 And here we go. Hello and welcome. My name is Shannon Kemp and 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 data management, metadata management and data governance working together sponsored today by data.world. 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, the Zoom defaults the chat to send to just the panelists may absolutely switch that to network with everyone. For questions, we will be collecting them by the Q&A section or if you'd like to tweet, we encourage you to share our questions via Twitter using hashtag RWDG. And 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 Atticus for a brief word from our sponsor data.world. Atticus, hello and welcome. Hey, everybody. Thank you so much for having me. I'm super excited to be here. Just to get us started, I'm going to go ahead and give us a quick intro, kind of a brief background on who data.world is and kind of our take on agile data governance as well. Just as a little bit of background on myself. My name is Atticus Ross Fountain. I'm a sales engineer here at data.world. Before this, I had a background in ETL tools, APIs and databases as well. So very happy to be here. Super excited to hear Bob's talk as well. So without further ado, let's go ahead and move on into that intro. So today really quickly to kick things off, I wanted to introduce who data.world is and talk a little bit about why this idea of agile data governance or really non invasive governance is so important. First off, data.world is different. We are the enterprise data catalog for the modern staff, but we are also relentlessly focused on adoption. We believe that there's such a big issue and challenge around data enablement and data literacy. And we think that data governance and cataloging are really key areas where you can start to make a difference in your team's current data literacy and your team's current enablement as well. For a bit of background, data.world was born on the cloud and we are focused on how we can really easily integrate with your environment and create data discovery within your organization. We believe in an Amazon and Google like experience for your data with consumer grade experiences for collaboration, lineage exploration and for searching your data as well. Being built on top of web technology means we're both open and flexible. We have no black boxes behind the scenes when it comes to interoperability with the rest of your stack as well. So when it comes to that traditional model when we talk about governance, we think of addressing the needs to have better visibility around data or being able to protect yourself when data gets into the wrong hands. A traditional data governance is about making sure that you're dealing with a strict regulatory environment, compliance needs and also industry standards so that you're able to do that effectively with data at scale. And in the past, those governance models have really been very top down and with a lot of steps for approval and processes along the way that ultimately create an environment where your data consumers at the bottom of a very long chain. In reality, this ivory tower approach to governance often gets sidestepped by those data consumers at the bottom of the chain, because they find it tedious and rather restricting and it keeps them from generating value as data consumers. Something that's really important in the world of governance right now is sort of the changing wins around governance. Governance now is becoming much more about enabling data consumers for data discoverability, rather than just data protection. So while application silos continue to create governance challenges for your organization, adopting an agile data governance approach can make a huge difference when it comes to generating that value. So in short, the traditional approach to data governance has been overly focused on risk avoidance and compliance. It's been using top down policies to really clamp down the process and it's created cumbersome processes and an ivory tower approach that's often not enabling those data consumers at the bottom. In contrast, we believe that data governance needs to be a benefit and not a burden. And I know that many of you are taking governance not just as a defensive but instead going on the offensive and creating more value from your data and actually taking the friction away if you can. So here we think it's about acceleration and not barriers. At data.world one of the ideas we're so excited about is the idea of agile data governance where you can really take a noninvasive approach to your governance, an iterative approach to your governance where you can really focus on collaboration around your company and also really focus on use cases instead of focusing on locking things down. Ultimately, you don't want to boil the ocean. You don't want to just take a platform approach. You want to actually solve problems. You want different areas of the business to find the data they need and put it to work. That's how you create value. So with an agile approach, what you can do is you can take your data producers and your data consumers, you can take out some of the middle men or perhaps some of those more complicated processes, and you can really focus on this flywheel. You need to be able to curate, you need to be able to audit, you need to be able to govern and you need to be able to document. And you also need to be able to do it as fast as possible. Essentially, you can iterate yourself towards more value if you can speed up this process. So one thing that you need to do, one thing that you need to think about is importantly how you can build that data front office. If you're trying to empower the use of data. If you're trying to make data governance work better in your organization, think about how you can create a layer where folks can find what they need, where it also interoperates with the rest of your data ops ecosystem. If you're using data observability or data quality tools, maybe you're using some policy type tools as well. You need to make sure that those things can work smoothly together. Ultimately, you want to understand your data supply chain and you want to provide a more self service experience around AI and machine learning and analytics for the rest of your organization as well. So lastly, if you enjoy these kinds of webinars and you love learning about data, data.world puts on a podcast called catalog and cocktails and it's available on all of your favorite social media platforms with new episodes every Wednesday. It's an honest, no BS non salesy conversation about enterprise data, and they drink cocktails while they discuss those trends in various industries, they bring on cool guests to speak and there's plenty of other good stuff as well. I think it's a ton of fun so do check it out if you have a chance and in conclusion agile data governance is really the way to go and I think that Bob is an excellent speaker, and he's also a great expert about all those things that you should be doing around metadata management and governance I'm very excited to hear the talk today myself. And with that I'm going to be passing the