 Hello, and welcome. My name is Shannon Kemp and I'm the Chief Digital Officer of Data Diversity. We would like to thank you for joining the current installment of the Monthly Data Diversity Webinar Series, Real World Data Governance with Bob Siner. Bob, just reminding me this is the 12th year of this webinar series, very exciting to kick it off this way. And today Bob will discuss data governance trends, a look backwards and forwards sponsored today by Alation. 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 and to note Zoom defaults the chat to send you just the panelists, but you may absolutely switch that to network with everyone. For questions, we will be collecting them via the Q&A section, and we encourage you to share highlights via your favorite social media platform using hashtag RWDG. And to find the chat and the Q&A panels, you may click those icons in the bottom of your screen to activate those features. And as always, we will send a follow up email within two business days containing links to the slides, the recording of the session and any additional information requested throughout the webinar. Now let me turn it over to Miles for a brief word from our sponsor, Alation. Miles, hello and welcome. Thank you. I'm glad to be here with you and I have what I hope we'll find a timely topic, which is, you know, how do we sell data governance during a recession. And so I want to start by saying that it's not just me. There's lots of press, including today. There's been recent press on this move to recession. And so how do we sell the value of data becomes a really important question. You know, unfortunately, if we look at our past, the last two recessions, the 2003 2004 and the 2009 recessions. Most people are still doing it the old way they're reacting to what's happening they go into survival mode they try to to do across the board head counts, and they oftentimes get rid of where the knowledge is in the organization. Unfortunately, this really doesn't deal with the uncertainty, the recession brings and in fact, there are organizations that really can grow and thrive during a recession. And then, you know, even as important as data is so critical to transformation. What happens if you slow down your data agenda, and you come out with an order that's weaker than others in the market place after. And then lastly, you don't want to have all the knowledge walk out this happened to honey walnut 2003 to 2004 and they made it change the next time there was a recession to make sure they didn't lose all the knowledge because they ended up in what they thought a system sunk. So how can we do better. Well, one of the things Mackenzie says this is the time of all times to invest in data and analytics, because it helps you make better decisions. And even though there is great uncertainty data is a great way of doing with it. Now to be honest, when I thought about data governance in the past. Tom Davenport did this article several years ago and Harvard Business Review, you can go get it if you want on data offense versus data defense. I parked data governance, solidly in the defensive thing, but I think this makes it harder to make the business care. And it doesn't allow us to share the things that we do in data governance that actually do drive business value directly. And so one of the things we need to do is we need to work to define data governance as being around the decisions that you need to make. And being able to cross that chasm in terms of the gap of what you may have and trusted data. So I think data governance has a big role to play. And organizations who get after data governance during recession actually can come out stronger. So some of the things we can talk about is how do we get the right source, how do we get the right quality, how do we certify that data is trustworthy. How do we make it safe to make decisions and make those decisions hopefully happen faster. I was not going to go through everything on this slide. The important thing is that we need to think about what goes into the descents column, and what goes into the offense column. And we do a lot within data governance to actually help drive the bottom line and the top line of the enterprise as you enter recession and we need to extol that so we don't have data. We need to go back agenda rather or rather than the front part of the agenda for corporate leaders. So some parting thoughts, offense is about generating values from data. It's making data sits for purpose, making people be able to make those hard decisions of where to invest and where to be invest during a recession, and hopefully what you're doing is de-investing is your de-investing in areas where you can actually drive efficiency in the organization. You know, obviously it speeds up the time to value, and it also goes to, you know, a real story around value. We aren't just about descents and data governance today. We're about how do you run a business better. So with that, Shannon, I'd like to turn it back to you. Miles, thank you so much for kicking us off in this great, with this great presentation, and thanks to Lation for sponsoring today's webinar and helping to make these webinars happen. If you have any questions for Miles, feel free to submit them in the Q&A panel as he will likewise be joining us for the Q&A portion of the webinar at the end. So now let me introduce to you our speaker for the series, 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 specializes in non-invasive data governance, data stewardship, and metadata management solutions. And with that, I will give the floor to Bob to get his presentation started. Hello and welcome. I think I need to take myself off of you. Boy, I don't think I've done that in any time in the last 12 years. Thank you, Shannon. Thank you for the great introduction. Thank you, Miles. Thank you for allation for sponsoring the webinar as well. I'm really interested in the topic of offense versus defense, but I thought this topic would be a great topic to kick off our 12th year. Talking about data governance trends, where have we been with data governance? Where did it even come from? And also to take a glance into the future to see what some of the trends are and where at least I see from the organizations that I work with, where data governance is heading. And one of the things that we're going to need to think about is what is going on with the economy right now and how is the recession going to play a role in some of the decisions that are going to be made around governing data and governing and managing data effectively within organizations. So quickly before we get started, just want to run through a few of the things things, a few of the things that I'm involved in presently. As you know, we do this monthly webinar series on the on the third Thursday of every month. Next month on the third Thursday, we're going to be talking about how data catalogs are the answer. What is the question that they are answering? So data catalogs are the answer. What is the question? I'll also be speaking at a couple of upcoming dataversity events. Events next week on Wednesday is the annual enterprise data governance online event, and I will be moderating a