 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 everybody as a data steward. Get over it, 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 upper right-hand corner for that feature. For questions, we will be collecting them via 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 this 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 David 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. Thanks for taking time out of your busy schedule to be part of the webinar today. I love the way that Shannon emphasized the get-over-it portion of the title. I was actually told that that might be kind of a not-so-nice way of saying it, but the truth is, just infill this way by the end of the webinar, but the issue is the data steward. And we need to talk about what that means and why it's necessary to kind of accept that approach. But I'm really looking forward to this webinar. It's one of my favorite subjects. It's one that I get challenged on quite a bit. So without further ado, let's get started. Before I kind of get into the main content today, I want to, first of all, thank Dataversity for giving me the opportunity to do the real-world data governance series. I've been doing it monthly since 2012. So we've now started into 2018, and we've got some great subjects that are coming up. In February, we'll be talking about formalizing data governance with policies and procedures. In March, we'll be talking about metadata governance and specifically focusing on vocabularies, dictionaries, and data. And I'll have a special guest during that webinar. As you probably know, the real-world data governance series takes place on the third Thursday of the month at 2 p.m. Eastern, and you can register for the webinar at a bunch of different places, TDAN, KIK Consulting, Dataversity. There's lots of ways to register for the webinar. Also wanted to tell you about the non-invasive data governance book, Sharon mentioned non-invasive data governance in the opening, and I talked quite a bit about it, so if you're interested in learning more about non-invasive data governance, you can find it at your best bookseller. Want to also mention a couple of events that I'll be speaking at in the very near future. In fact, one is next week, and if you're not aware of it, you should be. The Enterprise Data Governance Online Event, it's a Dataversity virtual conference, and I'll be one of the speakers in that event. It takes place next Wednesday. And Enterprise Data World 2018 is taking place in San Diego in April, so I hope that you will come to San Diego and check it out and introduce yourself when you're there. Two more things, the online learning plan, if you're interested in learning more about non-invasive data governance, please go to the Dataversity Training Center, and as Sharon mentioned, the Data Administration Newsletter, it was published twice monthly, it was published yesterday, a new issue, lots of great subjects, a lot of great writers that participate in publishing on the Data Administration Newsletter. And so in this webinar, in this webinar titled Everybody is a Data Steward, get over it, I'm going to be discussing five very important topics, and the first thing I'm going to talk about is, you know, why everybody is a Data Steward is actually an approach that you might want to consider following in your organization. So we'll spend a little bit of time talking about why everybody is a Data Steward and why that approach is different and why it might be better and it might make sense for your organization. We'll talk about how to recognize people in the organization as being Data Stewards, and oftentimes I compare and contrast that with assigning people to be Data Stewards or identifying people. I talk all the time about recognizing people for their relationships that they have to data and helping them to recognize that they themselves are Data Stewards and they have some level of accountability for the way that they work with data. And we're also going to talk about how to go about recognizing the Data Stewards, so basically formalizing accountability based on people's relationships to data. We'll talk about how this approach, the Everybody is a Data Steward or the non-invasive data governance approach has coverage of the entire organization. So rather than just having a handful of people that are Data Stewards and having them as being the ones that have the responsibility for the data, we'll talk about how the Everybody is a Data Steward approach basically has coverage for the entire organization. Last but not least, we'll talk about using this technique in the discussion around why this technique is unique and why it's better for the organization, using that concept to sell Data Stewardship throughout the organization. So oftentimes in this webinar series, I start out the webinar by talking about the definitions of data governance and Data Stewardship and those types of things. And for today's webinar, that's particularly relevant. So we'll talk about first the definition of Data Governance and then about Data Stewardship and why that can really be used to help to recognize that potentially everybody in the organization is a Data Steward. Governance is one that is worded particularly strong and it needs to be because we really need to know or we need to let people in the organization know that Data Governance is really all about executing and enforcing authority over the management of data. But the way that we're gonna go about doing it is we're not gonna assign people things that they don't pretty much already have at least in most situations. We're gonna talk about formalizing accountability and that's my definition for what Data Stewardship is. Basically a Data Steward is a person in the organization that has a relationship to the data whether they're a definer or a producer or a user of the data and they're being held formally accountable for that relationship to the data. So potentially if you go by that idea that anybody who defines data or produces data or uses data has accountability for how they define produce and use data then if we can get to the point where we can hold those people formally accountable for those relationships, they become Data Stewards. They recognize themselves as having a responsibility around getting the data to become better, better used, better valued, better quality throughout the organization. So basically when I talk about formalizing accountability it might imply that there's already some level of accountability that takes place in the organization and we'll talk about that a little bit. I'm gonna share with you some of the reasons why people think that the everybody is a Data Steward approach doesn't work and then we'll talk about why some of those statements that people have made just are kind of nonsensical where they're kind of silly. But we want to make sure that people that use the data and that's the easiest example, people that use the data need to know the rules associated with how they should use the data and it's not just gonna be that handful of people over there, it's gonna be