There we go. Hello and welcome. My name is Shannon Kemp. I'm the Chief Digital Officer of DataVersity. We would like to thank you for joining the current installment of the monthly DataVersity webinar series, Real World Data Governance with Bob Siner. Today, Bob will discuss data governance and data management untangled. 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 and with each other, we certainly encourage you to do so. And just to note, Zoom defaults the chat to send you just the panelists, but you may absolutely change that to network with everyone. For questions, we will be collecting them via the Q&A panel. 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. 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 introduce to you our speaker for the series, Bob Siner. Bob is the President and Principal of KIK Consulting and Educational Services. 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 your presentation started. Bob, hello and welcome. Can you hear me okay? Can hear you now, yes. Okay, sorry about that. That was weird. No worries. That's not the point of doing a weird thing, but I think we're ready to go. Except that I'll see you next time, there we go. Yeah. Thank you, Shannon. Thank you, everybody. I appreciate you're taking time out of your busy schedules for today. Happy new year. It's the first chance I've had to talk to you in 2024. I know we're well into, Shannon has is over 900 webinars now, but I'm really happy to have you here. Real happy to be doing the Real World Data Governance series with you throughout 2024. I know I always say this to Shannon, that the topics are really relevant when they seem to come up in the year. And we schedule these things a year in advance of some of the topics. So data governance and data management untangled, that is a huge topic right now. So really looking forward to sharing that with you. I wanna share with you before we get started, just an interesting observation that I've seen, because oftentimes it's us folks. It's the data governance folks that are asked to justify their relationship between data governance and data management. And why do we need data governance? It's never the data management folks that are being asked to say, well, why do they exist and what do they do? And so many organizations have had data management functions a lot longer than they've had data governance functions. And so we need to untangle and we need to couple them together and uncomplicate the concepts of data governance and data management. So again, a very pertinent subject, very appropriate for today. And I'm happy to have you here for this webinar and hopefully for the rest of the year. Before I get started, I just wanna quickly share with you some of the things that I'm involved with that are coming up and things that are happening now. As you know, this monthly webinar series takes place on the third Thursday of every month. And next month we'll be talking about optimizing data governance with data governance frameworks and maturity models and those types of things. I hope you'll join us then. I'll be speaking at a couple of data diversity events that are coming up soon. One is actually next week. I'll be talking about where master data management and data governance collide in the Enterprise Data Governance, the EDGO event next week. So I hope you'll register for that and you'll join us next week. I'll also be in Orlando speaking at the Enterprise Data World Conference talking about activating data governance and data stewardship roles at that event. I talk a lot about non-invasive data governance. I've written a couple of books you might have heard of, Non-Invasive Data Governance and Non-Invasive Data Governance Strikes Again. I have several learning plans available through data diversity. There's one that's gonna be announced soon. In fact, we're gonna be recording it starting soon on data governance frameworks. So I hope you'll take a look at that. KIK Consulting is my consulting business, KIK. Excuse me, stands for Knowledge is King and that is the home of non-invasive data governance. And in my spare time, I'm also a faculty member at Carnegie Mellon University in their chief data officer program. So what are we here to talk about today? We really wanna look at these two subjects, these two disciplines of data governance and data management. And there are still some organizations that somewhat use these terms interchangeably. But the first thing we wanna do is we wanna gain an understanding of what the relationship between data governance and data management is. So I'll start with some basic definitions and then start to map together some of the activities between the two disciplines. We'll talk about techniques to establish and optimize your data governance framework. I focus mostly on data governance but wanna talk about ways to establish and optimize your data governance frameworks. We'll look for ways to align data governance and data management across your organization. I'm gonna provide several real-world case studies talking about data governance implementations and where data management should have or did play a role in the successful implementation of their programs. And then last I'm gonna wrap up with navigating challenges and achieving compliance because we know that the data landscape is constantly changing in front of us. So let's start out real quickly with a bunch of definitions. I use these definitions a lot. I've kind of even summarized them down to less words than I usually use. Data governance is truly the execution and enforcement of authority over data. Some people cringe when they hear that definition. You have to word, at least in my opinion, you have to word your definition of data governance strongly. At the end of the day, you need to execute and enforce authority. The government's not coming to you.  So we need to find out how to execute and enforce authority. Stewardship is the formalization of accountability for data. So people define, produce and use data as part of their everyday jobs. If they're being held accountable for what they are doing with the data, they become stewards of the data. It's not something people can opt into or opt out of. Metadata, again, typically data about data, but I provide a little bit longer of a definition. Let's spend one second at least focusing on the data management definition. And this is just kind of a collection of definitions brought into one. It's really a comprehensive process of taking most of the action that people take with data, the acquiring, organizing all the way down to maintaining and assuring the quality of the data within the organization. So I'm gonna provide a little bit further of a definition of what data governance is by listing out some bullets as to what it's typically responsible for, and then do the same thing with data management. Look at who the stakeholders are, figure a way to, how are we going to map these things? How are we gonna untangle what data governance and data management is presently to the organization? So again, like I said, we're gonna start by with some key concepts on each of these two disciplines. We'll identify the stakeholders, wanna kind of map the processes together and I'll show you how I've gone about doing that. We'll talk about establishing policies and procedures because that's a big part of either data governance or data management and talk about the importance of collaboration and communication in the process of untangling these two disciplines and bringing them together. So the first thing I wanna say is, I don't wanna offend any people that are data management purists who might be attending this webinar or who may be listening to this webinar at a later date. I'm not meant, I'm not meaning to put data governance over or under data management, but traditionally within organization, people are looking at data governance as being the rules of the game, not the roles, although the roles play a big part of it, but basically the rules of the game. And then traditional data management is the playing of the game. It's the implementation of a lot of the things that data governance is telling us that we need to put in place around the data. It also is a little bit more, for the most part, a little bit more technical than data governance. In terms of non-invasive data governance, I often refer to it as people governance. People have referred to it as people governance because it's really people's behavior that's being governed in data management. It's the implementation of all the frameworks and everything that's being established by data governance. I'm not saying that data management is completely dependent on data governance. I'm just saying that there needs to be some level of collaboration, at least when it comes to policy roles and responsibilities and those types of things. So really data management is so much more and data governance is so much more. So I'm gonna jump into just kinda outline quickly what some of the core activities are in each of these two different disciplines. So when we think about things in terms of data governance, oftentimes we think of things in terms of policy setting and regulatory compliance.  