 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. Today Bob will discuss Data Governance Best Practices, Assessments and Roadmap sponsored today by Informatica. 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 it to just the panelists, but you may absolutely switch that to network with everyone. For questions, we will be collecting them by the Q&A section or if you'd like to tweet, we encourage you to share highlights or questions via Twitter using hashtag RWDG. And to find the chat and the Q&A panels, you may find those icons found in the bottom middle 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 this session and any additional information requested throughout the webinar. Well, let me turn it over to Cash for a brief word from our sponsor Informatica. Cash, hello and welcome. Hi Shannon, thanks so much for having me here. Looking forward to today's discussion and also learning from Bob's presentation. So I'm going to take a few minutes just to kind of set the stage, you know, as you all know today's webinars around data governance best practices and how you could take those best practices to build roadmaps to be successful in your data initiatives. So what I bring to you today is really a market perspective. Oh, there you go. We weren't seeing your, there we are. Okay. Yeah, just to bring in the market perspective, those who don't know me, I'm Cash Mehdi, I'm the head of data governance catalog privacy. And at Informatica, we are a cloud data management company based in Redwood City, California, but happy to share more details as we go into the presentation. So for today's topic, I thought for the audience, you know, I'm seeing a lot of people attending from different locations, countries and whatnot. I'm super excited to have you here. But my today's goal is to give you a perspective into what we are seeing across our global community of customers and the market. We see a bigger drive towards data sharing, which is top of mind for almost every data leader, chief data officers or as Bob calls it, chief data analytics officers, more and more. When we walk into any typical organization, we're seeing two perspectives, right? So on the technical side and then the business side, and each of these business functions have different personas supporting business priorities. And as you go from the left to the right, you move from highly technical to more of the business users who are non technical. What we are seeing more recently and over the past several years is there's a massive rise in the population of the business community. More and more companies have business users who need clean and trusted data to be able to perform their business activities. And then, if you look at the traditional investments that companies have made in the past, they hired a lot of technical users, like the data scientists, ETL developers, engineers. While that number continues to be a part of this organization as a core function. In comparison to the business community's rise, we see this number declining over time, and also due to the increase in the volume of several personas on the business side. We also see companies having a greater need for driving data literacy. And as Informatica, we're seeing a market where more and more customers are coming to us talking about the new self service model. And this is where users need clean and trusted data to be able to perform business activities. Now, I also want to touch upon some of the things that we're seeing at the top of the chain with the CDOs, chief data officers. And one thing we have observed is their common failures, you know, among the, you know, across different industries, community of CDOs. And in this slide I want to present one of those reasons why CDOs are failing. So number one, failure to have business literacy, and to put business needs first. To perform your business activities more successfully, more and more CDOs are required to have literacy. I've put out an example here, depending on the industry type, you know, you could be driving your customer experience, you know, if that's a hospitality business, or it could even mean having operational efficiency with your internal teams like the analytics, compliance teams, having access to that data to draw life predictive analytics and other business tasks, or even if it means you're lifting and shifting data to the cloud. All of these initiatives have one thing common that, you know, these leaders need to have business literacy and be able to put the business first in their data initiatives. And the second big one that I see is failure to connect data challenges to business outcomes. And I find this as one of the hardest thing that any data leader has to go through. Sometimes you might see examples of this where a leader might say, Hey, you know what, I've got a data quality problem. It's very scarce, right, unless and until that problem is set in the context of the business outcomes. What's the really the impact. And we have seen many customers like an insurance, reminded of a customer like New York Life, early on in their data governance journey. And what poor data quality could impact our customer experience, how do we help agents find more data, trustworthy data quickly to be able to, you know, acquire more customers, retain existing customers and whatnot. So, like I said, the second one is really tying your data challenges to your corporate goals, business outcomes. And this will help you buy, buy in the executive sponsorship, get more support from other cross professional teams. The third one is really the failure to address the human aspect because at the end of the day, many data leaders are at the forefront of driving change. And this change means cultural change, being able to articulate why a new technology or even a new data initiative will help your workers or employees be more effective. So CDOs, we see commonly, you know, they're looking for capabilities to create literacy across the organization. And sometimes you have to personalize this because if you're talking to a marketing officer or a product officer, their priorities are different, and even their cultures are different. So need to have a right balance of the enterprise as well as the local perspective. And the last thing that I would mention here is really failure to understand technology is a means to an end and not the end itself. And more than anyone else, being a leader in the data management space at Informatica, we've seen this numerous times where sometimes customers procure our technology, and there's a higher expectation that technology should solve all the problems. While technology can help you eliminate some of the labor intensive tasks, but still, there's some work that needs to be done, which is around culture, which is around articulating your quick wins and whatnot. So this could be another key area where beyond technology, what are the measures that we have to take to monitor and continue to communicate success. So for key themes around why we see CDOs are failing, maybe this could help you in terms of building in roadmaps by taking in feedback or perspective