 Hello, and welcome. My name is Shannon Cameron. I'm the Chief Digital Officer of Data Diversity. We'd like to thank you for joining the current installment of the Monthly Data Diversity Webinar Series Real World Data Governance with Bob Siner. Today Bob will discuss metadata will not govern itself metadata governance sponsored today by precisely just a couple of points to get us started due to the large number of people that attend these sessions you will be muted during the webinar. If you'd like to chat with us or with each other we certainly encourage you to do so and just to note zoom defaults the chat to send you just the panelists but you may absolutely switch that to network with everyone. For questions we would be clicked in via the Q&A section and to find the chat and the Q&A panels you may click those icons found in the bottom middle of your screen to activate those features. And as always we will send a follow-up email within two business days containing links to the slides the recording of the session and any additional information requested throughout the webinar. Now let me turn it over to Sue for a brief word from our sponsor precisely Sue hello and welcome. Hi Shannon. Thank you very much. As Shannon mentioned I am Sue Pollock with precisely. And today we're going to talk about how organized is your data workshop. Wanted to spend a minute talking about a data workshop. Okay so maybe you don't actually call it your data workshop but let's face it with the huge volumes of data that we're working with today, structured unstructured data, internal external and third party data and sources. And then there's of course the ever evolving systems and applications that make up your data ecosystems. This picture can sum up what your data workshop may sometimes look like or at least feel like right. Some of the requirements for organizing your data workshop. We're going to go through some of the things that you might want to consider. First of all, how can I find the data that I need. Modern data workshop requirements are asking for an intuitive business friendly data catalog that helps you discover those critical assets. You might want to know, what's the impact of the data where did it come from. So some of those things might include data impact diagrams lineage and relationships between data assets, so that you know that how they impact each other. How is your data being managed. What are those internal and external data policy management things that you need to consider to ensure compliance and reduce risk. How do I trust my data. You need to have data quality and observability, making sure that you're looking at data in real time to increase the confidence in those data driven decisions. How do I get access to the data. So you need processes that account for accountability and allow you to be more operationally responsive for more timely insights. And the most important thing at the end is now that you've done all those things what's my single source of truth, having a master data management solution or process in place provides a trusted system of record for your data workshop. So for looking at this holistically, a modern data workshop requires cross functional teams with the ability to collaborate and understand the full lifecycle of data pipelines in a single solution. You need robust data access quality and observability capabilities that are seamlessly integrated and critical metadata and master data is easily catalog discovered and managed so that your data workshop. Looks and feels a little bit more like this. So what does that really mean. It means having data integrity in your workshop data integrity is data with maximum accuracy consistency and context for confident business decision making. And in your workshop, everybody's journey to data integrity is unique, and it's driven by your specific business business initiatives, and precisely can help you at every step of your integrity journey. Leading data integrity capabilities going to go through a couple of those categories that we provide. Integrate is about modernizing your infrastructure for the cloud or eliminating data silos and automating business processes. Verify is about building data governance and quality into your data centric processes to ensure accuracy and consistency. Location is about leveraging location intelligence that's inherent in your data for more sophisticated analytics and actionable insights. Enrichment is about complimenting your core business data with expertly curated data sets to add critical context and increase value. And engagement is about creating a seamless and personalized omnichannel way to communicate on any medium at any time. Today, we talked a bit about the verify space there. And those requirements that are needed to organize your workshop. And lastly, as the leader in data integrity has modular and interoperable data integrity solutions that contains everything you need to deliver accurate consistent and contextual business to your business, wherever, and wherever it's needed. If you'd like to learn more, please contact us at precisely.com. And I thank you for your attention. I'm going to turn it back over to Shannon. Sue, thank you so much. And if you have questions for Sue or about precisely, feel free to submit them in the Q&A portion as she will likewise be joining us at the end of the webinar today. And Sue, honestly, I love the workshop analogy there. I so appreciate an organized scene and setting. I want to introduce to you our speaker for the series, Bob Siner. Bob is the president and principal of KIK Consulting and Educational Services and the publisher of the data administration newsletter, TDAN.com. Bob socializes in non-invasive data governance, data stewardship and metadata management solutions. And with that, I'll give the floor to Bob to get his presentation started. Hello, and welcome. Hi, Shannon. Thank you, Sue. That was a great presentation. I'm going to back what Shannon said, and I want to say I like the analogy of the workshop. And it plays so well into the topic for today's webinar, which is how are we going to categorize our data? How are we going to organize our data so that we can get the most value out of it? So first of us, understand that we're going to need to give people information about the data. We're going to need to give them metadata or data documentation, and you're going to need a data catalog or a repository or at least some place that people can go to understand the data. So there's a couple of angles of metadata, and you're going to need the metadata to organize your data, but you're going to also need your metadata to support what people need to get out of the data. And if you've attended my webinars in the past, that's great. You've probably heard me say the expression that the data will not govern itself, and it's true that it requires a resolute effort to govern your data. Well, the same thing holds true for that information that's going to help you to categorize your data. The