 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 building a data strategy, practical steps for aligning with business goals. 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. 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. We may absolutely change that to network with everyone. To open the chat and the Q&A panels, you'll 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, the recording of the session and any additional information requested throughout the webinar. And then let me introduce to you the speaker for 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 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 get her presentation started. Donna, hello and welcome. Hello. Thank you, Shannon. Always fun to do these. So as Shannon mentioned, today's topic is on building a data strategy. And hopefully we'll give you some practical steps. Saw some new names in the attendees, which is always great. So if this is your first time with one of these series, that is a series. So hopefully you can join us for what next month or some of the lineups coming up. And as always, one of the big questions, will this be recorded? Yes. And all of the previous webinars, the diversity is really great about that are kept online. So you can always go back and catch ones you might have missed this year and in previous years as well. So that is us. So what we're going to cover today, as Shannon mentioned, is data strategy. Super hot topic today. Shannon and I were kind of chatting before the session on that, you know, the things like artificial intelligence and analytics. And, you know, so many folks are looking at data now, and let's now look at it in a more strategic way, right? And if you're here because you're boss, your manager might have said, build a strategy. You know, I get it. That can seem really daunting. You know, just the word strategy can feel really big. And it is, it's a very important role if your title is strategist or you've been given the role of strategist. So, and we'll talk a lot about this in the session is what makes a strategy strategic. Is that alignment with the business and offering some business value, not just a list of stuff you need to do of architecture, right? So we'll give you some ideas, hopefully keep it really practical if you've been on these webinars before. I mean, that is kind of what I try to do, you know, do this for a living and happy to share everything I've learned through my scars, battle scars, and really try to demystify things like a data strategy. And what that means, you know, this is a data architecture series. How is something like a data strategy related to but different from your overall data architecture? So that's what's promised on the tin. And hopefully you'll get some value from this. And we'll have a Q&A at the end. So please do feel free to kind of pick our brains with stuff that you might be running into trying to build your strategy. So I often get the question of, you know, isn't there just a template I can just fill in? Well, there's some guidelines, but you know, the whole idea of being strategic is to think a bit outside the box. So it isn't as easy as just checking off a bunch of checklists. That said, these I have found, and this is, you know, we're giving you some of our best secrets from the company I work with. But these are kind of the steps you need to go through. And I always say you can maybe do some of them lighter than others or maybe some of these you have already, but don't skip any. It always feels when we do a strategy, and something goes wrong, we have a question late in the game, we forgot, you go back to this, and one of these questions didn't get answered from the beginning. So I like to use this just as a kind of simple guide. We'll show some other ways to look at this as well. The first and foremost, and I'll whack this one over the head over and over in this session, is this an alignment with a business strategy? To me, that really is the differentiator. What makes the difference between something like a data management plan or data architecture with a strategy? What makes it strategic is what are we, what are the business drivers? Who are we helping? You know, what's the ROI or the return on investment from this? This is the why? And how do you link back when you're at the end? Or was this a successful strategy or not? It really goes back to your business or your organization. And I use the word business, you know, widely, I know there could be nonprofits, you could be a school, you could be a government, but in general, you know, what's the so what of your organization and how is data helping it, right? Then you need to go through the classic, you know, where are you today? And where do you want to go? So your current state and your future state and that future state will be driven by your business goals. Do you want to be the next, you're a retail company, you want to be the next amazon.com and beat them and be the best online thing. Well, you're gonna need a data state to do that. Or am I, you know, Joe widget maker and we want to, you know, sell widgets in a more effective way, but we don't need to be an Amazon, right? So you really you need to know what the business is is pushing forward to really understand that future state, and then be realistic in your current state, you know, are, yeah, we want to be the next Amazon, but we're everything's in a spreadsheet, right? Or everything's on pencil and paper, not an exaggeration, I've seen it all right. And what are the technical challenges that may be keeping us from the future state? How mature is our data management practice today? There's a lot of maturity assessments out there you can use, but you know, be realistic. And then are we aligning with some best practices? Doesn't mean best practices are exactly what you do. We'll talk about that. But they're certainly a great guide, you know, full disclosure. I'm a fan of the data management association or Dama. You know, there's a data management body of knowledge, Dama DMBock. Absolutely look at that. Is it your Bible that you have to your checklist and you do it exactly like the book said? No, your every you organization is unique, but it's certainly a great guide, right? And then that helps you guide the future state, which not only is tech, but the people