around what not to do in your initiatives. So the other thing I would also point out is the IDC survey results. So for those of you who don't know who IDC is, they're a global market intelligence company, and they've recently done a global chief data officer engagement survey. And here are some of the results that you could see on the left hand side. So what the survey results talk about is 49% of global chief data officers. It's top of mind for them to simplify data access and consumption. And we see this again in our continuous sales cycles where we're speaking to customers across the board. The second metric here is really 54% ensuring data and analytics compliance. It actually reminds me of the customer example. One of the customers said, hey, cash, you know, we have our analytics teams, the biggest consumers of our data. What that also means is the biggest risk can be coming from analytics. How do we ensure compliance for our day to day analysts, data scientists who are working on massive amounts of data sets. So 54% of global chief data officers looking to ensure compliance around analytics. 39% enabling teams with self service. This is another big area and top of mind item that I see in my day to day talking to CDOs and other executives, where they're looking to create an enterprise wide self service. So there's one single stop for people to come in and look for what data is out there, being able to understand how they can use it. So they have usage guidelines and whatnot. So 39%. It's really focused on creating that self service capability and help move all the employees in concert when driving innovation and what not. This one right here, the second last 51% managing data complexity. This is quite interesting to me because we're in the 21st century we've come a long way in the data space. But yet you could see from surveys that still a lot of the leaders find data complexity. One thing I was recently reading was about the volume of data that's also increasing right so we've gone through definitely like a big data era where everybody talked about Hadoop and whatnot. If you look at recent statistics, the every 12 to 18 months, the volume of data that's getting generated is almost doubling for every company, and not to mention the knowledge around data because companies are consuming more and more data. And they have more knowledge about the data that's also doubling so it can lead to complexity around making sure data is fit for purpose for your business initiatives, your investments that you're making as a company. So I see this as a very interesting metric like more than half are saying they're managing complexity so how do we reduce this right. And lastly, 39% talking about standardization data sharing and consumption functionality. In this slide, you know, one thing I see with CDO stay in the life is, you know, we see them quite active in terms of focusing on business drivers, we're having more value conversations with, you know, data leaders in data challenges as well as cultural challenges is top of mind. And more and more CDOs looking for avenues where they can learn from other experiences so there's also a massive community that's being built across the board. I founded a CDO Academy here at Informatica which really provides CDOs to come and platform to come in and share what initiatives that they're going through and learn from other stories as well. It works lots and lots of CDOs consuming customer stories from across different industries. Doesn't matter if you're a CDO or a data leader part of finance. We see leaders taking inspiration from other industries, how are they tackling the problem, maybe in retail or healthcare, and bringing that to to the full. Technology is another key area where we see a lot of the leaders engaged in quick wins. And many other aspects so again, I'm, I'm almost over my time so I want to pause here and pass the ball to Shannon. Sorry to interrupt you. I just want to make sure we keep keep the schedule going. Thank you so much for some questions coming in and thanks to Informatica for sponsoring today's webinar and helping make these webinars happen. If you have any questions for cash about Informatica, 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. Hi, Shannon. Hi everybody. Thank you, Cash, for a great presentation. I know we could talk about the subject all day, you know, and talking about best practices is really important to me. So, first of all, thank you everybody for taking time out of your schedule to participate in this webinar today. If you have a brand new program or if you haven't started your program yet, or if you have a program that potentially I'm talking about data governance program that's not providing the results that you're hoping for, you might want to take a look at that program and see, you know, assess that program, assess it against industry best practices associated with standing up a program. And that's what we're going to talk about today. We're going to talk about, you know, if you're in a situation where everything's going great, then just continue to build on it. But if you need to assess what you're doing, or you need to assess your organization when you're getting started, I hope that we're going to be able to address a lot of those topics of how to do that in this webinar today. So thanks again for coming. Just want to spend a minute talking about some of the things that I'm involved in. I'm involved in a lot of things. This monthly webinar series, thank you again for coming. Next month, I'm going to be talking about who should own data governance, should it be it, should it be business, should it be shared services, who should it be. I also want to talk about, I want to tell you that I'll be speaking in person at the diversity data governance and information quality DGI Q East conference at the beginning of December in Washington DC. I talk a lot about non invasive data governance. So there's a lot of resources about non invasive data governance. There's the book there's the online learning plans that are also available through diversity. As Shannon mentioned, I publish a regular twice a month publication called tdam.com that the data administration newsletter, my consulting company is kik consulting. And on the top on the side, I feel I do some work at Carnegie Mellon University as an adjunct faculty member. I want to talk a little bit about so there's there's a list of things that I want to talk about today. First, I want to talk about building criteria, building best practices. So figuring out what the best best best practices are for your organization. I want to talk about defining best practices that you're going to use as an assess as an assessment tool in your organization, defining those for the the best impact that you can get from using the assessment. I'll talk about the process of assessing against the best practices, talk about the recommendations, and then ultimately this all leads to the development of a roadmap so want you to take a ready aim fire approach instead of taking a ready fire aim approach. And one of the best ways to do that is to start