 And here we go. Hello and welcome. My name is Shannon Kemp and I'm the Chief Digital Officer of Dataversity. We would like to thank you for joining the latest installment of the monthly Dataversity Webinar series advanced analytics with William Pinkbite sponsored today by Zen Optics. Today, William will be discussing is our information management mature. 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. We will also be collecting them by the Q&A section or if you'd like to tweet, we encourage you to share highlights or questions by Twitter using hashtag ADV analytics. And if you'd like to chat with us or with each other, we certainly encourage you to do so to open the Q&A panel or the chat panel, you'll find those icons in the bottom middle of your screen for those features. And just to note the chat defaults send just the panelists, we may absolutely change that to network with everyone. Hopefully we'll send a follow up email within two business days containing links to the slides, the recording of the session and any additional information requested throughout the webinar. Now let me turn it over to Hina from Zen Optics for a brief word from our sponsor. Hina, hello, and welcome. Thank you Shannon. Good morning, good afternoon everyone. Am I clear. Coming across. You are. Yeah. Okay, perfect. Let me share my screen. Let me bring up our presentation. And I will just ask that you confirm. It says host disabled participating participant screen sharing Shannon. Oh, yeah. All right. Can everyone see my screen. Looks good. Yeah. Okay, great. So, you know, really excited to be here and kick this off today. The, you know, there is our information management mature. I think that question is very important into where we are sort of in the overall ecosystem and sort of the time in terms of the genesis of data and analytics. Right. And so, when you talk about information maturity and where we are today. It's a journey that we've all been on over the last few decades that has seen an explosion in basically the building of, you know, large data pipelines, analytics, assets and tools out there self service bi. And all of that sort of ties into, you know, not just your data strategy, but also your information strategy right and that's where you know today organizations are spending a large amount of effort to figure out, you know, is the information management really mature right now when you think about, you know, the explosion of self service bi and analytics that happened as a result of the explosion in the data world. We've created a ton of content out there. We've created dashboards and reports and need these KPIs available. We've created tons of assets that are spread across the organization, but truly to understand sort of where we gain value out of that and really is, is the maturity in how that information is really being consumed is it really being used to make the decisions the way we intended to use the data and the information to make those decisions. We tend to find that across the organizations and larger enterprises, a typical challenge is lack of visibility. On sort of both ends where it's for the business users as well as for the teams that are actually supporting those business users, you know, the analytics teams, and, and these challenges are interrelated but they're sort of unique to each of these personas, but sort of a feed off of each other right. The business user is really struggling with, you know, lack of awareness because there is just, you know, so much out there as a result of the sprawl that has been created that there's limited knowledge on what is available, really to use in terms of, you know, access to that information, unsure of where to get it. Is it really the right set of information? Is it the right content? And then obviously leading to sort of experience issues right with aligning those assets with business context. Now, when you talk about sort of what the challenges are on the IT or the BI and analytics team side, the sprawl is, is evident, right, we all deal with that. And it's just a factor of this growth and explosion in the, in the, you know, just the maturity of data and analytics and self service BI, but really the processes around managing that, you know, are there large rails to really manage that, you know, so there's obviously constant, you know, requests to create new content leading to obviously duplication of content redundant content being created. And as a result of which people wasting time to go find that and you're also adding to that technology debt, right, that we all inherited as a result of, you know, the legacy BI stacks sort of evolving into the modern analytics and BI stacks as a result of the overall growth in analytics data and information management. So, so when we talk and we look at this and really help organizations achieve that information maturity and sort of identify even where they are in the various sort of information maturity stages that we're going to talk about within that framework, which is really simple and really will help sort of tie it back to understanding, you know, where are you today, what's as this and where do you need to get to be or where do you need to be. Our goal really is to unlock that power right off. Basically, BI and analytics and really empowering those users. So, how do we do this how does an optics do this. Zen optics is a bi and analytics hub. Our goal is really to simplify the discovery of those assets right help you immediately attack, you know, your as is information maturity stage and really help you understand. What do you need to do to go to that next level, right, it's not just about discovery. Once you've discovered the content, our goal is really to empower the teams that work with that content to use the information and collaborate along that information right and then actually help, not just the content. You know, the end users or the business users, but also the people that are responsible for that environment to really align efforts with objectives. We need to be in the value out of those investments scale the investments right and ultimately design a really easy simple consumable experience to drive usage and adoption. That's really the goal of sort of what we see as adding, you know, the values and optics in terms of what enterprises are trying to do to align with their information strategy that directly leads into delivering information maturity. We have, you know, a really, you know, I'm going to obviously pass on to Shannon and William, but just to kind of leave you with something we have a very interesting webinar coming up on the 22nd of June where Donald Farmer and Peter are actually going to talk about, you know, why your analytics strategy and not your BI tools need an update, right. You can also use the URL here to a case study with one of our