 Hello and welcome. My name is Shannon Kemp, and I'm the Chief Digital Manager 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 Steiner. As you join, you will be muted. I'm going to be tongue-twisted today. Today, Bob will discuss how to select the appropriate data governance tool. Just a couple of points to get us started. Do 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. Just click the chat icon in the upper right-hand corner for that feature. And for questions, we will be collecting them by the Q&A in the bottom right-hand corner of your screen. Or if you'd like to tweet, we encourage you to share highlights or questions by Twitter using hashtag RWDG. As always, we will send a follow-up email within two business days containing links to the slides, the recording of this session, and additional information requested throughout the webinar. Now, let me introduce to you our speaker for today, Bob Steiner. Bob is the president and principal of KIK Consulting and Educational Services and the publisher of the data administration newsletter, T-Dan.com. Bob has been a recipient of the DEMA Professional Award for a significant and demonstrable contribution to the data management industry. Bob specializes in non-invasive data governance, data stewardship, and metadata management solutions. And with that, I will give the floor to Bob to get today's webinar started. Hello and welcome. Much for taking time out of your busy schedule to share with us this afternoon as we talk about or this morning, depending on where you are, as we share a very important subject around or sub-subject of the subject of data governance and one that I tend to get engaged with a lot with the organizations that I'm talking to or that I'm working with. They're always looking to see if there's ways that they can better enable their program. They're looking for ways to improve the usage of governance to apply value or to add value to the organization. So like I said, this is a very important topic to me and to pretty much anybody who's trying to put a data governance program into place. And we're going to talk today about a couple different things. We're going to talk about selecting the appropriate data governance tool for your organization. But I'm also going to share with you, as I like to do a lot in the webinars that I do, different tools and templates and things that I've used with different organizations that might add value to you. We're also going to talk about, well, here, let's get to that in a second. We'll talk about the agenda here in one minute. But as I like to do before we get started with the webinar, I'd like to go through a couple current events. And as you know, I do a monthly webinar series. I've been doing it for several years now with Dataversity called Real World Data Governance. It's always on the Thursday of the month. At 2 p.m. Eastern, 11 Pacific. And the next two topics that we're going to talk about in the coming months. In October, we're going to talk about aligning data modeling with data governance. A lot of organizations do data modeling and they look at, I actually define data modeling as being a way of governing how we're defining the data of the organization. In November, we're going to talk about data governance best practices and lessons learned from implementing best practices within the organization. So there's lots of different ways to be able to register for the webinar series. You can do that on tdan.com at K-I-K Consulting or also through dataversity.net as well. So I hope to see you there, as I am very glad to see you here today with us for this webinar. I also wanted to share some information about a book that I published. Oh, it's almost three years now that it's been out. It's called Non-Invasive Data Governance. It's available through your favorite bookseller, whether it's Amazon.com or Techniques Publishing. You can find the book a lot of places. But I'm very passionate about non-invasive data governance. If you're interested in learning more, not only do I do a book on that, but I've also produced a learning plan, an online learning plan with Dataversity that's available through the Dataversity Training Center. So again, if you're interested in learning more about non-invasive data governance, please go there. I'm also going to be speaking in a couple weeks at a conference in Jersey City, New Jersey, that happens to be a Dataversity and DevTech International event. It's the Data Governance Finance Conference 2017. And I'm going to be speaking on the first day of that event. So if you're there, please come up and introduce yourself as a webinar participant. Last but not least, I wanted to share with you about the Data Administration newsletter. If you're not familiar with tdan.com, please go out to the site. It's actually published twice monthly now. And always on the first and third Wednesdays of the month. So it just so happens that every time I do a webinar, that the day before, there is a bunch of new content that is available on the tdan.com site. So now let's get to what we're going to talk about today. Let's talk about the agenda for today's webinar. And we're going to talk about how to select the appropriate data governance poll, but I'm going to address it from several different perspectives. And the first one is, you know, we're going to look at different categories of tools that you already even have in your organization that would handle some of the purposes of a quote unquote data governance tool for your organization. So we're going to go through a bunch of different types of tools and how you might want to use those for data governance before we jump into the next section, which is talking about using do-it-yourself tools and templates to nail down some of the things that you need for your program, but also potentially to help you to nail down those requirements for the tools that you need to select for your environment. Third, we're going to talk about how to define the appropriate tool criteria for your organization. And one of the things that you'll hear early in this webinar is as we look at the different categories of data governance tools that the one thing that's kind of the common thread among those tools is the metadata. So when we're looking at appropriate tool criteria for your organization, we're going to talk about it from a metadata perspective and from a data governance perspective. It started in the data administration and data management field as a metadata repository administrator and had to evaluate what tools were available way back when the tools have gotten a lot better. But I want to share with you some of the most important criteria that I've used not only in the corporate environment, but also when I'm working with organizations to determine what's the appropriate tool for them to use in their organization. Then I'm going to spend a little bit of time talking about how to manage the whole tool selection process, which can oftentimes be very detailed and very involved in your organization. So we'll walk through a series of steps that I suggest that I've used actually to manage that whole process of going through identifying the appropriate tool to be used in the organizations that I've been working for. And then last but not least, we're going to talk about the core components of that RFP. It's that typical document that individuals send to organizations, to vendors that have their products that basically outline a whole bunch of things associated with the tools that are necessary to help to implement your data governance program. So as I said, the first thing