 Here we go. Hello and welcome. My name is Shannon Kemp and I'm the Chief Digital Manager for Data Diversity. We'd like to thank you for joining today's Data Diversity webinar key elements of a successful data governance program sponsored today by Elation. It is the latest installment in the monthly series called Data Ed Online with Dr. Peter Akin. There's 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. For questions, we will be collecting them via the Q&A section or if you'd like to tweet, we encourage you to share highlights or questions via Twitter using hashtag data ed. And if you'd like to chat with us or with each other, we certainly encourage you to do so. To open an access either the Q&A or the chat panels, you may find those icons in the bottom middle of your screen. And just to note the Zoom chat defaults to send to just the panelists, but you may absolutely change that to chat, to network with everyone. We answer the most commonly asked questions as always. We will send a follow-up email to all registrants within two business days containing links to the slides. And yes, we are recording and we'll likewise send a link to the recording of this session, as well as any additional information requested throughout the webinar. Now let me turn it over to John for a brief word from our sponsor Elation. John, hello and welcome. Hello, Shannon. Great to work with you again. Always look forward to these sessions. And this one is this one is certainly no different. So it's great to be with you. Let me get to the top of my slides here. There we go. And it's certainly a pleasure for us to be able to sponsor Peter. I've long admired Peter, you know, he's a force in our industry with all the books published. All the articles written and all the education that he's given all of us. So it's certainly our pleasure to sponsor Peter today in this in this educational session. As you can see on the screen, I'm Elation's field CTO and I want to thank all of you for being here today. And for giving giving all of us this opportunity to convey some of what we're learning with, you know, from our work with our with all of you, frankly, and, and to, you know, to share that with you to hopefully have you take away some some best practices and some tips for your own work. You know, I do not want to do the typical vendor commercial so I'm not going to do that. I'm sure many of you are well familiar with elation. And if you're not, that's the reason our website exists I welcome you to to go out there and have a look. We are the leading data governance provider as assessed by, you know, organizations like Forrester. And the well in the upper right hand corner of the of their of their for quadrant, you know, the proverbial for quadrant assessment wave matrix. We're also the data governance partner of the year with with snowflake. We have many other, you know, accolades and we're growing really really quickly. But again, it all comes down to all of you and our customers and. And, and, and helping our customers succeed right and all that other stuff is, you know, secondary to say to say the most. So it really comes down to our customers so I just want to share four things that we are focused on as a company and most importantly our customers are focused on. And these all dovetail with, with Peter's material, I was really excited to see where I think 100% in lockstep around this idea of governed, you know, our guided data governance I should say, because in our customers world, you know, they're really focused on using a catalog to lead governance and there's a really important point that, you know, that there's this dashed line horizontally across the screen. And that's because we as a nation and I think it's fair to say our customers view the catalog and a governed catalog as a business application. So it's not a story platform, you know, pushed on business users and analysts and data scientists it's not a policy platform pushed on them. It certainly does all those things and they're incredibly important, but it's a guided environment again to use one of Peter's words that he's, he's going to teach us more about in just a few minutes. And, and so most, most importantly, it's their platform it's the analyst platform it's the data scientists platform it's the rank and file employees platform to find and understand and trust their data. It is made better by the governance program, and by the people who do the great stewardship work right that's and so this is really one of the fundamental principles that we've always been focused on and as more than ever before it's I would say it's, it's working, and it's working incredibly well. The second thing we've always been focused on again come back to this word guided. It is a guided stewardship experience with curation, or you might say crowd sourcing. By a great many people you know way back when it used to be that you know the notion was, well stewards had to kind of do it all right they had to not only define the rules but they had to input, you know all the metadata and they had to keep it all clean and they had to report on all of it. We all know that impossible task. The world today is changed, and I think that there's an expectation that stewards are going to be the mentors coaches so using a few different words than guided. I think there's a network of people involved in that network helps give lift and give scale right to to to to governing the data in all forms of its quality that's metadata quality its timeliness accuracy. It's also all those things for the data itself, not just the metadata but the data as well. The third thing is that we're very focused on driving community, which is kind of an extension of the prior topic right around this social capability but very focused on community adoption and participation. And I think that takes many different forms you know one form of community is this people aren't just consumers of the great knowledge that people put in but but they they share back. Right so that allows them to, you know, do better self service so they can, they can find their colleagues on their own who are the experts in particular domains particular subject areas. In the center diagram there they can also share their own knowledge and then it's sort of a reciprocal process right, the more people put their knowledge and they share what's working what's not working. That's about the data but that could also be contextually about the policies around the data could be around metrics terms and all those things. This makes a richer environment really makes up to my call a knowledge base if you will, that's living and growing. And then of course the steward guiding that entire process, helping people know what good looks like. And keeping the entire organization accountable for that. So, the last thing I'll share and then we'll get on to the meat of the material with Peter. We don't just take these high level concepts these three that I've laid out for you and leave it at that because we realize a lot of our customers. They really need best practices they need a starting point. And so we provide what we call the active data governance prescriptive approach. There's a lot packed in behind these but essentially it's this continuous improvement process that goes from establishing your governance framework and the the upper center, then moving clockwise all the way around populating the assets, deploying and employing you know stewardship, curating assets, applying policies, monitoring measuring and so forth, and iteratively building that up and that's another theme that I that I know isn't in Peter's materials is this idea of iteratively building muscles over time and layering on don't, you know, try not to bite off more than you can chew in the early days so we very much are very pragmatically oriented, believe in an in an iterative approach with this business facing process and the combination known as the catalog that's really governed well and gives people this ability to find understand trust and just have this this well of, of, of information that they can have confidence in, and really it's the combination of all those things that, you know that we see as forming this, you know, what can be an ambiguous term data culture but it's really the combination of all those things that helps start to build culture certainly starts to build data literacy again just to sort of segue back over to to Shannon and then Peter I think you know Peter is all about literacy right and many different forms and educating people. And so I'm excited to hear here from Peter today so I think I'm going to leave it at that we're focused on all these things our customers are seeing a you know success like I've never seen before. And I've been around the industry a long time 30 plus years and I've seen governance go through various stages of maturity and this is this is the most excited I've probably been in terms of seeing customers succeed with governance and have it be established as part of the ongoing culture of the company and in part of the ongoing operational process of the company so again super excited thank you all for ring here appreciate you listening me for a few minutes, and now Shannon and I'm going to turn it back over to you. And thank you so much and thanks to elation for sponsoring today's webinar and helping make these webinars happen. I see there's a couple questions coming in already and if you have additional questions for john or about elation, feel free to submit them in the q amp a portion of your screen as he'll be joining us in the q amp a portion at the end of the webinar today. Now let me introduce to our speaker for today Dr Peter achon Peter is an internationally recognized data management thought leader many of you already know him or have seen him at conferences worldwide. He has more than 30 years of experience and it has received many awards for his outstanding contributions to the profession. He has written dozens of articles and 12 books and as john was saying the most recent one is on data literacy, Peter definitely knows his topic on that. Peter is experienced with more than 500 data management practices in 20 countries, 20 countries, and consistently named as a top data management expert, some of the most important and largest organizations in the world have sought out his expertise. Peter has spent many multi year immersions with groups as diverse as US Department of