It's the setting up of the roles and responsibilities specifically focused around accountability, around the stewardship that I talked about earlier. It's about data quality control, although this one kind of blends into the data management side as well because data management is very active in data quality. In fact, data management teams have been active in data quality way before data governance came along, or at least the term data governance came along. And then strategic oversight. You don't see as much strategic oversight coming from data management as you do see coming from data governance at this point. So the key concepts around data governance, policy setting, regulatory roles and responsibilities, quality control, strategic oversight. That's just in a nutshell. It's a way to summarize on one slide because we know data governance can be so much more than just these things that are listed here. And there's a lot of sub components to those things. All right, let's talk about it from a data management perspective. Data management, they're not the same five bullets that I shared for data governance. It has to do with the handling of the data. The technical implementation, I mentioned that a second ago where data management is oftentimes viewed as being more technical than data governance, which is really more people focused, people in process focused, data accessibility, operational execution, data security and privacy. A lot of these things are things that the data management function focus on. Are they related to what I talked about in data governance? For sure they're related to what's happening in data governance. What we really need to do is we need to map together. We need to untangle and then kind of remap how data governance and data management relate to each other. So one step is one thing that we need to do is first of all, identify who the stakeholders are. Are the stakeholders from data governance the same as the stakeholders for data management? So in data governance, I've listed out here who are some of the primary stakeholders in data governance, executive leadership who's defining the strategy for the organization, the strategy to use data as a valued asset, moving forward, the data governance council that represents the different business areas within the organization, compliance and legal teams, the stewards, the business unit leaders, those are the primary stakeholders of data governance. And it's not to say that there aren't other stakeholders in data governance because your customers are stakeholders in data governance. Pretty much everybody in the organization is a stakeholder in making sure that your data is being governed properly. So I just wanted to list a few of the stakeholders of data governance. When we look at data management and we're trying to identify the same thing, I don't have the same list at all. If I say that typically data management stakeholders are the IT professionals, are the data analysts and the scientists that are using the data to help to make the decisions that need to be made by the organization, the database administrators, the operational staff, the architects, typically the key stakeholders. Although I guess you could say that all of the stakeholders for data governance would also be stakeholders from data management, but specifically the day-to-day activities of data management are focusing on helping these people within the organization. And I'd be glad to hear from you if you think differently or if you have others to add to the list, please consider adding your thoughts to the chat session or Q&A as well. So one of the things that we need to do in order to untangle data governance and data management is to map together some of those core processes that I just talked about. And I could have done this one of a couple of different ways. I could have drawn lines between and say, okay, this one relates to those, this one on the left relates to those things on the right or these things on the right relate to those things on the left and draw a lot of lines connecting the dots because I think you could almost draw a line between each of these and everything on the other side of the equation here. So I decided not to do that.  What I decided to do instead was try to put, bring together a statement that encompasses both what data governance and data management are focusing on. So enhancing data quality, improving compliance, efficient data usage, increased accountability, all of these types of things but let's make statements that relate the data management and data governance together. So mapping data governance policies to the data management activities ensures that your data is gonna be higher, gonna have a higher quality, the reliability and the confidence in the data is gonna be higher. Again, what I've tried to do with this page is just kind of bring together a statement and still instead of drawing those lines and drawing the relationship between those things, if you agree that these things are some of the core processes for each of the two disciplines, we've gotta start thinking about these things in terms of data governance and data management together. So again, here's just some ideas of how can you go about mapping those things that you're telling your organization that data governance is responsible for or those things that you're telling your organization that data management is responsible for. I've even noticed this was kind of pre COVID that the people from the data governance team and the people that had responsibility for data management, they weren't even sitting at the same tables at lunchtime. They, these were literally two separate functions and we've gotta find a way to be able to map and untangle how they've become separated and get them to collaborate and to work together. So again, this is just one way of doing it. Let's map the processes, let's put together some strong statements that we can stand behind as part of our organization. So one of the things that we need to do for both data governance and data management is we need to define and establish policies and procedures. So around data governance, you would think that they would be similar and I would think that you would want to mention the collaboration between governance and management and data management in these policies, in these things, in these artifacts and things that you're creating. Well, as part of the establishing of the policies and the procedures, there's the development of the data governance framework from a data governance perspective. There's the creation of the quality standards. Again, to work with data management to make certain that the quality checks are taking place and that we're taking the steps that are necessary to improve our data quality. The establishment of privacy and security policies. All of these are specific activities of data governance. These are the types of policies and the types of things that are being put in place. And I can tell you that from experience right now that with the organizations that I'm working with, these are truly a lot of the activities that they are focusing on specifically and it starts with policy. Policy guidelines, standards, procedures, whatever that order of hierarchy is within your organization. Policy always seems to, or something that would be related to the policy always seems to be first. So let's talk about the policies and the procedures as they would relate to data management. So they would be defining how the data is gonna be collected, how new data is gonna be added to the organization. What are the protocols for how the data is going to be entered? How are we gonna store this data and archive this data and provide retention for the data and get rid of data when we need to get rid of it, implementing quality assurance. All of these are specific policies perhaps that would be the responsibility of data management. But again, they shouldn't be created in a silo. They need to be created along with data governance because there really should be one set of policies and standards and guidelines for the organization, at least depending on what the size of your organization is, but these things should be, there should be a collaboration between data management and data governance in order to untangle any of the differences. And you should very clearly state who's gonna be responsible for doing what. And oftentimes that starts with the leadership of your organization. And is there a chief data officer or somebody in a similar position to that that has responsibility for both the management and the governance of the data because they are different things. They're very closely related, but they are different things. Another way of gaining understanding of the relationship is to take some other actions, is to clarify the roles and responsibilities. Make sure that the folks that have a responsibility for data governance understand what the roles and responsibilities of data management are and vice versa, and get them to understand. And not only that, potentially participate in the authoring of what those roles and responsibilities are. So they're not being brought to these folks and said, okay, data governance do this. Data management do that. Let's collaborate, let's bring them together. Let's clarify the roles. Let's facilitate whatever opportunities we have for sharing the knowledge that we have about the data. Because we're all pushing in the same direction. Align the strategic and the operational roles. Again, I'm just trying to suggest some activities that you can take in your organization if there is any conflict or if there is any discrepancy or concern around the differences between what data management is doing and what data governance is doing. Okay, so now let's talk about, let's spend some time talking about some of these techniques to establish and optimize your data governance framework. Because like I said before, most of my webinars here focus on the data governance framework. They don't focus as much on data management, but I wanna share with you just so when you look through the core components of a data governance framework, you're thinking of it in terms of data management as well. We'll talk about the roles and responsibilities, policies and standards. Let's talk about a few of these techniques. So if you've attended my webinars in the past, I talk a lot, I'm actually really excited about the data governance framework that I use. Because what it does is it addresses all of those things that at some point in time, you and your organization is gonna wanna make sure that you've documented and that you have an approach for. John Zachman used to talk about his Zachman framework and say that every one of those blocks in his framework, his framework for enterprise architecture, at some point in time, you're gonna wanna know about each and every one of those blocks. Well, that's the same thing for this data governance framework because those six core components are really the basics of a data governance program. The data, the assets that are being governed, the roles, process communications, all these are what I consider to be the core components of a data governance program. I've actually asked people at some of the presentations I've given at data diversity events and other events, am I missing anything? Is there any components that I'm not talking about? And you know what? The nice thing about the framework is you can customize it.  