 Hello and welcome my name is Shannon Kemp and I'm the Chief Digital Manager for Data Diversity. We'll want to thank you for joining the latest in the monthly webinar series, Data Architecture Strategies with Donna Burbank. Today Donna will discuss building a data strategy, practical steps for aligning with business goals sponsored today by Encorta. 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 by the Q&A panel or if you'd like to tweet we encourage you to share highlights or questions via Twitter using hashtag DA strategies. 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 chat defaults to send to just the panelists but you may absolutely change that to network with everyone. To open the chat and the Q&A panels you will find those icons in the bottom middle 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 and the recording of the session and any additional information requested throughout the webinar. Now let me turn it over to Mohit for a brief word from our sponsor Encorta. Mohit, hello and welcome. Thanks Shannon, glad to be here and I'd like to extend my welcome to everybody who has taken an important hour from their daily work and joined this webinar. So I'm going to go ahead and share my screen. So a quick overview of who we are. So we are an end to end data analytics platform, we were founded in 2014. We are a series D company with the backing of word class investors including Google and Microsoft. We are headquartered here in US with offices in UK and Middle East and have over 400 employees and that includes a total of 600 plus years of engineering work that is primarily focusing on solving the application data problem with a fundamentally different approach than the other data analytics solutions that you see in the market. So overall if you think about it, one of the key objectives of any data solution is to enable agile analytics for business and this is not to only include the ability to ingest the data as quickly and as efficiently as possible but more importantly give end users the ability to quickly respond to the changing business requirements for their data and analysis need. And this is especially true during the pandemic, right? I mean during the pandemic you can have new data sources that may become available at a short notice and users may have to act at a moment notice to analyze and make decisions based on that data. So the modern data solutions have done a good job in terms of acquiring the data but making that data available to the business in the format that users can understand and consume continues to be a challenge, right? So this has actually become a big pain point for the customers that we deal with and where we see huge bottlenecks and reporting backlogs that span over weeks and months if not years. And I think one of the key reason behind that is the need to physically transform and aggregate data before it can be served through the BI tools. And this not only obviously slows down the time to incite but also causes significant loss of the data in that process as well. So Encoder is built on top of the open data formats such as Delta Lake and Parque Files that supports the asset transactions to ensure that you always get a full and consistent view of the data at scale. And it supports things like time travel and a purchase park and can be used for machine learning and big data use cases as well. But the key differentiator with Encoder compared to other solutions is that there is no need to physically transform the data, right? The data always stays in the same shape and form as the data source, right? And the way we do it is we leverage the same primary key index and the joins from the source to link and transform the data, right? So there is no need to flatten the data set or create surrogate keys or create target schemas or start schemas, right? Once the data is ingested and mapped using the source metadata, it's basically ready for consumption, right? So this actually provides an unprecedented level of agility to the business to then be able to explore and then build insights freely without having to constantly model or remodel their data all the time. So and given that the data is always in the same grain as the source system, you have access to 100% of the data, right? You can use that data to summary level analysis. You can then drill down all the way to the transaction level details from that summary level reports while still maintaining the complete control over security and governance of the data. So here is a quick case study that I wanted to share with everybody on one of the one of the largest media companies in the world and how they were able to transform their business using Encora. So as you can tell, right, it's a fairly complex environment with five different solutions that included data warehouse, ETL tool, and of course a BI tool. And then they had multiple data sources with over 1,000 reports and 2,000 users. So even after setting up multiple servers to improve the overall system performance, the customer was still stuck with the daily data refreshers and it was taking more than two minutes to run an average query in their system. So with Encora, they were able to consolidate all of that into one reporting solution, right? So Encora, given it's an all-in-one, end-to-end platform, they were able to get rid of all the different tools that they had to stitch together to put together a BI solution for their users. So which basically not only improved the data ingestion speed but also significantly improved time, it takes them to explore and build new insights, right? So the data load frequency went up by 100x almost, right? So earlier, they were able to load data only once a day, but with Encora, they get near real-time access to data. They were able to load data every 15 minutes. The time to insight went up by 10x, right? So from nine days to less than a day. And this is for the complex insights that require new data sets or new business logic. So in summary, whether you are new to data analytics or a veteran in this field, I hope this was helpful and how Encora is fundamentally a different approach in solving the application data problems and how it can complement or fit into your data strategy. So feel free to reach out to us through Encora.com or for any additional information. We do have a free trial that you can use to experience Encora by yourself. So I would encourage everybody to check us out and sign up for a free trial and bring your own data, bring your own data to Encora and see how it works. So with that, Shannon, I'll pass it back to you. Thanks. Well, Hyatt, thank you so much for kicking us off and thanks to Encora for sponsoring and helping to make these webinars happen. Always appreciated. And if you have questions from Hyatt or about Encora, you may submit your questions in the Q&A panel and he will be joining us in the Q&A portion of the webinar at the end here. So now let me introduce the speaker of the monthly 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 a 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. Hello and welcome. Hello, Shannon. Always a pleasure to do these. Great to be back. Yeah, so just just before we start, we jumped to slide three. If you this is your first time visiting us at one of these Data Diversity webinars, this is a monthly series. And one of the questions that always comes up that I'll answer here and Shannon will mention it again, is that all of these webinars are recorded. So if you missed any this year or several of the past years