panel of chief data officers and people in similar positions talking about data governance in my session during that event. I'm also speaking at EDWD enterprise data world digital this year during March. So I hope to quote unquote see you there at those events. I always talk about non invasive data governance but I do have a new piece of news just to share with you. The second book, instead of being called non invasive data governance to is going to be called non invasive data governance strikes again. All the lessons learned in the perspective gained since the first book came out in 2014. There are online learning plans associated with data governance non invasive data governance, metadata governance available through the Dataversity Training Center. Shannon mentioned the publication he Dan, my consulting business is kik consulting, and in my spare time I'm also a faculty member at Carnegie Mellon University here in Pittsburgh, Pennsylvania, which is where I'm located, by the way. So, what are we going to talk about today, want to take a look at least a brief look into the past and see where data governance has come from we want to take a look at what's happening presently and you know you'll be. I'm sure that you would love to know what's happening with everybody but really the present situation has to do with what is your present situation. So we'll talk about that a little bit. We'll talk about how the trials and tribulations we've lived through over the last several years have evolved to success, talk about lessons learned. Talk about how the data catalog and the data governance tools fit in to the overall increase of importance of the governance of data and the governance of information within organizations. So we'll talk about the data governance tool explosion, and then we'll wrap up by talking about what is the future of data governance holds so again thank you for coming today. I typically like to get started by talking about a few just sharing with you a few of the definitions that I use. So if you're new to this webinar series, or you don't have a definition of data governance at this point. Your word my definitions pretty strongly so data governance is the execution and enforcement of authority. It doesn't matter what approach you take whether you take a non invasive approach command and control approach at the end of the day. You're being told that you need to execute and enforce authority over the management of your data data stewardship is the formalization of accountability a lot of organizations talk about how there's a lack of accountability for data within the organization. The ownership addresses the formalizes the accountability for what people do with data. So basically anybody who is held formally accountable for what they do with data, they're a data steward. And my definition of metadata is that it's data that improves both the business and technical understanding of the data. So, when we look at the past and we look at the president we look at the future. So data governance really haven't changed over the years. In fact, I think I've gotten a stronger sense of why I feel these are good definitions, when you need to execute and enforce authority that's going to get people to pay attention. If you tell them we can do this in a non invasive way where there's already levels of authority in place, we can formalize that it's going to help you a great deal as you move into the future. We're going to talk about the past and the present first before we jump into the future. So let's talk about where we've come from, where we are now. What are some of the prevailing tendencies that we're seeing within organizations or I'm seeing or I'm seeing at some of the other events where people are talking about data governance, and then we'll spend a little time talking about the lessons learned in the perspective game. So here I'm going to share with you a little bit of how old I am I was working in the data field back in the 90s back in the time that they were in the data administration, and you don't hear that term being used all that often these days. Excuse me, these days, but the data administration newsletter came out of the 90s, and that was the term that was being used to describe paying attention to the management of data and data modeling was very big in the 90s and it continues to be big, especially with the, the use of different types of modeling tools and different types of databases in the in the 2000s. The management became important data architecture was critical metadata repositories were being talked about in the 2010s. More and more data governance became important the conferences became better attended these webinars became better attended there was a lot more interested in data governance and data management and metadata management was all the rage as well during the 2010s in the early 2020s. Now where I'm saying we're in the early 2020s now. There's a prevalence of just the term governance within organizations. So it could be data governance. You can have a separate program or a similar program called or labeled as information governance. Data governance metadata governance, but one of the things that has become really important in the early 2020s is the use of the data catalog, the use of the data governance tool, so that you manage and understand the value in the understand everything that you need to know about the data assets within your organizations. So what's going to happen in the mid 20s and in the late 20s. Well, I've actually talked to several organizations that are using the term governance so often that they're talking about potentially having a chief governance officer over their data governance their information governance, their policy governance project governance. So, I think that you may see a move towards more governance focus, and also data virtualization and visualization and the use of the catalogs, at least from what I see is going to continue to become more and more important in organizations into the mid 20s. By the late 20s, when we are in most of us are in a better situation where we know about our data assets. I think that's where you're really going to see data science, artificial intelligence, machine learning take off, but to take data catalogs are still going to be there so data catalogs are actually a big piece of the present data governance within a lot of organizations. So let's talk about where we are now let's talk about the present I talked a little bit about where we come from, but where are we now. And that question really needs to be answered by where are you right now. So I know there's a lot of organizations that are represented on this webinar, and each of you are probably in a different state of governing your data within your organization. Some of you may be brand new to it some of you may not have even started so where we are now the state of the industry I'll talk about that a little bit here in a second but it really depends on your organization. And when you think about it oftentimes where you are now with data governance relates very closely to the effective use of metadata management data catalog metadata repositories, wherever you house your information