You might have different names for the core components or you might have different core components. But the idea of cross referencing the core components to the levels is really important. And then there's the organizational levels. And like I said, every organization doesn't define them the same way, but this is pretty standard. I mean, you've got an executive level, perhaps a strategic. I've had some organizations tell me that I have the tactical and the operational flip-flopped and they should be the other way. Again, build a framework in a way that makes sense to your organization. And while you're doing it, think of it in terms of not only data governance, but think about are there other core components to data management that could be included that we could actually expand this framework to include both data governance and data management. So I know that framework is kind of small. I wanted to provide a little bit larger of a version of it. And as you can see across the top, all of the core components that are necessary. Actually, when I started building the framework, I didn't have a data column, but we really need to be concerned about the data that's important to the executives, the roles at each of the different levels, the processes that are important. I find it to be an easy way to be able to describe all the core components of data governance. The more I think about it, you know, a lot of the levels aren't gonna change for data management. A lot of the core components for data management, may focus on the tools at the end of it, may focus on the process end of it, but they're gonna be some of the same core components. At least I believe that that's the case. And when we talk about the different levels from the executive down to the support level, oftentimes I've done webinars strictly on the data governance operating model that I share. But if you notice, each of the different levels are represented. And so we need to look at each of those core components from each of those different levels in order, and you're not gonna do it all at once, but in order to cover your entire organization, you're gonna wanna know each of those core components from each of the perspectives of the different levels in the organization. So I'm not gonna spend a whole lot of time talking about the operating model here. Again, I'll make it bigger so you can take a look at it, but you can see that each of the different levels that are represented within the framework are represented within the operating model. That's no coincidence, that's required. In fact, if you think about how would we complete the roles column of the framework, well, we wanna make sure that we're mentioning who this, that we have senior leadership support as a steering committee. We've got a council or some similarly named body and we've got the domain or subject matter stewards and the data stewards and all the other people in the organization on the left-hand side that are gonna be involved in making and assuring that our data governance program is successful. So some additional ways to be able to, besides for the framework, some additional techniques that you might wanna consider to establish and to optimize the framework if you're building out a data governance framework. The first one is focused on data governance policies and standards. And I wanted to share with you some of the things that at least I would consider as steps that you might wanna consider taking if you haven't already taken them. It's always good to start with an assessment. Instead of taking a ready-fire aim approach, it's good to have an idea as to what you wanna target your activities at, what you wanna focus your activities at accomplishing for your organization. So you wanna start with a maturity assessment, defining the clear governance objectives, also defining the clear data management objectives as well. Developing comprehensive data standards, all these things in terms of data governance policies and standards are things that you might wanna consider when you're establishing and optimizing your data governance framework within your organization. Data quality, again, that was the one item that I said really blends closely between data governance and data management. I'm sure there's others, but every organization talks about data quality, it might mean a different thing, something different to each organization, but because there can be quality definition of data, there can be quality production of data quality use of data, certainly all of those things are gonna bring data governance and data management together. So some of those techniques include implementing data quality metrics, working together, data governance and data management to define what those data quality metrics are, establishing the quality improvement processes. You might see that data management plays more of a role in integrating the data quality tools, but who are the customers going to be? Again, go back to the stakeholders. The stakeholders of data governance are certainly going to be recipients and beneficiaries of integrated data quality tools, conducting regular data audits, promoting data quality awareness. Again, these are some techniques that you can take to optimize your data governance framework. And then the last set of these that I wanted to share with you is it have to do with data privacy and compliance considerations. As I would say, every organization has data quality issues. Every organization seems to have accountability issues around data, but one of the things that they're not being permitted to skirt around or to avoid are the data privacy and the compliance considerations. So I wanted to share a couple ideas of techniques that you can do to optimize your governance framework around data privacy and compliance. One is to make certain that you're aligning with regulatory compliance. There's gonna be a data management side to that and a data governance side to that. Implementing access controls, conducting privacy. Again, there's a lot of things to share on the slides. I'll never get through everything that I wanna talk about if I go into too much detail on these things. But if you're looking for ways to optimize your data governance framework, and it's focused around some of these things that I consider to be core activities for both data governance and data management, these are some activities that you might wanna consider taking or steps you might wanna consider taking. So let's talk about what types of strategies. Okay, we recognize that we have data management and we recognize that we have data governance and they may not be communicating all the time or it may be unclear as to who does what and when and who's responsible and who's accountable. So we need to put together some strategies for bringing these groups together, for bringing these functions together. So the first thing is to look at the data structures, at the structures, specifically I'm gonna start with the structure around data governance. Talk about aligning your data governance effort with your strategic initiatives, with your business strategy, aligning, integrating the activities, getting the folks from data management to work alongside the people from data governance where it makes sense. It's not like organizations have resources sitting around waiting for things to do. Both the data management function and the data governance functions are extremely busy. If there's any duplication or if there's any way of them working together that's gonna make it more efficient and more effective, we as an organization should be looking at that. Data governance communications and training, measuring the effective of governance alignment, we need to look at all of these things. So what is one of the strategies that I suggest for organizing and aligning your governance and your data governance and your data management is again to go back to those clear roles and responsibilities that I just shared with you. Make certain that we are addressing the appropriate people at the appropriate levels of the organization. In most, I haven't really seen operating models with roles and responsibilities to find that way as a standard for data management. You see that more often with data governance. So if you define your data governance roles, you can utilize the stewards as a way to kind of build the data management activities into what they're doing. So again, in looking at the data governance structures, you wanna create the cross-functional committees. Many organizations have that steering committee at the executive level, which is certainly cross-functional. And then the data governance council or the data governance committee, whatever at your strategic level is also cross-functional. Implement your data governance framework, again, doing it across the departments. A lot of these are ideas as to again, ways that you can align governance and management across your organization. And you may be doing some of these, but as I started to peel away the levels of the onion in comparing the two, I started seeing that there's a lot of similarities between the things that data governance are doing and data management are doing. What we need to do is have a strategy for bringing the functions together. And organizationally, that makes sense too for many organizations. If data management falls under IT and data governance falls under risk or finance, that could potentially cause problems because they're reporting up into different parts of the organization, their goals, how they're being measured may be different, depending on who they're reporting up into. So again, it's not an easy thing to establish a role that has responsibility for both data governance and stewardship or I'm sorry for data governance and data management. But again, we're looking at ways to untangle the mess that has become the difference or the ways that data governance and data management work together.  