that I wanted to talk about today was different categories of data governance tools. So some of the tools that you might have in your environment and feel free through the chat to add in other tools that you might have in your environment and how you might use them as data governance tools. One of the great things about this webinar in the webinar series and all the webinar series through Data Diversity is that oftentimes there's a lot of good discussion that goes back and forth in the chat and in the Q&A section. But these are some of the tools that I've identified as being kind of core tools that a lot of organizations may have or may be looking at that might also be considered to be data governance tools. So we're going to talk about data definition tools and what different tools are you using presently in your environment to define the data and how the data needs to be... the quality of the data needs to be improved through definition of standards. Now, how are we defining that? Where are we already cataloging information about the data definition? There's the data production tools. A lot of organizations will either code themselves or use ETL tools and data movement tools. They're looking to make data tools to be able to gain access and produce data coming in from all sorts of sources. So we'll take a look at the data production tools. We'll take a look at the data usage tools because data usage tools are prevalent in a lot of organizations that are doing data analytics and insights and things like that. So there's a lot of metadata that's captured in the data usage tools that will also be helpful as we start to improve the understanding of the data and roll data governance out into the organization. We'll talk about some data administration tools, and that's kind of a high-level catch-off for those types of tools that are really necessary to manage any type of environment. Then I'm going to share some information on data quality tools and data process tools. Again, that you might already have in your organization or tools that you're either developing internally, but there's tools that you already have. I can almost guarantee that there's tools in your environment presently that you can leverage and that you can use in the absence of going out on the market and purchasing a tool. Now, there's a lot of great tools on the market that will add a lot of value to your organization, but oftentimes organizations start by using the tools that they have. In fact, I suggest, and most consultants would suggest, to take a look at the tools that you have in your environment before you start jumping to the outside to purchase or add additional tools to your environment. There's really one thing that's common among these tools, and that's the metadata. That's the information about the data, information about the data definition, production usage, all of those types of things. There's metadata in those tools. Oftentimes, and I'll talk about this in a little bit about if you have an enterprise metadata repository tool, it's like building a data warehouse with metadata because there's already so much metadata that is inherent in your environment that you may use, you may leverage, you may not leverage, that you might want to take an idea of looking at, well, what metadata do we have? What can we do to take that metadata out of that tool and make it available to people so that we can do that while we're going through the process of evaluating what tools are on the market and what tools might be right for your organization? The metadata is really the thing that is similar and consistent among these tools. Now, that doesn't mean that it's easy to access. It doesn't mean that it's easy to make that available, but we'll talk about that a little bit as we move forward in the webinar as well. So let's first focus on the data definition tools in many of your organizations, and I know this because oftentimes when I do webinars on data modeling and data governance, we have a lot of data modeling interest at Dataversity and through the Real World Data Governance webinar series. But organizations that do data modeling are capturing the definition of the data in the data models. Now, I have often joked about things like cheeseburger definitions, the definitions that aren't really well thought out or vetted and guaranteed, or at least confirmed in the organization. But the data modeling tools have that metadata, have the definition of the data. If you're going through the process of modeling your data, then it certainly makes sense to take advantage of that information that you're collecting as part of those data modeling projects and making that information available to people. So basically, with the data definition tools, we're governing the data definition. And you may use a modeling tool, you might enter that information directly into a business glossary or into a data dictionary tool. And oftentimes those tools are built in whatever tools you have available to you, whether it's a spreadsheet or a word processing document, and they're managed through a shared site or through a SharePoint site. Organizations have glossaries that they often develop as they're developing applications. The problem is that oftentimes those data glossaries and data dictionaries go untouched, meaning as the data evolves over the time and over the years, the data dictionaries and the data glossaries that are manual efforts oftentimes to keep up to date, they fall by the wayside or they're at least not kept current. But my suggestion is take a look and see what glossaries and dictionaries you have, because if you're looking to improve quality or you're looking to improve the understanding of the data, it's going to start with that data definition. So take a look at those tools. Now, I put the word data governance in quotes in the bullet point on this slide, and I have it on each of the slides because oftentimes data governance tools is a catch-all for a lot of different functionality that could take place in your organization, just like master data management tools will help you to create a master data environment, but they oftentimes have some governance aspects to it and some metadata aspects to it and definitional aspects to it. So, excuse me, data governance tools are kind of a catch-all. And if you go and you research data governance tools, you're going to find a series of tools, actually many tools that claim to be data governance tools that help you to manage the metadata about your data in your organization. Let's also take a look at the data production tools, and those are the tools that are used to govern data production in your organization. So you've got ETL tools, or you've got mapping tools, and you've done your data movement through cobalt programs or whatever tools you use necessary to move and combine and to integrate data. So you've got these ETL tools that have a lot of information and data is being produced. A lot of organizations still use spreadsheets to say that this is how the data is at the start and here's how it's going to look when it gets to the end of whatever it is, the project or the function that we're working on. Data entry forms and data acquisition tools, a lot of those tools have information about where the data came from that people are going to be using within your organization. And one of the things that they always want to know is where did that data come from? How is that data defined? Again, going back to the data definition tools. So a lot of the quote-unquote data governance tools don't necessarily keep track of that, but when