 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 Siner. Today, Bob will discuss how to govern your master data sponsored by InfoJix. Just a couple of points to get us started. Due to the large number of people that attend these sessions, you will be muted during the webinar. If you'd like to chat with us or with each other, we certainly encourage you to do so. Just click the chat icon in the bottom middle of your screen for that feature. For questions, we'll 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 via 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 the session, and additional information requested throughout the webinar. Now, let me introduce to you our speaker for today, Bob Siner. Bob is the President and Principal of KIK Consulting and Educational Services and the publisher of the data administration newsletter, TDAN.com. Bob has been a recipient of the Damon Professional Award for significant and demonstrable contributions to the data management industry. Bob specializes in non-invasive data governance, data stewardship, and metadata management solutions. And with that, I will give the floor to Bob to get today's webinar started. Hello and welcome. Hi, Shannon. Hi, everybody. Thank you very much for taking time out of your schedule to sit in another one of the real-world data governance webinars. Happy holidays. I hope you guys have a happy new year. And glad that you've joined us looking forward to continuing the series into 2019. So as Shannon mentioned, today we're going to talk about how to govern your master data. So I get a lot of questions about that. People talk about data governance and master data in the same sentence a lot. And so what we're going to do is we're going to spend some time talking about how to typically go about governing your master data for your organization. Before I get started, I want to run through a couple of quick things. Actually, there's a bunch of them on the screen, but just ways that I'm involved in the data governance industry, a lot of them have to do with my relationship and partnership with Dataversity, obviously the real-world data governance series. So we kick off the 2019 series with a webinar on what's a data steward to do. And also I'll point you to my book that's available on Amazon and techniques publishing called Non-Invasive Data Governance. I talk about non-invasive data governance a lot. I'll be speaking at several events coming up. In January, there's the Enterprise Data Governance Online event. In Boston in March, there's going to be Enterprise Data World and then the Data Governance and Information Quality Conference in San Diego later in the year. There's also an online learning plan that's available through Dataversity on Non-Invasive Data Governance. There's the Data Administration newsletter that Shannon mentioned and of course KIK Consonings. So I hope that's a bunch of resources for you as you are learning and you're heading down the path of data governance for your organization. So what am I going to talk about today? Today I'm going to talk about specifically how to govern your master data. So these are the things that I really want to go over and spend the time focusing on. The first thing I'm going to talk about is the connection between master data and data governance. Since a lot of people look at that, them as being connected at the hip, we want to talk about what is the connection between mastering your data or creating master data and implementing formal data governance practices within your organization. We'll talk about why and how master data needs to be governed. We'll talk about applying governance roles and other artifacts and things I'm going to share with you and how to apply those actions to master data processes and whether or not there's really such a thing called master data governance. So I've got some interesting answers for you on that question. And the last but not least, we're going to talk about the value data governance brings to master data within your organization. I'd like to start the webinars by providing to you the definitions that I use. So I want to define data governance and data stewardship because it really is the basis for the noninvasive approach. And so with data governance, there's a lot of definitions that are out there, but I use one that specifically has some teeth behind it. I say data governance is the execution and enforcement of authority over the management of data and data-related resources. And then stewardship, which is also often used interchangeably with data governance, is really the formalization of people's accountability for the management of the data and the data-related resources. So certainly however you define data governance for your organization, at the end of the day we want to make certain that we're executing and enforcing authority over whatever it is that we're trying to achieve with data governance, whether it's protecting sensitive data, improving the quality or the understanding of the data to match regulatory concerns and things like that. At the end of the day, we need to execute and enforce authority and that's really what data governance is all about. Now there's a bunch of different approaches to implementing data governance and I talk a lot about the noninvasive data governance approach, really is to take a look at what people are already doing within your organization and recognize that they already have a relationship to the data and if we can formalize that relationship and formalize the accountability that goes along with people's relationship to data, then we can implement data governance in a noninvasive way. And a good example of that is people that use sensitive data within the organization, there's not just some of them that are responsible for protecting that data and following the rules, handling rules associated with sensitive or confidential data, pretty much anybody in the organization that uses data needs to be the steward of the data. So we're going to talk a bit about that in the next webinar as well. But really what noninvasive data governance focuses on is how we're going to apply governance to the organization. So we're going to apply formal accountability through noninvasive roles and responsibilities, which I'll talk a little bit about later in the webinar. We're going to apply governance to existing processes or create new processes when necessary, but let's look to see what we're doing and make certain we're getting the right people involved at the right time. So really the goal of noninvasive as the approach to how we're going to apply governance, that really the goal is to be transparent, supportive, and collaborative. So let's talk about the connection between master data and data governance. And actually, since a lot of people use these terms, believe it or not, somewhat interchangeably, we really need to focus on explaining to people what's the difference between data governance and MDM is. And so as I mentioned earlier with data governance definition that it's the execution and enforcement of authority over the management of data. Well, governance is that authority. It's the way of being able to make decisions, get people formally involved as they need to be involved. So governance is truly the authority. And a lot of people look at master data as being the authoritative data to go to within the organization. So if we're looking to achieve authoritative data, by all means, we're going to need to be able to apply some level of authority to