customers, Janie, and learn more about sort of their journey on, you know, driving adoption and actually achieving, you know, larger or greater information maturity. And if you have any other questions or you'd like to sort of connect, you know, more than happy to do so my personal information is included here. So I'm always available to set up a call and connect. I'll hand back to Shannon I'm going to stop sharing. Hina, thank you so much for kicking us off if you have any questions for Zina Hina or about Zen optics. Feel free to submit them in the Q&A portion of your screen she'll be joining us at the end of the webinar with for Q&A. And with that to let me introduce the speaker for the series William McKnight. He's one of many of the world's best known organizations he strategizes his strategies, I can speak today for the information management plan for leading companies in numerous industries. He has a prolific author and a popular keynote speaker and trainer, he has performed dozens of benchmarks on leading database data lake streaming and data integration products. And with that, I will get the sort of William to get his presentation started hello and welcome. Hello, and thank you Shannon. Thank you especially Hina as well. It has been great getting to know Zen optics in the course of building this presentation and if you ever be I problem out there I do encourage you to take a look at what they have because I think it's unique on the market and I think it really speaks to a lot of situations. Anyway, today we're here to talk about one of my favorite subjects is our information management mature now it didn't used to be one of my favorite subjects, because I used to think well it's kind of a bad question. Let's just talk about what your next steps could be, but an information maturity model actually advises what those next steps could be. And I think trying to keep up in terms of maturity is actually a good thing, and I think it's highly correlated to the bottom line of the organization, not just I think there's been studies that show that. And so, let's start with a philosophy beyond the mountain is another mountain. And you may have heard me say this, I like to say it a lot it's a Haitian proverb. What I say here is let's take one mountain at a time, and do know that there are more mountains after you get over the one that you're on today and that's okay. And guess what, more mountains are being unlike the picture more mountains are actually being built beyond the mountains that we can even see. So the future is looming. The future is different. And I was observing to Shannon in the pre call that I don't really recall any period where there's been so much innovation so much happening in the broader space of data and analytics. It's really like that today and I've been in the space a long time so, hey, it's all good, as long as we're keeping up. And so let's get into seeing if we can measure where we are today and get some next steps going. We are in the business of data, no matter what company you're in, you're in the business of data today. The volume is exploding. It's becoming more real time. Big data is actually essential today to get under management it's not an option. Information usage differentiates the competition, how we use our data is really what the basis is for differentiation in the competitive marketplace quality is important information is reused. Even seldom use data like big data sometimes is essential to be under management, and to be accessible, well performing, and have good high non functionals and things like that third party information is essential to use. And that just explodes the information possibilities for us. We've all heard about the data marketplaces. And there's a lot going on there there's a lot of data there. I'm sure there's data there that you could use that you're not using, because there's so much data there. And some people are just beginning their journey on that and we'll see where that is on the maturity cycle as well as probably about 2425 other things as we go along here. This is sort of a presentation that's going to try to stay at, I don't know 5000 feet, because if I dare dive down into any one I will not get through all the topics so that's just how it's going to be today information is a key business asset. Yeah, I hope you believe that by the way I hope you feel empowered by that the fact that you work on a key business asset for your business. I hope you feel like you need to share that news share the good news, if you will, with the rest of the organization in an appropriate way, and kind of push the organization up push up the maturity, because that is really where it's at today here's some other quotes about data. I won't belabor them all but you see some of these prominent people saying like big data is that the foundation of all the mega trends that are happening. Yes, and every company has big data in its future and every company will eventually be in the data business so says Thomas Davenport I couldn't agree more. So for the first time really we have this economy that's based on a key resource information that is not only renewable but self generating running out of it is not a problem. Not a problem, and nobody's nobody's 100% you're going to find the very few people are going to be at my maturity level five. Okay, but you have to be somewhere on this journey you have to be pushing your way up you have to be going up, not down on this journey. So, let me talk about the data that's coming into focus here in this presentation data on data maturity. Okay, so what we do here at McKnight consulting group we look at our last 30, what I call intimate enterprise projects these are mostly in the course of your client, we get to know you pretty well, we can grade you out across our 50 questions pretty well, sometimes we don't even have to ask, we already know, because we've been told that's how we do our projects and otherwise. We look across, you know, many other enterprises that aren't clients we're just talking to them their prospects. We find that we've already have we already have 25 questions answered in the course of getting to know you. And so why don't we ask the 25 more and get through our questions so in our questions we asked for probe we do probing answers, not just we accept your answer, because everybody's a little bit different if you don't know yet in this business. The data, I mean, the terminology is all over the place it still is, and it's hard to keep up and certainly that's something that you want to be sure that you're on common basis with whoever you're talking to, before you dive in too far, because you could be talking past one another I see it all the time. So for probing answers 40 of them are on data topics, 10 of them are on business success and for publicly traded companies we use public information