baton back on to Shannon. Monica, thank you so much and yes I love that podcast with Tim Gasper, and once a Kata have them on our podcast recently to they're just, they are great entertaining couple of guys and you can learn a lot from them in that podcast. And if you would like and thanks to data.world of course for sponsoring today's webinar and helping to make these webinars happen if you have questions for Atticus and about data.world feel free to submit them in the Q&A panel, as he will likewise be joining us for the Q&A portion of the webinar at the conference. And let me introduce to 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 says she likes is a non invasive data governance data stewardship and metadata management solutions. And with that, I will give the floor to Bob to start his presentation Hello, and welcome. Thank you data.world thank you Atticus for a great presentation. Thank you everybody for taking time out of your busy schedule to sit with us today for the webinar. This is a great topic. A topic I've been looking forward to for a while. It seems to be so relevant for so many organizations right now, if you have a data management group, if you have a data governance group, who's responsible for what, where does metadata fit into it. So we're going to talk about data management, metadata management, and data governance and how these things should work together within your organization or how they can work together, and I'll provide you some examples of some things that some of the organizations that I've worked with have thought about when they are trying to relate these disciplines and bring them together and get them to work together just real quickly a couple things about myself. I'm very busy in a lot of different ways. I'm going to talk about this webinar series. Thank you again to data.world for sponsoring today. Next month I'll be talking about data governance best practices, how to do an assessment, how to develop a roadmap from the results of an assessment of best practices. I'm speaking at Dataversities Data Governance and Information Quality Conference East in Washington DC coming up in December. I talk a lot about non invasive data governance. So there's a lot of resources available to you there's the book I wrote on non invasive data governance, also some learning plans that are available through Dataversity on non invasive data governance, but also something that we're going to talk about how metadata governance is a real thing. Your metadata is not going to govern itself. You require people to be accountable for how the metadata is being governed. That's certainly one of the ways that these three disciplines can work together. Shannon mentioned the data administration newsletter tdan.com please go check that out new issues, a couple times a month kik consulting is my education business and my consulting business, and then I also in my spare time. I'm a young faculty member at Carnegie Mellon in their chief data officer program. Today what I want to talk about is basically how, when organizations talk about data management, there are a whole lot of different disciplines that are typically included under data management I'll share with you that the dama dm block the dama the wheel of all the different knowledge areas and disciplines that they say fall under data management, or we'll talk about the categories of disciplines that basically all are focusing on managing data as an asset. So we're all pushing in the same direction that way. I'm going to share with you a definition of data management that embraces all of these disciplines and you'll see that there's already a bunch of the definitions out there that you can use or that you can customize for yourself. We'll talk about the importance of metadata management and the use of a tool like a catalog like a data dot world to help actually help to assist in the governance and the stewarding of the metadata that's involved in each of these disciplines that are part of data management. We'll talk about how, how wide data and metadata require formal governance, and then I'm going to share with you a graphic that I put together in response to a couple of my clients had that kind of the same request around drawing boundaries between data management and data governance. So I just want to share that with you as well. I want to start really quickly with the definitions. And I always share the definitions at the beginning of my webinars. I word things kind of strongly. I word things to get people to sit up in their chair, take notice, ask questions, what do you really mean by this, I say data governance is the execution and enforcement of authority. It doesn't matter if you take a command and control, or a traditional, if you build it they will come approach or a non invasive approach. At the end of the day, you need to execute and enforce authority over data. You need to make certain that rules are being followed standards are being adopted those types of things stewardship is really the formalization of accountability. I talk about how potentially everybody in the organization is a steward of the data. They have a relationship to the data, and they're being held formally accountable for that relationship. They're a steward it's not something to opt into or opt out of. So stewardship is formal accountability for data. A data steward is somebody that is held being held formally accountable for what they do to data, do with data and metadata is basically data, it's data it's a form of data that needs to be governed itself, but it improves both the business and technical understanding of data that's really what metadata does. So just want to kind of reach into what they must says, and what they may uses as their definition of what data management is. At first glance, you know, if this is a very complex definition in fact it's very wordy it has a lot of pieces to it. And what I intended to do with the definition was underlying in different colors, the different sections of the definition. The development and execution and supervision of all these things of plans and policies and programs and practices, why to deliver control protect enhance the value of what you know the data and information assets throughout their entire life cycle. There's a lot of pieces to this, but this is a really great definition. It's not one that you can get people to understand, or necessarily adopt and be able to speak to off the top of their head, but it covers everything. But then they also go into more detail, and they break it down into specific disciplines. So data management. So I took their definition, and I don't get down a little bit or maybe I don't get down a lot. And I said that it is the coordination of all the data disciplines that Dave was talking about that we're talking about to improve the administration of data and data related assets. So that's really what data management is, it's kind of encompasses all these different data disciplines. And, you know, if we think about data management as being the coordination of all those disciplines to improve the administration of data. What's metadata management