out by doing an assessment of your organization. So let's start out with a few definitions as I usually do. My definition of data governance pretty strongly. It's the execution and enforcement of authority over data I don't care if you take a command and control approach, or you take a noninvasive approach. At the end of the day, what you need to do is you need to execute and enforce authority over that data if your data definition is going to improve your data production, your data usage is going to improve. You need to execute and enforce authority. I talk about stewardship as being the form, the formality, or should I say the formalization of accountability for data. So people who use data need to be accountable for how they use that data, which means we need to help them to understand how they can use that data, and then a data steward is basically somebody who's held formally accountable for their relationship to the data, and then metadata instead of just saying data about data. It's really data about the data that improves both business and technical understanding of data. So let's start out by focusing on figuring out what the best best practices are to use when you're going to conduct an assessment. Let's talk about selecting best practices. I want to share with you what I, what I consider to be some best practices for defining best practices, share with you the criteria that I use, and then share with you a list of best practices that are probably the most common or actually they are the most common best practices that are used by many of my clients when they're doing assessments, when they're either starting or trying to improve on their existing data governance programs. First thing first, we need to recognize that the best practices I'm going to talk about here are best practices associated with standing up a program. These are not best practices for people to grant to request access to data to determine a solution around data. They are virtually best practices associated with standing up a formal program, or if you have an existing program, looking at how well that program is performing within your organization. So there's a lot of industry lists that are available you can do a search on the internet and look up data governance best practices. I have mine everybody seems to have theirs. Please feel free to use the ones that I'm sharing with you today so I'll get to those in a couple of minutes here, but there are lists of best practices. Some of them might be more focused on the tactical or the operational perspective. These are more focused on the standing up of the data governance program within your organization. So what are some of the best practices for writing best practices. Well, the first one that I learned pretty early on in my career was they need to be written in the present tense. They don't want to write a best practice as we will do this instead you say, Okay, this is the best practice senior leadership support sponsor and understand what we're doing. That's in written in present tense. And so you're going to evaluate as to whether or not you are in line with what that best practice states, and oftentimes in organizations when they're creating these best practices. There's key words that are in there that people aren't going to necessarily understand, underline those words, make certain that you provide definition of those words that people may not understand. Be strong with your words, you know, don't the softer it is, the more room for interpretation there is. So what I say is be strong in your words be strong in your conviction, and don't have 100 best practices, typically the organizations that I work with they just find anywhere between four to six may go a little bit higher sometimes it even goes a little bit lower. So, when we're talking about best practices. And like I said these are best practices associated with standing up a program. What are the criteria that I use to determine whether or not their best practice. So the first one is, they have to be practical and doable in the organization so don't define a best practice that is just going to be unachievable within the organization that you're doing the assessment of your own organization or another organization. So they got to be practical and achievable. The second criteria is even more important it is. Will the best practice that we're going to assess against will we be at risk will the data governance program be at risk if we don't achieve what we're defining as best practice. So basically you have to answer yes to both of these. And you know you're going to assess as part of the assessment and meeting with people within your organization, you're going to assess strengths and weaknesses against each of these best practices. And wherever there's a gap there's a risk associated with that, and you're going to then create the recommendations and the actions that are going to feed your roadmap so hope that all makes sense. So, let's start. Let's really get to the point here where we define what the best practices are. And if I told you that 98% of the organizations that I've worked with have used this as their number one best practice, I'd be lying to you because it's 100% You need to realize the importance of senior leadership within the organization, supporting sponsoring and understanding what data governance is, and what it's going to take. So, as you're assessing your organization, how well do your senior leadership support sponsor and understand data governance, it may support and sponsor it. In the keyword there maybe understand how well do they understand what resources are going to be required, what tools what catalogs, what effort is going to be required. So that's the first best practice where are you in comparison to the best practice, there may be things that are already in place. You know you might have a consultant working with you or you might have a team focusing on this there's a lot of good things that can be taking place, but there can also be a lot of opportunity for improvement. So when you're doing when you define this as a best practice you may find that there's levels of understanding that need to be improved when it comes to your senior leadership. Okay, I can't spend too much time on each of the best practices, or we'll run out of time really quickly. The second best practice used by most organizations that I work with is, you know, basically that somebody has to have the responsibility for data governance. So a resource or a resource, or at least part of a resources time is allocated to the administration of the program. The program's not going to run itself. I always say, the data is not going to govern itself the metadata is not going to govern itself, the data governance program is not going to administer itself. You need to have somebody who has the responsibility for the program. So the question is you might evaluate really well against this as a best practice. You might have no idea where in the organization data governance should sit, you know, so you might want to assess against this as a best practice. The third one and I've done