 Hello and welcome. My name is Shannon Kempen. I'm the Chief Digital Officer of Data Diversity. We would like to thank you for joining the latest installment of the Monthly Data Diversity Webinar Series, Advanced Analytics with William McKnight. Today we will be discussing what does information management maturity look like in 2023. 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. For questions, we will be collecting them via the Q&A section, and if you'd like to chat with us or with each other, we certainly encourage you to do so. And just to note, the Zoom chat defaults suspended just the panelists, but you may absolutely change that to network with everyone. To find and open both the Q&A and the chat panels, you can find those icons in the bottom of your screen for those features. And as always, we will send a follow-up email within two business days containing links to the slides, the recording of this session, and any additional information requested throughout the webinar. I'll let me introduce to you our speaker for the series, William McKnight. William has advised many of the world's best known organizations, his strategies form the information management plan for leading companies in numerous industries. He is 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. William is a leading global influencer in data warehousing and master data management and he leads with Knight Consulting Group, which is twice placed in the corporate 5,000 list. And with that, I'll go to the Florida William to get today's webinar started. Hello and welcome. Hello, Shannon. Hello everybody. Welcome. I trust you can see my screen. Okay. Looks good. All right, so this is one of my favorite topics. And it's an exciting topic. I get asked about this a lot. Kind of the question comes in many forms, but one of the most common forms is how are we doing compared to everybody else, you know, compared to a particular company that's in your industry or your industry in general. And I love to give this presentation because I get to share my experiences and my perspective on things. Due to having a good birds eye view into a lot of your environments out there we've done some aggregation of information to show what you're doing, and feed that back to you and today isn't going to be simply I'm going to talk to you what we're seeing out there but I'm also going to expand a little bit and talk about where things are going, even beyond this year. And where I see information management maturity going because, and I'll probably say this 10 times but the, the maturity model keeps keeps changing. If you're looking at a static maturity model, one that you haven't changed in a while. It's probably no good anymore. So the components of my model that I'm sharing with you, they do keep changing, and they will keep changing and I think they're just going to change much more rapidly that's what I've seen happen in the past few years, the artificial intelligence overlay on to just about everything we're doing or at least we're thinking about its overlay on to everything we're doing. And we should, but that does mean that perhaps there are components of a data enterprise that aren't even in place yet that will be in place for leaders in the next three years or so. I'm going to initially stick to the information that we've received over the years from our clients. First of all, though, let me kind of stress upon you how important this is, we are in the business of information. So whatever information whatever business you're in, it's about your information. And hopefully you can see that a lot of companies today or at least the ones that I have the opportunity to come in contact with they want to be data driven. They know this fact, they don't know how often, and we will work it up together and get the architecture the technology, the strategy, the data, etc in place as you'll see. Because if you don't know that you want to be data driven, you do know some of these things information volume is exploding. Pretty much everybody is trying to manage more information today than they ever were before data is a lifeblood of artificial intelligence, obviously can't do AI without good data to train on, and then the data to act on. There's real time all the time, at least many components of business today are we are certainly encouraging. Most things be architected to real time today, even if those requirements are not in place, because it is an achievable goal. Information usage differentiates the competition. You can break down how you're competing with the marketplace. A lot of times it does come back to your information somehow information affects every single strategic objective of your company it impacts all stakeholders. It's reused, even that cell amuse data is essential to be under management because sometimes it's a needle in a haystack kind of thing but when you need it you need it. And so we are encouraging everybody to get all their data under management to high standards, high data quality standards performance, and all sorts of non functional standards as well third party information is essential to use. And some shops actually have more of that under management than they do their own data. And if you're not availing yourself of that marketplace, you are probably missing out and many more are with the advent recently of data marketplaces. So it's a key business asset that we're talking about here today. These are some quotes that I love and drag around a little bit here. And that is at the foundation of all the mega trends that are happening I won't read them all. You can see some of them there and another one that's one of my favorite is, we have for the first time in the economy, based on a key resource that is information. That is not only renewable, but it's also self generating running out of it is not a problem, but drowning in it is, and that's really our challenge that quote comes from john nayspin, by the way. So that is a challenge that we need to rise up to and the winners will overcome that challenge. So, here's the approach to the data that I'm about to show you. We, like I said, we get here at McKnight assaulting group, we have the opportunity to deal on a daily basis with what clients are doing in their data infrastructure and oftentimes we're brought in on day one into the most pressing problems and so we get right down to it's a great place to be for learning about so many different environments, and also, many of the vendors that we work with, bring us in on their data journeys with their customers so we learn a lot and the ones that we can get to some level of detail with, which are