well let's be consistent it's the coordination of all the different disciplines associated with defining producing and using metadata to improve the administration of the metadata and the things that are related to the metadata. So, according to Dana, these are the 1011 knowledge areas that make up that make up data management. And so a lot of us have seen these before I typically start at data architecture work my way around to the right end up in the middle with data governance. What's interesting what we're here to talk about today is data management in its entirety. But really the things as you're getting to the end of going through that whole cycle. I just talked about is the metadata and the data governance and how those three things really need to work together in order to for organizations to be successful in the way that they govern their assets. So, let's look at the Daniel wheel and a little bit more detail. And let's recognize that, you know, if we want to simplify this a bit. We can consider that that some of these things are really data definition disciplines. Some of that these things have to do with data production. Some of these things really focus on data usage. I typically break down the management of data into those three actions definition production and usage. I've been challenged on that but typically when somebody says oh well where does analytics falling or analytics fall under usage, where does privacy and security, you know they would fall under usage as well. What I want to do is I kind of tagged each of the areas of the data wheel and associated them with one of these three actions data definition data production and data usage. But then I started thinking about, well you know data governance, you're governing the production of the data you're governing the usage of the data, we can't just label data governance as being a data definition discipline, it's actually all three. And then I started thinking about data security and I started thinking about data quality and metadata, and I actually came to this conclusion that all of the different disciplines associated with data management, the way that data has laid it out in the in the they're all data definition data production and data usage disciplines. And because of that we need to make certain that we are doing the right things that we're setting standards that we're applying formal accountability for the management of these things. I basically took the demo wheel. And I changed it just a little bit I changed it kind of looks like the demo wheel still it's kind of re envisioned version of the wheel. But when I did what's really different with this picture and this image, compared to the demo wheel was I put took metadata management, and I took quality management. If you notice, I didn't talk about data quality. I just talked about quality management of all of these things quality management of unstructured data quality management of master data, you know metadata management to support all these things. So, this is just a kind of a different way of looking at the demo wheel, but instead of just putting data governance in the middle and having data governance applied to everything. And maybe this is the way you're already looking at things that metadata needs to be applied to everything too. And so does data quality. So it's a different version of the of the demo wheel. I'm just going to kind of use that as the basis for a bunch of the conversation here today. So, when it comes to data management I've already stated that it's kind of the coordination of all these data disciplines to improve the administration of the data. And I talked about how data governance is really the has to do with the authority it has to do with the enforcement, the formalization or should I say the stewardship of the data, the account and the accountability for the data. In fact, a good friend of mine, you've probably heard him speak at diversity events before Len Silverstone I always attribute this to him. He said that we shouldn't even call it data governance, we should be calling it people governance, because it's people's behavior that we're trying to govern, and we need to do that through authority we need to do that through enforcement. We need to do that through formality and formal accountability, where people understand what it what their responsibilities are for the data. Data governance is certainly a component of all of those disciplines that that Dama talked about that are in this kind of re envisioned version of the of the knowledge areas or the disciplines associated with data management. So what is metadata management will metadata management is the administration of the documentation. So as practitioners of data management of data governance, if you're involved in any of these disciplines that are around the orange circle, you understand that that under architecture, the metadata that we have the information that we have about the data of the organization is incredibly valuable and invaluable, or should I say incredibly valuable to the organization, the same thing about data modeling, same thing about security and privacy. How is the data classified, how do we use the data. If the data is classified as a specific way, you know, integration lineage, all these different things. So the metadata the documentation associated with these things are critical. So that's why I re envisioned this model to include, you know, data governance metadata management and quality management, right in the middle and speaking of quality management, basically your focus, you're focusing on making certain that all of these different disciplines are providing value to the organization. So we're going to govern unstructured data whether we call that unstructured data management, or we call it information governance. The metadata is going to be important the accountability for the unstructured data is going to be important, assuring that the quality of the information that's being defined and produced as used is important. I just think it's a different way of, of envisioning something that we've all had right in front of us for some time. So, let's ask the question so why should you care not only why should you care not only why should you care but why should other people within your organization care, especially if there is confusion between what data management does and what data governance does. And so, you know, first of all, I can tell you that at least in my practice, data management does not equal data governance. They are not the same thing. So I've heard people use those terms interchangeably although I've heard people use governance and stewardship interchangeably as well. Doesn't mean that they're the same thing data management does not equal data governance data management does not equal metadata management. I mean that is, you're not going to provide a metadata management solution and all of a sudden, your data management is going to be perfect. They're not the they're not the same thing. It's the management of the metadata versus the overall view of all the different disciplines and data management does not