about your data. That's where we are presently organizations are heavily engaged in in working on their data documentation and what they know about their data as a big way of being able to help to help themselves moving forward, just to kind of relate this to what Miles said earlier, and we need to continue to capture this information, because when we do come out of the recession. Those organizations that have collected this information and have their arms around it in our governing this information are going to be in a lot better position to prosper after the recession. I'll talk about that in the q&a later, but you know there's tools available right now in the present to help you to do a lot of things, help you to catalog your data to help you to monitor your protect your data, visualize your data, share your data, you know you can fill in the verb basically. There's a lot of tools. But what I'm finding, actually, you know, so many organizations and data mesh and data fabric are such hot topics right now, and go to everybody's favorite resource chatbot GBT if you haven't been to chatbot GBT. And yet, to hear what they say about everything you can have it right you a song right you a book right you a white paper. But data chatbot GBT refers to data mesh as a data governance approach and refers to data fabric as a data management approach. So if you're having questions about the difference between governance and management. What you're describing and I provided a summary here of what chatbot GBT is telling you about the differences between data mesh and data fabric. These are all things that organizations are presently thinking about. So what are some of the prevailing tendencies what are some of the things that I'm seeing within organizations you may be seeing within organizations, a lot of organizations are still getting started. The programs are still in the process of going through a maturity in gaining in levels of maturity. They started initial they get to a point where they're defined they're repeatable, they're managed, and then they start to exceed their expectations. So a lot of organizations are looking to mature their programs right now and that is one of the prevailing tendencies that I see out there. So the other one and I mentioned a little bit earlier is just the term the use of the term governance. So if your organization is has an information governance program, it may be separate from your data governance program, because organization governance is often thought of more to be concerned with unstructured data records management, content management document management. However, I also see as a trend organization starting to blend those two disciplines together the data and the information governance disciplines. As I said, the term governance is being used a lot of ways in organizations. So just the whole item of governance in general is very hot. It's one of the prevailing tendencies in a lot of organizations, especially in the organizations where the protection of sensitive information is very important. There are trends data catalogs and the automation of data catalogs and being able to ingest metadata into those tools and making it available to people is certainly a prevailing tendency glossaries dictionaries lineage. Some of these things are prevailing tendencies still, even though you'll see on the next slide they're also dinosaurs or you'll see on a slide. They're dinosaurs there are things that people have been putting in place back since data management was the thing when I was getting started back in the 90s in the field of data management. One of the most prevailing tech trends for data governance is that they are there to support your enterprise analytic endeavors. And the value of the data the value of the data in helping you to improve your decision making capabilities data governance is being aligned in many organizations with supporting the e a endeavors within their organization. There may be other prevailing tendencies and trends that you're seeing but these are the things that I'm seeing happening right now. What are a lot of organizations doing they're taking the lessons that they've learned over the past several years of implementing their governance programs, and they're reviewing them, and they're saying well what lessons can we learn from what we've done. And to be honest with you learning from experience is what works in a lot of organizations, but learning from what is working and learning from what's not working. It's painful for a lot of organizations to go through this. But again, one of the trends is how do we learn from what we've done. What, what do we, how do we recognize what has worked and what hasn't worked and how do we address those things. So, one of the first things is organizations are getting past the concept of trying to assign people into new roles. I know a lot I talk a lot in non-indicative data governance about recognizing people into roles, rather than assigning them or giving them new titles. So that's one of the lessons that have been learned by a lot of organizations is let's not add to the platter of the things that people have to do right now. Let's also build reusable components. Let's provide an ability for organizations for people within the organization to make us aware of different opportunities associated with the data. They don't all have to be issues. So sometimes somebody has a good idea as to how to use data to improve the efficiency of a process. That's not necessarily an issue. That's an opportunity. So providing an intake process for not only intaking issues, but intaking opportunities is important. And one of the things that we're learning is data literacy is really important in organizations. People need to understand the role they play with data. They need to know how to speak with data, how to work with data, depending on your definition of data literacy. You know, all of those things, you need to get the organizations to understand that data is such a valuable asset that they need to become literate in it. And the truth is, you know, one of the things that I see happening in a lot of organizations is that they are going through digital transformations or they're going through business transformations. So you're going to be seeing this, whether or not they have formal governance in place. And I think it makes sense logically, that if you can learn from what's happening during your digital and business transformations. It is going to be very non invasive you're going to be seeing who are the people that are making the decisions and start to formalize that. So you can add value to your digital and your business transformations by just associating the people who are active in those programs. And one of the people that are associated with your data governance program. And one thing I'm seeing too is that senior leadership are starting to recognize the importance of data governance more and more. It doesn't mean that when the recession hits that that it's not going to be something that's going to be cut back on. It's not advisable necessarily to cut back on that because as I mentioned before, you want to have this information collected. You want to have