 Hello and welcome. My name is Shannon Kemp and I'm the Chief Digital Manager of DataVercity. We'd like to thank you for joining the current installment of the monthly DataVercity Webinar series, Real World Data Governance with Bob Siner. Today Bob will discuss metadata governance for catalogs, glossaries, dictionaries and data sponsored today by Irwin. Just a couple of points to get us started due to the large number of people that attend these sessions. You will be muted during the webinar. If you'd like to chat with us and with each other, we certainly encourage you to do so. Just click the chat icon in the bottom middle of your screen for that feature. For questions, we will be collecting them via the Q&A in the bottom right-hand corner. Or if you'd like to tweet, we encourage you to share highlights or questions via Twitter using hashtag RWDG. 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 additional information requested throughout the webinar. And if you'd like to continue the conversation from the webinar, you can go to the dataversity community, community.dataversity.net to continue that conversation. Now, let me turn it over to Danny for a moment for a word from our sponsor, Danny. Hello and welcome. Hey, thank you, Shannon. Hello to everybody out there and welcome. Another great topic and great presenter from DataVersity. We're happy to be sponsoring, so very, very exciting. I won't take too much of the time and we'll let the time be spent on the purpose of the call. But my job here is just to tell you a little bit about Irwin and how we relate to the topic at hand. So, you know, if you haven't been following the story of Irwin, we were divested by CA Technologies back in 2016 and we've been a standalone entity now for, I guess, three years. Pretty exciting. Time flies when you're having fun. And building on our, you know, pedigree of data modeling with a lot of, you know, internal development and innovation combined with a couple of very strategic acquisitions, we put together a platform that really at the end of the day, I think, gives an organization the capability to deliver data intelligence to their organization. By bringing together, you know, a data catalog complete with data dictionary business glossary and extended business usage that brings a lot of automation into the maintenance and preparation of data over time, capabilities around data literacy where you can understand your data from the perspective of the business and access to all the different types of stakeholders in a role aware context and capability sensitive environment combined with our foundation in architecture around enterprise modeling. So, our data modeling, our enterprise architecture and business process modeling, bringing them all together into an integrated platform that is the most connected role based data management and governance solution out there. When we look at what we're trying to achieve for organizations, we're trying to give them a sustainable data capability intelligence and control so that they can achieve all the things that they want around their data and their data assets in the organization. So, whether it's, you know, traditional governance and defense against risks like, you know, making sure that you're in compliance, making sure you understand what needs to be private and secure, making sure that you understand that your data is complete and fit or really more on the offense side, which is enabling agility around the folks that are trying to leverage data and bring value to your organization, optimize the insights that they bring and assure that they can do that in a sustainable way over time. That's really what our goal is by bringing all of these tools and capabilities together and enabling the enterprise. At the foundation and very germane to today's conversation is our data catalog, which is very much focused and built on active metadata. What do we mean by active metadata? Well, we go out and we capture all of the metadata, but we bring it into an environment where you can actually use that to drive and practice data governance, to drive and practice automated and agile data preparation, and really make sure that as folks are applying data to analytics, to machine learning, artificial intelligence, whatever the use case might be, that they can find the data that they need, that they can understand that data at all different levels of abstraction, and really trust that data to leverage it in an effective way for the organization. And it's a place where really everybody comes together and works off of that same metadata based page moving forward. Now, in terms of connectivity, what you see here is just a flavor of the different types of infrastructure that we can connect to and catalog data assets through the harvesting and management of metadata. So all of your traditional data stores, all your new dig data and no SQL type environments, your cloud environments, your ERP environments, we can connect and bring all of that metadata, organize it, catalog it, and then map it to all of the rest of the important things that really allow you to really understand that data and not just data at rest, not just data stores, but as well an automated capability to connect to all of the data movement in your organization, whether it's traditional ETL, scripting languages, procedural languages, however you move data from one place to the next and integrate it for different purposes, we can also understand that, bring that into an environment that puts it in your control, both from a management and lifecycle perspective, but also the ability to build out new infrastructure, actually swap out different tools and capabilities and help you manage the process of modernizing your infrastructure as well to meet the latest needs. A big part of that is our natural language data mapping capability where we centralize all of your data integration work into a business-driven, non-technical environment and then leverage automation to actually generate all of the code, reverse engineer all the code that may exist out there, so we have a clear picture into that as well as the ability to automate the discovery and maintenance of lineage and impact analysis for the purposes of governance and anyone else in the data world that may be needing that to best understand where their data came from, how it got there and what it really means when it hits their dashboard or their report or the data store that they're going to then run their analytics or other algorithms against. Lots of intelligent automation and it's really about creating a sustainable operating framework and to a large extent to the topic of this WebEx govern the processes that manage metadata, that manage data governance, and really allow you to standardize your environment, make it much more efficient, much more effective and much more agile, whether it's the automated scanning and scheduling of metadata assets and versioning of that so that you have a clear picture of how your metadata foundation has moved. As I said, code automation for all of the major targets where we're taking standardized mappings and pointing it at different technologies and then of course improved visibility, understanding not just all of the different data that you have but how it moves through your organization, how it serves the business, where it came from and what happened to it on the way. So a very exciting, very very capable in terms of bringing all of those needs both for offense