we've been doing this, they're all available in kind of the digital library of Data Diversity. And then I hope next month you'll be able to join us as we talk in deeper about master data management. But if we go to the next slide, the topic for today is data strategy. Always a popular one, as you can see by the name of our company, that's literally what we do for a living. So I'm always passionate to talk about that. And I think, I mean, I'll talk about this later in the deck and often throughout the slides, is that really when we talk about data strategy, the thing that really differentiates the data strategy from just good old fashioned data management is also a question I get. What is that difference is really that business value in the business driver. And when you're thinking of strategy, the really the significant aspect of that is that it's going to drive a lot of the key initiatives in today's marketplace. Think of so many of the key successful companies in the globe, your Amazons, your Uber, et cetera, they're all data driven. They are data companies. So very strategic part of a lot of companies business plans. If we go to the next slide, you'll see in this probably isn't a surprise if you haven't been hiding under a rock this idea of this the rise of the data driven business. If you know a lot of our interest in data strategy does come from business users who may have seen a article like this. And if you look at the title, these are all business magazines, Forbes, Harvard Business Review, Wall Street Journal, et cetera, all talking about data and the data driven business. I'm sure we've all heard that famous quote of data science being the sexiest job of the 21st century. I think they meant data architect or data modeler, but I'll give them that data quality error. But again, that's a good thing. But how do you balance that need for business agility with getting that right architecture behind it? And we'll talk a lot about that. If we go to the next slide six, one of the things I've been doing with data versatility the past few years is, and I think we're going to be launching it soon for next year or this current year, is kind of a survey of data management professionals and seeing what are some of these key trends. What I am heartened to see is these statistics that you see over 70% of respondents felt that their organization sees data as a strategic asset. So you might sort of yawn and say, of course, nothing new there. But I think that's significant because I mean, I've been in the industry gosh over 25 years now, and that hasn't always been the case. I know when we would go to a lot of data versatility or enterprise data world type conferences, a lot of it was, yeah, we're not getting the attention of the business, we're an afterthought. And I kind of think, be careful what you ask for now because most companies data is top of mind. And with that comes a spotlight. So are we ready for that spotlight? When you look at the drivers for the data driven business, probably no surprise that that second bullet point that, you know, almost 70% is all about saving costs and increasing efficiency. I mean, that's been true probably since the dawn of humanity. One of the first things people saw on old, you know, rock walls and things were counting, right? Counting and counting how many bushels of rice you had, right? So that's kind of always been up a point of data management. But what I'm seeing more of and it is pretty exciting is this idea of digital transformation over 60% and that that's actually growing. If you look, again, we do this each year, that's over over 10% from 2019. And I know I'm seeing that with most of my customers that, you know, especially, and he mentioned kind of COVID, that sort of made digital a necessity, you know, companies that thought they could never could be digital are. And then you realize that your your data really needs to be right. You know, data really is that foundation of digital transformation. And that might sound trite or kind of overused. But it's absolutely true. Just was interviewing an exec yesterday. And that was her point of we're trying to go digital, we can't go digital without data. And I said yes. And that, you know, some folks realize that up front and are prepared. And some people realize that after the fact and have to kind of catch up. But either way, data is digital. So if we go to the next slide, seven, then I mentioned this in beginning, but it's probably worth mentioning again, this is one of the common questions, especially people who have been in the business for a long time of having we've been doing data strategy for a long time, it's kind of a new buzzword from the business. But I feel like we've been doing that and you may have again, data has been driving businesses since the dawn of time. But I think even more so with this digital world, it's increasing. And there are differences between data management data strategy. So I went a good old Webster's dictionary. I'm a data architect, data modeler. I like the definitions of things, right? Metadata is important. So I have my own metadata here. If you look at the definition of management, you know, maybe it's not the best definition there that the art or active managing, right? Or supervising or the judicious use of means to accomplish an end. That's important. But it sort of feels like housekeeping, you know, things you need to do, you know, I'm making things more efficient. I'm kind of managing my, you know, pencils in the pencil desk. It's important, but doesn't not sexy. It's not exciting. If you look at strategy, a, it's, you know, employing plans or strategies towards a goal, it's complex adaptations, achieving evolutionary success. The science and art of meeting the enemy under advantageous conditions seems a bit more dramatic. But also it's that idea of business goals and aligning what you're doing with data with your business. And again, it's in our name. We do a lot of data strategies and what my team always gets probably tired of me saying is what's the so what? I love data. I love business glossaries. I love data models. I love data architecture. But if you don't have the so what of why we're doing that, that's where it becomes just, you know, data management for data management's sake and not so much a data strategy. And hopefully that kind of clarifies. It's often what the one of the few questions we get of what do we mean by a data strategy? And to me, that's it. It's really aligning all your resources around that. So what, to achieve some goal. If we go to the next slide, eight, after that question of what's the data strategy, and I give an answer similar to the one you just got, I often get the question of like, literally what is it? Like, my boss asked me for a strategy. Is that like a document? Is there a template? Is that, you know, my slightly joke there, interpretive dance? What do I do to present this back to the interpretive dance? We could joke there, but actually one of my clients was getting her master's degree in kind of population health and science. And that was one of her options for her final thesis was you could either do a report with your data analytics or a poem or a dance. And the reason was some of the it's so easy to get lost in the data. Some of the research they were doing was, you know, how many people died this year due to homelessness? Yep. Yeah, I apologize. It looks like we are my slides aren't matching yours. I apologize, y'all. We have a connectivity issue. So I'm trying to drive the slides for Donna. So