that formality in place. You want to come out of the other side of the recession, and the overall feeling that I'm seeing from people is that we still aren't there yet. I'm not sure what their means, but organizations data governance is a program. It's not a project that has a beginning and end. So I'm not sure that you're ever going to reach your final destination. I think that's a prevailing thought that data governance programs are are are having a hard time to get gaining traction and becoming operationalized within organizations so let's take a look at how the trials and tribulations that we've lived through over the years can help us to evolve to success with our data governance programs. So we're going to talk about learning by trial. We'll talk about what some of the tribulations are and the consequences and how data governance and ever is an evolution, not a revolution. And then we want to talk at least spend a little bit of talking about what is the definition of what does success look like when it comes to a data governance program. So there's five things that I want to use just to kind of to use as a spotter to see, you know, what are the things that we're trying to do within our organizations, and how well are they doing for us. So one of those things is the command is the approach that you take. Have you taken a command and control approach where you've said thou shout govern your data, where you assign people new roles, you data governance has a perception and maybe even a reality of slowing things within the organization. Maybe that's the approach that you took or maybe you took a traditional approach, which I always liken to the movie field of dreams. If you build it, people will come to it, you build your program and hope people will gravitate toward it. Or did you take a non invasive approach. And did you recognize people and how well did that work out for you. So I'm not saying that that one of these approaches, I'm not really saying that one of these approaches is better than the rest. But you can learn from the approach that you took. And oftentimes, it's a, it's actually a combination of command and control and non invasive, or traditional and commanding control. So take a look at the approach that you're following and see what you can learn from it. And also the next thing is the roles and responsibilities. How have you defined the roles within your organization. Do you have strong support sponsorship and understanding from your executive level, or from your strategic level. How engaged are your tactical subject matter experts. Again, learning by trial, take a look at the way that you've defined your roles and make certain that they're working, that they're working well for you. And don't be afraid to change them because data governance just like any other discipline. It's going to, it's going to morph from time to time, depending on what's important to the organization, depending on the people of the organization. The support of the program is another thing that we can learn by trial. Do we have the level of sponsorship that we need. Do we have the level of resources that we need are we asking for too much or too little. I'll give you a point in a couple slides here that the success that we can demonstrate to the organization is often based on the capacity that we have to be able to help the organization. So taking a look at the resources is really important when it comes to what do we want to get out of our governance program, how are people participating, how are people people partnering with data governance. The value the program delivers the understanding of the program. I don't want to read through each of these, these items. But they're all very important where are you demonstrating value within the organization, and are people seeing that value. And if they're not then let's make certain that we focus our attention in the future on making certain that they understand the value that the messaging is getting across to the appropriate people in the appropriate way. So let's make certain with all of these different things that we're learning by trial, we want to take a look at leveraging what's working, and then addressing where there's opportunity to improve. That's one of the core tenets of non invasive data governance is look for where there's existing levels of governance and formalize that, but then also address opportunities to improve. And after doing that, you can address the different gaps and the risks that you have within the organization. This next slide looks a lot like the previous slide. And so when we talk about tribulations well what is a tribulation. By definition, it is a cause of great trouble or suffering. What have the approach that you selected what have the roles that you're engaging with the support that you have from the organization the value, the understanding, how has that caused you problems. I'd like to say it's caused you great suffering. Maybe it has caused you great suffering, but you want to make certain that you recognize where there are, where you have limitations and where there's opportunities to improve. And you want to address the encounters demonstrate the issues that are presently taking place due to a lack of governance and the implications of the approach that you select is command and control working is not invasive going to be easier to implement within your organization. These are all things that we can learn from the things that we're presently doing. So data governance is an evolution. It's not a revolution. I always suggest to the organization if you, you, the organization to organizations that if you view the organization as being a pie, you're not going to govern the whole pie at once. You're going to start with a piece of the pie or a piece of a piece of the pie, and you're going to incrementally extend it across the organization. Again, you're going to leverage, you're going to learn from your, the lessons learned from what you've already done. And you're going to start adding new perspective to how you're going to move forward with your program but you're going to do it incrementally, rather than trying to do it as a big bang approach. As I mentioned before, the speed that you can move that oftentimes depends on the capacity that you have. And the capacity is often by the data governance manager, or the data governance lead, or the data governance administrator or office or the chief data officer even, you know, the speed that you're going to be able to move your program forward is going to depend on the capacity that you have to be able to do things that you're telling people that data governance can do for the organization. I've been with critical data elements and in a previous and one of the recent previous real world data governance webinars, I had a great octopus picture on the slide, I talked about how critical data elements. They never seem to stand alone they seem to have tentacles into other pieces of data and other aspects of the business. So, if you're going to do this as an evolution, start with your critical data elements first, recognize that you're not just going to pick one element, it's most likely going to blossom into something that's