everybody that uses sensitive data as the best example. Say everybody that uses sensitive data needs to have some level of accountability for how they use that data and that means that it's our responsibility as the data governance practitioners to let them know what that responsibility is and help them to truly understand how they truly are Data Stewards. So before I kind of get started into some of the content I wanted to give you the best reasons, the best excuses for why people have told me that this approach won't work and you might be thinking some of these things yourself and I'd love to hear it from you if you're thinking about those things. But the one that's most popular is that people tell me that if everybody is a Data Steward then nobody is a Data Steward. And I'm sorry, but I think that's silly. If somebody uses data then they need to know how to use the data. So it's not just a group of people, it's everybody that uses data. If people are defining data they have the responsibility for making certain that the data is defined per business use and that good definition is given to that data. They have some level of accountability for making certain that the data is defined well. Same thing with producers. So I'll talk about the finest producers and users in a little bit. Other people have said, how could it be non-invasive to the organization if everybody is a Data Steward? And I'm gonna talk about that as well. The Data Stewards, I've heard people say we cannot educate everybody in the organization. The organization's too large or the organization is too complex. And I say, again, that's kind of silly. They say, why not? Why can't we educate people in the data and the information that they use to do their job? And what level of responsibility do they have for protecting that data, for making sure it's high quality? You can educate everybody. It takes time. It takes a program that has a valid and a thorough communication plan to make sure that everybody in the organization can be educated on why they're a Data Steward. You know, the other reasons it'll be too big, it'll make governance too expensive. We don't have time. Well, I have a good answer to that. You know, we're not necessarily asking for people's time when they become Data Stewards, depending on what activities our organization has them involved in. So people say we don't have the people, we don't have the time, you know, or our organization's data is governed just fine the way it is. The truth is, in a lot of situations it's not. In a lot of situations, organizations are at risk because people that define, produce and use the data don't necessarily have knowledge of what that accountability means to have that relationship to the data. So that's what we're gonna talk about today about why exactly, you know, what exactly is the Everybody is a Data Steward approach? And the truth is I don't use that name. I don't really call it that. I have referred to it as non-invasive data governance. I'll share with you how non-invasive data governance is different from other approaches in a couple of minutes. But the truth is that, you know, the way I look at it is that if you don't necessarily give people more work to do and you don't interfere with their day jobs, with their existing jobs, but you help them to understand that they have some accountability about how they do do their jobs, then that's a lot less invasive to the organization. And if you can get people to understand that their relationship to the data matters, it's really helpful to get them to agree that, yeah, you know what, I really am a steward of the data and maybe I have some accountabilities that I haven't really thought of before. And again, oftentimes it's the person that has the responsibility for the data governance program to define, you know, what those responsibilities are and to help them to understand that they truly are data stewards. So truly, when some people are in the organization are recognized as data stewards or all the people are recognized, you know, the amount of time that it's gonna require to be a data steward is really gonna depend on the activities that you have them involved in. If you're only asking them to be the eyes and ears of data quality issues in the organization and you educate them on a process for recording data quality issues, you're not really asking them to do much more than what they do already. However, if you're getting them involved in an ERP project, a transformational project for the organization and you're getting them involved because they're the people of the organization that know the data, then yes, it's gonna feel like these people are spending more time as data stewards, but the truth is that even without a data steward program or a data governance program, most likely you'd be engaging those people in those efforts anyway. So it's not really any time over some time that they would already spend since they are the knowledgeable people about different types of data in the organization. You know, the truth is that's kind of to continue on the theme of what exactly is everybody's a data steward, what is that approach? You know, it's really a non-issue, but people really understand already that people have relationships to data. You know, they basically, as part of their jobs, they define or they produce or they use or they define and produce and use data as part of their jobs. And one of the things that we can do as data governance practitioners is record information about who those data stewards are. So, you know, if we can recognize who defines, produces and uses data as part of their job, then we can help them to understand that it's appropriate for them to follow certain guidance that we have around those actions. So it's not difficult to get management of the organization to agree that potentially everybody's a data steward when you focus on the protection of sensitive data as one of those things that you want from your data governance program. So, you know, they're gonna agree that everybody that needs to or uses or sees sensitive data needs to understand and follow the rules associated with how they use that data. So the question is, if it's not difficult to get management to agree to the concept that people need to be held accountable for their relationship to the data, really the question becomes how do we do that? How do we put something like that in place? Maybe we haven't been doing that throughout the life of our data governance program. Maybe we've assigned people to be stewards. Maybe we've identified people and asked them to be data stewards. Well, there's really, there's an easier way. So really what we need to do is to formalize accountability based on the relationships that people have to data. So that means that we need to know what relationships people have to data. We need to record that information somewhere. There's a lot of great data governance tools out there that will help us to do that, help us to engage the people that define, produce and use data. But people do define, produce and use data as part of their job and