 Welcome, my name is Shannon Kemp, and I'm the executive editor for Data Diversity. We would like to thank you for joining today's Data Diversity webinar, Best Practices with a DMM. The latest installment in a monthly webinar series called Data Ed Online with Dr. Peter Akin brought to you in partnership with Data Blueprint. Now let me give the floor to Steven McLaughlin, the webinar organizer from Data Blueprint to introduce today's speakers and today's webinar. Steven, take it away. All right. Hello, everyone, and welcome. Thank you for finding the time and your busy schedules to join us for today's webinar. Best Practices with the DMM. As always, a big thank you goes out to Shannon and Data Diversity for hosting us. We're going to get started in just a few moments after I let you know about some housekeeping items and introduce the presenters. We have a one-hour presentation today, followed by a 30-minute Q&A. We'll try to answer as many questions as time allows, but feel free to submit questions as they come up throughout the session. To answer the top two most commonly asked questions, yes, you will receive an email with links to download today's materials and the webinar recording so you can view it afterwards. These materials will be sent out within the next two business days. You can also find us on Twitter, Facebook, and LinkedIn. We've set up the hashtag, hashtag Data Ed on Twitter, so if you're logged on, feel free to use it in your tweets and submit your questions and comments that way. We like keeping it to 140 characters. We'll keep an eye on the Twitter feed and we'll include answers to these questions in our post-session email. All right. I think we're ready to present the presenters. So first up, Dr. Peter Akin sitting right next to me. He's an internationally recognized data management thought leader. Many of you already know him or have seen him at conferences worldwide. He has more than 30 years of experience and has received many awards for his outstanding contributions to the profession. Peter is also the founding director of Data Blueprint. He's written dozens of articles and eight books. The most recent is Monetizing Data Management. Peter has experience with more than 500 data management practices in 20 countries and consistently named as a top data management expert. Some of the most important and largest organizations in the world have sought out his and Data Blueprint's expertise. Peter has spent multi-year immersions with groups as diverse as the U.S. Department of Defense, Deutsche Bank, Nokia, Wells Fargo, the Commonwealth of Virginia, and Walmart. He often appears at conferences and is constantly traveling. Fortunately, Peter is here in the office with us today, which is a rare treat. And we also have Melanie Mecca. She is the CMMI Institute's program manager for the Data Management Maturity Model and a primary author of the DMMs since January 2011. She secured corporate sponsor funding and created a methodology for DMM-based benchmarking of an organization's data management capabilities and has led DMM assessments for the Securities and Exchange Commission, the Office of Financial Research, Microsoft, Fannie Mae, the Federal Reserve Systems Statistics Function, the Ontario Teachers Pension Plan, and Freddie Mac. She has architected and implemented data strategies and organization-wide data management improvement programs and managed programs and projects to design enterprise data architectures, master data hubs, data warehouse components, and shared data services. She also has a master's degree in philosophy. How are you doing today, Melanie? I'm doing well. Fantastic. Well, with that, I'll go ahead and hand it over to you guys. Thanks, Stephen. Melanie, it's always a pleasure to present with you. We've actually done about half a dozen of these things recently, and so we're getting our dance steps down really, really well, so... Yes, we do. And some of our jokes as well. So, what we're going to cover today is, first of all, the motivations. And the real question is, who are satisfied with today's performance to hang up on this program about its way? Most people are here because they're really seeing areas a lot before they can get out of data management. We now have a fast forward. We're going to talk a little bit about how we got here, which is building some of this research. This really does take advantage of a 20-year position as you're starting out a long, long time ago. And it's really a community that has put this together because the community probably aren't aware that this... Then, obviously, we're having a meet-up of what is the data management approach model, which is the DMM. We're going to say that a lot. And if you haven't heard of DMM or CMMI, the figures you have for it, you'll be pleased to know that we've actually got the ability to do this a lot better. So, please focus on... at a much more detailed level than we were able to before. And finally, how it's going to be used, use cases and value proposition. So, let's get started, Melanie, and talk about the motivation around this. Very brief introduction to it. Okay, well, the DMM is essentially a reference model of best practices across 20 data management discipline process areas. And it is intended to be used as a guideline for process improvement and the establishment of enterprise programs for data management. And it is also very, very useful to evaluate in great precision how your program is performing across all of these disciplines at any snapshot point in time. So, it has five primary categories, each with a number of process areas. So, we see data management strategy. We emphasize that because we've all known over the past three or four decades that one of the biggest problems with effective data management is that it is approached as a project, it is approached ad hoc, and that the funding waxes and wanes, and it's difficult for an organization to turn its attention to as broad a topic as data management consistently in a sustained manner. So, that category helps address all of those considerations and how you can do that better. And you see data governance, which for us is a vertical process area, governance management plus the business glossary and its extension, the metadata management area. And this is all about establishing approvals, agreements, collaboration, and control over enterprise data. Data quality consists of four process areas that together comprise a complete 360 approach to data quality, starting with data quality strategy, again, which is rarely developed and often overlooked. In data operations, we concentrate on data requirements and provider management and the management of data across the lifecycle, including data lineage and authoritative data sources. And in platform and architecture, we concentrate on standards, architectural approaches, platform selection, best practices in data integration, and what to do with historical archived data. And the levels on the right, I'll talk more about them shortly. Essentially, level one is ad hoc. Level two is building capabilities and more at the program level. Level three is enterprise-wide efficiencies and best practices. Level four is continuing to refine with statistical analysis and a very strong use of metrics. And level five is continuous optimization and sharing with industry very sound practices. And goodness, I hope nobody's out there trying to take notes on this. We're going to come back and go into this in much more detail as we get into it. So just to give you an overview, thanks, Melanie. Sure. So it answers the question, how are we doing now with great precision and utility for all participants and what should we do next? And it's a very good baseline for integrated strategy and specific initiatives and improvements that are precisely tailored to what your organization wants to get out of its data and what its current gaps are. And as we mentioned before, the real origin of this is that people in the industry saw a need for it. Outside the industry, there's not even really a good awareness of it, but we've had some terrific help along the way. Your old organization, Buddha Allen, Hamilton, Lockheed Martin, Microsoft, Kingland Systems, many, many people contributed to this. And again, we'll come back and talk a little bit more about that as well. The DMM was designed to be architecture and technology neutral, which is very important for data management because as we know working in the field, essentially in the end, data is data and the practices to do a good job managing that data are very similar regardless of what sort of records you have in your data stores. So if you have an all-legacy environment or a huge sandbox with Hadoop and the latest technologies for big data, if you have a services-oriented architecture, this model still applies and is useful because it's about the business practices for the data. It's also industry independent, so it can be used by any industry. It is not slanted towards any particular industry. We have customers in technology, in finance, in retail, logistics. We have a lot of interest in the defense area at this time. So perfectly applicable. And the emphasis