metadata, and you're going to hear me say this several times throughout the webinar today, that the metadata is not going to govern itself. It's not going to define itself, it's not going to produce itself, and it's certainly not going to use itself. So the idea of metadata governance is really important. It's just like data governance. Excuse me, but it's really focused on the metadata, the data documentation of the organization. So quickly before I get started, just wanted to share with you some of the things that I'm actively involved in. I will be speaking at Enterprise Data World Digital coming up. Next week or the week after that's coming up very soon. I'll also be at DGIQ West and Dataversities event in June. I've talked a little bit about my book, the second book that will be coming out on non-invasive data governance. It's called Non-Invasive Data Governance Strikes Again, and that is what the cover will look like when it is finally out the door. There is a learning plan available through Dataversities Training Center that focuses on metadata governance. So if you're interested in learning more about what we're talking about today, please go there. It's Data Education Month. I can provide you with a discount code for you to, if you're interested in that learning plan. And then there's the KIK Consulting Business, TDN, like Shannon talked about, and in my spare time I work with Carnegie Mellon University here in my hometown of Pittsburgh. So we're going to talk about how the metadata truly needs to be governed. So we're going to talk first about what does it mean to govern your metadata. I think most people understand what it means to govern data, but what does it mean in the context of the data documentation of the metadata. We'll talk about how do we go about applying data governance to the metadata of the organization. Most people when it comes to metadata, they need to define what are the roles and responsibilities that are required to govern metadata. Somebody has to have responsibility for the metadata because it's not going to govern itself. It's not going to produce itself. So we'll talk about some metadata roles. And then we'll talk specifically about the people in the organization who you may not call them metadata stewards, they're data stewards, but they're stewards of the metadata. And then the last thing I want to talk to you about today is as part of governing your metadata. You're using the tools like precisely like the other vendors have out there about to automate your metadata and to make certain that the metadata that you're using to categorize and to organize your data is available to people and that it's up to date. So let's start first with just some quick definitions, and then I want to apply these definitions to metadata as well so I always talk about data governance as being the execution and enforcement of authority. And that's worded very strongly but at the end of the day, you know the government and the regulators and the business community is not coming to you and saying here's a set of rules, you know, follow them if you'd like, you need to execute and enforce authority. So you can, you can define data governance however you want but at the end of the day that this is something that you need to accomplish. So data stewardship as being the formalization of accountability. When I talk about non invasive data governance. I talk about how the fact that people are already defining producing and using data as part of their job. And there may be for informally accountable for that data. We can be non invasive by formalizing that accountability for the data and basically a data steward is a person who has a relationship to the data. And if they're being held formally accountable for that relationship and you've probably heard me say before, everybody is a data steward. I don't think the same thing holds true for metadata stewards but you know pretty much everybody in the organization defines and or produces and or uses data as part of their job and if they're being held accountable for how they're doing those things, they're stewards. And that's not that in terms of metadata. So, there are differences, I guess between data and metadata, metadata I even use the word data in my definition of what metadata is. And people call metadata data about data well I say it's data that improves both business and technical understanding of the data, and it has to be stored somewhere because otherwise it's just information that's in people's heads. It's not going to improve the business and technical understanding of the data. So what is data govern, what is metadata governance. Well then if we just use the same definition as I use for data governance. It is the execution and enforcement of authority getting the people to do what needs to be done in order to provide effective metadata for the organization. That's the execution and enforcement of authority over the metadata. I think a data steward is a person held formally accountable for their relationship to data, a metadata steward is somebody that has responsibility for defining the metadata for producing the metadata and for using the metadata in the organization. So let's talk about, you know, what does it even mean to govern data what does it mean to govern metadata so I want to provide with you to you, a definition of what it means to govern. And then think that we need to apply that same level of accountability of execution and enforcement of authority to the metadata because again it's not going to take care of itself, and we need to define what does it mean to hold people to be accountable. Now I'm basically going to walk through the data life cycle, and we're going to apply it to metadata, and with the summary of being that the metadata is not going to govern itself. So the first thing I wanted to do is just bring to you a definition of what it means to govern anything, not just data, and I added those wrappers around the pieces of the pieces of the definition. So basically with the definition of to govern something means to, to make an administer policy for to exercise sovereign authority to control the speed, and all of these things. If you're thinking about them in terms of your metadata, you may have policy I was talking to an organization earlier today that needs to document their data better and they need to put up there putting a policy or a set of guidelines in place to do that. So if we look at just these pieces of the definition of what it means to govern something. The definition of execution and enforcement of authority makes more sense. You know it's the collaboration of people across the, it's the harmonization of people and process and technology, you can have another definition for data governance. So really, when people come to you and ask you what it means to govern the data. Here's some examples of what is the definition of to govern something. And now think about we need to apply this also to the metadata. So if you're thinking