equal data quality it doesn't equal any individual one of these things actually it equals all of these things. And it really includes all of the disciplines that are associated with the demo wheel that are associated with this version of the data management, not framework but a data management diagram. That's why you should care because your organization probably is asking questions as to how data management, which is the traditional way of thinking. Mostly before data governance even started being used as a term within organization data management's been around forever data governance has been around for a long time, but not being labeled as data governance not as long as data management has been around. So, I also then grabbed a definition from data versity which is very similar, actually not only in how it's worded but the end result of all the different pieces of it of what data management is. So it's a comprehensive collection of practices. So they can data management agrees with this that data management is all these things, it's concepts and processes focused on doing these things for this data, you know from the beginning to the end of the life cycle. And IBM even says that data management is the practice of doing all of these different things, and then utilizing the data, excuse me to make it to improve our ability to be able to make decisions. So, please, let me take a look at the definitions if you have a definition of yours, share it with people within the webinar and I think that would be interesting to see how you're defining data management. Excuse me. So in a typical organization, and I work with a lot of different organizations, data management may include the delivery of these things may include data modeling and data architecture platforms. You know anything that was data warehousing or business intelligence could also include analytical platforms. Oftentimes metadata management with might fall under data management as well quality data quality assurance might fall under data management master data management if you have master data management within your organization. The transformation that your organization is going through a digital transformation or a business transformation, pardon me one second. Okay, let's try it again. Okay, so I mean in many organizations those disciplines that I just showed they're the typical traditional things that would fall under data management. But have conversations with clients. Is there a way for us to be able to simplify that even more. And really, the way that they kind of decided that that they would focus on is that data governance really focuses on the people of the organization, getting some data literate. Oftentimes data literacy might fall under data governance or be part of data governance or at least be a partner with data governance. But data governance focuses on getting people to understand the right ways to define data, consistently define the data understand what data has already been defined, so they don't have to define the data again, helping people to produce the data the way that the data has been defined for for best use within the organization, and people getting people to understand that they're the front end, they're the front piece of the protection of data the usage of data and how they use the data. Data governance specifically focuses on people data management and this again came from another organization but they said it focuses on the delivery of information based information technology based outcomes. I think you'll see from the diagram that I'll share with you in a minute that data management. And is it seems to be a little bit more information technology based, and you may agree with that or you may not agree with it but this is a way of looking at it that data governance really focuses on people, and it focuses on their behavior information security, when when you think of things in terms of data governance data management and information security information security focuses on the protection of sensitive information. It's not even the protection of classified information because data can be classified as being open, you need to have a catalog you need to have a tool so people understand how the data that they are using is classified so they handle the data and protect the data appropriately. That kind of comes back to being a data governance thing as well there's a relationship between information security and data governance, because that protection of sensitive information is a people is a behavioral focused discipline associated with the data. And typically that would be something that data governance would have responsibility for. Again that's just an example from a client and typically there's a lot of different types of data that organizations are considering that they need to govern. And we've done webinars we've done, done presentations at diversity events, where I've talked about the governance of structured data and the governance of unstructured data. There's information governance as a discipline is more widely being recognized as the governance of unstructured data. But you know what people are defining and producing and using unstructured data, probably at even a quicker rate than they're defining producing and using structured data within the organization and records management is as a practice within an organization has probably been around longer, if not as long longer than data management practices have been around. So talk to your records management folks again they're people to partner with that that's a different type of data that also needs to be governed that also needs to be stewarded that also needs to have metadata about it in order for people to use it effectively protected effectively and all those types of things. And then there's external data. What are you doing about your external data or your PII I'm sure that that as is something that your organizations are focusing on how things are classified how things are being handled. There's corporate data, there's intellectual property which I don't hear people talking about as much these days. There's data that is is owned and used and operated with the company that is important to the company that you don't want to share. That's the intellectual property it is another type of data that has to be included in your data architecture in your data security and all those things that are listed in the demo wheel, and then also in the in the envisioned, the re envisioned wheel that I shared with you. So let's spend a minute talking about metadata. And so I had shared with you earlier kind of a quick definition of metadata, but the real definition that I use is the one that's here. And again I break it into pieces because it metadata is data, and it doesn't really become valuable or useful to somebody until it's recorded somewhere where people can get access to it. The metadata.world would tell you that their catalog is the perfect place to record that data about your data. That's the tool to do it in. How is it being used it's being used to improve both the business and technical understanding of not only the