of the model is on evaluating the current state. So if you have a lot of future plans, if we show up on the site to evaluate how you're doing, we'll be looking at what is implemented now and what is coming to fruition in the next month or two. So at any point, you can do a snapshot and gauge your progress. And probably one of the biggest benefits of the DMM is its ability to foster collaboration and enthusiasm for stakeholders, governance, and even executives who haven't necessarily paid attention to it as much before. So if you have data, you can benefit from the DMM. And that's really important because we talk about data in context of Maslow's hierarchy of needs an awful lot. And the idea, of course, behind Maslow, which most of you remember from high school, is that if your food, clothing, and shelter needs are unmet, you're not likely to go home and self-actualize, whether that's writing, poetry, music. You could hear we were having a little bit of fun with the music before we got started because Milan and I both love music, and Stephen actually made his living as a musician for a number of years on that. So the point is, if you are missing the ability to have health, you're not likely to get up and write the great American novel. And data is an awful lot like that. It falls into the category of when you look at it from the popular literature of what I call the Golden Triangle that's out there. Again, these words have changed over the years in terms of the contents of the Golden Triangle, but everybody's out there having buzzwords. I had a terrific luncheon conversation with a good friend of mine just before coming here to do the webinar today. And they said, yes, we're trying to put in MDM. And I said, does anybody in your organization know what that is? And she said, no. In fact, the sad part is the business doesn't know what they're getting into with this MDM because we will put in the technology, but it's up to the business to define what the master data is. I could go on on that topic for quite a bit. I won't. But when you figure this part out, and this is really where collectively we've gotten to as a profession, it really represents just the tip of the iceberg. It doesn't matter which, if any of those things you're doing, almost all of them really represent the top piece. And yet what we're going to talk about today is more how to put in place the foundational elements. And these foundational elements are the five areas that Melanie described to you just a few minutes ago, governance, quality, management, strategy, platform, architecture, and operations, and to also understand that they are held together by what we call the weak link chain method. In other words, the foundation is only as strong as the weakest link in this foundation. So in this example here, and this is hypothetical, which doesn't represent any organization, but the data platform and architecture is held together with literally a tie band. That is not a good strong piece. You can see that chain is actually weaker than the other three chains. So rather than the organization putting more money into governance, strategy, quality, and operations, clearly the best approach is to put it into the weak part and bring the entire platform up to a level that is of a higher ability to support everything else. Again, the things in the Golden Triangle are really all technology focused, and there's nothing wrong with the technology. It works very, very well. But what's more useful to your organization in the long run is the capabilities that you need to develop, because in the long run those capabilities are going to give you better guidance as to which technologies to select and how to implement them. We do get a lot of questions over the years where people say, well, I understand all that, Peter and Melanie, but I need to go to the Golden Triangle stuff and we say, of course, you can do that, but it will take longer. It will cost more. It will deliver less value to your organizations and it will present greater risk to them than if instead you learn to crawl, walk, and run your way to the top. And that's a shameless ripoff of a Tom DeMarco line that he was always asked, can you do it faster, Tom? And he'd say, yes, I can do it faster, but it will take longer. So Melanie, now let's talk about the business results. So what you're after and the reason to do work on improving and building data management capabilities is to gain trusted data that assures customer confidence, your internal customers, and your external customers is applicable. That this data is fit for purpose does what I needed to do. Of course, if you have accurate, cleansed data that's properly architected and available for use, it does improve your analysis of risk, whether financial risk or internal risk, and analytics decisions. So whenever we talk to large analytics vendors and we talk about the DMM, they always look at us and raise an eyebrow and say, this is all a no-brainer. All of this is needed to have the best results. Also, effective processes prevent people from having to reinvent the wheel every time. So you get a lot of cost reduction over time and improved operational efficiency. So you can not only streamline your data layer and manage it better and control it better and define shared data needs and have better data quality, but you also have more efficient staff, which leads to happier staff, which leads to more creativity. So very important efficiency. And of course, regulatory compliance, the DMM provides a yardstick and benchmark to meet requirements for good data management by various regulatory industries. And you'll notice the bottom right-hand corner of that picture is a picture of a foundation. That is actually my barn at my house. We had a requirement to build the barn in the two-step process, making sure the foundation was in fact solid before the bank would give us the next draw on the loan and allow us to build something good on top of a solid foundation. So with that as a foundational piece, we are building on previous research here. Now, one of the questions we get at Data Blueprint all the time is we want to move our data management program up to the next level. And the first question is, well, what level are you at now? If you don't know, then you have no ability to move to the next level. And so you have to know how to put in time, money, and energy so that the data management really supports the mission. So when I was the U.S. Department of Defense reverse engineering program manager back in the late 80s and early 90s, we sponsored research up at Carnegie Mellon asking the question, how can we measure the performance of the Department of Defense and its partners that were helping it to do IT? And they came up with the CMMI as a result of this. In addition to that, they also said go check out what the Navy was up to, and that's where I had the very good fortune to run into Clive Finkelstein and John Zakman, who were doing work for the Navy at that point starting my particular journey on this process in here. So the SEI responded with an integrated process and data approach. Interestingly enough, the Department of Defense required the SEI to remove the data portion from that piece because they were called the Software Engineering Institute and they weren't recognized to have the ability to do anything in the data space. They actually gave that stuff to me. I ended up taking it around to a fellow named Burt Parker, who worked for the MITRE Corporation, did an internal research and development project for them. You can see some of the other folks that worked on this project. The actual papers out there, you can see the reference to it. In the IEEE Pro, I think it was a thing. If you need a reference for that, let me just get some of this little note and we'll send you on that. But, again, that worked for a little while. Now, of course, what it's done is made its way back to its rightful owner, which is the CMM Carnegie Mellon University's CMMI Institute. And, Melanie, the Institute? Yes, owned by Carnegie Mellon University. It was formed and evolved from the Software Engineering Institute, which is a federally funded research and development center. And it continues to support and provide all of the CMMI offerings, our reference models and classes and certifications that were delivered at the 20 years at the SEI. So now the organization is for profit, but yet it still plays the role of a primary brand for reference models and associated services and training and certification. And it is focused on responding to the overall industry in the market. As you can see, we're small, small but mighty. And with a tremendous worldwide footprint. Again, you can see there's over 10,000 organizations that have adopted this process in 94 different countries, 12 national governments. It's been translated into a number of different languages. And there are a number of partner organizations, and we'll circle back around the partner organizations, because you need to have some specialized training in order to be certified with CMMI in order to implement this. There were more than 1,200 appraisals done in 2013 alone in order to do