webinars on this in the entire subject of the webinar is focused on roles and responsibilities roles and responsibilities are the backbone of a data governance program. Are your roles and responsibilities associated with data governance well defined? Have they been vetted? Have they been talked about? Have you consulted with other people? Have they been approved? That's a best practice because a lot of the tools and templates and things that I share are really based around the roles that you've defined in your organization. Who's the steward? Who's the owner? Who's the council? Who's, you know, who should this issue go to? So the roles need to be well defined and vetted. And so typically that is it practical and doable that you can define the roles? Heck yeah. Is your program going to be at risk if you don't define your roles? Yes, there's no doubt about it. The third one is actually similar to the previous one. Instead of being the roles, it's the goals, the scope, the expectations, the measures of success that those things are defined and approved. If people don't know the goals, is the program at risk? If people don't know what's in scope or what's expected of them or how you're measuring what value is being added to your organization, you know, are you at risk? So is it practical and doable to do these things? Certainly, especially if you attend, you know, a plethora of data versity webinars, you can certainly learn how to define what your goals and scope and expectations and measures and all those things are. And I'll share one more best practice with you and that has to do with policies and guidelines, standards, standard operating procedures. You know, are these things documented? Are they being followed? Do you have a policy? Do you need a policy? Is it practical and doable that you can do every one of these things? Yes. Are you going to be at risk if you don't have a policy? Well, if your organization requires a policy that's signed off on at the highest level of your organization, yeah, you're going to be at risk. If you don't have guidelines for how people need to behave, you're probably going to be at risk or standards or standard operating procedures. Again, this is a series of five best practices and there are more to them than that, but like I said, you want to start with a finite list and start doing your assessment there. So how do we define best practices or as we're picking which best practices we're going to use in our assessment? We really need to begin with the end in mind. What is it that we're trying to achieve and let's at least direct people in our conversations around best practices that are going to help us to get at least those building blocks of our program in place. And then involving your target audience and then predicting what your target activities are going to be. And then I'll just share with you some of the things that I've heard in some of these meetings, these interviews and assessment meetings that are done. Okay, begin with the end in mind. That's a Stephen Covey best habit, I guess. And, you know, it's really hard to get where you're going. So if you're going to try to create a roadmap as to where your program's going to go, it's going to be hard to achieve it if you don't define what it is. So take a ready aim fire approach to find target behavior. Every journey starts with its first step, you know, make certain that even the best practices that you're defining to do your assessment are directed towards activities that you think are going to be important to your program or even potentially the existing program. Where are there things that could be improved within your programs? How do you like that picture of me as a young kid? And I don't know if I necessarily wore a t-shirt like that. But okay, so let's talk about involving your target audience. So you can select your target audience by business function and a lot of organizations do that. You can select your audience associated with a subject area or if you're focusing on customer data governance or the data governance of customer data or product data or student data or whatever the domain is that you're focusing on. You may want to focus, you know, get some of the people that are going to be involved in the assessment meetings from those subject areas. Select your audience if you have a council. What areas do they represent? So very easily you can figure out who the appropriate audience is for your organization. And like I said, while you are defining your best practices, think about those things that are going to be really important to your organization moving forward. First of all, somebody needs to run the program and we need to know where it's placed within the organization. So we want to make certain we have best practices that are going to get information from people that will help us to determine potentially where the program administration and placement should be. Standing up a strategic level, many organizations start by defining a data governance council, you know, defining roles and responsibilities, making certain that you can, you have a communication plan to address all levels including your senior leadership and your partners and everybody that you're working with, having a communication plan, having a data documentation platform, you know, like a data catalog tool or a web or a cloud based data management tool, you know, having policies and guidelines, making certain that we don't try to do this all at one time and that we're going to be incrementally deploying this in the organization. So this can be some of the activities that you're thinking about when you define your best practices and recognize that when you meet with people, no matter how you break them out and I give you gave you a couple different ways that you can do that. At some point you're going to start hearing repeat from people. So especially if you're going to assess against the best practices, you're going to talk to them about senior leadership support you're going to they're going to tell you need a policy or standards they're going to demonstrate that there are pockets of governance in different parts of your organization. They're going to talk about the lack of those three things of documentation. They don't have resources they don't have time, or nobody's accountable for anything you're going to hear a lot of repeat when you're doing those best practice assessment meetings. So before we even hold the meetings we want to make certain that we prepare the audience that we're going to be with we don't want them to come into the meetings blind. We want to share with them and I'm going to give you an example here in a second of a of at least the front page of a questionnaire that I've used to kind of prepare the audience for those assessment meetings, and we'll talk a little bit about what it takes to structure and what questions you might want to ask in those meetings, the structure of the assessment assessment meeting itself and then talk about conducting the conducting the assessment. So when we start with preparing the audience, you know you might want to develop a