the last 30 intimate enterprise projects, probably spans back now about a couple years. These are mostly our clients that we've done at least a power hour, or a full implementation for and special kind of power one that gets to this level of detail I might add. And it's confidential we're not revealing client names here or anything like that that's not what it's all about we did, we do have 50 questions that we get the answers to their probing. We're going to probe on them until we get a correct answer, 40 are on data ten are on business, and we're looking for the correlation between advances in different areas of data management within the enterprise and advances in what is all about business. And so we do find that there is high correlation here. We find that progression within the shop in data topics now we, we come to the table with what those topics are the topics that we're dealing with all the time. Data legs data science data warehousing master data management data streaming, these sorts of things which you'll see in a minute. So we bring those topics and there are there's definitely maturity levels within those topics, and the shops that are at the higher levels of maturity and most topics. Those are the ones that we're calling high data maturity shops and from this chart, you can see that there is a correlation there to overall business success so we put every company into a bucket, one through five. One through five for data maturity, as well as business success, and we do find a correlation here. There's a clustering of you all or a clustering of enterprises I should say, in the lower maturity levels, that's to be expected. And then there, there are a few that are pulling away from everybody else up into the fours and fives and so that's where you want to be, because they're having not only great data success they're having great business success. And everybody's a winner in that scenario. It's a win win. And so, before I jump into it, I, if you've heard some of my presentations you know I love refrains of this type of this type of phrase. Today I'm sharing with you beyond the mountain lies what more mountains that's right more mountains so this is another way of saying what I said at the outset which is that our industry that is data. It keeps advancing, it keeps changing and very rapidly, and I've been in the business now 25 to 30 years, and I've seen a lot of change and I will say that it's probably changed more in the past few years than it ever has in the past, and I see no letting it up so let's hang with it. This is a good thing. It keeps giving us new things to think about new things that are stimulating to help our business with, but let's take one mountain at a time as we go. So this to me, the fact that there's going to be more mountains built. That's exciting. That's a sign of a great field. You have picked well if you're in the data business and I think most of you probably are so congratulations on that. I'm going to stop maturity modeling so I broke everything out into four categories, strategy, architecture, technology, and organizations, and so the capabilities emanate from the presence of these items shown within the business. I should give you a sense of priority. And this is one thing that I want to stress to you today is that what I'm showing you is how organizations progress with their data journey. Not everybody's the same. I'm not trying to say that, but there are some commonalities that we see in terms of the steps that must be taken as you move on up the maturity models. And so you can't skip levels either. You need to go from a one to a two to a three to a four and hopefully to a five. Now that's the, I don't know if you call that bad news, but you can skip levels but you can move through them rapidly. If you get the wind sailing in the right direction. So that's what I want to help you with here today is get your momentum going is your momentum going in the right direction for your data environment, or is it going in reverse, or is it standing still, it needs to be going forward. And by the way, maturity levels do tend to move in harmony. We don't see very many shops that are a five in strategy and a two in technology, these categories were strategically selected, and they do move in harmony so you'll probably you might be one number off between them, but you're not probably going to be two. And if you are, well that could be true but I would ask you to really think about that. The midsize or a smaller company you can add one to the measurement that you're going to see here, you're not going to be held to the same standard that a larger enterprises. And frankly this is geared a little bit more towards a larger enterprise but you smaller and midsize companies hang in there because it's mostly for you as well, and it gives you some hopefully even more aspiration, as we go along here. And I will say momentum is paramount, get the momentum moving in the right direction. Okay. I'm thinking about you. I'm thinking about you, the information management professional, and I want you to have success. And I know that it's, it's a multivariate means to that success within your organization and within your career. One of the things that I tend to think that your, your success your personal success is going to be measured on data maturity I'll start with that because that's why we're here today. It's only a component though, and I'm going to acknowledge that right here right now, satisfying internal customers from an IT or a, let's say you're not an IT but your technology professionals out there, professional out there in the business department. So it's kind of a remote from central kind of IT role. Most people are technologists or their business people still even even today when we're kind of saying we don't have centralized IT anymore. That's kind of true, but that's a roundabout way of saying satisfy your customers wherever they may be. And finally, what's emerging and growing as a piece of the puzzle for your success is delivering on business ROI. We in data, the spotlight is turning to us. The spotlight is shining bright on us today because of all this clamor to be data driven, all this information that our executives know, and we need to step up, I think, within order within our organization. We need to be more assertive than what we have been in terms of where the, where the business needs to go. The possibilities, we know them better than most people in the business because we deal with AI we deal with we're learning about streaming we're learning about data lakes and data science and all the things that I'm going to be talking about