and defense and at the end of the day Erwin's purpose in the world is really to activate your metadata and really drive the data value chain, bring all of the people that are concerned about data, consumers, governors, managers, anyone that may be out there and dealing with data in your organization, bringing them on to the same page so they can really get to value much faster with their data assets, get the largest return on the opportunity that those data assets represent and really allow you to leverage the data in your organization to drive innovation and transformation in your business. So with that, I think I will hand it back to Shannon and we'll get to the good stuff. Danny, thank you so much for this great presentation. I love it. And if you have questions for Danny, he's going to be joining us in the Q&A after Bob's presentation. So feel free to submit those questions in the bottom right hand corner of your screen. So now let me introduce to you our Bob Siner, the speaker of this series. Bob is the President and Principal of KIK Consulting and Educational Services and the publisher of the data administration newsletter, TDan.com. Bob is better recipient of the David Professional Award for a significant and demonstrable contribution to the data management industry. Bob specializes in non-invasive data governance, data stewardship and metadata management solutions. And with that, I will give the floor to Bob to get today's webinar started. Hello and welcome. Hi there, Shannon. Hi, everybody. It's really good to have you with us today. Thank you, Danny, very much for a great presentation that you did. You know, when you talk about data value chain and data intelligence, I think that's some of the things that we're all trying to achieve in our organizations. And I'm going to kind of be blunt with everybody right at the start of this webinar. The fact is that if you don't have metadata, if you don't have metadata that's effective, it's going to be very difficult to improve your data value chain or to achieve any level of data intelligence. So and the fact is that if you have metadata, which most organizations do and find to be very important, the fact is that it needs it's not going to govern itself. It needs to be governed and there needs to be people that have responsibility for the metadata. So I'm looking forward to this subject today. It's a great subject and I've been talking about it a lot recently. And I hope it's of interest to you because I think of the bottom line from this session is that the metadata will not govern itself. Somebody is going to need to be responsible for it. And that is what we are going to talk today about. Before I get started, I just want to run through a few quick things. And this list seems to be ever growing. Certainly, I've been doing this webinar series for some time next month at this time on the third Thursday at 2 p.m. I'm going to be talking about activating your data governance policy. And I've been talking about having a special guest on my webinars for a little while. And in fact, I'm going to ready to announce that Len Silverston, who is famous for Zen with Len, will be the guest on my webinar next month. The Data Governance Book, the Non-Invasive Data Governance. Please take a look at that. I'll be speaking at a couple of diversity events coming up. The first one is in just a few weeks and it's Enterprise Data World. And I'm going to be speaking on, again, how to govern your metadata as part of that session. I hope to see you there. And I'll be at the DGIQ event as well. One thing that I have to announce that's kind of new is that for some time I've had the Non-Invasive Data Governance Learning Plan on Dataversities Training Center. We've just added a second set of classes called Non-Invasive Metadata Governance. So if this topic interests you, please go out and take a look at it and see if it's of interest to you as well. Then there's always the publication Shannon spoke about and KIK Consulting, which is now not only the home of Non-Invasive Data Governance, I guess it's become the home of Non-Invasive Metadata Governance as well. So please take a look there. These are the few items that I'm going to talk about today. It's a great topic and it could really take a heck of a long time to get through each of these, but I'm going to go through them quickly and at least give you some ideas as to some of the things that you should be thinking about if you're going to implement metadata governance in your organization. The first thing that I want to address is really that relationship between data governance and metadata governance. And I've received the question before, if we're going to govern our metadata, don't we just apply the same thing that we have in place for data governance? And the question is maybe it's perhaps, but it really depends on what you have in your organization. So we'll talk about how data governance and metadata governance are the same and how they're different. Then we're going to jump into really the topic of what type of metadata are we talking about. We're talking about metadata in some of the tools and things that Danny talked about in the data catalogs, the business glossaries and the data dictionaries. Then we'll talk about, well, how do we get the most value out of this information that we are recording in these data documentation tools? We'll talk about governing the documentation in those tools. And last, we'll speak about measuring the effectiveness of governed metadata for your organization. So I'd like to start with a series of definitions. And again, I want to go through them relatively quickly, but you'll see that there's a trend in each of these definitions. Or should I say, there's a level of consistency in these definitions. And they're not necessarily the definitions that you need to use, but I suggest that you take a strong look at these and at least consider them or implementing pieces of these in your definition. So I define data governance as the execution and enforcement of authority. And those are worded pretty strongly and they've got a lot of teeth behind them. Some organizations don't like that strength in the definition. But really, truly, at the end of the day, if we're going to govern something, we need to execute it and enforce authority over that thing that we're governing. Data stewardship, and some people use governance and stewardship interchangeably, where really data stewardship has to do with the people in the organization and formalizing levels of accountability for the data that people define, produce, and use. And basically, the difference between the data stewardship and a data steward is that the steward is the person in the organization that has the relationship to the data that they define, produce, and use if they're being held formally accountable for that relationship. And you know what? The same thing holds true for metadata stewards, people that have responsibility for metadata, if they're being held formally accountable for that metadata. Now you have the people to do the things that you need to do in order to be successful and to deliver all of those things that Danny spoke about earlier today. And then my definition of metadata is that it's data stored in IT tools because it has to be recorded somewhere and it improves both the business technical understanding of the data. And the one thing that I wanted to point out here too is that the consistency in these definitions, if you look at the