Defense, Deutsche Bank Nokia Wells Fargo the Commonwealth of Virginia and Walmart. And with that, let me turn everything over to Peter to get his presentation started. Hello, and welcome. Welcome. Thank first of all john I'm speechless thanks for all the kind of things that you said about me I'm certainly looking forward to continuing the discussion we get through my material here in a quick second but absolutely pleasure to be with everybody Shannon wonderful to get us started here and continuing on john's positive note I think it's important to highlight that most data governance efforts are making progress in the right direction there's things that can be done to speed it up and I also believe that that sense of community that he spoke about as a really critical element as well as the iteration portion of it so thank you john for bothering to drive we did not rehearse this but again it's I think very very nice to pull into this. So we're going to talk about data is confounding characteristics and we're going to move on to four key elements in my experience that describes what a practical and a growing and an evolving data governance program should look like in this and I'm just going to dive right on in with the old elephant and blind persons analogy here and of course you're familiar with the normal version of this, but data is also in a similar kind of a context here and that people approach data from different perspectives. And so they think data is different things very similarly to the way the blind people in the elephant did. Again, there's been confusion and all of this because it has thought that data is a business problem and if they connect to the server my job is done, where his business thinks it is managing it what else with somebody with a title chief information would actually be doing well unfortunately data has fallen through and into an enormous chasm between it and the business and it is our job as data professionals to try and repair that damage and restore harmony and the business on all of it and today I'm going to move off into a different kind of a context So this is, first of all it was interesting to me to find out that the title of the book was actually the princess on the P instead of the princess and the P. And that actually gives a very good analogy the management is actually understanding and we'll get to that and storytelling but the question is what happens here if we don't get the data piece of it right it locks in those imperfections for the life and it restricts additional investments and leverage that can be occurred. And each of that organization spend 20 to 40% of their it budgets migrating converting or improving data. And this is part of the cause of data governance is to start to reduce these types of steps and use data in more direct service of the organizational mission. Of course, failing to do that makes everything take longer costs more deliver less and present great to risk. Thank you Tom DeMarco as always for this and one of the first aspects of this to understand as data professionals. Is that part of our task is to figure out and help the organization figure out what is the wheat from the chaff in all of these the problem is, of course, it's a bigger tasks and most people realize organizing that process. One first ask the question is well organized data worth more and the answer to that I hope is a resounding yes but if you have trouble articulating that to anybody just simply remind them that before. The computers were around books were organized with lots and lots of metadata and removing of course the spine from a book and having the pages just sort of flutter about unnumbered would not be a good way to impart knowledge so sometimes a negative example is what you need but clearly demonstrates the case that organized data increases in value and yet in our organizations 80% of the data that is out there is redundant obsolete for trivial. And most enterprise data is never analyzed so we have these vast majority of data that is out there and the only argument I ever get on this is that it's not 80 it's 85% in my organization or something perhaps worse. It's best qualified to accomplish this and what this has left us with collectively as a concept called dated debt. It slows progress again same for things that Tom DeMarco was talking about, but it's the idea that this is going to slow things down think of it as as you know bad stuff in your veins keeping your heart from pumping correctly and what you want to do is of course get back to zero, but that involves undoing some stuff and requiring new skills and this is part of the exciting world that's going on in the governance area let's just give you a value perspective on it real quickly. This is an article from Forbes obviously last year and it talked about in 2020 which was not a great time for the airlines American Airlines had been valued at $6 billion by the market, but the advantage program data was valued between $20 and $31 billion by the United had similar numbers here. The answer is to why this occurs is of course in fact dated debt let's jump on to our next topic then, and that is keeping data governance focused practically so the first question is how old. Are we as a profession in the data world and if we compare ourselves to the accounting world that we're not very old. We can get ourselves back to Augusta Ada King and her wonderful insight that this weaving loom it was something controllable and that the control that they were using in those days could also be programmed to control math what an insight onto this. So, while accounting has been around for 8,000 years. We really have been around maybe for 300. If we expand it so this newness is important and one of the things we do is to try to figure out what is happening out there now this is a wonderful survey I'm going to spend just a minute here and I urge you to check out the survey at www.dantage.com Randy of Tom have done a great, great service here. The data here is actually 2022 and I realize I'm looking at the slide and seeing it's a little confusing right at the moment. But the question is, are you driving data with business know are you competing on analytics are you managing data as an asset you can see these numbers get abysmally worse. They have other numbers they have other numbers that they've put together but the most important statistic that they took away from this is the upper right hand corner where it says 2018 and 2018 the question was to these thousands of business people who are working in the business and analytics world are your problems primarily technology or people in process and you can see the answer in 2018 was 19% 80% and over the years has just continued to hover in that same 10 to one or five to one ratio which just says we need to be looking at people in processes and 80% of the data challenges are people in process based, and this is the goal the focus this is why governance was invented and this is why we're going to do data governance in these areas. So let's start out and just put some context on it your corporation whatever it is has some form of governance probably can't say that authoritatively, and people will be familiar with it it is getting better about being governed. The idea of aligning it strategy with a business strategy and then producing measurable results coming up with metrics and a return on investment and then specific folk I in these area notice by the way in all of these folk I none of them are on data. And so that's an interesting piece. Again, corporate governance it governance. Now let's look at some definitions of data governance and I'm not going to read these two. These are all good definitions put together by some friends and colleagues on all of these including David Dimbock that is here. Those are difficult definitions though to explain to somebody in elevator ride. So I like to use a very short definition data governance is managing data with guidance, and that of course immediately begs the question would you want your only non degrading non depletable strategic asset managed without it and the answer is of course no. As we work our way up the food chain in our organization is the question changes just slightly, and that is that people get further away from the actual data they think they're not involved but in fact they are involved they are in bad data decisions and they don't know it. And this is a problem leading to what I call the bad data decisions spiral again that as governance professionals you'll have to be aware of this so business decision makers and technical decision makers are not data knowledgeable. This leads to bad data decisions poor treatment of organizational assets and poor organizational data quality and poor organizational outcomes. There's a big challenge right there for starters in organizations. And if you heard Morgan Freeman he says this is wrong. I just like the way he says it in there on this. Let's take it another step though. Most organizations miss this one and they say okay so what's an example and the most common example that I have seen literally a dozen plus examples of since the pandemic started is the instance of an organization, installing Salesforce by a time in order to make a technical IT deadline, and that that same organization unfortunately learning very quickly thereafter that the concept of having Salesforce filled with bad data is a very difficult one for customers to understand the difference between and the customers just say Salesforce isn't working for me whereas course Salesforce is working, but garbage in garbage out we'll come back to that a little bit. But the most important question then around this topic is how not how should we govern all this data, because that's way too much and it's you don't have the resources nor did you do it the right question is, how do we include this data and I'm sure we include this data within the scope of our practices. And, regardless of what the answer is document the reason for why you do that because of course you don't have the resources to do everything that you'd like to do. And that is the reason for strategy strategy exists to help focus the mind. It didn't really become a popular term in the business world until 1950 or so when the business consultants discovered it and made this thing of a grand plan and I do mean a thing in the sense that the strategy. There's somebody the other day that had 126 page data