how data is defined, produced, and used in the organization. So governance is that activity that assures master data is authoritative. And I ask the question, without data governance, can master data really be the authoritative source? So we use the one that said that this is the data that we need to manage and that this is the version of the data that we need. So in order for data to become master data, there needs to be some governance that takes place to make certain that it's the right data, defined, produced, and being used the appropriate way. So what does it mean to master something? So if you look in the dictionary, you'll see there's a lot of different definitions, but one says it's to become very skilled or knowledgeable about something, become or to defeat something. Basically, the definition that I see most organizations using around mastering something is that you're creating the go-to version that everybody in the organization trusts. So if you're a master chef, people go to you to get advice as to what's the best way to cook something. Well, if it's master data, it's the data that people want to refer to that they can trust and that is truly the data that is to be most used within the organization. So oftentimes, you'll find data governance initiatives focused around specific subject areas of data. So we're looking to make those subject areas into kind of master places for people to go to get the authoritative source for the data that they need to do their job. So again, what does it mean to master data? It means to provide the data in a single authoritative source and to use it across the organization in multiple systems, applications, or processes. So to provide a single source of business data that's used across all systems applications for the entire enterprise, that's typically what organizations refer to as their master data. And so the interesting thing when we're looking at the connection between master data and data governance is that the mistakes that organizations make for one, they tend to also make for the other. So there's really a close connection between master data and data governance that they have a series of similar mistakes that organizations make that prevent them from being successful. And let's talk about what some of those mistakes are. The first one, and I oftentimes talk about this as being the number one best practice for data governance programs, data management programs, master data management programs, is that you need to have executive and strategic level of understanding and support. You know, I always say that it's the support, sponsorship, and understanding of the senior level. That's a best practice that we're all trying to achieve. And really the understanding becomes the most important aspect of it, because they're very willing to, well, sometimes they're very willing to support and sponsor the program, but do they truly understand what we're doing to achieve master data within our organization or to achieve data governance within our organization? Oftentimes, you know, one of the reasons that these types of programs fail is that there's really lack of accountability or responsibility to make the program happen. There's a lack of the definition of best practices. So define what best practices are so you can take a ready-fire, a ready-aim fire approach instead of a ready-fire aim approach. You know, lack of critical analysis of the present environment. What do you already have in place that you can take advantage of? And what are the things that you can leverage opportunities to improve within your organization? And the inability to select and follow a suitable approach is certainly also an avoidable mistake that organizations tend to make. They not necessarily have a roadmap as to what they need to produce master data within the organization. So what are some of the more of the top avoidable mistakes? Well, there's a lack of planning and the ability to support this as a meaningful initiative across the organization. So getting the right people involved at the right time or having a framework of roles and responsibilities associated with governing your data or governing your master data specifically. Making data governance and data quality optional lack of focus on communicating to people what we mean by governing data and what we're trying to achieve when we're implementing master data within our organization. I've seen a lot of organizations that take a big bang approach. They try to tackle too much. And so typically what I suggest to organizations is they take an iterative and incremental approach to how they govern data within the organization and the same thing could be told true for master data. I see organizations that have supplier data governance initiatives or customer data governance initiatives. And that's a really good indication of a specific domain within your organization. Sometimes the domains are so large within the organization that trying to bite off the whole thing at one time is a little bit too much or a lot too much because, for example, customer data is used everywhere within the organization or supplier or vendor or student or whatever your focus is. Even if you're focusing on a specific subject area it makes sense to take an iterative and incremental approach. So perhaps do an aspect of customer, do an aspect of student or whatever it is that you're focusing on for your organization. So let's talk a little bit about why and how master data needs to be governed in your organization. A lot of organizations, there are a lot of organizations that focus master data as being all about the data. And then there's others that focus on master data as really being a process of achieving that level of quality around a subject area of data within the organization. So the ones that look at master data as data they typically start with a subject area or a part of a subject area, as I mentioned before. Those are the domains or subdomains of data that we're focusing on. Or they focus on data as a system or record or single point of truth from a specific application. So some organizations will look at their existing applications and say, this is where we want people to go. All that master data. That's not really what master data is all about but there's a lot of companies that do that. It's an easier way to be able to say that we have master data. But then we need to demonstrate that we have a high level of governance around that data so that we can make certain that people have a high level of confidence in the data that's associated with our master data. When you look at master data as a process, if you're familiar with my webinars and hearing me speak or write, primary actions that people take with data. They define data, they produce data, and they use data. Certainly by all means, if you can think of other actions that people take on data that don't fall under one of those categories, please let me know that. And I've put that challenge out to people before and typically when they say analytics, well analytics falls under using data and data modeling falls under defining data and things like that. But basically, we need to make certain that if we're creating a master data resource for our organization, that we're doing a good job, we're doing a formal job of how we define the data that's going to go into that data asset. We're going to make certain that we know how the data is being produced and we're going to make certain that the people that are producing the data understand