as well now we're not always looking at the full company. When we do this, if you're a fortune 50 company. We, we, you know, we don't take in the whole company we take in your division, your department what have you, and we look at how well that is doing because that's what we can get our arms around here. The question in data topics does approximately equal data maturity is what we found. So that's what that's a basis of what we're assuming here. So if, as you get further along in your data late get further along in your master data management your data governance etc the things we're going to throw on the table here. That is what we are considering maturity, you're doing more, not less with important things. The, the questions and answers are always under NDA or friend DA so I'm not going to be identifying individual companies, we've probably done this for close to 50 companies at this point. And we try to keep it on a rolling 30 basis here so it's a it's a bit it's a bit of a journey. And when we started out, we had everybody into nice 20% buckets right there was a 20% at the low end 20% at the high end and three of them in the middle right. What we have done over time is we interview new companies, and we add them in, we find out well their profiles really more aligned with this one, this bucket or that bucket. So we don't, we didn't rejigger if you will, what the different buckets meant. Now, we could not hold everything else constant, but figured that this all shakes out over the course of the 50 data points, and the questions get updated to so it's a moving target. I wouldn't spend any more time on the approach, I wouldn't get to the meat of it so I won't. And I will say though that here we have the fit the 30 companies and how they were graded out on data maturity and business success. And there's all sorts of mathematical things we can do about it but I thought I present the raw data, and you can think about it as well you can see that there is a correlation. Now line right up the right in between here and it's going to be approximately a nice straight line from low data maturity, low business success, high data maturity, high business success. Now you also see that there's a bit of an overweight in terms of the data maturity being low. Like I said, we took the business success and we held that constant, and then we looked at the data maturity factors. There are more companies in the low categories the one in the two, then there are in the higher categories as a matter of fact we only have two of them in category five, but hey, they really do represent a unique profile so we had to create that, create that space for them. And I think it's going to be continued to be pretty hard to get up there. So where are you. Where are you in your journey. Well, you can say after today, where you might be in this so if you get out of pen and paper or pencil if you will. Write this down category strategy architecture technology and organization. So we're going to make a little ticking marks. After these number after these categories, as you learn about them and learn what category one or level maturity level 12345 mean to you. Find yourself in there. These capabilities emanate from the presence of the item shown this is not a capability model, although it is sort of indirectly one, but we went to we went beyond the capabilities we know what you need. Technically, architecturally to achieve those capabilities today. If you want to be on top of predictive maintenance you have to be on top of your big data and and that probably means a data lake so let's we measure out your data lake maturity, and that from there, the possibilities explode. So this should give you a sense of priority. Why should it give you a sense of priority, because William didn't sit here and just think about Hmm, what is 12345 look like it's based on data. And we have found in our walk here as consultants that companies do tend to follow a pattern of going up the maturity cycle from 12345. What's next is probably what's next for you, depending upon where you are today. You can't skip levels, can't skip levels, we don't encourage you to even think about it. If you're a one. And you want to be a five you have to go through 234 sorry to say but you can go fast, you can go fast, and it can probably take as little as for a good organization that's focused maybe six months or so to bounce and that's really aggressive. And that's really keeping the focus there maturity levels tend to move in harmony. We didn't find too many companies that were very divergent between these categories. If you're a one in strategy, you're not going to be a five in technology. If you're a foreign technology you're probably not going to be a one in organization, you have to bring that up in order to get to the foreign technology and so on so very infrequently did we find organizations were more than one number differing across the categories midsize and smaller companies. Yeah, if you're in one of those, what you consider a midsize or smaller company you can add plus one so you go ahead and give yourself a ticking mark right down the, right down the pike there right down the column. I would get one because I am these are progressive approaches progressive elements of maturity that a midsize and smaller company probably is not going to get to momentum is paramount. I can't stress this enough. It's about momentum. If you're a one, don't fret, but get moving, get moving up the maturity cycle today and get that momentum going again, you can move from one to two quickly. If you got the focus and I did not give all these maturity levels 12345 anything but a number. I didn't say you were a laggard if you were one, and you're a supreme example of all the possibilities if you're a five, you can work that in yourself. I think, though, for most organizations, you're going to need to be at my three. All right, some of you need to be at four because you're in a an industry that is more aggressive about data. So what about all this stuff. Well, the information management professional what I found and I've managed quite a few of them on an interim and longer term basis. It kind of comes down to this user satisfaction we are internal consultants that's how I like to look at information management professionals inside of organizations, we have to have high user satisfaction, sometimes that's a much more dominant piece of the puzzle than the other two. Over time, I have found that that gets kind of pushed a little bit more to the background, and these other things are becoming pretty important and by the way if you manage information management professionals out there. I encourage you to think about how you're grading out those professionals. Are you just using user satisfaction, because you should be using other things as well. Business ROI. Business