here today. It's really incumbent on us to take this information and strategically and appropriately start working them into your organization's plans so I hope to be a small part of your planning here today as well. Now, what would be great is for you to take out a pencil yet an old fashioned pencil, a piece of paper or you can do the same spreadsheet if you like as well and do just what I've shown you right here on the screen. So strategy architecture technology and organization, total and average down the left score be the heading of column two and then next steps for column three. That's a very important column, because these are some of the areas that you can grow into next within each of the categories. And so as we go along here, keep score, grade yourself for each of the categories strategy technology architecture and organization. The good news is everyone is maturity five but don't write down a five just yet because you might be a maturity five but that but for what year. Okay. Maybe it was 1990 that you're a maturity level five for okay ha ha very funny. I want you to be a maturity five for 2023 or at least 2021, but we are actually grading for 2023. So, and by the way, everybody gets a one. So put a one start scoring yourself right now. Give yourself a ticking mark right down the strategy architecture and technology and organization columns, those four columns. Give yourself a ticking mark and don't write it in pan or don't take up all the space because hopefully you'll have more ticking marks to add as we go along but everybody's at least a one at least in my book, and a one is not not too exciting to talk about. It's kind of all the bad things so let's get all the bad things out of the way first. Hopefully you're not going to stay here, but some of you are obviously going to find yourself resonating very well with all these things right okay the data warehouse is a struggle. And that's the way it is, and will always be. And we'll just struggle along with it. And artificial intelligence is for others. I hear this sometimes out of enterprise and I'm just shop where I hear executives say well we studied the matter, and it doesn't apply to see it applies to you. It does apply. Your infrastructure is overwhelmed. Maybe it's dated. And it hasn't been attended to the only thing that's been attended to is projects that build on the old infrastructure, putting it further under under stress. Maybe you have a one vendor mentality, which I'm increasingly starting to believe is a maturity level one marker where you go to, and I won't name names but you go to your big vendor for everything. And whatever they say is what it ends up being. There's no competition there's no consideration of other tools. It does matter. For example, a data warehouse is not a data warehouse is not a data warehouse. They're all different. They're all different in terms of capability okay there's some commonality for sure, but they are different in capabilities and I do a lot of performance benchmarking, and I can attest to the differences, not only in speed, but also in functionality, there's a little bit of, how shall I say it, a little bit of leapfrogging going on for sure there's a little bit of monkey see monkey do going on. In terms of the capabilities but nonetheless, they're different, you got to consider the differences, your tools might be outdated cloud costs out of control if you're even in the cloud at maturity level one, I should add that. My data skills turn over it's hard to keep good people that understand these things about about data architecture about these high data technologies data science and so on, an organization that's resistant to change. If that's you, you're a maturity level one organizational silos organizations don't work together. They actually have this silos, they actually have this mentality in maturity level one that is my data, and nobody else can see it in the organization, even if it could help them. It's my data. That's the mentality, and you have multiple overlapping data stores, you have a skepticism in general about the cloud. And there's a deadlock. It's going to stay that way for quite a while, because there's no organizational way of breaking through I mean we could go on right. I'm going to throw a couple more out there though you got to hear this excel is the number one bi tool in use there. Okay. And there's really no connection in these environments to the word architecture. It's almost like a dirty word in material level one you're trying to do something, something big something that has legs to it for the future. And instead, you're always kind of circled back to do this or that project and and that's okay. That's a reality. You must do projects, but you must be able to do them on higher and higher levels of data architecture and that's a strong point that I want to make here today as a matter of fact if there's one big piece of advice that I have to help you in maturity level one. It's this get an understanding of this, that you must achieve business goals, while increasing data maturity. So I've dealt with a lot of you a lot of it professionals data professionals in general over the years. It's a bit of an undercurrent I think to some that goes something along the lines of, well, I'm not, I'm not getting budget to do these big strategic things that I want to do and I may, I may have kind of fed that a little bit with, you know, ideas but that's right. That is right. You do need to achieve business goals with that budget, but at the same time, you need to be increasing data maturity. As I said before that the spotlight's coming to us in the data business, and we have this heightened responsibility. Yeah, we have a, we still have that responsibility though to achieve business goals, while we achieve data maturity. So remember how you're graded out as a data professional. It's the three pieces of the puzzle. Yes, solving customer, doing customer satisfaction is one of them. Data maturity is another one in business ROI is yet another one. So don't forget those those second, the second and third one that I mentioned there. And too often, we just put our head down and we give up. And we say, okay, I am going to make that customer happy no matter what. And even if I'm sacrificing the future, speak up, come up with great plans and achieve business goals. Yes. And you must achieve increased data maturity as well. If I have a say in the performance evaluation of data personnel at shops, and I've contributed to quite a few of this. So sneak in there that they must improve their area of data at the same while at the same time, achieving business