what impact they have on the quality of data across the organization. So we certainly want to govern the process of producing the data to assure that we have high quality data for the organization. And the one that's a no-brainer is really the process of using the data. If data is classified a specific way, it falls under a specific set of handling rules and anybody in the organization that uses the data needs to make certain that they follow those rules. Same thing holds true about regulatory rules and compliance rules or even intellectual property within your organization. It has to be protected a certain way. Anybody who has access to that type of data needs to be well aware of the rules that are associated with how they handle the different types and different classifications of data within the organization. So master data, we talked about it as being a data focus, there's a lot of different aspects of the data that are associated with master data obviously. And that's the quality, the security, usage, structure and design, all of these things and we're going to go through each one here real quickly just to talk about what do I mean by data quality and what do I mean by governing the process associated with data quality and security and usage and all of those things. So you can see there's a lot of different aspects data aspects to master data that need to be governed. And the processes that are associated with each and every one of those and if you can think of other things that need to be governed as well to make it master data please share those in the chat group or in the Q&A area and share them with people because there's a lot of different things that people do with data and we have to make certain that if the data is going to be considered the authoritative source for the organization that we're doing a darn good job of making sure that the quality is high. The data is being protected that it's being used properly and all of those things that you see on the screen right now. And then there's governing master data as a process. The other side, I said people look at master data as data, look at master data as process, but we need to govern the processes associated with all those things that were on the previous page. Data structure and design, a lot of organizations will create data models, logical data models, conceptual data models, physical data models. Well, I've been known to say before that data modeling is data governance and to a form and to an extent it really is. It's some governance around the definition of the data. So the data structure and design needs to be governed. We need to make certain that we are modeling appropriately, that we're sharing it with the business community as we need to to make certain that we're capturing the appropriate requirements for data definition. Then there's data storage, movement security, like I said before. Let's kind of walk through each of those and let's talk about them in terms of master data and data governance. So the first one is data structure and design. So obviously we need to govern certain things that are associated with these steps of the process, of the definition of the data. We need to design and structure what our master data is going to look like within the organization. So we're going to govern the design and the structure of the master data. We certainly need to master the metadata that's associated with the master data. Certainly the understanding of what that data is, how it's defined, where it came from, who's responsible for it, what are the appropriate values, what is it called, how is it defined. All of those things are metadata and unless while we're defining the data we take that opportunity to collect the appropriate metadata. We're certainly not doing as well as we can to govern the data structure and design of the data that's going to become our master data. We need to govern the accessibility to metadata. There are organizations that put data governance programs in place specifically focused on giving access to the appropriate people or reviewing the present access and making certain that we have an auditable trail to make certain that we can demonstrate that we're giving the appropriate people the appropriate access to the appropriate data and a lot of that information is also collected within the metadata. Do we want to make that information available to everybody that's using the data? Do we want to make it available to people outside the organization if we're setting up, for example, a marketplace where we have customers coming to our website? We certainly want to make some of that metadata available to those people as well, but do we need to make information about the structures of the files? Probably not. So we need to make certain that we're governing the access to the metadata. And again, when it comes to the structure and design of your data, we want to make certain that we're governing the validation and approval of the design of the data that you're going to use for that data resource. Governing the data storage. And there's a lot of things. People who focus on data storage and data retention and those types of things know that it's a very important aspect of data management in general. So we want to make certain that we're governing the master data storage appropriately, that we know how that data is going to grow over time and that we plan for it, who gets access to it, the security, how long we need to keep that data around. So data storage is not such as simply as keeping it on a disk drive somewhere. It's all these different things about the data. If we're going to have master data, we need to make certain that we're governing these aspects of data storage within the organization. Data movement. So a lot of us are working on ETL, extraction, transformation, load of the data, the lineage of the data. Where did the data come from? And that's certainly something when people are accessing your master data sources. That's one of the questions they ask. Where did the data come from? What did you do to the data to get it to be the way that it is? Because I know before the master data became used within the organization that they spent a lot of time manipulating the data and making it the way that it needs to use. And now they're expected to be able to go to the master data source and just use the data the way that it is. They need to have that type of metadata about how the data was extracted, where it came from, and what you did to the data to make it the way that it is in the data resource. So we need to govern the movement of the data, the selection of which data is going to be included in our master data, what we're doing to the data, how we're transforming the data, calculations, derivations, or examples of transformation, the loading of the data, the migration and the conversion of the data. All of those things are things that people are going to want to know when it comes to utilizing the master data the best that they can within the organization. Governing data security, I talked about that a little bit. We need to know how the data is classified and what the rules are that are associated with how we can handle data that's classified a certain way. And the access and the risk associated with master data, certainly when it comes to data security they have a big interest in making certain that this data only gets into the appropriate people's hands. So again these are things that need to be learned associated with data security not