strategies. Wow, well done. The original term before 1950 was described as a pattern in a stream of decisions, and that's much more of a process or an iteration if you will. Then it is a thing. So let's take a look at three quick ones just for example, Walmart had a former business strategy and I'm sorry I went back a slide instead of forward. Walmart had a former business strategy here that most of you are familiar with and in fact everybody on the Walmart Express and in Bentonville and every Walmart employee understands that Walmart strategy is everyday low price and if you make an error that supports their strategy that generally will be supportive of the decision that you made around that that's a very simple strategy it's a pattern in a stream of decisions. The example of strategy is Wayne Gretzky's and it's got a wonderful little Wikipedia article but basically what it says is he skates to where he thinks the puck will be. Because of course the alternative is to chase something hard and plastic that travels much faster than you do around the ice which probably not a terribly fruitful activity strategy example number three here is sort of a way in which our military something here if we're here the good guys and the bad guys are over there, we're going to employ one type of the strategy if our position happens to be here, where we're the good guys and we have a high ground and the bad guys have the low ground. We're going to employ a different strategy than we would if the bad guys have the high ground, and we have the low ground so again these pattern in a stream of decisions is a much more important concept because this pattern guides work group activities as people are working in these this is how they are taking guidance this is how they understand their group members will work in the same way they do. And let me just point out that here is a relatively complex data governance environment that one organization is attempting to put together. Notice it's more complex than the strategies that I just described to you. As a data strategy, the key is to give the organization some sort of high level guidance that focuses on business goal achievement, because that's where you're going to have people having questions. What are we trying to do and why are we trying to do it or what are we trying to do first. So it's how data can be usefully applied to support the organizational strategy and involves a balance of proactive and reactive measures in this area. Data strategy then drives the organization, excuse me organizational strategy drives the data strategy and the only purpose of a data strategy is to develop increased data support for the organizational strategy. So the question of data strategy then factors into data governance by articulating what are these data assets doing to support the strategy how can we make them better in that process, and the feedback of course comes back how well is all that working. We get the work done in governance oftentimes through stewards and john mentioned that they were much overworked and underpaid and I certainly agree with all of that. We're still learning as a profession to get out of here but nevertheless it is the stewards that we turn to when we want something to actually be implemented in that type of a context and let's just take a quick look at it. The goals the organization should be supporting data strategy expressed as business goals are something tangible so that we can claim that we've had a movement, the needle has has has moved in this area boy movement that's on the bad didn't The data governance group should be talking on metadata and the language should be metadata that's spoken in these areas failure to do so is very problematic and of course what we really want is the stewards to be doing this type of work, expressing these metadata goal business goals and how they impact each other and tying it together and of course the perfect tool for something such as this in the general starting place for many data governance initiatives is the data catalog that goes into that. The governance role then has to also be understood and this is something that you'll have to work out with your individual organizations right now and most organizations we can claim that they have less perspective into the whole process and they'd like to have but they will start a data governance initiative and most people understand data governance improves data over time and there's nothing wrong with that but it oftentimes is perceived as slow relative to the business challenges that the organization is facing. And so they also say, can somebody go out there and actually fix something you know let's not just make the next generation of engines coming off the assembly line better. Let's in fact fix the ones that are out there at this point in time. So the data improves as a result of focus into this. Again, this will give better perspective we can now start to mature the organizational structure add in the components of the data governance program and start to celebrate things that happen in the data world and while that's a good thing. You'll notice that the expression that I put between data things happen and organizational things happen is the approximately equal sign. And the reason for that is because we're not as good at that as we'd like to be and we need to get better at it. We're very good at celebrating data things that happen. I was talking to somebody to job interview the other day and they said yes and so tell me what you've done so I wrote 10 reports. It's great did anybody use your reports to make any money. I don't have an answer that question what that is a place we need to go yes I'm sorry to bring each of the cold realities but budget different processes are in fact going to require just that type of an approach. So let's give some value around this concept here right now we've got people that are managing data assets and trying to get better it outcomes and trying to do some support for strategy and maybe there's a knowledge worker improvement exercise. This gets to confusing though and people don't really understand that there's usually a root cause to all of these that comes about. Much much easier fashion, and that is to say that most organizational challenges. First of all have a root cause of data. And if they don't, they don't really exist as challenges we haven't articulated them precisely enough. That said the challenge is not recognized as a data challenge by those experiencing the challenge because it's filtered through some sort of business processor it system. And so really remains for somebody to connect to these dots and say these are poor results all stemming from a hidden data factory that perhaps exists within your organization again thank you Tom Redmond for a wonderful term to describe all of this. And that root cause analysis is a part of data governance and can only occur through the type of community efforts that talk about building up eliminating data that requires a team with specialized skills, they're going to deploy a repeatable process and develop for themselves sustainable organizational skill sets that they will be able to articulate. I like to say it's kind of like a fire station way to think of it. Most people don't imagine that the fire people are out there fighting fires and I do like to say harmless paper clips and duct tape are valuable data governance technologies around all of these things although the paper clip is the biggest barrier to management and one page is really about what you want to do with them. But the idea is yes there will be some clever things that will do in data governance and policy driven things that will take some time, but just as the fire department doesn't sit around waiting for a fire to happen. They go out and they do all sorts of education and knowledge training and awareness and just generally promoting the idea of fire safety these are wonderful things to do. And the focus in the data governance area has to be similarly divided between proactive and reactive, according to the organizational strategy so you can see it comes back into play. Lack of data governance is costing organizations millions in productivity in siloed and redundant efforts and poorly thought out hardware and software purchases delayed decision making. Again, waiting till initiatives get much worse and as I said before, consuming 20 to 40% of it spending annually. Again, keeping it practically focused on strategy is key to being successful in this another key is the ability to exist at a programmatic effort in this data and it operate at a different cadence or rhythm data is not a project. IT has gotten very good correctly at creating new projects out of it. It's a sensible way to do it and it's been the best way to improve the overall throughput on this. But data evolves and more importantly it evolves at a different cadence. And this concept means we need to separate and make external to and most importantly precede system development activities. So these data programs have to drive the IT programs whereas it has been the other way around up to this point in most organizations. Most will come down and say what's the difference between data governance data management it can gather in sort of the policy level which is why some people say it takes a long time. It's a good idea to focus in on practical things that you can do in terms of now as well as later, but it says guidance and direction, and I'd say that all information, not marked public should be considered confidential. Firehouse metaphor is the idea of going out and being active sometimes but also being proactive in the times that you're not actually fighting fires data management on the other hand is the business of planning control and directing information assets to their target by solving specific challenges around those areas. And yet, even this level of understanding that we now have among us is problematic in organizations. Most people, you know, there's data, we've got data management data governance we just talked about the differences what happens. I guess that's the good old Charlie Brown blah blah blah. They don't appreciate the difference so don't try and explain it to them it just gets confusing instead talk about your data program. Because the key is to drive home the fact that there's no way that you are ever going to not need your data program, your data program will last at least as long