And what are some of the ways that we can go about doing that? Well, the first thing is we can integrate our data goals with our business objectives. We can incorporate data insights into decision making. Again, a lot of different steps of actions that we can take as an organization to align data governance with the business strategy. Oftentimes that's in the strategy itself that data governance and data, what the definition of the purpose and the function of data governance is and the purpose and the function of data management is. Integrating the two together, adopting some type of a unified framework, sharing in the objectives, defining objectives together, collaborating across the board on activities. I mean, to me it makes sense and some of the examples I want to share with you here in a minute, there's organizations that had meetings that brought together governance and management of the data in the same meeting so that we could collaborate and we could have those types of understandings as to what activities each of us were working on. Coordinating the data lifecycle management, throughout the data management lifecycle, there is an activity for governance, there is an activity for management. This should certainly be aligned. And then the last thing I wanted to share here is around the strategy, around the technology solution. Additional strategies for aligning governance management have to do with communication and increasing people's awareness on both sides of the equation. Getting the folks in data governance to truly understand what the purpose and the mission and the activities of data management are doing. We need to increase the awareness through communications and training. That means improving the training of people on the data governance side and on the data management side and exposing that to the entire organization so that people understand when they hear these terms, they're not the same thing. Just because they have the word data in them doesn't mean they're the same thing. There are in most organizations or at least in many organizations, they are specifically different functions. And we need to do that. We need to increase that awareness through communications and training, through measuring the effectiveness of the program, developing the KPIs and reporting. Again, if we're looking for ways to align governance and management, we can do it through the KPIs, through the reporting, through the feedback loops. Again, the longer we keep these as separate functions, the longer people are gonna view them that way. And to some degree, they are separate functions but we need to communicate together. It's not as though data management and data governance are completely different. They are sharing in their goals, which is to again, to manage and to get effective use of data as a valued asset of the organization. So I'm gonna run out of time real quickly because I knew there were a lot of slides for the amount of time that we had. I wanna go real quickly through some case studies, specific implementations of data governance and maybe share with you a little bit of oversight as to where data management played a role in that. In a lot of the situations in many of these organizations, data governance and data management were separate functions. So let me go through some of these quickly. And I'm just gonna talk from experience because these are the things that have happened recently with organizations. One thing you'll find with each of these different case studies with each of these success stories is that the ones that were more successful, they had a higher level of executive leadership support and sponsorship and most importantly, understanding. In fact, I wrote an article published and on LinkedIn this week about support sponsorship and understanding. Again, you'll see that the organizations that are most successful are not the ones that are fighting against the top of the organization. They're the ones that are being empowered by the top to be successful. And in this healthcare example, the chief medical information officer was the person that was driving the success, the successful implementation of data governance and the successful integration with the more technical people in the organization that we're focusing on data management. So again, this organization, they also had a very strong, they hired a very strong data governance manager. That's another thing that I think, or I don't know if that was their exact title, but that was their responsibility, maybe the data governance administrator. You'll see that the organizations out of these examples that were successful, they had strength in that area as well. Not only executive leadership, but the people that day to day were implementing the solutions. Here's one in a banking example. This one is working on being as successful because they're fighting an uphill battle within their organization. The people that have responsibility for their data governance program, they're spending a lot of their time down in the weeds and doing those types of things that are important to keep data management and data governance moving forward within the organization, but they haven't established data governance. They don't have an established chief data officer, but they're giving lip service to it, but when push comes to shove, the resources need to be applied to it too. So like I said, the one thing across each of these case studies where they've been really successful is that the leadership sees the need for these things and is really standing behind and empowering people within the organization. In a retail example of an organization I'm working with, again, they're down in the weeds every day. They've developed their operating model. They're enhancing privacy and protection. They're looking for unique ways to be able to use data across the organization. What's interesting in this organization is that the chief financial officer, CFO, was the person that was really driving data governance from the top. It was very specifically data management focused, customer data management focused, and it was very MDM related. So some of the topics about MDM and data governance, I'm gonna talk about them at EDGO next week. So please join us then, like I said, but this is again, another example of organizations that are using a data governance framework to be successful. In the manufacturing, I've got many manufacturing examples. Again, the core item that was most impactful on their success came from their leadership support. If they didn't have leadership support or they didn't have leadership sponsorship, typically it's easiest to get support and sponsorship. What really happened was we needed to spend a whole lot of time getting their executive leadership to understand what data governance is, understand what data management is and how they relate to each other. Oh, data management, we've had data management forever. What's this new data governance stuff? That's what I talked about at the beginning of the session is that we seem to be the ones that people are coming to us and saying, well, where does data governance fit in? Cause they seem to get data management, but they don't seem to get data governance as much. Government, public sector, working with a lot of organizations in this sector as well. Most of the time they have a mandate or a directive that's coming from above. And again, the one consistent thing through each of these case studies was that senior leadership support sponsor and understand what the organization was doing and stand behind it. And it's not a constant battle of, do they support us? Do they sponsor us? Do they understand what we're talking about? So what are some of the things that you should consider for data governance when you're in a large organization? Again, for a sake of time, just gonna run through them somewhat quickly for you is the scalability. I have a couple of organizations I'm working with today that in a conversation with a client, I shared what I'm doing with one with the other. They said, wow, you're doing a lot of the same things. But the thing is those organizations are vastly different in size. And so what are some of the things that they need to be considering as they're moving their data governance program forward? Well, number one is the scalability. When you've got a smaller organization, there's less scaling to do, but scalability is still an issue. Again, you can't flip a switch and have governance come on to the entire organization. You've got to be able to take it from one area and expand to other areas in the organization. Do it incrementally. There's the complexity of management in the larger enterprises. Stakeholders are more vast and more widespread across the organization.  