Hello, and welcome. My name is Shannon Kemp and I'm the Chief Digital Officer for Data Diversity. We want to thank you for joining the latest in the monthly webinar series, Data Architecture Strategies with Donna Burbank. Today, Donna will discuss emerging trends in data architecture. What's the next big thing? 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'll be collecting them by the Q&A panel. And if you'd like to chat with us or with each other, we certainly encourage you to do so. And just to know the chat defaults to send to just the panelists, we may absolutely change that to now work with everyone. To open the chat and the Q&A panels, you'll find those icons in the bottom of your screen to enable those features. And as always, we will send a follow-up email within two business days containing links to the slides, the recording of the session, and any additional information requested throughout the webinar. Now, let me introduce you, the speaker of the series, Donna Burbank. Donna is a recognized industry expert in information management with over 20 years of experience helping organizations enrich their business opportunities through data and information. She currently is the managing director of Global Data Strategy Limited, where she assists organizations around the globe in driving value from their data. And with that, let me give the floor to Donna to begin her presentation. Donna, hello and welcome. Hello, Shannon. Happy 2024. Good to kick off another year of these events. We've been doing this for quite a while. I mean, these are always popular, especially this sort of emerging trends one that we always kick off the year with. But if this is your first time joining us, hopefully this is a good thing for you that we have a whole year of lineup and we have this as a monthly webinar on data architecture strategy. So you'll see a wide range of topics across data architecture, some of which we will highlight today as trends. This is a little spoiler alert. And the other nice thing about diversity is that all of these are recorded. So, you know, if you're able to not catch one and you have to catch it later always better life because there's always a vibrant Q&A. But these are all available and as well as past years as well. So welcome if you are new to this, this session. What I'm going to be talking about today as Shannon mentioned is what is the next big thing so much evolves in the industry as Shannon said is actually almost 30 years now I've been doing this. And so much has changed in the industry but in some ways, sometimes it's the same foundational things we've been working on right so how with you know and I full disclosure I think it was in my bio I have been a software vendor and I get the space and I understand it and I love it. The vendors are always sort of trying to create the new next big thing to, you know, kind of create some hype so it can be very exciting but also very challenging to be in this industry to really how to cut through what is hype and what is actually real practical value and what's different what's the same thing repackaged right so what we will try to do today is kind of talk through that and hopefully give some guidance as you folks do this in your day job so just some clarity in the beginning. You'll see some data data about data here. So, based on a research paper that diversity and my firm global data strategy have been doing for, God, I think five years now on trends in data management so you'll see some actual data driven figures from there. One of the news is this is available free for download either on the diversity site or our own site which is global data strategy under the resources white paper section so we'll give you a sneak preview into this but there's a whole lot more meeting research in there. And that's always asked because we are data folks is, you know, what is the survey says what is the sample size. It is over, you know, almost 300 people 285 folks from 35 countries and over 25 industry, and we will go into that a little bit more. But it's a really nice cross section and we've been doing this for a while and they're able to truly see some trends that we will talk about as we go so hopefully you'll enjoy that as well. Why are we talking about this because, unless you've been living under a rock. You heard this term data driven business or data driven organization, and it is true more and more organizations are competing on data as a competitive driver. And you'll see both of these have data as the main topic but their business periodicals right forms Wall Street Journal this isn't you know, diversity was do you expect to talk about data these are business journals talking about data and I think a lot of you are probably you know have have we have the job we do or on this call, because your management is asking that question how do I become more data driven or be a data driven organization and what does that mean, but more importantly how do we support that from the data architecture perspective and again, what side what's not because I'm sure we've always had all all, all have had that topic of you know CEO reads something in the paper and says you know can we do AI or should we be doing a data lake or you know what's the data mesh and should I be thinking of it right so, as you go through, hopefully, some of these trends can give you some meet behind what are people really doing and getting some benefit from, and one might be a little bit of hype so. So, the other part of this is that when we say the data driven business or did driven organ know not every, you know, organization is, you know, retailer for profit even or you know this government agencies. It's really helpful to kind of see what is that span of the different folks in a variety of industries and I found this one super interesting and again I've been doing this forever. And back in the day for the rest of you who have been doing this forever to you may remember it seemed like the only folks really doing data management, we're sort of finance and government right and you'll still see one of the top two up there. But what I find exciting and on a good day why I really love my job is just the growth of the variety of industries if I just think of our practice over the five you know past several years. We've worked with museums we've worked with not profits we've worked with a bunch of different fun retail companies or video or media companies right everybody is schools government agency everybody is doing data right not just the big one so I think that goes in that list we've probably touched in the past several years as well. And I find that super exciting and it's also probably why a lot of you are on the call it also will explain as we go through some of the trends, kind of the level of maturity of some of these practices that yeah maybe a Wall Street company has been doing something like data governance for 20 I hope they have if they're holding my money. That may be someone new like entertainment company that hasn't particularly done a lot of data driven in the past maybe not so much so that might explain some of the findings will show later but I find this interesting to me it's that long tail, you know some of those percentages that I'm sure we'll see growing retails a big one I know we see a lot of that. And you think of an Amazon.com Walmart you know those are some of the