as your HR program going forward and it needs to have the same level of public support around that. If you haven't seen this before it's bad on me I am president of Dama International, and quite frankly, this is a wonderful piece of articulation about what does it mean to do data management. Unfortunately it doesn't actually show anything about doing it's got a lot of very good words on there and allows us to articulate the idea and a nice framework around that. What most organizations fail to understand from a data governance perspective is that it's going to be involved and that's of course why it's at the center of the Dama wheel. For example, when organizations start out on their journey in data they don't generally understand the rule of three which is likely that you're going to need three parts of this pie, if you will, in order to get a complete focus on things that an organization may start out to build sort of a data warehouse and get the idea that they should have data governance and quality because perhaps this is the second or third time that they built the data warehouse by the way the average is seven. And in doing this the second time through they said yes we need to put quality in before we get started and also we want to make sure that we have governance that make sure that's going to occur going forward so it's not just the technical design of the warehouse but making sure that it's full of good quality data. Well, that may require still a third application of it which may then say we need to focus in on metadata, instead of data quality and again that these shouldn't be one versus the other it's mainly a cyclical and say what are we going to focus on this cycle. You'll notice we've given two X points for data warehousing and data governance one X for metadata management and if we go to our third example on this as well, maybe now we've decided the reference and master data might be a strategy that would be better applied to this specific warehouse in order for us to do this. The idea is that data governance is going to be involved in almost every aspect of data going forward as you started. And the idea that we're going to be done with it at some point in time is nuts. You have to get the ability to tell people that. Now, when they ask what are we doing. Tell them the question is not what are we doing the question is what are we doing first. The first is things that further the organizational strategy will make a Venn diagram for data that is used by the business that needs to be improved and add a third component here of saying if we have a choice. Let's practice some additional data skills that perhaps we have not been able to up to this point and focusing in on all three of those is really sort of a sweet spot. Let's allow us to go forward and do this and notice this is different because we've practiced in the past what we call application centric development and that's the idea that we've taught for the past 50 years you have strategy, then you should apply it and data information falls after the fact on that this ensures that the processes are very narrowly formed around the application and limit your data reuse as a wonderful book on it by Dave McComb. So the wrong way to think about data strategy is to look at it as organizational strategy and then it strategy and then a data strategy coming below it Morgan Freeman. Yes, sir. Thank you very much. I do appreciate that. This is the correct way to do it and it's the idea that data strategy is going to influence your it strategy to a greater extent than your it strategy is going to influence your data strategy, meaning that we only need to change everything and change from strategy driving data and information and then it projects. It's challenging and it requires a lot of rice bowls to be moved but if you think about it the data as the programmatic piece that doesn't change much is much more stable over time should in fact be the framework that we use then to drive outward on it projects this will allow us to create the process architecture the way it should be for the organization instead of an application and app driven pieces and finally maximize our data reuse around that the talk on data strategy does a little bit of that as well. So, to try and get improved effectiveness data governance is central to data management, and therefore also central to digitization efforts. We're going to take a look at those in the next section up here, but you've got to decouple it from the it strategy in order to make it work. Next one is to gradually add ingredients and I want to shout out to Mark. Coming up with this wonderful insight he was doodling online so we weren't actually physically present he said data from digital I'm not sure what that leaves but if do know that if I go the other way and subtract digital from data I've still got the data. This is a very insightful way of thinking about it because of course you can't just go digital by spelling the word data it doesn't work and consequently it becomes problematic when organizations try to add it afterwards very much like time to engineer security or other type of capabilities into your framework afterwards so once again if you start with garbage data and the perfect model you're still going to read reap digital garbage results, maybe a new term there. Our term for it of course is garbage in garbage out or G I G O and this is true of course whether you think in the center is data warehouse machine learning machine intelligence block check AI MDM data governance technology, whatever it is and worse. We're probably doing these multiple imports of data in parallel to the same processes so the first place that it makes sense to take a look at is to see if you can harmonize some of the process flows around this now harmonizing the data will take you even further that's a different issue but the idea here is of course, you need to get a handle on what's going on here you can't just assume that it's been set up correctly and of course once then you pump with quality data into it. Can you expect quality digital results anything short of that you're going to get imperfect results over there. Just to pick on the machine learning community for a quick second which no disrespect to them but they've invented great learning algorithms, but they are almost completely bereft of good data to have and that failure. Fortunately to look ahead and try and attempt it is the main thing that is holding that progress in AI we're not going as lofty as AI. We're going to go for a data sandwich. Now the idea is that in order to do what our organizations do they are trying to leverage high performance automation. That means as a component of data literacy, a data supply that is probably of uneven quality and a data standard use that is maybe perhaps of uneven quality as well of course, trying to get to perfection is not something you can snap your fingers because they group process that must occur from collaborative means over time in order to do that, but we've got to do it because we need these three pieces to work together. And of course this cannot happen without investments in engineering and architecture so another key component in this area, even though we're gradually adding ingredients is to say we want to do it in an engineering architecture fashion. And that's all the way to India this tea farm in India where I found behind the cash register the sign that said quality engineering and architecture work products do not happen accidentally of course that's a quote from Jay Edward Deming and I had to go all the way to India to see it but then again I put the word data in there as well because of course it is equally as true now I'm calling you today from this location. I'm here and I'm showing you a picture of our barn which had to pass a foundation inspection in order to get started so this series of photographs documents the fact that the barn had a good quality foundation that passed a foundation inspection in fact it was a condition of the loan that the bank gave me in order to do this before further construction could proceed. They needed to verify the validity of the inspection certificate. It makes a good business sense, yes, absolutely it makes good business sense if I build a large barn on top of a poor foundation, I will spend more money on that bills and I will paying off the loan, and the bank wouldn't be happy. Given that as well but there is no it equivalent, and this is a challenge for us. So one of the things we talk about in data governance are these frameworks is the idea that we're going to eventually build these pieces and but not right away, and not right at the beginning where we know the least about what it is that we're doing these system of ideas allow us to organize to prioritize to determine where progress is again just a couple little rules in the building trade right don't put up your walls until the foundation inspections past, but once you've done that put on the roof so that you can work immediately in inclement weather and make it all dependent on continued funding. So now these next few slides are going to go by very quickly remember this is recorded and that's what I'm intending I'm not going to walk through them, but instead show you that the one framework I am most familiar with is the data governance framework from the data governance framework simply is showing as an input process and output diagram these are some of the original diagrams they haven't changed much over the years this was from Gwen Thomas, who I believe will be coming out to bid us in June we are from Diego for DJI Q, our friend Rob Steiner put this one together. This was from the IBM data council, this could be data governance council this one was particularly comprehensive I found Jill Dushay that baseline consulting I believe is the author of this particular one this particular organization decided it was important enough from a governance perspective to give them a logo of some sort I'm not sure why you would think sale boats and college professors but maybe I'm jaded I don't know. Back to our context in here the reason for those frameworks is to try and look at something to build likely none of them are perfect for you but if you find one is close to you. It is a great starting place in order to go forward. Now, speaking of support the it world of course has to support what we're doing here so we're writing them in right away