only for master data, for any data within the organization, but certainly for the master data resource that you're collecting. The quality. We know that we need to govern the processes that are associated with the quality and certification of the data especially if it's going to be master data. Who was it who validated the data? What rules did they apply? So what is the level of confidence that we can have in that data and has that data been certified? And what was the certification process for the data when it became master data? Understanding the definition of the standards and values again don't want to read every line to you, but there's certainly I want to point out when it comes to governance that we want to have a formal process in place for making certain that people can let us know when there's issues with the data that they're working with and also that we have a formal process in place for resolving the data quality issues as well. Data usage and I know I talked about this earlier as well, it's the use of the master data and who can see it to make certain that people who can see data that needs to be protected understand how that data can be shared, how that data can be printed, how that data needs to be handled. And not only that, especially when it comes to master data, when that data is passed on from one resource to the next are we sharing the rules with people that are receiving the data that we understand to be the case as we're handling the data. So certainly you want to make certain that you're defining the rules associated with data usage but you're also sharing it with people that aren't necessarily accessing the data firsthand. Data function requirements there's data function requirements and then there's data system requirements and again we want to make certain that we know that we have a plan that is data centric when we're focusing on creating a master data solution for our organization. So making certain that we have requirements that we're documenting the requirements that we're vetting them with the appropriate people and that we're approving them for use within the organization and that just comes to data function requirements but the same thing also holds true for data system requirements. We need to have a process to collect the appropriate requirements for the organization. We need to make certain that we document the heck out of it get it in front of the right people so that we're doing the right thing that we're implementing the appropriate systems and front ends to data that we're making available widely across the organization. So now let's talk about applying governance roles and actions to master data. So I talked about rules, R-U-L-E-S a second ago now I'm going to talk about the roles what do people do when it comes to the data. So I oftentimes suggest the use of an operating model and I'm going to share that with you in a minute that has a bunch of different components to it and so typically there's different levels that are associated with the organization. So there's an executive level there's a strategic level tactical operational and even a support level and we'll talk more about that in a second as well but then there's the domains of data what subject areas are we focusing on and then there's the diagrams and the tools that we can use as communication artifacts for people to get them to understand the value and what is basically how the master data in the organization is made up where it came from and all those types of things. Then we're going to talk about the roles and the responsibilities as well. So let me first just start by talking about those levels and typically for data governance I suggest that you at least consider looking at all these different levels when you're starting a data governance program and this would certainly be true if you're starting by focusing in a master data area. You need to have an executive level the enterprise leadership when we say that senior management support sponsors and understands the activities of data governance these are the people that we're talking about they're the people that are the steering committee potentially they're not necessarily the next level down which a lot of organizations also have a strategic level that they call a data governance council or data governance committee or something like that and that's often times made up of different people that represent different business areas or business functions or business units across the organization. Then there's those people and this is really one of the prime roles that we need to look at when it comes to governing master data is the tactical level. Who's going to look at how this data is defined across the organization and determine what we're going to use as the definition or the use of the data in our master data version. So when we talk about the tactical level I typically talk about data domain stewards or enterprise subject matter experts. They need to be involved in the master data initiative so we need to make certain that as we're creating master data we get the appropriate people involved from across the organization that's critical to make certain it's going to be master data for everybody and not just for one specific group with their definition of whatever it is that you're mastering. There's the operational level. The people in the organization that define, produce and use data as part of their job we need to get them involved. They need to be recognized as potentially being the stewards of the data within the organization and then there's the support level which I typically include as IT. You know certainly the team of people that have responsibility for implementing governance whether that's an administrator or a work group or a team of people on the stress support level then there's also the partners the people in information security, the people in audit who we want to work with to make certain that our governance program is dotting every I and crossing every T for the organization. This is the diagram that I share most often within my webinars and it's broken down exactly as I stated you've got the executive level at the top which is typically your steering committee your strategic level, tactical operational and then all the different supporting functions of a governance program. I know that I'll be doing a webinar sometime into 2019 that goes through a complete set of roles and responsibilities associated with data governance so if you're interested in learning more about that please join us at that time. So we want to govern domains of master data this is really the way that a lot of organizations get started. They focus on a specific subject area and they title their data governance program something to be associated with that subject area of data. So many organizations will start with customer data governance because customer seems to be one of the most widely used pieces of data or subject areas of data across the organization. I've also seen supplier data governance financial medical made metadata governance or medical data governance again we're just labeling the type of governance on the specific subject area now that's not necessarily an issue until we start getting asked the question as well can we apply the same level of governance to other subject areas within the organization and typically if you're creating a governance program to focus on one specific subject area you want to make certain that you don't need to redefine your roles for another