data, but the data processes and who the stewards are who the owners are. So all the different assets that are associated to the data. And metadata just being data about data which I think it is I think it's a valid definition. I've had many clients who have started to talk about metadata in terms of being just data documentation. And so it's data documentation that people need that's that's severely lacking in a lot of organizations. It's data about data but it's, it's the data documentation so you know if you're going to build a data documentation library if you're going to build a data catalog. They can serve a lot of the same purposes. And, you know, if another good friend of mine I talked about Len a second ago but Danette McGilvery who's done a lot of presentations at dataversity events talks about the five W's. And she always wants to ask why and then ask them that ask why again and again and again and you'll really get to the core root of what the requirements are they're required to do analysis, I started thinking about her five W's. And then I started thinking well what is metadata really it's the who what why where when, and even these days, some of the how has been incorporated into it so it's the kind of the five W's plus and plus an H. It's like a good name for a rock band. So, but that's really what the metadata is it's the who what why where when and how of the data. And if you want people to make the most use of the data. That's where they're going to get their answers is from the catalog is from the data documentation. And the truth is that metadata. It has to be recorded somewhere because if it's in people's heads. It's not really a value to people. So it must be recorded. The other thing too is it must be kept up to date. And so you might have a definition of a field when when data, a database or a data resource was originally created. But that might have changed over time and it probably did change over time and business rules have changed associated with that data over time, it needs to be kept up to date. And that could be made available through a tool or through some mechanism. So when I go back to my definition and I'd say that it's data recorded in it tools, those tools could be a spreadsheet, probably not the easiest place for people to get record get access to it could be the back of a napkin at lunch you could draw a data model, and that could be your, your recording and that's your it tool. Again, not necessarily the way that you're going to want to share that with people. It needs to be recorded somewhere. A data catalog tool is the direction that most organizations are going when they're taking data governance seriously. So when I say that that metadata is data recorded in it tools. Well that could be associated with the definition and the production and the usage of all those different disciplines that we talked about. The definition of the data definition of your unstructured data the definition of your architecture, the definition of your master data, how that data associate or how that discipline and the production of the data that's included in those disciplines. So all that information is metadata, and then even about how that information can be used, who can see that information, you know all the usage, you know metadata is about the usage of the data that's also included in all those disciplines. And so when you think about it metadata. So we said data management metadata management and data governance and there's a I guess a hidden reason why metadata is put in the middle of those three things is because metadata really becomes the backbone of each of these disciplines. And if you've listened to my webinar series before, or if you've gone to the kayak a consulting website. You know I talk a lot about how data will not govern itself. We can expect our data problems to get better. If we don't hold people accountable. If we don't formalize accountability. Well the same thing holds true with metadata. The metadata is not magically going to improve. It's not going to governance a government itself. You know I talked earlier in the session about the non invasive metadata governance course, the governance of the metadata is a thing. So just keep that in mind, people to talk about metadata governance as much but just like data governance. It's, it's, it's, it's, it's brother in crime should I say is metadata governance as well. So, all these disciplines in this model, you're not going to be able to operationalize architecture, or unstructured data, or master data or integration, or any of these disciplines without metadata so the data disciplines that are shown here are operationalized through the use of metadata and the data disciplines show here, they're really only going to add business value to the organization. Yeah they might add some technical value but really to add the business value that you're expecting. They're going to do it through the metadata, and all of these data disciplines here, they depend on the metadata. So, again, the metadata is the backbone metadata is not going to govern itself. You know the reality is that metadata governance is a thing. So when we're talking about data management metadata management and data governance working together. You can do that through the, you know, through effective management of your metadata. Let's talk about why data and metadata, why both of these things if we recognize the data and metadata are different even though metadata is a kind of data, it's data about data. Why do data and metadata require formal governance. In order to be successful around the definition of data or the definition of metadata, you're going to need to execute and enforce authority. That means that somebody's going to have to be responsible for something somebody's going to need to be accountable for something. And just like I say governance requires the execution and enforcement authority over the definition production and usage of data. There's the execution enforcement authority over the definition of metadata and the production of metadata and the usage of metadata. There are so many different types of metadata that you can manage within your organization. Somebody needs to have accountability for defining what metadata is going to add the most value to the organization. And you also have to think what's it going to take to get that metadata produced and who's going to have responsibility for that. And then the same thing. Why are we even doing any of this if we don't know who's going to be using that data. So these things so data and metadata both require the execution enforcement of authority over these things. And it comes to again to definition production and usage of these things. So okay so I talked about that in terms of governance let's talk about it in terms of stewardship. And the metadata is not going to define itself. You know, somebody needs to say these are the pieces of metadata that we're going to care most about, and that we're going to collect these are the ones that we're going to produce. These are the ones that are that people are expected to use well, we're already doing that in many