 Hello, and welcome. My name is Shannon Kemper. I'm the Chief Digital Officer of Data Diversity. We would like to thank you for joining the current installment of the Monthly Data Diversity Webinar Series, Real World Data Governance with Bob Seiner. Today Bob will discuss the role of metadata in a data governance program sponsored today by Irwin by Quest. Just a couple of points to get us started due to the large number of people that attend these sessions. He 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. Just, and just to note this Zoom chat default to send to just the panelists, but you may absolutely switch that to network with everyone. 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 and the recording of the session and any additional information requested throughout the webinar. So I would like to go over to Sue from Irwin by Quest to get us started and for a brief word from our sponsor. Sue, hello and welcome. Thank you, Shannon, and I have just set my timer to talk to you all real briefly about the subject of metadata catalogs and data governance and how Irwin by Quest approaches it. So next slide here, starting off. So Quest Irwin Suite has been recognized as a leader both in data modeling and data intelligence industry for some time now. It's been known as a best of read Irwin modeling technology for over three decades and here's where we focused on enterprise data standards. With the investment into our data intelligence suite, we work to help reach the ultimate goal of deriving data value and improving data literacy across the entire organization. So that is our mission and our goal. And I think it falls well into what we've provided our clients with over the years for the last three decades with Irwin modeling. Next slide. So talking about metadata. Data without context is definitely just data. For example, the difference here is between delivering a sales report with just numbers or understanding exactly what those numbers are and how those numbers came to be. So as far as the as much of the proliferation of data that's out there in our data and analytical environment today, it's pretty easy to just pass on numbers and shout out numbers to everyone without any sort of context or curation. And from a metadata perspective, that's exactly what we want to deliver is context around those numbers metadata is giving us the who what when where why, and how questions answered about the data data governance technologies and catalogs they're actionable to help us find track, track, protect measure and remedy really longstanding issues that we've had with our data. So that's the goal. Metadata in my perspective I've been in this industry for over 25 years now. Data has really moved just in the last couple years from being the foundation for data governance efforts to the foundation of data and analytical efforts through the transparency and the automation of helping clients. It's really looking at not just protecting data, but disrupting with the data as well. So data governance has given us that protection against are helping us show up and be audit ready ready for regulatory requirements privacy and security. Now it's giving us that transparency to deliver what I call or what I have heard called controlled freedom. So having that business context wrapped around the data, having transparency into the controls around the data gives us controlled freedom to enable and use the data across the organization. Next slide Shannon. So sometimes it can take close to six to nine months to just deploy a business glossary from a data governance perspective and we see clients push off collecting metadata inventories and lineage until after they've deployed a business glossary. And from our perspectives there's not a whole lot of value in the glossary itself if you're not actually improving the data, wrapping it around the data and providing that context to the data itself so connecting the head to the body gives you real life use cases on how you can improve and use the data. So we don't want you to start with a blank slate and what we approach our customers with is why don't you start with your long standing enterprise architecture standard standards and models. Your enterprise architects are probably the best and the brightest in understanding the data across it in the business as you start to deploy a data intelligence initiative and they've worked for years with your business and it on these data standards. So from our perspective we have what's called a smart connector between the actual enterprise models. We bring that in and we leverage that really good information into the glossary itself so you're starting with critical data elements you're starting with PI tagged information. You know where your sensitive data is. And we also bring in some of that good business logical information on how the data is moving so that you're not just looking at data lineage through the technical lens and understanding the physical components of the data that's moving. But you're also looking at the logical names in the business rules as you're as you're monitoring these data pipelines. Next slide please. So from our perspective, we're not just opera operationalizing the data, we're operationalizing the trust in the data. And right now, I really feel the stakes are high on data, and just as high as keeping up your critical systems keeping up your servers, keeping everything running. So if you really feel like you have to operationalize and automate those processes that keep your data good and clean. Thus the concept of data observability, if I can say it right data observability. So if you've ever heard of zero defect data, that's something that clients are really trying to charge towards right now and that's reducing the time that data is down or defective inaccurate or erroneous. So basically data that can bring your systems down if you really don't take a good look at the impact and understand the propagation of that data across many different systems. So how I'm going to ask you how are you reducing the data downtime inside of your organization and wouldn't it be great to report that you have you know 99.9% uptime in your actual data intelligence practice. There are little capabilities that that we like to promote when you're talking about improving your data is data lineage. So to be able to observe and validate these critical data pipelines data quality to be able to measure the reliability of the data itself, and then everything is wrapped up in this business context around the data so that you can raise the data literacy across the organization. So why has metadata management been so difficult over the years and from my perspective what I see is the vast array of legacy versus modern technologies that we're supposed to keep up with. And we've also had the challenge of vendors that can only stay at that high level of not just glossary but that high level of understanding from application application without actually