subject area you don't want to redefine your processes you don't necessarily want to redefine how you're going to communicate with these people you want to make certain that you're using a model that is repeatable within the organization so if you go back to the slide where I talked about the operating model of roles and responsibilities that's not really specifically focused on one subject area it's something that can be used for the entire organization so if we're done and we've been successful at implementing customer data governance and we want to move on to supplier perhaps we just need to find additional data domain stewards or subject matter experts our counsel potentially would be the same our stewards potentially would be different because they're the ones that are using the supplier data or defining or producing the supplier data so again you don't want to necessarily focus your program on something that's specific for a single domain you want to make certain that it's repeatable across multiple domains within your organization so what are some of the examples of domains well again I had a client recently that said well I was calling them data domain stewards and they said well you're really talking about enterprise data subject matter experts aren't you I thought about it for a second and said well for sure if those are people in your organization that people go to to get information or they're the person that seems to have all the knowledge about a specific subject area potentially they would fill the tactical level of the model of roles and responsibilities that I've provided before and you could then identify or recognize additional subject matter experts as you extend this across different parts of your organization there's a tool that I also share in a lot of my presentations I'm not going to go into a whole lot of details on this but I call it a common data matrix and if you look at it kind of from left to right you start with the subject areas of data and the sub-subject areas of data and as you work your way across the diagram you'll see that we've got IT and we've got different business areas and that business area can be repeated to the right and we want to know who in what part of the organization uses or defines or produces what data in what application or system and to use a common data matrix or a tool like this to get your arms around who does what it will really help you to identify who some of the people are at that tactical level the domain stewards, the subject matter experts for the data across the organization so if you're interested in this we typically make this available to people after the webinar but this becomes a very valuable tool in knowing who does what with data across the organization another one of the processes that we need to focus on is communications with people to let them know what work is going into the master data the value, the quality of the master metadata and how they can use it and again this is just a matrix that I've shared in the past that helps to formalize a process associated with the governance with the communications associated with the governance of data within the organization and you can do one of these specifically for master data if that's where your initial focus is so again when you identify across the top is the different groups or parts of the organization that you identified in our operating model you identify the things that need to be communicated down the left hand side I know it's only a two-dimensional matrix but it helps you to get an idea on the things that need to be communicated who they need to be communicated to because we know we're not going to communicate with the executive leadership team on the far left the same way that we're going to communicate with people in information technology so we need to have a plan associated with communications and we can certainly build that specifically for master data but typically you would create something like this to go along with a data governance program and again if one aspect of that is master data we want to make certain that we're covering the things that need to be communicated and who they need to be communicated to across the organization so I asked at the beginning whether there really was such a thing as master data governance so to some people they'll say yes they'll say yes there is something called master data governance it's when you focus your data governance program on improving just the value of the organization's master data and again I mentioned that that could be a specific subject area within the organization you could say yes that there's such a thing as master data governance because it's a discipline of assuring that the data that's provided as part of MDM is of high quality or high value it also focuses on those steps that I mentioned earlier that we followed to make certain that our data is mastered when we provide it as part of our master data solution so on the flip side of that is there such a thing as master data governance and this is the answer that I would probably use more often is no there's really a discipline called data governance and it can be applied to a whole bunch of different data within your organization it can be structured data content management record management it can be focused on protecting sensitive data it's a single discipline really called data governance and it doesn't require an adjective in front of it because as I mentioned earlier we're going to use some of the same roles to address one business area or one subject area as we would for another subject area you want to be consistent in how you apply governance across the organization so just to call it data governance or enterprise data governance or information governance whatever makes people in your organization or helps people to understand what it is there's really typically one discipline and we can apply it to different subject matters within master data so the discipline doesn't require the adjective the roles and responsibilities pretty much stay the same you know it's not typically a separate program from data governance so if you have a data governance program and you have a master data governance program and they're not working together well typically that wouldn't be what I would suggest in the organization at least there would have to be some symmetry or some utilization of both if there are two separate programs but typically it would be one program called data governance and it would focus on master data as one particular type of data that we're focusing on within the organization so there's lots of different ways to go about mastering data governance and the way that I've seen it when I look out there at the organizations that provide things like certification that there's not really a single organization that I would point to but there's a lot of information that's available from multitude of resources. Dataversity is a perfect example of that you can go and you can find information about data governance and a whole lot of disciplines at dataversity.net obviously you know another way to go about mastering your data governance is to follow a best practice approach there's a lot of different approaches that are documented out there please go take a look and see which one makes sense for your organization share experience with other practitioners when you go to events like some of the ones I mentioned earlier talk to other people about what they're doing you share what you're doing share