underlined words on this slide, this is always about the data and data related resources. So you need to identify for your organization, what are those data that are most important to you, that you need to govern, that you need to have people that are held formally accountable for, and that you need to have metadata for. And then look at the available other resources that are associated with that data and make certain that you're governing those and you're stewarding those. And that's really where metadata governance comes in. The idea of making people or at least taking people in the organization and holding them formally accountable for the work that they're doing around the metadata. Because like I said in the opening to this session, the metadata is not going to govern itself. There needs to be people in the organization that have the responsibility for it. If it's going to be captured, if it's going to be kept up to date, if it's going to add value to your organization. So looking at those definitions, I also want to share a definition of noninvasive data governance, which basically applies all the principles that I kind of just talked about, where we're going to take the people who have relationships to data and we're going to apply formal accountability to their relationship. So if they use the data, for example, there are rules associated with how that data can be used or how it can be shared and they need to be able to follow those rules. So we're going to apply that formal accountability. We're going to use noninvasive roles, which I've done webinars completely on that subject. We're going to apply governance to the process rather than define everything as a data governance process. And we're going to assure that the definition, production and usage of data assures all the things that we're talking about that we need within our organization, compliance, security, privacy, and all those types of things. So really the truth is that noninvasive really describes how we're applying governance in the organization. We want to be transparent, supportive, and collaborative, but we want to see what we can take from noninvasive data governance and now implement that as part of our metadata governance or our metadata management program within our organization. So that's an easy way to be able to transition into the next, really the first subject that was on that set of bullets that I spoke about, which is what is the relationship between data governance and metadata governance? And so if we take my definition of noninvasive data governance and we just replace data with metadata, it holds true. I mean, what we want to do is apply formal accountability and have the right types of roles and responsibilities and processes defined to make certain that the definition, production and usage of metadata rather than just the data itself does all those things that we want governance to do. And again, we're talking about this is how we're applying governance for metadata across the organization. And we're going to ideally, we would try to do that in a noninvasive way. So the first thing that I really suggest to organizations when they're looking for that relationship between data governance and metadata governance is that they grab a framework. Any framework will do. This is the noninvasive data governance framework. I know it's really small on the screen now. So I'm supplying a bigger version of it. And so this is just a typical framework for data governance. It has the core components. It has the different levels of the organization that you're looking at it from. So, but what I really want to focus on is, and I could spend hours just talking about this diagram in itself. And I'm going to supply a white paper to Shannon so that she can put that out there when she sends her follow up email regarding the webinar. But I don't really want to focus on the details of this slide. What I really want to focus on is just those components that are across the top. So if we're going to look at metadata and metadata governance from these perspectives, then that's going to really help us to make certain that we have the appropriate level of governance in place around the metadata, around that information that's improving the understanding of the data for people in the organization. So there's really the five critical components that I consider that I talk about a lot as part of your framework, and those five components would be here. And so I've taken them from left to right and I talk about the data roles and responsibilities and who has authority for what. The data process is obviously a really important piece of this as well. Communications around the data, the metrics associated with the data, the tools associated with the data, that's all, these are all different pieces of what I would consider to be the core components for a framework for data governance. But now if we look at this as from a perspective of a framework from metadata governance, you know what? I don't really think that the roles, the components change a lot. Maybe the roles are a little bit different. Maybe the processes are different because they're more focused on data documentation than the data itself. You know, the communications about the metadata, what's available, what's available, where it's available, what it takes to go get it and how it can be used and those types of things. They're still critically important about the metadata as well. We want to measure how well metadata, the metrics associated with the metadata, how many people are using it? Are they getting value out of it? Is it saving them time? All of those types of things. And then the tools themselves, you know, we need to have tools. All the things that he talked about, all the products that Erwin provides, those are basically metadata tools, things that will help to enable certain activities within your organization. So we can look at that framework from just a data perspective or we can look at it from a metadata perspective. I don't think the components really change too much. But the question really becomes, are these components handled in exactly the same way between data governance and metadata governance? And my suggestion is no, but they're not handled in the same way. There are a lot of similarities between them. And I'm going to run through some of those in the session today. Like I said, there are distinct differences in metadata roles versus data governance roles. And I'll go through that in some detail, but certainly we can have conversation about that in the data community, which I forgot to mention at the beginning of the things that were going on. Please go visit the community at dataversity.net. I've already set up a stream for conversation in regards to the different topics that I'm talking about today. So I hope you'll join us there. But yes, there are distinct differences between the metadata roles and between the data roles. And we'll talk about some of those here briefly as well. So I found when I was putting these slides together that I had the word data governance slash metadata governance and I had the word data slash metadata all over the place. I just had it too much. So I don't want these slides to look confusing, but I abbreviated the data governance slash metadata governance as GGMG and the data slash metadata as D slash M. And so when we're talking about the different roles that we need, and this is the first component of that framework, the roles that we need to govern the