we get to the point where we're looking at the criteria for metadata, you'll see that being able to extend some of the products that are on the market today to capture information that they may not capture right out of the box is something that you want to consider because otherwise you're going to be stuck in the same box as everybody who uses that product and you're going to need to manage what data they think is most important rather than give you the capability of being able to go in and add whatever information you want to be able to collect into that tool. Talk about data usage tools which are often used to govern data usage within the organization. And many organizations are using things like MicroStrategy and Tableau and a lot of these other tools that are available that do incredible analytical capabilities, but not all the organizations use those tools and have a place where they have a catalog of the different reports that have been written and the different data sets that have been created upon user request. I'm working with an organization now that really needs to bear down on that and to start keeping track of whatever information they've already provided and that's already available so that people aren't creating and recreating and re-recreating the same reports over and over again. So there's report writer tools, there's report cataloging tools, there's spreadsheets that people keep this information in and then the analytical tools that are on the market. Again, a lot of those quote-unquote data governance tools don't necessarily have the capability at least out of the box unless you go looking for specific tools that do this as well as a bunch of other functions. But the data usage information is incredibly important to getting people to get easier access to the data and better understanding of the data that they're using. The data administration tools, again, I mentioned that as kind of being a catch-all for a bunch of different types of products but they're used to govern data administration, however you define data administration within your organization. And in my background, in my upbringing working in the corporate world many years ago, I was that metadata repository administrator and I know firsthand that populating it with the information is not as easy a task as some of the vendors may lead you to believe. You need to have the ability to get your metadata from wherever it is, it's a native source into a central location where people can access a lot of different types of information about the data that's being used in the organization. There's also change management tools for keeping working in development environments where things are in test and production or have been pulled out of production and put into production, those types of things. There's change management tools that, again, have information about all the different artifacts in the organization that make use of the data. So we want to know what the status of those different artifacts are so having a change management tool is probably already in place in your environment somewhere. The question is, is there any metadata in that tool that will be valuable to you or valuable to your program to be able to share it with other people? Then there's the DBA tools, the database administration tools, and the truth is that when it comes to if you have your databases in Oracle or SQL Server or DB2 or Flash files or whatever database system that you have, those tools basically run on their catalog and their catalog is a bunch of database information which is metadata that will help people to understand when they need to access specific pieces of information in specific databases. A lot of organizations, their DBA environment, if you're not aware of them, you could talk to your DBAs. They can tell you what tools they're using in their environment that you might be able to take advantage of. Again, really the point of going through all these different tools that you might have in your environment are or that you might likely have in your environment is that there's a lot of metadata that's already being captured that you can make available to people that can help you with your process of governing data across the organization. And the data administration tools basically are governing data administration. Then there's the data quality tools and a lot of organizations, as they focus their data governance program on something that's meaningful to the organization, meaningful initiatives. They want to make sure that as they start these initiatives, they're starting them down the right path. They're governing the quality of the data. And the quality of the data can be quality definition of data, can be quality production of data, quality usage of data. It can also include, you know, what information do we have about that data. So a lot of the data quality tools for governing data quality are things like data profiling tools or even data issue logs and things that you may keep in your organization. Or if you're in certain industries, I know a lot of the financial industry use scrubbing and matching tools and householding tools and things like that. So there are tools in your environment that you can use. And again, that catch all of data governance tools. When you're going out to look at data governance tools in the market, make sure that you're documenting those things that are most important to your organization. And we'll talk about some of those in a minute as well. And last but not least on my list of categories of data governance tools are data process tools. So workflow management tools, decision-making process that you already have, data flow diagrams. You know, a lot of organizations use these types of tools, whether they're part of a formal tool set or something that they've just developed themselves. So again, really the core message out of this section of the presentation is to take a good hard look at what tools you're using in your environment already, see if you have access to the metadata, see if you can understand what metadata exists in those tools, so you can see if there's anything of value, even if there's that tidbit or that golden nugget that you use that will help people to all of a sudden get and understand the data in Fill on the Blank, in your MDM solution, in your data warehouse, in your data lake. You know, all of those tools, there's a lot of those that are out there. So please, as a first step, as you're looking to select the appropriate tool, take a look at what tools you already have and take a look at what functionality are we using out of those tools that would be beneficial to other people within the organization, within your organization. So now I'm going to spend a little bit of time talking about some do-it-yourself tools and I'm going to share with you a bunch of templates. And as Shannon mentioned earlier on, we're going to make those tools and templates available to the people who are attending this webinar and who signed up for it, as we usually do. You know, a lot of the tools and templates that I share, we always make them available as part of these webinars, as part of this series. So I want to take a look at, you know, what it really takes in an organization to purchase a tool. And then we're going to take a look at what it is it takes to develop a tool. And I'm not going to go into too much detail on this, but I want to just at least outline for the things that I think it's important that you think about when you're going to purchase a tool. And the first thing is that somebody has to be accountable for the whole