goals over the next year. If we're still sitting at the same place from a maturity perspective let's say you're in data integration. We're still in the same place. We haven't moved. We're just still doing the same things we're not we haven't moved to real time we haven't moved to streaming. There's no thought of Kafka anything like this, then that's a problem. Okay, now that's all the bad stuff let's get that out of the way. Okay, now I'm going to be talking about more or less good things maturity level to starts to starts to sprout some good things. We all have that one ticky marker right. So beside data strategy, under maturity level to you can give yourself another one if I'm going to describe you here, you have emerging data standards, data standards are a thing. They're not bad words. You have some data decentralization. There is executive awareness of data. They, they get that data is important, whether they've followed through or not that might be a matter for a higher maturity but you can tell from something some indicator that the executives get it, you're doing some self service bi. Oh my, please tell me that central it is that doing all the reporting in your shop still in this year of 2023. You've moved to at least off of that, and you have some self service be up, you have a cloud first direction, and you might be surprised to see. Well, why is that in maturity level to I thought that would come along later. The data tells us that there's a clustering of of enterprises at this level, maturity level to that have a cloud first direction. So if that largely describes you give yourself a tick. I won't keep saying to give yourself a tick as you go. So basically you've got the pattern, and we'll move on to architecture. You have a central data warehouse. And by that I mean on a relational database platform a snowflake redshift tarry data synapse big query kind of thing. You have emerging platform heterogeneity, meaning you're not still trying to do one size fits all. Everything goes in a blank database. God forbid one database that it's just that's not flexible enough for today's organization I won't strongly make that point because I got to move on but I that is what we see in maturity level to and a data lake is in development if it's not in production it's at least in development so these now these are just some of the highlights there's a lot more data behind this but this is your slide where and what I can do today. But still I think it's good enough that if that largely describes you you can move yourself up to level two for architecture now let's look at technology what's happening in technology for maturity level to have master data management at this point or it may not even be on the radar but you have some master data sharing going on, maybe there's a database somewhere that people are increasingly utilizing to get all their blank their customer data etc. You're starting to use third party data. Data integration is starting to be at least some chatter about machine learning and how it might automate some of the things that you're doing your data integration may not be just streaming levels yet, but at least you're not doing old ETO. And you've realized that ELT is a more preferable way to go about your data integration, and at least you're beyond reporting, and you're moving into dashboards. So if that's you, you're a two for maturity level to. Yeah, organization wise, okay, cannot forget this component. Okay, they all have to move in tandem, and the organization is largely going to help pull up all the others. So you and I may be more interested in the technology and the architecture, and that's all fun. But we better be sure if we want to advance in our technology and advance in our architecture or strategy as the case may be that we're also advancing the organization now the organization can be one of the hardest ones or the hardest one of these to move along here, so we got to do it. There's some data governance now in place, not full data governance, not all subject areas, they don't necessarily meet yet, but at least there's some data governance, you have an agile methodology you're doing, old waterfall for every one of these data projects, and you have data specialist data is not something that is tagged on to or tacked on to what's really an application specialist an application developer that okay I'll do some data on the side here just just enough to make my application work by the way. If there's such a thing as a data specialist they're very important, and every organization needs them and at level two you've already started to break that out. So, that's maturity level two. Now, if there's a big learning and making the move up to level three. This is my big learning for you. You're always focused on the data architecture, as well as data access. If you're a low maturity shop you're always focused on the bi you're always focused on that tip of the iceberg that's above the waterline that everybody can see oh the report the dashboard. There's something wrong with the report of the dashboard, then fix the report fix the dashboard, and do all sorts of gymnastics. We don't even consider changing and improving the data infrastructure. That's what we need to be doing more of though. That's what that's the iceberg below the waterline. That's the big part of the work effort. Too often we just work on that part that's above the waterline when the real leverage in the organization is at the data foundation. Now, I used to say, I want to build my data foundation. I want to build my data architecture, so that you can overlay any bi tool out there on top of the data. My data is already going to be screaming out what you need to know, you just need a tool to lay on top of it and see it. And I still like that. I still think that's largely a good direction for any shop. Now, before I dive into maturity level three, let me say that I think everybody needs to be at maturity level three. So, if you're not here. If you, let's say you, you're not at least two ticking marks for every category. That's a problem. If you're not able to give yourself three, you need to be more or less sprinting to maturity level three by let's give you a deadline. Next year this time, no later than that, because remember what I'm showing you here today as maturity level three, probably going to be two a year from now. We've got to keep up, we can't skip levels, we've got to keep moving. So let's start with data strategy. We acknowledge that there's a layer in the architecture for data. It's, it's another refrain of that we're, we're separating data and application into data owners and