getting down to the column level and seeing what's happening to the data as it's moving and as it's being integrated across the organization, not being able to read through some of the code and not providing business value. And there's a lot of different ways that I could talk about about how to provide business value, but I would say, providing a modern user UI experience is at the top of the list. So being able to provide our end users our business users with very familiar online capabilities that you see on Amazon and Facebook. Those are capabilities that you want to look for if you're looking into the technology itself. Next slide. So the third pillar around data quality. This is something that we feel like it needs to be embedded in this visualization of your data. It's prompting you to where you need to go do some profiling, because anytime you see that big spaghetti mess of data lineage, or maybe just all the redundant data you see today, that's going to prompt you to say, hey, I need to go profile this data. I need to improve this data and I need to secure it because it's the most critical data inside of my organization, or maybe it's PI information that's associated to the CCPRA program, understanding both the head and the body, all together in one story is really what's going to allow you to have more reliable results with the data, bringing that practice to the data itself is extremely important. So at the end of the day, embedding data quality into your lineage and into your catalog will provide you with that overall confidence that you need to lead your business based on the data and rely on these insights from your newly treasured data and analytical environment. With that, I'll pass it over to Bob. Thank you so much for kicking us off and for thanks to Irwin by Quest for sponsoring today's webinar, and to help make these webinars happen. And if you have any questions for Sue or Irwin by Quest, feel free to submit them in the Q&A panel and she will likewise be joining us for the Q&A portion at the end of the webinar today. Thanks 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 TDAN of the data administration newsletter, TDAN.com. Bob specializes in non-invasive data governance, data stewardship and metadata management solutions. And with that, I would give the floor to Bob to start his presentation. Hello, and welcome. Thank you Sue. That was a great presentation. And you know what, I really enjoy working with somebody who is as passionate about a subject as I am passionate about a subject. And that subject that Sue spent so much time talking about the metadata is critical. And today, I mean this topic, it's great to touch on this topic every once in a while. But we want to talk about the role that metadata plays in a data governance program. So first of all, thanks, thank you everybody for taking time out of your busy schedule to sit in with us and talk about the role of metadata in a data governance program. Today, before I get started, I just wanted to share a couple of things that I'm actively involved with on the side, of course, besides for the webinar series on the third Thursday of every month. Next month, I will be talking about glossaries, dictionaries and data catalogs and how they result in data governance. I was fortunate enough to speak last week in person at the DGIQ West conference in San Diego. I talk a lot about noninvasive data governance. So if you're interested in learning more about what noninvasive means and how noninvasive data governance can be applied in your organization, please check out the book. It's now available in five different languages. I think that number is going to be increasing sometime soon. One of the topics I'm going to talk about today in significant detail is going to be the governance of the metadata. I agreed with everything that Sue said about the importance of the metadata, the context for the data. And one of the things that I'm going to really focus in on a lot in today's session is the fact that in order to be able to provide effective metadata through a tool, through a catalog, through a glossary dictionary, whatever tool you're using is the metadata will need to be governed. Somebody will have to have responsibility for the definition, production and usage of that metadata. And we'll talk about that a little bit more as we move into the session. And as I mentioned to the data administration newsletter tdan.com this week, we will be celebrating the 25th anniversary of tdan.com. And so it is great to have a lot of you as readers. Please check it out if you're not familiar with it, but lots of great content there. kik consulting is my education is my consulting and education business, and I'm also an adjunct faculty member on the side at Carnegie Mellon University in my hometown of Pittsburgh, Pennsylvania. So what are we going to talk about in today's webinar really want to start with a definition of metadata and I think Sue had shared a definition of metadata, and everybody talks about it as being data about metadata. But I think if we can provide a really digestible understanding of what metadata is to the organization, that will ensure that people recognize the importance that metadata plays in data governance in data management, in general, and how it's about using metadata to, to really to improve three basic actions that people take with data and I always try to simplify things as much as I can. And when I talk about three different actions that you can take with data I would say that hopefully, some of you will recognize that and say that there's others. But if you think about it, perhaps they fall under one of the three actions that I'm going to talk about here in a minute. We'll talk about positioning metadata in the organization to support where the organization is making its biggest investments in terms of data. Talk about a little bit about roles and responsibilities associated with the governance of metadata, and one thing that I stayed all the time and I think it even runs in a banner on my kikconsulting.com website is that the data will not govern itself. The other truth is that if you want to really take advantage of your metadata and improve the role of metadata in your data governance program, you have to realize that the metadata is not going to govern itself either. So it requires things that people in the organization to have formal accountability around metadata in order for it to truly add the value that is expected from metadata within your organization. Let's start with definitions of a couple different terms. I want to start with definitions that I typically focus on just data governance in general. And I've shortened my definitions even to just try to make it more succinct, but data governance I say is the execution and enforcement of authority over the data. A lot of people cringe when they hear that definition they think it's worded too strongly. I hear the harmonization and the centralization and the cooperation of