It's not as though these things don't apply in a smaller enterprise, but obviously it's a different scale. Some other considerations for data governance and regulatory compliance. Again, look for ways that data management is also focusing on regulatory concerns, on audit trails, on privacy and security and find ways for your data governance initiative to partner with your data management initiative. There, believe me, there's enough for data governance to do and enough for data management to do that data governance is not typically trying to infringe on what data management is doing. And the same thing holds true in reverse. You don't see data management functions trying to take over some of the governing functions. Instead, what we should be doing is sitting at the same table together and finding ways that we can address regulatory mapping, audit trails, data privacy and address these things together. Considerations for data governance in terms of data monetization. That's not an area that I have a whole lot of knowledge around, but organizations that are trying to monetize their data look for ways to be able to bring your data management and your data governance functions together. Because these are, again, these are real world examples of what your organizations are trying to achieve. And if you've got two separate functions, trying to achieve the same thing, you're gonna have, it's gonna be a harder situation.  We should always be looking to communicate together, collaborate, coordinate, cooperate together. That starts with some of the things that I talked about at the earliest part of this webinar, which is let's define specifically the functions. Let's define specifically the stakeholders and let's map these things together. And again, the same thing for healthcare and privacy compliance. So there's a lot of different ways, there's a lot of opportunities in our organization for our data governance and our data management functions to work together. And like I said, early in the session, you know, it's important to be very clear as to who's responsible for what because organizations aren't gonna be successful with compliance. They're not gonna be successful with security. They're not gonna be successful with quality. If data management is fighting against data governance, if there's not a partnership between those two functions. So the last thing that I wanna talk about before I turn this over to Shannon is I just wanna give you a couple of lists that I have of things that you again, might wanna consider when you're trying to bring the data governance and the data management functions together. First, we need to understand how data governance and data management must become untangled in order to address these types of things like data privacy regulation security and cybersecurity. So I'm just gonna flash through them quickly and give you an idea. And I hope you'll refer back to this slide deck from this webinar when you're considering what are some of the things that we can be doing as an organization in order to bring these two functions together. So around data privacy regulation, stay informed about global privacy, work together as an organization to improve the global regulation awareness across the organization. The data subjects, the data subjects rights and the cross-border regulations that organizations put up with every day. From data security and cybersecurity perspective, here's again, just some ideas as to ways that we can bring our data governance and our data management functions together. You know, you think about access control. In fact, out of the five that are on this screen right here, the ones that I find most organizations are really having problems with is making certain that the appropriate access controls are in place. Data governance can't do that by itself. Data governance can define the standards. They can define the roles and responsibilities. Oftentimes it's gonna be the data management function that has the responsibility of the implementation of the access control mechanisms. Data classification and sensitivity, you know, training on data handling. I'm gonna pick out that one from this list here and say that, you know, somebody in the organization has to have the responsibility for improving the awareness that people have around data and what they do with the data. And they have to understand the rules and how they can use the tools that are available to them to follow the rules. So training on data handling is really important. Again, another opportunity for data governance and data management to work together because you're not gonna solve the data handling and issues within your organization by having these two functions operating on their own. Data retention and data deletion policies. Again, opportunity for data management and data governance to work together. So again, I know I've shared a lot with you in a very short period of time. Again, just think back to what I started with in the clear definition of what do we mean by data management? What do we mean by data governance? If we define them the same way, people are gonna be confused. If we're very clear about the differences and we share those with people across the organization as part of their data awareness, as part of their data literacy, people will start to see that data governance and data management, they're truly two separate functions but they need to come together and they need to collaborate, coordinate and like I said, work together. And again, some other ideas around the evolving data landscape. These are again, all ideas of bringing our data governance and our data management functions together. So like I said, I've gone through several items pretty quickly for you. The first thing I wanted to do was to provide the understanding of the relationship between data governance and data management. So we started out with definitions of these of each of the two different disciplines. We talked about techniques to establish your governance framework and to optimize your framework across your organization. Strategies for aligning governance and management. I went through very quickly but I went through some real world case studies that showed data governance being implemented. And I talked about how the importance of the senior leadership support sponsorship and understanding how important it was to the success of those specific initiatives and how data management plays a role in each of those different things that I outlined and each of those organizations had done to be successful. And then I talked about navigating the challenges and achieving compliance because we know that the data landscape is forever changing. And with that, Shannon, I am losing my voice and I am going to send it back to you to see if we have any questions. Well done there. Well Bob, thank you so much. And just to answer the most commonly asked questions just a reminder, I will send a follow-up email to all registrants by end of day Monday with links to the slides and links to the recording. And before we continue, Bob, I want to say congratulations on 10 years of non-invasive data governance since the launch of your book, Non-Invasive Data Governance, that's amazing. Thank you very much. It makes me feel old. You can't feel old because then I have to feel old too and we're just not going to go there. As you and I talked about, I mean, the idea of non-invasive data, the idea of it came from back in the corporate days. So the concept's a lot older than 10 years. Finally, when my publisher convinced me to put the book out, that was 2014. So thank you for the congratulations. Absolutely. Well, diving into the questions here, Bob, you compare and contrast Dama Data Governance Institute and DCAM frameworks. How much time do we have? Well, you know what, they're all good. I would say that. You need, if you're doing an assessment, if you're looking to see, to evaluate your data governance or your data management functions, they all have really strong breakdowns of the functions and they give you a lot of good questions and things that you should ask. Do I, I don't feel stronger about one than I feel about the other. I think it really makes sense to pick the one that seems to be aligned with your organization the best, but there's not one that stands out, at least in my mind, stands out over to the other. I tend to use all of them, at least in bits and pieces, but it's a great question. And if anybody has some ideas that they wanna share, please do that through the chat or the dataversity community, because there are a bunch of models to be able to follow. I think that my framework that I shared in this session could be used to help you to address everything in all of those models. All right. So do you have some examples of data management, data governance efficiency metrics? One that quickly comes to mind is an organization that created a substantial intake process for issues. And so they weren't being, people would not be submitting issues specifically around data management or specifically around data governance. They would just be submitting issues and submitting opportunities that they would have. And this was a great example of data management functions and data governance working together because those reviews of those issues and opportunities that came in through the intake process were done together. And the decisions were being made together as to who is this fall under? Is this more of a data governance issue or a data management issue? And it was divvied up to the appropriate people. So that's a good opportunity of an organization that actually took these two functions and kind of married them at the hip, at least proactively. Now, reactively, it might be a little bit more, a little bit different of a story, but proactively for people addressing new issues and opportunities, that's one idea of what one organization did. So data security, cyber security, privacy, et cetera, are often held by the IT information security team. How does that team play with data management and data governance? Well, you know, when I talk about data governance, I always refer to the idea, especially non-invasive data governance, I refer to the idea that there are other governing functions taking place in the organization. And all those ones that you just named, Shannon, those are all governing functions. And data governance does not want to take over the responsibility for those functions. And data management doesn't want to take over the responsibility for those functions either, but oftentimes you'll find that data management and those things