 All right. Hello and welcome. My name is Shannon Kemp and I'm the Chief Digital Manager of Data Diversity. We would like to thank you for joining the current installment of the Monthly Data Diversity Webinar Series, Real World Data Governance with Bob Sinner. Today, Bob will be discussing data governance versus information governance sponsored today by Ona. 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. And for questions, we will be collecting them by the Q&A. Or if you'd like to tweet, we encourage you to share our highlights or questions by Twitter using hashtag RWDG. And as always, we will send a follow-up email within two business days containing links to the slides and the recording of the session and any additional information requested throughout the webinar. Now, let me turn it over to Kevin for a brief forward from our sponsor, Ona Kevin. Hello and welcome. Hello, Shannon. Thank you so much. I'll go ahead and get my screen share started here. And if you can just confirm that that's showing. That looks great. Fantastic. Well, first and foremost, thank you and thank you to Data Diversity for allowing Ona to sponsor today's session. And thank you to all the participants for joining. My name is Kevin Cox, and I am the Lead Solutions Architect at Ona. I'm super excited to be here today to talk a little bit about what we do. Ona is a knowledge integration platform allowing anyone to unify, protect, search, automate, and build on top of their organization's proprietary knowledge and tech stack. Today, business is powered by workplace applications with collaboration and communication tools leading the way. The adoption of applications like Google Workplace, Slack, Salesforce, Dropbox have all grown tremendously in the past few years. And of course, we know that that adoption has been accelerated over the last 15 months by the global pandemic. And this is creating new challenges for enterprises, not least in the terms of privacy and information governance. With the rise of cloud-based and hosted workplace applications, knowledge is taking on new forms and formats, and it's multiplying in scattered data silos. In the past four years, the average organization has deployed 175 applications, which is an increase of over 68% from the same time period prior. And so this makes enterprise knowledge fragmented and difficult to access in most organizations. And it's costing businesses huge amounts of time and money just for searching for information. And so Ona's knowledge integration platform can connect to over 30 of the world's most popular workplace applications, collect and process all of that data in real time, enrich it with machine learning and natural language processing, and then we create a rich index to enable critical workflows. We currently have products that are helping enterprise organizations with information governance, risk and compliance, and knowledge management. We also have an open API that will allow other companies and partners to build their workflows on top of our knowledge integration platform as well. So we're really seeking to provide a holistic solution for your enterprise's needs. And just to give you an idea of what Ona is capable of doing, I'm using the example of a Slack export. And if anyone on this call is familiar with the Slack export, you're probably used to what we're seeing on the left. And what we see here is a standard JSON export, which really has an increasingly common method for SaaS or cloud applications to share their information. Now, this format is great for machines and even technical audiences like myself, but for most people, it's challenging to understand what's happening over there, much less to identify what's important from all that noise. And on the right, you see that same data presented in Ona. As I said, we connect to these best of breed applications and when use our technology to make sure that your teams have the ability to search and identify critical information no matter how technically proficient they are. I don't want to keep you any longer from Bob's amazing presentation. So I'll go ahead and wrap things up, but I'll be around to answer questions later on or please visit us at www.ona.com to learn more about our solution, how our customers are enhancing their critical workflows, and we'll also be happy to set you up with a tailored demo for your team. And with that, I will pass it back over to you guys. Thanks, Kevin, so much. And thanks to Ona for sponsoring today's webinar and helping make these webinars happen. If you have questions for Kevin or about Ona, Kevin will be, as he mentioned, joining us for the Q&A portion of the webinar at the end. And now I'm going to introduce to our speaker for the series, Bob Sinner. Bob is the president and principal of KIK Consulting and Educational Services and the publisher of the data administration newsletter, tdan.com. Bob specializes in non-invasive data governance, data stewardship, and metadata management solutions. And with that, I will give the floor to Bob to start his presentation. Hello and welcome. I guess I need to take myself off of mute. So hi, Shannon. Hi, everybody. Thank you very much, Kevin, for a great sponsorship and for the information that you shared. There just seems to be so much information that is hidden in our organization in apps and in different places. And you know what? We need to get the most value that we can out of that information. And a lot of organizations are referring to it now as data governance and then organizations are referring to it as information governance. I think Kevin alluded to information governance in his presentation, but let's talk about what the difference is between data governance and information governance, or even if there is really a difference between data governance and information governance. So I've got a lot to share with you today. And I am looking forward to really kind of differentiating between what data governance is and what information governance is. And I'll give you some examples of what some organizations have selected and why they've selected and how they've gone about selecting what they are calling this discipline. But before I get started, just want to run through a couple of things real quickly with you. Actually, there's a lot of things, but I'm going to go through it very quickly. As you know, this webinar series, third Thursday of every month, next month, I'm going to be talking about data governance and data science to improve data quality. I talk a lot about non-invasive data governance, and I authored the book, Non-Invasive Data Governance, which is available at your favorite bookseller. I speak a lot at dataversity events. I spoke at the data architecture online event yesterday, and I hope to be speaking at the DGIQ, Data Governance and Information Quality Conference in December in San Diego. I have learning plans available through dataversity on non-invasive data governance, non-invasive