biggest companies on the planet and they are super data driven. And one of their competitive differentiators is absolutely data. So, you know, I found this an interesting statistic. So, the other one that's interesting and it's one old big pie chart which to any we love to hit part talks. So that basic question. Do you see data as a corporate asset. And yes, and for the younger folks on the call or people new to the industry that you know so used to things being data driven. People say yeah, and I feel like the old old grandma here well in our day we had to walk up hill both ways and it wasn't necessarily always considered a data as a corporate asset and many of you may have met me or seen me at a lot of the businesses and I've been doing those for probably 20 years now. And in the day the biggest topic was always how do you get the attention from the business, you know, how do we get the, you know, really get a seat at the table. And I say now be careful what you ask for because now the topic of these conversations is, you know, how do we go at the speed of business and how do we really engage all of this business stakeholders with all the different needs, you know, it's very rarely that nobody cares. Right. It's that too many people care and how do we manage that so I see this as a positive obviously that no, you know, we would love this to be 100%. But you know I think there's a lot of yes we know data is important but do we truly treat it as an asset, right I think very few people would say that management doesn't care about data at all. There's caring and there's there's nurturing to we truly manage it you know you can even say I don't know we're a taxi company do we truly treat our taxi cars as an asset, I've been in plenty of taxis that I don't think they do right. So doesn't mean that it's not important but are we truly managing it in the right way. So as we move ahead I want to talk a little bit about this business aspect of data management, it's a huge part of what we do every day in my practice is that data driven data man a business driven data management, I mean if we're not focusing on the business of the org drivers why are we doing anything. And I'm not just doing it from my own personal perspective, but also this played out in the research that this is one of them we talk about what's the next big thing. A lot of it is you know how do we have business and data really become more of a cohesive group and rather than two separate us versus them. So one thing I like to think of when we're looking at business drivers organizational drivers. We're looking kind of offensively, you know, which is kind of that that that classic carrot and stick you might wonder about these pictures right so that the carrot is, I'm thinking about opportunity I'm thinking of benefits I'm a growth mindset think of, you know, a stereotypical Silicon Valley startup, we're going to be big we're going to be large we're going to move fast and break things and we're going to be the best. And watch out for us right, very often offense or sales driven type of organization defense it might be more on regulation or risk reduction and it's more of a mindset of caution. You know maybe a medical device testing company right, I would hope you're you're sort of defensive or you know in some some companies are a bit of both think of I don't know insurance or financial services. Obviously there's a growth mindset but regulation and risk reduction and caution is a huge part of their business right so whenever I'm doing anything in data management for a client or for myself. I like to think of these, this aspect of our are we thinking of offense in our data management are we building new dashboards to see new business drivers and and beat the competition, or, and or we are looking more defensively are we trying to, you know, do data lineage for audit and things like that but generally a company has a focus or a mindset and it's really important to think of that. One of the reasons I bring that up is one of my favorite questions in the survey that we have is it's what's driving the need for data management because to me this is the huge so what me I am a full fully self proclaimed. I'm a nerd, and I love data management and probably would just do it for the fun of it, but I probably wouldn't get paid to be just for the fun of it business folks want some sort of ROI so why are people what are the business drivers for data management. And I know this is a data architecture webinar data management is a super set of data architecture but bear with us I think they're so closely related. The number one reason for data management that has been number one and every single survey we've done in the past many years is gaining insights through reporting and analytics. I would also include things like AI machine learning kind of embedded in that advanced analytics, but most business people were good or bad. When people are thinking of data they often think of their dashboards reporting analytics and that sort of thing. Number two is saving cost and increasing efficiency I actually love that because we often try to quote sell that data management something like master data management. If you're in don't have nice organized customer information or product information or supply or information you're becoming much more efficient inefficient right so a lot of you know companies that are looking to do data management really are trying to be more efficient be digital transformations another big one again, I expect this one probably to go away after a while because it's become so much of just the de facto way of doing things it's sort of like the dot com boom, you know everyone's the dot com now. You're not a dot com you're missing something right, but that is a big area of you know data is the foundation for digital if you don't have a good product hierarchy how can you sell your products online right. If you don't understand your customer base how do you target them online, etc, etc, and you will see regulations and risk, which kind of your classic, you know stick rather than Karen. But you'll see some of the others and I won't read through them all because I'm sure you have been reading them as I as I ramble, but you know customer satisfaction product quality revenue growth. I like to is the one towards the bottom there. These are just the top ones is not every single one, you know improving outcomes health education. We tend to and maybe because it's easy we sort of talk about customers and products. You know we work with a lot of you know higher ed universities or government agencies and it's how do I support student outcomes how do I have better. You know citizen experience from a local government right. It isn't always about how do we make more budgets and sell more money. So I think this is important to remember as we focus on data management, which is the why. And in the past few years we do, you know, do this every January in terms of when the survey comes out kind of what are the trends for the coming year. I like to do my unofficially called carrot or stick a meter. And what does this mean. I, when we look at this, you know, which of these drivers are more of that offense where we're kind of Karen and we're driving and what is more of a defensive. And you'll see one I kind of split the difference that saving cost and increasing efficiency. So something