that you just named, they do fall under IT. And again, it's a matter of bringing the more business-focused data governance alongside the more technical-focused data management. You should see I'm waving my arms around here like you can see what I'm trying to explain to you. That's, you know, I kind of lost my train of thought, but that was, that's just one way to look at it. So Bob, is data management and risk management the same thing? And data risk management, I should say. Data management and data risk management. I'd say that data risk management is a component of data management. I think that most of the frameworks and the models that were just described by people would agree with that. That risk management is that, well, because risk management can accompany a whole bunch of different types of risk. Data risk management is certainly one aspect of the overall risk management in an organization. I love it. So we've got about seven minutes left, a little lesson. So I'm gonna get in as many questions as I can here. Do you think with proper data governance since it's the foundation, all other areas of data have been increased percentage of success? Wow, that's a question. You know, we've been doing these webinars long enough that you get questions that you never, you never have gotten before. I do think that data governance has helped to elevate people's, elevate the conversation around the different aspects of things. So repeat the question again, because I wanna make sure that I address it. Yeah, absolutely. So how do you think that with proper data governance since it's the foundation, all other areas of data have an increased percentage of success? Well, I think that all of the, if you look at the demo wheel, for example, there's a whole, there's, I don't know, 11, 12 knowledge areas that are around the outside of the wheel. But what do they have in the middle of the wheel? They have data governance. So I think that data governance, not only will it help to elevate the success of those different areas, but the roles and responsibilities, the formalized accountability, the working alongside data, alongside data management has to be in place. So yes, I think that data governance is going to have an impact on all of those things. And the fact that people are talking about data governance more and more gives us the opportunity to elevate in people's eyes throughout the organization, what all of those different disciplines, that data management is focusing on. So I think that's a great question. Thank you. Trying to slip in as many, and keep the questions coming, everybody. So because Bob gets the questions and then he'll type up the answers that we'll include in the follow-up email. So, but I'll try and get in as many verbal questions here as possible. And he'll lose his voice after we're done with it. So how critical is it to have technologies, so example, a data catalog, data quality tool, MDM in place, and at what point is an organization ready for these? So I think these could be topics for complete webinars. So how important is it for them to have those technologies? It's very important. I don't know if I would say that you need to start out with the technologies. In fact, sometimes you won't even know what the requirements are, what you need out of the tools and the technologies that you're gonna acquire until you start setting up some of the foundational items. But how important is it to have the technologies? If you want people to know what data you have, want them to have confidence in the data, can help them to find the data. You wanna protect the data appropriately, grant access appropriately. You need to have these types of tools available. So that's my question. My answer is that you need, they're very important to the success of a data governance program. But I've also seen organizations buy the tool first and then try to adjust their program to match the tool. That's not the recommended protocol. Number one, it's a blessing to have a tool to begin with. So if you have a tool first and the rest of your program is following after, consider yourself fortunate because you have a product, you have a tool, hopefully it's an appropriate tool that will help you along. So you don't necessarily need to buy a tool, but if you're given a tool as you're getting started, take advantage of it because the data governance tools, the catalogs, the data dictionaries, the glossaries, the those types of things are gonna be instrumental to your success. Indeed. Okay, we got about three minutes left. So I'm gonna slip in at least one more question here. So I would love to get more information about how to host a successful domain council and steward forum and helpful content to share with the stakeholders. Are there any books or demos to show agendas and content? I mean, I'm writing those down, okay? So domain council and a steward forum. I don't know of organizations that use the term domain council, but I guess if you're bringing together like minds that are focused on the same subject area or as I put it data domain, having a domain council would make sense. Having a steward forum, I actually am working with an organization actually two organizations that are setting up stewardship programs and they both revolve around including centers of excellence. So do I know of any resources that people can go to? Now, I'd be happy to have a conversation with you whoever you are who asked the question, but I would say go to data diversity. Do some searches on the keywords that you're looking for and take advantage of all those resources. I don't know if they'll specifically talk about data domains and stewardship forums in using that language, but I think you'll find that there's a lot of beneficial information to you there. We may have one more. I should turn for one more. Could you clarify the difference between the tactical and operational organizational roles? They seem very similar. Well, and so in my mind, they're very clearly different. So the tactical, if you consider tactical, tactical is a cross business function and operational is within a business function, at least in the operating model that I shared. And so I talk about the operational people, most organizations try to solve as many of their problems as they can and as low of a level within the organization as they can, push them down to the functional level. That's the operational level. That's why the amount of area within that part of the triangle or the pyramid is greater. Then you have the tactical level and that is, and it literally says on the operating model, cross business function. So now these are people that are, where we're trying to break down the silos of data, the tactical level, those are the people that are looking at data across business functions and typically the operational folks are the folks that are specifically within a given business function. That's the way I address it. Perfect. Well, Bob, thank you so much. Again, there's so many additional great questions out there, but I will get those over to Bob to get answers about and we'll get a follow up email by end of day Monday to all registrants with links to the slides recordings as well. And I hope to see you all next week as Bob gives another presentation in part of Enterprise Data Governance Online. I'll throw that in the chat as well too. I'm really excited about that agenda. So I'm excited for your talk there too, Bob. Thank you. Hey, we're talking about data governance and data management here, data governance and master data management there. We can do a whole series on these things. Yeah, indeed. I'm not surprised MDM came up today. So it's a hot topic. All right, Joe, well, thank you so much. I hope you all have a great day. And again, we'll get you a follow up email by end of day Monday. Take care. Thanks, Bob.


So good afternoon, my name is Jason Watson I'm delighted to be your new host today on what is now episode two of the third season of the show First of all, I want to say a big thank you for Tom Tom Hall who was out my predecessor and did a such a great job I'm sure is watching this and of course a massive thank you to you the audience for allowing us to give Giving you an excuse to keep doing these shows. Yeah Great look we love spending this time with you geeking out on all topics around Windows Server 2022 and It's come up to the end of month four now at Microsoft for me and the huge part of our culture is all about learning Which I'm loving quite frankly and but the idea of this show is to share that wealth and you know expand on that learning culture So each episode of from rock to the cloud We will have some of the world's most foremost figures in Windows Server to help you get whatever you need To know or just what you want to know about Windows Server And as always if you have any questions about the episode, please make sure you pop to me into the the comments section below So let's look at today's agenda. Yes below. Thank you very much Pierre Today's agenda and today's episode is all about managing your Windows Server with Azure For the next 30 minutes, I'll be catching up with yours truly here. Mr. Pierre Roman, I'll let him introduce himself in a second And we also have some elements as always a little bit of fun That that you guys can get involved with so please do stick around So From here on in We would like to introduce you to Pierre Roman who today is joining me Pierre Can you please introduce yourself to the audience who's watching? Yes, my name is Pierre Roman I'm a senior cloud advocate with Microsoft and you mentioned that the foremost experts The the world at foremost experts were on this show. They couldn't be here today. So you get me No, I've been I've been in it for and I'm going to age myself here for over 30 years How many more over How many more over 30? Uh, four more over 30 Yeah, well you've beaten me by two actually Pierre so uh, also show my I think it must be the gray, right? Yeah, you can tell by that's why I shaved the head that way that my my head my whole head is not silver But in that time we've managed everything from vax and and mainframes to servers to and now cloud So we've been go we've gone through multiple transition periods in the world where Like technology drastically changed the way we do things and cloud is here now and we're in the middle of this shift uh to the cloud and Don't get me wrong. Uh, we are at some point going to be mostly cloud-based, but I think that and Uh, Jason zander our uh, corporate vice president for uh, azure The last on the last in person event that he spoke at Uh said that's iBrid is now we realize that iBrid is our customers end state So when you're talking to Marketing people and you're talking to sales people are you talking to a technologist of any kind?  