but I'm now going to talk about things on the left hand side of the orange and I'm being less specific and the roles more formally defined and of course the opposite on the other side. Similarly above the brown line, they're more likely to govern encounter govern data but spend less time doing it whereas down the bottom half is where people are going to dedicate their time and encounter that government set of data so these are also components to think about whether this makes sense for your organization or not it's pretty generic as that goes and many organizations will draw a line around the left hand side of the diagram say this is our data governance organization and we put some motivations around it leadership is responsible for obtaining and maintaining access to resources, listening to data and feedback that comes from making some decisions that we asked the stewards to help out with taking some action so that things actually change around there. Looking again for additional feedback, additional ideas and obviously providing some guidance back should be the iteration loop that we're looking for notice I've simplified the diagram slightly in order to perhaps make it more palpable as we go forward. Finally, another component of this just to tie it all back to where we were going is that there's a list of things to be done to get started. This is not comprehensive please don't think of it that way. Although I do want to point out that john's diagram could be taken in the same way and I want to make sure that I expressly clear what I've seen a lot of organizations try to do is to go through this list on the left before they have experience in this. I don't believe elation is proposing that I think what they're suggesting is gradually layer these things in as you get better with it because of course the cyclical approach and I think we're entirely aligned in that respect. So, I'm not saying, don't do this but I'm going to quote quote herb Cohen who has written a great book on negotiating care, but not too much. All right. So let's go a little bit further forward again we can look at goals and you can do the same thing with these that I did with the frameworks. You can look at each of these and spend years trying to get really good at it. You can put yourself put an effort on it evaluate that effort and decide whether was sufficient or perhaps more should be included in this. And the key of course is to look at all of these things and say oh my goodness. You know that seems like a lot to get started, and I want you guys to think instead of a different word, which is of course evolve. I don't want to go through and try to do a perfect job on that because we're not practiced at it. In fact we will do this exactly once if we do it well although there are others that are starting in a repeat mode on this I'll pick on art my own Commonwealth of Virginia. And that's a target in this case which is that they're on their third specific iteration of trying to implement data governance well and it's not that they haven't had a good effort at it but it's been hard and they've been overly dependent on champions and that has been a challenge So the point of this slide and I'm actually going to go back and build it again because I was battling around it. This occurs once whereas the part that you're going to repeat occurs on the right and if I'm going to get good at something. I get good at the thing in the bottom right hand corner. And if it looks a lot like plan do check act. It's very similar in that context and that's the way it should be thought of because the way to go through and build your data governance program is to see what your organization needs. And what your organization needs is still probably an unknown when you compare back against what we're looking at specifically. And what does the organization need to change. What are the things that are going to help your organization, use your data in better support for the organizational strategy, and the loop on the right hand side just like all plan to check act loops on the right hand is exactly what you need, combined with what we were also talking about the community now once again I wouldn't open it up to the community again a bit of guidance at this point just to give you a symbolic kind of thing. Lots of organizations I've gone to and spoken with them about this. And when we get to that stage they then say well. Okay, so I really wanted to be part of the data storage group but I wasn't appointed so I'm just going to sort of kick the sand over here and grumble about things. But I don't think that's really what you want to do so, rather than appointing your data stewards Council appoint the first round of data stewards. That way there can be a next round and the next round you can even do better than you did perhaps the first round. Things like that are much better way to approach it whereas I've seen organizations try to come up with very precise details on definitions of 16 different types of stewards in order to do this. And that doesn't quite add up to the way you'd like it to head up around that. So, again, the part on the bottom right hand side is what you're looking for an environment, being able to work within this get people and processes together around here because that's where the data challenges are. We've got a couple of stories now to drive into and the reason for that is quite simple. It does no good to be able to support data governance on strategy by a programmatic effort that you gradually get better at by adding scope of data, adding scope of stewardship adding scope of subject areas whatever makes sense and probably some combination of everything I've at least described there in order to get started, but it does you no good if you can't justify it and storytelling has to be a key piece of this. We're going to talk about a couple of quick stories here as we get back towards the top of the hour and see where we'll actually come back and have your discussion around this. The first one is that when people start going digital. It's kind of important to be able to have at least an anecdote to say, Are we thinking about this correctly. And the answer maybe yes or maybe no but a good way to do it is to look at what happened with respect to the world's first ever tweet. So this is a screenshot of the NFT of the world's first ever tweet that Jack Dorsey sent out. You can see back in 2006. Okay, setting up my stuff for Twitter again down here in the bottom. Now, the process for this was that the individual this study was going to sell the NFT and he thought it might make a proceed of getting more than $50 million in order to do this and again I think he was a bit disappointed the people were going for $48 billion and they got $280 as the bid so digital is not something that you can simply throw words at. I will also point out to us by the way we're on a data call of course at this point. There are more chief digital officers out there than there are chief data officers. That's a challenge around things as well. Okay, so you've now got a story that you can take with you to help your organization think perhaps more clearly about what their focus is in the digital strategy area. A wonderful group that I worked with here for the next story is a group that had a very good way of expressing their local culture had done this they said getting access to data around here is like that Catherine Zeta Jones where she's having to go through all the lasers now obviously we're dating ourselves as millennials in that context. But the key is this is a very cultural touchstone it was the idea of course if she was good at weaving her way through and achieving the prize, you know that would be fantastic. But of course you don't want people to manage it was quite surprised when they found that this they thought they had solved the problem because they had purchased the data warehouse, and that should have solved their problem in their minds. And again, from a perspective here, somebody bought some technology, but the technology was not correctly implemented, and they didn't fail they failed to include the people in process components of that implementation which would have made the data warehouse implementation more successful around that. So, whatever hooks it is that your organization has use them don't steal somebody else's they're not quite as much fun, given that sort of the, but certainly in your organization there are people that talk around this way. In order to do this. I was actually up on Wall Street at this time it was fascinating to see what was happening here but on a particular day usually in just a minute. That bar layman brothers went under and told itself to Barclays, basically, and as part of the deal. There were 179 contracts that Barkley said, we're not going to buy those no matter what's happening we'll buy the organization will bring the name along we'll get everything that comes with it. We're not going to buy those 179 dodgy contracts and that was, of course, the reason Lehman Brothers was going down as well was because they had gotten into some very strange things again big short. Certainly read up on it going looking at that. This is exactly of course what happened now in the process of doing the sale of course lawyers were involved and they went through, and they used a spreadsheet and they hid the contracts that they didn't want to buy so there were 29 lines in a spreadsheet that were hidden and then handed to a first year associate. Again, junior person if you get the drift there. And of course it happened long after business hours this has been written up you can see the headlines on the other side of the screen here and yeah it's late night and there this love work you know we go through in format, and then quite get so the end of the sale closing was September 22 2008 in there, and they went before the judge and said here you go but they became unhidden by this associate that was working on this. And the judge they fielded and the judge went back, but nope sorry, that's what it was. So, you can believe around Barclays, the data governance around how to use spreadsheets is phenomenally good, it's effective and they do a really commendable job for unfortunately having to absorb the data debt that came along with the Lehman Brothers sale in that. Another British example in this. During the beginning of the pandemic. Why on earth, do you imagine that a health department