people in process and technology. That's all well and good but at the end of the day, in order to truly have a substantial data governance program, you need to be able to execute and enforce authority over the data. So I'm going to talk about doing that in a noninvasive way, which is to recognize people in the organization for what they do and to help them to get better, but at the end of the day we need to follow the rules that are being set up for the organization. Second definition, and this has a lot to do with metadata as well as we get into talking about metadata stewardship, but data stewardship is basically formal accountability for data. Using sensitive data as an example, you're expected to know the rules and protect that sensitive data. If you're defining data, you know the rules associated with providing good definition to that data. So people that define data produce data and use data as part of their everyday job. If they're being held formally accountable for how they define produce and use data. That's basically a data steward. If you look up the word steward in the dictionary. It is somebody who takes care of something for somebody else. And that's, that is the basically the Webster definition of what a steward is. And so it's not a data owner it's not a, it's basically a person that's there. And if they're being held formally accountable for what they do with data, they're a data steward. And that's that in terms of metadata. So are we going to execute it and enforce authority over the metadata. Well if we expect the metadata to be defined properly to be produced in such a way that it will be able to be used by people in the organization if we expect them to be able to use it. Yes, you're going to need to execute and enforce authority over metadata. My definition of metadata is more than just data about data. It's data that's stored in it tools that improves both the business and technical value of data. And I'm going to go walk through that in a little bit more detail here in a second but you know one thing I want you to think about is that people are if people are being held formally accountable for their relationship to the metadata. If people are being held formally accountable for putting good foundational business description to data, rather than cheeseburger definitions for data. They're stewards of the definition of the data. So we can recognize people who are metadata stewards the same way as we recognize data stewards. So, you know, metadata as a term as Sue had said has been around for many, many years. If Sue has really only been working in the field for 25 years, she's a young and because I've been working in the field of metadata, even a little bit longer than that I think. But metadata was certainly one of those buzzwords that came along, just like data warehouse data science analytics. Now some of these things have truly caught on in our really part of the, the language that we use to describe the data management industry. Some of them we don't know what like data mesh and data fabric they're being used but as we learned in the conference last week there's not a whole lot of companies that are doing those things. Well yet those things are still considered buzzwords. So, one of the reasons why it's really important to have a strong definition of what metadata is, is because you want to get people on the same page. So, I shared my definition with you before I just kind of want to walk through it quickly just kind of in pieces even to describe that it's not, it's not just data about data it's not just context for the data. The first thing is that metadata is a form of data in your organization. You know, if you want to know what, what data type it is what the val and values are what the description is what the business name, that's all data. And so let's just start with an understanding that metadata is data, and it doesn't really become valuable to anybody in the organization, until you record it somewhere. Recording it in an it tool that could be a spreadsheet that could be a word document. It could be the back of a paper napkin when you were sitting at lunch and drawing a data model with one of your, your closest colleagues at lunch. It's not the best it tool, obviously, but, but it has to be recorded somewhere and that way, if it's recorded somewhere and there's somebody who is responsible for the recording of it and for the value of it. And it really can start to add value to people within the organization. And I say that it's data recorded in it tools that improves the business and technical understanding. So back in the earliest days of metadata, it was focused on being a technical asset to the organization. Well now metadata has become more of a business asset, anybody that any talk to any of your data scientists, or your analytical the more that they have confidence in the data the more they understand the data and these are business people for the most part. So we're no longer just focusing on the technical audience and metadata itself doesn't actually just have to be about the data itself. It could be, it could be information about who the owners or who the stewards of that data are. So any data related assets, it metadata can also be information about the data but information about the people or the process or the technology associated with that data. So just real quickly to summarize my definition of metadata is that it's data recorded in it tools that improves both the business and technical understanding of data and data related assets. I know that's a mouthful, but it may tell you a little bit more than it's just data about data. So now let's talk about where we can use metadata in the organization to have impact on different actions that are being taken with the data. And so I typically break the actions that people can take with data into three and it's, they can define data as part of their job. They can produce data as part of their job and there's a lot of different things associated with the definition or the production of the data, but they can use data as part of their job, or most likely, they're going to do all three at this and maybe even at the same time. So if we recognize that who is defining the data who's producing who's using the data, and we formalize their accountability for that. And we do the same thing we recognize who's responsible for defining what metadata we're going to manage. Who's responsible for putting definition to that metadata and actually producing that metadata, or ingesting that metadata from another tool or something like that. And then who's responsible for using that metadata we need to know who these people are in order to truly have effective governed metadata within our organization and if you use the three actions. It'll really simplify the way you as an organization will look at data stewardship and look at metadata stewardship. And so, again, the whole concept of stewardship is that it really describes a relationship that people