you said slide eight, but that's what is data strategy strategy versus management. Oh, next slide then. What is it really? A slide nine is data driven business. Is there one that has a pile of papers that says what is it really? We have a thinking issue. We have a thinking issue. Sorry. Maybe I will send you another email with the latest. Sorry, y'all. We have some technical issues. So trying to make it all work here. Gotta love technology. You now know our secret. I'm literally on a landline. I'm at a client that doesn't have a connectivity. Okay, let me send you that as we go. And I'll just keep babbling away as I do. But that is a question people get in terms of what would be the format. And I kind of joked about interpretive dance. But in terms of that client, it was, you know, a lot of the research she was doing was again, how many people died due to homelessness or lack of housing this year, or how many folks have, you know, issues with AIDS and very weighty subjects. And sometimes we get so into the data, we kind of forget the so what again, that was a very impactful so what. So I do at least have one sort of strategy that was done in a poem interpretive dance, probably not what you want to do for your CEO, however. So I often say a PowerPoint, partly because a PowerPoint is a really good way to tell a story. A PowerPoint makes you concise. You know, kind of that classic 10, 20 slides, not more. You know, the actual strategy might be hundreds of slides with all the detail in the back. You may have a Word document. In fact, we're working with several kind of federal agencies or countries that have, you know, UK, the US have a published data strategy that generally is going to be a Word document, right, of, you know, hundreds of pages of the or, you know, dozens of pages that this is our data strategy. I would say if you are in a company and you gave someone the big heavy Word document, you've, I don't want to say you've failed, but that'll probably end up on the shelf. We come in often and are given, you know, to say we need to revamp our strategy. We tried to do it last year and it just didn't land and we're given kind of the, again, 100-page, 50-page Word document and we look at it. We say, you know, there's nothing wrong with this. Actually, it was all very solid data management, but why didn't it land? It wasn't sold. You know, it didn't either align with the business or even if it did, you know, people didn't know when everyone was ATD now or everyone's busy and it probably was not presented. So that's why I, if I were to pick, I would say put it in a PowerPoint. So in terms of what, yep. Sorry, John, I thought there was a break there. I'm on slide eight. I'm on slide eight with the pilot papers on it. Yes. Yep. Okay. Great. In terms of the format is one thing and then what would that include kind of a section headers? Again, there could be lots of things, but generally, well, always I would say, start with the business. Why are we doing it? What's the case for change? What's the value proposition? Think of it, you're selling to a CEO that probably has, you know, dozens of priorities. Why should he or she invest in this data thing that's probably very obtuse, probably not something that they can understand more of than buying a new building or hiring new people, right? You're always selling whether you think, whether you like that or not, the world's all marketing, right? And then kind of you've got their attention. And then what's the so what? So what's the issue now? Either the opportunity or the pain point. You know, it's kind of the easier one to find the pain points. We can't do this because I would caution and I am a data management professional proud card holding member, but we tend to be seen as negative. And we often are negative because when you think of it, a lot of things we're doing is solving problems. But think of an exact, they're often opportunity driven, right? And having someone come in is like, well, your data quality is terrible and you can't do, I mean, that often is a way to get attention. And often that's a very relevant thing. But when you can, could it be, Hey, we would really get a leg up on our competitors if we could, or maybe switch, we'll flip the script a little bit to be more positive, because, you know, you're trying to make this something people can get excited about and people get more excited about opportunity than the problems. And then what do you do about it? Again, often we're doing strategies. I always remember this, like the analysis, you go to the doctor and they say, Yep, you're sick. Here you go. That's not so helpful, right? But here's your sick. Here's the medicine you can take or exercise more or whatever it is, reduce your stress. Don't just go to your CEO or whoever you're selling this to with a problem. They want a solution. And then some sort of roadmap for how and when are you going to do this? That's great. You've sold me. I love it. It's going to be a year, a month, you know, can I build it in steps? And when might I get this kind of ROI and benefit? So again, that might be obvious. I find, you know, writing down the obvious and sticking to it is a really helpful way. Because again, when you're talking about a strategy, you want it distinct and kind of clear. So now if we move to slide nine, when we think of the value where it talks business, I like to clarify between business optimization, which is becoming a data driven company, and business transformation, which is becoming a data company. I would say most companies should do the one on the left. That's kind of that classic when you saw the stats from data diversity. You know, how do we be more efficient? How do we eliminate manual efforts? How do we grow our revenue with better marketing campaigns, better, you know, data about our products that we can enhance? I think you might say, yeah, we've been doing that forever. Again, since the beginning of time, data has been helping companies make more money or helping nonprofits serve more people. But data now with so many tools and technologies can help you do that a lot better and more efficiently. I would say any organization should be looking at it that way. Some organizations choose to become a data company. And when we advise corporations, we think, you know, you may not want to do this. It's not for everybody. It's not a good or bad thing. It's just this opportunity makes sense for you. But this is where data is the product. Can you take your data and monetize some of it? Could you sell it for ad revenue? Could you build a new product from it? We've got some, we had a big manufacturing company that actually they shipped a lot of their project products and kind of Latin America where there were a lot of roads that couldn't maybe have some big trucks on it and certain bridges that you couldn't travel on certain you could. And they had a lot of data from the IOT of their trucks that they knew that. And so what they did is kind of create an app. They give a ways or a map quest or Google Maps for the shipping industry, which is very unique to kind of back roads and heavy trucks. And they monetize that completely different from their business. But they saw an opportunity, especially in an industry, they were kind of heavily reliant on the construction industry, which can go up and down. So they want to kind of another source of revenue. And that was a great idea. Are there new business models? Think of Uber. Is it a cab company on data, maybe? Or is that a completely new business model where they