this. Now, the reason this is important is because, first of all, there's some good science that shows that organizations that adopt the CMM, CMMI Process Improvement Framework, which is what it's technically called, perform better on budget and better on time performance. And if you'll notice, this is against ITEL, which is a very popular piece right now, slightly better than ITEL. ITEL actually shows good results. Which is the object orientation piece, and that's not got as good results. COVID, which is actually producing worse results and, disappointingly enough, CMMI also is not produced. Excuse me, I said CMMI. PMI, excuse me, sorry, Melanie. PMI, not showing good results in here. I'm sure that PMI actually has some better capabilities in that, but at least this particular study, they did not show up high on that. Now, when you look at this in terms of the overall portfolio, there are a number of flavors of the CMMI. Again, service delivery, product development, supply chain, workforce development. These are all variants of it. And most importantly, because of the breadth and depth of this, really the popularity of this approach, it means that your management will also be familiar with it. So this is not something new that's coming out of left field for them. It's tried and proven technology with really good results. So because data management is broad and complex, it is and has always been challenging, both to wrap your mind around it if you're a group of executives saying, what have I got and what should I do with it? But also as we say, it requires a planned strategic effort. So it is not a project or it is not a disconnected program. It is a lifestyle that envelops all parts of the organization, anyone who creates or manages or touches the data assets. So the reference model helps you evaluate those capabilities and see where you have gaps and see where you have strength to leverage, which is equally important. It also is written primarily oriented at the business, so it helps to unify the understanding and the priorities of business IT and data management. As many of you know, at some organizations, there's not a lot of unity among those groups. So the DMM helps to bridge those gaps and it definitely helps in getting a shared vision and a plan for collaborative and sustained capability building. So you can see the history of the model. It took a long time to develop. We had over 50 authors, over a period of three and a half years and 70 peer reviewers, including Peter Akin on the phone here. He is first because his name starts with an A and I always like to see that. So anyway, it was a long journey and a very enjoyable one. So we had probably the authors all together and if you had the peer reviewers, it would just floor you. But just the 50 or so authors have between 1,000 and 1,500 years of experience designing, architecting, and implementing programs, projects throughout the entire spectrum of capabilities. The consortium approach we used focused on practical wisdom. There wasn't one statement in the model or one contextual elaboration that could get past our group because everybody was in a very kind way having everyone else run the gauntlet of all objections for anything you wanted to add to that model. So it had a lot of people ready to pounce on you if you said anything that was too theoretical or hadn't been proven in practice or you couldn't give a good example for. But why did we write it? Why have we spent so much time on it? We did it because all of us in our careers had wished 25, 30 years ago that we'd had something like this so that we could help change and enhance the organization's perspective in all portions of the organization and so that we could quickly pinpoint where we needed to go. So we did it for ourselves and for all of you to make your life easier and simpler. A question that comes up regularly. I'm the past president of the International. Melanie's been associated with the group for a long time and people ask, well, how is this working with the DIMBOK? And Melanie, the answer is... The answer is that the two products are highly harmonious. Almost everyone who was on the author team over the years are big fans of the DIMBOK and we've all used it in our work and we all refer people to it as well as to the CDMP knowledge classes that accompany the DIMBOK. So we have a very good relationship with DAMA International and at this time we're offering discounts for our courses, which I'll mention again later, to all DAMA members and we are planning to harmonize some of our educational offerings as a first step in further collaboration. So right now I would say the organizations are in relatively complete harmony in terms of what they want to foster in the industry and how they want to help professionals. Again, it's just a group of us, as you said. This is designed for us to be used by us and it's evolving in a way in which you'd hopefully expect to see really good collaboration all the way around. So let's dive in. What actually is the maturity model? Some of these facts we've mentioned, we talked about our sponsors and the development period. We released it last year, August 7th and one week later we did our first data at WEMADA about it. We had over 80 organizations all toll involved in developing the model and the model is big. It's approximately 360 pages altogether. It has 320 practice statements across the 25 process areas and it has categories and it has over 520 functional work products and that's something that we're very proud of because that was all of the authors and the peer reviewers taking a look at how do you functionally describe useful work products to support the behavior and the processes that people are doing and want to do better. So far, the people who've looked at our work product list and assessment haven't had any trouble interpreting their intent so we don't do titles, we do descriptions and your organization has lots and lots of work products and we would say you don't know about all those work products until you actually focus on them. So our model emphasizes behavior, effective, repeatable processes that will stand you in good stead and be easy to understand and everyone can do roughly in the same way so they don't have to think about it and reinvent things. And then to leverage and extend those process areas across the organization for maximum value, bang for the buck, reusable artifacts, reusable methods. The work products, of course, include policies, processes, standards, designs, templates. Templates are very important. We're going to talk about them in data quality a little bit. If you have a really good template for how to report data profiling results, you have made a beach head for everyone who comes after you. They don't have to invent the drill down or how it should roll up or what the dashboard should look like. In the terms of each process area, we carefully designed the model to the best of our ability to evaluate the process area. Let's say business glossary. Take it right out of the model and use it standalone and you can evaluate the process area stand alone. So that reflects real-world organizations and they're wanting to focus on one discipline or another, one project or another. The model is very flexible in that regard. However, we spend a lot of time indicating dependencies and relationships in the text of the model from the time you create the data until the time you purge or archive it. When you look at the model, Milan has already outlined the five areas that are there. We also talked about briefly the five levels and you really do get one point for just having a pulse. It's a low standard. It's kind of like getting a DNA exam by signing your name, but everybody does have to start somewhere. One of the funny things that Milan and I hear when we go out and talk to people about this is that everybody's a one. And what that really means, though, is that your data management practices are informal and ad hoc. And they're dependent on heroic efforts and tribal knowledge to a degree that's probably a little bit unhealthy for most organizations. To get to level two, what we want to do is look at putting some definition and documentation around us to get the processes to what we call managed. That somebody is, in fact, at level three we get up and say they are defined so that we have them to be standardized and they can be applied consistently outside of the work group, because what happens in most organizations is that work groups develop their own data management practices. They sometimes do this with complete ignorance of what's going on in a formal discipline. We'd use an example a lot where we worked with a group of about 100 chemical engineers and they are terrific at chemical engineering, but they know very little about data management, so they weren't able to... they developed a good series of data management practices, but they weren't optimal in any way, shape, or form. And we were able, by applying this, to improve the productivity of that group. Of course, once something is standardized and used consistently, now you can start to measure it to see whether doing it better this way or