data programs around formalizing accountability for definition of data, making certain that the definition is of value to the people who are going to read the definition to make certain that people who are responsible for producing the data, understand how the data is going to be used, and how the data should be defined. So again, like I said just governance is necessary for the definition production usage of data and metadata stewardship is necessary to formalize accountability for the management of these things. So without the execution and without the enforcement of authority and I know those are worded quite strongly, your data is going to go ungoverned I probably could have added in here your metadata is going to go ungoverned, unless somebody has the accountability and the responsibility for that data and for that metadata, without formalizing the accountability the data and the metadata are both going to go on steward it as well. So if you're looking for reasons to do these things well if we want to improve our data landscape we want to improve our metadata landscape. You know, let's make sure that we're applying. We're executing enforcing authority over that data meaning that there's rules that there's formalization of accountability. And we need to make certain that the execution I guess I had on the previous slides. So that's just the execution enforcement over the definition. It's execution enforcement over the production and the usage of data as well and the same thing holds true for the formalization of accountability. So, again, I'd love to challenge you that if there's another way to look at this beyond definition production and usage. If you can define something that doesn't fall under those, I'd love to hear from you, and see what you have to say about those things. These are basically the actions that people take. And you know what the way they take these actions can be different. If it's structured data, or if it's unstructured data, if it's metadata, rather than structured or unstructured data. So these are the actions I'll take. And like I said, many times in many of these webinars. The metadata is not going to govern itself, it requires formal governance. The metadata is not going to govern itself. It requires formal governance. You know, this is one of the ways we can think of things when we're talking about how we can get data management metadata management and data governance to work together. What I wanted to do in the last few minutes that I have and then turn it over to Shannon to see if we have any questions today is share with you the graphic that was put together, because I had several clients that were asking the same question. And you know what's really funny is if you look under data governance versus data management versus information security. It's under data governance. And again, this wasn't intentionally even left in this diagram, but it says with limited capacity and limited resources. Because in many organizations and including the ones that this was developed for, they did have limited capacity, and they had limited resources. Let's take a look at these. But before we even look at these in more detail, you know, let's focus on the partnership part in the middle. And the partnership piece of this diagram in the middle talks about that formality. It talks about accountability. It talks about having formal and people accountable for process and procedure. It talks about the coordination and the cooperation between these functions, the operations and the communications. But then the one question that I tend to get a lot about this is, let's see if this graphic will work well. Where does data analytics fit in? Is data analytics the responsibility of data management? Is it the responsibility of data governance? Well, typically I don't see it as being the responsibility of data governance. I see it as being at least the technology portion of it as falling under data management. So I'd be curious to hear from you. Where does data analytics fall in your organization? Is it a separate part of the organization? Do we need to include that within this diagram? Again, like I said, the organizations that I was talking to when this diagram was created, it was mostly around data governance and data management, then information security was thrown into it as well. So let's take a look at each of these things real quickly in the time that we have. Well, what is data governance responsible for? Oftentimes it ends up being, and again, this isn't the perfect model. You may want to create something similar to this for your organization, but you don't necessarily have to use all the things that are included here. It is a framework, it is kind of a way to view things. But roles and responsibilities, the formalization of accountability, that being data stewardship, having formal policies and procedures. Oftentimes the delivery of the glossary and the dictionary, and even the data catalog itself, even though that's a technology tool that's going to enable these programs to be successful, it's going to be part of the responsibility of the data governance group. And then everything else under data governance has to do with the behavior and the accountability facets of the overlapping disciplines, metadata, quality, taxonomy. Again, fill in the blanks with what's important within your organization and what falls under the auspices of your data governance group. But it is the governance of structured data, unstructured data, external data, all of these things that are listed here. So again, data governance again is very people oriented. And as Len said, you know, maybe we should consider that this is people governance and not data governance. And then data management is oftentimes, and again I shared this with you a little bit earlier in the slide deck, it oftentimes has to do with data architecture and platform, and maybe even the metadata management tool, or the data catalog tool itself, data quality, it's also utilizing these types of things and again, it's typically responsible for the management of all these different types of data within your organization, and then kind of the no brainer of it all is the information security. They're responsible for the classification of data, the handling rules, you know, but it's interesting because data governance also can can get involved in assuring that people know the rules, and that they're, again, it's a behavioral aspect of data management. So I hope that breaking down the data management metadata management and data governance this way was helpful to you. The things that we talked about today included the categories of disciplines and I shared with you the data model and all the different pieces of what can be data management. I shared with you some definitions of data management, you know the importance of metadata as kind of the building block, the backbone to these other disciplines. I also talked about why these things require formal governance, and I hope that that tool or at least not the tool the graphic that I shared with you will help you in really clarifying the differences between these things