process of purchasing and selecting the tool. And typically it's really good, it would be, excuse me, really meaningful to your organization if that person had a little bit of, you know, people are looking up to that person and really trust that the person is doing a good job and making sure that you're matching the requirements that you have for the tool with the tool that you're bringing in. And so having a resource that's accountable for acquiring the tool is always important. You know, having a platform to deliver the tool on is always important because a lot of the tools, some of them are cloud-based, some of them run internally on things that you have within your organization, but you need to consider the platform that you're going to deliver the tool on and how you're going to make the information from the tool available to end users and to people within the organization. You're going to need to define your requirements for what you want the tool to do. And that's probably one of the most important items on this list. It's not the most important because, you know, if you listen to what the vendors tell you about how the tools work for them, the fact is that the tools do do what they say they will do, but they don't necessarily give you the insight of what it takes for you to get the information into the product that's going to allow you to use that capability of the product. And oftentimes that's kind of the iceberg that's hidden underneath the surface. That's all the work that's necessary to show that nice shiny object above the surface. So the requirements for what you want to do with the tool or what you want the tool to do are important, and we're going to talk a little bit more about those in a couple minutes. There's always the development time and the testing time if you're not going to use the tool that came directly... use it directly as it came out of the box. There might be some development and testing and setup time that's required for it. So you need to understand all these things as you go to purchase a tool because it's not going to function for you the way that you want it to function for you right out of the box. It will do what the vendors say the tool will do, most likely, but it takes a lot of work in order to get there. And then you also need to consider, well, what's the training and education of the people? How long is that going to take and when can we schedule that? And do we need to take the people that are going to have the responsibility of implementing the product, put them through some type of a boot camp? So the training and the education, the administration and use of the tool, the maintenance of the tool, these are all things that you need to think about. It's not as simple as just buying a product, installing it and having it do the work for you because there's a lot of work that needs to be done by your organization to make that tool valuable to you. So now let's take a look at what it will take to develop a tool. And you know what? It's a lot of the same list that I just told you as to what it will take to purchase a tool. You're going to need resources that are accountable for developing the tool and a platform to develop it on. You know, it makes sense for defining your requirements for what you want an internally developed tool to do. And again, in a second here, I'm going to share with you several templates and tools that I use that might give you a good idea of what you want your data governance tool to do for you within your environment. There's the development, testing time, training, administration, maintenance. All of those types of things are real important, but it's pretty much going to require resources either way, whether you're purchasing a tool or whether you're developing a tool. And you really need to understand what the steps are that are going to be necessary to make those tools valuable to your organization. So now what I want to do is I want to spend the next couple of minutes talking about a bunch of tools and templates and things that you can create and that you can build yourself that will help you again to define what are the requirements for usage of a tool within your environment. And I'm going to talk about a data governance maturity model, and I've done some updates to the one that I shared, I think many years ago, kind of using the basis of the capability maturity model to develop a data management maturity model and now a data governance maturity model. So I've added some things to that that I'm going to share in a second. Then there's the operating model. And as I've always mentioned, that the roles and responsibilities for data governance in your organization are really important. So having a solid operating model of roles and responsibilities is always valuable to have to be able to explain to people what role they play in data governance and to really clearly think out the use of your executive level, your strategic, your tactical, your operational levels and all the support levels that are necessary in order to enact your data governance program. So I'll share the operating model. Then there's the common data matrix, and I've shared that a lot in the Webinar series. And I'll share it with you again because it seems to be the tool that so many organizations get so much value out of it right out of the gate. The racy matrix for matching up people in the organization to their steps and their responsibilities within a process. And then a communication plan matrix. I want to share with you quickly each of those. I actually think that each of those could almost be a Webinar within itself to describe the use. If you have any questions about these things, please let me know either through DataVersity or come to me directly, and I'll be glad to talk to you about how these tools are being used. This is the maturity model, and I've shared something that looked like this before, but this has a bunch of information that was added in association with the client that I am working with now. And they wanted to really be able to show have we evaluated their level of maturity around data governance, and have we defined an outline of the steps that are going to be necessary to move from one level to the next. So if you look at this, it's really still very much based on the Software Engineering Institute out of Carnegie Mellon University here in Pittsburgh, their capability maturity model. There's now a data management maturity model that's out of the CMMI, and a bunch of different organizations have different models, but this is one that I find is very easy and very valuable to the organization that you can show people where we are and where we want to get, and what are some of the primary steps that are necessary in order to get there. So if you're at a level one, basically you have no strict rules or procedures. Your data is in multiple redundant formats. There's no effort to inventory what exists. So you're pretty much at that initial level, and as this document shows, my take is that 30 to 50% of the organizations operate at that level between zero and one. And if you're there, or let's say you're at the one level and you want to move to the two level, then it's a great idea to be able to express to people what are the steps that are necessary to do that. So the data begins to follow best practice. Data roles are defined, but not necessarily institutionalized yet. The technical aspects of the data and the metadata are managed, but they're not necessarily, the quality of that information isn't necessarily managed. Now I don't want to go through each and every one