it's all sorts of different data that they're defining, producing and using. It could be metadata, it could be master data, it could be big data, it could be smart data, it could be small data to be used to define the data within your organization. There are people that are defining this data, producing it and using it as part of their everyday job. And it also includes all the actions that people take and I've challenged people on this webinar and in presentations that I've given that really the three basic actions people can take with data is as definers, producers and users. So if you can think of something else that doesn't fall under one of those realms, then please share that with us. But basically people define, produce and use data. So simply stated, pretty much every activity that people do fall under one of those headings. Nobody's gonna deny that as a fact. Management most likely will tell you that at least again in the situation of protecting sensitive data that everybody that comes in contact with that data needs to protect that sensitive data. And in a lot of cases it needs to be recordable, it needs to be auditable. You need to demonstrate that all the people that are accessing this data need to have accountability for protecting that data. So I'm gonna share with you a couple diagrams that I've used in previous webinars. And the first diagram that I'm gonna share with you is something that I call the data governance operating model of roles and responsibilities. And I'm gonna show you a little bit bigger model, bigger picture of this model in a second. But basically I've described this in other webinars and in other presentations through data diversity and such, but there's a lot of different types of roles that are associated with the data governance program. There's roles at the operational level that are really business unit specific. There's tactical level roles that kind of crossover business units that are strategic and there's executive roles. They don't necessarily participate in a very time consuming way, but they participate in the activities in the operation of data governance nonetheless. On the left side of the diagram, there's people that support the data governance program, the data governance team, the data governance office. So we know we need to have that specific role and then there's people in IT and project management and regulatory and compliance that are basically partners of the data governance program, partners of the data governance team. So this operating model is what I often share as being the, but you may not call them what I call them, but you need to have these types of roles represented within your organization. So the little block in red basically is down at the operational level and those people are the people that define produce and use data as part of their job. So let's call them the data stewards. And so in some organizations, they've gone as far as differentiating between the different types of stewards in the organization. Some people have responsibility for defining others' producing, others' using. Sometimes it can be two of the three, sometimes it can be all of the above, but the fact is that the bottom two levels of that pyramid are the people that I consider to be the data stewards of the organization. And so there's not necessarily just one type of data steward that we have in our organization. Now we know that we have these people at the operational level that define produce and use data and they have accountability, or at least we want them to have accountability for how they define produce and use data, but that's typically within a business unit and very business unit specific. That's why I have another level of steward that I call the data domain steward. And I'll share with you other names that people have called the data domain stewards, but those are the people that have accountability for domains of data across the organization. So we'll talk a little bit about that in a minute. So under the operational stewards, and basically I have two slides that look similar to this one, we have definers, producers, and users of data. So I kind of hammered that in a little bit, but we'll talk a little bit more about that in a second. But at the tactical level, those people that have responsibility for data that crosses business units, I tend to refer to them as data domain stewards. Enterprise data stewards is what my clients presently call them. I had a client that I defined the data domain stewards to and they said, you know what, they're really the subject matter experts, so why don't we just call them subject matter experts? So these are people, again, that have responsibility for looking at the data across business units rather than just specifically within a single business unit. And if we know if we're looking to try to get to a single point of truth or we're creating a data warehouse and that's going to be the place that people are going to go for the data or a master data management solution, we need to have people that represent the interests of a specific subject area of data across the organization. And those are the tactical data stewards. So when I say everybody is a data steward, I'm not meaning to say that everybody is a tactical data steward. I'm really meaning to say that everybody is an operational data steward, again, based on the relationship that they have to the data that they define, produce and use. So who are the data definers? So these are people, obviously, who define data as part of their job. So the question is, do we know who these people are? Do we have a record of their names? Do we record them, their names? Do we know that for XYZ project, for XYZ transformation, the people that have the responsibility for defining the data? Do we know who they are? Have we really identified what data that these specific people define and is their specific process a governed process for how people go about defining the data? You know, we want to know how are these people defining the data? What's the process that they use? Do they have any formal accountability for putting solid definition to the data? Are these the people that we're going to hold formally accountable for defining the data in these major initiatives? And then I ask the question, why does it matter? Well, you'll notice that when it comes to the definition of the data and improving the understanding of the data and improving how the rules associated with the data are defined and transmitted and communicated to people across the organization, it's not going to get done unless somebody has the specific responsibility for doing that. So that's one of the reasons why it matters. We want to know who the data definers are, what data they define. The data governance office can assist those people with a metadata plan and perhaps a tool to make certain that we're consistent in the way that we're recording definitions. And I've talked in the past about something I call cheeseburger definitions where the definition of a cheeseburger is it's a burger with cheese. Well, how many of your organizations