like reporting analytics or digital transformation or customer satisfaction. Those seem very much to me kind of driven by business needs we want to grow we want to understand our customers, etc. Saving cost almost seems your classic, you know, kind of stick right I'm, I'm trying to cut costs but my case is that improving efficiency generally when we're working with customers and it's something like MDM and it is one of these customers that are, let's move fast and it's going to be lean and mean and an efficient that to me is the carrot version of saving, you know, saving costs might be we're not growing so, you know, let's just cut cut folks or cut cut, you know, departments or cut cut buildings. Efficiency is hey we want to be lean and mean to grow faster right as they're kind of two sides of a similar coin but that's why I kind of split the difference on that one but what's interesting here is this is a very carrot driven opportunity driven right so a lot of us in data management tend to be sort of risk aversion we feel like we're just maybe keeping the lights on for the business but I don't think the business feels that way right people want to be data driven for a reason. So I think we need to keep that in mind. What I find interesting in a word I did not coin, but I like it, you know, anecdata right well it does what I just showed match what I see in the real world right so we are data driven, but we know that a lot of our information or evidence is driven on personal experience is something that came from a survey like the one we're basing this this presentation on. So, the next thing I'll show you is not from the diversity global data strategy survey but from some real world experience I'll kind of share so I do a lot of workshops with the diversity if you've been to any of the onsite events you might have seen me there. And when I do a session on data strategy or management. I often ask this question as you're trying to look at the strategy of your organization is your organization more on the offense, or defense right because if you're doing a data strategy or a date management plan, you should be thinking of this this on the why right and I've already covered this that, you know, offense is all about growth profitability customer satisfaction competitive advantage. Defense is more about compliance regulation avoiding audits fraud, you know, security and privacy concerns.  Most folks have an aspect of that or even different people within the org, you know, maybe sales is very red and all the offense and growth maybe legal right or privacy group, or maybe a little more of the defense which makes sense. So what I tend to do is I ask the people in my workshop we may have 70 people in the workshop. Where do you think your organization is on the spectrum. And my anecdote of people raising their hand. Generally, it comes out that it's sort of 80% sick and 20% carrot, which to me doesn't judge right because when you look at the surveys of business opportunities.  When you look at the different orgs, you know, are we going to say that, you know, retail and, and you know, manufacturing are very, you know, kind of only risk averse I would put some of those and very much the you know, generally these conferences is a cross section of most industries, it didn't seem to jive with me and really I think it's just we as an industry in a way, we're, we're kind of paid to be the data governance folks the data architects of folks that make sure nothing looks right that that is we are sort of naturally many of us and I am super stereotyping across all this so noted but we tend to be more defense because that's what we sort of feel a lot of is what we're keeping the lights on we're making sure the architecture doesn't break, we're making sure the data is right and creating but but what's sort of the risk of that can almost in some ways conflicting mindsets and bear with me following picture, but I often say the good news if you are a data centric professional, you do have a seat at the table right business people want to be data driven they want kind of that trusted advisor to be able to talk to and say hey what could we do with dashboards, you know how could we be more efficient with master data, but we're often seen as kind of the, the downer right so my very odd picture bear with me.  So just think of that you are at the table, and not that you need to be all Polly Anna and you know, doesn't matter, you know data quality doesn't matter and, and data architecture doesn't matter and governance doesn't it certainly does. But can we flip the script a bit. And again, you know, not only think of the stick but think of how do we, you know, think of that difference between cost reduction and efficiency in a way it's similar but can you word in a different way, right. We're more lean and I mean we can grow better and things like that so just think of it I think it is kind of a personality type thing or it's what we're focusing on things so I do it myself I'm, you know, up all night trying to fix something or especially when I used to be kind of more on the, you know, real hands on coding tech, and, and I spent all night fixing the bug and I go to do the demo and what do I focus on I focus on the bug I just fixed. Really Donna 90% of the application works, but what's top of mind are the problems or you know this just kind of again that's what we're often focusing on but maybe something to think of as we think about what is the next big thing is because I do feel that it's the that the blurring of the problems between business and quote technical or data management because the tools are getting easier to do the business drivers are so data driven, we really need to be more. We use the term data literacy, but I think it goes both ways is business literacy for data people right so humor me with that weird picture but there's something I'll remember if nothing else. So I this kind of leads into this next slide which I have found interesting because it has evolved over the years since we did the survey, you know, and back in the day when you said who's driving data management, it was maybe your CIO or your CTO or the it team. Absolutely not that anymore over and above anything else that sort of and again this is a multiple choice right so it isn't only it is an end. You'll see the the breadth of different roles, but very often led by the data governance role the data governance lead. To me that makes sense because data governance lead is one of those roles that kind of needs to span both business and understand business drivers and work with data owners and data stewards, but also understand data architecture and data management best practices and be able to talk with the technical team as well. So that makes a lot of sense. You'll see the chief data officer is number three. I often see data governance lead as a stepping stone to something like a chief data officer, because what is a good data governance lead do they have to evangelize they have to build buy in they have to understand the business and tech which really is a chief data officer role right. So often that is an evolution if you are a data governance lead and that is a goal of yours to be more of a chief data officer I think that's absolutely reasonable. So what I think is interesting and has evolved you'll see in the color coding. The brownish there is 2022 the dark blues 