of the levels, but this is a great tool, a real solid tool for you to be able to do a self-evaluation or to have somebody come in and take a look at what your organization looks like and tell you where they think you fit on this scale if you're looking for external resources to do that. You know, the most important aspect of this tool is setting up the steps to take you from one level to the next level. Now sometimes to get to the optimizing level or to the managed level is done by so few organizations. You may just want to get to level two or to level three. And if you can demonstrate that you've done that by following the steps that you've outlined, this becomes a very valuable tool. It's not a software product, but it's a tool that can be used, it's a template that can be used to help people to understand where you are and where you want to go as an organization. Another one of the tools that I've shared in the past and I'd like to talk about is what I call the operating model of roles and responsibilities. And as I said, we've done webinars on this whole topic where we've gone and dissected each of the different levels. But it's important to define your roles and responsibilities. As I show you some of the additional tools and templates that I'm going to talk about today, they're kind of color coded with the operating model. So if you see yourself in the pink or the yellow or the orange, you can see yourself in these other tools as well. It's very important for you to define roles and responsibilities as the backbone of making your program successful. And the operating model is part of that set of roles, tools and templates that we're going to share as part of this webinar and as we always do. This tool is probably the most important out of all the ones that I'm going to share with you. And I'm going to break it down because I know that this is kind of small on your screen. It's kind of hard to read. So I'm going to dissect it by each of the three sections of this diagram. But the common data matrix is a tool that you can develop yourself to inventory what information and what data is very valuable to your organization, who has responsibility for it, who the domain steward or the subject matter expert is for the data, where it resides and what systems and who in the organization uses that information. So the common data matrix is something that when I used to share it, when I would start out giving presentations at the diversity events that I would speak at, and then I found that people stopped paying attention to me either because I got really boring or because they felt that it was really important for them to just capture the moment right there and start to collect the information that I'm talking about collecting within your common data matrix. So I'm going to start with the left section first. You can see on the bottom of the screen that as you walk across each of the sections, I'm highlighting which ones are the ones that I've kind of blown up on the screen for you. But it's very important and I'm going to see if I can use a tool to draw on the screen. Give me one quick second here. There we go. So if you take a look at the top part of the matrix right here, it basically has a legend of each of the different colors. So if you recall the pyramid diagram that I just shared with you, the color at the top was the strategic level. That was the council. And then there was the yellow level, which was the subject matter experts for each of the different subject matters of data. So here you define customer data and all of the different subsets of data that are associated with that primary domain. And as it says, the yellow block. So we've got a yellow block right after the word customer domain and down the right-hand side of that matrix. If there's a person that's a subject matter expert or a go-to person for that information, you want to collect who they are. And a common data matrix is a great place to be able to collect that information. Now, if you remember, I'm going to go back one slide here real quickly. So if you look at each subdomain of the main domain and you think of the fact that this data could exist in three or more systems or maybe just a handful of systems, kind of look at that as it continues out throughout the rest of the diagram. Because those same three, for that subset of that domain that we just discussed, where does it reside in ERP, MDM, Enterprise Data Warehouse? Who are the people in IT that have responsibility for that data? Who's the IT resource owner and the system resource, and the business resource owner? If we work our way across the tool even more, we could take each business area and break it into sub-business areas and identify who the people are that use that specific type of data within that domain in what part of the organization. And if you have a shadow IT area, you could also collect that information in the Common Data Matrix. So the Common Data Matrix is a great tool for you to be able to know what data you have, what data you really care about and that you want to manage, and who in the organization has responsibility for that data across the organization. Again, like I said, I could spend a whole day talking about the Common Data Matrix and I have in the past. But for the purposes of this webinar, the idea is just to share with you some things that you might be able to develop within your own shop that might add value to your program right out of the gate. So now this is a racy matrix, and a lot of you might be familiar with racy. Oftentimes I refer to it as RASCY, and I've added an S for supportive, and I actually have that highlighted down here at the bottom of the screen what each of the letters stand for. And then if you look at each of the different roles that are defined across the top, the colors in the roles line up with the colors on the Common Data Matrix and on the operating model. And here's a series of steps, and here's the different people that get engaged, and we can highlight who's responsible, accountable, consulted, informed, supportive along the way. And again, it's a tool that you can develop yourself for the processes and procedures that you are attempting to govern that will help people to understand that if they see themselves within the operating model of roles and responsibilities, they can also now see themselves within the steps of the processes that you're governing as part of your data governance initiative. So a racy matrix is pretty common in a lot of organizations, but my suggestion is to align it with the different roles that you've defined as part of your operating model of roles and responsibilities for your organization. And one of the last tools I want to share with you is the Communication Plan Matrix. So we all know that communications and awareness is a very important component of a successful data governance program, especially for organizations that are just getting started. And again, it's kind of color coordinated with the other tools that if we, the way that I break down communications is in three ways, and each of them start with a letter O. There's orientation communications, onboarding communications, ongoing communications. And if we can identify what those things are that we want to communicate in part of each of those phases of communication, and we can then, we have identified already through our roles and responsibilities, who we need to communicate these things to. And we know that we can't necessarily communicate with your data governance council the same way that we communicate with our operational data stewards. So the things