that way works. Again, this is a standard A-B experiment that Amazon and Google and the rest of the web tries on us all the time to say, will they buy it better if we do it this way or will we get more results if we do it that way and we can compare and contrast and measure. And once we have measurements, we then have the ability to optimize. And when we understand that, we can say, how is it working for our organization? Is it best to do it this way or is it best to do it this way or is it best to do it this way for this organization and that way for that part of the organization? Different parts of your organization are made of management in different ways, depending on the specifics of the strategy of each area. Those of you that are familiar with this basic model will understand also that it's the basis for TQM or ISO 9000. Although the ISO 9000 joke in this is kind of interesting, it says, so you don't care how bad our processes are as long as we follow them consistently. It's actually an official Dilbert, and there you can look it up on the web. That means you're at a level three. You are using standard consistent processes, but you're not actually measuring them or deciding whether they are, in fact, optimal for your organization. Now, as I mentioned, these five levels are key. And of course, what you're trying to do is go up each level. The Melanie mentioned earlier that there are 20 specific areas. We grouped them into the five super categories, if you will. But we want each of those other areas to be joined at that level and to come up at that level. Now, the problem is that these things are only as good as the weakest link in the chain, as we mentioned before. So if you have 20 areas that are all at level five and one that is still at level one, that does reduce the entire organizational score down to a level one. That's, of course, not optimal. And so what we want to do is to help organizations work their way up the chain in a much more orderly fashion with a lot more guidance that you get from this process. As you do this, you also start to see that the risk to the organization becomes much less and the quality increases. Similarly, the amount of ad hoc ad nests. Good words. I know. Good words, right, exactly. But the ability to reuse increases as the ad hoc number of ad hoc operations decreases on this. And, of course, the last part of this, which is something key to all of us, is that the stress levels of the organization go down. And you find you end up less time and rework and effort and a lot more clarity increasing that process as we go through it. So this is the way in which we'd expect to see these things progress through the organization. Now, Melanie, the next piece on this is really the difference between capability and maturity here. And a lot of people ask us this question, so we wanted to develop some responses. Yes, but I want to return to something that you said about optimization is going to look different in different organizations. That is very, very true. Your artifacts and work products will not be identical. Your process development for these disciplines will not be identical, and that is completely appropriate. Just like the DIMBOT gives a knowledge base that is kind of core best practices, the DMM gives the core best practices path, which is not meant to be implemented as is. That would not do your organization a service. It's meant for you to use it as you create your own sort of enhanced reality for data management. So people always ask, so what's this difference between capability and maturity? What the heck is maturity? We went through this for many years on the CMMI side, and people wrote tones and tones and encyclopedias about it over the years. But I think we can disambiguate it quite simply. A capability is the ability to do something. So when you're looking at your organization with respect to the specific practices contained in the model at each level, you are determining whether or not you're doing it and doing it effectively. And the work products that support capability are essentially, as Peter said, documentation. And they include the whole suite of documentation that we often use and are familiar with. Maturity is the stability of the process such that it is very unlikely to degrade over time. And that's important because data management is forever. As long as you have data assets, you'll need to continually lean into doing a great job to make your customers happy and your internal and external clients confident in the data. So if you have process stability, it's simple things that we all have heard about like having a policy, instituting training, let's say for assessing data quality rules, providing quality assurance, how well are people doing data requirements, and things like that. So the model contains a set of what we call infrastructure support practices, which essentially, if they are also met, indicate that this process at whatever level it has achieved is mature. So you managed to quote Cheryl Sandberg in their millennium after research. Anti-degradation insurance, you could also call it. There you go. And resiliency, which is another key concept. So this is now the structure of the DMM. And looking at this just from a, you know, what does this mean here? And I think this is a really terrific model, but you do need to explain it. So for each core category, there are multiple process areas. I gave you an example, business law three or data management strategy or examples. Each one has a business purpose, which is stated in a couple of lines, and has introductory notes meant to tell anybody what the organization gets in benefit for doing this well, what is the context in which this discipline is usually found in an organization and other orienting information. It has goals. Each process area has a goal, which is a business goal that by doing it well, you get to achieve for the organization. It has core questions that are aimed at level three so that if you can answer all those core questions in the model affirmatively, then your benchmark finger in the wind is, gee, we're level three. We have all of these things. Related process areas are included to indicate core dependencies from process area to process area. But if you're evaluated, you're only scored on the functional practices from levels one to five in the process area. And the infrastructure support practices, if you're interested in determining how mature those practices are. So lots of information to support it. Yeah, this is really how the model is organized. And if you do get a copy, and we should probably tell people where they can get a copy now, Melanie. Yes, just type in cmmidmm, and you'll come right to our link, and you will be able to have the title page, which talks about training, downloading the model, and we have a lot of information and white papers there, too. So for each core area that we talked about before the five, you get this nice overview. And this is a really good example of data management in the sense that this simplistic model here, once you understand what it's doing, becomes a guide to looking at the rest of it. And now let's talk a little bit more about the content of each of these areas. And again, we really are celebrating this in the sense that it's just something we haven't had before. So here now are the 20 practice areas on the left-hand side of this diagram. And then just briefly, data management strategy says, are we going to manage data coherently in our organization, or are we going to let everybody do what they think is best, which is how most organizations are doing it today. And I say that with a database of more than 500 companies where we've measured these things empirically across that. Similarly, data assets deserve professional management the same way as HR, as a profession, the same way as finance is a profession, the same way that leadership is a profession in our organization. We want to see the similar types of respect done for the data management profession. Similarly, we don't want people to try and get all their data perfect. It's not worth it. But really try to understand what does fit for purpose mean for your organization in there. And we want to make sure that your architecture implementation is appropriate and that your lifecycle implementation is appropriate for your organization. Finally, Mellie's mentioned a couple of times we do have the supporting processes as well. Again, you can see that each of these break down into additional areas on the left there. We are not going to walk through these next five areas. We've sort of appropriated them for you and we're leaving them with you for reference. So again, here are the five process areas. You can see they're broken out, each one governance, quality, platform and architecture and operations. Again, we're not going to go through these with you. We have other things that we want to copy. Sorry, I'll bring to your attention on this, but they are out there for you to reference for future. That'll give you a little bit of a taste of what's going on out there. Now, how do we use this? And really key here is that there's some natural events that are