metadata, first of all, there needs to be somebody that has the responsibility for overseeing the program, for overseeing the metadata activities, for making certain that we're defining the appropriate metadata that needs to be used by our organization, that we're producing it and that we're using it effectively. There needs to be an administrator, basically somebody who oversees the governance. And a lot of the organizations that I work with, when they talk about right sizing the solution for their organization, if it's a small organization, typically, instead of having a team, they might have a single person or a part of some person's job as the administrator of the program. And so we know we need to have at least that role when we get started. Then there's the data stewards and the metadata stewards. And so these are people within the organization that have now been given formal responsibility for their relationship to either the data or to the metadata. So it's like I said before, if somebody uses the data and there's rules associated with how that data can be used, they need to know those rules. They need to follow those rules. And we need to be able to monitor the fact that they're following the rules. So there's not just data stewards, there's metadata stewards as well. And I oftentimes break the stewards into three categories as the definers, the producers and users. If you've listened to my webinars before, you know that I talk about those three categories of activities that you can take with the data all the time. And so there's the people in the organization that have the responsibility for defining what metadata needs to be collected, how it needs to be collected, where, when, all of those things, somebody needs to define the metadata. Because if you say we're going to have a metadata program, the first question I would ask you is, well, what metadata is it that you're governing? So we need to have somebody who has that responsibility or your requirements won't be defined. It's going to be much more difficult to acquire that tool that's going to match your requirements if you don't really know what your requirements are. And then there's the producers of the metadata. These are people, perhaps the people that are doing your data models, the business analysts, the business people that are working with you. These are basically the makers of the data and the makers of the metadata. And we need to identify that that's one of the roles within our program. And the same thing with the metadata users. We want to make certain that we're collecting this information that's going to be useful to some people across the organization. So from all of these five different roles, that's just the first piece, the first component of what you need in order to put formal governance in place around your metadata. So let's talk about the second component, the data governance and the metadata governance processes. And again, I'm going to go back to the same three actions, the defining, the producing and the using of the data or the metadata. Well, you need to make certain that you're defining the process or that you have a process defined for how you are going to figure out what is the appropriate metadata to be used for your organization, that you have a process for making certain that that metadata is being produced and that's people know about the metadata and they have access to it so that they can use it. So we know that we need to set up processes around the metadata, just like we do around the data because, again, going back to what I said earlier on, the metadata is not going to define produce and use itself. We need to have people in the organization that have the responsibility to do this if the metadata is that valuable to you and to your organization. The communications, there needs to be communications around data governance. I've talked about that before, I've done webinars on that before. Well, the same thing has to hold true for the metadata governance as well. And I usually break the communications into three levels. There's orientation communications and that is kind of general communications about the data governance program or even about the metadata governance program. You know, what is it about? What are we trying to accomplish? What's in scope? All of those types of things. The orientation communication. But then when we ask somebody to do something and we get them engaged, the next thing that we want to talk to them about is getting them on board. Let them understand what we have available for them to use and that they have a level of responsibility, but we need to communicate that to them. You know, certainly during the onboarding, one of the things that I find to be communicated most effectively is the role-based activities. What role are you in? What are we expecting from you? When are we expecting it? And those types of things. And then there's the ongoing communications. And like I said, this could be a webinar within itself. It has been a webinar within itself when we've talked about putting together plans for communication associated with data governance. But now the same thing holds true for metadata governance as well. Maybe you should keep your eye out. There might be a metadata governance communications webinar somewhere down the road. But we know that communications is the third component of the framework that that needs to be addressed within the organization. The metrics. We want to be able to measure improvements in efficiency and effectiveness and all of these things. We know that, again, as a component of a data governance program, we need to have metrics around our metadata because people are going to ask why even bother? What's the value that we're getting from having better metadata and making that metadata available to people? And there's lots of things that we can measure, including the efficiency, the effectiveness, understanding, you know, what data documentation is available and how is this helping us to do what we're really setting out to do, which is to improve our decision-making capabilities within our organization. And then the last component is the tools. And, you know, so you've got data definition tools and you've got metadata definition tools. You've got data production and metadata production, data usage and metadata usage tools. All of these things are really important, but what this does is this really leads into the next subject, which is, what are the different tools that are associated with data or metadata definition production and usage? And if you'll notice in the red bullets, the ones that are in that first category are the ones that we're going to continue to talk about today, the data catalog, the business glossary and the data dictionary. So again, when we're looking at all of those different components of a successful governance program, tools are a piece of that. And certainly if we're going to improve the understanding, we're going to need metadata to help people to understand the data better and that information is going to be collected in your catalog, in your glossary, in your dictionary, in other tools. But let's take a look at those three tools specifically for the webinar today, which leads to the second subject, which was what is the metadata that we're going to collect in these different data documentation tools. And so that's what we really want to address. What metadata are we going to include in our catalog? And there seems to be a lot of confusion