a little bit bigger than that, engage working teams, address the intake process for the issues and opportunities that I mentioned earlier, and make sure that we're divvying up the governance. Not everything should go to data governance. Not everything should go to information security or the program, or the application development team, compile a toolkit and share it with people as well. Let's talk about the definition of success. So many organizations that I work with they start with a purpose statement for data governance, and I just wanted to share with you an example of what one organization said it was the purpose of data governance, using strategic data with confidence. And that was what success was if we could get people in the organization to have confidence in the data that they're using to make strategic decisions. That was a definition of success. You know, incremental success beginning early is start addressing those things that are low hanging fruit or things that are not going to require a ton of time, or resources, but they're problems that are just beckoning to be resolved. You look for improvements in access availability all those things that are listed on the screen. And then measure your organization what success looks like and then create metrics and measures that will help you to compare where you are now to where you are at the end of the year. This is a good time to do it at the beginning of the year. And you could measure it again at the end of the year or halfway through the year. Some organizations are defining success as revenue that's driven from data. What happened most of the time that happens. Often I see it in more extreme cases. Many organizations are looking for cost savings driven from data. So that's more a more practical expectation for people in the organizations is that we're going to save money we're going to be more efficient. We're going to be more effective. We're also going to be able to make better decisions or decisions that we're more confident in. So let's talk about leveraging lessons learned governing data where it lives building efficiency into process, making, you know, making governance fun for people within the organization and activating your stewards by getting them to share what they know about the data. Let's talk first about governing where data lives. And if you're familiar with the concept of data mesh they oftentimes talk about or write about having accountability for data at its source. And when I talk about non invasive data governance, I'm talking about formalizing that accountability. So you're going to look for the people that are at the source of the data that know the data the best, and we're going to formalize their responsibility formalize their accountability around the data. We also need to recognize that accountability for the data is at every phase, not just the definition production the usage of with the retention of the data of the elimination of the data accountability is going to be necessary for your organization at every phase of the data lifecycle. And governing data where it lives is really a common best practice for organizations that are successful with data governance, trying to recognize those people that presently have ownership of the data, have the knowledge of the data. They need to play a key role in the governance of data in most organizations. So one of the other things that organizations can do to leverage their lessons learned is recognize if they're following something that I typically refer to as a data governance bill of rights. And it's not the rights of the people it's more the word rights is in quotes and if you can see on the right side of the screen, on the right side of the screen that the word rights is in quotes. So the data governance when it comes down to it is getting the right people or the right person involved at the right time, using the right data for the right purpose, with the right decision that they need, or with the right information about the data even to drive to the right business outcome. So that's where organizations are looking to take their existing governance programs move towards implementing something like a data governance bill of rights. And that means that we need to know who the people of the organization or we need to catalog that information somewhere. And one of those good places would be within your data catalog. We need to activate stewards by sharing intelligence. It really improves. It proves the intelligence to all the knowledge workers when people have a mechanism to be able to collect their knowledge about the data. So we need to use the stewards by facilitating and coordinating stewarding activities. In fact, the, the most recent issue of TDM calm, I talked about providing a toolkit to the stewards to allow them to get active in collecting their intelligence, so that it can be connected to your data catalog tool. So it will increase collaboration and cooperation between stewards. You can use this information to engage stewards through gamification making this somewhat interesting or should I say even fun for the organization. Let's start to implement different aspects of gaming into data governance. So we keynote at the DJI Hue East conference a couple months ago that talked specifically about the gamification of data governance. These are all things that we can do to activate stewards as we learn from what's worked in the past, and what we need to do as we move into the future. So we're going to activate these folks by getting the stewards to recognize themselves as playing an important role and playing an important role in the confidence, the level of confidence the organization has about their data. And I talked about making data governance fun. Incorporate gaming activities and read about what makes a good game and look to see if there's any aspects of gaming functions I've written articles about data governance as a game data governance as a puzzle. Let's see what are their what aspects of data governance can be used to make this interesting to people to get the stewards of the organization to actually want to purchase it participate. So providing them a toolkit maybe one way that you can encourage them to start collecting and inventorying some of that metadata that's going to be so important to the organization. If they are expecting to be able to govern the majority of the data within the organization, create friendly competition between business divisions and business units and departments. You know, who has their, who's recording more issues or how many of their critical data elements have been defined, or even recorded, and also find ways to provide reward and recognition for positive activity. I think data governance, my friend Len Silverston had said one time and I know I've referred to him before that we should call it people governance, because we're really governing people's activities. And one of the things that we can do to encourage strong data focused activities is provide reward and recognition, recognize those people in the organization that have gone over and above to make certain that their data is well documented, and that that data across the organization. So, let's