and how they need to work together in your organization. So with that, Shannon, I am ready to turn it back to you. See if we have any questions. Thank you so much for another great presentation. And just to answer the most commonly asked questions just a reminder I will send a follow up email to all registrants by end of day Monday for this webinar with links to the slides links to the recording and anything else requested throughout. So this is like again also for Atticus will be joining us here in the Q&A so diving in here. Security and privacy are in the production color in your redrawn demo wheel but you said security is a usage activity in the defined produce and use roles. Shouldn't it be blue instead of green. Maybe. I mean really again it's just a model that you can use because I think you can actually say just like I went back to the demo wheel and I said initially yeah that looks like it's a one or a two or a three. I covered that up with one two and three. So yeah you're right it's it's again just a way to kind of break these things down and it and yes I think that you're right that I would reconsider how I have that drawn. That's what I would do for your organization that's what I would suggest. Okay, feel free to jump in on any of these. And do you think it's critical to engage the main data stewards to come up with the data model define the entities attributes to reduce the business metadata and ingest into the catalog and metadata tool. And metadata wouldn't have that quote unquote critical thinking path and tend to be full of ambiguity. Well I can't, I can't for the life of me understand why you would want to come up with a data model without including your data domain stewards, the people who are the subject matter experts associated with different types of data different systems of data, different areas of data, those types of things so yeah I believe that it's really important to engage those people to define what the in it with the key buckets of data are going to be within the organization, especially if these people are business people. And they're the ones that have the business knowledge around those subject areas, by all means you should be engaging them to, to include them in in whatever thought is going into the definition or the modeling of that data. And again, Atticus I'd love to hear what you have to think about that. Yeah I was going to say I would plus one on that I absolutely agree and I think that that's something that we had data.world would agree with and that those people can help you start to build out your data model. And it's someone that we engage with early as we're going into the implementation process to help get an understanding. And as the question mentioned we do think that that's how you add in that critical thinking path and start to really showcase your business context alongside of your technical context as well. Yeah and you know what I think there's another way to look at this as well because, even if you don't have your data domain stewards defined, and you're going to do a data modeling exercise. So most likely you're going to pull into those conversations the people that are most knowledgeable within the organization to help you to do the model, and they're going to be by doing that you're going to even recognize who those data domain stewards are who the subject matter experts are, because you're going to want to talk to them anyway. So it can work both ways. Yes, you can engage these people, if they're already defined, but yeah you can also use this process as a way to be able to determine who your domain stewards are 100% yeah I agree with that as well. And data management really a sub discipline of data management, shouldn't it be, for example should it be part of data management policies practices etc. Atticus you want to hit that one first. On that one. Yeah, I think it is a bit of a sub discipline of data management but I kind of like what you had to say Bob about how it's kind of people management as well so I think that it's a bit of people management in that too. I mean, in the metadata management I think that really it's a, yeah I guess if you want to consider all of those things then you would also consider data governance to be a sub discipline of data management, if you're using the DMBoc definition, or any of the definitions that I shared, you know yeah it would be a sub discipline. And in fact I know a lot of organizations that are measuring their data management maturity will include metadata as being one of those sub disciplines under data management. So yes I think it's it's core to basically all of the different disciplines associated with metadata, or I'm sorry associated with data management. So I say yeah you can include it as a sub discipline. What is the difference then if there is one between data ownership and data governance. Well, data, I don't, I particularly try to stay away from the word data ownership because it implies exactly the opposite of what stewardship implies. So stewardship by definition is somebody a steward if you look in the dictionary it is somebody who takes care of something for somebody else. An owner says this is mine it's mine I get to make the decisions because I own this. So I don't like the term owner as much as I like the term steward ownership and even stewardship and governance there is a relationship between data management because it's really, at least in my mind it's how you associate people in decision makers with the data that you have within your organization, and then data governance is actually the application and the use of those people. So I don't. I think there's a big difference I think they're definitely not the same thing. What I do is that data protection is becoming more important and the data model and your model does not incorporate it properly how does data governance interface with data protection. Well that's pretty tough. This is my model does not incorporate it properly I'm not sure that how I would incorporate it differently. I'd have to go back I'm not going to go back to that slide right now but I think there is something around data protection and data security and data privacy. And so how does data governance interface with data protection well, their partners, their partner they're both trying to govern data. One is trying to govern the protection of the data and the security of the data the other is trying to govern overall behavior of the data so that's how I think they partner together. I'd say that data governance doesn't want to take on data security or data protections, responsibilities and data protection or data security doesn't want to take on data governance is a responsibility so it's really a partnership. Yeah, I would agree with that as well I would say it's kind of a balance you don't want to lock it down too much but obviously you don't want to have it to pre blowing either so I think it's taking a good balance is great. It seems to be a massive overlap between what is a legal data privacy protection team does and a data governance function is there an industry delineation between these two areas, especially depending