2023 over time we've started adding roles because they were so many right in answers, you know chief operating officer was one folks kept writing in so we put in the I team the you know, a lot of these business driven roles have kind of been added so I find that kind of again that long tail is really exciting and the high level of C level folks that are driving things so we talked about CIO and CDO probably not a huge surprise that they're driving data management. What I love to see is that CEO and the CFO and the CEO right that means that from the very top the business is really understanding that data drives the business and they want more importantly a seat at the table themselves right so they really want to help drive the data management. Do they have all the skills to do that. No, they understand the business but that's where a good data architected good data management lead can really help bridge that gap. So, moving on. This is one of my absolute favorite questions on the survey because it validates what I see all the time. I have a lot of comments and cuffs with people who disagree with me, especially at a data diversity conference and people folks will say, you know I can never get the business involved interested in a data model, or a data architecture diagram, and I get quite bold and I say well and then you're doing it wrong, because I have, and I will stand by that, you know maybe outside, because I absolutely seen the opposite and this data proves that darn it here right that when people say, what is some of the, you know, of using a defined data architecture. Number one, what and number one and two is collaboration across it and then collaboration with the business, right and that may seem odd if that hasn't, you know happened here in your daily world. So I think data architecture is something like a data model, I use conceptual and logical data models all the time to have conversations with business folks and very often they say thank you so much I've never had this explained in such a clear way you've been able to communicate my business problems, whether it's a hierarchy for a product hierarchy or, you know, the customer relationships that's all, it's their world right and the data model is a way to explain the business and it together. I would also put out a good old fashioned like system architecture diagram here's your source to target, you know lineage of how your sources go into the warehouse and get reported out to your reporting and analytics platform. I mean I've had a lot of business people say thank you for showing me that that finally clarifies how some of these data flows might cause problems and things like that. If it's done well it actually adds clarity and again, you don't want to bore business folks with a really detailed physical data model or something like that but I think at that high level kind of you know conceptual logical type it's a really great way to explain as well as with it right if we don't have a map of where we're going. You know how do we collaborate together so that one's that one's really hit home with me and then you'll get the rest of it right increased in quality consistency, etc, etc. That one we don't have a defined data architecture makes me sad but but we will have that go down in the future years I'm sure. More people see the value so that one interesting because not generally I think when people say, you know, why do you have a data architecture, you know collaboration with business probably doesn't come top of mind but absolutely is a helpful way. So, we talked about governance so all of what we talked about, you know, I don't get to governance lean driving data management data architecture collaborating with the business. That's really a tool of data governance so you'll see that across the past three years. You know data governance has is popular and has grown most folks are doing it, but you will see a lot of folks feel that they're still in the lower level of maturity. Or they're just starting to implement so you know the folks at a higher level of maturity you'll see at least self identified. They're fairly low, you know, are we self critical as an industry and we can know we can always get better. Maybe does it take time to fully have a governance framework to kick in at 100%. Also, you know you can get a lot of benefit out of data governance right out of the gate so I don't want to depress folks who, you know, are just starting, but to make it, you know truly saying that's going to be true for any department HR finance you know you don't, you don't get 100% when you first start. I also think the reason for this is when we think of all those industries on that chart in the very beginning. So many new types of industries are now becoming data driven that they literally are just starting with data governance I know, again in our practice, a big part of it is you know, I start out becoming data driven. And we'll talk more about that have started with their dashboards and then have some issues with trust in the dashboards or trying to become digital and move their products to the web and find out that their product data isn't really in a state that they can do that well. And then they go back to, can we have some governance place. So that kind of to me explain some of that. You know that we're either starting it now or that we're in the initial stages of maturity so anyway interesting. So, the other thing is what are you planning to implement in the future and I'm starting to sound like a broken record data governance data governance, but it is true. The other one which I love to see because it's a big part of what I do for living a status strategy and what's the difference between something like strategy and management. Well, a lot of it is the alignment with the business and making sure that your data architecture and your data management plans are strategic and they align with what the business is trying to do to me that's almost the core definition of what a strategy is versus what governance or architecture or management is. Then you'll see some of the other cast of characters we're all familiar with data quality master data metadata. And so what I found interesting, and we should have talked around it you'll see some of the ones towards the bottom are still initiative plan like self service analytics data science AI machine learning. So it's not that that at my again, this is part of data and part, Donna's opinion, so hopefully, or a bit of data right so based on things up and seeing with my clients and in the industry. People absolutely want to do data science and AI machine learning and self service reporting analytics but get to a certain point and say data isn't quite there yet so let's go back to the fundamentals and make sure we have governance and strategy quality and master data, etc, etc. So, I think that these don't work against each other it's telling that same story of want to do the cool business driven stuff. Got to get that foundation right first. So, on that note when we say, what are you absolutely using now so this previous one is what are you looking to do in the future. This one the green one is what are you actually actually doing today. I'm surprised because it was one of the top drivers earlier business intelligence data warehousing self service analytics are still top of the heat right because that that's what people