metadata governance, and then the most recent one is focused on business glossaries, data dictionaries, and data catalogs. Shannon mentioned the data administration newsletter. You might be familiar with that free online publication. Please go check it out. Lots of information that's being published several times a month. Please go take a look at that. KIK, Consulting and Educational Services. KIK stands for Knowledge as King. The focus of my business is on knowledge transfer. Just recently, I started doing adjunct faculty position. I accepted an adjunct faculty position at Carnegie Mellon University here in my hometown of Pittsburgh in their Chief Data Officer or CDATAO program. What are we going to talk about today? Let's talk about what are some of the similarities and the differences between what people refer to as data and what they refer to as information? I think that has a lot to do with what you're going to call your program. We'll get to the point where we'll talk about labels for programs. I'll get to that a little bit later in the webinar, but we're going to start out by talking about the similarities and the differences between data and information. I'm going to provide some definitions of data governance and information governance. I'm going to share with you some examples about how some organizations have selected what label they're putting on governance. We're hearing big data governance and master data governance. There's a lot of different types of governance, new ones being introduced every week almost, but I want to share with you how some organizations have picked what they're calling their governance program. I'll do a couple brief case studies and then provide you at the end of the webinar with some considerations for how you might want to go about naming your program or taking a look at your existing program and see are we naming it appropriately for what we're trying to achieve with the governance of the data and information assets. Just real quickly, I want to start at the beginning with my definitions of data governance and a couple other terms. I define data governance in some very strong terms. I call it the execution and enforcement of authority over the management of data. I know it's worded strongly and makes some people cringe, but at the end of the day the reason why we're putting governance as place is to execute and enforce authority to make certain that the right people are doing the right thing in the right way with data across the organization. If we look at that as my definition of data governance, well, what is information governance? I think the same definition applies. It's the execution and enforcement of authority over the management of information and how does information differ from data? Well, we're going to get to that in one quick second here, but we can still use the same definition even if we call it information. I think one thing that we need to do is to explain to our organizations what the difference is between data and information. Stewardship is the formalization of accountability. I talk about everybody in the organization potentially being a steward of the data, metadata being that data about the data, the information that's stored somewhere in the organization that really helps people to understand and get more business and technical value out of the data, and then metadata governance. Again, I'm just going to go back to my original definition and plug in the word metadata. So we're executing and enforcing authority. That's basically what governance is doing. So does it make a difference if we call it data governance versus information governance? Well, first, let's look at the second word. Let's look at the word governance and see how it's being defined. And if you look at the dictionary definition, you can see they use some pretty strong words to exercise authority to control and to direct, to rule, and to control. There's a common theme in the definition. So the first thing that a lot of people think of when they think of governance is that the term governance itself is very scary. And so organizations are thinking, well, do we need to call it something else other than data governance or information governance, something that will be more acceptable to the organization? And yes, there are ways to be able to make the term governance less scary. It's not going to be by adding the word data or that word information or the term metadata in front of it. You need to really focus in on the approach that you take to governance. And as I mentioned before, I talk a lot about non-invasive data governance, and that the focus of non-invasive data governance is to formalize things that already exist, at least to the extent that you can before introducing the governance of data as being something that's really brand new to the organization. So the subtitle of my book is the path of least resistance and greatest success. Certainly we can temper what people think of data governance or information governance by putting the term non-invasive in front of it. It will certainly grab their attention and they'll want to ask you or and you need to have an answer ready for them as what do you mean by taking a non-invasive approach? We're not going to go into that in detail today. I'm certainly available to talk to anybody who wants to learn about taking a non-invasive approach. But the big question is what's the difference between data and information? And, you know, we shared with you what it means to govern, but we really need to differentiate between data and information. So let's start out with the question of what is data? Very simple question. You think we would have covered that many times before? I'm not sure. There's a definitive answer. So, you know, a lot of people say, and if you look up the definition of data, it talks about data being facts. Well, the fact is that data isn't always factual. So if you asked me to record in your spreadsheet how tall I was and I said that I was seven foot four, that's now a piece of data in your system. But is it a fact? No, it's a piece of data. And so data is not always facts, but a lot of people think that data at least that's the fact as I presented it and believe me, I am not seven foot four. So other people think that data has to be in a digital form. And the truth is that everything that we do in our daily habits is data based. I mean, if you are back in the office and I hope you'll be back there sometime soon, you know how long it takes to get into town. You know how long it takes to park your car and to get to where you need to go. That's data. It's not recorded anywhere. It's in your head, but that's still considered data, you know. So we need to put some definition around what data is. And I typically say that there are no real facts without data to be able to support it. And that's really not a political statement. So it's hard to define data without using the term information. Some organizations