have with the data, a relationship that people have with the metadata, and then we want to use the metadata to improve these actions so without further ado and I've already talked about them quite a bit. Just to tell you what the three actions are that people can take with data. People can define data and I'll talk a little bit about different disciplines associated with data definition and different activities that your organization might be that really have to do with data definition. And then there's data production how is data introduced to the organization. Where does that data come from who's responsible for that data from the from the moment that that data kind of hits the doorway of your organization, and people can use the data they can use it to do data science analytics they can do it to do predictive modeling, they can use it to issue reports, or to make decisions. I would challenge you and I typically challenge people in all the presentations that I give and you can do this through the chat is if you see any action, in addition to defining producing and using again the concept, trying to keep it as simple as possible. You know, are there are there any things that are missing by that so people in the organization basically become stewards. If they're being held formally accountable for what they are doing with the data with how they're defining the data with how they're producing the data if they're being held formally accountable for that, or they're being held formally accountable for using the data, and the truth is that everybody in the organization defines and or produces and or uses data as part of their jobs. And if they're all being held formally accountable for it and you've probably heard me say this in other webinars, and I probably say it a lot, if not too much. Everybody is a data steward. Everybody in the organization that either defines and or produces and or uses data as part of their job. And if they're being held formally accountable for those actions that they're taking with data. They're a data steward. So I've written articles called everybody is a data steward to get over it. We need to understand that if we're going to have entire coverage of the organization. Everybody is a data so now is everybody a metadata steward, probably not, because you're probably not going to engage everybody as a definer producer and user of metadata as well. But, and because pretty much everybody in the organization that does anything with data is a steward, they're going to benefit from having having metadata, they're going to benefit from having governed metadata that they can trust that they know is accurate. That they know that there's formal accountability for that data, and for that metadata. So, again, the concept of the three different actions that people can take with data. If you think about the data definition actions taking place within your organization, you may be doing data modeling. It seems like data modeling is even though Erwin has been around for as long as I can remember data modeling is I wouldn't say it's a dying art but it is something that organizations do to really put solid definition around their data. If you have initiatives that are presently underway to create a business glossary, or to create a data dictionary, or even to implement a data catalog. Most of those things are data definition activities, data production activities. Are you integrating data are you pulling data together are you creating new data. Are you bringing data in from external sources. And there's a lot of different actions that you as a purveyor of data user of data within your organization, where new data is being produced. Those, you know, we need to know where those activities are taking place, and who has responsibility for it. And really the no brainer of it all is the usage of the data and the analytics that is being going on with data and just the access to data, and the protection of sensitive information. Those are all data use actions. So again my challenge to you is and I know I've been saying this for a long time that the three actions people can take with data or they can define it. They can produce it and they can use it. The three actions that people can take with metadata is they can define it, they can produce it, and they can use it. So, if there's other things I'd love to hear from you I always read the chat after the session is over. But is there something again I'm trying to keep it as simple for explanation purposes as possible. So, again, getting back to the concept of stewardship, because I really said that the title of this is the role of metadata in a data governance program. I've had several clients that have actually referred to it more as a data stewardship program than a data governance program and their focus was on recognizing who does what with the data across the organization. And that information about who does what with the data across the organization. That's metadata. And it'd be great to be able to know that if you're going to make a change or there's a change to a business rule or a change to a compliance rule or there's a new compliance regular regulation that is is being that we need to follow, you know what data precisely is going to be impacted on that. So stewardship really describes a relationship that people have for data, if they're being held formally accountable, and that is formally accountable for the definition of the data. So let's be consistent in the way we're defining data consistent in the way that we're producing data consistent in the way that we're using data. And the fact is that this formality, doing things similarly across the organization. Let's talk about the silos of data. If we had been consistent in the way that we define data across the organization, there would be a lot less data silos, because the data that the connective tissue the critical data elements that connect databases together would be more efficiently within the organization so formality leads to consistency, which leads to efficiency, which ultimately leads to effectiveness and that's basically what everybody's looking for within the organization. So when you're thinking about the role that metadata plays in a governance program, and you're focusing on the metadata or you're focusing on the data. Because in order to really make this part of governance and to make the metadata governed, you need to identify who these stewards are and help them to do a better job of governing and of storing the metadata beyond the data itself. So, let's talk about what types of metadata are going to improve definitions anything that you're going to include within a business glossary or a data dictionary, or something like that. Anything that's going to help people to have confidence in the data to to know where the data came from. And then you think about what metadata that people need to help them to understand how the data is being produced, or how they can use the data how it needs