about, you know, execution and enforcement of authority, you know, controlling the actions or behaviors of people to keep under control. I didn't make these up these came from free dictionary dot com. I'm not sure where they got their definitions from, but I mean it is. I need to tell people that the metadata is not going to magically appear that some people in the organization or need to have the responsibility for that metadata. So the first question that I get a lot and I actually get some pushback on this is using the term being held formally accountable. And what does it mean to be held formally accountable I can't tell you how many organizations come to me and say, we don't have a level of accountability around anything, let alone data, let alone metadata I mean metadata, the documentation for many years has been more of an than we thought, but now we have better ways to be able to create our metadata workshop. Maybe I don't know so maybe we could talk about that in the q&a but you know we need to organize our metadata as well, and we need to have people who are recognizing what is the most valuable data for the organization. And so people and they people that need to put definition to that metadata, then there's people that have responsibility for producing that metadata, and then certainly there's accountability that goes along with using that data using that metadata as well so holding people formally accountable can take different can take different forms. Sometimes it needs to be written into people's job descriptions maybe they're already doing some of these things, but they need your assistance to help them to do it more formally to capture it somewhere and make that metadata available to people across the organization. There are so many different types of metadata that could potentially be managed, just defining that metadata that's going to add value to the organization, somebody has to have that responsibility, and then like I said somebody has to be responsible for producing that data. I joke a lot in the webinars about cheeseburger definitions for data. And what I mean by cheeseburger definition is that the definition of a cheeseburger is a burger with cheese, the definition of a student account number is an account number for a student you're using the same terms in your definition. So if we're going to get business people to produce quality metadata quality definitions, we need to help them to understand what goes into a good definition so, and we need to hold them accountable for providing definitions that are meaningful, not only to themselves but meaningful to people in the organization who are going to access this metadata. And then we need to hold people for people formally accountable. There's no excuse to say well I didn't know how that information was calculated. When that information about how that data was calculated is available to people. So we need to hold people accountable for using the metadata as well. And I talk a lot about defining producing and using as the basically the three actions people can take with data. Well, we can apply those to metadata as well people define metadata, they produce metadata, they use metadata, and basically any other actions fall under one of those things, and we can think of these in these defining producing and using in terms of metadata, where not only do we need to define which metadata is important to the organization, but we need to put definition to that metadata. So we need people to understand what a good business definition looks like what acceptable values what what lineage means what business rules mean and those types of things, we need to provide definition for the metadata itself. Like I said somebody has to produce that metadata. So to provide them standards to follow, and to assure that the metadata is being produced. These people become metadata, metadata production stewards basically. And then, you know, like I said for the, the use of the metadata to improve discoverability to improve confidence. That's really what it means. That's what we're hoping that people that are going to use the metadata are going to get out of the metadata. And that's right in terms of the data lifecycle so I saw this diagram on the internet I thought I would share it with you and where it came from, but it talks about just some basic steps in the data lifecycle. So there's the creation of the data the storage usage archival the destruction of the data. And I think about that in terms of metadata as well. So, something, somebody is responsible for creating the metadata. It needs to be stored somewhere. I mean, right now a lot of organizations have their metadata scattered throughout the organization. Their workshop, it may look like many workshops where it's multiple places where the data is being categorized and being organized. But the storage of the metadata is part of the lifecycle. We've talked about a little bit about the usage of the of the data and of the metadata, but we need to think about metadata if we're thinking about the fact that we need to govern our metadata. We should really be viewing it from the perspective of the entire data lifecycle, and therefore metadata lifecycle as well. And the metadata is not going to create itself. It's not going to destruct itself. Somebody needs to have the accountability for doing those things. So, you know, again, like I said, you're going to hear the expression from me a lot that the metadata will not govern itself. Basically, the metadata is a form of data, and the actions that that are taken with metadata really are very similar to those actions taken with data. And we talk about the metadata not defining itself, the metadata not producing itself, the metadata not using itself, somebody has to have the accountability for it. However, these people, just like the stewards of the data. You can't go and tap them on the shoulder and say you're a data steward, start doing data steward stuff, because they're not going to really understand what that means some people may understand it but most people will not. Well the same thing holds true for your metadata. People who are defining what metadata is useful to the organization can probably use some formal direction and how to figure out what metadata is the most valuable to the organization and how are we going to define that metadata. So, the point of the last three bullets here is that these folks who are the stewards of the metadata they need formal direction from data governance to because it's not just going to be the people on your data governance team. If you have a data governance team that are going to be doing these things it's going to be other people within the organization and they need your help. They need some formal direction around how to define what metadata is important. How to assure that that metadata is being produced properly that's being kept up to date properly, and people aren't going to know how to use the metadata until you share it with them. And we'll talk