use the term data and information interchangeably. But as you'll see, the organizations that are implementing information governance are focusing not necessarily always on the same thing that people who are implementing data governance are focusing on. So data is facts, data is statistics, data is, you know, I wanted to share with you what some of the different sources people go to, how they're defining data. It's pretty hard to define. It's hard to define data without using the word information. It's hard to define the word information without using the word data. Yet we all seem to know what it is. We know it's that information asset. And you know, when Kevin was talking earlier about all of the information and all the data and all those different places, you know, we need to govern that information if we're going to maximize the value that we get from that data. So let's start out by, I'm going to give you a piece of data. Here it is, a free piece of data. And that piece of data is the number or the text of 1299. Okay, so now what are you going to do with that data? Well, you don't really know what that data means, right? Is it an address? Is it a quantity or reading a dollar amount? There's really no way to tell. So this is data. This is not information. You know, so you look at it 1299, it could be a lot of different things. I want to share with you an equation that I use that really hits home with a lot of people. And it's just common sense that if you take data and you add context to it, you add the metadata to it, it becomes information. So data with context becomes information. That's the difference between data and information. You have data is just the piece of information. See, again, I'm using the term information to define data. But data is the 1299. Now that I tell you that that's a dollar amount, I guess I need to tell you more, maybe it's $1299 or $12.99. But that, you know, is a quantity or is a dollar amount based on the context that has now been added to that. So the funny thing is now, if you look at information, it's not very consistent either. But from my experience, a lot of the organizations that are implementing information governance are focusing on unstructured data. And that would be data that is not necessarily in a database or in a table. It could be recordings, it could be audio, it could be video, it could be documents. And that's a lot of the information that Kevin shared that is scattered throughout the organization in those different apps. Well, if we're going to govern all that information, you know, maybe we want to refer to it, we want to refer to it as information instead of just data. So in, from my experience, a lot of the organizations that are actually labeling it as information governance are focusing more on unstructured data. And I'm going to give you some examples of that here in a minute. And in fact, many of the organizations that are implementing information governance instead of data governance, it's really an extension of a discipline that's been around for a long time. And that is records management. So information governance, if it's an extension from, from, if information governance is an extension from records management, you know, a lot of the records managers out there are focusing on content, they're using content management systems, as compared to other places, other repositories, to store their, their information or to store their records. So if data governance is the execution and enforcement of authority over the data, and if that data includes both structured and unstructured data, we don't need to do anything to change the definition. In most common, in common industry language, I think that most people would agree that information governance applies mostly to unstructured data. Now, I can tell you that I've seen organizations do information governance that are focusing on structured data and unstructured data. But it's good to ask that question as to what is, what are you defining as that thing that you're saying that you're going to govern? And in the past, in these webinars, I have shared with you a framework for data governance. And so the question is, can you use a data governance framework to govern your information as well? And I'm going to go through just real quickly, at least the, the items across the top, which are the core components of a successful program. And then the different levels or the different perspectives that we need to look at those core components from. So for example, in the webinar that I did last month with Dataversity, I talked about the roles. And I actually went and said, okay, at the executive level, the roles were called the steering committee or something like that. At the strategic level, the data governance council at the tactical level, they may be the data owners or subject matter experts and so on. So that's kind of how you read this governance framework. And so then if you're going to apply it in the noninvasive way, which again takes a look at those things that are already existing in your organization, the way that you complete the framework may be focused on making certain that the messaging that you're trying to get across from your framework focuses on being noninvasive. But the reason why I'm showing you this is to say, okay, in the data governance framework, those components are data and roles and responsibilities are a key concern processes and they need to be governed. Communications needs to be governed, metrics, tools, all of those things need to be governed. So now let's look at that from an information governance perspective. Well, now the data is unstructured data and structured data. So again, where we have the same components, we really don't need to change a thing. The disciplines may be very similar. What we call the roles may be a little bit different, but we still have the same components to our program data roles, processes, communications, metrics and tools. And if there's something that you can think of that goes beyond that, you know, I rack my brain thinking about that all the time, is there something else that we could add as a core component of a governance program. And to be honest with you, I find that these six pretty much covered. I started with five, and then I added the data component and said, well, I hadn't thought of the data component before for the framework for the program. And then down the side of the framework, you know, the levels of the organization and in most organizations, it's set up to be executive level, a strategic level, a tactical operational and the ever important support role for a data governance program. Well, now if we're going to look at that for information governance, you know, what the organization doesn't change, you still got to look at things from the executive perspective, from the strategic tactical operational and the support perspective. So at first glance, at least that framework that I shared with you a few slides