questionnaire or a survey. So you might want to send the survey to more people that you're going to then you're going to have time to meet with. And actually give them the opportunity if you can look on in the image on the right to click a look to check a little box that says, yes, we want to have a meeting with you, rather than filling out this form or rather than providing information to you another way. So, you can also define your definite have provide your definition of data governance and what the criteria or that you use to determine if something was best practice, and then what you don't see in this image is the pages behind it there's a page per assessment per best practice I'm sorry that asks a series of questions and we're going to talk about those questions here in a minute. The next slide. So what are the questions that you're going to want to ask people either in the survey, or so they know that you're going to be asking them, or in the assessment meetings themselves. So the first question and the fourth question and the fifth question. Oftentimes, you're not going to ask the people in the meeting those questions but you're going to want to answer those questions, or have your consultant answer the question. Why is this best practice for our organization. What can what are we doing presently that we can leverage towards that best practice, we want to stay as non invasive as possible right so there's things that we can leverage. So what are they if there's opportunity to improve. Where is their room for us to improve. And then it really becomes the job to your job to look at and say, Okay, what's the gap between what the present, what the, the present practice is what we're doing presently, and the best practice. You can articulate that gap and then you can articulate what the risk is associated with the gap. Oftentimes, then the results of that are, you can formulate from the findings recommendations and we'll talk about the recommendations here in a second. So when you develop develop an assessment. Here's an example of a structure of a meeting sample agenda of a meeting you do the introductions you talk about the program history, you, you go back over the definition and the purpose. You spend some time talking to whatever audience it is to understand what their challenges are around data whether it's definition or production or use challenges. And then you can jump into the assessment, and then you jump into the best practices is what I'm saying and then you follow that up with some next steps. So typically these meetings. And again, I was kind of sharing with you, some of the best practices that I've seen for for holding these assessments is hold them at 60 minutes, don't don't schedule more time than that if you need more time than that schedule more time, but make certain they know that this is not a one shot deal, and that you're going to have a future of communication. When you get to the point where you have flushed out your recommendations from the assessment and the findings of the rest recommendation, you're going to share them with these people. And you're going to ask them are these are these the recommendations do these make sense based on the things that we talked about in our meetings, and then you're going to incorporate the feedback into the assessment. The recommendations are going to flush out of this. And so the next topic we want to talk about here is what do the recommendations look like. And so oftentimes, you know, you hear here's just an example of what some of the recommendations might be from an assessment is you need to find and approve roles and responsibilities. You need to require a communication plan. We know that you can't communicate with your executives, the same way that you communicate with people at the operational level, or people at the strategic or tactical level of the organization. You know that when you're onboarding each of those groups, you need to communicate with these groups differently. So you really require data, data governance communication plan. So you know that your organization is going to benefit greatly from a data documentation platform, whether that's a data catalog, or a metadata repository or whatever tools are best suited to fit your organization. So you know that that's going to be the case. And so or at least you're going to most likely hear from the assessment meetings that people don't know what data exists they don't know where it is they don't know the definition of that data they don't know it's related to other data, they don't know the lineage or the ownership or the policy associated with that data. You know you're going to hear that from people so one of the recommendations is that you know we're going to benefit greatly from creating a data documentation platform. And then the fact that you're going to do this incrementally in the organization through actionable use cases. So for example I have a client I'm working with presently. He's doing it a transformation from one system to another system, and they're mapping all the data elements from all the systems that are going to now map to this new system. And almost every one of those data elements and as how they're being mapped can almost be viewed as an individual action will use case within the organization that you have a field that has race and ethnicity in it, and you need to integrate those, or any of the EDI concerns or DEI concerns, the equity diversity and inclusion concerns. So you need to have a data documentation platform and you need to do this incrementally within the organization. I'm just going to walk through each of these three real quickly with you. If you've attended these webinars before you've probably seen my data governance framework. I'll be talking about the data governance framework in the DGI Q East conference coming up in a couple of weeks. This is a an image of the framework where we have the specific core components of a successful program across the top and all the different perspectives that we need to look at each of those components from. So the roles and responsibilities you know going into your assessment that you're going to need a set of roles and responsibilities. So guide your questions towards understanding if people know what their role is if they want to play a role if they need to play a role if they recognize themselves as already playing a role roles and responsibilities are going to be key. And if you look at it again like I said across the top, you've got those core components data roles processes communications metrics and tools. And you need to be concerned about each of these things from each of these levels and each of these perspectives I'm going to share with you, kind of a filled in version. This version is forever changing and being updated but if you just focus on the roles column from each of the different perspectives or or parts of the organization, you've got your leadership team you've got your council, you've got your owners of the data or your subject matter experts of the data you've got your operational stewards, and then you've got everybody else in the