a little bit in a couple slides from now about what does it take to get people to come to use the metadata. So let's let's talk about how to apply data governance to metadata. And again, the theme continues with, you know, applying it to the definition of the metadata applying it to the production, applying it to the usage of the metadata. And I also grabbed something else from the internet because I thought it very aptly described what it, what's the value of applying the formalized accountability that I keep talking about. Again, my definition of data stewardship is formalized accountability and definition of a steward is somebody that is being held formally accountable for something. Well, these are some great bullet points that were defined as to what is the value of formalizing that accountability. Well, one of the first things is that it really provides that signal to the organization to make the the management and the stewardship of the metadata a priority within the organization. You know, it signals to the organization when you start to formalize accountability around the management and the governance of metadata, it signals that it's, it's no longer business as usual when it comes to data documentation and metadata. It's really going to have people in the organization that have as their responsibility, the metadata, again defining it producing it and using it, but it's no longer going to be that metadata that data documentation was an afterthought, because these days it's not an afterthought those organizations that are doing that are focusing on enterprise analytics, they need the metadata in order to support. The use of that data you need to increase the confidence that people have in data. What are some of the other things that the value of the formalized accountability is, it activates due diligence. So now, maybe your, your present situation is that there aren't people that are being held formally accountable for metadata. Well it starts to activate the due diligence around metadata. It's the first formal process to determine what metadata we're going to manage, and that we're going to make certain that metadata is produced and made available to people in the organization that are using that data that the metadata is going to explain for them. It provides the transparency I don't want to go read through every one of these bullet points but I thought this was a very good article, and maybe you want to note it and go to look at it what does it mean to hold somebody formally accountable, or what is the value that we're going to manage from formalizing accountability across the organization. So, first thing that we know and like I said I break it down to these three actions of definition production and usage, and I always challenge people to say well are there any actions that I'm missing. And so if there are some in your organization then add them to the list. There's nobody telling you you have to use definition production and usage. My experience has shown that, you know when people talk about analytics as being an action, you know it falls under the usage of the data, you know protection of the data protection of sensitive information, it falls under the usage of the data, but it also falls under the definition, because the rules need to be defined. So, if we're going to apply governance to the definition of the of the metadata. We need to look at all the different types of metadata and where the that metadata comes from and select the metadata that people are going to use, how are we going to know if they're going to use it, we need to ask them what information do they need to make better use of the data that they're accessing. That's going to give you a good indication of what metadata is going to provide the highest return. To describe the metadata like I said before provide the standards for the metadata through formal accountability to assure that business value is coming from the metadata that might mean staying in touch with those business the business community. And see is this adding value to you. Where does it need to be delivered to be most effective to you. So it's through additional formal accountability to make certain that the business is getting access to the metadata. Just like I said if you create just like Sue was saying, if you create a data workshop, it's only going to be useful to people if they can go in and they can make use of those tools that you're providing to them. That's why that analogy works so well. And through formal process to define, you know, where do people want to receive the metadata. Can you deliver it along with the data so they're not backing out of one application and going into another to get to the information that they need about the data. Okay, so we can apply governance to metadata definition, we can apply it to metadata production. And this is getting people in the organization to produce the metadata that has already been identified as providing the highest return to your organization. You'll see these bullets are very similar across through each of the three applications of governance to these actions. To the formal accountability to make certain that the metadata that's being produced is of high quality. I'm going to share with you a use case here in a couple minutes of what I implemented my first metadata repository tool, and people went in and challenged the metadata in the tool and they wanted to know if their metadata was in there, if it was being kept up to date. We'll talk about that as being an aspect of governing the metadata as well. Through a formal process to define the value that's coming from producing the metadata. If you're adding additional steps to people's jobs to create the metadata. We don't have to find the business value for them what's in it for them, what's in it for the other business users of that data. And through a formal process to define who are the best people in the or who are the most appropriate people in the organization or maybe to stay as non invasive as possible. Who are those people that are already producing the metadata. What can we do to help them to produce a higher level of quality metadata for the organization. And then the last one was applying metadata, applying governance to metadata usage. So how do we let people in the organization know what metadata is available. They don't know how they can use that metadata, get them involved in providing information back to you about the metadata that is providing the most value to the organization. So we can apply governance to metadata. More simply by just focusing on the definition of production and usage and hopefully some of these ideas will help and will resonate with you. I want to spend a couple minutes talking about the exact about the different roles that are necessary around governance. You know, and I, in my operating model and I'm not going to be talking about the operating model today. Now, I break it down to these five levels so I want to walk through them real