had some really interesting data? Went to a really interesting presentation from some of the Uber engineers and the amount of data they get from the flights that are landing at airports and the traffic and how many people might be expected. So how many drivers? Maybe it's completely a data company with some cabs and drivers on top really, right? New business model. So again, that's something to think about is how far do we want to go with data? I often tell a story working with two very similar companies in the similar industry, one of which when we sort of said, where do you want to be? They said, we want to be in the left. We want to sell more widgets and do it more efficiently. We're not going to be a data company. We just want to sell more widgets. The other company that sold very similar widgets really did want to be a data company and tried some new business models, both for very profitable and very successful. So again, not one is not better than the other. The one on the right though, this idea of becoming a data company is pretty exciting and it is getting a lot of people interested in data. If we go to the next slide 10, what I want to do is I'm still in the industry is that I find this very interesting, this kind of interdependency from, you know, I think always your business strategy should drive your data strategy. What are we trying to do as an org? Are we a hospital, a school, a startup in Silicon Valley? And then what are our goals and how can data support that? That's sort of your, from the previous slide, the one on the left, how can we be more efficient with data helping that? What's sort of exciting is the one on the right becoming a data company is that data can inform your business. So your data strategy can sometimes flip and then it's a kind of a virtuous cycle. So that's what is kind of fun about being in data now. So if we go to slide 11 with the framework, if you've seen my webinars before, you've probably seen the framework, but we do get good feedback on it and it is a helpful way to kind of frame things. So when we're talking business and data strategy, we're at that sort of top of the framework, which is what was this top down alignment with what we're trying to do and what are the goals. But the rest of it is important as well. I don't want to belittle that in this presentation, but we are going to focus a little more on the business side because we're talking strategy. A lot of the other webinars this year are more on the architecture, so we will cover that if that is of interest to you throughout the year. But also in a data strategy, is that bottom up? What are we talking about? You know, if you thought of in court as earlier slides, you know, it's all of the above, right? Is that data lake? Is it data warehousing? We have IoT, we have documents, and so what is it that we need to manage? And then kind of going up that stack of how do we integrate data like that? Do we have the right metadata around it? The lineage, the definitions, the glossaries, and then kind of that level three of how do we manage it? You know, it's a quality good. Do we have the right architecture to support that? And then how can we do the kind of front-end things like BI, the analytics, AI, machine learning, or things like master data management in terms of, you know, what we see in our practice, this idea of master data is becoming one of the big drivers of a lot of organizations. Again, if you're trying to go digital or trying to do anything, master data drives that. That's your products, your customers, your employees, your patients, your students, your locations, right? All of that that drives your business is master data. And then we'll talk a lot about this in this session, that second row, the data governance and the collaboration. And we often get questions about that, why'd you add collaboration with data governance? And again, I've been doing this for probably too many years, I want to admit, but, and I'm a super technical person, love the tech, but the more I'm in this business, I realize what makes the tech work is the people and the culture. And with very few exceptions in all my career, I don't think anyone has, you know, come to business to work in the morning and said, you know, I want to have really bad data, or I want to create silos, or I want to, you know, make sure my data isn't operable. What people are doing is trying to get their job done, right? And they may not see the bigger picture. So generally, I feel, and maybe I'm naive, but getting all of those people in the room, so they can see the effects of other groups or make joint decisions about what can be shared or what should be managed or prioritization of things, that's data governance. It just works a lot better. You know, the carrot works a lot better than the stick. And there's plenty of sticks that can help you with governance, regulation, risk. But we're all human and tend to get more excited about positive things. And building that culture is a really great way to get data governance. And all of that is the foundation of a full data strategy. So if we move to the next slide, again, how do you prioritize all that stuff at the bottom when there's a lot to do? And I, you know, I'm always looking whenever I do a strategy for what are those quick wins. I'm not naive that I know it may take three years, five years to do everything in that framework. But you can do something in three months, six months, nine months, depending on your company, that's really going to show some business value. And that's going to get people excited to do more. If you don't do that, people aren't going to wait. You may not be in business three years from now if you don't do some of these things. So kind of looking around, what where could data be the fulcrum with a little bit of effort, you can get a, you know, a lot of load moved. So could it be cleaning up some data quality for a marketing campaign that's going to sell more stuff or help more people, right? I had a boss way back that when we'd be working on something, he's like, are you just rearranging the deck chairs and the Titanic? IE, is this going to offer any value or are you just doing busy work? And he's kind of a rough guy. But that stuck with me. And we do a lot of things during the day. Let's focus on the things that are the most impactful. So if we move to the next slide, some of the ways to look at ROI or making the business case, I mentioned at the beginning, are some of the classic ones, but it's always good to look across these. There may be more in your industry, but decreasing costs or increasing revenue are probably going to be the bigger ones. Even if you're a non-profit, you need to kind of keep the bottom line in mind, right? So decreasing costs I often find is the easier one to quantify. And I would say do this at the beginning of any effort, because what you want to do is kind of look at that before case. Manual effort is a big one. Often we'll go in and look at a company and maybe the dashboard is great, maybe the quality is great, and maybe management doesn't see that, but that's done on the back of a lot of people's weekends or nights. It takes three weeks to build that report because we're cobbling it together with spreadsheets and word documents. Getting rid of that wasted labor is often an easy one to kind of quantify. Are there inefficient business processes around these, etc., etc. That can also lead to increasing revenue. Some people get nervous, well, if my people aren't doing this will they lose their