ago, it's going to work for information governance just like it would work for data governance. So there's a lot of similarities in the disciplines of governing something. And it just really comes down to how are we defining it? Are we defining it as data, as the bits and bytes in tables, or information being those same bits and bytes in tables, but also the unstructured data and information, you know, that is what is going to really dictate to you what you're going to want to call your program. So when we look at data governance versus information governance, and we do it side by side using each of those six core components that I just talked about, well, the data one, that's the topic of this webinar, what we're talking about, the difference between data and information, the roles are going to stay the same, the communications are going to stay the same, you're going to need to orient people to the concept of governance, you're going to need to onboard them, you're going to need to provide ongoing communication. The tools are somewhat the same, you've got metadata, you've got metadata about unstructured data too. And a lot of the data catalog tools have that capability to be able to collect information about or collect metadata about the unstructured data. And you need to activate your data stewards as part of your program. So what I would say is we really need to focus on the structured data processes and the unstructured data processes and perhaps define what the differences are between those and how we're going to measure the effectiveness of our program. So there may be specific metrics that are focused on structured data. And then there's other metrics that would be focused on unstructured data. So if we look at, again, but there's a lot of similarities between even the processes or at least the categories of the processes associated with data governance and associated with information governance. And if you've attended my webinars or my presentations in the past, you know that I break the activities that people can take with data or information down into three categories. And pretty much every activity that I know of falls under one of these categories. You can either define data, you can produce data, or you can use data. And again, I challenge anybody on this webinar to see, is there another activity that we can take with data that I wouldn't feel comfortable saying fits under one of these three. So we know that we need to govern how data is being defined. We know we need to govern how data is being produced. And certainly one of the first places that a lot of organizations focus is we need to govern how the data is being used. Well, the same thing holds true for information governance. How are we defining our information, even if it's unstructured data? How is that data being, how is that information being produced? And who has the responsibility for producing that data? I always talk about, when I talk about stewardship, I say that, well, if you are responsible for producing or defining or using any one of these either data or information, you are a steward, if you're being held formally accountable for how you define produce and use that asset. And certainly information usage is the same thing. You can see that the processes, even though they may be somewhat different, they are categorized the same. There are processes associated with defining information, producing information and using information. And then the last one of these things I want to share with you, just kind of doing the side by side are the metrics. So these are the things I said, the processes and the metrics are different in information governance and data governance. Well, they're going to be different in how they're defined, but you can use the same categories, just like in processes, defining, producing and using either data or information. Well, with the metrics, it's the same way. You know, we can define business value metrics. What is the business value we are adding to the business by governing our information assets? How well has information governance been accepted into the organization? What's the quality of the information? How are we protecting that information? What level of confidence do we have in that information? Even though they look side by side to be very similar, some of the things that we measure associated with data governance versus information governance may be different, but at least there will be a consistent theme between them. And most organizations are looking for business value metrics, acceptability metrics, quality protection and confidence metrics. How confident can we be in the data and the information that we're accessing? All right. So let's talk about, first of all, how organizations select the label to put in front of the term governance, you know, as either data governance or information governance. And then we'll talk about, well, how many different labels are there and is a label even necessary? And what is it telling the organization when we apply a label to what we're calling our governance program? And then we'll kind of wrap up this section by talking about selecting the appropriate label for your organization. So first of all, what does it mean to label governance? And if we just start with the term data governance, you're really defining what the specific data is that you're governing. So certainly by calling it data governance versus information governance, you may focus completely on your structured data versus also including your unstructured data. I've seen organizations that have evolved from data governance into information governance because they've started to apply the same level of discipline to the unstructured, they were applying to the structured data to the unstructured data. So certainly the label can kind of direct the activities that you're taking with your governance program. They can also influence people's perspective and they can get people to ask questions and there's a lot of different labels. So we need to be very careful as to what label we put on our governance program. These labels can also indicate where people can look for business value from the governance program. The truth is that if we start adding too many labels to what we're calling governance, it potentially may cause more problems than it solves. So if you call it information governance, people may say, well, the industry has kind of settled on the term data governance. Why are you calling it information governance? Well, there's a big call for people calling it information governance now as there's a lot of information governance conferences and information that's available about information governance as well as data governance. So what are the different labels that are being used out there presently? So there's the obvious one. There's data governance and then there's information governance. Again, it could mean or it may not mean the inclusion of unstructured data or