process governance and tools. Of course, I'm not going to hear you had to push tools and too often, you know, I used to full disclosure used to be in the tool vendor space, right? I have left that now. But I see too often, you know, my again, my manager told me to build a data strategy. What product do I buy? Well, I was cringe, right? Yes, you'll meet products. You're not going to do this in pencil and paper at the end of the day. Please don't start there, right? The product or products you buy should be driven by your business goals and your current and future state, right? And then another thing that I often see kind of skipped on a road on a strategy is the roadmap, right? It's not enough to just have this great vision. What actual are we going to do three years down the road? And then next week, right? Because a strategy has should be looking several years out. Otherwise, it's a tactic, right? However, if you don't have a if you have a long term vision, but no steps to get there without those tactics, you're not going to get there. So that's the balance of looking ahead, but do it through a series of quick wins along the way. The other part that's often forgotten or is this idea of culture change and organizational change management part of that is marketing and communication part of that's really changing the DNA of the organization to be data driven and data first, if that's in your future state that you want to get to, right? So all these are fairly obvious questions, you know, it's not rocket science. But it is helpful to go through this checklist. Are any of these things I haven't thought through? Or do I have an answer for it? Again, some of these may be already existing in your organization. Maybe you absolutely already know what the drivers are. Maybe you don't. And that's some questions you need to do. Maybe you already understand the data landscape. Maybe you don't, right? So it's a good way to just kind of have a checklist of some really easy questions to kind of guide your strategy. So starting in and we'll kind of loosely follow this in the presentation of these phases. So starting with that, the business drivers, right? You know, this rise of the data driven business, everyone is talking data across all industries, nonprofits, government schools, retail, everything. And what I like about this slide is that these are all mostly business, you know, magazines, right? Forbes, Harvard Business Review, Wall Street Journal are all talking about the data driven business or data, data, data, right? And so it's probably if again, you're here on this webinar, because someone asked you, what's our strategy? Probably because one of your, your business leaders either heard another company, a competitor who's using data effectively, or they read it in an article or they're on a, you know, business podcast, and we're talking about being data driven, how can data to be a differentiator, they're hearing about artificial intelligence and hearing, how do I be prepared both from a governance and from a technology perspective to really leverage something like AI or analytics or business intelligence, etc, etc, etc, right? So this is key to making your data management planning data strategy. What's the why, you know, and when we run projects in my consulting company, that's my team is sick of hearing me say, what's the why, what's the so what, what is the thing that's going to move the needle for the organization that we can do through data? That's what makes it a strategy. This slide I like as well, because it's from the World Economic Forum, right? And we're all data friends here. I love diversity. But it probably not a big surprise that someone on Dataversity is talking the fact that data is important, right? It's sort of a syllogism, right? But this is the World Economic Forum. And what they're saying is that in the past, and there's a bit of a dated slide now, but what made you the top, you know, market cap publicly traded companies were your products, right? So that I was Walmart, and I had the best supply chain, the best, you know, marketing or I'm Exxon, and I'm kind of owning the energy market. And now, in today's market, and you could say, yes, Amazon still sells things, but what makes Amazon's value is how it manages its data, its recommendation engine, it's the way it manages its product hierarchy and how you have a good product master data to sell on Amazon alphabet, literally, you know, Google is literally a data company, right? So I think what this is nice is that someone like the World Economic Forum, which the fairly well known group saying that data is driving the economy, it's not, you know, we're changing into an information age, not so much a manufacturing goods and service age. So we need to be prepared for that. So the other question I get a lot, and hopefully this webinar is helpful for you, that's why we're doing it. So I try to always want to get questions incorporated into future presentations. What do we even mean by a strategy? So go back to the good old Miriam Webster dictionary. I'm a data management person, so I love my definitions. You know, the answer to what a data strategy is, is really in the word. So we start on the right. What's data management? I've heard people kind of cynically ask me, isn't data strategy just data management with a cooler title, you know, where I'll just have to rename things? Well, maybe, but not really. So when you look at management, it's judicious means to accomplish an end. Or I hate this definition, it's the art of managing, it's not helping much, but you're supervising something, right? And that's what we do a lot in data management. Yes, my glossary is up to date. I have an inventory of my data assets, my data warehouse is optimized and has all the right, you know, metrics on it. That's fine. It's not a strategy. A strategy, look, is you're looking towards, you know, achieving a goal. It's complex adaptations to for evolutionary success. And it's the science and art of meeting the enemy under advantageous conditions, right? And it's a bit more lofty, right? We are thinking big picture. And it's really getting, if we look at number one, it's the so what you're trying to achieve an organizational goal. And that's really what makes it a strategy. So yes, data management is an underpinning of a