are trying to get to. We don't see AI machine learning and data science here as much as it doesn't mean there's not an interest I think again that's an evolution. Again, some of the foundational things like security governance architecture are supporting that really what people are trying to do is get insights from the business to drive their organization so I think these are telling some of that similar story. So, we're talking about data warehouse on that previous one and to me when I hear, you know, business intelligence and data warehouse together in the same sentence that doesn't seem weird to me. But that isn't the only tool in the toolkit anymore. And so we always ask about this idea of a data lake. And again, if you've been looking in the industry there's this buzzword bingo right we have data warehouses data lakes now data lake houses now data fabrics there's a lot of different things so we sort of broke that down. The specific question was around, are you going to get a data lake and so number one answer was no, which is fine not everybody has a use case for a data lake. You know, we'll talk about this later. Still, we're talking about a lot of the data driven reporting. Show me how many many widgets I sold by region by product over time. Kind of structured data warehousing type reporting doesn't nothing wrong with that is a lot of the information that drives a business. However, there's a lot of other unstructured especially as we get into things like AI and machine learning and things like that so the second that's most popular. This does resonate with what I see all the time in my day job is that a data lake is used in conjunction with the data warehouse, which to me and, you know, we can argue semantics or the or the exactly what that means but that's a data lake house to me and you can have your cake and hit it to in a certain way that we can you know that kind of ELT instead of ETL right that we can't you don't have to limit ourselves to only the things we're going to slice and dice by in our bi report. We can get a broader areas information it's sort of that that broader landing zone for familiar with kind of late landing staging warehouse kind of world. Well, just think of your, you know, I know I'm oversimplifying for folks that may be critical on the call but you know just think of your data lake, you've broadened your landing area right I don't just land my ERP structured data but it could be video files or streaming data or, you know, a lot of other non structured data as well, or just raw data. I don't know how I'm going to transform it let's just take it from its raw source and be able to use that for things like, you know, exploratory analytics. Absolutely if that's your only solution data lakes can become data swamps very quickly. So I would not just say dump everything and good luck to you later I would say I'm old enough to have seen a complete Gartner hype cycle come and go, which was data lake, where you know for a while and you know the history rolled their eyes when we heard it about and we don't need these things like data warehouses instruct ODS and structured data just data lake is the new way to go. And like anything it's not either or which and right so that data lakes have their place they aren't the band I'll be all where you can just dump stuff and magic happens right by the same token you don't have to structure everything into facts and dimensions or relational data or things like that. So hopefully that's an interesting kind of metric of kind of what other folks are doing as the back end for some of that be I an analytics that we talked about it's one of the main drivers. So I always find this one interesting because what can be very confusing if you're in the industry is what platform do I use you do I use a data lake. Should I get away from relational and my old fashioned because I'm using a relational database should I be using you know something different like a cloud, you know based graph pattern database or something right. And again, it's and not necessarily or there's a lot of options out there but when you look and this has been true year over year every single year we've had the survey, the number one data source that folks are using our relational databases so you'll see here. This is still this was from 2023. The number one platform and use is relational on premise databases right not even in the cloud you'll see cloud has been moving up is number three there. And the thing that keeps me up at night will forever make me sad is number two is that spreadsheets is one of the top data sources now. I love your spreadsheet I love spreadsheets to they are an excellent tool I use them every single day, but as a data management platform for an enterprise, they are not the solution to use right so I mean better than pencil and paper but the fact that people correctly self identified kudos people are sort of hiding things and pretending, you know we're not doing that anymore. But I would say, you know, this has been true as long as I've been in the industry, you know, what are the top database platforms out there well spreadsheets and then Oracle or you know what so I want to name vendors but get the idea. So, interesting, and that what I also find interesting and this has been consistently true across the different years we've been doing this is the legacy systems mainframe co ball, etc right so, you know, for the boomers and Gen X or whatever out there, your code still working right we can we can kind of knock, knock some of the old fashioned folks but a lot of that code was well written and it's still running a lot of the national services and other, you know, big government and big agencies out there probably because they've been doing it so long. I could also say that maybe they were designed and built very well. Also, so also think of that. As you have some new people on the team and when we this isn't the topic of this presentation but you know automated metadata discovery are you going to have 20 year old out of college read cobalt copy books. Probably not but you know are there tools they can extract some of the metadata for that person and get the knowledge from that yes there are so there's a lot of good tools that can help with this disparate set of systems out there. There are a lot of options I guess I get slightly disappointed in this and this isn't the following when you read the full paper you'll see that you know people are using a lot of the, you know, you know key value pair and I guess there's that kind of non relational or or graph databases and things but not as much as maybe I'd like to see given that there's so much exciting stuff out there in the industry it really is what's running your business is relational database spreadsheets and some cobalt. So that's fine and so but where are people headed right and what you do see is when you look ahead. There are some different things folks are considering that maybe isn't just the relational database what I think you'll see here is that even with the relational database, it is now much more going to the cloud. I don't see a lot of net new development on on presses databases doesn't mean you have to go to the cloud I have, you know, there are many companies out there that either have their own data center or their own databases as reasons not to both price and security and other things so you'll also see