job? No. Hopefully they're doing stuff that's going to drive profitability, right? Doing things that they should be doing rather than kind of wasted effort. Or, you know, what about how can we optimize price with better analytics? I already mentioned the marketing campaigns, but we just worked with an insurance company that was really, really good at data-driven recommendation engines when their agents were on the phone. What's the best customer to be talking to, best based on customer profiles and past efforts of that client? Totally data-driven and it was really successful for them to, again, not rearrange the deck chairs and the Titanic, but focus on those important things. A risk, again, is super important if you're in the financial industry or healthcare or, actually, most things maybe isn't the sexiest one to lead with, but again, you know your management. That might be the one thing to lead with. We have to do this because of HIPAA. We have to do this because of GDPR. We don't want to get sued. We might be, we worked with a restaurant. That was a huge part. You'd think they were all about selling and getting more people into the restaurant to sell, and that was part of it. But a lot of it was, are we going to have the right allergens on the menu so that no one gets sick and sues us? I hadn't thought of that, but that's, it was a big part of that. How can they trace the food from where it came and what was on the menu to show if there were any, you know, I don't know, peanuts in it or something like that. And then reputation. That's often one that maybe is harder to quantify in terms of numbers, but often is that kind of gut feel that helps you sell it. Like wouldn't we, actually, I have a colleague, he sometimes speaks with me on the University of Nigel Turner, and he loves to tell the story of his local, what do you call it? I guess we call it a pharmacy boots or whatever. They keep selling him ads for, they have his gender as female and they keep sending him lipstick coupons and things like that. And he always tells that story because he, I don't think would look good in lipstick. He might want to try it, but that's not his bag. And he finds it very funny that every month they're spending all this money on ads for, you know, again, new perfumes and new lipstick and things like that that he would never buy. And not only is that not helping their campaigns, and that is probably losing revenue, but it makes them look sort of bad. Right. I mean, we've all probably had those stories I want from a hospital got somebody else's medication sent to me. That's not only that's a reducing risk, but I'm not sure I want to go to that hospital. Where are they going to send my results to somebody else? Right. Or the classic one I hear from management is we don't want to be in the newspaper. And often that that's kind of a reducing risk. But, you know, do you sort of look, or I always give a positive example. I am a consultant, and especially in the before times that have lived in airplanes. And I would say my, my preferred airline does a really good job of, you know, I call and I need to change my flight. And they'll say, Miss Burbank, are you trying to fly from, you know, Denver to London? Do you want the same flight you did last year at this time? And I say, yes. And I, you know, that could be creepy, but actually that's a very good customer experience based on data. So again, that's kind of that softer stuff. But often this is stuff that's, you know, do you ruin your recommendation by having a marketing campaign that's, you know, based on a completely different persona, like a woman versus a man, or are you really targeting your, you know, frequent flyer really well and knowing her flying patterns that kind of some, some of that softer stuff. Okay. If we go to the slide 14, the next slide, one that's often forgotten is what is the risk of doing nothing? And that often can be kind of an impetus. It's easier to do nothing, right? It's easier to keep the status quo. When you're trying to sell something for it to your management, inherently, you're trying to get them to do something different. So by kind of quantifying that if we do nothing, this is what we might lose in terms of either wasted time, opportunity cost. Often when we, well, always when we go into a strategy, we will look at the competition and say, you know, do you know that your competitor X actually is doing Y and Z, and you might want to keep up with them or whatever it is with you, don't forget to do that because that's going to help kind of move that momentum forward. If we go to the next slide 15, as you're working, I've talked a lot so far on kind of convincing your management and talking upwards, but really you need to, and I'll talk a lot about this at the end when we talk about organizational change management, which is a big part of any strategy, talking to a lot of different stakeholders. So when we come in and do a strategy for an organization, it's a little different because we're consultants and the first thing we do is talk to a lot of different people and we spend a lot of time really thinking through that. You want to get the execs, of course, you also want to get the people in the field, the people answering the phones, the people manufacturing the cars, the people serving the patients, the teachers in the classroom, whatever that is because they're feeling the actual pain and then the managers. So all different areas cross-functional from finance to HR, etc. Because you're doing a few things. One is you're trying to get all of the pain points you're supposed to understanding people's needs. You're also getting allies. Often the people we're talking to end up being the data stewards and the data managers with governance or your champions. When we do this well and we do sell the management, it's not us talking. One of our more successful ones, marketing came with us and finance came with us and they sold for us, the data people. That works a lot better. Often, and my advice to you, often it's so easy. We want this to be our strategy just because, of course, we spend a lot of time on it. A lot better when you're not talking and other people are selling your strategy. You want everyone to feel like it's their strategy. It should be. It's the company's strategy. So the more people that are chiming in and agreeing with you is great. So you're doing several things there. That cartoon, yes, there are data capture cartoons. That's from one of my books and we often have this up because often when we're talking kind of our sponsor and we say we want to do interviews, are people going to have anything to say? I said, oh, you wait. All you have to say is tell me about your data and they'll go on and where the weirdos that actually find that interesting, I suppose. But everybody uses this data, even folks you might not think of and most people have some pain points. So if we move to slide 16, I would, as I mentioned, give that some thought. Don't just talk to a bunch of people and that's easy to do. What you tend to do, human nature, talk to the people you know. I did a management training class and probably one of the, you know, usually you learn one thing in a class that sticks with you and the one they had you do is just find someone in the room that was the most different from you as you could think of. And I found a short guy with black spiky hair that was in the legal department that I'd never talked to. And he looked different than me. He talked to everyone in the meeting. He had a different, and he became