organization is already governing their function may not be called governance but you know it governs and information security governs and privacy governs and all those things govern. They're just not doing the same things that data governance, for example, are doing. So, also the next so the next time was the communication plan and as I mentioned I know this is really difficult to read on the screen, but one of the things I mentioned before is that you cannot communicate to all people at all levels of the organization in exactly the same way. So you want to have a plan for how you're going to communicate to these people. So you've got different roles and responsibilities and just like in the, in the operating model in the framework that I just shared with you I kind of like to carry the colors over from one to the next, but each of the columns in the communication plan represent a different piece of your organization, a different role that is being played as part of your program. I always break the communication into three. There's orientation, which can be somewhat similar to everybody in the organization, but when it comes to onboarding these people. I think a specific onboarding communication specifically at the target group like the council or the stewards of the organization, and then there's ongoing communication, which is metrics and measurements and new tools and new ways of doing things and just ongoing types of communication. It's a little bit larger version, it's I know the writing is still really small on it, but you kind of get the idea so the categories that fall under orientation onboarding and ongoing are down the left hand side. You've got the different audiences the different parts of your operating model roles and responsibilities across the top. You can start to fill in. That's the message we're going to send to these people what tool are we going to use it, how often are we going to communicate this message to them, you know those types of things so you can truly flush out a communication plan data documentation platform. Again, the data catalog tool the the cloud based data management tools that you're working with. Now I'm just sharing you an image on the right hand side here that came from an organization where they were looking at the subject areas and then really three levels of metadata, the glossary level the dictionary level. And then what they were calling the data catalog level, which was the physical structure of the data, you've got your standards on the left. You've got, it's called one thing in Salesforce it's called another thing in the data lake. If you want to know that information, you're going to need to know that information. So, a data documentation platform is certainly going to be a recommendation. If you don't already have it. And if you do already have it. Maybe there's better ways that you can utilize that within your organization. If you ever hear any of these terms thrown around ownership stewardship lineage quality to all of these things. These are metadata terms, their data documentation terms, I'm finding a lot of organizations like the use of the word data documentation rather than metadata because you seem they understand what data documentation is. All right so then the next piece is actionable use cases, and focus on those pieces of critical data that really have a lot of value within the organization but let me kind of share with you that just because you pick a few pieces of data or even one piece of data. There's going to be a lot of other pieces of data that are related to that so I like to say they have tentacles into other aspects of the organization. Things that are low hanging fruit things that you can deliver some value on that aren't going to require system changes, and, you know, huge organizational changes or process changes, create working teams validate documentation. Again, I'm just stating this as being a recommendation that would come out of your assessment is that we're going to do this. We're not going to try to do this all at once we're going to do this incrementally. And this is some of the ways that you can figure out what are the most appropriate actionable use cases for your organization. The last subject that I want to talk to you about today before I turn it back over to Shannon is the delivery of the roadmap. So that's the reason so doing the assessment first doing the roadmap second that makes sense because you want your roadmap to be based on the recommendations that come out of your assessment. So we've talked about all of those things. So what are we going to include within a roadmap, we need to take those recommendations, and we need to convert them into things that we can act upon actionable streams, we need to provide a notional timeline, we need to do some resource planning, who's going to do what and when. There needs to be budgeting so let's let's talk about all of these things. So when it comes to actionable streams. So this is an example of some things of streams, although I have two actionable stream fives. I don't know if anybody saw that as a data quality problem, but they, this is just again like I said a sample of actionable streams, and that are fairly typical for data governance, you're going to need to deploy your data governance administrator, you're going to need to know where they're going to sit in the organization, who they're going to report to what resources they have, you know, all of those things so deploying a data governance administrator to go back to that number two best practice. You need to run the show. This is going to be that person data governance manager data governance lead data governance administrator data governance office if you're fortunate enough to have more than one person who's focusing on the data governance program. Can your chief data officer be your data governance administrator, well depending on the size of your organization, I've actually seen that happen. And not the person that ultimately in the long term is going to run the program for you. Okay, deploy an operating model of roles and responsibilities, develop an act upon a communication plan, design and deliver a documentation platform, and all these things make sense incrementally roll out your program via initial use case and then future use cases and figure out how to intake those use cases, design and deliver those things that are necessary to your program like the policy like the guidelines, like the standards, and then deliver a set of metrics again this is just a representative list of what some of the actionable items would be that would come out of doing an assessment. A notional timeline you can take those actual streams and you can put them sideways, and you can try to put beginning dates and end dates, and recognize that it's notional because it's notional for a reason which means when things change when resource when things become a priority, where things become decreased in prioritization. Everything could change so things could slide to the left they could slide to the right. They could be eliminated completely, but you want to create a notional