or should I say different levels of understanding as to what information needs to go into a data catalog. So we'll talk about that for a second. We'll talk about business glossaries and data dictionaries as well. There seems to be more of a consistent understanding across the industry of the glossaries and the dictionaries than the catalogs. And there is a lot being written on catalogs. And I know Danny mentioned it earlier on and certainly the Erwin product has those capabilities as well. But when we're answering the question of what metadata should we collect in these tools, the answer is that the answer isn't the same for every organization. There are some common practices in what organizations collect, but the one thing that we have to keep in mind is that gathering the requirements for these things, we need to understand what metadata we should collect because there's a lot of different types of metadata that's out there. Now how are we going to collect it? Where is it going to be entered? I said metadata was data stored in IT tools. Where are we going to house that metadata and when are we going to collect it? Now how are we going to govern the metadata because we know that the metadata will not govern itself. You need to have people associated with those activities. So let's take a look at data catalogs. And like I said, there's some confusion or should I say inconsistencies, at least from what I've seen about what information is stored within a data catalog. So when it comes to defining what metadata you need associated with the catalog, it's going to really depend on what information are you collecting within your catalog. There's the data inventory, which really is the metadata that's specific to the different data resources that you have in your organization. There's metadata about the ownership and who are the people within the organization that have responsible or responsibility for the data. I typically try to shy away from the term ownership. I think stewardship is much more appropriate because it's the data is typically owned by the organization. But I wanted you to kind of understand, I wanted to use Linga that is well understood. So data ownership, you know, we might need to collect that information in our data catalog, the information that's specific to the owners and the subject matter experts for that data. The classification information, the reports of data critical or critical data resources, a lot of organizations seem to be starting with critical data elements and focusing their initiatives on something that they can put their hands around or put their arms around. So your data catalog, the metadata that you're going to collect is really going to depend on how are you going to use that catalog within your organization. The business glossaries are more understood. I think they're more consistent. And I've seen organizations that focus primarily on semantics and business terms and subject areas and functions. I've also seen organizations that have recently started to collect things like process flows and rules. And the truth is that this information, if you're really sticking to that semantic level, it's not always connected to the data. It may just be this is the language that we use within our organization. And if you get, once you get that information collected and you have data dictionaries for your data resources in your organization, at that point, you might want to think about rationalizing, okay, we have students as one of our primary categories of data. What information do we have about the steward? Let's link to that in the data dictionary and I'll show you a diagram that demonstrates that in one second. Well, this is the beginning of the diagram. So again, the business glossary is that we're looking at the semantics and the terms and the subject areas and those things. At some point in time, we might want to rationalize that to the dictionaries, to the data that we have, to the standards that we've defined for the data in the organization. So we want to have the business glossary as well. And we need to collect the information about it somewhere. And in one of these tools, like the Erwin tool, it is a perfect place for that information to be collected. You want to build a catalog, you want to build a glossary in the dictionary, look at the tools that you have, look at the tools that are available to you, and then ask yourself the question, do we want to link between the vocabulary and the dictionary? And then at some point, you may get to the point where when you document your data dictionaries, now you want to be able to rationalize the data dictionaries, not only to the glossary in the business terms, again, like saying, I want to know what information do we have about student address, and then all of a sudden you can get a list of those components in the dictionary, but then the data itself, what are the physical names, what are the databases that these exist in? And again, a lot of the tools that we've mentioned are available to have the ability to be able to link these things together. And so just one more slide, kind of on that, and I shared this in a webinar that I did, I believe it was called the Three Levels of Metadata, and I think I spoke about this last year at one of the Data Diversity Events, but you've got the business term and then you've got the data dictionary, you've got the vocabulary level, the data dictionary level, and the data level, and there can be connections between all of these things. So if you want to go from the business term down to what are the standard names of the fields that we use to define that business term, and then what are those pieces of data called within the systems that we're using? So you've got the standard name and the system name, and then at the bottom level you have the physical data level. You've got the business system, you've got the business resource, a database, and so you might want to be able to link those three things together. You know what, these aren't going to happen by themselves. Somebody has to have the responsibility if you want to get this type of information out of your metadata, somebody needs to have the responsibility for making sure that each and every one of these things are being catalogued. Okay, so now let's talk about how to maximize the use of the data documentation in each of these resources, and you're going to notice that these things are very similar from one to the next. And what do we need to do to maximize the use of data documentation in the data catalogs? Well, the first thing is that we need to make certain that the metadata is correct, or that it's accurate, that it's up to date, that you're making it available, that people know that that metadata is available and they know how to be able to go after that metadata. So if we can assure all of those things, first of all, if they go to the metadata and they find that it's not accurate or it's not up to date, or it's difficult to get a hold of their, the chances that they're going to utilize that resource are going to be diminished significantly. So we want to make certain that if we're going to try to maximize the data documentation, we're going to do that by making certain that the metadata is accurate, that it's up to date, that it's available, all of those types of things. And again, going back to the communications, we need to make certain that we're communicating with the people in the organization what metadata is available, how they can get