have definitions like that? And how many of your organizations through the people that have responsibility for defining the data, it's kind of a last second thought to have them actually apply a solid business that is definition for the data. So we need to know who has the responsibility for defining the data and we need to engage them in how they go about defining the data. The data producers, well basically those are people who produce data as part of their job. And the question is, do we know who the people are in the organization that have the responsibility for producing the data? Do we have a record of their name? Do we know who to go to when there's data issues for new data that's entering the organization? Do we know what data these people produce? And do we have a specific standard process or guideline for teaching them how to produce the best quality, the highest quality data that we can have? You know, you might wanna ask yourself, how are these people producing the data? Is there a best practice around producing the data? Well, certainly that's another activity that a data governance office or a data governance team can get involved in is helping to educate the people that produce data on why it's important for them to understand the impact of the data that they produce, you know, to help them to understand that because they produce data, they have a specific responsibility and they're gonna be held formally accountable moving forward for how they go about producing data. And again, the question is, why does it matter? Well, obviously it matters because these people are the ones that are creating the data that's gonna be beneficial to the entire organization. And so the last of the three actions that I talk about are the data users. And I've said this before, that I think this one is the one that's really truly a no-brainer. You know, we need to know who's using what data across the organization so that when there are rules that need to be applied to how that data can be used, these people are educated and trained and become aware of how the data can be used, how the data can't be used. So the questions I have for you are, do you know who these people are? Do you have a record of their names? I'm gonna share with you a tool in a minute that I've shared a lot. I call it the Common Data Magics that helps you to record who's doing what with the data across the organization. So you wanna know what data these people use and is there a process for them to gain access to that data? Do they have to become certified or at least knowledgeable in how the data could be used to become a user of this data? How are these people using the data? Is there best practice for that? Well, truthfully, I think if you go to your management and you say that you're gonna educate everybody who uses sensitive data on how that data can and can't be used, they're gonna say that's in line with what they're thinking is. So basically, if you look at everybody in the organization and you look at whether they define, produce, or use data, that could be virtually everybody in the organization. So the fact is, everybody's a data storage. Get over it. And here I'm gonna talk to you about things to consider when you're thinking about taking that approach. So what are some of the things that we can do to formalize accountability based on data relationships? I'll share with you 10 things to consider. So the first one is, do we know, have we identified who in the organization is accountable for the data per each source of data? And maybe per each subject matter data. Do we know who in the organization is accountable for that subject matter? Who knows that subject matter? Can be the person that can be a decision maker, potentially, in the organization. So basically, you wanna know per source of data, per subject matter of data, per system of data, who are the people in the organization that define, produce, and use that data. So again, the common data matrix will be a tool that you can use to record that information. However, some of the other tools on the market may do a better job and be easier to shape, to fit your needs of your organization. But we need to know those things. We need to know who is accountable for the sources, the subject areas, and the systems of data. And the truth is, as I mentioned this before, data governance, the data governance program should be held accountable for helping to hold these people accountable. So oftentimes, it's really just a matter of recognizing who in the organization does what with the data. And the data governance program, the data governance office, can have the responsibility of managing that information. So we're looking for the people in the organization that are probably, at this point, informally accountable for the definition, production, and usage of data. And we want to record that and help to train them and educate them on what responsibility they have for the data. So there's questions that organizations ask themselves. If you're looking to improve the enterprise view, the enterprise perspective of the data, you may need it to articulate to your management what value that is going to bring. And the question is that oftentimes, when they ask for reports, they get different results on the reports based on people's understanding of the data, which really confuses the heck out of them, and they really want to know, well, why am I getting different answers, depending on who I ask? Well, because we don't have an enterprise view. And in order to have an enterprise view, that means that we need to have somebody who is ultimately accountable and responsible for the enterprise view of that data. So I've heard organizations tell me that nobody's accountable, and that's not true. I mean, typically, they'll agree that people are accountable for how they use the data. If you state that nobody is accountable for the data in your organization, ask yourself if that's really what your management thinks. If management thinks that nobody is accountable, then they're going to tell you we need to find a way to hold people formally accountable. So how are decisions made? Are decisions being made? If there are decisions that are being made, there are people that are responsible for making those decisions. And the question really becomes, are these people of the organization the data stewards? So who's a data steward? Basically, anybody in the organization that defines, produces, or uses data as part of their job if they are held formally accountable? And a data steward is a person that defines, produces, and uses data as their job, basically the same type of description, maybe just a little bit different, but they all emphasize the same thing. Let's identify who defines, produces, and uses data, and let's help them to do a better job of defining, producing, and using data. So when we ask the question about what are we holding these people formally accountable for, here's a list of some things. I'm not going to get through each of them individually, but when it comes to definition, there's certain things that people have