like the charter and principles and the role-based activities and the metadata and documentation that's available to support your data governance program, what we might want to present that to the different audiences at different levels of detail. So having a communication plan becomes very key to the success of your data governance program because it makes sure that you're addressing each of the different roles that you've defined as part of your program and been very specific as to how to orient people to data governance, how to get them on board, if they have some specific responsibilities that you want them to take responsibility for, or even the ongoing types of communication. So having a communication matrix becomes extremely important as well. So let's switch gears in the few minutes that we have left and let's talk about appropriate tool criteria. And as I mentioned, I'm going to look at it from a metadata perspective and I'm going to look at it from a data governance perspective. And again, as being a metadata repository administrator in the past, I know that a lot of these things are real important and if you don't look to the outside to learn about these things, a lot of the vendors may not choose to talk to you about these things unless you ask specific questions. And so the first item on the list is the metamodels and the software releases. And the reason why I put that up at the top is that the databases that these vendor products are based on mostly have models, or should I say metamodels, of how they store the information in the tool. And if you want to have an open environment where you're going to be able to get access to the metadata without using their tool to do it, you're going to want to know how the data exists. So it's really models of the metadata associated with your database environment, your data modeling environment, because you might have multiple database tools, multiple data modeling tools that all need to be mapped to a common metamodel within the repository. And then understanding about software releases, how often do they come out, when they come out, do they change the metamodels and to understand what type of impact that will have on your organization is really important. And I talked about extensibility a little bit, where the idea of, well, you can either use the metadata tool or the data governance tool the way that it comes to you out of the box, or you may have things that you're already collecting or that you want to collect that you want to be able to add to the tool. So being able to extend the tool becomes very important. The ability to define your own ways of loading information into the tool become very important. The self-defined loads, the ways that you can represent the different roles that you have associated with managing data in your data governance program, you might want to have that information available in your metadata repository tool as well, as well as be able to take your metadata, your metadata data warehouse, basically, that you're building and integrate it into processes. Do your analytical tools give you the ability to, as the people are creating reports, to hover over certain key terms or certain element names and have it give you the information about, well, this is how that data was defined. This is where that data came from. So being able to integrate your metadata and your data governance tools with process becomes very important in your organization. A couple more requirements for purchasing a metadata tool is versioning. I don't know how many of your organizations have this as a situation, but I know that I run into it all the time, is that things have the same name, but they really have different meanings within your organization. Or they have different names, and they have the same meanings. But when you have things that are named the same, you need to be able to control the versionings of which version of account number do we want to use? Do we want to use marketing's account number, or do we want to use finance's account number, or do we want to... You get my point, is you need to be able to differentiate between different versions. Communications becomes a key. End user requirements for the metadata tool, that was the beef on the industry when I was getting started, was that the tool was great. It housed a lot of information, but getting it into the hands of people to be able to use it was always something that was very difficult. So think about that when you're developing your requirements for purchasing a metadata tool. Training and education, what are the resources? We talked about some of the things that it takes in order to create a data... Or to really be able to bring in a tool to purchase a tool or to develop a tool. So think about the resource requirements as well. So that's about 10 different requirements that I wanted to throw at you quickly to help you to understand that selecting a metadata tool, there's a lot of things to think about, besides for the flash and the glitter that are oftentimes shown to you in demonstration. Ask the question about what does it take to get our metadata into the tool to make it as useful as it's just been demonstrated to us. Then there's the requirements for purchasing a data governance tool, and a lot of them are the things that we already talked about in this webinar. The business glossary capabilities, the ability to customize things, the extensibility, the ability to customize roles and responsibilities to match what you have in your organization. Your workflows, your policies and standards have a client now that's looking for a metadata and a data governance tool to catalog all of their reports and their data subsets that they're making available. One of the things that they want to do is make data more accessible to people. There's a process that gets bogged down because all requests get thrown into the same bucket. But if we can tell them that some of this data that you don't need to go through this detailed process, we have that information already collected. You have a data governance tool that can help you to do that or that can be extended to help you to do that. That's a really good thing for your organization. So workflow for issue resolution, data policies, master data, all of these things that you really want your data governance tool to be able to support these types of initiatives within your organization. So what are some of the more requirements for purchasing a data governance tool? Whether it's data lineage and impact analysis and profiling and quality, all of those types of things. And so when you're talking to the vendors or as you're developing tools yourself, take a look at what is it going to take to integrate our data governance tool or our metadata tool or our conglomeration of tools that we've developed ourselves to get them integrated into the process so that people can get more value out of the metadata that's being collected as part of your data management or your data governance initiative. Let's see, next slide. So out of all these requirements that I just talked about, really what you want to do is you want to map the tools to addressing your most basic data governance needs. And so I've created two pages of what I consider to be most basic data governance needs. So our first one is getting your executive or your senior level to support, sponsor, and understand what it is that you're doing. When I do a webinar on best practices, I'll tell you that if I told you 99% of the organizations define that first one practice I'd be lying to you because it's more like 100%. They