if it's focused on unstructured data, it may or may not include the structured data as well. The other one, the other expression that I hear the other label that I see more often is master data governance. We're focusing on the governance of master data. I'm not sure we need to call it master data governance, but it certainly is data governance that is being applied to the master data. But again, by asking, by adding the name master in front of it and calling it master data governance, what does that imply to the organization that you're only governing your master data or that you're governing other data beyond that? And the same thing holds true. I don't see too many organizations actually referred to it as metadata governance, but if you'd call your program metadata governance, that may be the only thing that people may think that you're focusing on with your governance program. So what are some of the other labels that you find out there? And I'd love to see in the chat or in the questions as to what are you calling your governance program? There is certainly big data governance. That is something that they started talking about as early as when they started talking about big data. And if there's big data governance, why couldn't there be small data governance? I've seen that written about a few times where we're focusing on instead of the big, the voluminous data of the big data, the small, very finite and focused data sets. So I've seen organizations call that small data and refer to it as small data governance. Again, are we going to be governing more than just the small data? That's one of the risks that you run by using this name. Recently, I've seen BI data governance as a popular expression. And again, just like master data governance, are we just governing the BI or the business intelligence or the data warehouse of our organization? I've seen another term that's just BI governance without the term data in it. And typically, BI governance is the governance of the process associated with business intelligence. And then last week, I heard the term artificial intelligence governance and without the term data. And again, I think it's focused on governing the discipline of artificial intelligence. But if you add the word data in there, and now we're adding lots of labels to our term governance, artificial intelligence data governance, certainly to me, implies that we're just focusing on AI data, just like BI data governance, just like master data governance is really implying to the organization that we're only focusing on this specific type of data. Why do we need to do that? It's my opinion that we should just call it data governance or call it information governance and apply at least mostly the same discipline to all of the different types of data that are important to our organization. I've seen things now called analytical data governance or analytics data governance. Again, that's defining what specific data you're focusing your data governance program on. Now, the last one that I'm adding to this list here is the term non-invasive data governance. But that one's different from the rest of them that you see on this list because non-invasive is describing the approach. So I usually talk about there being three approaches to data governance. There is the command and control. You will do this. It doesn't matter how busy you are. Just kind of that iron fist. I don't see organizations calling it command and control data governance. That would imply probably something different than you would intend or even traditional data governance. And I liken that to the movie field of dreams. If you build the program, people will gravitate to it. It's just like if you build it, they will come. So I don't see organizations calling it command and control data governance or calling it traditional data governance. But if you want people to ask questions about the approach that you're taking, calling it non-invasive will get people's attention. And they'll ask you questions as to what do you mean by non-invasive data governance. So that label is a little bit different than all of the others, which are really describing what type of data you're going to govern by using the term non-invasive data governance. That's really implying the approach that we're going to take to govern the data. So the first question is, is a label necessary? Certainly it's part of the messaging. Just by calling it data governance might be too vague, but it's really going to come down to what do you mean by the term data? What do you mean by the term information? Is this going to be a progression from data governance to information governance to knowledge governance? There's a lot of progressions that we could take. So a label is good. It is helpful, but a label can also be limiting. And then when you have different things like master data governance and big data governance and different labels for your data governance programs, there's a possibility that you might have multiple types of data governance within your organization. And the question is, is that really necessary within your organization? So the label can be limiting. It may imply that, okay, we're governing only this type of data. There may be another type of data governance within our organization. Looking at the way that you label your governance program, just be careful as to the message that you're sending to your organization, and just make certain that you're not sending the wrong message to your organization. So what does the label tell the organization? Well, it tells that governance is focusing on a specific category of data. It may answer the question of who's even sponsoring the data governance activities or where in the organization they're going to be able to look to get value from the governance, whether that governance is of data or that governance is of information. So as I mentioned before, if you take the term BI data governance or AI data governance or MDM data governance, or for being master data or metadata, and you drop the term data, that becomes a different meaning. So when you call it BI governance, at least again from my experience, you're seeing that we're now trying to govern the processes that are associated with BI. We're governing the processes associated with AI. And there's a possibility that you may have different types of governance in your organization. My suggestion is try to reign it in under the same umbrella instead of having different factions working on different things. Be consistent. You can be consistent in how your roles are defined. Just go back to the framework that I shared with you. There are so many similarities between the two. Why have a duplicate function in your organization? And I'd love to hear from you if you have both a data governance program and an information governance program. And I'd love to learn why you have two different names for what you're calling your program. So in industry in general, I find