application owners. That's a level, that's a hallmark of maturity. Now we're doing some reactive AI. Maybe it's just for automation that's the most basic type of AI. And that's starting to be in place at maturity level three shops. This is programming to provide a predictable output based upon the input that it receives. So, reactive machines. They're always responding to identical situations in the exact same way every time. They're really learning or conceiving of the past or future. Now, obviously AI can do all that, and we need to get it there. But at least we're laying up AI foundation. This is what we're finding that maturity level three shops are doing. So this is your base standard. And by now self service is the dominant model. So give yourself a third ticking mark for strategy, if that's you. Architecture, you have multi year architecture direction and plans. And hopefully something things like today influence those plans, some real world situations and thought leadership influence those plans. Now you're not going to get there tomorrow but if you don't have a plan you don't know where you're going, you're just going to write it out right where you are same tools, same old architecture, same everything. So, we got to have that direction we got to be looking for opportunities to move us in that direction so the next project that comes up. And you're already under deadline I know. I know how it works. You're already under deadline, but you got to pause, breathe, and be sure that you're doing it right to modern standards that you're building it out efficiently. So that a year from now, you're not circling back on some of the things that you're doing today. You have a data lake in production. So data lakes. So this is on for me, they're on cloud storage. They're for data science. They're most of the data all data types. We know what a data lake is right. We have one of those in production data virtualization is in place. We know that we're not putting all data needed for every query in one platform. It's spread around by necessity we have multiple platforms is some data in multiple. Yes. Some data is only in one, and that's okay to most stages probably like that. But we have data virtualization in place to to get it all under control for query reported dashboard so on. You have measured data quality levels. That's right. You're not just doing data quality. You're measuring it. You can say, we're at 78% today. That's pretty low. We're at 95% today. 94%. We were 94% yesterday. Yay. So the data warehouse is getting better in terms of data quality we're actually measuring data quality now may not be very sophisticated measurements, but it's an indicator that you're attending the data quality and that's what's really important here. We're moving data around and, and getting it there. Right. You're managing many data types. And there are some data lineage in place now data lineage is becoming increasingly important in organizations they're going to go and you're going to need to be increasingly able to say where that data came from and what changes that went through. Maybe a data catalog could help with this, but at least you're doing something around data lineage at maturity level three. And now data catalogs that's going to come in here and technology and as well as some other things that you see here is quite a bit. Quite a bit for maturity level three. And you might say well why didn't you put that under architecture, some things could go either way. And so I did my best. So technology your data warehouses in the cloud. And what your shops do. They don't have the on param data warehouse. It's in the cloud. They're using graph databases for their relationship data, and most shops have relationship data, and dealing with it and a relational database is seldom really good enough for modern standards. You have master data management in place of a major subject area. Well, you don't have customer product and your 10 other major subject areas but you have at least one that you're doing master data management with you're using the data marketplace that's that third party data. Well marketplace where you can get a third party data use of the data catalog, the integration is streaming and your integration is reusable, not every pipeline is from scratch. Okay. Now, let's move on to organization data governance, it really took a step up here from charity level three. You're doing it by subject, subject area across most major subject areas. It's a big jump. You got the momentum from two to three, and by three. So now you're sitting here with data governance where you're actually having meetings, you're developing standards together. You're focusing on data that's going into projects that are in process right now. It's a beautiful thing. And there's still more maturity levels to go with data governance. Some people asked me, Well, how come this wasn't. This is so foundational how come this wasn't in place at maturity level to. Well, I think my answer is, it's hard. We're finally here at maturity level three. And by the way, if I was sitting here with a blank piece of paper, not sort of influenced by all the shops that I deal with. Yeah, I'd probably draw it up that way. But I'm sharing with you what I'm seeing out there in the, in the world, organizational change management, that has been added to data projects. This is a beautiful thing because we're acknowledging the people side of change. We're acknowledging that what we're doing is not just changing systems. We're changing people. And actually, I gave a whole presentation on organizational change management prior in this series. So I encourage you to go back and look at that at data versity net, or on YouTube. You have a chief data officer now. You have data scientists more than one, and you have strong dev ops. It's reusable. It's reusable. There's this defined procedure for getting something from development to production getting it there with agile and getting it there rapidly. And that's dev ops to me. So there you go. Hopefully you were able to add a few ticking marks for maturity level three. And some of you have put your pencil down. Okay, I understand. But hang in there and, and listen on as we move on to maturity level four but to get there. What we're going to do is follow the data profile to the right platform. So what is a big separator as we go from three to four is the four shops tend to acknowledge that there is a right platform for data. And they're not just winging it anymore. They have their data lake they have their warehouse they have their MDM they have hubs all over the place they have application