access to it and what value it will bring to them in their part of the organization. So when we look at how to maximize the use of data dictionaries, the same thing. We need to make certain that the metadata that we're collecting in the data dictionaries is accurate, that it's up to date, that it's available, people know about it, know how to get to it. All of those things are extremely important when it comes to maximizing the use of the data documentation in each of these resources. And again, I know I keep parking on this and I know I said this at the beginning of the webinar that this is the one item that you really need to take away from this webinar is that the metadata is not going to govern itself. If we want the metadata to be accurate up to date available, then we need to have processes and we need to have people within the organization that have that as part of their responsibility. So here's a couple of things that I'd like you to consider. And I've found it to be very beneficial to organizations when they are collecting these types of metadata that deliver the definition of the definition metadata that I spoke about earlier, deliver it with the data. There's nothing more difficult than having to get out of an application and go into another application and pull up the dictionary and try to pull up a definition of a term or of a critical data element when in fact if you can deliver that metadata along with the data itself, it's really beneficial. And that's one way that you're going to find that you're really able to maximize how you're using these different data documentation resources that I talked about. All right, so let's talk about governing the documentation. And the first, you know, I seem like I feel like I'm repeating myself, and I really think that it makes sense to kind of save some of these things over and over because they've got to pound the message home. But what does it really mean to govern the documentation? Well, simply stated, we got to govern the definition production in the use of the metadata of what metadata of what documentation we're going to collect because there's a lot of different types of documentation that are out there, govern the production of it, make certain that we have people that have it built into their job, that they need to collect a business acceptable definition of the data as they're defining new data for the organization, things like that. We need to make certain that there's somebody that has the responsibility for producing the metadata that we're going to use. And then we want to govern the use of the documentation, make certain that the right people in the organization are getting access to that information in a timely manner. And so once we go, okay, what does it mean to govern the documentation? And we talk about the definition production and usage of that documentation. The next question is, well, what else do we need? Okay, so if we have somebody who has the responsibility for all three of those things, what are the other things that we need? And the fact is they really go along with the framework that I mentioned earlier. All of the components of the data governance framework that I shared at the beginning of the webinar are the other things that you need. You need roles and responsibilities. You need processes, communications, and those types of things. So if you're really going to ask, well, what do we need in order to govern the documentation and these resources? You're going to need people that govern the definition production and use, but you're also going to need to define these other core components of what it means to govern metadata for your organization. So just kind of going back to the framework that I shared at the beginning, if we're really looking across the top, we're saying we know we need to define the roles processes, communications, metrics, all those types of things. But when it comes down to it, you know, we need to look at them from different perspectives. So like I said, this framework, I could talk about it for a long time, but you might want to take a look at it closely and say, do I need these things under roles? Do we need an executive level role, a strategic, a tactical, operational? And that's how this is set up. And that's how I would expect this to be used if you're interested in learning more about the framework. So the last subject that I want to talk to, before I turn this back over to Shannon and we start the Q&A, because it looks like there's a bunch of chat and hopefully questions as well, we're going to talk about measuring the effectiveness of the governed metadata. So what about the metadata can we govern? And one of those things can be the breadth of the documentation that's governed. Are we collecting information about the definition of the data? Well maybe we need to collect information about how that data is being produced. And what's the lineage? Where did the data come from? So we need to, we can always measure how much metadata we're governing and demonstrate that we're governing more, or the completeness of the metadata. If you already have a data dictionary or a business glossary, how complete is it already? Have you even started it yet? And so you can certainly measure the completeness of the documentation. You can measure the accuracy of the metadata, or you can give percentages as to how much of the metadata has actually been validated or certified. I have joked from time to time about something that I call a cheeseburger definition. A definition of a cheeseburger would be a burger with cheese. And maybe that's not the quality of the data definition that we want. And so we're going to go through our dictionaries and we're going to say, okay, well 90% of the definitions were good, 10% need help or need to be improved. So we're going to measure the activities that are taking place as we try to improve our metadata. You know, the number of people involved, the people that are educated and trained and what metadata is available. And the number of people that are accessing the metadata, these are all things that you can measure within your organization. And you're going to need to measure these things if you want to be able to demonstrate to them that the governing of the metadata is just as important or is, I won't say it's more important than the governance of the data, but they certainly go hand in hand. Anything that you're trying to do with your data as part of your data governance program, having better or having improved governed metadata is going to assist in making whatever that outcome is a little bit better or maybe a lot better. So again, what about the metadata can we measure? I have found that a lot of organizations that I've worked with recently are focusing on this confidence factor. You know, what confidence do people have in the data? How do they know that it's right? You know, we can we can measure their effectiveness of the documentation, the delivery, but when it comes to it, what are we really trying to achieve when it comes to data governance? We're trying to achieve confidence in the data. And that's, you may not state that in your mission, but a lot of times, really what we're looking for is confidence that the data is and then take any of the quality dimensions that you're looking for, accessible, accurate, complete. Any of those that are the different dimensions that you have for data quality, confidence that the data is all of those things. And how are they going to get that