responsibility for, production and usage. We want to document what those accountabilities are. And once we've recognized people as being data stewards, help them to understand that there are guidelines for things that they do in their daily job. So the fact is, again, everybody potentially is a data steward and we need to find a way to get over the idea that that's going to be too big or too expensive or too complex for the organization. So there's operational data stewards that work in business units or technical units. They have hands-on knowledge of the data. They define, produce, and use the data. The data domain stewards, as I mentioned before, these people also work in business units or technical units, but they're recognized as being knowledgeable individuals or potentially decision makers. So if your organization strives for continuous improvement, we need to identify who these people are that have accountability at a subject matter level, at a domain level, at an enterprise level, so that they can help to point us in the direction to have definitely an enterprise view of the data that we define, produce, and use. So I mentioned the common data matrix before, and we usually share that as a result of this webinar series because I use this diagram a lot, but we need to know who in the organization defines, produces, and uses data, so we need to have a tool, at least one, that we can develop ourselves that will record by domain of data, by sub-domain of data, where that data exists in the organization, who in IT has responsibility for that data, who in the business units have accountability for that data? So I talk a lot about the common data matrix, being a tool that helps you to document and record the metadata, the who aspect of the data, who does what with data across the organization. So if you're interested in talking more about the common data matrix, please let me know that. There is an association between the common data matrix and the operating model that I just shared a little bit earlier, and the one that I've defined in the different roles and responsibilities. So I'm kind of color coordinated, should I say, between these two diagrams, where you see the operational stewards in the one diagram, you can also see them in the common data matrix. So one of the practices that we should get involved in as data governance practitioners is recording who in the organization defines, produces, and uses data, and oftentimes it can be in a form as simple as the common data matrix that I'm showing here. So now let's talk about covering the entire organization, because that's really important. And the approach, the data governance approach that you've selected to use in your organization is often associated with how you go about relating people to being or associating people to be data stewards and what data steward roles there are. So I'm gonna spend a quick moment here talking about three different approaches to governance. There's a command and control approach where people are assigned to be data stewards. There's kind of a traditional approach, kind of a if we build it, they will come approach where we ask people to be data stewards because we're identifying them as data stewards. And then there's the non-invasive approach that I talk about, which really focuses on recognizing people as data stewards. So if you're interested in learning more about the comparison and contrast of those three approaches, there was a webinar that I did back in June of last year through Dataversity, a real world data governance webinar that talks about how I compare those approaches and there's also articles on tdan.com if you're interested in that subject. So the first one is command and control and that's the one where they come forward and they say you will do this and you assign people to be data stewards. Well, the truth is that it's not really practical for you to assign everybody in the organization to be a data steward. So assigning, the first thing that happens when you assign somebody something is that it immediately feels as though it's over and above things that they're presently doing and that's one of the things that we're trying to avoid by taking a non-invasive approach. So we define oftentimes in the command and control approach, we define people as being the owners of the data and therefore they're the data stewards. But the question we need to ask is, are these really the only people in the organization that have formal accountability for data? The truth is that you need to identify everybody and I mean everybody who defines, produces and uses data as part of their job. In this traditional approach, as I said before, it's kind of an if you build it, they will come approach where you identify people who you think are data stewards but we typically end up asking them if they accept the role of the data steward and that's not necessarily gonna cover the entire organization either. That's gonna cover the few people that you're identifying as being the stewards of the data and oftentimes those are also the people that are the owners of the data. So again, ask yourself the question, are these the only people that you need to hold formally accountable for data across the organization? So typically the answer to that question is gonna be no, we want everybody in the organization to you've probably heard this in terms of other subjects where they say everybody is a steward of the earth, needs to take care of the earth. Well, think about that in terms of data. Everybody in the organization that has a relationship to the data needs to be held formally accountable for that relationship. So the last of the three approaches is the one that I talk about the most, the non-invasive approach where we recognize people as data stewards based on their relationships to the data. We market the program as this is work that is either already being done, people are already doing this or they should be doing this. And it's not something that's brand new and it's really, you're defining it as we're gonna find a way to make certain that everybody in the organization is held accountable or at least understands the impact that they have on data across the organization. And to ask that question of does this cover the entire organization? Well, I would venture to say that it does because if you define or produce or use data and we're addressing your concerns that potentially everybody in the organization is a data steward. So I hope I'm helping you to get over it so far. Let's talk about leveraging the technique to sell stewardship across the organization. So one of the things that we really need to do is to start is to get our leadership to concur that everybody who defines data as part of their job needs to follow the rules. They need to help us to record the metadata. We don't leave that as an afterthought. We get the people that we've identified and we've recorded as being definers of the data. We've helped them to help us and help the organization to put better definition to the data. So basically everybody who defines the data should be accountable. And