that number two is no longer spreadsheets and I'm not sure if just people don't want to say it out loud. You'll see that it's still number four, people at least admitting they'll be there but you will see that cloud object storage which is sort of that cloud data lake so this sort of says to me. And I'm seeing that a lot too. Again, you do are able now to kind of have that idea of the best of both worlds that you can have the power of the data lake storage and the scalability of that, and the power relational databases because you know relational databases are absolutely your, your only tool the toolkit, but they're really good at what they do right if you're looking at, you know, referential integrity and data quality checks and things like that. That's kind of your relational model. And also it's again that that and condition not everything is either or I've seen a lot of companies kind of rationalize their data into master data stores or data warehouse type stores, or structured data, yes, or whatever, and then apply from those cleanse data sets, your more exploratory graph patterns and, and things like that. So you can have the best of both worlds that I'm cleaning and understanding and managing my data in the relational world. That's a great way to feed some of the, you know, data science and more exploratory type analytics so you can find this one fun maybe next year I'll be proven wrong and real time streaming will be the number one item or something right so but we will see I expect really if I had my crystal ball relational databases aren't going anywhere and they really haven't over the past So what is the next big thing you came to this webinar to hear it and I feel I may have kind of disappointed you it isn't the big sexy stuff. It's kind of the fundamentals right so yes is there sexy stuff or exciting stuff or whatever word you want to use in terms of gaining insights reporting and analytics. Is there exciting stuff with analytics and interesting machine learning AI is so much more opportunity with things like self service bi even absolutely so that is I think some of the, you know, carrot versus stick stuff. However, what seems to be a trend is may folks start out with something like self service analytics or start out wanting to do some AI machine learning and then very quickly realize that without that foundation without things like data governance without cross functional business and IT alignment, which is supported by data governance underpinned by a solid data architecture run on a mix of relational data like storage which I think is maybe it's not a big new thing anymore but I do see that growing. That that almost is whereas that was pretty next generation a few years ago, it almost is the fault way of working for so many companies now that you can do so much in the cloud, as well as and it wasn't a big focus of the survey. You know, maybe next year that'll come out or maybe it's just a new way of working of. I see less of these kind of static architectures where I'm building a warehouse only or an ODS only or a data lake only and it's, whether it's you know capital F data fabric from a vendor or just lower case data fabric which sort of means to me, you know, ecosystems of solutions because, you know, a lot of these cloud vendors allow you to have kind of a date data lake house and also some real time streaming patterns or some, you know, great tools for data science and AI and so there are so many different choices I really see that kind of this next big thing is a lot of interrelated ecosystems of solutions where and I think the data lake house where, although I think the name is goofy. I'm saying it being recorded. The concept makes a lot of sense and I think that lake house and who knows what word will be created for that. I don't know, a lake community right where we have so many different tools with you not only relational they exist already right in a lot of these cloud platforms you can have, you know, no sequel stores and you can have a lot of different patterns that aren't relational, so you can have these fit for purpose use cases. That's my sort of prediction that wasn't in the survey but we're sort of heading there. I think that just makes a lot more sense as long as you know when we develop architectures for clients I like to just call them zones in a way right this is our relational zone where we're doing some cleansing or reporting from and this is our data lake pattern zone or this is our streaming data zone right because so much, you know, think of cell phone data or iot data. A lot of that is real time. You manage that in a very different way than you would, you know something for your, your master data right so again it doesn't mean master data goes away, they can work together so good old quote from Michael Jordan right get the fundamentals down and the level of everything else you do will rise and I think that makes a lot of sense in the data management world right yes is there so much really incredible amazing opportunity to drive the business through data and through your data management and are there amazing different you know innovations coming out with real time data and with you know pattern detection data and AI machine learning.  But right you can you need those fundamentals, you're not going to jump like Michael Jordan right so think of that as you're thinking of your architecture it isn't old fashioned it isn't Stogee or you're not the only stick amongst carrots so think of how we, we, we word that right. Foundation for future success it's not stop everything you can't possibly do any reporting or analytics or AI machine learning until we do all of this but you need the right foundation to help grow this in the future so just a kind of a different way to think of it that way. You know business intelligence analytics absolutely still a driver operational data as well but really behind analytics teams to be a lot of what the business is focusing on. And can we think of whatever we're doing a data management think of it as that that stick that offense that business value approach, because the stuff we're doing that can feel like a stick, like governance like data quality like metadata. I started my career with met with the bat in the old days we called the metadata repositories now their data catalogs, one of the hottest things out there, and I still have to almost pinch myself that something like metadata is now such a cool hot trending thing. Why because people want quality data I mean it makes sense. It's just maybe not what you think of as top of mind, when you're thinking of, you know, data driven business these are absolutely the foundations. And again, relational databases will still be there they're not going anywhere. Are there other tools in the toolkit. Absolutely. So, hopefully there's some things to think about hopefully you found the research interesting again download the white paper from either our website or a diversity. And if this was an interest of you please don't hesitate to join the rest of our lineup next month is on data strategy so if this idea of business alignment with architecture was of interest to you will be doing a whole lot of more of that. Next month because that's really the underpinnings of the data strategy. We do this for living if you need help let me know my emails on the first page. And I want to open it up for questions