oriented databases. And that's just kind of the world we're in right now. Do I think like an accordion that it's going to kind of pull back at some point. Yeah I do. And I see sprouts of that on the horizon. But today in terms of the actionable future. It's very broad and varied. And we don't have to sit here and sharpen the pencil all the way down and, and figure out okay houses data going to be used how we, how are we sure to make it scalable. Largely, these platforms today are scalable enough for what we're doing. But we got to get it into the right platform by understanding the profile of the data, the profile of the data will ultimately drive the usage of it. Over time, whatever, whatever you're thinking about the access to the data, whatever you've developed through interviewing people and whatnot. That'll all go away. Once that data is out there accessible, ready, clean, perform it. Managed is the uptake will will will happen. And so it's that profile that you want to focus on to get the data into the right platform. So maturity level four. All right, now, now we're now we're stepping up. Data is an asset in financial statements and by executives no not not the, not the Wall Street financial statements because they don't ask for it yet. But we're ready to go in maturity level four with that level of information. We're doing predictive analytics we're predicting the future. We're predicting it well. We're not off on all our predictions, and we're trusting in the predictions to the point where we're changing the future. We are tuning the future to what we want it to be through predictive analytics. AI data parsing. This is where AI is now recommending data quality rules and the data that we should be using it is doing auto classification and parsing of the data. AI is curating insights already at maturity level four. Now we're probably down to about 15% of enterprises out there are here. And it's good to know from an aspirational perspective where you're going and frankly where you're probably going to need to be in a good year or two time. Architecture Kubernetes. It's a dominant architecture level four identity management is happening now. Rest API's for communication across the applications across the data stores. Data lake house is in place now we had the lake. Okay, we had the warehouse. And now we are embracing a lake house architecture. And I am certainly not saying a fabric or a hub is out of the question at this point. As some of you have heard me say they are not mutually exclusive. And you have full data lineage at this point so some of you can give yourself that fourth mark under architecture great good for you. Technology wise now MDM is across more major subject areas I decided to bring MDM into the analysis, and some enterprises shook their heads about that. They're not believers but more and more becoming believers in master data management over the course of time the data catalog. It's not just sitting there, getting some auto population which is just not working today, frankly, but it's being populated is being attended to manually. And there's some help being provided to make it a real asset data observability we've all heard about this. That's becoming a thing for maturity level for searches augmented and interactive. Yeah, it's not. Let me run a query and that'll be it. We can think, we can think and get data and think some more and think more deeper, we can do that on a repetitive basis with our data at this point, and the analytics are live, they're real time. And we're doing API match we brought in a third party that has all sorts of API that we can use. Great organization wise comprehensive data governance at this point all the things that you are thinking that data governance is all those aspirational pieces. They're in place now from maturity level for, and you have a chief information architect or equivalent. Yeah, not too many of you will have that yet. I've been advocating for this for years that there is a seat at that executive level about architecture, not just CTO CDO large organizations, you can wear multiple has but really, I mean really large organizations, you got to break it out. So CDO chief information architect. So if the CDO in a midsize company say is doing the chief information architect roles, which is largely the parsing of where data is going to go the architecture of applications, the maturity modeling which we're doing today, that sort of thing. Okay, great. But in, in many shops it's going to require special focus, and now you don't only have strong dev ops, you've adopted that to ml ops. And so your ml is becoming much more fluid, much more attuned to the characteristics of the data that it's seeing much more organizational organizationally relevant. And that only happens when you have strong ml ops and you're able to iterate with your ml. You don't have ml ops. And I'm not saying you have to have a product for this by the way it's not what I'm saying here. I'm saying strong pipelines for machine learning, do products help of course they do. And maybe that's what some of you are going to need. That's another conversation, but either way, you have strong ml up for maturity level four. So what's left. Many of you are looking at this going, I wish I was that right there. Stop. Well, there's more to go there's a few of you maybe we're talking 34% now that are the top of the top, the best practices as they like to say the success stories the magazine covers and so on what are they doing well in order to get there. We have to acknowledge that to do more to do information management right does not require more expense or time. It requires, it takes know how and concentration. Yes, focus if you will knowledge and focus. So you're getting a little bit of that knowledge here today hopefully you have a program for keeping up your knowledge base. And you need that you need to be having an organizational focus on doing it the right way and moving it forward. So what's left I'll go kind of quick here because I know it's not super applicable to many of you at this point, but data strategy wise hyper personalization. We're getting of our customers, bucketing of our suppliers, their individuals, and we know this, we're not only doing look back and look ahead analytics we're doing prescribed prescriptive analytics and getting in front of that road, and we're paving it in the way that we want it to happen and we're seeing we're procuring or sorry, we're producing information products. Actually, I think a lot of us in data, I would say 25% in about seven to 10 years are going to be slowly working on information products within our