confidence? They're going to get it from the fact that they have information that's going to improve their understanding about it. In other words, that they can talk about the confidence that someone's being held formally accountable, confidence in their understanding, confidence that the appropriate tools are being provided to give them access to that metadata. All of those things are things that we can govern in association with the use of metadata within our organization. So I know I went relatively quickly through the slides, but I hope that they were logical and that they made sense. So the things that I talked about was first relating data governance to metadata governance. Are they the same thing? Not exactly. Are they similar? Yes. Can we take some things from data governance and use them in our metadata governance? Certainly. And some of those things are like the framework and the things that I talked about during the webinar. We talked about the different metadata that you should think about collecting in your catalogs, glossaries and dictionaries. We talked about maximizing the use and governing the documentation. And the last thing we hit was measuring the effectiveness of the governance. And there's lots of things, as I mentioned, that we could govern associated with our metadata. So with that, I am going to remind you that next month on March 21st, my special guest, as it says I'll have a special guest on here, will be Len Silverston. And we'll be talking about activating your data governance policy within your organization. And with that, I'm going to turn it back over to Shannon. Thank you for another fantastic presentation, as always. And just to answer the most commonly asked questions, just a reminder, I will send a follow-up email by end of day Monday for this webinar with links to the slides and links to the recording and anything else requested throughout. So diving right into the questions coming in, aren't the data stewards responsible for the corresponding metadata, unless it's the technical metadata we are talking about? My question back to you would be, are they? If you make that as part of their responsibilities and you hold them formally accountable for it, then not only are they a data steward, they're a metadata steward as well. So I hate to kind of turn the question back around on you, but it really comes to how are you defining the role of the steward? And if you're putting metadata responsibilities into that, then you don't need to have people that you would even label as being your metadata stewards, you just use your data stewards to do that. Feel free to jump in at any time. We want to light you back into the conversation. Yeah, I'm here. I agree to a large part with, you know, we don't want to make this so complicated. You know, the only maybe different perspective I would bring in is that, you know, there are folks that are system owners. So if you can, you know, organize them into the into the mix to business perspective that most data stewards. I would say that you know, that there's a different way to implement it in every organization, depending on who you have in place. But I think that we shouldn't have the metadata standards being too far away from the good business perspective that brings to the table. I agree with that. Question a lot, you know, what's the ROI of the metadata? Could you share some meaningful measures that will resonate with business stakeholders who are funding the metadata initiatives? You know what, I oftentimes suggest that looking what the ROI that you're getting from your data warehouse. What's the ROI that you're getting from your master data solution? What's the ROI you're getting from your ERP implementation or your analytical platform and understand that if you're going to build your analytical platform before you have the understanding of the data being documented somewhere, it's not going to add as much value for you as if you have the metadata before you start to build out your analytical platform. So people look for ROI for metadata and data governance and I typically suggest, yeah, I think there's ROI. I mean, I know we're improving process, but we're doing we're not just creating the metadata for itself. We're doing it to support specific business mission or business critical events that are happening with our organization. And if we want to get the value out of those and there and so many of those are associated to data, their data oriented initiatives that we really should be looking for the ROI from those rather than the metadata itself. And I don't know, do you have anything to add to that? Yeah, no, I think that Delta is the key, right? You know, in a lot of cases, a lot of this is finding understanding, preparing and then the amount of time that we spend actually delivering insights and value, you know, through something like an analytics to an organization. And really, it's about, you know, measuring the Delta between, you know, when people were doing it in an ad hoc, you know, with no specific managed foundation, and then, you know, see the results that come from, you know, come from having this capability, you know, increasing the level of self service, how much faster do we get to the end result that we want? I think there's other, you know, more abstract thoughts in terms of looking at lost opportunities and things that we weren't that we didn't take on. Because we didn't think we had the capability or the or the resources to get that done. But, you know, net net, I think, I think, you know, Rob's debt on, which is, you know, the data is what's being measured for the impact on the business. If you look at what's being spent around data versus around things like data governance, metadata governance, metadata manage, that, you know, it's, it's that it's a multiplier of 10 or even 100 in some cases. And, you know, so you want to tie to those and tie yourself to those metrics and then focus on the delta, how you improve that process, things that you've done around metadata management. I, again, I concur, the delta is important, which requires that people have to take a benchmark of their present state so that they can measure what that delta is. And some people don't think about that. They look to improve and say, okay, now let's measure it. Well, let's measure what we're doing now. Let's measure what we're doing in the future. And that's where the delta will really come out. Some of your explanation, Bob, you know, what's, there's a little lack of understanding why there's a parent distinction between data governance and metadata governance. Metadata is a component of the data in an organization. Why aren't they just managing governance the same? And, you know what, in most organizations, there isn't. And maybe that needs to change because a lot of the organizations that are doing data governance say that they don't have as strong metadata as they need to support the, to support the data governance. So I think that's, I think the person who asked that question really hit the nail on the head is to, well, why do we need to make it different? Well, we need to make it different because metadata is a type of data and it needs to be managed. It needs to be governed. You know, I've always talked about the idea of master data governance and how is master data governance different from data governance? Well, it's really not. In metadata, because it's the documentation that we're focusing on, as I highlighted in the webinar, there are