I think it's not that difficult of a sell to the organization to say, now let's spend a little bit more time making certain that the data is well defined as we begin these projects. And it's not gonna necessarily cost the organization a lot more money, but it will cost some time to make certain that good definitions are being given to at least the most important data to the organization. So these people are the data definers. Another way to leverage the technique of sell stewardship is that, I think management will agree with us or we need to get leadership to concur that everybody who produces data has some accountability for the quality of the data they're producing. For understanding the impact of creating another customer rather than using a customer that already exists as an example. So the people that produce data have to have some level of knowledge as to what's the importance of the data they're producing and why it's important for them to produce high quality data. So basically we need to get leadership to agree that people that produce the data need to be held accountable for how they produce the data. So the quality, the understanding for making certain that we're collecting all of the appropriate data that we need for our organization. And then last but not least again is the data users. Everybody that uses data really needs to be held reformally accountable for how they use the data. And in some organizations that we need to start there and we need to say well we know we've got users and we know we need to share the rules with them so really isn't this a given? So we in some way shape or form need to record who does what with the data, who uses the data so we can whip a little knowledge on them about how the data should be used or how it can't be used and what the rules are associated with the use of the data. Usage is not optional. The government's not gonna come to us or the industry's not gonna come to us and say protect this data if you want. Now the data has to be protected so therefore everybody that has access to that data needs to understand the rules associated with accessing that data and sharing that data. So in several webinars in the past I've talked about something that I call the Data Governance Bill of Rights and you see the word rights is within quotes so basically the idea of data governance is to get the right people involved at the right time in the right way, use the right data and so on and so forth. Well the fact is it all starts with getting the right people involved at the right time so we need to know who in the organization defines, produces and uses the data and get them involved, if they're domain stewards or operational stewards get them involved in the right times in the projects so that they recognize that they're adding value to the organization and that they are potentially data stewards. Now we don't need to change their title, we don't need to call them data stewards but we as data governance practitioners again need to know who the people are that are defining, producing and using data so that we can engage them and get them involved in the appropriate way. Talked about something that I call messages for management about data governance because the term data governance can be scary to a lot of organizations. You know people think that it's gonna be a large, complex effort and a lot of the reasoning for that is that we sell it as such. We sell it as being large, complex, very expensive but the truth is that if you take a look at your organization you already have people defining, producing and using data and to some extent there's probably already some level of governance around that data. That governance may be very informal, it may be very inefficient, it may be very ineffective but the fact is there's already people that have accountability for different aspects of the data and so the truth is that we can formalize how we govern data by putting structure around what we learn from people that they're already doing. So using the common data matrix to record who does what with the data helps us to understand why it's important that these people recognize themselves as stewards and that they get in line with the program of making certain that data is managed as a valued asset of the organization. So another message is we can formalize what we're doing, we can certainly improve what we're doing and there could be a laundry list of things that we can improve but certainly we can manage risk, we can manage quality, we can manage coordination of cooperation and communication around data. The fact is that oftentimes data governance programs if you follow this approach does not have to cost a lot of money. In fact, most of the time data governance programs really cost primarily at least before it comes to the point where we have tools or we bring in tools, data governance only costs the time that we put into it and that's why I suggest considering the non-invasive approach to data governance as that structure for helping you to define, helping you to record who defines, produces, uses data and get them involved in those efforts. So the last thing I really wanna talk about is how we communicate with all these people. So certainly we need to create a communication plan because the communication is not gonna take place itself and if somebody has to have responsibility for it and if you recall the operating model that I shared basically has a whole bunch of different audiences and the truth is we can't communicate with all of the audiences in exactly the same way so there's different messages for the different audiences. We need to communicate in such a way that the timing is appropriate for the different audiences. We can use different tools to deliver the communications. I'm working with a client now that's setting up their SharePoint site and doing a newsletter associated with data governance. Again, make the topic of data governance interesting and fun if you can believe it to the organization that these people recognize themselves as having some level of formal accountability for what they do with the data and if it comes to creating a formal communication plan here's an example of a model again, kind of color coordinated with the operating model that I shared before and the spreadsheet, the common data matrix that I shared before but if we know that we need to communicate these things to the organization and we can break them down into orientation communications onboarding and ongoing communications that there might be a different way that we communicate each of these things to the different audiences associated with our governance program. So we might communicate with our data domain stewards differently than we communicate with our data stewards and the operational ones that I've been talking about we may communicate differently with them than we communicate with our council or our ELTR executive leadership team. So it makes sense to use the approach of everybody being a data steward but making certain that we follow the communications that's necessary to keep everybody