ROI. Yeah, that's right. That's right. Since when is the information management professional been responsible for business ROI. Well, since about maybe two years ago. And now we're bringing the ideas to the table that the organization can use to deliver that because we have the ideas. Okay. Again, I want you to feel proud. I want you to feel assertive about your role in the organization as a data and analytics professional. Finally, are you growing your data maturity. So you're going to learn about that here today. And you can definitely expect your professionals to be pushing up the maturity in their level. That's what that's what I do inside organizations. I say, Okay, next year I want to be at the next level you're an ETL or your data integration professional, what have you. We're out of three. Next year I want to be at a four no matter what else, you know, comes get thrown in the way, no matter what the prior, how the priorities change, et cetera, et cetera. You got to think about that next level because we can't keep doing what we're doing today. We have to move it up. So here's your score sheet here. Here's a full score sheet for you. I didn't bother giving you a downloadable file just make it look like this. Hopefully you've already started. So you're going to get a score, and then you're going to get some next steps. That's what's really important here. What do you do about it? What do you do about your low score? What do you do about the fact that you're trying to achieve higher maturity. So keep your score, grade yourself by category, strategy, architecture, technology, and organization. So, and another thing I'd like to say is everyone is a maturity five congratulations. You are a maturity five, but for what year. Okay, are you maturity five for 1990 or 2022 we want you of course to be higher maturity for the present time. So maturity number one, this is kind of fun. I have a little fun with this. When I when I have given this class in person and I give it as a full day class, or one hour webinar like this or session like this. I get a lot of heads nodded when I talk about maturity level one infrastructures overwhelm cloud cost out of control I'm just throwing out some characterizations of maturity level one. So you saw my data you can know that you're not alone. If this is you organizational silos, multiple overlapping data stores so maybe the same piece of data is stored 10 times, just because, just because number 10 didn't know about number numbers one through nine, or it just wasn't right, or they didn't know how to work with what numbers one through nine did so let's do it again. This is true for data stores this is true for reports. Kind of where Zen optics comes in by the way, one vendor mentality we're going to get everything we need everything we possibly could use from vendor XYZ from our favorite vendor. That may or may not be the best approach as a matter of fact I say, Okay, you can bring them into the circle but that is not the best approach today. There's too much differentiation going on. There's too much leading edge stuff that you probably need to be availing yourself of that you won't find in your current vendors stack. And I can say that, be not even knowing what your current vendor is because I study them all. That is true for all of them. Some of your tools might be outdated. Hopefully they're not out of support that's really outdated, but by outdated I don't necessarily mean just out of support. I mean that they're not keeping up with the demand anymore, and maybe, maybe you're in a situation where the users don't demand anything anymore because they've just been kind of put in their place. They've just lost the data, and that is not where you want the users to be. And if that's if they're sitting on their hands, you've got to change that resistance to change is replete at maturity level one. Everybody's scared of change. They're scared of the 25th person out there that's going to say why did you do this. That is holding things back so we got to get over some of these things so I had a little fun with maturity level one, not trying to make fun of anybody's environment I never do that. I always just talk about what the next steps are on the journey. And that's what's important. And I don't talk about, I don't belabor, how did we get here. Today's a new day. Today is day zero. Okay, you're going to learn some things today today's day zero now tomorrow's day one you got to start putting things in action. Okay, no more excuses, we got to start moving forward because again, the information management professional is going to be judged based upon that so for each of these levels that was one. Again, I'm going to get a little bit more refined now as I get into two three four and five don't worry but for each of these levels team, I have found to get to the next level, there has to be a light bulb that goes off and that's my little light bulb here and on the slide right. There has to be a light bulb that goes off in order to get you to the next level. Frankly, there's probably quite a few, but the one that stands out as I think about those organizations that have made the jump from a one to two is that they understand, they find the understand that you have to raise data maturity, while you accomplish business goals, because there's too many organizations out there, the sitting on their hands are not moving forward, because well I don't have a budget to raise data maturity. Nobody's asking me to raise data maturity. You know how many in 25 years of consulting you know how many times I've been asked to raise data maturity, probably zero, probably zero. Oh, I've been asked to raise it while I do something else, and that I do, but just come in and raise data maturity. No, that doesn't happen you don't get a budget for that you get a budget for targeted marketing you get a budget for predictive maintenance. You get a budget for fraud detection, etc, etc supply chain management customer management etc. Okay, so while you're doing all that, you don't always do it the same way that you did everything else before. This is not the time for cookie cutter approaches to applications this is time for progressive approaches to application so the next initiative that is put before you think about doing it in a different way than you have been doing things. Now, this is how I'm going to structure maturity levels 234 and five there wasn't much to say about one so I didn't do it this way. Okay. So I'm going to take each block strategy architecture technology and organization, and I'm going to talk about it. So let's start with data strategy and this is where you get your pencil. Get your get your pen and I don't see if you can give yourself a ticking mark and raise this from a one to a two okay data strategy and these are the main things. These are the, these are the things