the term data governance is mostly focused on structure data. I find that the term information governance focuses on structure data and unstructured data, or perhaps just the unstructured data, or even records in organizations where it's now an extension of records management. Metadata governance, that's actually a discipline that has to be in place whether or not you're focusing on structured or unstructured data. We're talking about the documentation, we're talking about the context to add to the data. And as I've said many times before, and I will say many times again, that metadata, that information that adds the context, that adds the meaning to the data to make it become information, it's not going to govern itself. Somebody in the organization, yes, you're going to have metadata stewards, people that are defining, producing and using metadata within your organization. So my suggestion is data governance, information governance, metadata governance, I would shy away from selecting any of the other labels because again, you're trying to imply that you're only focusing on certain data. It's typically a single discipline across an organization. So let's look at a few organizations and let's see how they went about selecting their names. I'm going to talk about what they're calling their governance program. So let's start with the financial services, a couple of organizations within financial services, one called data governance, one information governance, and then a couple other examples of data versus information governance. So in this financial institutional trading organization, which is not located here in the United States, they are focused on data sets and data products. And they're focused on specific domains of data as they're governing their organization. They're moving their data into a new analytical platform. And that is the opportune time for them to start to apply governance as they move to that platform. But they are focusing strictly on structured data within this platform. And they're recognizing ownership, they're assigning certifications levels and confidence levels of the data as it leads into the platform. There was never a question. They were going to call it data governance. They're not even thinking in terms of unstructured data at this point. So logically, it made sense for them to call it data governance. Another organization in the financial services industry that focuses on federal home loans, they call it information governance. And their program is basically based on principles of records and content management. And how did they get started? Well, I don't know if you know what the acronym MRA stands for, but that's a matter requiring attention. And if your examiners give you an MRA, that's not a good thing. So they needed to address how they were governing their records and their content management, and they called it information governance. So focusing on the protection of sensitive data, extensive content management solution, extensive DMI, data management inventory. They called it IG, they called it information governance. And their slogan was that they were igniting the organization. So they ignited the organization with information governance. For them, it was never a question. They never used the term data governance. They started and continue to call it information governance. In fact, the one client is a shining light example of how you can govern information within your organization and it not be the bits and bytes of data, just the bits and bytes of data in databases. Another organization was focusing on accessibility to data. In the US, one of our national laboratories, they focused on structured data. They called it data governance from the beginning and they continue to call it data governance today. And again, they're focusing on structured data. Another organization, now this is a funny story. This is one of our larger insurance companies here in the United States. When I came in, they called it information governance. And that was a directive from the person in top of data within the organization. And when I asked him, why was he calling it information governance versus data governance, he said, because we've tried data governance three times and it's failed each time before and we're not going to be able to call it data governance again. So we cannot call it data governance again. We're going to call it information governance. They focused on the quality of data. They were focusing on very structured data, but they still use the name information governance today because they've been more successful with information governance than with data governance. Again, not the ideal reason for calling it information governance, but call it what makes sense to your organization. So organizations are, there's a bunch of different options for what you're going to call your program, whether you call it data governance or information governance, or you call it data management or metadata management, or whether you call it something else. The name that's being used by most organizations that I see these days and that's written about the most is data governance. And in fact, if you're going to look for resources online to learn more about data governance and more about noninvasive data governance, you're going to find more information in terms of data governance than you're going to find in terms of information governance. But I think that that gap is quickly closing as information governance becomes more used in different organizations around the world. When you are selecting your approach, make certain that you're considering what it's going to mean to your organization and how people are going to accept that information. Consider labeling it with the name of the approach that you're taking, what specific type of data you're focusing on, and what the purpose of your governance program is. It may be better to use the term information and to refer to structured and unstructured data than calling it data governance, which mostly in most people's eyes is viewed as being mostly structured data. So right now the term information governance is most often referred to this way when folk people are either focusing on just the structured data and the associated metadata, unstructured data as well, or the combination of structured and unstructured data within the organization. So there's not a simple answer. I can't tell you, you should call it data governance. You should call it information governance. It doesn't make sense for somebody from the outside to tell you that. If you want to associate it with what type of data it is that you're governing, I think that's the best way to determine whether we call it data governance or information governance. Oftentimes the organizations that are implementing information governance, it refers to things like records management, library and information science, content management. That's typically what I see or whatever