enterprises. And it doesn't matter what enterprise you're in, you're going to be kind of on that order. And you're finally at this point doing limited memory AI, which learns from the past and builds experimental knowledge experiential knowledge, excuse me, by observing actions or data. And this type of AI is using historical observational data in combination with pre programmed information to make predictions and perform complex classification tasks. It's widely used something that you're doing at five architecture wise, your data is fully discoverable itself describing. You have a microservice and a containerized analytical architecture, not just operational architecture, and you're using multi model meaning you're not just let your, you're using all the capabilities of your database to deal with multiple data types. And largely, this comes into place when we're talking about what we used to call no sequel databases that can now handle your key value can now hand your column to store your document store all in one your graph store sometimes as well. Sometimes all other kinds of data as well. I think I gave a presentation on that as well. Technology wise, you have complete enterprise MDM. Your databases and your processing is at the edge in your IOT architectures we're doing more and more at the edge maturity level five shops have not only databases at the edge, but they have AI at the edge. And that is seemingly becoming ideal in architectures embedded databases inside of applications, you're building your applications with embedded databases, and you have policy management in place this is for security, beyond what the database provides. These are tools largely that provide do do things like you can tell it to provide access to all data for central office employees. You can tell it to mask all PII data. And it happens across the board. Can you imagine these days with the importance and the criticality of data, trying to do this without policy management well most of you are, but there is a better way and maturity level five is doing it that way. Organization wise, pervasive data governance it's now just part of the fabric of the company. People don't question it anymore. When people come up with new ideas they think about data governance. They think about how that can be an asset to them. And by the way, in these organizations, data governance didn't lead with the stick. They led with the carrot. They said, hey, look at what we're doing and how we can help you. Not. Well, we're going to go get executive management to to make sure that that you use us. That doesn't last. You've got to build the carrot. And I think this is in place in these organizations, especially to handle the escalating cloud costs that are going on out there. Now, come back saying that channel next year. What will I be saying about these while I'm starting to have an idea but I really don't know. I think a lot of this is going to be influenced by artificial intelligence, and that's probably going to be true for the next several years. And the machines able to understand and remember even things like emotions and then adjust behavior based on those emotions, as they interact with people, this sort of self aware AI is AI that's building upon itself designing itself, growing itself. There's going to be more and more of that happening. So, where is this all going I just got a few more slides here to talk about that. Because if you're laying down plans today for the next five years. That's a challenge. Number one, you still ought to do it. Number two, and you got to take a lot of AI into consideration as you do it and place some bets I don't have any problem with placing some bets just stay flexible about it. Keep an eye on this space, but we are at the start of general AI now that's an opinion because that's not really super well defined. AI is when the machine has the capacity to understand or learn any intellectual task that a human being can. So we've opened this new chapter in machine learning. The most striking feature about it is its general, which means we can give it high level tasks. One day will be able to give it a high level tasks like run my company. I'm tired of running my company run my company for me. Come up with the big decisions. Things like that. So, but that's down the road, but we're at the start of that. And so that tells me, we need to be able to consider AI in everything that we're doing as an organization that's where it is. We will eventually adapt to large language models, which is all the rage right now right, like we have adapted to other technology, but will be changed as a result. So stay flexible stay open to change. And a lot of that change is going to be right in this area here. And with your designs that design and frankly AI learning how to create itself. These are all becoming possible. Create AI systems that can spawn new AI systems that take you to deeper levels of growth deeper levels of business gain deeper levels of analytics that are in use deeper levels of customer engagement. It's more sophisticated than what humans can design. If you're keeping yourself to what you can design, what I can design what people can design. You are being highly limited today. You've got to embrace what these possibilities are because I think this is going forward, and you can place your own bets, but I think this is a keeper. And that's one of the easiest things I think I can say Google research scientists recently released the system that can identify objects in real time with remarkable accuracy. I don't know if you can see it on the, on the screenshot that I grabbed there of it, but it's identifying all these things. How can you adapt that to your company as a insurance company as a pharma company. Well, it probably take me five minutes probably take you five minutes to come up with 10 ways. And if you can't come up with it, maybe ask AI, and it can come up with it. And you can get some ideas there. I'm trying to open you up a little bit. And the whole, the whole thing about coding. And I think in particular coding around data integration. I think that's going to have a lot of, shall we say opportunity to automate. We're going to start to see that in the next few years so these are things that are going to take my maturity model that you're going to see next year if you're back here, and I'm back here, or if you catch me down the road. Frankly, if you saw this presentation before you didn't see this presentation before because it keeps changing. And that is, that is just where we at where we're at. I'm looking beyond 2024 now frankly I'm looking, I'm looking a little bit well