going to be appropriate here. Mellie? Yes. Maybe you can put the bullets in a little more quickly, Peter. I like to see my bullets. Okay, so use cases. The obvious use case for the model and its overall grand design was for bullet one, developing or enhancing a data management program and developing a strategy for data management for an organization. So the entire model covers the basis for all of that, all of the considerations, disciplines, work products, and behaviors that you need to have a good program. If you are about to do a major architecture transformation, let's say you are going to transform five major legacy data warehouses into one or two new streamlined top technology data warehouses. That's a lot of data and it's a lot of shared data, so the DMM is an excellent preparation for launching those efforts. At the last couple of data governance conferences, where we presented and had a booth, people kept coming up to us and saying, and I know you get this all the time too, Peter, we tried data governance once or twice and it failed. And as they say this, they're looking unhappy and they're kind of looking around the room, feeling like a failure. So one of the things we have found, that the DMM, although it is a basically academic kind of quality document in these little abstract statements with supporting information, it tends to focus people very well on the details of what they do and the details of their roles and responsibilities as a line of business staffer for the data. So we have found that every time the DMM has been used, there's been a big rise in energy and cooperation and enthusiasm for data governance. Also, a lot of times governance programs, as many of you know, can fail if there's no clear agenda. You know, sometimes the C exec will say, set up data governance and that's all the direction that the leader is given. So what are we trying to accomplish? What do we need to do? What's the corporate vision? All of this is helped by the DMM. And we already mentioned analytics. If you want top quality analytics, you not only need your great modelers and simulators and data scientists and technologies, you need really good data to start with. For data quality, we've noticed over the years that very few organizations have a real data quality program and that's leaving a lot of value on the table because data quality in the end is what you're after, right? Usability, fitness for purpose, that is one of the main goals of doing data management disciplines. So DMM helps focus you on it and I've said those four process areas are kind of a 360-degree approach to data quality in the organization. Metadata repository, what should we do? How do we enhance this metadata model? What are the best phases? All of these are assisted by a DMM preparation and many other things besides. Any multi-line of business focus. So we compare it to an energy audit or to an executive physical and the organization is the patient and you are the doctors and nurses and the DMM is the series of advanced tests and at the end of the day they tell you, okay, this and this and this isn't good, your blood pressure is high, you need your low iron, whatever they may tell you and if you do this, this, this and this you'll improve your health and then they also give you a complete lifestyle plan. So the DMM can be compared to that. Absolutely. And don't just go out and run a marathon without doing some prep work for it. So the two comparisons here, if you look at them, excuse me, the two sets of components then are the five areas that we've talked about already on the left-hand side here and now the levels that you can look at on the right-hand side and what you can do with them is to look specifically at certain things. I mentioned before I've got this database of companies that we've been looking at over time here and the idea is this is the insurance industry. So those of you that are in insurance may look and recognize yourself here that, oh my goodness, the average insurance company does not have managed data management practices. That's, again, a fairly low standard in terms of all of that and we'd really like to see that come up. On the other hand, the good news is if you're in insurance and you're doing better than that, you're actually way ahead of the competition. Here's an example that we gave for an airline at one point and the idea was that there were one, one, two, two's in a one and you can just imagine management looking at me and saying, so what? And I say, ah, but here's the competition and they go, oh, we're the ones and they're the twos. That's not good, is it? The answer is no, it's really not. If this portion of the competitive strategy is important to your business. In addition, we can compare them also to all respondents that are in the area here. All of these, I'm not telling you which airline it was in here, but you can see that the clear roadmap going forward would then be to address the ones and make them into twos instead of trying to take the twos and make them into threes. All of these, of course, are anonymous with the exception of one that the World Bank told us that we could use here and they asked us to look at their Treasury Group, their Information Systems Group and their International Finance Corporation and you can see that this is, by the way, a sub portion of the model. This is not just the overall results, but you can see that most of the Treasury Group and the Information Systems Group did not have actually terribly good results, but when you look to the business portion of it, here was an example of an organization that was actually world-class. So the answer here was don't go out and hire a bunch of expensive consultants to help you improve your practices. Walk down the hall and ask your colleagues what they're doing because it is actually world-class type of an operation. Again, terrific results on this. One last little set of charts here before I turn it back over to Melanie and that's the idea that in this era of big data, which I, by the way, just officially, guys, we're in the new post-big data era. If you didn't know that, that ended last year. The data's getting smaller? No, it's not getting smaller, but big data didn't work. It's what it really comes down to. And I say that jokingly, but big data is a governance issue. Big data by itself was not enough to solve problems. And so that's a very, very interesting set of findings. We can hit that in another webinar down the road a bit. But just overall, these results from 07 to 12 are not statistically significant. And so consequently, we end up with a problem where if data is continuing to increase, as Melanie said tongue in cheek there, we do need to have something else come back and address this. And so Melanie, really measurement ends up being confidence for people. Absolutely. Because for one thing, if you are a data management warrior, and many of you are, you've been at this for a long time through one kind of implementation after another, for you and your stakeholders, good metrics and measures are very important to prove to yourselves your accomplishments. For example, we did a webinar on data diversity with Ally on January 27th. And they had put in metrics, they were tracking very carefully, for example, how many terms they had added to their regulatory compliance, high priority data elements. And they had gone from zero to 3,000 in about a year. So that's a huge accomplishment, you know, where they got governance approvals and agreements on all of those terms, and they were working on data quality rules for them. So if you have metrics, you're proving to your executives, your stakeholders and yourselves that you're doing well. It also allows organizations to gauge their achievements against peers in the industry and definitely helps to get funding. If you can prove to your funding committee that you have done a good job and show them the benefits and metrics to support that, it will make your initiatives easier to fund. And it will enhance your reputation. Go ahead. Sorry, it's not that it's getting better, but it's getting better in concrete, measurable ways. So if you are starting to use the DMM, if the organization is a little bit bold, we advise starting right out with an assessment. People come up to us, by the way, and say quietly in the back of the room, my organization isn't very mature. Should we wait before we do an assessment? And our answer is always, the DMM is for you at any time in the organization's capabilities. In fact, if the organization in multiple areas is less mature and has fewer capabilities, it's even more helpful to give your governance groups and your stakeholders a view of what the future looks like and the path that you're going to encourage them to walk on. So what we did was we invented a method with great care and painstaking input from expert executive facilitators and meeting experts to maximize the stakeholder participation in determining how the organization is doing and minimize the time. Nobody wants to spend nine months analyzing their data management program. That's looked at as wasted effort. So essentially this is fast, very high energy, very