quickly with you. Typically, the people at the executive level of the organization are pretty hands off when it comes to the metadata but they do need to support sponsor and understand the activities and what it's going to take to govern the metadata to provide that value so that people have trust and confidence in the data. So we need to make certain that we are communicating effectively with people at that level to get them to support sponsor and understand what we're doing and what it's going to take. They are the people that assure the capacity of the resources. Again, these people are not necessarily hands on when it comes to metadata management or metadata governance. And then there's a role for the strategic level of the organization and oftentimes that's representing representation across business areas. They're the point of escalation if metadata is required that's not being produced. We want to make certain that we engage people at the strategic level of the organization so that the appropriate decisions can be made around whether or not the metadata is even necessary. How can we get people to move off the dime and really start to take metadata and the governance of the metadata seriously? At the council level or at the strategic level, they assure that there's a capacity at the organization, the resources that would provide the capacity to govern and to manage the metadata. That's typically what we're expecting from people at the strategic level. And then there's the tactical level. And these are people that are looking at and have responsibility for different domains of metadata. So it could be data definition, metadata, it could be data production, data usage, metadata, it could be business rules, it could be data protection. If somebody has to define, locate where that information is, somehow manage that category of metadata, that tactical steward, that tactical metadata steward is critical to the success of metadata governance in most organizations. And so usually I refer to them as being metadata category subject matter experts, SMEs. That's what most organizations refer to them as. They may be the decision makers around the metadata. They may be the ones that set standards around the metadata. And they would certainly be those individuals that would be right in the middle of any center of excellence or community of excellence around the topic of metadata in your organization. And then there's the operational folks. And these are the people that daily are defining producing and using data. And they're also now defining producing and using metadata, because it's now built into what they do rather than being added as something that's additional. And then there's the support functions in the organization and a lot of these functions themselves. They're producing metadata. The information security group has a lot of metadata about what data exists in the organization, who has access to it, what data they have access to. I'm sure in human resources they have a lot of metadata about data in their HR systems. Now all of these different groups are potentially partners of data governance, and when it comes to metadata, they're partners of metadata governance as well. And so one thing I won't talk about today is where does metadata governance reside in the organization. You know I'm just going to touch on it briefly here in a many organizations. The governance of metadata falls under the people that have the responsibility for for the governance of data in the organization so it could be the chief data office, or the data office in the organization or the lead. But somebody has to have the responsibility because well what's the name of this webinar, the metadata will not govern itself. So, let's go back to the definitions that I shared with you of executing and enforcing authority of being formally accountable. What does it mean to execute and enforce authority. Well, the whole concept of governing your metadata may be new to your organization so immediately it may feel as though it is invasive. So if you can build it into the culture of the organization it becomes a lot less invasive. So people need to understand that again the metadata is not going to produce itself. We need people to have accountability for doing it and we need people to have accountability for getting that metadata into the hands of the people that use it. So in order to be effective with our metadata we need to execute and enforce authority over that. I wrote a subject I wrote a blog recently on a vendor website talked about gamifying data governance. Well the same thing can hold true for gamifying metadata governance. If you create friendly competition within the organization is to get groups to identify their critical data to document and create data dictionaries and business glossaries for the terminology in their business areas. And you turn those into competition. Well data governance, you can actually gamify metadata governance, the same way that organizations are becoming effective at gamifying data governance. And the bottom line is we need to hold people formally accountable. So, again, going through the what are the responsibilities when it comes to the people that are the data, the metadata definition stewards, selecting the appropriate metadata, providing definition for that metadata providing guidelines. This is where metadata starts. And, and I've written before on the TDAN publication that we spoke about a little bit earlier. You know what are some of the categories of metadata that we can consider those categories are forever growing, especially when it comes to data mesh and data fabric and data virtualization and visualization and all the tools that you have in your environment. So there's a lot of metadata being created. The question is, do we need to manage all of it, do we need to govern all of it. The answer is no, actually somebody needs to be looking at what is most valuable to the organization and then putting definition to that so that when we get to the point where we produce the metadata, we have some standards for what we're accepting for that metadata, and providing the guidelines and the standards, and you know the people that are defining the metadata also a lot of the time have the responsibility of considering how well that metadata is being is being used how well that metadata is being accepted across the organization. So then you know there's stewards there are the people that actually enter the business definitions into your glossary, or that enter the definitions into your data models if your organizations are producing metadata through your data models. These are metadata stewards these are metadata production stewards, do we need to change their title. No, do we need to help them to understand they play an important role in getting the metadata produced. So people in the organization can understand and have better confidence in the data. Yes, we need to hold them formally accountable for that. And when it comes to the