official would have to know whether or not they were using the right version of Excel. The key there is the right version whatever that means. But if you have a version of Excel that causes it to drop rows after you a certain level and not happen to give any error messages as a result, you under counted the number of people who were working on the virus in this case. So it's a very, very problematic situation and clearly led to a number of situations here where we're now thinking that the virus counts not do of course spreadsheets or bad technology but nevertheless is one third of what the actual count is in here and this of course contributes to that general sense well you can see with each of these stories so far there's been some kind of a grip but what did happen with Lehman Brothers and you know and there was a real story didn't come out yes and how did it turn out yes the judge turned them down right. You know what, what about the concept of Catherine data Jones actually get it yes, she and Sean Connery I think both got the gold and came away and deck Dorsey of course, and the tweets well you know who knows what's going to happen with with all of this. There's a great article and wired called there is no inherent reason to trust the blockchain, which is a well worth reading going through that. The last story here that we're going to do sorry to switch there is one about fan blades which is very boring. In the sense that they are not of course boring but if you're a jet engine made manufacturer, and you're able to put sensors on these engines, you might get from one sensor probabilistic forecast of the maintenance. These things are high high high precision instruments fascinating engineering unbelievable quality control around this to this extent that when we have the one engine failure that we know of at this point flight 1381 from Philadelphia couple years ago they were able to trace the error back to the decision that was made in the factory around this so it's a fascinating fascinating science for this but think what happens if you go 100 sensors. And the idea is that instead of understanding, generally what's happening we can find optimal monitoring targets fine tune, which means the maintenance can be improved and safer. This means that we don't have to store things that we needed to in the past that we can now try to get to better ways of understanding the systemic risk with which we approach these very very important organizations and for this one in particular. They saved a total of one and a half billion dollars over the time, or you can better believe that the data governance group was careful to run around and help do this and the reason for this was because this was just one example of the part that this one system made in the organizations move into the digital world. They had changed their model transformed it entirely from a product oriented model to a service oriented model there is no, no more wrenching organizational transition that can occur beyond going out of business on that. And yet they managed to accomplish and accomplish it in a phenomenal way through in particular the use of storytelling around here so to take it to the final sort of conclusion here. There's not much point in making your data better. If you're also not going to make your people's use ability to use the data better as well this gets against back to the literacy component. So data, there is an even understanding that people have approached it from different perspectives and for good reasons understand data to be different things. There's nothing wrong with that but our failure to understand it as a unified unified view with fractured views and unfortunately it all adds up to increasing organizational data debt. I call it a stupidity tax and some of the groups that I work with around this. It's a very harsh thing to say but it's nevertheless, as true as it's going to be. So the focus then and what does it get to just a data governance program need to have it's got to be focused on strategy. The best we can tell is that the more you support strategy the more feedback you'll be able to get because people will understand what it is you're doing, and be able to help you correct the course around this again the crowd sourcing model to take into a data governance can only be effective if it has the same level in the organization of continuous support as the HR group, because it is central to what happens in data, and it's got to be not subsumed by an IT strategy, given all of that. Finally, we look to add ingredients bit by bit again start with a small section of this make just a couple stewards get them to work together as a team, spin the data up. Yes you'll get bigger bang if you go for bigger but I've also seen with bigger becomes bigger risk. And this is the same thing that the chaos report has told us all in all the projects that work or the efforts that succeed the programs that support to the various degree are small programs that grow to medium size over time as opposed to large size given all of that because what we're doing, whether it's digital or just data sounds crazy for me to say just data right. It's dependent on high speed automation in this, and that there's got to be some sort of a framework that we understand as a starting place but perhaps not the vision we're going to implement fully at the sense and that evolves this sense. But also if we can't tell the stories around this, none of this does us any good. So the need for data governance is increasing it's a new discipline. Therefore, there is no one best way but there is a better way for your organization given those constraints. Keep it focused on the four elements that I've just gone over. And remember that the goal is to get better at what we do over time, because the more literate the organization it is the easier the transformation is now I don't normally recommend books on this because some of them are better than others but this is a good book, very good book by John Ladly and I'm showing it to his twofold one. He wrote the first version 10 years ago and I asked him what it changed between the first and second version he said everything. That's a good measure of how things are going in here. A couple website references for everybody on this again. My own books are on sale at the moment that are out there and we've got some upcoming events including the first. Excuse me sir, we can do we're going to be in San Diego for DG IQ. So we'll be able to see Shannon around there. And I think Shannon is the top of the hour so it's time to bring john back on and look forward to your discussion everybody thanks so much. Hello, and I hope to see lots of people in San Diego in June. Very excited about that. So, and just to answer the most commonly asked questions just a reminder I will send a follow up email by end of day Thursday for this webinar with links to the slides the recording and anything else requested throughout. And so many asked earlier to if they can get a certificate of attendance for any of our webinars you can email info at data versity net for a certificate of attendance and see if it applies to for your continuing ed for any certification you may have. So, lots of great questions coming in here. With this goes to john initially your presentation in the beginning with the data mesh paradigm, how isolation aligning was distributed governance at the data domain level. Yeah, thanks Shannon and Peter. Wow. Thanks for all that great content that was that was amazing and I have to say your audience is amazing because this audience because I watched the numbers there was no drop off. I mean it's amazing. People are really absorbed absorbing all all this content. So, yeah, and I didn't know what that little yellow blue yellow thing was in your lower left hand corner into your last slide I love that was to say like good. Good things with our data. What bad things it still equals bad results something like that. Anyway, that's great. That's a wonderful conclusion on that yeah thanks. Anyway, back to the data mesh question so yeah, I think we live in exciting times data fabric data mesh hot topics super hot topics. I think data mesh as articulated by ThoughtWorks is particularly intriguing bringing some of the object oriented and you know development world into the data world is certainly a challenge and refreshing. I think one of those things for those of you not familiar with that and by the way we just elation we just did a for a part blog series on fabric and mesh, which I would point you to as for the resources but for the for the blog that I did on the mesh. The mesh is really about not evolution as Peter's talking about revolution and it's really about taking the centralized capability to build things data products and push it into the business domains into the business and have it wholly owned in the business. And I mean that in the most dramatic ways possible so so with that comes this desire is expressed in the concept of data mesh to have federated governance where these domain teams. trying to you know do their own thing yet adhere to governance practices principles. By the way it's sandwiched to go to Peter sandwich analogy which I love it sandwiched by doing the same with infrastructure trying to not reinvent the wheel. So, so where does, you know elation position in terms of or how do we fit with distributed or federated governance across these domain teams with data mesh. They've extremely well. You can express as many domains and sub domains as you want inside the catalog. Those domains certainly fit with the build teams and the agile teams that are going to be distributed in the business, what better place in the catalog to express central policies, and then have those policies be attached and actionable by multiple multiple distributed product build teams out in the business so I think you know we already have our customers on that path essentially will be doing more in the space over time. Oh, absolutely 100% but our customers are already on the journey to use again going back to Peter's word evolve. And we're super excited about that they're already on the journey on fabric and mesh, depending on what flavor they want and by the way spoiler alert spoiler alert on the four part blog series we don't think those are mutually exclusive we think those are complementary actually. So have a read. Nothing gets easier does it john. Yeah, right. I mean this is really