to be protected, how that data is I've even seen organizations that have defined specific handling rules associated with the day that the way that data is classified, and they even included that as metadata that they shared with people when they were accessing data that was classified in certain forms across their organization. And, to be honest with you to identify what the most important metadata is that that will help to for with data definition production and usage. These things are not going to happen on their own there really needs to be a resolute effort, and I always say that the metadata is not going to govern itself it requires people and we're going to talk about that in a second, what are some of the different roles that are associated with governing within your organization. So I wanted to spend a few minutes in this webinar talking about how we as as practitioners can position metadata within the organization, and I always suggest that we take a look at where we are really where are organizations making investments in data. I know that that several of my clients are caught on the trail of doing digital transformations or business transformations. I see organizations that are creating new CRM systems or, or implementing new versions of SAP or, or other ERP applications within their organizations I see acquisitions going on within organizations of organizations acquiring other organizations, or even acquiring new tools and products. This is the day to day activities. So what we really need to do is focus on how can we assure people in the organization that metadata is going to be important to support all of these things. So what I hear most from organizations is I hear them talking about digital transformations and digital transformations do go way beyond data, but digital transformations are also pretty much all about the data. They can have to do they have to do with processes and the use of information, but digital really implies data, the, the business transformations they, they imply data, really all of these things, you know, organizations are looking to become more digital and more business centric, more application centric in some cases. The fact is that really all of these things so if you see transformation efforts taking place in your organization. Look for ways that having additional context, having more understanding more confidence in the data through the metadata is going to add value to the organization so that's what I mean by positioning metadata to support where the organization is either talking about, or is actually putting, you know, as actually applying money to these types of investments, and then there's integrations their system and application integrations data and information integrations organizations creating digital discovery and analytical platforms. Well the folks that the truth is that each of these initiatives, typically have those things that are on the lower right hand side of the screen. They have a project plan, and they have somebody who's accountable for it. They have, even though they may not be purposefully doing it they're collecting metadata, they're recognizing who the most important people are in the organization pertaining to that data who owns that data, who's using that data, and that whole process of creating system and application integrations of building new analytical platforms. For the most part they're governed. When I suggest to all of my clients is piggyback on those initiatives. Let those initiatives help you to identify what your critical data elements are. Use those initiatives to help you to recognize who the owners and who the stewards of the data are, because if they're not engaging those people already, and they're not focusing on that data already what are they doing. If they engage the appropriate people they're going to focus on at least what they feel is the most appropriate data benefit from these other investments that the organization is making and demonstrate how metadata is going to add value and how governance of that data is going to add value. I mentioned acquisitions I mean that's another place where organizations are making investments whether it's in new software or new applications. Maybe that they're acquiring completely different organizations I was working with a banking system in the Midwest that was acquiring several banks several times a year and just trying to integrate them so the acquisition of other businesses and trying to align the data together where where would metadata come in handy for to help people to align different aspects from completely different organizations. And what I'm talking about the data is going to become critical to those alignments to those to successful acquisitions, and you know most of the time these acquisitions, they oftentimes blend with the integrations and the transformations that I just spoke about. And again, think of them in terms of these are already projects, they're being directed they have metadata that's being collected. They've got ownership being identified. There is already governance being applied where the organization is investing money. There cannot be data governance the way that you and I are talking about data governance it's certainly not metadata governance. That's where data governance can step in and make certain that these investments that the company or the organization is making are really going to, going to pay off there they're going to have the ROI, the ROI is not going to necessarily come from your development of data governance or your development of a metadata management program. And all the other activities that are taking place, day to day to maintain your data environment there's maintenance performance connectivity access security all of these things that are typically part of normal upkeep within your organization. Oftentimes, even these things have projects that are planned they have people that are accountable for it. So the big advantage of that information leverage that and that will help you that that's metadata. That's information that's going to help you to to roll out your data governance program across your organization. So when I talk about return on investment. I'm talking about the investments, other investments, not necessarily the investments in metadata management or data governance. The cost of doing business or should I say cost of doing business. Effectively, it's going to require that your senior leadership support sponsor understand what you're doing. And you need to, as always position this in coordination with other investments that are taking in place in your organization, and don't necessarily look for the value specifically from data governance, or from metadata, look for the value that it is adding to other investments that you're making within your organization. So I'm going to run out of time real quickly so I'm going to go through these at least pretty quickly the different roles associated with data governance in the