differences. So you might not need to have a formal metadata governance program per se, but you need to have people that have accountability for defining, producing and using the metadata. So that's how I would answer that question. I think it's great and I think it speaks exactly to why there is need for metadata governance within an organization or somewhere. It is what it is. And then when you think about metadata description, there's rules. There's the technical piece. There's so much more. And I think metadata has so many more layers and has, quite frankly, a level of complexity that an individual data element. So I think that you have to look at it differently. You may come up and get best practices that share from side to side. You don't have to figure out what needs to be in a data element, right? The data element is the data element, but you definitely need to know what your standards are around metadata, especially as you start to get out of the physical world and into more of the semantic and the business world where, you know, you may be leveraging tools like modeling tools and stuff like that to go out and create this metadata based on tribal knowledge, not necessarily connecting to a piece of technology. Those standards are very, very important because once you get those standards in place, then it's easy to follow and it's easy to fill in the blanks. But again, I just think metadata has a lot more levels of complexity and a lot more levels to it in itself is a reason why you need to look at it somewhat separately. Okay, I agree. And so it is great you're exposing these concepts. And can you give some examples of these as it is, the current presentation is highly conceptual. There's also another request, you know, for examples of specifically of catalog law through your dictionary. Well, you know what, I'd love to, maybe we had a further date we should do a case study that kind of walks through some of these examples. But I just to give you as an example from an actual client that I'm working with right now, they're focusing on protecting or should I say securing classified information, probably something that is related to something everybody is doing. And we want the people in the organization that have access to the classified information to handle it in the appropriate way. So there's metadata. There's metadata that talks about the data and then how it's classified or what pieces of the data within that resource are classified a certain way. And what are the rules that are associated with handling that data that's now been classified as public or confidential or whatever your classification is? You know, the metadata, how can we expect people to protect the data if they don't have information about the data? How do we expect people to improve the quality if they don't have information? Think of it this way. If you're trying to manage your own finances, how can you manage your finances if you don't have data about your finances? How can you manage your resources if you don't know who they are and what their availability is? So in any case, if you're trying to get more value out of your data, you need, and you know, I'd be glad to have the conversation offline or in the community at dataversity.community.dataversity.net if people want to talk about those types of things. But, you know, we really need to make certain that people do understand, do have a storyline behind where the metadata is specifically going to add value to people within the organization. It's a great question. And Rob, when you look at, you know, creating a data model in a data model, you know, there's the mechanism of a template that specifies, you know, extended metadata that needs to be in the model and then there's a model score card that you then validate that model before it goes out into production. A clear example in one way of managing and governing metadata is to have those mechanisms in place and the processes behind them that say when we do something, when we create a schema of some sort, that it has to have X, Y, and Z there before that schema and the, you know, the design of that schema is considered valid. And to me, that's a real simple one and I think it's one that people have been doing for a long, long time knowing that they've been governing their metadata through that process and through some of the automation that the tools provide behind that. And certainly to stay non-invasive, you want to look specifically at what are you already doing? If you're already doing that, then let's leverage that and tell people that is governance of the metadata. You know, if we're going to identify the metadata that's going to be most important to people, we could take a step back and we could just say, what types of questions do we get about the data? People don't have access to that information. And if we could just keep a running list of things that people want to know, where did the data come from? When was the last time it was updated? What are the valid values? Whatever it is that they're asking, that will give you an idea as to what you really need to be focusing on when it comes to the metadata that you select and then you need to put a process around it if you expect people will get value from it. All right, I think we have time for one more question here. I have read your book, but I'm struggling to get a program off the ground on its own. GF Suggestions had had a jumpstart this journey. I've been making ground on stewardship over ownership but hard in a political environment. Hey, yes. Well, first of all, thank you for reading the book or for going through it. The steps that I would suggest is that we need to make certain that our senior leadership understands that there's an alternative approach to the kind of hard core iron fist approach to data governance. That we can, that there is levels of governance that are taking place within the organization that we can formalize. And if we can do that and we can demonstrate to them, it's not all bad. There's things that we can take advantage of. It might get them to sit forward in their chair and listen to you a little bit more. You also need to have somebody within your organization that has the responsibility for doing these things. Back in the session today, when I talked about the administrator, you're going to be immediately at risk if there is not somebody in the organization that specifically has the responsibility for data governance and metadata. That could be the same person or maybe it's not, but it really depends on your organization. But if you don't have senior level support and understanding of what you're doing and you don't have somebody that has the responsibility for making this happen, you're going to fail. So that's typically where I suggest that organizations want to get started. Yep. Very good. I love it. What a great resource for these great presentations and for the Q&A portion. But I'm afraid it brings us to the top of the hour. We have a lot of questions coming in. Do feel free to keep submitting them. I will get them over to Bob to answer and get that included in the follow-up email, which will go out by end of day Monday, which will also include links to the slides and links to the recording. Thank you both for such a great presentation. And thanks to all of our attendees for being so engaged in everything we do. We really appreciate it. And thanks, everybody. Hope you all have a great day. All right. Thanks, Sharon. Thanks, Danny. Take care. Bye, guys.