on where they sit within organizations you typically separately. Don't necessarily I think we can we can kind of refer back to the answer to the previous question right is that they're the data privacy and protection team. I don't know that there's an industry standard for how these things are delineated, but in most organizations that are successful, at least from the ones that I have experienced with the data privacy protection team. They're partners of data governance and they, they're again they're not, they're working together they're pushing in the same direction, rather than being separate entities. All these questions coming in. So, I think we have time for a few more we've got about eight minutes left. How do we make sure that metadata gets to the user to provide the right context data catalogs are great but what ways can we work with human behavior to actually ensure people don't have to work harder to find what they need in the catalog. I'm going to let Atticus start. I was going to say it was a data catalog question. So as far as how that goes on the data catalog front, essentially the way that we work with human behavior is partially by engaging that agile data governance model. As I mentioned before, part of the process is engaging those people that are knowledgeable about the model knowledgeable about your business context, giving them a simple easy to use interfaces our goals so that they have a way to provide that value in a collaborative tool. And we also really leverage the power of the graph, which is the same technology that Google uses to make sure that you're served the right metadata we want to give you a Google for your own metadata. That's essentially how we make it easier for them to find that right context, the same way that you're going on Google to find which movie it was you watched last weekend, that's how we can provide that context to make it easy. And I think that, you know, you've got to build it into what people do you've got to put enough information in it and it's got to be organized in such a way that people can be able to use it and that's the strength of, you know, strength of a tool like data. Kind of put that forethought into, you know, what is going to be the best way to be able to link things together. And ultimately the information that's going to be in your data catalog. It's going to have it has to be a value to somebody. If it's not a value to somebody they're not going to take the time to learn how to get to it, or even entertain the idea of using a data catalog. I mean, first of all, make certain that there's information in there that's going to be valuable to people, and then you've got to help them to understand how they can use it to improve their job in a day to day basis because, again, it is a human behavior I'm glad you called that out I mean it really is, you know, getting people to say you know what, first of all they're not used to having this type of information available to them. They're not used to having this kind of organization so they're going to be happy about it they may challenge you to make sure that the right information is in there, but that's a good thing too. And so you want to engage with them and I think that's how you kind of build it into their everyday behavior with data governance professional, what will be my part in data quality. It depends. It depends on if there's somebody in your organization that has the responsibility for data quality, and then you're just going to be a partner of them because it's going to there's going to be a behavioral aspect of it as well. There's nobody in the organization that is looking at data quality, oftentimes data quality data governance group might be the group to initiate data quality activities, and you're not going to really be able to do data quality activities until you've started to record. I mean you know that some of your data is right and some of it's wrong you've got to define what right means, and what the standard is so that you can compare it to the data to see if the data is of high quality or not. And so those those again a lot of those are behavioral things but then there's also kind of a technical component of it. So I would say as a data governance professional. Your role is to make certain that the data quality group is doing the right things in engaging the right people in the right way in bringing in maybe data data management individuals and getting them engaged as well. So it's going to be your role is going to be whatever you define it as. Is that a cop out of an answer is that a good answer. Yeah, sorry I'm having issues with my having technical issues actually probably user issues. This is real I don't think it's a cop out yeah. It's a real world right. So yeah, and I don't think it's got bad at all I think it's good you know and certainly we can inquire to add more but we've got just under three minutes here so I can slip in at least one more. Do we have any. Yeah, could you please explain what the data governance department does and does not do because governance is often confused with governance is often confused with data governance. Atticus you want to hit that first. Let's see. I'm a little bit unclear on the question if you want to get a stab at it. Okay, so data. So, you know it's interesting the way that the question is worded because governance is often confused with data governance. And in fact I had a conversation with an organization recently about them creating potentially a chief governance officer role. So governance is the execution and enforcement of authority over whatever it is that is being governed. If it's which side of the road you drive on if it's you stop at red lights, that's all governance so executing and enforcing authority to get you to do that is what that type of governance is all about. What does the data governance department do. Wow that's really such an open ended question because it's different in a lot of different organizations sometimes they have a team of people who are bringing together working teams and solving problems and addressing opportunities, and those types of things. Sometimes it's one person. And so many of you on this in this webinar, maybe a one person team. What do you do and what don't you do. It really depends on how people are going to engage with you, how you can engage with them, where your strengths are, where you have tools that will be able to enable you. It's a great question, but it's not something you know maybe we hold a whole webinar on that subject at some later point. Well that does bring us to the right to the top of the hour here. Bob, thank you so much Atticus great to have you join us as always and thanks to data.world for helping to make these webinars happen. Thanks to all of our attendees who are so engaged in everything we do we do have Bob we will get the answers to the remaining questions out to you in the follow up email that I'll send out by end of day Monday, with links to the slides links to the recording, as well as the additional information. Thanks everybody. I hope you all have a great day. Thanks Bob thanks Atticus. Thanks everybody.