spend a little bit of time talking about the data governance tool explosion, the tools of yesterday year business glossaries and dictionaries repositories and catalogs, and then spend a minute talking about the data catalogs of the future. Well, I already mentioned earlier in the webinar that I'm old I've been in this industry for quite a while. Years ago when governance wasn't even being called governance yet there were a lot of tools that could be used to enable the data environments in the organizations, they weren't called data governance tools. That's relatively new over the last 10 or 15 years. So there were tools that were metadata repositories, they were not as powerful they were not as automated. They did not have incorporate machine learning and intelligence into the tools as much as they do now. They had tools that were tools there to manage data. As I mentioned earlier business glossaries and data dictionaries have been around since before I started in the data field. People want to know what terminology to use across the organization, yet they still seem to have a difficult time syncing up on a single terminology or taxonomy dictionaries of data resources have been important since the beginning of time. They used to store these things in metadata repositories, a lot of them were mainframe based, you can log into the mainframe, and you could in a cryptic way try to find your metadata. Now the tools are cloud based now vendors are providing tools software as a service. The data dictionaries and the business glossaries, like I said they're as old as the dinosaurs business terminology has always been important to organizations organizations have always been trying to do. It's a common business language. In fact, a lot of the solutions within organizations these days are what we have multiple names that we call something, or we have multiple. We have the same name being called to has several meanings, and we as data governance practitioners can focus on providing context to the name so we don't name them exactly the same thing. We have different meanings within the organization. So business glossaries and that terminology data resource documentation data dictionaries. Those have been around for a long time. They're still very important. You know, as we're going into the recession we may want to look at what can we do to set us ourselves up for success when we come out of the recession. The glossaries in the dictionary are still the most used functionality in many of the data catalog tools, certainly having this information documented improves the findability of the data, the understanding of the data. And as I talked about in that in that purpose statement, if you're trying to improve the confidence people have in data to business glossaries in the data dictionaries will help to improve people's confidence in the data. Also, there's that you're going to need to spend time rationalizing the metadata in your data catalog tool, making it make sense to people connecting the dots, unless the tool has the ability to be able to do some of that for you. So there's data catalogs of many colors. And in fact, for a client recently and Attorney General's office, they were forced to do a market research on the available data catalog tools, because they needed to include everybody. Back in the 2019 and 2020, they found 318 vendors that use the name data catalog in their product description. And so I'm a certain that that number has even increased. So there's lots of repositories. There's lots of catalogs that are out there. People don't use the name metadata repository anymore. So that's great for those tools that have have exceptional automation and machine learning and that's top of mind to these organizations. And another thing that's really important when you're implementing your catalog tool is to look for ways that you'll be able to integrate it with other tools within your landscape. So this great data governance tool explosion as we look into the future. It's only going to get more complicated work with the vendors to make certain that they're addressing your needs. And I think that the catalog will be a big benefit, benefit to all of your data initiatives moving forward. What is the data governance tool of tomorrow look like I already mentioned this a little bit there will be increased automation and machine learning. So they've been talking about trying to deliver the metadata delivering the context of the data along with the data. I think you're going to see more of that within tools. It's going to integrate with other tools that you work in your day to day environment, not only some of the reporting tools and the analytic tools that you have but just other office tools that you're going to find that more as we move into the future integrating with spreadsheets and those types of things. So this is what we're hoping for and I know what a lot of these the vendors are around in the data catalog space are hoping for is that this will become integral to business work and actually the data catalog in the future. And maybe even now is the face of your data governance program. I'm going to spend the last couple minutes just doing a little bit. I don't want to call it prognosticating the talking about the future of data governance. I am not a prognosticator nor is this prognostication. I don't know if I've used that word before you were going to need to the ability to govern all types of data here let me walk through these, and then I'm going to turn it over to Shannon, and see if we have any questions today. I'm not a prognosticator I don't know if you're familiar with, with the punks of Tony Phil, the groundhog on the right hand side of the screen. He's a groundhog that lives somewhere around this area of Pittsburgh Pennsylvania, and he comes out, and he predicts whether there's six more weeks of winter, or it's going to be an early spring, and his records not real real good but you know I'm many things I'm not a predictor of the winter, I can only share with you what I see. And one of the things that I see is data governance is not going away. Even though we come he come into hard times financially in a lot of organizations, we need to keep the keep the faith number one, but we need to keep the momentum moving forward. And there's a lot of things that are necessary to do that but having somebody or having a group of people focusing on this is really important. And we know that if we want to get to a point where our data in our metadata is governed well within our organizations that this is not going to happen magically that data and metadata will not govern themselves. And you know I think that more organizations realize that when I talk about how everybody in the organization that has a relationship to the data is a steward of the data, if they're being held formally accountable for it. I think that's a good thing and I see organizations are looking to hold everybody in the organization accountable for how they define produce and use data. One of the things the future data governance holds is that it's not just going to be data instruction databases and applications and systems that need to be governed. That's the traditional data