my biggest ally because when I needed legal advice, he was there and when he needed help with data, I was there. Right. So it's easy. It would have been easy for me to talk to the data group, guess who I know. So think, look at your company's org chart. Are we talking, think of what your company does. Are we talking to a broad range of people? And if you are an architect, we like to model things. We actually model out the people depending on how complex it is. Sometimes we not only have their roles, but how they like to communicate, what their communicate, what their goals are, a little personality analysis there. So then you're really understanding what makes them tick and how you can help them because everyone's going to have a different viewpoint there. So if we go to the next slide, 17, I mentioned that data governance is sort of a sister effort to a data strategy because that has to do with a lot of the people and the policies and the procedures that are going to keep this going forward. So when we talk about data-driven business, when we, again, these surveys that we sort of do yearly, 76% had a data governance initiative in place, or we're planning one in the near future. That's positive. I'd like to see that hurry. Or of course, it probably is by now, but most organizations are realizing that, and maybe if you've been in data management, you're sort of not in your head, of course. But to me, it's like trying to run a company without a finance department. How do you do that? Who manages the money? So with governance, who manages the data? And as with finance, everybody in the organization manages the money, right? You can't just go on a business trip and not do an expense report. So although there's a finance department, as is there's a data management department, it's really the responsibility of everybody. And what I like to see is that last bullet is that over half of people specifically said that improved collaboration was one of the big benefits of having not only data governance, but data architecture as well, which is what we see. And I was pleased to see that it's a great way to get kind of business and IT together. If we move on to the next slide, kind of with our house, when we look at a data governance effort, we like to use kind of a structured framework, and this could be, and I think is actually a whole presentation in and of itself. But governance is one of those things that often people are kind of speaking across purposes, and they're both right. So when some people think of governance, they're thinking, I'm thinking of the org, the people, the data stewards, the data champions. Some people are thinking more of the tools, your metadata tool, your data lineage tool. Some people are thinking of the data management, the data models and the glossaries. And some people are thinking of the processes or the policies and the workflow, and they're all right, and they're all important. But they don't always work together. And as part of working together, it's that kind of that top, we all working towards the same vision and strategy and working on the same things. And we have a culture where everyone's kind of working together. So if you go to slide 19, on that note of culture and org, one of the things we often do is we start with that data governance framework, which is the org structure for data governance. And this is a little meta, as they say. But in doing the org structure for governance, you need to look at the org structure of the company, because that's going to be very different. The org structure for governance on the right is almost your classic, you know, DEMA, DMBOK, if you're familiar with that, the data management body of knowledge. We kind of have a hierarchy. There's your execs, there's the data governance steering, you have your kind of in the field data governance committee. That is often where we end up. It's a very good framework. But that doesn't work for every company. Some companies might be more distributed, more federated. We had one company that they wanted their data governance structure be more of kind of concentric circles, where it was just much more collaborative and less top-down. And what you don't want to do is go to a company just kind of box that hierarchy and show them something like something on the right. So again, you really want to give that a lot of thought. Again, often where we see the governance fail that's not aligned with the strategy is people just say, let's get a bunch of people together and talk about data or the people we're already working with. You really need to be strategic about that. How do you organize a stewardship? Is it by data domain? Is it by business process? Is it by org? Is it by region? All the above? And do we have the right people at the room at the right level? Another thing that can go wrong is you have, and we've seen it all, we had one where they had senior level execs going through and doing match merge strategies for master data and spending their whole day seeing if John Smith and John H. Smith were the same person. Very valuable thing, not a very valuable thing for the execs to be doing. Funny that they didn't get funding for the next year. Or the other way around, people who are not at a strategic role because there's no one leading that data strategy making decisions at the field level. If your DBA is implementing, your data engineer is kind of implementing your business roles because they have to do something and no one's telling them so they're doing the best thing. So again, getting the right people in the right room at the right level. So going to the next slide, 20, we've kind of touched on all of these, but this was I found really interesting from one of our earlier reports that when we said, what are your key drivers for the next several years that the top ones were data strategy, data architecture, and data governance, which is what we see a lot of and I think makes a lot of sense. They're kind of all again, sisters to each other. The strategy drives your data architecture and then the governance really supports that architecture and strategy. So I would say they are kind of your triumvirate or your three pillars of anything you'd need to do. If we go to the next slide, 21, this is again something we do each year in terms of a key part of a data strategy is the technology. I don't want to belittle that, are we going to go to the cloud? Are we going to stay on prem? What platforms are we going to use? Are we doing a data warehouse, a data lake, both? But it shouldn't only be that where we sometimes see data strategies fail is that it's only a technical plan and has nothing to do with the business. I'm a tech geek. I get it. I love to play with the new stuff. I want to do IoT and data streaming and digital twins. There's so many things out there. Does that make sense? That wouldn't make sense for my business, right? So you need to really what makes sense. What I find interesting here is that despite all of the really cool technologies out there, what still runs the business is your good old-fashioned relational database because they work for what they do. Are they the only solution? No. If you're only using relational, I would highly recommend Look Elsewhere. But I think at the other extreme, a lot of vendors may say, oh, you don't need a relational database anymore. I'm like, how do you do some basic referential integrity? They are very good at what they do well. Are we seeing more of the cloud? Absolutely. But I find that an interesting chart. If we go to