what issues that you're having because that's typically one of the best ways to get information from people who are feeling the same sense of pain that you're feeling. So share experiences with other practitioners and you're going to find that those other practitioners are going to share their experience with you as well. Attend future webinars and conferences that's a no-brainer but certainly learn from things that you're doing and what works and what doesn't work within your organization. So another question is is it important that you, you right there who's listening to this webinar that you master your data governance skills. Well the truth is that many organizations are looking at data governance and looking at master data as disciplines that they want to focus on within the organization. And the development of those skills are typically repeatable. I mean some of the things will work from one organization to the next. I've never really seen two programs that operated the same exact way but there are certain skills and roles and ways of doing things that are repeatable and transferable from one organization to the next. And you can also apply data governance implementations that target certain behaviors within your organization. So you know that a lot of organizations are looking for people that are educated and knowledgeable about this specific field. Master data governance is typically a very valuable skill to have at least from the governance perspective and also the master data perspective but if you've got both you've got a set of knowledge that may not be as plentiful across organizations. And lastly I want to talk about the value that data governance brings to master data. So typically when organizations govern their data they do it one of two ways. And I asked this question often to large groups and you know a lot of people tend to focus on the reactive data governance which is we've got a problem we need to fix the problem without necessarily being proactive in building data governance into the steps of their SDLC for example or for their methodologies that they follow or working with their project management folks to make certain that data is not an afterthought that it's built into the process. So building the application of stewards including the subject matter experts or the domain stewards into your existing project methodology or building a tool or building something that says okay this is the methodology or the process that we use to identify and record data quality issues this is the process that we follow to resolve those issues again that's a level of governance and you need to make certain that you have a data quality methodology associated with your master data effort and if you're going to identify data quality issues then by all means you want to have an issue resolution process as well. And I want to share with you a couple of graphics that represent that this is one that I've used for some time it's a simply again a two dimensional matrix that has the steps of a process down the left hand side and the different roles and responsibilities that you've associated with your program across the top and I've seen organizations start to add an S to the term racy and that S stands for supportive so it's responsible accountable supportive consulted or informed. But you want to make certain that the people that have been identified into roles that are associated with specific data are engaged at the appropriate time in the and this example of a data issue resolution process. I want to share another diagram too but it's a lot harder to read because the text is a lot smaller. I don't really want I'm not looking to go through it in detail but this is a proactive set of actions that an organization took to improve the quality associated with what they call critical data elements CDEs you seem to use this here that term used more often through the organization through the process of identifying what are the critical data elements all the way through to assuring quality of the data of those critical data elements and all the different roles that are associated with governing that data being able to document who does what creating who's responsible who's accountable who's consulted informed even taking it to an additional level of detail beyond this and being able to say okay during this specific step these are what the people in that role do it's really great to have that information documented because again you want to show the process of how the data got to become master data within your organization. So master data metadata change management so obviously if you're creating master data and if you've been listening to what I've been saying is you want to make certain that you have the metadata information about that data available to people so that they trust where that data came from how that data is defined how that data changed who uses that data how it can be used all those types of things but I've often time shared you know I kind of look at metadata as being a three tiered architecture really and here's the architecture you typically have the vocabulary or oftentimes it's called the glossary at the very top where you have business terms and then at the data dictionary level you could have standard data names and you might want to consider using standard data names in your master data and then you also might have those fields that are represented in some system and they have the they're called ABC field name or some other context is added to them or some other abbreviation and you want to be able to connect those standards to how that data is represented within different organization within different locations and systems and data resources within your organization and then there's a third level of metadata that becomes really important as well which is that data about the data in the databases in the data catalogs and things like that so you can often look times look to your DBMS to the catalog for a lot of metadata that's collected within that tool and the really the challenge is to get the metadata out of the DBMS and get it into people's and to also have people that have time and effort that's associated with creating the data dictionary and the business glossary so typically having a structure like this and being able to say that we're not only things from the business terminology level but down to the system level the application or the dictionary level and then down to the physical level of the data itself is a great plan when you're getting started but the thing is that once you collect this metadata about the data that you're going to focus on the specific master data you also want to have a process in place so that you're governing how that information changes so when you collect that information it's a snapshot at a point in time and again this is just an example of an organization that had if people wanted to change definitions to the terminology in the glossary or they wanted to change things at a lower level within the dictionary or things like that they needed to go through a formal process where the requests were made to the data governance manager who bounced it off of the steering committee and did their research and made a decision as to whether or not that change was going to be made or wasn't going to be made. Again this is a level of governance around the master data to make certain that however whatever effort you put into defining that master data up front that you keep that information and that