interactive. It depends on consensus. All of the stakeholders have a role in how the organization is doing and your job as a facilitator is to help facilitate consensus and allow them to give their points of view from different lines of business and different projects. There are supplemental interviews of executives and to get more business context to the results based on the DMM and extensive work product reviews. So that essentially is the evidence. If you say you're doing something and you're following it consistently, you will have artifacts to support it. You'll have meeting notes. You'll have compliance documentation and so on. And then the report at the end is basically the score according to the DMM, the findings, which are gaps and capabilities, the observations, which are context and how to improve the gaps, the organization's strengths, and some targeted, very specific recommendations. Essentially what should you do next based on everything you've told us and everything we've learned from the DMM? So this is very useful for an organization and one of our big pieces of advice is when you do an assessment, before you even start it, plan for what you're going to do when you get it delivered because you get a report and an executive briefing. There's a tremendous level of interest and enthusiasm and momentum. So basically you should be ready to go to your executive committee with some initiatives to fund immediately afterwards. You should be jumping on the results basically. And here is a sample at the end of the workshops really before the end of the first week of this three-week compressed engagement. You get an exact score of where you are. So this one page is supported by a very detailed spreadsheet with notes and examples. And if you see it, if you go back for a second, Peter, sorry, the white line is level three. We recommend that organizations start their journey to achieve level three because that is where you have maximum efficiency and a very good value for the dollars and efforts spent. You can go beyond level three, and it's wise to do that in areas that are of particular focus due to your business strategy. So if you are a data vendor, you really need to double down on quality, right? So that's the kind of thing. So if you need to be continuously optimizing with perfect metrics and to demonstrate to customers that your data is pristine, you would continue to advance in the levels in those areas. The assessment results provide very, very good preparation to come up with a unified data management roadmap for the entire organization. And this can be worked into a sequence plan, which we recommend not only for the accomplishments themselves, but to keep harnessing the enthusiasm of data governance participants. And this is a benchmark of many organizations who have used the recently published version of the model. And as you can see, these are all the different process areas at the bottom, the slanty text, which you cannot read, but anyway, you'll be able to read it on your slide deck when you download it. It shows the tremendous variation in capabilities and achievement of these organizations. And I always like to say, don't worry about the level five. That is the Federal Reserve Systems Statistics function whose primary mission has been perfect data with perfect data quality for the last number of decades, and they really have aced it. So nobody should feel bad about that. If there's part of the government we'd like to get good, that's certainly one of them, right? That's right, because we support economic stability and we're very happy that they're using good data. So why do you do it? To engage the lines of business, you know, person after person, conference after conference, talk after talk. People keep coming up with this, and Peter has heard this for many, many years in all of his endeavors and from all of his clients. I can't get the business to pay attention. So the DMM is really powerful at engaging the business. You know how you're doing, you know what you should do next, and you're using an industry-wide standard so that begets confidence. You know, it's not just a couple of you in data management telling the executives what's wrong and what's good and what they should do, but you're basing it on a standard that is independent. It helps improve the climate for an organization-wide program because more people understand the needs and what their role is and what they should do. And it does help to create a shared vision and purpose. And of course, as we've said, since it is a measurement instrument, you can precisely measure your progress over time and report that. Familiarly, we've got a little bit of time crunch on this here. I'm going to skip over the Microsoft case, sufficient to say that Microsoft, another set of organizations did participate in this, gave us some terrific results in terms of benchmarking in this. But let's just dive right into how it helps the organization. A step-by-step path for improvements that's outlined in front of you. So it's like shining a light on the path. That helps. The common language helps everyone understand what progress looks like and what the good looks like, which helps. The work products help a great deal because they give you a clue about, oh, I don't have this document. And look at that. Very, very nice to kind of put the light bulbs in your mind about how can I support this new process that I've created. And the practice statements are able to be understood by business, IT, data management, executives, anyone who needs to look at them. So you have a common language. It helps the professionals as well. Yes, it helps the professionals because it helps you educate your organization or your client organization about their roles. It helps you give them the enterprise perspective which is kind of the elusive internal guidepost that everyone needs to develop in this industry. It does immediately lead to actionable and implementable initiatives. They kind of drop like ripe fruit from the assessment. And it's obvious to everyone. So everybody is going to improve your funding. They're representative in the workshops. Go back and tell them we need to do this. Very good for business cases. And also we have a certification path with a defined skill set, extensive training, and industry recognition for your own expertise. So there's an entire ecosystem that's been developed around this. Again, it's not just the standalone model, but a lot of things that go into it. Again, we don't have time to cover them here at this point, but it is sufficient to say there's a product suite, there's a whole series of training opportunities, a partner program, and a lot of good results already to date, even though the program's only been around a year. So I'm going to finish up here with just a couple of thoughts on this, which is the idea of where do we go next. And one of the things that this has been most helpful for some organizations to look at is trying to get support for the top data job. Now, the top data job is currently conceptualized as a chief data officer. Actually, I like the word top data job better because then you don't have the argument over whether you need a new chief or not. But clearly the idea is that somebody's got to be in charge and focus on these issues. Similarly, there is an absolute fiction out there that says you can develop your software requirements at the same rate and same process as your data requirements. That's crazy nuts. This can help you to start articulating that and making the business case for it because data is not a project. It is simply unreasonable to expect that data can be done in the same fashion that you develop software, which is more of an agile process in order to do that. Similarly, again, data is not the same as creating new systems. And so our mantra here is really to say that data needs to be separated from, it needs to be external to, and it needs to proceed system development activities. And the DMM is something that you can use to help organizations understand what and why from all that perspective. And we've got just about a minute left to go, Melanie, the top of the hour. The executive perspective then here is. We call it the CDOs of a top-data job person's best friend because you can engage the light of business. They can understand step-by-step, but very, very quickly the strengths and weaknesses that the organization currently has. They get a very good education about their roles. It shows what needs are there for the data management program. And as you know, the top-data job is 80% managing sideways across the organization. So it really helps winning hearts and minds and getting everyone speaking along the same lines and looking forward in the same way. So the real key for this is that in the past, it was up to sort of everybody to figure this out. Now with the CMMI's help, we have the ability to go through and look at this from a number of different perspectives. But relatively speaking, it's our first steps towards standardizing what's going on in terms of the process of improving this. So with that, we're at the top of the hour and again, we'll turn it back over to Steven