organization and therefore potentially also involved in governing metadata within the organization. And so I've shared this diagram quite a bit in presentations that I've given, you know we've got an executive level within an organization, a council maybe in place in your organization for governance, you know the subject matter experts or the domain stewards, the operational people, you know you've got people who are administering, not only the data governance program but maybe administering your catalog, the partners of data governance the working teams. I want to walk through each of these. I know it looks kind of complex, and it's kind of scary a little bit at first, but my suggestion is that this is probably the way your organization is set up. And instead of trying to plug your organization into a model like this, I suggest doing exactly the opposite. Take it and overlay it over what already exists in your organization. So I'm just going to walk through these levels quickly. There's the senior leadership level. They typically already exist within an organization you may refer to them as a steering committee, they typically don't have any specific role associated with data governance or metadata. Their most important role if they have one is to support sponsor and understand why these things are necessary in the organization. Your programs are going to be at risk. If they don't support sponsor and most importantly understand what metadata is why it's necessary, the effort that's going to go into place, or the effort that needs to be in place in order to govern it. They may approve policy, you know they talk about governance and stewardship and metadata, as maybe a line item on their agenda. They may, as one of their key contributions is recognize who's going to be on the next level down. And that's the data governance council, or data governance committee. There are different names that organizations use for that. Oftentimes you'll find your executive sponsor of data governance of the data governance program or metadata within that strategic level. Again, they're not going to be very hands on in the management of the metadata, they're going to review policy they're going to support sponsor understand they're going to meet regularly prioritize issues. Again, they're not very hands on necessarily, they may want to hear the status of the programs, but they're not day to day going to be defining producing and using the metadata. And then there's the tactical roles and so when it comes to the tactical roles, those are people that are looking at data, looking at metadata across the business areas instead of specifically within a single business area. And it's the hardest, this is the hardest level to determine who these people are at the tactical level who the domain stewards, who the data subject matter or experts are within the organization. But these folks are typically instrumental in a data governance program success story is where we're going to break down the silos we're going to start looking at data to be consistent across the organization. And at the operational level, these are the people that are day to day defining producing and using data and using metadata. So, oftentimes, you know, you are going to have people at the tactical level and at the operational level, who are going to have the bulk of the responsibility around collecting the metadata that's going to help people to make use of the data that they're responsible for. I think you're going to find in most organizations that at the operational level, those are the people within the organization that really have to roll up their sleeves and put good definition to data, good put good definition, good production to data. And then there's also the folks that are using the data as part of their job. Again, everybody in the organization is a data steward, and we'll really only get to the point where it where we have complete coverage of the organization, when we consider the fact that potentially everybody in the organization is a data steward. There are support roles the far left hand side of the operating model, typically there would be a data governance administrator, somebody who's running the program. Oftentimes the data governance administrator is responsible for the role of metadata in the data governance program, until you get to the point where you have a catalog tool, and you might need somebody in the organization to administer the tool itself. There are groups within the organization that are already governing but they're governing things that they don't call them data governance there's it, which is the governance of information technology, it security which is the governance of the security of the data, HR of the people, you know all of these groups are governing. We're going to partner with them when we're building out our governance program, and then there's the working groups as the groups that are typically formed ad hoc to address different issues and different opportunities within the organization. So if you can build the responsibility for metadata into the activities of these people and it becomes just part of their job, you've won the game of data governance, because you've gotten to the point where people in the organization. In the second nature they view data as an asset it's part of what they do, especially if they're being held formally accountable for the actions that they're taking with data. So the last thing that I want to say before I switch it back to Shannon to see if there's any questions for today is the one thing that I really want you to walk away from this webinar, and not forget that there is no magic solution to pixie dust I used to joke that I had it on my shelf in my office that I give you some pixie dust you sprinkle it over the organization, and all of a sudden you'll have the metadata that you need to improve people's confidence in the data. I'm sorry that really doesn't exist if you didn't know already. The fact is that the metadata is not going to define itself. The metadata is not going to produce itself it's not going to use itself. Let's talk about those things quickly. There's a lot of different types of metadata that are available to you in your organization. Every one of your tools in your it environment have metadata in them from your data quality tools your data definition. I wanted to say from your data definition to production to data usage tools. There are a lot of different types of categories of metadata. And you know I talk about them in one of the online sets of courses that I do at the University but somebody needs to define what specific metadata out of all these different categories, we're going to focus on. And then it needs to, you know, not only understand what the categories of metadata are but what is the specific metadata, the information that's going to add value to it. And somebody needs to define what that metadata is they need to know where that metadata originates where it