recognize that if they don't have their senior level support, sponsorship, and understanding that their program is going to fail. So when you're looking to purchase tools or you're looking to develop tools, keep in mind that if you can use those tools to improve the senior level's support, sponsorship, and understanding, that in itself is enough value to have for a tool. Program accountability and responsibility is one of your basic needs. Definition of best practices, maybe you as an organization want to not only define what best practices are around data governance, but you want to share them with people and allow them to see what are some of the things that we're trying to achieve with data governance, and if we don't get to these things, we're going to fail. We know we're going to be at risk. So when you're defining best practices, oftentimes people do a critical analysis of their present environment. They identify things that they're doing to leverage to support the best practices. They also look for opportunities to improve, associated with the best practice. Those things are often used to help to develop the recommendations that are going to feed into whatever roadmap you have for data governance within your organization. So planning and the abilities to support meeting initiatives is one of your most basic needs from data governance. Well, how well the tool that we're developing or producing, how well can it help us to achieve that? If you have other basic needs of roles and responsibilities, communications, as we talked about, sizing the quality and the governance solutions, you want to look for tools that will help you to stay iterative and incremental in your approach. So for example, if you would use the common data matrix tool that I just shared a few minutes ago, we want to understand, we don't need to do this all at once. We may start. I have a customer who's focusing on vendor product in customer as there are three primary domains of data, and they're building common data matrices for each of these. Again, it's something that can be done incrementally, and you could stay iterative in your approach as well. So again, you want to make sure, as you're looking at your tools, that you can map them to your most basic data governance needs in your organization. So the last thing that I want to share with you in the last couple of minutes we have before I turn it over to Shannon for questions or for comments is to talk about the whole tool selection process. So this is a series of steps that I suggest. You're going to define your requirements. You're going to define your selection criteria. If you're going to need to purchase a tool, you're going to need to define funding and who's going to be the users of the tool. Often that is used to determine who's going to pay for the product. Where's the cost of the product going to be split between who in the organization is going to use that tool? You want to identify the appropriate resources that you're going to use to not only to evaluate the tool and define your requirements, but to also manage the tool process once you've brought it in-house. Research products to match your requirements. Select the finite number of vendors. Craft and send your request for proposal. So the first thing that I often suggest is that when you're talking to the vendors, you need to set a reasonable timeframe and schedule. And don't be afraid to ask them to respond to you quickly, but then do them the favor as well of responding back to them quickly as you can. So don't have the vendors, this is at least what I suggest, don't have the vendors respond and then wait for six weeks to give them an answer. If it's gone from ten vendors to three or down to whatever the handful is that you're going to have come in and do a dog and pony show, at least they really appreciate it. I'd appreciate it if you would tell them that they're in the running or they're not, but set up a reasonable timeframe. And then these are basic things. Issue the RFP, confirm that a response is coming, give them the opportunity to ask questions, respond to the questions. Again, you've seen RFP processes before, and all of these types of things are things that are going to help you as you go through the process of selecting the appropriate data governance tool for your organization. So that RFP that we talked about, which is often one of the big first steps in selecting a tool is to develop the RFP. So oftentimes there's a targeted cover letter, there's objectives for what you're going to use the tool for, requirements and capabilities, weighted how are you going to determine what's the best tool for you. Key dates like I just shared. There's a lot of information. And so what I want to do is I want to share with you a picture that I used at a client recently to define what their requirements were around metadata and around data governance. And they were focusing on improving understanding of data. And they looked at it from the semantic level, from the business level and from the technical level, what information and metadata did they need that was going to really help them to evaluate what's the best tool for them. And feel free to use something like this or talk to me about it. But there's a lot of information even in this that will help a vendor to say, well, here's really where our strengths lie. Here's where we can help you as an organization. But being able to share your objectives with them, sharing with them how you would go about scoring them based on the criteria that you've defined. You might not necessarily tell them how you're waiting things or adding weight to these things, but you want to at least have that internally as a way of scoring what products evaluate in what way. And that becomes very beneficial as you're going through the process of selecting the appropriate tool for your environment. So basically, I know I shared a lot of information in a relatively short period of time, but during the webinar today, we talked about all those different categories of tools that you may already have in your environment or that are available to you with the metadata. We talked about creating your own tools, defining criteria, how to manage the selection process. So we covered a lot of different things. I've seen that there's been a lot of chat going on during the session today. And with that, I'm going to turn it back over to Shannon to see if there were any questions in all that conversation. There's quite a few questions coming on. But if you want to submit questions, it helped me make sure I don't miss any. Make sure you submit them in the Q&A in the bottom right-hand corner of your screen so much chat going on. I just love it. It's so awesome in the community. It's just so engaged. So, and Bob, thank you, of course, for this great presentation. I will answer the most commonly asked questions that we receive just to let everybody know as a reminder. I will send a follow-up email for this webinar by end of day Monday to all registrants with links to the slides, links to the recording, and anything else requested. Also, if we don't have time to get to all the questions in the next few minutes, we will bobble right up the answers. So, keep those questions coming in because we'll get those answers to you as well in the follow-up email, along with all his matrices and all that lovely stuff provided. So, diving right in, Bob. So, which of these tools qualifies as a data profiling tool? Data quality is not my forte, but data profiling tools. I know there's tools like Trillium that are on Trillium and look at some of the keywords