quality of the metadata production. I listed a handful okay and I listed six of the dimensions of data quality. Well they hold true for metadata metadata as well. The metadata has to be accurate has to be complete has to be consistent timely unique has to be validated. You know you don't want a cheeseburger definition for your data or you don't want a definition from just one part of the organization's perspective, if you're setting a standard for that data across the organization, and then formally accountable for the usage and if we're going to hold people in our organization formally accountable for how they use the data it's typically going to be appropriate use compliant use responsible ethical. Sharing data with people, should they be sharing the rules associated with appropriate compliant responsible ethical use. I would say most people would say yes they should do that. So one of the ways that we can for the role of the metadata steward hold them formally accountable is to improve their knowledge and their trust of the data by providing them the metadata. That's going to help them to use the data appropriately to use the data in a way that's compliant responsible ethical and so on. So the last subject that I want to talk about before I kick this back to Shannon and see if we have any questions today is it's really important because I've talked a lot about people's behavior associated with defining producing and using metadata. But there's a lot of things that are there's a lot of aspects of a lot of the tools in the marketplace today that we need to consider to help us to govern the metadata. So automation considerations, we need to spend a minute talking about that. Talk about change management I'm going to share that that case study or that use case with you about change management and the importance. I want to realize what aspects of metadata governance can technology actually govern and what aspects do we need to be manually governed by people. And then I'm just going to wrap it up by saying again and again and again that metadata will not govern itself so if you walk away from this webinar with one thing. It's great to be repetitive, but the reality is if you walk away understanding that you need to apply formal accountability for the metadata that I've done. I feel like I've achieved what I'm trying to achieve in this webinar. So the first thing is, when it comes to automation, well, even without automation. If people go into your workshop and they can't find what they need or they go into your environment and they can't find the information about the data, you're going to lose their confidence it'll be harder to get them to come back again. So keeping metadata current is important. Change management if you can automate the change management, it basically eliminates the manual aspects of it. And we all know that if it's going to be a manual, a manual step that it may or may not get get taken care of. As long as you know people need to be held accountable for following maybe there's more likelihood that it will be followed but automation will help to keep your, your metadata in your tools current. Wow. Okay, so I was trying to change slides and it didn't happen but okay here. So let's talk about change management, needing to reflect reality. So back in the early days of my career I was a metadata repository administrator for Blue Cross of Western Pennsylvania here in my hometown of Pittsburgh, Pennsylvania, and when we started loading metadata into our tool into our repository tool. It was very ungoverned. All we did was scan seven days a week, 24 hours a day scan information into the tool, but then we realized that oh if somebody goes and changes something, then the repository is going to be wrong unless it somehow knows to recapture that information. So there was actually a time where we purged all the information out of the tool, because it was in there but it wasn't kept up to date, and then we did not load anything into the tool again, until we had a process to assure, or an automated process to assure that the metadata was kept up to date. So the first time we started introducing the repository tool to people, somebody challenged us and said we changed the file layout, I don't know if they use that term, that much these days, we changed the file layout, we're going to check in the tool to see if it's actually updated, and it's accurate and we just did this last week. I'm very surprised by the fact that it was kept up to date, that's because the, we had built into the system the ability to recognize that a file layout changed, and that we purged it and we loaded it into the tool. Change management to reflect reality is extremely important, and that's a big part of metadata governance. And as I mentioned quickly before, it is difficult enough in organizations to get people to come to your data catalog the first time. But if they come to it the first time and they find metadata that's old, or not complete, or inaccurate or doesn't provide value to them. They're going to lose confidence and it's going to be a lot more difficult to get them to come back the next time. You know, the fact is that the metadata itself has to reflect reality within the organization in order for it to be successful, and we should look to try to automate that change management as much as possible to take some of the burden off the metadata stewards, the people that are producing or defining producing and using the metadata. Let's talk about what aspects of this technology can govern. Well it can govern. It can be the storage place for the metadata in your organization. It can you can with a lot of the tools, they have processes that will notify people if the metadata changes or if they're, or if somebody just has an idea to change a definition. Anybody who is linked to that metadata in the tool could be appropriate could be appropriately notified when something's going to change. A lot of the tools have the ability to step by step, help people in the organization to govern the process around the metadata. So that's another thing that technology can govern is that process to engage the appropriate stakeholders process to engage the appropriate metadata stewards. It can also recognize when things in your environment have changed, and it can automatically scan them into the tool at least that's would be what I would strive for, again to decrease the burden on the metadata stewards. And then with the technology can govern is the ability for people to use the metadata to discover what data they have in the organization and to deliver that data to them. There are aspects of metadata governance that have to be manual. You know, especially when it comes to creating a business definition for providing the acceptable values for data, those are basic forms of metadata that typically respond requires that somebody has that responsibility. And if nobody has that responsibility, then it's just not going to get done, but somebody needs to govern the metadata