you have a process, a change management process for updating that information over time. Otherwise again your data dictionary is just going to be a snapshot of what the data looked at a specific point in time. You want to make certain that you have change management associated with your master data or really any metadata within your organization. So there's five things that I talked today. We started out the webinar by talking about master data and data governance and the relationship of those things. We talked about why and how master data needs to be governed. We also applied different roles and responsibilities of data governance to master data processes. We answered the question as to whether or not there really is such a thing as master data governance and it's probably still up in the air for you but I think that there's data governance that's applied to master data and if you want to call it master data governance feel free to do that. But then if you have data governance around other data what are you going to call it? You've got to think about that to think in advance and then the value that data governance brings to master data. So I want to thank InfoJix for being a sponsor of today's webinar. I want to thank all of you for attending and I'm going to turn it back over to Shannon here in one second. I just wanted to give you a reminder that we started the 2019 real world data governance webinar series with what's the status to order to do. So I hope that you'll join us on January 17th. You can sign up at all the places that I've listed there and with that what I'd like to do is turn it over back over to Shannon. Bob thank you so much for this and another great presentation and for helping us to wrap up the 2018 year. This is our final webinar of the year, 94 webinars we've produced. So this is fantastic and I love that everything's been going on here. We got, of course, have a ton of questions and going on here and I just want to let the attendees know I'll be sending you an email afterwards in addition to the follow up email that will go out which I'm hoping to get the follow up email out by Friday at the end of the day tomorrow before the holidays for you instead of Monday but we will see. So but for sure we'll get that out as soon as possible but in addition I'd like to invite you to join us at community.dativersity.net like you did tested out carry on the conversation we're going to be setting that up I'll get you in full on that moving forward. So Bob diving right into the questions here I would like to I would think that if a tool is being used for MDM that a good portion of these data structure and design aspects will be provided present with a tool agreed? You know what I think that and there are different suites of tools that can be used for master data and as long but you know the use of the tool itself has to be governed so making certain that the right people have access to the tool that they know how to use the tool that they're well educated in it and yes a lot of those steps will be available to you through the tool but that doesn't necessarily mean that you have a plan as to how you'd like to use that aspect of the tool so on one hand I'd say yes it's nice that there's master data tools and master data suites that provide with you with the capability but again it's just a piece of software you really need to govern people's use of that product to make certain that you're consistent in collecting the metadata and using that tool effectively for your organization. What process do you suggest to assemble and profile online and offline customer data? I'm not sure you kind of cut in and out on that but offline customer data you know in order to profile data in general you really need to have a definition of what's right about the data what is the definition of the data the right definition and the right values and things like that so it's hard to know what's right and what's wrong unless you have a definition of what's right so if you can work with I guess you said offline customer data resources to get an understanding as to what the quality or what the standard is for that data then it makes it a lot easier for you to profile that data and then be able to report the statistics that certain data falls within certain range and some data falls out of certain range but you need to know what's right in order to be able to determine what's wrong and I'd say that's pretty much for any data resources that I've seen. We've had a lot of requests for your spreadsheet tool as always so I will make sure and get that out to everybody in the follow up email so Bob I'd like to I also like that data issue resolution process slide there's just so much good stuff I think it's the whole deck and any other exhibits that they would like as well so we'll get you the slides as well but anything you want to come out you know what it's before you go out purchase a tool there's a lot of tools and templates that are out there so I appreciate the fact that you find the ones that I share to be helpful but then there's also others that are out there as well find a tool that makes sense to you that can be done practically that you don't need to get through a budget process to it to achieve but certainly that matrix tool the common data matrix is the one that's requested most often in my presentations because if you're going to do data governance around any data specifically around master data you need to know who uses who does what with that data across the organization and if you're not involving the appropriate people from different business units that are using that data perhaps in different ways you're only going to get a percentage of what you need you really need to to understand how the data is shared across the organization and by all means we want to share the common data matrix the racy matrix again it's not something that I came up with it's a tool that's out there on the market it's just a set up of a use of a tool that coordinates in steps of a process with the people in the organization that have some level of accountability for that and so you know I'm glad that you can use them if anybody is interested in having conversations on any other tools don't hesitate to reach out through the community community.dataversity.net I will be out there so if you have questions more about the tools please address me there you recommend any training on how to actually carry out metadata collection and storage or recommended tools you know what there's a lot of resources that are out there I wrote an article on TDAN many years ago called questions metadata will answer and there's a lot of different subject areas because metadata is a pretty wide definition especially if you go with the data about data definition you know we there are metadata tools that you most likely already have in your organization that are collecting metadata that might be useful to people if you could get it out of the tools and into people's hands so you know my suggestion is you know go and look first of all define what you want out of your metadata so that when you go looking at tools you find tools that can meet your requirements because without requirements it's very difficult to select the appropriate tool so start there and then look and see what's available but the metadata itself becomes a backbone to successful data governance or master data governance so certainly focus on metadata requirements first and then you know if you need to collect them in a template or a