engaged as a data steward. See, I wanna wrap up the webinar and then turn it back over to Shannon to see if there's questions. I see a whole lot of chat at least going on and the chat box is there, please keep that coming but what I wanted to do is I wanted to tell you my specific rules for becoming a data steward and they're pretty direct and you may agree with all of them, you may agree with none of them. The truth is that basically a data steward can be absolutely anybody in the organization and I hope I've made that clear through the webinar today. So being a data steward is not necessarily a title, it describes a relationship to the data and it's not necessarily a position. So you can have multiple people in different positions in different roles in the organization who are also stewards of the data again based on their relationship to the data. Now a data steward is not typically hired to be a data steward. A steward doesn't have to have the title, as I mentioned before, of data steward. There are some organizations that have their organizations of data stewards but that's not necessarily the way that you need to go so you don't necessarily have to have that as being somebody's HR title that they're a data steward. Oftentimes a data steward doesn't need to have to be told how to do their job. You might set up guidelines for them for how they define, produce and use the data and the truth is that if you try to send these folks out to get some type of public certification or industry certification around being a data steward that's really associated with what they do in their job. So how they define and how they produce and how they use data, that's not necessarily gonna be taught in any class. And one thing to definitely keep in mind is that there's gonna be more than one data steward for each type of data. If your PII data, your personally identifiable information is used by a whole lot of people then they all need to know the rules. So there's more than one data steward, more than one data steward type actually for each of the different types of data. And oftentimes when I talk about data steward training and getting your stewards to understand or recognize that they're data stewards the data steward training needs to be focused on formalizing that accountability that we've talked about throughout the session today. So basically there's five things that I talked about during this webinar. The first one is I described what I meant by everybody is a data steward and how that approach is different but it covers the entire organization. How to recognize people based on their relationships to the data and help them to formally understand that there is some accountability for producing data. There is accountability for using and defining data but the data governance program, the data governance office can have a major role in defining that and helping people to understand these things. The non-invasive approach, the everybody is the data steward approach really is it covers the entire organization. And so the nice thing is when you can get that to be auditable and you can demonstrate that you know who these people are and that you're educating them in a way they need to be educated you truly have coverage of your entire organization. And you can use the way that you recognize people as data stewards and leverage that technique to sell data stewardship across the organization. So with that, and I feel like I've provided a lot of information in a short period of time I'd like to turn it back over to Shannon to see if there's any questions today. Bob thank you for another great presentation and of course we've got a lot of questions coming in so just to answer the most commonly asked questions I will be sending a follow-up email by end of day Monday with links to the slides and links to the recording of this presentation and we've already had questions and inquiries about receiving the matrices and I will send links out to those as well in the follow-up email going out to everybody. So Bob you know can a person be in multiple relationships for example data producer and user what's the best practice there? Without a doubt. I mean you'll find that there's gonna be multiple that people are gonna have multiple relationships to the data. Oftentimes the people that are defining the data for use within their part of the organization are also important power users of that data. Certainly when people are defining the algorithms or defining the ways that data is being manipulated as data moves through the organization the people can be producers of data too. So without a doubt to me it would be best practice. Oftentimes organizations don't necessarily break them down into the finest producers or users. They define them as operational data stewards and that takes into consideration that they most likely have more than one relationship to the data. And you know what do you recommend when you recommend to start if the data governance program is just getting started? That's a great question because if you're gonna start now this is more of the kind of the grassroots effort. This is kind of the bottom off effort where even before you soldier management on the need for data governance when you're getting started you might wanna be able to go to them and say look we've identified what data is classified as being highly confidential. Just again to use that as an example. And we've identified all the people in the organization are all the different parts of the organization that use and consume that data. Well we're gonna go out and we're going to address their knowledge of the rules associated with how they can use the data. So if you're getting started with the data governance program this is truly kind of that grassroots effort that if you record who does what with the data then you can take that information to your leadership and get them to concur as I mentioned earlier that these people are truly the stewards and that the program really needs to address all of them and again that's just a good step to take when you're getting started. Yeah it's a great question. So what tools resources should be used to help data stores create data definitions that are consistent among all functional groups? In my experience there's some really concise and some struggle on data definition. Yeah there's no doubt about that. So what I suggest to you is that you attend the enterprise data governance online conference next week when I'm talking about business glossaries and data dictionaries and the need for governance around those activities. There's a lot of tools that are out there that will help you to record the information but a lot of organizations that I hate to say this they even start out with spreadsheets and Word documents and SharePoint and tools that they have available to them. It's not a matter of necessarily out of the gate where you record the definitions I mean that definitely will have some how people can use them but there's ways there's tools that you probably already have within your environment. Just be consistent in the way that you