content. Somebody needs to define the quality standards for how that metadata is going to get entered. You know, a lot of the things that I've talked about so far, there really needs to be formal stewardship around the metadata around the definition around the production and the usage of metadata, and really as a bottom line that is what metadata governance is all about. And it's going to require people who know the right things to do but it's going to require people to define what those right things are to do within the organization as well. So again, I just said I was going to end on this note and I've said it many times the metadata will not govern itself but if you today order from me I have pixie dust that you can sprinkle over your organization that will set up your workshop like Sue talked about, and can't know I don't. There is no such thing as magic pixie dust. The metadata won't govern itself, just like other metadata in the organization it has to be governed, it has to be stewarded, and the quality of the metadata and the value that you're going to get from your organization is going to depend on how well that metadata is being governed. Don't provide them cheeseburger definitions, provide them with information about the data that is going to help them to get the value that they need out of that data. So quickly before I throw it back to Shannon what did we talk about here. We talked about first but what does it mean to well first I shared a couple definitions for you. What data governance is and metadata governance, what stewardship is and what metadata stewardship is, then we talked about, what does it mean to govern metadata and people may ask that question. That definition was helpful. We talked about how to apply governance to the definition production and usage of metadata. We talked about the different roles in the organization and their relationship to the metadata, the role of the metadata steward, and then we kind of wrapped up by talking about that automation and that change management and how important it is when it comes to governing your metadata. So metadata governance is real. It is a real thing. The metadata will not govern itself. Maybe that'll be the last time I say that today, but I'm going to take it back to Shannon to see if we have any questions today. Thank you so much for this great presentation. And I certainly am going to put in my order for magic pixie dust. It's running out. It's a short supply. So if you want some, please let me know. And if you have questions, feel free to put them in the Q&A section. Just 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 Sue, we invite you to join me back in. So let me dive into the questions here. So do you think it's best practice to govern business metadata with the domain models, EDM with taxonomy and ontology and just to the data catalog. Well, I'll answer that and then I'll let Sue answer. If your metadata is categorizable, or if you can build a taxonomy for your metadata, that might be the most appropriate way in your organization to figure out who those tactical stewards are or who's defining the category that's necessary from that category from within the taxonomy. So I think that it really depends on your organization, but if you are taxonomy based and you can find a way. I don't know of too many organizations that have have created a taxonomy for their metadata but I'm guessing that if you do a search on that or you ask chat GPT, it'll give you somebody's example of how they tried to do that. I would say it does depend on the organization you're absolutely correct. I mean, that's where you need some more pixie dust right the one size that fits all to all of this life would be so much easier. But in this consistent situation I would say you know the key thing that you want to look at is whatever solution you're using or whatever you're designing has to be flexible enough to adapt to whatever your use case is and, and, and that's really what the bottom line of it is. So, I'm concurring with what you're saying and it is, it's pixie dust but there is definitely definitely a case for going in that direction if you're set up that way. Yep. Yep. metadata will not use itself that seems to ignore machine learning which will use the metadata as well as taxonomies ontologies to use the existing metadata to make decisions and produce more metadata. Does that statement make the disambiguation of text data in combination with the taxonomies and input metadata. You know I learned something new every day and I think that that question even just provided me a perspective. Right that that the that machine learning can make use of metadata so it can automate the use of metadata. So many people think about the usage of metadata as opening up a data dictionary opening up a business, a data, a business glossary, or a data catalog, or a data marketplace to find the data that is relevant to them in their organization, but there are other uses of I will I'll back up on something that I said earlier that you can automate the use of metadata in those organizations that are prepared and are able to do that so I think that was a very good point. I think looking at these. The thing that caught me from that particular statement is this is that metadata is, it's a wealth of information I don't even think we've tapped the way that we can totally use it, but you're absolutely correct what I'm hearing here. Number one, it's not going to govern itself and number two, it's a huge, it's a huge undertaking to govern all of it. So let's first of all say, all metadata doesn't need to be governed. Your critical metadata does need to be governed and that's the first start of your process is determining what is your critical metadata. That's the tough part right. And then from there you can put those processes in place but the other thing that kind of appealed to me as far as what was said there was the use of machine learning to help automate and help take some of the manual effort out of this. With the volumes we're talking about here there's nowhere this can be done manually all the time, no way. And, and, and having observability in place where you're actually looking at changes in your metadata and that's being flagged and alerted, or even rules put in place through through those type of through machine learning. That's the only way you're going to be able to do it in the long run. We're not all there yet right everybody's learning everybody's growing but that's still something that we're just starting to just tired to get our arms around how much we need to really go in that direction to make it sustainable. I agree with you. We're getting there but it's we're not there yet, you know, our journey is never ending or always have somewhere we can go. Yeah. Wondering if usage comes before storage. Well I don't know how you can use it unless you can get access to it somewhere I'm not really certain. I mean if it's the governance of the usage or the governance of the storage. Well, and I saw