suitable for a slide is more where this came from you might say but I think this gives you the essence. And I think this is enough for where you can give yourself the mark. Okay, emerging data standards. I didn't say they were complete at this level, but they're emerging. You're even think you're thinking about it whereas you work before data decentralization, you're getting data more out to the masses. There is executive awareness of data. Sometimes at maturity level one there's no executive executives are aware of data. They're only aware of business things and applications and oh yeah they work on data, by the way, but data is a thing, and that starts to emerge. When you get to level two partial self service BI didn't say total partial self service BI it professionals are starting to get out of the way between a user and their data cloud first direction. Yeah, this wasn't maturity level two last year. That was more like a three. Now it's a two now it's sort of expected that an organization has a cloud first direction. And again I'm not sitting here championing any anything here. I'm sitting here telling you that a material level to organization has a cloud first direction. I happen to think that's a good thing. Architecture wise central data warehouse says way hard warehouse or warehouse is emerge at maturity level to believe it or not there are some organizations out there that don't don't have what any reasonable person, I guess, would call a data base, but they do at maturity level to there's emerging platform heterogeneity, meaning you get the fact that in 2022. It's not one size fits all. Everything doesn't go into an Oracle database everything doesn't go into a SQL server database, necessarily, you think about it. You don't have you don't you don't have right fit platforms across the board, but you do have emerging platform heterogeneity, you have a data lake and development and this again this is another thing that wasn't here last year. When I talked about this that was a maturity level three thing. Last year but now we find that. Hmm, just about everybody out there is getting into a data lake at maturity level to and beyond so notice the asterisk here. What I when I say central data warehouse to me that's in a relational platform data lakes in a cloud storage platform none of this other fancy stuff. Technology master data sharing you I didn't say master data management at maturity level to it didn't say that, but you're all sharing master data from somewhere, at least a little bit. You're starting to get that concept technology wise third party data is utilized and by the way sometimes the category is difficult to assign for some of these things. Machine learning chatter I'm not saying machine learning algorithms are in place and running the business, but machine learning machine learning chatter has begun data integration ETL and ELT, which I think we've mostly found is a better form of data integration I didn't say streaming. We'll get there. And there's a lot of dashboards which will go away as we go up the maturity cycle by the way organization wise there is data governance I didn't say full data governance, not for the entire enterprise not for every subject area. This data governance doesn't necessarily meet every month, etc, etc. But there are pockets at least of what they're calling data governance pockets of business interests, trying to get their arms around this important asset. You're finally using an agile methodology at level two, and you have data specialists, not just simply application people that we happen to do a little data on the side. You know that data requires data specialists and I'm not saying some of those application people can't be great at data. It's just their focus might not be on it. So you have great people that are data specialists doing the data work the modeling the architecture, the data integration, etc, all the DBA work, etc, etc. So that's matured level two. So go down your list. If you if this if this is you at least, okay, maybe you're beyond but if this is you at least give yourself a ticking mark for level two in these four categories, and we'll see where we are as we go along here. The big idea to move to level three is that you have to attend to both the data and the data access ecosystems. You have both. And we finally, I think, learned that we have to attend to the data ecosystem. Most people on this call would know that not everybody does. Some people focus completely on the BI. Well, that's okay as long as somebody else in the organization focuses on that data layer under the waterline. But you also have a BI ecosystem. Yes, you do. You have a BI ecosystem, and that's not going anywhere. And that can be great. And that could be doing special fancy things, even AI upon data across the organization so you have to get that under management to not just the data. All right, so when you get that realization you move into maturity level three again I said that this is where most of you need to be. So hopefully some of you can give yourself some some marks for maturity level three and data strategy you have a knowledge that there is not just data in the organization. But there's a data layer to the architecture, and that data layer is special. That data layer is heterogeneous, and that data layer is managed by data professionals. You have reactive AI now for some automation so you got some AI in place and maturity level three the most basic type of AI is reactive AI. This is programmed to provide a predictable output based upon the input it receives reactive machines always respond to identical situations in the exact same way every time, but it doesn't it automates. It automates whatever and so when I get asked well how do we start our AI journey, look for things to automate, look for things to automate self service is now the dominant model. And that's how far self service has come in the past few years to be at maturity level three. Not four, not five, but three architecture wise, you have not just your current architecture but you have multi year architecture direction and plans. You have an idea you have a goal we have a vision for where the architecture needs to go in let's say three years, and let's say five years. It's not just where we are today you understand it's fluid. The architecture is flexible. It's not rigid. And this is well understood when you have those types of plans in place the data lake is in production now wasn't not just development but you have a data lake. You are using data virtualization at maturity level three. You understand that sometimes you need to run a report and snowflake needs to reach out to Oracle and get some data or SQL server needs to reach out to terror data and get some data for the report and that's okay. Notice it's not the dominant model, but you bring that in. You have measured