the question here and I certainly didn't mean to say anything about elation with this concept but what, what he's describing to you is that they built the catalog correctly the first time. In precisely anticipating the kind of structural need that he just represented there in the federated sense. And so that's one of the lessons of governance here is that the decisions that are made in data governance are important it's not to say that we should be you know delaying them or that because they're particularly weighty they take a long time there are right and wrong answers to these things. But the key here is to catch this now I found no better illustration than the class that I was teaching this semester was a totally online business class. And I had a young individual on there who kept saying to me, this stuff is easy I do this all day I'm a tableau developer. And so I got the individual to describe to the class. And what their role was I said well I go to this great place and they give me great data and I turn the great data into great stories and everything's wonderful and john you're laughing at this point right exactly. There's a lot that's going on in the background and this was what we were building in our class was the background for this, as well as telling them they had to learn a little bit about the environment that they were working within. So it's very much a matter of not as many have experienced this as they should and the description here is how to draw more people into this because I, as you know, believe strongly in this organizational data literacy concept. And the idea is that if you're going to have two candidates who are equal in all other respects you should find a way of figuring out which one is the more data literate and hire that candidate over the other one that's the best way you have to improve your literacy around that but I'm going to stop talking right now I'll talk to the thing anyway thanks John is great great response there. Love it. So, um, we definitely do not want to discourage any potential data storage. So what should the expectations be on the first round of data stores in terms of roles and responsibilities, and how do we get executive support for full time data stewardship positions. So the second question is the easiest one, which is justifying a full time role. Think about the option that you're given oftentimes I'll give 10 people 10% of their time and that's going to help you out in this area right. Well no the switching costs are way too high and we know that all the studies particularly during the pandemic where we've got authoritative data now on multitasking, and we just suck at it. So we definitely want the full time approach to this in here. And when we have this first round of stewards what we're saying I like to think of it as a team the data team again I don't even like to try to particularly articulate outsiders beyond this data program concept but the data team here is going to help you in putting in things that will keep more data debt from accumulating but in addition that we're going to clean up the existing data debt. And unfortunately the scariest movie that I think I ever saw in my life was a PBS program. It was a close up of a person being flossed for the first time in like 10 years or something like this and it's just the most awful images you can imagine I'm so sorry to put that in your heads now on this but many people think about that as getting in with technology and not having any interest in this you've got to find the passionate ones, put them in place make them a team focus them on some combination of proactive and reactive, and then get them started on saving organizational dollars or helping the organization make more dollars on the top line. Yeah we just add to that. I really appreciate the question I think it's a really central question I think having a this sounds kind of cliche but executive support and telling a positive story about the impact to the organization that this role will play. I think it's is one key, you know you, what better way to communicate to everyone the importance and to make someone proud to want to be a data store. What Peter said during his talk was critical which is, you know I can't remember the exact words Peter but you basically I interpreted as, you know, do do do do the things you can, you know and evolve, right and make it iterative and make the steward a participant and finding the right things and finding the best way, right. Think of it as having them not just have responsibilities that are dished out and sort of you have to check all these boxes every day but you're, you're helping us build this. You're, you're part of finding the right solution and we're going to iterate and we're going to get this thing right I. And that combined with the rewards of recognition and the understanding universally of the, the impact of the role can can have people really excited and excited to go on the journey understanding. It won't always be easy. Some things will work some things won't work. Right. There is a mint of money to be made by somebody and I'm not smart enough to do it. I know that you've seen in your career, people sitting against the walls when we're talking about this and they've got their arms folded and their business experts you want to find those people and become friends with them because they know where everything is in your organization. And then we turn around and we say oh well they're too old to be a steward, you know or something equally as dismissive of the expertise that's there and their ability to reverse engineer which can put your governance program light years ahead of where others are around this. The key to it is to say to them, there's got to be some measure, some connection with what we're doing, and what's happening at the organizational strategy level if we can't articulate it to management. We're not going to be able to do this at all. And so, again, that catalog gives a piece and I'm going to toss an idea out here for you maybe everybody else will weigh in on their thoughts. And in my mind, I drew catalog and the approximately equal to the information architecture in there in the sense that the organization has to have a language that is going to speak when I'm asked to explain what an information architecture is to executives. I say it so that the entire organization, the people in the organization on the business side the people on the technology side, and the systems of the organization speak the same language. They have the same vocabulary in there. So do you think of a catalog in that same context. I do, I do. Slightly different analogy but let me try it on you. You know when I when we look at the pace of change in in architecture in general data architecture, all the changes in data sources and BI sources and this just this rapid explosion we've all experienced in our career. You know, we we search for the search for the one anchor point right the one thing that can can be what we can go to no matter how much change is going underneath the covers and it's we can rely on it to find things and understand their context and know who the experts are and to me, you know, I'm going to I'm going to sort of steal the word data fabric here, you know, the catalog. I think there's a reason it's at the foundation of Gartner's description of data fabric and that's that it is the one reliable thing so you know it, I guess I'll go negative for you for on you for a second. I see a lot of reference architectures by, by consultants which will go and name big big consulting shops and they have the, they have all these boxes on a chart and they're one of 50 boxes this catalog, and they're kind of treating it like it's your, you know, it's, it's kind of the new flavor of your grandfather's data dictionary. And I think they're, they're, they're, they're really not understanding its power as this fabric across all this change that really empowers everyone, people that do governance including stewards but the business. And that kind of leads me back I mean to that you mentioned literacy again Peter and, and, and, you know, and I think it helps, it helps literacy and culture and all those things and, by the way just final thought. We're actually going through a process of trying to, you know, help put together sort of an open standard framework around measuring literacy and measuring culture. And this, it's, it's not easy because there's a lot of subjectivity in there but super excited about that work so anyway, thanks for the chance Peter appreciate it. I hope I hope you didn't didn't distract you with that I added the word trusted to the catalog because you were talking about key piece right that they have trust in that sorry Shannon we need to get back to your. Yeah. I love it I love the discussion many I knew you to be a great pair that you probably could do hours in a whole conference would be wonderful cocktail party. It's awesome. Well, among state of people, I probably go quite well but. Perfect. Speaking of, you know, along the same lines, you know and cataloging. So, is there a way to make cataloging cataloging of business terms and reference data easier. The business metadata cataloging and viewing does not seem to be as easy as it is with technical metadata. That was for me I assume, or was that for Peter. Before John. Yeah, but I'm sure Peter has an opinion too. Yeah. Okay, now I appreciate the question. You know if anyone wants to follow up through you Shannon afterwards and I'd love to be able to have a direct one on one I mean, I know, you know, we have customers managing and using you know, thousands if not tens of thousands terms and reference data and we're very, very successful with that so I'd love to understand this person's experience and then help them out. Are we always doing more sure we're always doing more we have a release that's coming out this week and you know there's more there's more on glossaries, we already have an extremely powerful glossary capability but we're, we're kind of just doubling down a lot of the stuff and trying to, you know, extend the usefulness, even from where we are so anyway I'll leave it at that you know unless I get into specifics I probably couldn't say anymore. That said, I think it's a good response and it gets back to the idea that you're not probably going to go out and populate your catalog populate your glossary in a quick one time. And what you're going