here and see what sort of questions you guys have. All right. Thanks, guys. So yeah, as Peter said, it's now time to ask questions. So feel free to click on the Q&A window feature at the top of your screen there. And you should be able to take questions through that. So go ahead and start it. The first question I have is, basically asking if you could just talk a little bit about the DIMBOK. Hey, if you have any questions, I think the next version of the DIMBOK is still in progress. And I think that, again, the current president is looking at a January release to review on that a little bit. And it looks like Shannon says we're having a little bit of voice issues because I might be picking up... I hope you can get the next question. Does the PMMI framework offer data management at the metadata level? Oh, Mike, we're cutting out a little bit. Do we have any written questions that we can take a look at while these work on the sound? Sorry about that. Is that sounding any better there? Much better, yes. Okay, great. So the question I asked Melanie was, does the PMMI framework offer data management at the metadata level? The framework, of course, is not software. It's a compendium of best practices organized by successive capabilities. Metadata management is one of the process areas and metadata is referenced throughout the model, much like governance is. So data quality and metadata have a huge dependency. Metadata and architecture have a huge dependency and all of this is indicated in the DMM. All right, fantastic. Got another quick one here. In the DMM CAPMAT model, why is defined third and not first? Because essentially defined is a more stable, more documented level of maturity and level one is essentially ad hoc, meaning that the organization can have wonderful achievements, even top quality award-winning analytics and visualization systems while not being able to integrate operational systems and not having authoritative data sources and not having good shared data, et cetera. So you can have a lot of unevenness in the way the organization grew before. And let me add on this too, Melanie, that some people who are just seeing the DMM for the first time aren't aware that there is actually quite a long tradition of research. Again, 20 years' worth of research into some of these areas. So if you wanted to get into that, we can point you to some papers that would lead you down a very nice rabbit hole and get to a good, solid answer on all of those answers. Yes, and if you email me, I'll toss you right over to our model architecture expert and then you can really get in depth. Okay, got another one for you here. Are there different considerations in the CMMI framework for spatial versus tabular data? This is articulated here. It really works for both. And when you say spatial, some people think of spatial or non-tabular and tabular data. It really works with any kind of data and that's really the nice part about the level of abstraction that this has been put together at. And Melanie, are there any prerequisites? Let me try that word again. Prerequisites that an organization should meet before doing a DMM assessment? No, not at all. That's what I mentioned earlier. It does not matter if you have been working on building capabilities for the last two decades or if you are just starting your journey to pull together a program. So the DMM will, if you have a lot of capabilities, the DMM will validate your achievements and progress. And if you're just starting your journey, it will point you in the direction of where you should be heading and allow you to get agreement on that very quickly. Great, thank you. And can organizations adopt DMM via the documented model or is formal assessment and training required? Great question. Yes, organizations can. When you purchase a copy of the model, we now have, by the way, enterprise licenses available at various user tiers like software only much, much, much, much less expensive. But if you purchase an individual copy of the model, you are able to use that copy in your work and you can extract from the model for work products like spreadsheets and presentations and so on. We also offer our presentation slides. Anything we've presented publicly, we allow people to use as long as they retain our logo. So yes, you can use it in your work. What you can't do is turn it into a product for sale or resell it in any way. Similar to a demo. Great, thank you. Right. Are there best practices for data validation in the DMM? And is this included in the data profiling under data quality? Data validation. Go ahead, Peter, would you like to respond to this? So I think that that would fall under that category, but validation is one of those terms that hasn't been quite standardized all the way through yet. So it may mean different things to different organizations. But certainly, you know, under quality, that would be an aspect of it. There's probably likely several other places that validation would show up. So really key to approaching that question would be to look at the model, figure out what validation means to your organization. For example, if you're working in, let's say the Defense Department and you're doing target acquisition, validation may take on a very different meaning than if you're just trying to identify a customer and doing identity resolution within there. Yes, and if by validation, you mean either source-to-target mapping, which we talk a lot about, life cycle-to-data-to-business process mapping, or governance validation or requirements validation, all of that is in the model in four different process areas, where it appears. All right, and I'm sorry, Peter and I are having to do a funny little dance here with this headset because for some reason on the speakerphone we're breaking up with the headset. It's fine, so the next question. Is a chief data steward really necessary? Well, in the DMM classes, one of the things we talk about is the difference between the data governance function and the data management function. So the data management function in our model, it's in the data strategy category. Data management is essentially the group or groups of individuals who are essentially full-time data people, and it may function slightly different in one organization or the other, but they are the ones who are essentially the water carriers and the shepherds of key enterprise products, for example, the metadata repository, the business glossary, an enterprise data model if you have one. They also provide the continuity throughout the different governance levels because they are usually responsible for helping with the governance dashboards, and the reason is because they're focused on it, they're focused on the long-term and the key products that go on and persist forever, and you need them. And really, if we take a step back and say, look, if there's not a single person in charge or having the responsibility, then it's up to everybody, and of course, if everybody's in charge, nobody's in charge. So while a chief data steward's not really necessary, what you're really asking the question is, you know, what sort of leadership should be around this? And when the organization looks at how it's managing, so non-depletable and non-degrading, durable, strategic asset, it's deserving of the same kind of respect that your fiscal assets are, your HR aspects, your HR resources, other things that are useful in there. So, you know, maybe not a chief data steward, but somebody's got to be put in charge because if nobody's in charge, everybody's in charge, and you heard me say that once already. Yes, and because the focus of the DMM is at the enterprise level, of course, the biggest set of data that you'd be looking at through the DMM would of course emphasize shared data or critical data for some purpose, for example, regulatory reporting or competitive advantage. So, you know, in that sense, the data stewards are typically not full-time on the job. They unfortunately get two jobs, their day job and their governance job. So, typically, they do not own some of the long-term persistent products because it would not be feasible to place the responsibility and accountability there. Great, thank you. All right, here's a good question for you guys. Do you have or can you recommend a reading list that a person should read first, second, third? You know, maybe just a couple of great books that maybe you guys have used throughout your careers. So, as Melanie has mentioned a couple of times, the idea that there's a lot of reading material on the DMM, you can go to the website and see some of the public reports that are available to download and things like that. But in addition to that, certainly I would recommend my book on the Chief Data Officer, which talks about some of the challenges that have been encountered in the past in organizations during this. Melanie, any thoughts on book lists? Well, I really like... I used to be a big fan of data modeling books. I don't think you can read too much about data modeling. I like total information quality very much. I like the book, The Data Asset, very much, because it has wonderful examples of the cost issues with