slide 22 of the next slide, a big part of most data strategies are this idea. How do we get that trusted data? How do we build those trusted data sets for either self-service analytics or enterprise analytics? And that takes a lot of things that we've been talking about, good data architecture, having the right data quality, of course, privacy and security, metadata about what that information means, where it came from, and then the governance to who deems it as trusted. Do we all agree? What was so classic of, well, I trust my number and I trust my number, but those numbers don't match. So can we all even agree? And again, both of those numbers might be right. How do you calculate total sales? Well, I include wholesale and you include just retail. Neither one of us is wrong. We're just different. So that's where data governance can come into play. Slide 23, I like to stress, I know it's a busy slide, but generally with a data governance and data architecture, there are certain tried and true architectural artifacts that I couldn't do a strategy or an implementation, even more important without them. Your good old fashioned data model that are never going to go away because those drive the rules of your business. A business process model to show where that data is used. Your data architecture diagram. This is one kind of your system level view of the org. When we do a strategy, this is often what's missing. Again, not because people are bad. Often they have a great diagram of their own system, but nobody has had the time or the remit to really look enterprise wide. How do all of these fit together? And that's where strategy comes in. What are the policies and rules that align with that? And how are we monitoring things like data quality? What can scare people, especially when you use something like strategy and then you use something like we need an architecture diagram that just seems heavy and big and unwieldy. And to give people credit, it's because oftentimes they are big and customers might take you two years. But what we always do and I recommend is just do them in small chunks, kind of like ads. Let's pick a piece of the business and do one process and do the architecture around that and start to get that in place with some wins and then you build it over time. Don't skip them, just do them at a higher level. Slide 24, and I won't go into this in all detail, this kind of summarizes a lot of what this presentation does. A lot of people like this as kind of a one-pager. Again, I've been given a strategy or building a new data program. What do I do and where do I start? So you don't necessarily have to do these in order, but it is kind of a handy checklist. Again, if things aren't going well, what does I skip? Do I senior level support? See that unfortunately too often. You might have a great architecture, make great dashboards, great everything, but nobody cares yet. You've got to sell it to them. Or maybe I have support, but do I have the right architecture? I kind of use this as a checklist. What people forget often is the stuff at the bottom. Deliver quick wins, communicate, and then communicate again and communicate and really show that over time and do that change management, which we'll talk about. To go to slide 25, plan your roadmap. Don't just begin willy-nilly and in that roadmap should have many things. Have the different areas. It's not all or nothing, right? A bit of governance, a bit of analytics, a bit of master data, etc. Have some quick wins as well as a long term. Be realistic. Don't go to management and say we're going to do a quick win in three months and that's it. It may take you two years to finish everything. So be honest there. And then what staffing you're training you might need for that. If we go to slide 26, maybe obvious, but it's so hard to forget this, is that you need to A, align with what the business is doing. Again, are you rearranging the vectors and the Titanic or are you aligning with the big company strategy? And then even more tactically, there is a big strategy and there's a big marketing launch in November. Can we do our data strategy launch to align with that? How can we help people? Or we're a school in the semester starting in September. Can we help with kind of the onboarding of students or whatever it is? Or there's an audit coming up, something that not only you're aligning with a macro vision, but the micro things that are happening. So you're a help instead of a hindrance. Just quickly before, I know we do want to get to questions, but I know we do a lot of this in our practice and we even have some folks in our team that are kind of trained in organizational change management. And this is often what is forgotten that any company but the strategies that tend to win have some sort of, this isn't change management like software change management. We probably need a better word, but this is how you change the organization. How do you change hearts and minds? And there's a lot of different methodologies, there's proci, that's one we often use, but they all kind of go down to the same thing. Am I aware of the product problem? And that was kind of that selling. Do I care? What's in it for me? Maybe there's this new software that that's nice, but how is it going to help me? And then the knowledge about that and then reinforcing that over time. So if we go to the next slide, maybe some examples of that little people bubble. Hey, great, that's great. Should I be worried about that? Should be excited about it. What often helps is are other people in the org excited about that? When I talked earlier about getting your stakeholders and getting other champions, maybe I never recycled before and I think it's stupid, but suddenly I'm at a party and everyone else is putting their stuff and recycling. I'm the only one that doesn't. I kind of feel like a jerk. So maybe that was a strange example, but generally if the whole company's starting to get excited about that, I start to pay attention. And then okay, I pay attention. What do I do? How would I use this? How would it help me? And then that reinforcing over time, what we tend to do is launch and then go over to something else. Remember, other people, you've been working on it for a year. This is the first time they've heard about it. So that's often where we see things fail. You forget because you're excited about the next thing. Do more training. Show, remind people how this helped. Remind them what it looked like before. Remember what we used to do through paper? Now it's all automated. Isn't that great? And we tend to move on. So that might be the biggest takeaway. We've had a lot in our training, more questions about this. How do I keep that org change management moving over time? So slide 29 in summary talked a lot about the business side of data with a data strategy, but to me that is what a data strategy is. And it's orchestrating that people process tech around that. There's definitely a tools aspect of it, but pick the right tool for the right job and have a roadmap that builds over time with quick wins, but have it tell that story and make sure everybody sees themselves as part of that story. So before we open up for questions, if we go to slide 30, just to remind folks, if you're interested in master data management, that's what we'll be talking about next month. Slide 31 is a blatant plug that we do this for a living. And if you need help, come find us. And then slide 32, we will open it up for questions. And I'll look to you, Shannon, to help us with that. Over