That's not true Yeah, iBrid is our end state but because of that we had to come up with multiple ways In easier ways to manage All of your machines regardless where they may be In the same way because nobody wants to have to learn Three different sets of tools to manage three different sets of machines Indeed not efficient silos.  I don't know about you No So shall we jump into today's topic? Absolutely. Are you ready? Yeah, so, um As I say the topic title is managing your window server with azure So i've got some questions pierre that i've kind of prepared I hope you're gonna be in a good position to answer them. I don't want to catch you out by any surprise or anything I'm sure that's not going to be the case. So let's look at question number one Shall we how does cloud services improve your on-prem environment? How does cloud services improve your environment? No first thing Is that cloud services are always evolving? So as New threats are coming in new management um paradigms are being Brought forward Those tools will be evolving and if you take advantage of them You don't have to Deploy everything again on-prem like I love System center Configuration management and all of that system center type of management tools that that are still available to date and are great great um management tools But anybody who deals with them knows that whenever there's an update It's it's a project in itself just to roll out the updates and you need an infrastructure to manage the infrastructure So you end up like a chicken and the egg. Well, how do you manage the infrastructure that's managing the infrastructure? Yeah by using cloud services you kind of Eliminate holes kind of layer of complexity over your on-prem environment But we're not just talking about on-prem environment. You may have servers on-prem. You may have servers in aws You may have servers at a hoster Uh somewhere else down the road Uh and some in azure Who knows maybe all of them all of the above maybe gp gcp as well You don't want to have to have different sets of tools for managing all of these or having Four times the same set of tools in four different locations. So you don't have a A top-down view of all of the responsibility areas that you have You don't have a top-down view of the health of your environment you don't have a top-down view of Whether or not attacks or occurring and where they're coming from and where are they going? So when we're looking at cloud What cloud services can do to help you manage your windows server environment? That's what it does is it simplifies that it allows you to take advantage of best of breed And all of the innovation that's being put into it all of the ai that's being put in the back of them Because you can collect as many logs as you want like I mentioned I've been in the business for a long time like one of my first job My my my role on monday morning was to go to the server room and log into every server and go as windows server Uh and basically go to events viewer and look for any red Stop signs on the event viewer and then investigate whether or not this was an actual critical that we needed to deal with Or was that just informational and so on? brain human brain's not meant to be going through hundreds and hundreds of lines of Of logs by by line 200 your brain's not even registering trends. You're seeing Yellows and greens and reds, but you're not registering any trends Yeah, absolutely. I mean this i'm assuming these are the days before you were utilizing more of a manager of manager type In the very early days of windows server Yes, yes very very early days before the tools like sccm and config manager Became available, but that's that kind of tells you what the involvement How evolved it's become But now the those that ai model that's sitting behind for example azure monitor or or Insight in azure It's already knows and it's constantly learning. It's taking Uh telemetry from from microsoft from our own for example security groups and for our management groups and our networking groups And it and it's uh putting models together and it's applying those models to your data To surface up potentially like threats potentially uh issues that are going to come up Um trends in terms of okay your cpu has been going Slightly on the upward trends for the last six months and at that rate we expense we expect that your Your workload is going to run out of resources on that server in six months So it does give you the opportunity to say, okay, what am I going to do with it? Am I going to move it? Am I going to add more resources to it? Um, am I going to split the load across multiple machine like it it gives you time as opposed to Getting the alert that oh that machine is out of resources and they're like now you are scrambling Yeah I was going to say in the risk of also taking something down right in the old days It'd be a reactive kind of situation. Nobody's running around frantically to go and fix things, right? So to be predictive is obviously a good thing Which kind of brings me up to the next question then Pierre so Kind of alluded to it to an extent now. What tools do you need to achieve this really? For windows server Uh, there's there's a little linchpin here and I and I talked about it this week on another show Uh, I it's it's almost like the lord of the rings. I like it's the it's the precious It's the one the one rule the one ring to rule them all. Uh, and I'm talking about azure arc So our arc enabled server Which by the way is free. So you deploy that to any server And what it does is it's identified. So if you got servers windows servers in azure The tools are available to you no problem But if you got on prem servers and if you got servers on other platforms Or at hosters you can deploy those agents to that server and what that does is it creates an identity from that server into the cloud Once you've got that identity that relationship between that on prem server and the cloud services Now it's really easy for you to say, okay on these servers that are on prem In a to b us wherever they may be and say I want to deploy patch management to those or update management as it's officially called. I want to deploy Um desired configuration state. There's a desired state configuration to them. I want to deploy Auto managed to make sure that they're backed up that they're protected I want to make sure that sentinel is using the logs of that machine so that we can if there is a Break-in if anybody gets compromised that we can actually trace and investigate Where where it came in and and how and what's been compromised So all of these require logs required metrics to require data And that is provided by that link that azure arc provides So can I just be clear then all those logs and data all still sit within the server arc is just extracting what it needs to understand Or is everything ported back up into arc as a central console back into the azure cloud? How does that work exactly no arc arc? Which is also in some cases misunderstood arc is basically the facilitator Arc creates the relationship and the and maintains the identity of those servers in azure Arc doesn't send any data over and only sends metadata About that server in terms of maintaining the relationship. So in terms of compliance and stuff like that No, no issues there Now if you decide to start monitoring that server using Other tools like azure monitor or microsoft defender or microsoft sentinel Or update management in that case you need to start ingesting some of the data in in terms of What all the patches are installed on each server and that gets brought in By azure monitor. So you roll out azure monitor agent So you tell basically arc take the azure monitor agent and deploy it on my servers So it deploys the agent and then azure monitor starts collecting that data It only collects what you tell it to connect to collect And it is uh, you manage where it's sent. So you basically create your log analytics workspace. So if you're in In england and graven and you have a a requirement to stay within the confines of the country You can define I want my log analytics workspace to be in in uh in england Or for me in canada And then once your machines are connected to it. They only send their data to that location So you control where and how much data is being sent But once that data is the in the azure monitor in the log analytics workspace Then other services like update management like sentinel start using that data in order to surface up This server is missing a critical update or that server has been compromised or there's been 20 admin log on Attempts on this particular Workstation or server and then you can start acting Or reacting to that or planning Based on the metrics that are being uploaded Yep, got it. So I think this is kind of rhetorical and you alluded to it earlier already alluded to it earlier in terms of cost Where does the cost aside? Is there a cost? I think you mentioned as your arc is is free of charge But i'm assuming once the other tools start to then Take effect, then there's probably going to be some kind of cost to that would that be a first thing Yes, so arc is to establish that relationship and exchange of that metadata. That's