seem to be at odds with the IT group. Last month, I gave a webinar with Data Diversity talking about aligning data governance with data management. Oftentimes, the data management is the responsibility of IT, so we just need to acknowledge that. We need to look for ways to be able to align what we're doing with IT and we can do that using the maturity assessment of where are we now, where do we need to be, where does IT get involved in that, using the framework to also start to identify where does IT play a role? We know that they make up one of those bridges within the framework that I shared with you. If you look down at the support column or I'm sorry, you look at the roles column and you look at the bottom where there's support. I talk about IT part, about data governance partners. IT is definitely a partner of data governance and it's going to be, it'll do great things for your organization if there is a close working relationship with your IT department. I don't want to preach to you too much about that, but data governance and IT, that these things they need to work together. So it's how do we navigate the challenges associated with scaling the program? We need to make certain that whatever we define out of the gate is going to be scalable to the organization. If we define an executive steering committee, we define a data governance council and it's only going to address part of the organization. Well, what happens when we take our program global? We need to be keeping in mind how we're going to leverage the things that we're building as we move our program forward. So design and deliver a flexible governance framework that's adaptable as you're growing. Plan for your roles to evolve because they're going to evolve. Again, I haven't seen an organization yet that the way they define their roles initially out of the gate is the way that they're actually implementing them at this point in time. The key point here is to make certain that you are taking into consideration how your program is going to scale across your organization. So in the last couple minutes that I have before I toss it back to Shannon, I want to give some examples of some organizations in a bunch of different sectors. Excuse me, how they've used the framework, how they've used the maturity model. I don't really have a lot of time to go into a lot of detail, but if you're interested in how you may use a framework or a maturity model in your organization, just ping me and we'll be happy to set up a conversation to talk to you about that. So let's go through a bunch of these different industries and talk about how they've used the framework to the maturity models. Well, one large banking system in the Midwest was focusing on compliance and risk management. That was what they were focusing on. They actually used their assessment as more of a risk assessment in each of the knowledge areas, each of the subject areas that they had within their maturity model. They also looked to use their framework to identify the key data and the levels of risk at the different levels of the organization. And then they even parlayed that into using it for the tools and the metrics and things that they had in their organization. So the financial sector is just one great example of a risk-based approach to using the maturity model, using the framework to help them to move their program forward. Healthcare, I was working with a CMIO, recently a chief medical information officer who seemed to be that person at the top of the organization that wanted to overhaul how their organization focused on data and provided quality data. They wanted to set themselves apart from their competitors and they wanted to do that using the data within the organization. They also created a framework. And they also, before I even got started with them, they had a maturity model and they knew that we need to advance in these areas and that they didn't have the knowledge internally to do it. That's always the best reason to reach out to somebody outside. It is somebody who's worked in your industry before somebody who can articulate to you an effective approach. A lot of that articulating of the effective approach has to do with a framework and a maturity model. Because I know a lot of organizations get started with an assessment because I always say, and I've said in these webinars before, instead of taking a ready-fire-aim approach, you want to take a ready-aim fire approach. And the only way to do that is to truly start to assess where you are presently versus where you want to get to. And I've mentioned a couple, and that's certainly what a maturity model does for your organization. You can certainly use a framework to do the same type of thing. Another organization that is a national organization here in the U.S. in the retail industry, their entire focus was on improving their customer experience. And they were wanting to improve their customer experience through data. So you can just think about the terms that the framework that they're developing has everything to do with improving the customer experience, optimizing the supply chain, making certain that the stores have the appropriate product, knowing how products were being sold together. This is all data related. So in the retail industry, they recognize that, yeah, they don't sell data necessarily, but everything that they do, it has to do with data. So they know that implementing an effective framework or an effective maturity model is going to help them to get towards improving their customer experience through the data that they have. Government sector, I've been working with several government organizations over the last several years. They're looking at they're kind of, I won't say they're behind the rest of the world, but they have the best of intentions in what they're setting up to move forward. The problem ends up being with the cross agency communications and getting people to all push in the same direction. And you can use the framework. You can use the maturity model to help to communicate to these people what's working, what's not working, where do we need to focus? So government sector, there's a lot of government work in data governance work in the government and again, they're looking to implement frameworks and maturity models to help them to plan how they're addressing data governance moving forward for the organizations. And the last one I want to share with you is a manufacturing organization and there's a lot of these that realize that they're not just creating widgets, they're creating data. How do they even know what the most appropriate widgets are to create unless they know what their customers are interested in? So there's a lot of organizations, the manufacturing organizations that are looking to optimize their data governance programs. And again, my point is that you can use a framework to start to define those core components at all the different levels to find the maturity model that will help you to set targets for what you're trying to accomplish and measure those accomplishments effectively within your organization. So I know I just quickly kind of briefly just touched on a few of these case studies. Curious as to your experience, are you using a framework or are you using a maturity model to help to move your program forward? So again, I thank you for sitting through this webinar today. I've shared a lot of information I feel like in a very short period of time we talked about the pivotal role of the governance framework. We talked about the maturity model. I even shared some ideas as to how you can tailor these frameworks and models to address the needs that are specific within your organization. We talked about what are some of the challenges that you might have when you're starting to move your framework and your maturity model forward. And I hope I shared some ideas as to how you can address some of those challenges. And then the last thing I did was walk through the case studies, just to share with you that there are organizations doing this. There are organizations using my framework, using other people's frameworks, certainly