collect those tools that collect that metadata, that definition of the data, that production, where it came from, those rules about how it can be used and if you're consistent in the way that you record that information then hopefully at some point where you need a tool to move forward you at least have the metadata prepared so that you can reuse it or repurpose it within the tool that you choose. And it would have been nice to offer computer dial-in or sorry I'm not reading that so I'm not necessarily related to the conference. So my role in my organization is data steward. However I do not define, produce or use data. I'm more of a data manager or guardian. I verify that those who define, produce and use data do so according to established business rules am I a data steward? Yeah well you know again like I said there's different types of data stewards and there's not really one catch-all for everybody as the same type of data steward. So it sounds to me from the description that you just gave in the question that that person has kind of a higher level of responsibility around that data. So I didn't say that everybody was a tactical level data steward. I just said that everybody who defines, produces and uses data is an operational steward. So it sounds to me as though the answer is yes. You have formal accountability for that data but it may be at a higher level. So you are a data steward, you're not necessarily an operational data steward unless you define, produce or use data as part of your job. How does the matrix work in an organization with thousands of people? Are there any tips for making it work? Yeah that's another great question. And the fact is that it doesn't work very well for an extremely large organization. It may help to some degree to have multiple common data matrices to address different parts of the organization. However, to keep the left hand side of the matrix very similar in the definition of the data domains. Instead of recording everybody's name in the common data matrix, you may just want to put an X in the block where this part of the organization uses this specific data from this specific application and where you may want to use something like the acronym CRED to create, read, update, delete to define what actions that part of the organization takes. Yes it's very difficult to store everybody's metadata about everybody who defines, produces and uses data in a common data matrix. I would love to address that question offline if somebody wants to ask me that question offline but the common data matrix is pretty much a simplified way of knowing who does what in the organization. For larger organizations, you may need several of those. Yeah that's a great question. How do you factor in disposition to the model and managing the information data footprint? How would you approach integrating records management for data that might be considered a record? There is a similar concept in records management for stewardship. You know what? I mean that's another great question. These are all great questions. First of all records are usually groupings of data. So if you have, instead of going domain by domain and system by system and element by element, you could go record by record. You could go for unstructured data. You could even define unstructured data and who has accountability and who uses it in different parts of the organization. So again there's not necessarily an easy answer to that but the common data matrix can be very flexible. I mean you can define it to, or you can produce it to be used in your organization and have it focused more on records of data than on individual pieces of data. It really is up to you as to how you wanna structure that to be used within your organization. So is disposition of data part of the definition of production of data? So disposition, I'm not really sure what they're meaning by disposition of the data or is this basically how the data gets, it's either how the data gets disposed or how the data gets circulated throughout the organization and who gets access to it. I'm not really sure what they're meaning by disposition but again I'm stating that if you know who is defining producing and using that data then define for your organization how you mean to use the word disposition and then make sure that you formalize accountability based on that disposition. So can a lot of the stewardship and responsibility to describe the automated, where is the borderline? Yeah, I don't think so. I don't think it's very easy to automate who your data stewards are. I mean I think that I don't know what you would be asking of them unless you had some rules that said for any new databases that we're creating you have to go through this process and it requires them to enter validated or vetted definitions of the data. I don't see data governance as being something that's automated. Maybe the usage of the data stewards, the engaging of the data stewards can be operationalized that way but I don't see automation really playing. I'd love to hear if other organizations have automated their data stewardship process but I haven't really seen it. All right, I think we've got time for one or two more questions here. So are we saying that everyone interacts with data in a number of different ways and since there's a lot of different types of stewards, everyone is a data steward? Is that what the summary is? Well, basically yes. I mean if you know that somebody has one of these relationships to the data then first of all, if you didn't have that information before, record it somewhere so you have it somewhere. But it basically helps you to put your arms around people in the organization that most likely have some informal level of accountability. Boy, I'm glad there's only a few questions left because I'm losing my voice rapidly but I see that starting with this as kind of a context for identifying and recognizing who your data stewards are will be beneficial to most organizations. So what if the rules conflict? What if the rules conflict? Rules, are you... Are you LES? Okay, so the rules conflict. I'm not, again, I'm not sure what the person is asking. If the rules conflict with how they're using the data, well typically the rules are the rules. The government's going to tell you to protect sensitive data. The government's going to tell you to protect health information and customer account information. So LES don't usually conflict with anything. They're the rules. They're what need to be followed and we as the data governance practitioners need to make certain that everybody in the organization understands those rules. Okay, well that does bring us to the top of the hour. Bob, thank you for another presentation. Thanks to all our attendees for being so engaged in everything we do and all the great questions but that is all we have time for. Just a reminder, I will send a follow-up email by end of day Monday with links to the slides, the recording and the matrices. And we'll get that out to everybody again by end of day Monday. I hope everyone has a great day. Thanks so much. Thank you everybody.