with Shannon so over to you Shannon. Thank you so much for another great presentation here and interesting insights into 2024. And just answer the most commonly asked questions is to remind her I will send a follow up email to all registrants by end of day Monday for this webinar with links to the slides and recording, but diving in here Donna. And defense instead of being determined by the organization's industry doesn't it have to be more to do with the specific business concerns or opportunities the firm is facing and wishes the data strategy to help resolve. I think that's fair I think it's again again above both and so I think definitely the industry. And partly it's just your your wording right so I think you know not that I've ever made a mistake but I think I was talking to one of our education clients and I think I used the word monetize student data. And they they were just shocked that I that I was talking about selling kids data online that's not what I meant I meant of looking for business opportunity so some of that was just wording right. But every every company is going to have that purple zone of maybe some initiatives maybe I am got some work with several companies right now we're trying to do a big, you know, customer drive or student drive patient drive whatever it is but but the privacy around customers is paramount so there's a perfect I want to do the, the carrot, but there's a stick aspect of that that I want to make sure the data is protected in order to do that. I would say even though it wasn't in the question, you know, I might have mentioned this before to it's even different people within each organization, right, not to stereotype but you know, maybe, maybe finances more cautious or legal is more cautious and sales and marketing might be a little more carrot driven right so it's definitely nuanced, but I do think thinking of it in that mind like which conversation am I having, because often you're doing the sales pitch and you want to make sure you're using the right language if there's someone's doing a big sales campaign, you don't want to lead with, well can't do any of that till the you know qualities cleaned up you know just some of it's a way of presenting what you're trying to do you could say hey we're trying to get the quality right so we can have this awesome campaign you know it's just kind of flip in the script a little bit. Hope that helped. Definitely so Donna, what are so many. There are so many definitions levels of data strategy so what do you consider are the key elements of a data strategy. No, I will answer that quickly but I will refer to the, the questioner to that's the whole topic for next month session. But I would just say it's simplest and how we generally do it a lot of it is you know defining key difference here with data strategy versus data management is what are you trying to do as an organization how do you align with the business and what are you trying to achieve that's the biggest one understanding where you are, whether it's a maturity assessment current state architecture understanding. We want to be the coolest AI driven company in the world but we're still on spreadsheets well it kind of shows you the gap right and that leads to where you want to head in terms of what in my future state architecture. We want to be the best most awesome thing we're on spreadsheets we want to get to a cloud based lake house. These are the steps to get there and then that's the roadmap, which is how do we get there because it's going to take you years to get there what I mean it's a strategy right if it were six months it would be a tactic. So a strategy by definition looks far out a long term out the steps to get there, and then don't forget the change management plan, which is, you could say that's marketing but it's really winning hearts and minds right because you can have this great plan. This everybody across the organization want to come with you so hopefully that helps. So, is there a specific architecture that is best for advanced analytics and machine learning. Um, well, I, yeah, that's a hard question. I mean, I do think this idea of the data lake house or even a kind of a data fabric where you have, you know, it's a lot of these cloud platforms. The idea that you can load some of the raw data where you need to, you can have different, you know, timing, it doesn't always have to be kind of batch overnight can be really important. And then you have different tools within that platform. So I think a lot of these kind of, I call it a fabric which is kind of an ecosystem of tools which is also kind of a lake house right it's, you know, but but I don't want to say it's only something like a lake because, you know, things like master data and do I have the right customer list and things like that is generally a mix where you have some of the trusted data sets, but also the flexibility to have some more exploratory as long as you know what lives where right don't mix the two is this a trusted source, or are we looking at, you know, social media analytics where it really is more trending or ideas, you know, we're not we're not going to base our financials off social media trending that's that would be the warehouse. But we don't want you wouldn't necessarily put social media trending, you know, warehouse that doesn't make sense. So it's more that ecosystem approach. Awesome. So, great questions coming in here, you know, has the concept of data culture bubbled up in your analysis. It seems like a catch all term for data governance strategy quality, etc. Would you agree. Absolutely data culture is huge. And that with their in the survey itself, there was a bit of that the other word that kept bubbling up was data literacy. And on last year's trends we did talk a lot about that I prefer kind of data culture because David literacy almost sounds slightly insulting. I think it's almost not literate. You know, I think I talked about it earlier. You could say to you need to be business literate right, but I think having a data culture not only from the governance side of are their owners to people take accountability do they understand that they have a role in data quality, but even just from the data driven are people using the dashboards to make a business decision, or is it still got feel right off don't tell me you tell me how what my customers are like I've been working here 20 years I know my customer right. Data culture is very broad, but that's where in that previous question about how do you do a data strategy, we always include a organizational change management component right because how do you bring the hearts and minds are everyone along and everyone knows their role in a data driven or so yeah absolutely it's a big part of it. And is it a good idea ever to access the ODS to access ODS to internal business users or data warehouse, or to give access I should say. Well, ODS is probably one of those things that has I think has the worst definition. I mean, at its core I think an operational data store is not necessarily a staging area right because I think of a warehouse maybe it's landing staging, you know warehouse and ODS is often used truly as an operational data store and then yes it could be shown to the business users because the warehouse might be widgets over time in growth but an operator is truly that operational layer data that you're not hitting