lands. They need to make the metadata available to people in the organization and the metadata will not define itself so somebody needs to do that. The metadata is not going to produce itself either automation is good a lot of tools, talk about being able to automatically ingest the metadata from those tools. That's a great thing but the fact is it needs to get into those tools in the first place. The automation of keeping the catalog up to date is very good, but it's got to originate somewhere and somebody has to have responsibility for that metadata where it originates. So somebody must be accountable for creating the metadata. Oftentimes having standards for your metadata creation are going to be something that you might want to consider in your organization. But you can call these people metadata production stewards. They're the people that are filling in the definitions in your data models. They're the people that are defining the transformation rules as data is being moved from one place to some to another place. And so the last thing is the metadata is not going to use itself. So we need to make people in the organization aware of what metadata is available, help them to understand the value that they'll get from the metadata, help them to understand how they can gain access to that metadata. A lot of organizations I work with are trying their darndest to deliver the metadata along with the data in their analytical environments. Access to the metadata is extremely important. Feedback, creating a feedback loop with people who are accessing the metadata so you can understand what information is of benefit to them and where there needs to be improvement. That continuous feedback loop is very important in driving continued success of metadata management and then building, just building metadata and building the data catalog into the way people work. Now we know that the metadata is not going to use itself. The reason we do it is to improve people's understandability, digestibility, the value that they get from the data. One thing I really want you to take away from this session is just to keep in mind, the metadata will not govern itself. It will not define produce and use itself. Somebody has to have responsibility for it. So activate your data marketplace. It oftentimes starts with kind of a metadata marketplace. And it's really nice to kind of end my webinar this way in saying that really these things right here are truly the role that metadata plays in a data governance program. So again, I'm happy that you spent the time with us today. Give me a chance to walk through these subjects with you. You know, start with a good definition of metadata. Talk about or relate how metadata can be used to improve how data is defined, produced and used. Position metadata to support the big investments in the data. Define clearly the roles associated with your, with the governance of the metadata. And just keep in mind that the metadata is not going to govern itself. And with that Shannon I'm going to kick it back to you. Bob thank you so much for this great presentation. And thanks all our attendees for being so engaged in everything we do if you have questions feel free to submit them in the Q&A portion of your screen for both Bob and Sue. So timing in here. What do you, how do you get started with creating a minimal metadata with simple tools and showing proof that it is essential to an effective data quality program. Oh, that's a great question. I'll take a stab at it and then so I'll be glad to hear your comment too. I would start simple. I would start around definitional metadata first meaning that I would start by focusing on business terminology on business glossary on data dictionary that seems to be where many organizations are kind of getting their start within the field so I wouldn't try to manage all your metadata all at one time right out of the gate. I would do it incrementally. I would make certain that I have people in the organization that are going to use want to use that metadata. But start with the definitional metadata I think you mentioned data quality as well. Start with data data quality standards. You know you're not going to be able to tell what data is good or what data is bad quality until you know what's good. And that's going to be in your standards. So I don't know Sue any thoughts on that. Yeah, I would add on to that to start where you're bleeding the most because you don't have any metadata. So if you're flying blind out there somewhere or you're seeing where data is really impacting negatively. I think that's your reports or campaigns and things like that that you want to hone in on that area and look at your baselines today. So where are you having your most manual efforts, and it probably is in your spreadsheets, and the way that you're documenting them today, and make sure that you have a baseline and then start there and show them the value of how more efficient and effective you can be with the tool. That's a great idea. So what data standards must be uploaded in the metadata repository can you give one example of data standard and its use in metadata repository. Sue you want to hit that first. Yeah, sure. From the data standard perspective there's a lot of data standards and it kind of depends on the type of industry that you're in. So for instance in the financial industry we see a lot of folks start off with some of the regulatory standards, first, and if you don't have that documented somewhere, or you have an anthology that you want to bring in. That's where we would work to leverage that sort of information. So I would look at what industry are you in and what standards do you need to apply from a from an industry perspective, and then make sure you have a contextual hierarchy defined where you can have local definitions versus enterprise definitions versus industry definitions because they could all be different, but you don't probably want to spend the time and money making them all the same, because sometimes it is you are not going to do it the way the industry says to do it. But, but you want to understand what that is so have a way to compare that have a way to throw it into a hierarchy and and understand it from that perspective. I really like that concept of focusing on the things that are that your organization is being held accountable for first, you know, make sure that you have the appropriate standards in place there, I mean that is that makes a lot of sense. There you're not it's not an option that you're being given to follow these standards it's, it's a regulation and if they're, it just it makes sense to kind of focus on those as kind of the initial standards. The other things that I would again come back to, and I know I talked about it a lot in the session today is definitions around data, or say standards around data definition around what we're calling things within the organization. Around how we're collecting a true business description of that thing or even