governance that most people are talking about information governance is is caught fire. Many organizations even either had records management, or that type of a function within the organization. They're starting to consider marrying that up with data governance and making certain that data governance and information governance structure and and unstructured data are being addressed. There's corporate governance and mentioned earlier organizations are over using the word governance, maybe even to the point where a chief governance officer might be something that would be considered moving IT governance has been a thing we just need to make certain that when we look at the future of data governance, we're taking into consider all the different disciplines and all the different types of data that need to be governed within the organization. There's a lot of new technology and this is just a list of several that I got from a couple different sites but in the future, we're going to need to be able to govern across and through all these different technologies, we're going to need to take advantage of AI, we're going to need to be able to provide customer communications about data embedding the business intelligence, all these things. Data governance has to be applied to these things, or they should be applied. It may not happen in the next couple of years, but certainly by the end of the decade, I would see data governance and these terms being used hand in hand. One of the most important things is let's start by governing the data where it lives, you know, the place where it lives, with whom it lives or who has responsibility for it, how it lives, how it comes to be, who's defining it who's producing it using it, you know, when it lives what it lives on the future of data governance for most organizations is just to try to take their existing programs if they have them and build them into what people do. It's a lot less invasive that way than if you try to, if you, if people perceive data governance as being an add on to what they presently do. And the last thing I want to share is there's going to be less and less invasiveness in organizations, there's going to be more and more recognition that governance is already taking place. You're going to hear the term governance more and more. Executing and enforcing authority again going back to my definition of data governance is going to become more commonplace, because we're going to be able to we're going to need to be able to demonstrate auditable proof that we're executing and enforcing authority over data. Consider your options for what approach makes sense to your organizations. And I'm hoping, at least in the future of data governance that more and more organizations will start and stay non invasive in their approach. So with that Shannon, I'm going to, I'm going to turn it back to you here. I just talked about the past and present trials and tribulations, all the way through to the future of data governance. With that Shannon, I'm going to turn it over to you to see if we have any questions today. Thank you so much for another great presentation. And just to answer the most commonly asked questions just a reminder I will send a follow up email by end of day Monday for this webinar with links to the slides and links to the recording. So diving in here for Bob and Miles, you know, what it what I would like to know what your thoughts are on what a company can do from a governance standpoint that can enable the data engineers to have improved throughput. In other words, can what can data governance do to improve the data engineering science and analytics. I'll be glad to take that and then Miles see if you want to take a crack at that as well. What I have found is that oftentimes to engage the engineers. It requires that they're going to be, it's going to be need to be held accountable for the data that is associated with their job. So for example I worked with an organization recently where the engineers said data is not my job it's somebody who needs a job and certain information that was needed for the analytics and for the engineering of products wasn't being collected. So the organization came to the realization that if they're not going to do it we're going to need to have somebody who does it. So recognizing that there is going to be needed a level of accountability. I think you're going to find that analysts and that engineers are going to be held more accountable for data moving forward. That's something that you might want to consider moving forward. Yeah, I just add that you know, obviously we want to make their lives easier. We want them to be able to use the tooling to be able to discover where data is at to make their job work. Obviously for them integrations with various tool sets are important and so, but let's not lose sight and I know Bob this is going to be near and dear to you. We need to involve everyone in the organization and data governments and data. If we fail to do that then the process becomes an engineering exercise on. I agree with that. That's why, you know, when I say everybody is a data steward. Well, some people may be more related to the data than others, but people need to understand and become literate around data. And that includes the engineers. How familiar are with you with the data as a framework and interested to know your thoughts on that. That's good. That's an open question. The data, the data framework to me is a good tool to help you to understand the knowledge areas that fall under data management. And so I think that you see over time as as the data framework evolves. It's going to the those areas of discipline are going to continue to evolve to so I think it's a good thing it doesn't solve the problem to you because data governance as for example is one subject. It's smack in the middle of the demo wheel, but it, you know, it only takes into consideration certain things I'd say just do your due diligence, but use the demo framework as a very good starting point. Miles, what do you think. Yeah, I think the only thing I would say is that we shouldn't think as the presentation I gave to narrowly about data governance, it isn't just how you do compliance. It's, it's how you make good data that's useful to soak so obviously other elements of the demo model are impacted by data governance as well. Yep. That's a good starting point. It is. Yeah, so I'm very important question Bob. When do you anticipate your new book being released. Oh boy okay and now I'm going to have to start telling my asking my we're expecting that hopefully by the end of the quarter, maybe even before that so it's it's coming along quite well. I like it. We look forward to the from a federal perspective, we are being expected to inventory all data assets. How do you recommend leveraging data stores to catalog all data assets. Well, first of all, I mean, I think that's really appropriate in and again I'm not trying to push you towards the TV and publication but look at the article that I just shared about providing tools to the stewards of the organization to help them to utilize first of all what are the important data assets that need to be inventory and providing them with