to you. Love it. Yeah. Thank you so much, Donna, for this great presentation. And just to answer the most common last question, as just a reminder, I will send a follow-up email for this webinar by end of day, Monday with links to the slides, the recording and anything else requested. So just diving in here. So are you aware of any data-driven cities or counties? I work for a county government and this is intriguing. Very much so. In several levels, open data is a big part of either federal or local agencies. What can we make public to some of the information about transportation, about COVID, about education, sharing data sets? Some of it is putting more services online. That's digital transformation, right? Some cities and counties are being much more literally data-driven with IoT and footfall traffic. And we're going to build a street and we know from sensor data or traffic patterns what we're going to do. And making that public has been a big part of that, of how you made that decision. And there's a lot of data nerds out there, especially kids in school doing data science projects and things. I've seen some companies do a hackathon by making some public data sets open for the cities and counties. We're doing a big project now for a Middle Eastern country that's really trying to be more data-driven and what do they publish and what don't they? So yeah, absolutely. And in that sector, it's definitely a growing thing. And for a lot of good examples, you can Google and find some good stuff out there. What's next? I love it. And feel free to join in at any time. And if you have any examples as well to add in. But let me dive to the next question. What is your definition of data-driven organization? Are there levels of data-driven? Yeah, that's a great question. I mean, one level is that macro is the one I mentioned in the beginning. Are you a data-driven or are you a data company? That's probably the biggest. But one I might kind of refer to, almost if you think of kind of the famous Gartner stages of analytics, there's kind of the descriptive and just deprecating to the companies. But a lot of companies are still there. Do I even know my total sales by region, by rep, by just getting that, can I understand that company, my company and have the metrics around it? A lot of companies still aren't there. And then get to more prescriptive. Do I know what's going to happen in the future or predictive? And then can I drive behavior from that? So that insurance company that I mentioned is that every sales rip gets a data dashboard and that's how they do their sales calls. That's much at the higher level of can I drive behavior? So one is are people asking questions when they make a decision even looking at a dashboard? Is the data right? Does everyone know they have a part in it? Two, data is so inherent of how you drive your business. That's really a key part of everyone's actions. So you had a lot of different levels and then a lot of places to start. But I'm curious if Mohit, if you had any other thoughts on that? I totally agree with you, Donna. And I think giving business users the freedom to explore and build insights or data insights is absolutely key in all of this. And this is what I tell all of my customers, too, that there is a lot going on. There is a lot to think about. There is a lot going on in this space. And like they say, Rome was not built in one day. Similarly, data analytics is more of a iterative process. It takes trials and errors. Sometimes you have to fail first to understand and win later. So I think having an agile data strategy that can help you pivot and change course based on either your business requirements or the questions that you're getting from users is also one of the critical areas for you to be successful. That's a great point. A lot of industry-specific questions here, too. How would you propose building a business case if you are a public health government agency? A business case. Yeah, I think it would be different. Again, when I spoke in the beginning, what does the data strategy look like? Often these kind of public health, when I said, do the PowerPoint, everyone should do a PowerPoint. But often these kind of public, it is more of that a published data strategy and showing the benefits and making that public, making it public, showing some of the benefits. Again, we're working with a couple governments right now and there's some of its monetization in terms of efficiency and some of its public benefit. We had a few that with COVID, that was a big showing statistics to COVID and how that's linked. A lot of folks, that was finally where people became data driven. They kind of got what that meant. But I think for the public health or work, how do we work with other agencies doing things similarly? We're working with one big org that's kind of doing a lot of kind of AIDS and COVID research and a big part of their benefit was the alliances they have with how they can share data. And it's not just about them, it's about kind of the wider community. So yeah, definitely different than selling widgets or manufacturing widgets. But some of the same fundamentals apply. The what's in it for me, is it saving money or cost? Is it working for the greater good? I think it's some of that mission driven. And I'll shut up and let Mocha dance in the middle. But we've had some good success with kind of the non-profits or we work with some hospitals and just creating a mission statement for why we're sharing data. And one of the research companies we're working with in public health, that really helps them of, you know, we at our core want to share data and help the world. And at our core, we're working with really private health information. And we all understand that balance. So when we're pushing back, we're not trying to be not team players or whatever, but that is a very difficult situation we're in. And just kind of saying that out loud really helped people that, yeah, this is a hard problem and we all want to solve it together. But Mocha, did you have any other thoughts? I'd be curious. No, no, I think you covered it all. I totally agree. I think you know, sharing, sharing data, you know, even at the peer level, right? And I think this is one of the things that we are looking to do and in Kota is to also, you know, how do harness the power of, you know, everybody who is using, you know, in Kota as a data analytics solution and not just limited to within your company. And I mean, how can you collaborate across organizations to be, you know, to be more successful in this area? Well, I love it. Well, thank you both so much for this great presentation, but I'm afraid that is all the time we have for this webinar. Thanks to you so much to our attendees. You are all just the best, so patient. And thank you for working with us as we struggled through a couple of technical issues. And just love that you're sitting there helping each other out throughout the webinar. It's just awesome. And yes, we will get you some more information about in Kota and make sure that you have get the links to how to get more demos and we'll bring them back for more stuff. They are also sponsoring the research paper that Donna was talking about this year in Kota is. We'll have the survey going out March 1st as part of our March EDU. We celebrate data education month in March. So take a look for that survey coming out. Well, thanks, everybody. Hope you all have a great day. Thanks to the quarter for sponsoring today. Donna thinks as always. Mohit, thank you. Thank you.