data quality levels at maturity level three. You can say our data is that quality level three just like I'm doing the maturity level here for your overall data and analytics, you do that for your data quality. And you, you raise that number for awareness. You're managing many data types at one and two, it's alphanumeric that's the dominant model, you haven't got into other things like JSON, XML, Avril, parquet, but at maturity level three, you're into it, and you're able to deal with all these data types. There's some different ways you might be dealing with it, but you're dealing with it, you got it under management, and you have some data lineage that's starting to seep in now as being pretty important in the days that we're in now of compliance. And all DUIs, your data warehouse is in the cloud at maturity level three, it's no longer on premises, it's in the cloud. You have a graph database for all your relationship data. So relationship data, you're not force feeding it into the data warehouse anymore, you're actually doing real graph things with it. You got master data management of a major subject area. So you're well into development if not in production now of your customer, of your product, of your whatever is a major subject area to you, you got at least one under your belt or almost under your belt at this point. You got master data management, that's how important master data management has become. You're using the data marketplace. Now we hear a lot about that I don't need to belabor it, but you're using it you're bringing in that data. You're using the data catalog. I didn't say it was fully populated. I didn't say everybody's bought into it. I didn't say, you know, you have your business metadata in there complete, but you are using it. It is starting to provide value. That's where the data catalog has come. You see how much. See how, see how the stack has grown here from maturity level three integration now you're using streaming integration now you're using reusable components. You're just creating every integration from scratch you're going to a company maybe that has the plethora of integration routines that has already been done at your disposal organization wise data governance now is by subject area across most major areas so data governance is pretty advanced at maturity level three organizational change management now has been added to your data projects. You know that you can't just create a database and put it out there and they will come. You, you have organizational change management to bring people along with your data projects that's how important data projects are. You have to have that component you have a chief data officer. You have data scientists, you have strong DevOps. I didn't say MLOs, but you have strong DevOps. You know your path to production well. And it's documented. And you exercise it on a regular basis. That's maturity level three. Give yourself some marks. If you're at maturity level three. Now the big idea to get to four is that the data profile will drive the platform selection. The data profile will drive the platform selection you understand heredity already. But you make much more wiser decisions as you get to level four and you look at the data profile is it unstructured data is it alphanumeric data. Is it going to be accessed in just reporting. Is it going to be used in machine learning. Now these are some of the things that drive a platform selection again it's not one size fits all anymore. We're talking maturity level four, we're way, we're way past that now. We're not trying to cram everything into SQL server databases Oracle databases, DB two databases, except for what have you. You know that today in 2022 you can't do that. Now maybe down the road maybe in 2032 there might be one size fits all there. There is some signs actually on the horizon I won't I won't get into that too much right now. The science that there may be some translitical databases that might make sense for some consolidation, which would be nice. But anyway, I got to move on maturity level four data strategy. Now your data is an asset and financial statements it's on the, on the in the vernacular of the executives, they talk about data predictive analytics now you're, you're not just looking at the rear view mirror, you're looking ahead, you're predicting with reasonable certainty what's going to happen, and you're able to get in front of that and change it because of data AI, you're doing data parsing this is. I would recommend recommendations, as in for data quality that you should pursue, which data you should use automatic classification of data and automatic parsing of data. These are things that we don't have to do anymore AI can do that, and you're doing it at maturity level for AI is also coming up with curated insights. I'm not just using AI as bi, we're using it for what it's ultimately good for AI curated insights insights that you're bringing into the business architecture wise Kubernetes. Yes, why am I bringing up an application thing, because it permeates data as well. It has started to permeate data at maturity level for you are using identity management. And basic security identity management tools, rest API's, you're accessing data through API's now, you're also probably at this point, using a service that provides you with dozens or hundreds of API's that you can choose from data lake house data, it's kind of a loaded term, I know, and for some reasons I hate to use it, but you know, it's the data lake and the data warehouse are now married. Okay, they're now working together. And to get them to work together. It's not just you create a lake and you create a warehouse and introduce them to one another. There's some work to be done, but you've done that work at maturity level for you can now run queries through the warehouse that will reach into the lake, the data lake, and get the rest of the data there seamlessly. For example, and you have full data lineage, full data lineage at maturity level for technology wise master data management is now beyond one major subject area. It might be two, it might be three, but it's gone beyond your customer your product what have you and you've you've you've got the vision, you got the value. You got the excitement of master data management you're moving on data catalog is now populated, maybe not 100% at this point, but maturity level for organizations have a well populated data catalog, it's not just sitting there. The data catalogs unfortunately are just sitting there, but not a maturity level for they're actually using it. And you're doing data observability, not just data quality, but data observability through the pipelines, you have visibility to the pipeline, your search is augmented. And it's interactive it's not just running sequel. It's not just whatever you can think of in the