to do is take and put in place a culture that values and understands and wants to reduce the imprecision the uncertainty the doubt that exists in organizations that isn't trusted data in there. In each cycle of this you try and chip off a little bit more at what you're trying to do. So maybe we don't go after the a's because they're first, maybe we find the data that's going to make the biggest difference, and attack that particular set the area where the organization has experience challenges and the only way of course you're going to find that is by putting the councils together that john described as well and I didn't articulate them quite as much but it is what these individuals are doing in order to keep a float. So the question is, how do we understand what's going on from other perspectives, and it's never going to happen just because it gets lucky. Instead, what's going to happen here is you're going to have a group of dedicated individuals which I went to the wrong slide sorry guys. And I have a group of dedicated individuals who are going to become specialists at this area that's the whole point of the teams that you're looking at, and they will be able to connect the dots to understand that going forward here we can fix data at a root cause level, as opposed to an at a root cause level, and only by doing that in that type of a context, are you going to actually be able to come up with really good results around the organization so again, the data things are disguised they don't look like data problems but I assure you, there is a problem that exists that doesn't have data at the root cause of it and be able to pull it together and the catalog is just one of the ways of pulling together the vocabulary the other part of it is pulling together the people in process issues, which are what we're talking about in terms of those communities. And the goal is to have people meeting and talking about data regularly. If you've ever worked for an organization that has gone through a safety transformation, and I have worked for several of them, and it is an amazing thing to see, it doesn't happen overnight. It takes time, but when the organization starts out every meeting that you go to in an entire year, and they start out with a safety minute, you know the culture has changed there they're not simply paying lip service to it. And the same type of transformation has to occur here and that is not going to happen by Friday, no matter how hard to work at that particular process. So, hopefully that gives a bit of idea around the cataloging function great place to start, it allows to your gamification components to as John was describing he probably doesn't like that term, but it is the kind of thing that we need to involve younger people in. It is also a good way of building up use properly a good way of building up expertise and acknowledged expertise in organizations. So it's a wonderful wonderful development is something that we should pay more attention to, but it hasn't been a huge part up to this point of what we're looking at. I think we're ready for the next question channel. Okay, and I just want to address there was a question in here, asking where to get your books. And so I put a link in the Q&A also in the chat there you can get them on Amazon or from techniques publications which is Peter's anything awesome. Yes. You're welcome. I love it. All right, so now this next question, it does not have such a short answer. We've done a webinar on this next question in fact there's a couple people who came in with the same thing. Very similar, you know, data management and data governance has to be interchangeably so how important is maintaining the distinction between these two. I'll talk about the difference between what, which is sort of a requirement and then how which is an implementation component, and to think about them in that sense, certainly there's going to be overlap at least in the vocabularies that they're describing and other types of things. But the real question is, you have to identify that there is work that needs to be done by the organization. In order to create to eliminate the data debt that's out there. And if, if the data governance group is busy then there needs to be another group formally set up to implement these changes and simply not the case that you can say, oh, we don't need to do data anymore. Around this. John, how do you want to dive in on that one to give you enough. Yeah, absolutely. Yeah, I was just thinking about your, the matrix you shared right where you had all of the arrows between the four boxes that I don't know how many people remember that but it's it struck me as, you know, we talked about decision rights a lot so so the difference between data management data governance to me. Yes, there's obviously budgetary motivation and reporting lines of reporting and all that stuff is super important. But ultimately what I think I think about is, people have to get work done, and people have to know what their responsibility is and I think of it as almost like a super powered racy matrix right decision rights for matrix. And so let's take something very, let's take something very specific right as an example so let's say someone goes into the catalog, they find the world's best table about customer. They see 18 other people other colleagues have uploaded it looks like exactly the right table. They click through on a particular column last name and they see a warning from someone who just ran a query an hour ago. And it says looks like the data's gone bad. They click the button and say, you know, click the button to for somebody to remediate well who remediates. Is it Stuart is it a database administrator is it the system owner who owns the table that owns the column. Someone has to take a ticket and go do some root cause analysis and they have to solve it. So that's where I come back to at the end of the day you get down to getting real work done and decision rights. I think you just have to have that spelled out. People have to understand so ultimately you're going to cross boundaries between data management and governance back and forth and back and forth. And people just need to know what that what that map is. I think that's that's the way that we think about it. That's the answer I could give Shannon, because that is definitely a half day topic, but that's the shortest I can do it is for sure. And Peter, were you going to add something to that. No, no, I was going to say next question is just perfectly heavy. Yeah, you know, I mean the question came in a couple times and we feel free to, you know, expand, you know, it says so there was an answer, you know, in my mind, it's just exactly what John said there's some work that needs to be done. And if nobody's doing the work then we need to, you know, create a new individual if we've got people that are under employed then we can certainly utilize them again I don't want to sound callous about it what we're doing is not rocket science but it is a skill that you need to learn and understand and it makes sense to specialize and it as opposed to just saying, Hey, you should rotate through here and in six months you'll know you need to know no it doesn't work that way it's a very different process and we need to dive further into it. I'll give, I'll give one other example. Do we have a moment, Shannon. We do absolutely. Okay, okay. So in our active data governance process that I said is prescriptive and people can use it if they find it useful. You know we talk about three dimensions of stewardship business technical compliance stewardship. And, and then we say okay for different asset classes terms metrics business process description, all the data, data source table, column file file, you know that for all these classes of assets, and you're going to have, you're going to have attributes on them right and you want to have deep attribution right because that gives people the greatest context and they can learn, understand many things about about those assets. But different assets on let's take something simple again like a column. There could be assets around classification. That's, you know, is it is the PI I information right is it is it sensitive personal information. There could be a related policy. There could be other data health data quality. And if those go bad right that's more technical. There could be a business titling business description. You know top top business consumers attachment to lineage and business reports. It doesn't mean you have to have necessarily a steward for all those roles but when you route the compliance issues, what who do you route to right so again going back to decision rights crossing boundaries. What groups and what people and what groups have responsibility for remediating, making decisions, making new policy right all that stuff and so you think you just need to map it out it's hard work. But all those routes, if you will all those. Think of them as swim lanes if it helps right need to be need to be mapped out. And then failure to do it is sort of like leaving the checklist off the plane right and that's one of the things that's been most helpful in aviation safety in terms of improvements in those areas. Thanks John now great good stuff. Um, that's it those that's all the questions right now so. I love it. It's been such a great conversation. Again, and thank you john and thanks to elation for sponsoring today's webinar. Thank you. Webinars pleasure. Yeah. There's lots of great comments in there. And if you have any additional questions feel free to submit them and I'll get them over to john and Peter. So, again, thanks everybody and reminder I will send a follow up email by end of day Thursday for this webinar with links to the slides and links to the recording of this session I'll have links to the Peters, where you can buy Peters, many, many books. And then the most recent on data literacy, which is awesome. Also include a link to we recently had elation join us for a diversity demo day data catalogs. Seeing see a demo of elation as well off of our website there. So, and of course you sign up for more demos and in person demo personal demo from elation as well. So, thank you john thanks Peter so much thanks everybody hope you all have a great day. And I'm happy to see a bunch of you in June and DJ IQ john. Absolutely. Thank you Peter. Thank you everyone. Thank you Shannon. Appreciate it. Bye. Okay bye bye.