tools to help them to do it. So for example, giving them a tool or giving them a spreadsheet or something that in a consistent manner, they can collect all of their data resources, all their information systems, the main spreadsheets that they're using the reports that are being generated. So you can get your stewards. You can engage and leverage your stewards by activating them that way, giving them but they they you want to make it as easy for them as possible. So that's why I said provide them the tools that they can use, but I think it's a great idea to leverage your data stores to catalog at least the most important data assets. Yeah, one of the things I like to stress is that we kind of need to think, maybe turn it on its head, the original concept of data governance and by that I mean, we thought what we would do is we go figure out all the data we should have collected and we'd force a top down and I've talked to so many see I hated that process for a whole bunch of reasons. What we need to do is to let, instead of trying to do a top down exercise where you figure out everything in a vacuum. What we need to do is to let me let the catalog leave the process don't generate value for the business but at the same time, figure out what data is there. What data people are using, who are the people who are using that data, and then let that guide your process of data instead of trying to figure it out in abstraction. Let's find the data, find what people are using and drive it out that way. But do you agree that getting the stewards activating the stewards leveraging the stewards to catalog so I agree with what you just talked about may help you to determine what your most valued assets data assets are. Yeah, you don't want to just go and try to catalog everything, but be very focused on what you collect and make sure you understand the value that's being added, whatever you're asking the stewards to do. Right, and I'm, you know, I know this is part of your whole thinking but I'm thinking that data stewardship should be an organic exercise let's go discover who cares about this piece of data, and then activate them to help govern the data and the only way to do that is to find out what data people are tending to use and then relate that back with all the things you talked about the machine learning and the AI to really make this more of an autonomous process then something that is manually contrived. Yeah, and you know I think that I love the use of the term organic I might have to steal that from your miles at some point. It's very non invasive. It is very. So, Bobby you mentioned data catalogs metadata management and data stewardship in the non invasive governance. Can you go over briefly on the data quality aspect of data governance from a non invasive perspective. So data quality is typically one of the actions that you want to improve through data governance. So, having people. So if you're looking to improve the quality of the data that that honestly just it begs the question of do we have a standard do we understand what good data looks like, so that we can understand if the data doesn't look the way that we've defined it for business use and business purpose. So, the relationship between data quality and data governance is, you know, engaging the right people at the right time, making certain that you have the right documentation about the data, doing the checks, then changing people's behavior. When it comes to how that data is being created, if that is the source of your data problem so data quality and data governance they're very closely related data governance as a discipline. You can do data quality without having some level of data governance taking place. You can, you can do data governance though without having data quality in place. So that's how I see the relationship. I mean, I think, I think in many respects data quality is a form one of several types of data governance but some people are going to start more on the defensive side. I think doing something to work the improbability of data is important. And the reason is if you ask business users, and Bob's definition earlier was a great example that what they want is data that's trustworthy. So when they inspect data they want to know that it's safe to use to make a decision or a data model or a new report. It really doesn't matter what the use is. It really matters. And so it really does matter that we fix this. This is a great area by the way during a recession to invest in because if you can fix customer data, for example, you can come out and transform customer experience coming out of recession. Perfect. So, I think we have time for one more question here. So what has been your experience with marrying information management to data governance. So, I mean, the terminology has been interesting over the years there's been information management data management information resource management. What I talked about earlier was marrying information governance to data to data governance. And there's people that have the responsibility for defining data whether it's structured data or unstructured data. They have the responsibility for producing that structured and unstructured data and using that structured and unstructured data. And if we can get people to not only govern the structure data but recognize that they're being held accountable for how they're you know, protected information, classified information, how they're transmitting it, how they're sharing it. I mean, I think that's going to go a great way towards bringing together the disciplines around unstructured data and structured data together. So that's how I'm reading your question is it's more how do we marry information governance to data governance than information management to data governance. And I would just say that, you know, historically we thought about it as information or data management. If we go way back to the man with the cane and things like that. But, you know, the reality is, I, one of the things I really applaud are these notions around data ops, where they're starting to consider data governance for example is a key element of how you do data ops. And so we need to think about the process by which we create data the process by which we create information, and increasingly this I think this Bob needs to be in your slide. We're hearing people now want to govern models and things like that. So, I think governance is going to expand to include the analytics models and making sure that there's no bias or other issues coming into data. I like that I appreciate that consider it added. I love the collaboration. Well, Bob and miles thank you so much for these great presentations and the Q&A but I'm afraid that is all the time that we have for this webinar. So, thanks to all the attendees for being so engaged in everything we do in addition to, we'll get the, I'll get the questions unanswered to Bob. So we can get those out in the follow up email to which we'll go out to all registrants by end of day Monday with links to the slides and links to the recording as well. Thanks y'all hope you all have a great day thanks Bob thanks Miles and thanks to elation for sponsoring. Absolutely. Thank you.