using maturity models to improve and to optimize their programs. So with that, Shannon I've talked out, I'm going to turn it back to you and say thank you to everybody for attending this today and see if we have any questions. Well, we have so many questions. They started rolling in right away, which I absolutely love if you have and we'll get to as many as we possibly can here and answer the most commonly asked questions. Just a reminder I will send a follow-up email to all registrants by the end of day Monday with links to the slides recording and anything else requested throughout. So diving in here Bob and just to point to anytime if any questions we don't have a chance to get to I'll get over to Bob and we'll include those in the follow-up email as well. So Bob, how important our data domains and sub-domains in your framework and maturity model? Wow, that's a great question. They are really important. They're going to be important for people to be able to find information about the data. They're going to be important. The one thing that I will say about domains and sub-domains is that there's never a perfect answer. You're going to develop domains and sub-domains and there's going to be different people within your organization that think that your data should be categorized or sub-categorized differently. Domains are critical to getting started. For example when you are getting to the point where you are going to unleash a data catalog or a data marketplace upon your organization most people are going to look for the specific information about the data starting with domains starting with subject areas sub-subject areas. They are critical to the success. The only problem that I've seen and I've seen this with an organization that I'm working with right now is there's a risk of trying to be too perfect because again it's like setting up a taxonomy for your organization. That is very important but again it's going to evolve with time. So I guess to quickly answer the question, they are both very important domains and sub-domains and if you're not thinking about them now those data stewards I always talk about operational data stewards and tactical level data stewards those tactical level data stewards are typically associated with different domains or sub-domains. I often refer to them as data domain stewards at the tactical level and I have organizations calling them domain data stewards and they have different types of data stewards but domains are critical and you don't worry about getting them perfect get them good enough that they provide some positive effect on your program. That was a long answer to a short question. That was a great answer that great question. Is accountability the responsibility of the data owner or the data steward? Yes. That was a very quick answer. Accountability has to be it has to be built into everything that people do. People that just use as an example if you have a room of 100 people and they all use sensitive information in the organization, you're not going to just point at half of the room and say you 50, you're accountable for protecting this data per the roles per the rules that are associated with that data. You're not going to say that because the other 50 could cause great risk to your organization if they don't know and understand and protect the data. They're accountable based on their relationship to the data. If you use sensitive data, you're accountable. If you define data as part of your job and if you're being held formally accountable for not defining the data for the 100th time but looking to see what already exists before we go and create something new, you're accountable for the definition. People that are producing the data, they're accountable too. So I'd say accountability is the key and stewardship I say I've done webinars called everybody is a data steward, get over it. The fact is that if we don't account for all these people that have relationships for data within the organization and help them to be held formally accountable for what they do we're going to be only covering a percentage of the organization and that's the whole idea of the non-invasive approach, kind of going to throw that in at the end here is let's recognize what people do with data and let's help them to be held accountable instead of making them feel like this is brand new to them. Thank you. Bob, do you think it's necessary to have both a data owner and a data steward? So it's kind of in the same way I know what your answer is going to be. I like that though. I don't like the term owner because it implies that it's their data and they can do with it what they want. The term steward if you look it up in the dictionary means somebody who takes care of something for somebody else. So an owner in my experience is often times a critical role even though you may not want to call it that call it a data trustee or something else because somebody has to ultimately be responsible for owning the decisions. So I would say that it's important to have an owner role or somebody it's required that you have somebody who is the decision maker. You can't have all if everybody is a data steward you can't have everybody be a decision maker. So that's how I differentiate between the owner and the steward. Bob, how do AI and gen if it AI fit into your vision of data governance? I know it comes up every webinar. It's a hot topic. It's an important topic. It is an important topic. The way I feel about gen AI and those types of things is that it is not coming for your job. It is coming to your job. I read that from somebody else's article. I can't remember their name. But those people who know how to use generative AI and know how to use it effectively will replace people who don't know how to use it. So it's very important. I'll just throw that out there. That's my soapbox in terms of generative AI. Where is it going to impact data? How does it fit into your vision of data governance? Are the new data volumes and risks brought on by AI and generative AI currently embraced by your frameworks? Are there new additions to your framework and maturity models? That's a good question. In terms of the framework itself, I look at it as being another application of data. When big data came around and all these things came around, they're different applications. So yes, it's riskier. Yes, people are going to get access to the data. Therefore, you have to have more confidence in the data. You need to have more rules associated with who can see what data as part of your generative AI solution. Where does it fit in? I don't think that I would create a separate column or a separate row for generative AI. It's more of a tool. It's the data that's associated with your large language models that you truly need to govern. People need to have confidence in and that needs to be restricted that only the appropriate people can see it. So governance, it fits into the framework. It's just that there's nothing specifically in the framework that calls it out unless you develop your framework to include it. I think I can slip in one more question here. We've got about two minutes. Should a data modeler be discovering the data concepts for a data domain or a data steward or a business analyst? Should a data modeler be doing that? That's a great question. I think that the data modeler can be the facilitator to get that information out of people. People from my background, people who are good data modelers knew how to ask the appropriate questions within their organization to get down to matters. I think that that's my on that subject is we need to engage I'm kind of losing my train of thought but that's what I'm thinking about that subject. That's perfect. That brings us right to the top of the hour Bob. There's a lot of additional great questions. We'll get those over to Bob and we'll get the answers to you in the follow-up email which will go out by end of day Monday with links to the slides and links to the recording as well. Thank you Bob. Thank you everybody. Appreciate it. Hope you all have an amazing day. Thanks Shannon. Thanks everybody. See you again.