in the chat that somebody asked a question about, you know, what's the best solution for storing your metadata if you don't have a tool. And you know what that's really depends on you know again depends on your organization is you've got to record the information somewhere in order to give people access to it because if it's if it's just knowledge that's in somebody's head, and it hasn't been written down. It's not very valuable if it's been written down and people can't get access to it. It's, it's very important. I think if you can give people access to distributed metadata that's a good value to the organization. First, but you know I would say that you're going to have a hard time governing your usage without the storage piece. I agree. And what came to mind for me is are we talking about streaming data, right. That's another another conundrum, as far as it might be there before you have a chance to actually even store it but that's, we're not there yet. Right. And for change management automation, what tools do you use to manage that. So I'll, if Sue wants to talk about that from precisely his point of view. I'll just let me add at the beginning. Back in the day when I was a repository administrator I used a piece of change management software that anything that moved from development into test or into production. So that meant that if a table was being moved into production, or it was being moved into test, we could actually capture the metadata associated with that table in test in highlighted as being having a status of test and not being in production. So, being able to tap in in an automated fashion to the tools in your environment to recognize where things are changing would be complete automation. If you just had people add things that they've changed to a list, and you scan that list, that would be partial automation. But if you had to re enter that information just straight into the tool, that would be a complete lack of automation. I don't know as soon what do you think about that. I was going to say a couple of different things as far as precisely as concerned right as far as a change management as far as metadata or governed that only say govern data metadata in particular. A good governance tool should have a strong workflow a strong change management policies and process there. As far as data itself, you know, now you're talking about master data management, right, which is also something that we do so those are also part and parcel of larger solutions so that it, I mean, you. There's no sense in having those solutions if you're not having some type of documentation as far as change management policies. I guess if I had to pick I'd put storage before usage. But you know you may not be how be given the resources to work on that, unless there is a usage use case a business use required for that so I mean I could see it going either way but if I have my vote it's going to be. Let's cover the storage of that metadata and then make it available to people and get them to use it. And I would say with the way the world is right now. That's probably the, that would be the predominant focus I would agree with you. How can we calculate tangible business impact or value from metadata management solution. I'm going to tie it to data governance real quick and I'm just going to give an example of one organization that I worked with who defined their purpose for data governance as to use strategic data with confidence. Okay, so just to just simple a handful words to define the purpose of why they were putting governance in place because they wanted to use strategic data with confidence. What are the aspects to that there's the, what is strategic data. So you can't I agree with what Sue said you can't govern all your data or all your metadata, the same way you've got to be able to recognize what is your strategic data, what is your critical data. The other piece of it is, is to manage that or to use that data with confidence. What are you going to take if people are spending 90% of their time wrangling the data and 10% of their time analyzing the data because they don't have confidence in the data that they have access to part of that reason might be that there's not the definition of the data there's not the documentation provided with the data. I would say the business value if you are going to plan to use strategic data with confidence, you could apply metadata the value that comes from managing your metadata to impacting those things now the one problem with that is now how do we articulate how do we quantify that. And that I think would be another subject for another webinar is how do we quantify the value of metadata management, but that's how I, how I view it I say, you know, look at the purpose of governance in general or data management and recognize that you can't manage anything without information about that thing you can't manage your finances, if you don't have information about your finances, your HR team can't manage your people, unless they have information about your people. You can't manage your data without information about your data. There's your business case for why do we need metadata, because the data is, because we've got a situation now that needs to be addressed. 100% and once again what I'm going to say is that. One of the things that that we talk about here precisely is the fact that whatever you're governing the, we have a strategic services team that is very very industry recognized as far as being able to help define. I'm not sure we're going after those high value what are those business objectives what are those objectives that we're trying to get to, and what is the month you know where we going as far as our metrics to measure that. And anything that's being governed needs to be linked to that you need to be able to say this, these assets are directly linked to these objectives, and this objective is being measured this way, so that we can look at the progression of better governance or better management is direct, you know, when we have better data governance scores we have better data quality scores, those are directly impacting the way that this metric is moving for these business objectives. So I think that's imperative. I have a couple other questions but I'm afraid that is all the time that we have for today. But if you have additional questions feel free to submit them in the Q&A I'll get those over to Bob and we'll get those answers put into the follow up email which will go out by end of day Monday with links to the slides and links to the recording as well for everybody. Sue thanks for joining us again always lovely to have you and thanks to precisely for help sponsoring today's webinar helping to make today's webinar happen. Thank you so much. It's been a pleasure and it's I love this group they're always so engaged it's great. Likewise. Thanks so much. Thanks y'all. Yeah, thank you so thank you precisely and thank you everybody for coming look forward to seeing you next time. Bye bye.