moment. Your search is talking back to you that your search is saying go here go there, your search is helping you out with your queries, etc. Your analytics are live. They're not on day old data, they're on data that's happening in the background and changing as, as we speak, right. You're under API management tools, kind of like what I was saying before, but technology wise you're there with API's amateur level for organization wise, you have comprehensive data governance now. And oh by the way, I found in level four and level five there's a predominance of legal owning data governance in the organization that surprised me a little bit. And that is a trend that I see, probably worth talking about the chief information architecture equivalent maybe nobody with that title, but the data architecture is recognized at the chief level at the seed level. Now, not just a CDO, the CDO typically doesn't get into architecture too much. The architecture has a seat at that table at maturity level four, and you have strong ML Ops, you're beyond DevOps now you've applied those principles to your machine learning it's that far a long. Wow, good stuff hopefully you've been able to give some of you have been able to give yourself a mark or four for maturity level four. Now let's go on to maturity level five this is the Holy Grail, if you will. It doesn't take more time or budget to do it right. It takes knowledge and focus. And that is what maturity level five organizations have realized it's it's we can do this. We can do this we can get the knowledge, we can get the focus and we can do it. It's, it's, we don't have to, we don't have to go back for these ridiculous budgets, because we have to add on to our inefficiencies. But we're adding to efficiency at this point, we're adding our knowledge in our focus. So hopefully I'm giving you a few things that you need to go and build up your knowledge on. Okay, data strategy. Now we're into hyper personalization. We're getting into prescriptive analytics, not just the predictive predictive said oh this is what's going to happen. And remember I said maturity level four we've been able to get in front of that a little bit. In level five, the machine is helping us get in front of that by telling us what we should do by prescribing the next best tasks for us producing information products. Our information is turning into a real ROI Center for the organization, you found a way, and you have limited memory AI this is learning your AI is now just not just doing automated stuff. It's using the past and building experiential knowledge by observing actions or data. It's using historical observational data in combination with pre programmed information to make predictions and perform complex tasks. So this is great this shows real progression in AI architecture wise your data is fully discoverable. Maybe it's fully populated, the catalog is fully populated now users are able to use it. Your data self describing, you have a microservices and a containerized analytical architecture, not just application architecture, but your analytical data is also in this model. And you are using databases for multi model usage, you're using that capability is probably it's possibly just sitting there right now, as a possibility in your database if you're not at five, but at five they're using, they're using multiple data types to their usage within their databases. What does technology look like well. Now you have complete enterprise MVM close the loop I'm not saying your journey is done with MVM it's never done. But I am saying at that point you you have all major subject areas, mastered with high, high data quality and real time distribution, your data you have databases and processing at the edge. IoT if you have an IoT architecture, you don't just have a flat file out at your edge, you have databases and you have processing at the edge and I'm pretty sure the next year. The slide is going to say artificial intelligence at the edge because that is possible now to special chips, but it's possible. You have embedded databases inside of applications embedding is not just for vendors. You have data prices as well. And you have policy management practices within your organization probably tools to help you with that organization wise pervasive data governance. It's not questioned. They have a seated all tables, they've been providing value to have been sitting off in a corner, saying you know throwing off, you know, rules from the from the hill, but they've been integrated into applications, they being the data vendors right they've been integrated into applications, bringing value to the applications as well as to the enterprise for the longer term, raising level maturity for the next application, and the next generation, if you would all be on. We see a creep in of FinOps into organizations. And we find maturity level five organizations are there now I also threw in multi hybrid cloud. Yeah, that's why you need FinOps, so that you use the right cloud. Okay, for the right application, however, I'm not saying that it's not till you get to maturity level five that you're using multi or hybrid cloud by no means. That's probably a two or one. Okay, but I am saying that for FinOps, you need you, you put your FinOps over multi and hybrid cloud environments, and I'm not, I'm not sure that multi or hybrid cloud is a real factor in maturity so I've left it off. I'm not saying that more mature organizations are multi hybrid cloud. And I'm not that and if they are, they probably are because they're doing more with data right but that I don't think it's necessarily a factor in being mature, it just is something that happens. So that's 2022. There you go, hopefully you put some, put some more markers on when I talked about four and five. And if we had time for interaction I would ask you what your scores were, and, but you know now, you now know, and grab this presentation if I went too fast, come back to it, look at what that next level is so if you're a two in data strategy, what does three look like, hmm, what do I need to start putting into my timelines, what do I need to start putting on my roadmap. Next year, by the way for artificial intelligence, we're going to see machines with theory of mind AI that's going to, they're going to be able to understand and remember things like emotions, and then adjust the behavior based upon those emotions with people so really getting things done. And you know, to get things done you have to go through people, and people have these things called emotions, and theory of mind AI will be all over that. And then there'll be, I don't know about next year. I don't know how much of this will happen next year but self aware AI is no doubt on the horizon. But if you're saying wow that's so far out there I just don't know when possibly my organization will get