beyond 2024 now I'm looking out 1020 years. I'm looking at quantum computing, computing that is based on qubits, which can be any proportion of the one in the zero states at once. I'm looking at some early signs, some early research that we're getting an exponential seeing an exponential speed up in performance, beyond whatever I'm doing all this benchmarking well beyond what I've been able to do with marketable products today. This is where all calculations can happen at the same time. This is going to be real effective at searching large databases, and thousands of times faster than a traditional computer. Now your attention there. This is going to change the major players that we deal with in the next, say, 20 years. And so, yeah, and we can go around and look at all these data on the balance sheet I mean, we've already got the rules there right it's an internally created asset. There is a corresponding cost for acquiring or building the asset. This is a depreciation cycle, somewhat to it anyway. And it's used in a similar manner across all companies well that's the criteria right there for something to be on the balance sheet data's meets those criteria so it's a matter of time before gets on the balance sheet. And you might say we have to deal with that too. Edge computing edge AI are already starting to identify it, as I went through the maturity model, the need to store data will be reduced. Hmm, I thought it was growing. Hmm, yeah, well, I don't know that we each individually are going to have to store all the data that we're going to use I think there's going to be more of a kind of a collective way that the data can be there for us. So we're going to go along here as we're getting to the end, given the rise of generative AI, those companies with strong monetizable data and dedicated use cases will be the winners. Majority of data jobs will be automated. I'm sorry to put that on the conference room cable there and walk away, but think about that, the need for explainable AI will go away, kind of a rage right now but I don't see it sticking will win out and take over that and automated data discovery, abstracting us even more from our data platforms. Alright. The enterprises that are that are going to be formed in the future, maybe in your industry. Whether you're an insurance pharma health care finance consumer goods, except for manufacturing hundreds of companies are going to be built around an AI API for something like chat GPT we're seeing that already. The I try to keep up with the AI companies that are coming out and it is just overwhelming. So a lot of them are based upon API's to things like chat GPT so you can argue they're not really, they're just using chat GPT yeah okay. But that's where companies are coming from startups will not be able to create the AI themselves, but they can use the API is kind of what I just said nearly every industry in your nearly every verticals being transformed today. Using these techniques and software and statistical models to make predictions and drive business forward in a way that they're not able to with only humans. Alright, so finally, summary of the whole presentation. There's more maturity and moving in perfectly than a merely perfecting perfect perfectly defining the shortcomings. Yeah, a lot of us can can say well that's wrong and that's maturity level one and that's a this that's a bad, but let's move it forward. So that you're not dealing with that every time that you're trying to get something done in the organization. It's already out there don't be afraid to fail. Have an open mind. Pick your battles is another way of saying that, and no plateaus are comfortable for long remember beyond the mountain lies what more mountains and the resistance that you might be getting. If you were to be at maturity level five, everybody would be happy to be at maturity level five. It's the journey. Have you paid a journey that they want to get on. Are you the right person they feel like you're the right person to lead that journey or your, your kind of component of it. That's number one, but it's everything laid out the architecture of the technology and the organization. That brings us to the end of looking at what information management maturity looks like. There's a lot of different profiles out there. And I've given you the 12345. Hopefully you're looking at that and going, I can do better. And I've given you some ideas, maybe for moving to the next level in all of the categories. And with that, I'll turn it back to Shannon to see if we have any questions in our time left. Thank you so much for another fantastic presentation. I love this, this, this presentation specifically so I'm just going to dive right in here we got a couple minutes so strong dev ops on slide 14 you had strong dev ops, what about data ops. Okay. Very good question. I'm kind of in my categorization upon a lumping data ops in there with dev ops. And I am saying that as you do data operations, you're advancing the data layer, you're doing that for development activity, most of the time. And so, while there are some unique things that you need to do. And you're moving data components through the pipeline. I'm still kind of calling all that dev ops. So, yeah, that's that's terminology, but I'm definitely thinking that's in place. More or less for the higher maturity shops and something we should aspire to. Okay, I'm just going to slip in this very last question William we've got just a few seconds what about block change that beyond the maturity model. Did you say blockchain. Correct. Yes. Okay, blockchain. Wow, big, big, big, big conversation there is still finding its footing. I think that it will have some play in our world. Eventually, I did not see it or do not see it so much in information management organizations within enterprises today, and that's why I didn't find its way onto the model at all not even maturity level five is doing the blockchain when it comes to the things that we do so not saying that doesn't have applicability, I can sit here and think of things as a matter of fact I gave a presentation on on blockchain and how it can be applicable to the things that we do, but it's still, it's still immature, I'll say, and still working its way into into fruition. So William, again, thank you so much for another fantastic presentation but that is all the time we have scheduled for this webinar. I just reminder I will send a follow up email to all registrants by end of day Monday for this webinar links to the size and links to the recording. William thank you so much, and thanks to our attendees. Thank you. Thanks y'all have a great day.