bad data quality. And the DMM, don't forget that. I think, well, speaking of which, I think you guys touched on this a little bit, we had a question that says, what about the DIMBOK and DMM and reconciliation with Dama? I don't think I would use the word reconciliation. I think I would use the word harmonization, because really, we are all from the same profession, and most of us have had the same teachers and attended many of the same conferences and learned many of the same disciplines over the past 30, 40 years. So it's simply that the approach of the DMM is more of the measurement instrument approach versus a compendium of best practices. So our progress outline is kind of sequential, right, from a low level of capability to a greater level of capability. And in addition, the DMM does not contain some of the content that the DIMBOK does, which is a little more technical. So we don't have data design in the DMM, and that is not because data design is not deserving of great importance. Of course it is. You are not going to have a good architecture or data stores without it. But we consider it to be more technical, and the sweet spot of the DMM is that unification of the business data management and IT perspective. So everything that's in the DMM was intended to be understandable and usable by line of business staff as well. So we don't get particularly technical and we leave some things out. If you think about it, the DMM is really focused on the profession itself. And excuse me, I said that wrong. The DIMBOK is really focused on the profession itself and helping the profession to define and articulate its mission in there. The DMM is really talking about the intersection of how data management intersects with the rest of the business and host environments, if you will. But anyway, good collaborations going on there and we're anticipating some more results in that area as we go forward. Yes, and one of the plans that we have and we're finalizing now is we will be issuing our current certification Enterprise Data Management Expert. We're about to graduate our second class. That is a big deal certification because it requires quite a number of years experience to qualify for it. And our training is 13 full days of training in three successive courses. You can shorten that a little bit because we have an e-learning self-paced web course available for our Building Capabilities class which shortens the first three days to eight to 10 hours of self-paced instruction. But we're also going to be issuing a certification that is a little easier to attain for people who don't have decades of experience in the industry. That will be coming out later this summer and the plan is for Daima to add this as a co-recognized module for the CDMP. So we have a lot of harmonization plans. And give me an excuse to mention Steve Lucy who is the first person to have actually passed that, right? Absolutely. Steve Lucy in fact got the best score on the exam. So there he is. I know you're out there, Steve. You did a great job. Thanks, Melody. So notice that we also have another certification that we're working on. And this is based on our 20-year history with the CMMI Scampi appraisal method. So we're going to be convening a working group about what makes a reliable audit that auditors can have confidence in and organizations and industries can have confidence in. And then we are going to be adapting the Scampi method which has been proven for 10,000 organizations to the DMM content so that people can have a formal benchmark and published appraisals. And that will be out next spring to early summer. All right, thank you guys. I've got two more questions left. If anyone listening out there wants to submit some more, please feel free to click on the Q&A tab and shoot them my way. The next one I have is, which process areas should be examined first when applying the DMM model to an organization? Is there a sequence for the process areas that makes sense to evaluate? There is a...go ahead, Peter. Would you like to respond to that? I was just going to say it really, the question has more to do with what's important to your organization. We won't say that there is no difference at all. We'd like to start, for example, with an organization and given other opportunities there. But if your organization is facing a major quality challenge, then that may be appropriate for your organization to jump in there. Some organizations are... let's say if an organization has an initiative about to start, let's say that they are about to do a technology selection and implementation for a metadata repository. They would probably want to start with data quality, data governance and data operations. Which contain the process areas that are most closely linked with successful implementations for metadata. So it definitely depends on the organization. We've had organizations just pull out a category. One large financial organization was given the directive from on high, set up governance for data quality. So we worked with them only with the four data quality process areas, because that was their mandate. So what I mentioned before, it's quite flexible. Okay, great. Got a couple more questions rolling in here too. The next one is, is the DMM available as a spreadsheet document or maybe an automated tool to use for assessment and use the responses to derive computed results? Well, currently, our scoring spreadsheet is a spreadsheet at this time. In terms of further automation, there are a couple of vendors who can take in the DMM and automate the user interface. For our purposes, currently the spreadsheet is good. However, we're working on a new derivative product based on the DMM, which is called Compass. And that is a compressed version of the DMM for the specific use of business data governance leaders to assess how their line of business is doing with its progress. So that one is, it has a lot fewer text. Of course, you always have the DMM full model as the backup reference guide, but it's a much quicker process. So we're working with Wells Fargo to develop that. And that should be available to other organizations sometime this fall. Okay, great. And I've got one more question here. It says, there's no data warehousing section within the DMM. Which section would you say most closely relates to that in particular? Well, you know I'm an enterprise thinker. I have to say all of them, but I would also say data integration is the most tightly linked to data warehousing. The reason data warehousing and master data management are not in the DMM is because you're taking a step into the solution space, which we by design did not want to do with this version. Some of us wanted to, some of us didn't, and the consensus was we were going to stick with the fundamental data management practices for 1.0. And really that's one of the beauties of this, is that it is in fact easier to look at this from an abstract perspective than it is to dive in, because the minute you start to address any one flavor of data warehousing, you now have competition of, well have you done this one, and how does it work for this vendor, and that sort of thing here. So Melanie, that was a great session. I appreciate you joining us as always on this. It's a pleasure to work with you on this. Here's your contact information. You'd love to get questions from people and get more information out on that in order to do that. And of course we've got some more events coming up here, but again, thanks so much for joining us here, and I'm going to turn back over to Steven and Shannon. Thank you so much. Great fun. All right, thank you guys so much. Thank you for your questions. If you have any follow-on questions, feel free to email us at one of those many various ways you can get in touch with us that we've mentioned before. So again, thank you everyone for participating in today's event. We hope you guys enjoyed it. Thanks again to Data Diversity and Shannon for hosting us. Once again, you will receive today's materials within the next two business days. And our next webinar, which will be August 11th, is going to be Trends in Data Modeling. Hopefully you'll be able to join us for that as well. As always, feel free to contact us if you have any questions. So thanks everyone, and have an awesome day. And thank you everyone. Thanks, Peter and Melanie. Thank you so much for joining us again for another fantastic webinar. And one of the most important questions that we would get all the time, of course, is receiving a copy of the slides. Just a reminder, I will send a follow-up email within two business days. So by end of day Thursday, with links to the slides, links to the recording, and anything else requested throughout the webinar today. And so just to let you know that we will be getting out any questions that were left unanswered if you have more questions, go ahead and feel free to submit them in the Q&A as mentioned and we'll get you answers to those in written form in that follow-up as well. I hope everyone has a great day. And thanks again for attending another Data Diversity webinar. Thanks, Peter. Thanks, Melanie. Thanks, Steven. All right. Bye-bye. Thank you.