other types of data or information is of concern to your organization. Just again, be careful with how you label your governance program because of what it might imply to your organization. Some organizations just want to call it data management. But if you look at the demo wheel, data governance is positioned right in the middle of that wheel. So data governance is an aspect or a knowledge area in terms of the disciplines associated with data management, but they haven't been defined as being the same thing. And they don't have them defined as being the same thing, should I say. And there are lots of organizations that are presently even trying to determine what's the difference between data management and data governance. I think that's another topic for another day. But typically data governance could be referred to as people governance, because we're focusing on governing people's behavior as they're defining, producing and using data. Data management focuses on delivering projects on time and within budget. That's one of the things that I see for data management more than for data governance, that focus on the system projects and being on time and within budget. Data governance focuses on a lot of those behavioral aspects of managing data that include the policy, the standards, quality management, stewardship, literacy, documentation, and those types of things. So there's a difference between data governance and data management. I haven't really always understood why organizations would want to call it just data management instead of calling it data governance, or at least that aspect of data management, calling that data governance. Metadata management, well, again, that one makes the most sense because we're talking about the data, about the data, the context. People use the term data documentation more now. I see that being used not equally with metadata, but that's really describing what metadata is. If we call it metadata, people are going to ask the question, what do we mean by metadata? So some organizations are referring to that as data documentation. And metadata management basically has to become a part of your program, whether it's a data governance or information governance program. So oftentimes, metadata management focuses on the glossaries, the dictionaries, and the catalogs. It doesn't replace data governance. It's really there to help to enable the programs. And like I said, I was going to say it again, the metadata will not govern itself. So we require to have the governance of metadata, even if we're not necessarily calling it metadata governance, or pick something else, pick something else to name it. The effect is that you really need to just get started getting people to understand what role they play with the data or the information or the master data or the business intelligence data. I would just warn you that it doesn't really make sense to have multiple programs that are named differently in the organization. So my suggestion to you is just consider naming your approach non-invasive data governance and take that type of an approach. And then you're really just adding the label that refers to the approach that you're taking. Yes, you'll still get questions about what you mean by non-invasive, but that's a foot in the door to be able to tell people how we're already governing data. We're just not doing it as formally as we need to. And we can take a non-invasive approach and be just as successful or maybe even more successful than if we would take a command and control approach. So thank you for listening to me today. I talked a little bit about the differences and the similarities between data and information. We talked about how organizations call it data governance, information governance. We even talked about how several organizations went about picking the name or the label. I shared a couple examples of organizations and then I just wanted to share some additional considerations for you as to when you go about naming your program, just be careful what you name it because it's how you name it is going to persuade people or help to determine how your program is viewed within your organization. So with that, Shannon, I'm turning it back over to you to see if we have any questions for myself or for Kevin or for both of us. Thank you so much for another great presentation. And just to answer the most commonly asked question, just a reminder, I will send a follow-up email to all registrants by end of day Monday for this webinar with links to the slides and links to the recording along with anything else. And if we don't have time to get to all of the questions, Bob will write up the answers and we'll get that in the follow-up as well. So keep those questions coming for everybody. So diving in here, so if you wanted an all-encompassing approach, could you not call it data and information governance dig for acronym? I like that. This captures the notion of structured and unstructured and could bridge a larger type of organizational engagement because often folks think data governance is someone else's interest or responsibility whereas information is more inclusive. You know what, I had never thought of it that way before that we could actually have DIG instead of DG. There is nothing that would be stopping you from calling data and information governance. And in fact, if you're doing that, I'd be curious as to how people are responding to that. I think it's great. I think you need to name it something that's going to be appropriate for your organization. And if data and information governance covers what you need, you're covering structured, you're covering unstructured data, just be ready to answer the question, why are you calling it data and information governance? And tell them. It's because we're focusing on both structured and unstructured data. I don't know, Kevin, do you have anything to add to that? I think you nailed it. I'd be curious to see how that would be received as well. I love the creativity. Yeah, it is creative. I love it. There's somebody in the, I can't help it. I got to call it out. Someone in the chat said, deglaze to specific semantic extensions, which we didn't want to be called diggers. But I do, I like that. DIG, so you know our conference in December is data governance and information quality. So you can add the Q on there. You know, yeah. I dig it. That was awesome. So there's a comment in here, Bob. So fourth, analyze data. So the fourth, you had the three, and then what about the fourth? Say that again, I'm sorry. It's just a comment. It says fourth, which analyze data. Analyze data. Well, that's a use of data, right? So I think that was from my challenging. Is there something beyond defining, producing, and using, and analyzing, and protecting? And you know, there's a lot of other things, but I would say that analyzing data and reporting on data, that falls under data use. And so I'm sorry, I'm going to reject that as being a