standards for acceptable values and what their meanings are, I mean I'm getting very nitpicky here and very granular, but things around the definition of the data standards around the production of the data standards around the usage of the data including all those regulations and all those rules that you just talked about. So, I think one of the things if you really don't know where to start. Start simple, start by saying okay what definition what standards do we need to follow as an organization, and then maybe start jumping into, you know what definition and production and usage standards do we need. Absolutely. Perfect. So, assuming the majority of information exchanges are still file based, and many users are using simple operative systems such as windows not allowing to manage a custom metadata layer, which is the current alternative, regarding a realistic metadata oriented strategy adoption. Wow, I'm not sure I understand the question, can you try to say that I'm sorry. No problem and maybe we can get some clarification to assuming the majority of information exchanges are still file based, and many users are using simple operative systems, such as windows, maybe older versions of windows, not allowing to manage a custom metadata layer, which is the current alternative regarding a realistic metadata oriented strategy adoption. I'm going to take a stab at it. Thank you. Just to use as an example. SharePoint. Okay, so SharePoint is something that many organizations use or you have a file based system so really what I took from the first part of that question is that there's data everywhere. It's being managed differently in different parts of the organization, and that's why I said SharePoint is kind of a, an easy target to point at. It says a lot of organizations started to implement SharePoint, but they did it in a kind of a Wild West format, where they was off doing their own thing, and there was no way to be consistent. So many organizations have backed up on that, and have decided to take a more structured approach to how they're using things like those file management systems. The fact is that there's not an easy answer to creating a metadata layer that's going to give you knowledge about all your data everywhere. But it starts by, at least in my mind, you should start in a focused area. Start with a certain type of data assets and make certain that they get governed. And then take what you've learned from governing those and the metadata you're providing for those and extend it into a bigger piece of the organization. Do it incrementally, but focus on things that already have a start rather than starting from scratch. I don't know if I answered your question, Sue, you have anything to add to that. No, I think you did a better job than I could. I would also maybe add on to that that maybe if you're a company that's born digital this might be a reality but companies that have legacy and modern environments it would be really difficult to do. Yeah. Well, we've got just about four more minutes hearing and trying to fit as many questions as I can in the last few minutes here. So how are data custodian and sewer is different in terms of roles and responsibilities. I think that they, you know, I'll again I'll take a stab at this but it depends on how it really depends on how the organization defines it. I mean a data steward data production steward somebody who is producing the data or maintaining the quality of the data could be considered a data custodian. I just say that there's not a single definition of what a data custodian is across the organization. That's why I like to break it into, you know, responsibilities associated with defining data, producing data and using data. So I don't really know that there's a huge difference except for maybe a data custodian is just one part one action out of those three that I mentioned. I agree. That one was easy. And keep the questions coming because if we don't have time to get to it I will get the questions over to Bob to get those answers in the follow up email. And so I think I can squeeze in one more here by six to my nine months to deploy, meaning just to set up the technology and full catalog, fully catalog the data sets, or ancillary activities that add value such as curation classification ingesting and consolidating metadata from external sources. I think you're referring to my projection or what what I had on that slide about how long it takes to set up a business glossary. And I would say, primarily setting up the glossary and yes adding in some tags adding in some context hierarchy classifications. Yeah, I would say that that's really what we're seeing out there. And how long it takes to set up a glossary so I would say it would include those ancillary activities as well. And you know what I mean it's, it really depends on when you start that nine months and when you end that nine months because there can be incremental things that you can deliver along the way that you don't have to wait until nine months to be able to demonstrate value. Even if you're collecting your business glossary definitions in a spreadsheet or you're making them available to people until the catalog is up and running. Again the discipline associated with the defining producing and using of the metadata is really what matters. And if you can if you have that metadata. The catalog really becomes the delivery tool it becomes the marketplace that people where people want to go to get information about the data. But don't hold your asset, don't hold your asset and not release it until you've got the tool, you can start making it useful earlier than that so you know nine months is a good, you need, you don't want to set expectations that are shorter than what you're going to need so I think that's a fair assumption. And I would just add on to that you know look at your outcome what type of value use case are you trying to get to first what try what are you trying to tackle first and usually the use cases and I need to create a business glossary the use cases. I need to understand the definitions and the privacy information as I'm looking at the data as I'm looking at this report and roll it out by use case that that would be my perspective. I think that a lot I think he tied to the business value that you're adding and not just the action that you're taking to provide that value. Yep. Thank you. All right well perfect timing that brings us right to the top of the hour. Just a reminder to everybody I will send a follow up email to all registrants by end of day. It's Tuesday for this webinar Monday is a US holiday with links to the slides links to the recording and we'll get you the answers to the additional questions posted within here. Thank you everybody and thanks to our went by quest for another sponsorship always great to have you guys joining us to really appreciate it. And hope you all have a great day. Thank you for having us. Yeah, take care. Thanks to thanks quest. Bye. Bye bye.