source systems you're you're having an internal group. So it isn't necessarily sort of a thing that goes before the warehouse is its own first order thing of operational data for reporting and analytics that that's fine to me. That makes sense. Yeah. So, Donna, do all of this data breaks Microsoft Fabric and snowflake do they all have the same data architecture pattern in the analytics offering. I never like to talk about vendors specifically but I do think in general that they all have similar ideas right right and they all have strengthened weaknesses let the vendors battle it out themselves but yeah when I was sort of talking about that ecosystem where yeah I can have a lake storage or different you know tiers of you know a lot of customers that sort of silver bronze gold or whatever instead of instead of you know warehouse and everything else is just different patterns of storage. A lot of them have the kind of real time streaming stores and a lot of them have very different AI offerings machine learning offerings bi tool offerings. And I think they're all trying to battle it out amongst themselves of where they differentiate but I think that pattern of the cloud offers you a whole lot of different. Again I call them zones, you know you may have a key value pairs zone for your website stuff which is very different than your warehouse for your relational fast and dimension trying so I do think that's a trend and I think it's a good one. It's just use it wisely and that's where an architecture comes in. You know what do you put where don't just dump stuff in there and we know what's going to happen. So hopefully that helps me lots of great questions here so don't do you recommend star scheme is still in the clouds and say Databricks. Yes. Very rarely get a just a de facto answer like that. We actually did last year on the university it should still be out there we did you know is the star scheme is still viable. I think yes for for several reasons. Well it's not just not just for performance anymore right so can some of these systems now just be a lot faster and could you dump a bunch of stuff and have it be performant. Probably, but one of the nice things about a factor dimension is that business logic right and a lot we still do a good old fashioned bus matrix right what are your what are you measuring what's the definition of that measure. What do you want to slice it by in terms of a conformed dimension that links with your master data what do you mean by department gossip and doing this for 30 years that's still going to be a question that every company. And it doesn't mean they're behind it means that's still a complicated thing is that a finance department of sales department and when you slice by department what does that mean. It's a good old data warehouse with those facts and events and really it's just it's structured in a way I mean it's a pivot table and Excel. Right we still use those right it's a very logical way of organizing things, which you know an architecture isn't only about how do I store the data in a fast cheap way so I absolutely think it makes a lot of sense and a lot of these platforms still have a semantic layer in a cube and things like that so. Yes, I do think it still exists. Perfect and we've got about five minutes left so don't know you know where would a supply chain fall in. I think that question came in when we were talking with the different industries I think. Yeah, yeah, super early on. I think, I think each of those industries has a supply chain aspect so you know retail has a supply chain is one of those key stakeholders. I think or maybe came in when we had the list of stakeholders so yeah I don't think you can write that in this year and when the survey comes out. But yeah, I often think supply chain is a big person that a data one of the people in the data governance ecosystem that has a huge seat at the table in terms of product data in terms of supplier data. So I think they're big players I think they weren't listed in any of the graphs but it doesn't mean I don't think yeah some of the other marketing wasn't on there either and I think that's a big always a big stakeholder as well but yeah good call out I think supply chain. And when we talk about efficiency and lean a lot of the folks that we work with that want master data management, it's kind of supply chain or someone lean manufacturing things like that that people are looking at so yeah good call. Our non relational little cloud databases increasing or decreasing in popularity they are providing proving to be popular in the energy sector even though one could argue it is an old technology. So yeah I think that probably came up when we were. I didn't I do believe that the, the white paper does have the trends over time, and either unfortunately or not let's look at this one, you'll see that we kind of a big jump there non relational cloud databases, they've kind of stayed static over time. Yeah, I'm maybe it's because that that percentage of the use case is, you know, not everybody needs them. I think they're powerful they still exist and you're right they're not even released like a data lake isn't new anymore. These things have had around for a while. It really in terms of this survey. It really hasn't ticked up tons over time. And I don't know what that means is it people who take this survey or kind of limited in their thinking of cloud or is it, you know, I, I haven't seen I still do in my practice see a lot of the core business driven use cases in a lot of that, you know relational but there's also streaming and, and graph and so yeah I guess I guess my my gut is that I see a little bit more of these and what I see day to day, but the survey this particular survey, it really hasn't like jumped up over time or even gone over time. Don't know. TBD. We'll see. Yeah, it's it has stayed pretty consistent. So, you know, we've only got three minutes left, but and probably has been a whole and, and I know we have this topic on on the list this year, but do you what what do you recommend the application of data governance will be. Yes, I'll do a blatant call up for I think that's an April will have a whole data governance data architecture. But yeah I think you saw on this in this particular survey data governance popped up everywhere so I mean I can only see that increase. I do hope we see and I hope I don't make people dizzy going back to old slides here. The one of we're in the initial stages. I mean I get a governance is pretty consistently been growing over time of people are absolutely interested because of the reasons we said they want to be data driven you need good data, just make so much logical sense. I would love to see the initial stages start to get to that higher level of maturity and hopefully, excuse me in future surveys will see that over time. Alright, was that the last one or do we have one more we squeeze in Shannon you tell me that was the that was the last question. Yeah, and perfect timing because we are right at the end of the year. Well, Donna thank you so much for another great kick off to another year very excited about the upcoming webinars. And again just a reminder to everybody I will send a follow up email by for this webinar by end of day Monday with links to the slides and links to the recording. Thank you everybody I hope you all have a great day and thanks Donna. Thank you.