fourth category, but it's a good idea. And again, if it makes sense for your organization to break it down that way, my own means use it that way. These are just considerations for things to do. Do what makes sense for your organization. So what about IoT governance? So okay, so there's IoT governance, which if you follow what I talked about in the webinar today, that would be the governance of the processes associated with IoT. But if you call it IoT data governance, I guess you could do that. But the same thing holds true with artificial intelligence data governance, IoT data governance, machine learning data governance. Yeah, those are the uses of the data. I personally don't go down that direction. I wouldn't call it that. It is certainly IoT data has to be governed just by the pure nature of the beast in the internet of things. But I wouldn't call it IoT data governance. IoT governance has a different meaning. But you know what? Hey, again, I'm going to pick on you, Kevin, and see, you have a thought on that because you're focused on a lot of those apps that may be connected through the internet of things. Would you call it internet of things governance? I don't know that I would. And thanks for calling me out there, Bob. Because I'm with you. I think it's all about the internet of things could be just depending on your industry and your organization, you know, speaking with people that are in the industry of growing crops. And they use a tremendous amount of IoT devices. It's providing them with just a whole set of data, right? You think like soil conditions, moisture, etc. This is all data. And it all needs to be governed in some way, shape, or form. I think the fact that it's coming from these sensor probes or these other IoT devices is, I don't know if that's, you know, I don't know if that's the core of it, right? It's about what is that information? What is it? See, you got me, Bob. What is that data? How is it used? So I don't know. I don't know that I would specifically call out it as IoT governance. You know what? It's a category of data, I guess. It's the data that's being defined, produced, and used in the IoT circles. But I don't think it is. I have never seen yet an organization with a program called an IoT data governance program. So where does deleting data fit in? Where does deleting data? Excuse me, sorry. That is truly silenced. I don't know how that came through on my phone. So deleting data is a, you know, so I believe that that would fall under the production of the data. I guess deleting data and getting rid of data and, you know, having schedules for deleting data that no one's ever brought that to my attention before. That could be another category, but I wouldn't think that that would fall under the production of data. But it's good. I like that I'm being challenged with different things to different activities that people can take. And Kevin, feel free to jump in at any time, too, if you have additional comments for any of these. I'm going to put cruising down. We've got a few minutes left. I think I can fit a couple more in. Can you provide examples of business value metrics? Of business value metrics, certainly. So people spend a lot of time wrangling data. So people who are data analysts and data scientists spend 80% of their time. Well, that's just, I'm just throwing that out there as a number. It's not, it's not hard and fast, but 80% of their time wrangling the data, getting the data, the way that they need it in order to be able to use it, to be able to analyze it. The question is, you know, if we can reduce that nonproductive time and give these analysts more time to do what they're skilled at, analysts, what could they do with that time? What decisions could they make? What research could they do? What's the business value that could come from that? You know, business value could be reducing risk, business value. You know, I talked about business value and acceptability. Acceptability is truly something that you can measure by seeing how well the organization is adopting governing practices. But business value could be the return on investment from any other, from a data warehouse that you're building, or a data lake that you're creating, or an analytical platform that you're building. If people don't trust the data, they're not going to get, they're not going to get as much value out of these investments that the company is making other places. Those are all business value. It's hard to say that the data governance program, although organizations do it, you know, we can save $7 million a year by implementing data governance. It's going to be a stretch to get people to believe that. But if we can say that we want to increase our user base in a certain application that we have built, and we can do that by improving the confidence in the data, therefore get ROI from these other places, that's true business value. So those are some of the things that I consider business value. I don't know, Kevin, you have anything you want to add to that? Yeah, I like to, you know, I always think to whatever you talk about, we're going to save the organization, you know, so much money. I always want to make sure that that's grounded in reality, right? So if you're looking at your expenses, if you have, you know, because of the inefficiencies, or because you are relying on, you know, third parties to help, if you can, if you can gain not only that time efficiency, but you can reduce, you know, the spend on, you know, people, systems, things of that nature, then that's going to get people's, that's going to catch people's attention, right? And that's definitely something, as I talked about earlier, the complexity of all these various data sources and wrangling all that information for really whatever the purpose is. There's usually an added expense to making that happen, right? So if you can increase those efficiencies and you can reduce that spend, then that's going to be something that the business is really going to appreciate. I agree. I love it. Well, thank you both so much for these great presentations and for answers to these questions. Again, you know, with this all the time we have, but Bob, I'll send over these questions to Bob and he'll write up the answers to the questions we didn't have time to get to today and I'll include that in the follow-up, which will go out by end of day Monday for this webinar, with links to the slides as well and links to the recording. And, you know, just Bob mentioned the DGIQ conference tomorrow is the last day for the call-up for presentations. So if you'd like to submit your presentation, you feel free to go and do that at DGIQ 2021.DataRescue.net. I'll put that in there. But I hope you all have a great day. Thanks to Ona for sponsoring today and help making these webinars happen. Really been a joy for you to join us today, Kevin. I hope you all have a great day. Thanks, everybody. Thanks, everybody. Thanks, Al. It was a blast.