timeline. So at least people know when you're going to address those things that are actionable in your in your plan. You need to have somebody who captains the ship okay you need somebody who is the administrator. People in the organization are already solving problems they don't need data governance to solve problems that they're solving problems. They may not be solving them well they may not be solving them timely. They may not know what problems exist or what problems are being solved, but people are already solving problems. And you want to know and the fact is that they're going to continue to solve problems. You might just add a little bit more structure around the how they solve problems. So we're not adding time for them to be involved in solving things in a structured way. All we're doing is replacing the things that they were involved in to solve other problems. We're just doing it in a structured way in a documented way. So people are already spending time solving problems. People will be spending time and solving problems. Refocus that time on the actions that are appropriate and effective so if they're doing things that are unproductive you know you hear about data wrangling. How much time and they spend strangling the data and how much time are they actually spending and analyzing the data that the way that they need to, you know, focus the actions on the things that are most appropriate and most effective in your organization, and help people to know that you're not intentionally adding more time to what they're doing. If they're already busy 120% of their time, or 100% or 90% you can't really add to that. It's going to be better use of their time. So resource planning is really important in your roadmap. And remember at the end of the day just kind of going back to the definitions that I shared, you need to execute and enforce authority. That's what the bottom line is that's my definition of data governance. That's my definition of data stewardship. You need to do those things when it comes to budgeting and time and money. The people cost, you know people ask me, I tell people that they don't really need to budget a lot for data governance they need to focus on it's going to be the time that people spend on it. That's going to cost the most money. Actually the tools are going to be very helpful to enable the solution of the programs but people, there's a people cost associated with it. There's a capital cost associated with it. Certainly going to be operating costs associated with it. And one of the things that I have heard many times is that there's never a right time to get started with data governance. And I'd like to kind of flip that around a little bit and I'd say okay well then find a time, because there are a lot of things that are going on. Most likely I don't know your specific organization, but there's a lot of things that are going on right now you're doing digital transformations, you're implementing new systems you're a lot of things you're creating data mesh and data fabric, and you're you're making data more readily available all the things cash talked about earlier, you know you're making data available latch on to something like that document the heck out of it, recognize who the students and owners are assess how you did it, and then you can move your program forward. So I'm done, I'm done ranting and raving for today. I'm certainly going to be in the, in the Q amp a, what are the things that we just talked about we talked about what are those criteria to select what the best best practices are going to be for your organization, how to define those best practices, how to assess against them, focusing them on program success, and then delivering a roadmap. And with that, she and I'm going to turn it back to you and see if we have any questions. That's a great questions coming in Bob thank you so much for another great presentation. If you have questions for Bob or for cash feel free to submit the Q amp a portion of your screen. And just answer the most commonly asked questions just a reminder I will send a follow up email by Monday Monday for this webinar with links to the slides links to the recording and anything else requested throughout here so diving in. How do you make data governance more people centric versus policy process system centric any tips. Sure, I'll touch that first and then cash I'd love to hear your feedback on that as well. Data governance is a people problem. Data governance, a good friend of mine, he's been on my webinar before you know him through data diversity I'm sure Len Silverstone said we shouldn't even call it data governance. We should call it people governance, because it is the formalization of people's behavior get them to define data, according to standard produce the data appropriately use the data appropriately, it all comes down to behavior. Data governance itself is data centric policy is very important because that kind of lie sets up the guidelines and the things that need to be done within the organization demonstrate the things that are most important so I think data governance is people oriented it doesn't. If you emphasize the policy that's what people are going to recognize. If you emphasize the people aspect of it. I think that's what they're going to recognize. I'm sure you have any thoughts on that. Yeah, no I definitely agree with what you're saying there, Bob. I think one of the things I would add to your comment is making data governance people centric is really about building those experiences for the end users right so sometimes. I think you come out with a great framework and you know great use case, but there's no connection from a people standpoint like how does that relate to what I'm doing every day, like, you know if you look at the, you know personas day in the life experience, you find people having hard time finding data or understanding the data they have to go to multiple systems applications. Sometimes they also have issues around quality, which might be impacting other business outcomes, or if it's related to their productivity, right. So I would say to make data governance really people centric think about the user experience for them and have that storyline ready to be able to communicate. So that's one part, and then the other is, as you know Bob described in his framework, you know the communication aspect across different levels of the organization. So, you know data governance is not happening in a silo you need people from both sides business technology. You know sometimes I find clients talking about hey data governance should be really business driven it driven. You know what most data governance projects that have been successful. They are the ones that have the right kind of diversity when it comes to data owners technology leader representation business representation architecture, or even if you slice and dice your data by different domains. You got to have that representation from a people standpoint and I really like what Bob mentioned, it should not be data governance should be people governance. I credit Len for that but Len says that he