tool you create on your own by all means do that but then there's some great tools out on the market like InfoChicks that has a lot of information that really covers you know the data across your organization so take a look at those there's a lot of value in those tools you're coming out with metadata training soon I am coming out with metadata training soon I wasn't sure whether I should announce that or not but I'm in the process of recording an online set of classes through DataVersity training center that's called non-invasive metadata governance so it's applying the rules of non-invasive data governance to the effective management of metadata so we'll be talking more about that in January so stay tuned hopefully we'll have it available for you soon love it Bob do you have any definitions of master data versus MDM or are they one and the same well I master data management is the process of managing master data metadata management is the processes and the tools and everything associated with governing metadata so I look at MDM and master data they're used interchangeably I don't really find much of an issue with that have any solid definitions for each one you know what I can provide those in the response that we'll give back to folks I don't know if I really define them differently like I said master data there's a definition of that that's earlier it was used earlier in the deck the processes associated with creating master data are really all associated with master data management but I'll certainly be willing to provide a definition for each in master data governance applied to transactional and non-transactional data well you know I mean it's you know a lot of times master data is you know there might be a transactional aspect to it really I mean you're again you're focusing on the definition of production and usage of the data so if we can make certain we're governing that for no matter what type of data if it's transactional or analytical you know people want to know what data they're using where it came from how it's defined and all of those things so you know I would say that it's pretty much you know it would be used pretty much in the same way across the organization time for some more questions here what is original data retention time frame for the governance of metadata a retention timeline for metadata well that's not one that I think I've been asked before you would think that as long as the data is out there and it's useful to people or that it under needs to be retained that you would want to have metadata associated with it if you ever get to the point where you retire the data and there's nobody who's using it for any reasons even to set up models or anything from years gone by and then by all means feel free to get rid of the metadata or at least mark the metadata as being historic as compared to something that's live to be used right now I hope that answers the question definitely let us know if it did not and just move in on here so any additional considerations when external teams are involved in measuring and mastering and governing the data for example retailer's item is governed with the help of suppliers well you know what I mean I have seen I've worked with an organization recently that had to build it into their agreement with the vendor with whoever was providing data from the outside there are some companies that are in such a position that they can demand that there is metadata that is associated with the data they are receiving from that entity but there's a lot of organizations that don't have that area of leverage I suggest again try to build it into your partnership policy with whoever you're sharing data with and help them to understand the value of creating the metadata that goes with the data they provide but yeah it's very hard to dictate to an external source what they can do and what they can't do unless you have some level of leverage against them. What point is an organization's size or complexity of their data governance process that teaches hesitate a tool versus a series of processes without a tool? Well you know what I mean I've worked with a bunch of organizations that are working with some now that talk about right sizing their data governance effort so if you have a company of 200 people it's going to be different than if you're an international company so it may be that you get more value out of a tool if you're a larger organization having difficulty getting to the data if you're a small company and a tool is going to resolve an issue for people and get them to use the data that you're building you know you might be a small company but your assets and your value might be very high you need to make certain that they understand the data so again it just becomes very important for organizations to focus on making certain that they don't overdo it and create committees upon committees if their company only has a few hundred people you really need to right size your governance effort. What was the system you mentioned a bit ago for metadata? I'm sorry can you say that again? Yeah sure. What is the system you mentioned a bit ago for metadata? Well there are tools the first one I want to mention is InfoJigs I mean they are a I-N-F-O-G-I-X they are a sponsor of the webinar today so again thank you very much. They're a tool that certainly manages the metadata in the environment so take a look at them that's the one I believed I mentioned earlier. Quality sources for assembling a good business glossary. Quality resources well the way that I found organizations to be successful in doing that is that they get the appropriate people and they give them the time and the ability to be able to create your glossary to create your data dictionary. If you're going to define the tiers of metadata the way I discussed with the glossary and then the dictionary and then the detailed physical metadata the physical detail metadata is typically already there it's probably being harnessed within a tool the data dictionary is you know you certainly want to give people the appropriate time to maybe look to see if there were definitions for the data that was created at some point in time but certainly that again the data dictionaries that were developed when applications are developed oftentimes become a snapshot of a point in time so if you're going to do that then you want to make certain that you have resources that will be able to focus on looking at those definitions and validating them to make certain they're still effective. Business glossaries again it takes resources to go and look through and I think we did a webinar on this not too long ago that you can go through handbooks and manuals to pull out the most important terminology to your organization or go to your website and identify the key terms that need to be defined. Once you've done that it really becomes a matter of resources that can take the time and go through a governed process to provide definition. And that just brings us to the top of the hour Bob thank you so much for this great presentation and helping us to wrap up the 2018 year what a great year it's been thanks to all of our attendees for helping to make it such a great year and being so engaged and involved in everything we do. Again feel free to check out the community that we've set up for you to continue these great conversations and I hope you all have a great day and a happy new year. Bob thank you so much. Thanks everybody appreciate all the questions have a happy holiday. Bye