there. Well, I've already given you the answer. You go, you go from wherever you are to the next level to the next level to the next level to the next mountain. Get over that mountain. And then you're on your way. It's kind of like, if you've never swung a baseball bat when am I going to hit a home run. How about, how about you just make contact with the ball first and then we'll go from there. That's what I'm saying. And I have just some final words of advice as you go on your information management journey. There's more maturity and moving in perfectly than a merely perfectly defining the shortcomings and saying oh we're not doing this right we're not doing that right. So let's define what we can do right. Build your credibility. Build credibility that that greases the skids for your recommendations to your organization. If you don't, if you don't have if you're, if your recommendations aren't getting residents in the organization. Think about that bully. Do you have the credibility yet to deliver it. And don't fret, just build it. Don't be afraid to fail. Have an open mind. There are different paths, by the way. And people can have different opinions. And this is something that in my consulting I encounter all the time. I might have a way forward, but it's going to take, I don't know 100 person hours to convince the organization that that's the way forward whereas somebody else has a way forward that's different. And we'll get us there, but it will take, I don't know 50 more person hours to for it to happen well do the math. Probably easier just to do it their way and we get to the same result. That's what I'm talking about. No plateaus are comfortable for long. You'll have to re score yourself next year come back. And let's see how you are doing. Keep in mind that you got to get you got to get on the path, the journey, the resistance that you might be feeling either personally or within the organization. It's maturity level five who doesn't want to be maturity level five. It's the journey. It's the pain in the journey it's the setbacks in the journey. It's the this and the that in the journey it's the do we have the right people. Can we actually do this. Build your focus so that you can move forward. Hopefully I've answered your question is your information management mature. Get your scorecard now and answer that for yourself, and hopefully you have some actions there to move you forward in something very important information management maturity. I'll turn it back to Shannon now and get your questions. Thanks so much for another great presentation just to answer the most commonly asked questions. Just a reminder I will send a follow up email by end of day Monday for this webinar with links to the slides and links to the recording. So diving in here William with regards to slide 12. Who defines this and industry body. That last word you said Shannon and industry body and industry body. No, I guess I would say you defined it, you being in the enterprise, because this is reverse engineered from what enterprises are doing. This was originally what the second lowest profile, or what's the word for. I would say Dessau, but that's for 1020% second lowest 20% whatever that word is will come to me after the presentation. The second lowest look like this. And so this is this was not defined by me or an industry body is defined by you in the enterprise. I invite you to jump in here where whenever you feel whenever you want to jump in. So to so where do you see the data ball and the maturity model. Okay, so I'm going to say that I'm not sure that that's necessarily correlated to maturity. I find that some organizations are doing it some organizations are not. It doesn't preclude you from being at any level of maturity. If you do a great. I think it enables some of the other things. So I'm not I'm not saying don't do it or anything like that I'm saying that if it's an enabler or some of the other things I talked about that are more correlated to maturity then great. If you do data vault great. You obviously are attending to data governance you obviously are attending to your central data warehouse and your data standards and things like this which helps you in raising up your maturity. Perfect. And what are the dependencies between the four categories that impede progress progressing to the next level on one category before maturing to the other. Those those were the slides that I that I had in here. So this is what get this is what you this is the light bulb that goes off as you go from one to two. You realize that you can raise data maturity, you must raise data maturity, only while you're accomplishing business goals. That's what maturity level one they don't get that. And so you have got that in order to get to the next level, and so on, and so forth. These are the big ideas that you need to get to the next level. Yeah, I just want to add to that I think you know what William said earlier about, you know, focusing on the people really and it's about the change management, as you go through those levels, really empowered the people, right. You know, you know, you're defining these models you're actually scoring yourselves so it's really about empowering yourselves to sort of move through those different maturity levels I think. Yeah. Perfect, I think we have time for at least one more question here. What do you mean by embedded database in applications older applications were based around a database. I mean that the, the database is. I don't want to use the word embedded be wrong, but the database is exclusive to the application, the database has sort of cash the data that's important to the application is not just reaching out with SQL calls to a database obviously you know most applications do that. But when you build applications for your user community, you build them with the databases that are maybe, maybe there's an extraction from the main database like the data warehouse that's appropriate and cashed for that particular need. And that database is embedded maybe you have your users are accessing through the devices, like mobile devices, and you are caching a database at that, at that level at the edge. That does have a lot to do that statement did have a lot to do with edge architectures, but it has to do with really all applications as well. Perfect. Well, that does bring us right to the top of the hour here. And William, thank you so much for these great presentations and thanks to Zen optics for sponsoring today's webinar and hoping to make these webinars happen. And thanks to all of our attendees for being so engaged in everything we do always appreciate it and love it. And again, just a reminder, I will send a follow up email by end of gay Monday with links to the slides and links to the recording. William, thank you so much. Thanks everybody. Bye. Thank you. Bye bye.