thinks he got it probably from somewhere else somebody out there. I just want to add one more piece to the people oriented aspect of it. And I just want to kind of give you a real life example. I'm working with a manufacturing company. One of the things that they need to measure is man if it's manufacturing instruments downtime. The people in the plants need to record what the downtime is. It's not a data governance problem but it is a data governance problem. It's a people behavior problem people were leaving the plant without recording the reasons why the technology or the reasons why the instruments were going down. That's a problem. Ultimately the people weren't getting the data that they needed to be able to analyze that. And it came into data governance did data governance solve the problem. The meaning of the behavior of the people is what solved the problem. The data was the result of it. Just want to kind of throw that out there as an example of where people's behavior are really the focus of what data governance is. Makes sense. So any tips advice and how to enlighten understanding of data governance at the senior management level. Is there a universal example that works. I don't know cash you know of a universal hi I'll make one up but no I won't make one up but do you know what. Not that familiar there. Yeah no not yet. Yeah, I think that the scene. First of all senior management more and more these days are growing through the ranks of organizations and they understand more and more the importance of in the complexity of data management data governance within the organization. You know I often suggest that if you can talk to your key business stakeholders and you ask them what they can't do because the data is not there to support they're doing it, or you ask them what they're going to do, or what they could do if they actually had access to the data, and you took those answers to your senior leadership. They want to build in aha moment, because they don't want to hear from you, what people can't do they want to hear from the people that can't do it. And you got to get that into the right context so I think you can do that. That's kind of a word of advice to kind of take to your senior leadership to have them say, you know, this things are not going to improve unless we focus an effort on it. I mean, unless we make the investment in our people in our technology and our process to be able to do those things. So I think that's the moment that I would look for. I think Bob, now that you mentioned that I may add to this question. You put me on spot there with the aha moment there. So what I see with the senior leadership, it reminds me of a customer example of who actually was a leader at this company, and you know, I had the job to evangelize why the company should be doing data governance. And one of the things he did is looked at what the CIO and the CEO talked about as the company goals. And then went in, assess like what are some of the data challenges that I could relate back to what my CIO and the CEO is talking about and what's really important to the company. And, you know, when when this leader presented this idea, like, you know, the homework to the CIO, it got the CIO is blessing that oh yeah, absolutely these are some of the things that we want to fix these are important to us. So I took that response like you literally an email screenshot after the meeting, and went around different business functions to show hey look, CIO is on board, CEO is on board to get more, you know, business leaders buy in so I see that one area. I would also point to leaders who are looking to define this aha moment is to really think about the culture and the readiness of the organization. If sometimes you go out there you talk about data governance. Sometimes people think governance is like police action, oh you're here to control everything that I'm doing. I think what's not, but it's really not it's really providing data as a service right so you also have to balance that cultural aspect and build that messaging. And the reason I initially said like, not really a one size that fit for all. It's because every organization is different when it comes to governance and there are several variables as Bob has pointed out that you have to consider when formulating that strategy. And you know what data governance is scary and so I call it not not all my clients refer to it as non invasive data governance but if you take a non invasive approach you don't have to call it non invasive. It's it makes it less scary it makes it less command and control. And then people want to know how you're going to do it and you have to be able to answer that question to so it was a very good question because as we stated or as I stated to the number one best practice. So you need to get your senior leadership to have that aha moment, because they're going to be without their support sponsorship and understanding, you're going to be at risk at some point. And I think we've got time for at least one more question here and if we don't get, and there's going to be questions obviously that we don't have time to get to but for this webinar we'll get those answers to in the follow up email. So what is, what if your trajectory on the roadmap you show us get some of those streams you outline for example do one and two three skip four and five do six and seven can't be done because for not being done. You know what that was just a representative list that was not a list that you know if it works for you great use it if it doesn't. I mean so the fact is that you may not have the resources you may not have the capacity resource wise. Resource wise being people or money or time to be able to do all those things presently so you know what, what do you do is you, you, you, you don't lose fact lose light of the fact that you're going to need to do these at some point in time. You build it into the schedule, you know record if you are if you can't develop your data documentation platform, you can be consistent in the way that you collect your glossaries and your dictionaries and your terminology, you can be consistent in the way that you document your critical data elements, or who your owners are, or who your map where you're mapping data from when you're transforming data from one place to to another place. So there's things that can be done. While these things are are on hold, or if you don't have the funding or the time. That's my thought about it again that was just a representative list. All right, well that does bring us to the top of the hour here. Bob thank you so much for another great presentation as always cash thanks for joining us today it's been great having you here and thanks to informatica for sponsoring the helping to make these webinars happen. And of course thanks to all our attendees for being so engaged in everything we do again just reminder I will send a follow up email by end of day Monday for this webinar with links to the slides links to the recording. And that answers the questions that we didn't have time to get to today. So thanks to y'all hope you have a great day.