and some of the things that they do. I think that a lot of the tool suites have data profiling tools built into them. So if you do searches on some of the keywords associated with what the practice of data profiling says, it should highlight for you what some of the top data profiling tools are on the market. Again, I don't necessarily focus on the tools all that often or on the quality tools, but that would be a good way for you to be able to go. And I think you can see people that are already flashing answers to who are data profiling tools in the chat. I love it. It's really great when the audience is engaged like this. Yeah, absolutely. So since the central data dictionary metadata repository has been a flop over the last 50 plus years, are there any smaller, easier to use, less expensive, complex federated dictionary-like tools? Yeah, so that's a great question. We had a client actually here in Pittsburgh, Pennsylvania, who wanted to get an enterprise metadata repository tool for $25,000. And it's not easy to find. You're not going to find... The organizations that want to have those enterprise metadata repository tools, there's going to be mostly the larger vendors that have the enterprise capabilities. If anybody here knows of organizations or products that are not very expensive, please list them in the chat or in the Q&A. My suggestion is that you start out by looking at the tools that you have already and see what functions they will serve for you before you go out and buy the enterprise repository tool. But companies like CAs to have a metadata repository tool, there's tools like ASG and Adaptive that have large mainframe or large system repository tools. There's not a whole lot that are smaller that have a lot of different capabilities, but I agree with one of the comments that I see here that it really depends on the size of your organization and how many users you need and what metadata you want to collect. Again, my suggestion is research the smaller size tools. Look at the vendors that are out there and see which ones might be the appropriate size for your organization. So are data governance and information governance truly different or is it a marketing gimmick? How you define data versus how you define information. So the way that I define information is that it is data plus metadata equals information. So if I gave you a number 15 or 1-5-0-0, you wouldn't know if it's an address or a dollar amount or a quantity or anything like that. So having important loss, what was the question again, Shannon? Is there a difference between data governance and information governance? Okay, so yes. I've worked for organizations that have called it information governance instead of data governance, and this is just a funny, quick story. When I asked them why they called it information governance, they told me that they had called it data governance the first three times they tried it and it didn't work for them and they had to call it something else. So that should give you by itself an idea that data and information can almost be interchangeable. Typically, if it's truly the data plus metadata piece to turn things into information, we'll have the metadata components as being something very important as well. So I think I look at them as basically being the same thing, to be honest with you. So what's your assessment of some of the industry-leading governance tools, pros and cons of the different... that may be a bit... that's kind of a loaded question. I don't know. We can get to that in a few minutes. Tools will do great things for you. Ask them questions about what it will take to get your product to be able to do the things that they are demonstrating to you. Meaning that, like I mentioned before, there's the iceberg with all the work that takes place before that, Calibra and Informatica just acquired a tool out of Belgium called Diacu. And they've now integrated that into their Informatica suite. IBM has tools and all these repository tools like Adaptive and ASG and a lot of these vendors have tools that you could use for metadata. But again, as I mentioned earlier, and I know it's said a couple of times, take a look at what tools you already have in your environment and see what you're using already and see what the tools handle for you. One of the things I would suggest is go to the dataversity.net site and look at their primary conferences that they hold. So for example, their EDW conference or their data governance finance, data governance winter, DGIQ conferences, and look at who some of the vendors are that are sponsoring and supporting these things. Now, I'm not to say that they're the only tools that are available on the market, but they'll give you a good starting point. I saw that somebody had mentioned, look at the Gartner report or look at the Forester report if you're familiar with their magic quadrant as to what tools are available on the market. There's a lot of them. I know that some tools are used more than others. Some tools have much larger marketing budgets than others. So that's why you may have heard of them. But do your due diligence. Do your research. Join a data governance group. Ask those people what tools they're using. And I'm sure that you can get a lot of great information about what tools are available on the market today. You know, I think we have time for one more question, but do keep the questions coming in, because again, I'll get those answers to you from Bob in the follow-up email. So any ideas about free open source, you know, Apache Atlas as a data governance and metadata management framework and tool? You know what, I think that's a great question for the masses, for the numbers of people that are on this webinar. I do not know of any open source products, but I'm guessing that there are some. If you have information about free tools, you know, please put that in the chat, put that in the Q&A, and we'll make that available to people as part of it. I don't want to make things up for you, but you know, I'm sure there are some great open source products out there, or at least good open source products. You know, open source products come with some issues themselves. They may not be perfect. They may not be supported by anybody. But again, if you're looking to do things for less money, then take a look at what tools you have first. Take a look at what tools are open source on the market, and I think you'll get your answer there. I'm afraid that is all we have time for. We are at the top of the hour here. Again, if you have additional questions, keep them coming in, and I'll leave it open for here for a little bit for you guys to keep submitting, and we'll get answers out to you. And the follow-up email, which will go out by end of day Monday to all registrants with links to slides to the recording as well as those additional answers and some of the additional materials from Bob that you can use to reference as well. Bob, thank you so much for this great presentation, as always. And thanks to our community. I just love it when it's so on fire like that. I just love... It is all about community. That's why we do these webinars, to help build community and help build support for each other and networking. So I love it when it works. We see it works. So I hope everyone enjoys. I hope everyone has a great day. Again, I'll leave the Q&A open here for a little bit, so you can keep submitting your questions in. Happy fall there, Shannon. Yes, happy fall. I know. Indeed. Thank you again, everybody, for attending. I look forward to seeing you next time. Cheers. Thank you. Take it out. Bye.