free. That's included in your subscription no extra cost When you start collecting logs and metrics from all of those machines Because you control how much of it you're ingest and what you're ingesting There is a cost for that log ingestion On log analytics, it's not a huge cost It's still significantly lower than if you would have to deploy your own System center configuration management infrastructure because you don't have to pay for servers. You don't have to pay for licensing You don't have to pay for anything else You pay for the ingestion of the data into log analytics, then if you deploy Sentinel or or Update management, then you're charged The same as you would be for an azure machine and I don't have the the the cost Associated with me. I try as much as possible to stay away from cost and licensing I I'm more of the tech guy and I leave the licensing and cost to the sales people And all the people out there who are clearly looking to sell these things and Kind of make some money at the same time, right? Yeah, I'm just looking at how we can use the technology in order to bring value to the Enterprise. I know there's a cost associated with it But the cost is you you have some control over how much it costs because you have a control over how much data you're ingesting And then for like azure sentinel, there's a cost per machine to be protected And I think as you mentioned Sorry, I think as you mentioned as well and one of my former roles as infrastructure management and tools and so on and so forth And you know They sometimes take a lot of effort to maintain If there's you know to the point that cost comparison with something like azure Doing some of this work or all of this work in comparison to the build your own type scenario Is going to be pretty significant regardless, right? It's absolutely going to be a significant considering too that We're now looking at hybrid. So it's not just on prem machines, but it may be your hoster's machine And maybe your aws machines. So imagine if you had to set up this environment Let's set it up four times and then set up some kind of of link so that they could Exchange that data so that you could have a top-down view of your entire environment Yeah, are you gonna say single pane of glass? I was going to but i'm trying not to as much anymore because it's it's the term that's been overused I think Yeah, I have a thing I have a thing for for for marketing terms like everybody that keeps talking about Skilling when yeah, I just okay. We've been calling it training for like ever. Why are we suddenly have to change the term? Indeed. Yes or empowerment perhaps So in terms of all that where do you think you know the benefits would be who would you know? Would it help security companies or government departments? For example, where do you think the best area of of where this fits? I'm going to give you the consulting answer and say it depends but I'm sharing a slide here That's kind of like a top-down view of everything that we have And if you're looking at that one where you have your your services across your infrastructure and your infrastructure can be on prem in azure somewhere else anywhere And then through azure arc and that's metadata and that identity that's created You can have like defender and sentinel monitor azure policies, which we management loves especially when you have Uh compliance and regulations that you have to apply to because you set up a policy and you can apply to all your machines regardless where it is So in terms of password management in terms of auditing in terms of guest configuration management You can set policies And even have remediation tied to those policies. So if a machine drifts out of policy It can be brought back into policy update management inventory management azure auto manage, which is newly added to that list where the backup of that machine the auto Updates all of the normal stuff that you would normally set up on a machine Just gets turned on by default Yeah automatic you just say auto manage this machine and it takes care of the rest in the background Of course, there's always a cost associated with some of those services But there's something in there for there's something in there for the it guys and and girls that need to uh Have a visibility and alerts into resources and utilization of those resources through azure monitor azure monitor alerting Uh, there is something in there for the security groups that with microsoft defender and microsoft sentinel There is management For azure policy update management inventory management like there there are a series of tool To basically cover everyone And it's just a matter of turning it on Once the uh the relationship with azure is created with the arc enable the arc agent And that's what I call like the agent is like that the agent that rules them all because it will manage installing all of the other agents As you require them Once that relationship is done then you can take advantage of all these tools and that list is just growing Indeed. Yes. I mean the seems to be new services coming out of constantly, right? And yeah, and I wouldn't be surprised if at some point We don't end up having no difference in terms of what's available in terms of management tools for whether or not your machine is On-prem or whether or not it's in the azure cloud Yeah, good Great. Listen. Thank you. We're going to do some recapping of some of these key points towards the end of the shelf here Um But now we're going to move on to what I think is you know, this is my first time I've doing this Let's see what happens This is the part of this show. It's called the server acronym review Um, and I think like everyone involved in the tech world I kind of we all love a long confusing acronym and certainly my time in microsoft I thought I'd had lots of acronyms before my friend, but my goodness me there is There is it's just a plethora of acronyms out there So but look luckily for us the producers have found a few server acronyms to show us We're going to put ourselves on the spot. I haven't seen these up to now. Clearly you haven't Um, so let's see if we can guess what they are We'd love you guys out there to pop your thoughts in the comment section below Tell us what you think about these acronyms. Uh, tell us what you think of us, whatever it may be um, but let's Let's let's move ahead now. Um aas or do I dare say it arse Well, I was going to say my dyslexia kind of played tricks on me there for a second Uh, I thought it was something inappropriate, but anyway, um Air as a service perhaps I'm thinking I was thinking No, was I thinking azure as a service? No, it can't be can it azure is a service automated Something system automated audit system. We're gonna get some clue as your analysis services Okay, well, I should have known that one You should have I'm still learning clearly. I'm month four. So, you know, I've got an excuse here, right? Um, shall we move on to the next one? Sure Oh database management system Hey, I was gonna say that Yeah, this one has been around for decades. So the acronym is not new Uh, the acronym was not do we have any more out there? We've just got the two I think that could be it. So look I've been taking a couple of notes here. So let me try and recap if I can um in terms of, you know Overall azure arc scenario arc enables that on-premise So interconnect It comes at no cost Um, if you start there for consuming services around azure, there clearly is a cost But then if you start offsetting that against other options It's still a significant saving massively improved simplicity operational simplicity Gives you obviously a you know a single pane of glass Across both your on-prem And your public cloud azure Uh infrastructure, shall we call it any cloud Any cloud has got oh, yes, of course because we can move it into different clouds as well, right? um, exactly And you know things like the AA models the data as I say simplifying the operation um Anything else you want to add to that in terms of recapping? well This is the the main tool in terms of managing managing windows and linux servers in a hybrid model There are other tools, uh, such as like for example the windows admin center Which is kind of like managing servers on-prem, but also is now a dedicated Plane a blade on azure, so it's embedded into azure to manage servers So so and those tools have kind of like overlapping capabilities and sometime complementary couple capabilities So in on-prem through the windows admin center, you could go to the hybrid center and basically enable Or turn on an arc identity through it. So there's metal. That's just another way of onboarding a server into arc But windows admin center is Also a great set of tools if you don't have a cloud footprint So it all depends on it all depends on where you are in your journey Some customers are going to be On-prem only some customers are going to be cloud only and the majority of customers are going to have a foot in both worlds So tools are there regardless. Uh, what your situation or where you are in that journey Absolutely. Absolutely. Listen piet, thank you so much for your time today It's been an absolute pleasure meeting you for the first time listening to your Expert knowledge of one of our world's leading experts. I know you said you weren't but from what I heard today I would completely disagree um and For you guys out there, please keep an eye out Right here on it obs talk linkedin or youtube for the next episode, which I will again be hosting for the rest of this series I'm not quite sure who's going to be joining us next time. But hopefully they're going to be well equally if not Probably not near as good as what pier has but i'm going to say that to next guy as well or lady for that matter So look remember to drop your thoughts below and thank you very much for joining. It's been a pleasure. Thanks again, pier Thank you. All right. Thank you Cheers. Bye. Bye


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