strategy. You can't have a great strategy without the management. But it's taking the data management up a level to really say how we're using data for strategic organizational advantage, right? The other thing I get a lot, especially because a lot of us data people are very literal and we love our checklist and boxes and all of that. I'll have folks come up after a workshop or something and say, I get it. I sort of understand what you're saying, but really like what is it? My boss told me to write a strategy. Is it a document? Is it a, you know, what is it? And I say, you know, it I'll give you the consulting answer. It depends. And it may, you know, some organizations, you know, the US government has their published data strategy, which is a big word document and explains all of it. I will say though, if for an average kind of retail organization or publicly traded company, start with a PowerPoint. Why? Because it it's sort of by definition helps you tell the story. It's keeping it short and sweet and graphical. People can really get the message. And I've just come in too many times where someone does have the 50 page strategy with a big old white paper, and it may be fine. It may be, you know, correct. And it's just too much and people don't read it. It sits in the shelf, right? So I think that the idea of a PowerPoint and how we do them again, I'm giving you all our secrets of, you know, maybe the executive summary is the 20 page message, right? Can you sum up the so what the where we are where we need to go with the ROI benefit is in the story. And of course, because we're conscientious, we data management people, you know, hyperlink to an appendix, maybe there's 100 pages of detail in the back, but the story should be concise. And that's why I say a PowerPoint. Yes, after that, again, your government agency, maybe you need to publish out our word document that says or I know a lot of universities do that as well. This is we are now ready to say this is our strategy for the public. You may do that, but I would say don't start there. Right. Start with a short and sweet. You may see my little joke. They're interpretive dance, right? You know your audience, right? Probably wouldn't say go to your head of finance and dance about your data strategy. But you know, I kind of just I'm a silly facetious person, but we did have one client. I've told this story before I know, but you know, she was getting her master's degree in data analytics, and she was working for a nonprofit. And in what she was doing her analysis on was homelessness and and basically social outcomes. And she was using data to manage that. And as a result, they could have written a paper, done a presentation or write a poem. And I kind of giggled at first. And she ended up doing the poem. And the whole point was, do they really understand the impact of the data? So it would be great to say, OK, I've done this great, you know, Python analysis. And it says, great, look at homelessness is going up. Yeah, we see a graph going up. Well, that's actually horrible. You know, there's people living in the street. That's not a good thing. So by understanding the impact of the data, that was why she did that through a poem. So still probably wouldn't be an interpretive dance unless you're a dance academy. And then maybe that's the perfect way to speak to your your stakeholders, right? But I think that's kind of just a interesting way to say know your audience, right? What is your audience want to see? Maybe if your your sponsor is finance, you should have a whole lot of data analysis and find spreadsheets and ROI. Maybe that's heavier on the ROI side. Maybe if you're a social services, it might be more in the storytelling or how you're going to help people side, right? So you know the audience, but do think that through. But I would say if you need an intro now and you're not sure, put it in a PowerPoint because that really helps you kind of keep it clear and then tell that story again and keep it succinct and actionable. So the other thing to think about is we're aligning with the business goals is I like to differentiate between business optimization and business transformation. And what do I mean by that? So business optimization, you could say we've been doing that since the beginning of time is basically taking data and using it to make your your data more efficient, right? Why do I say it's just the beginning of time? I'm a data geek. That's fine. I'm proud of it. You know, if you go away and I did, I went to Egypt and you look at the pyramids and the hieroglyphs, you know, or a lot of each other, you know, you look at the cave dwellings and there's, you know, pictures on the wall. What were folks doing? You know, they were counting grain or they were counting how many animals they had counted. You know, basically it was early dashboards, right? Different ways we could take understand our business, which was grain in the ancient Egypt or hunting animals on the cave dwelling and counting and becoming more efficient and using data to drive our business, right? But now, you know, how do we know how many widgets we've sold? How do we know how much, you know, how efficient our team is? How do we know our marketing campaigns are doing what they're doing? Generally, that's being data driven. And I like to say it's doing what we do, but doing it better. Business transformation is not just being a data driven company, but becoming a data company. And I would just be careful not everybody needs to be a data company. We don't all have to be a Google, right? And what would be the difference? Say, being data driven, I'm a taxi company. And I want to know how fast my drivers are driving with the best routes, how much gas we're using. That might be using dashboards to be data driven. Data transformation is maybe an Uber, right? We're really Uber is a data company with some cars. They're leasing up their people, right? Where data really is the product or monetizing information. Are there data sets you could literally sell? Think of a Bloomberg or something, right? Where it's literally their product is data and analytics on data. And you have an entirely new business model. I'm using data and data really becomes your business. A lot of organizations do this. I used this story before too, but we were working with two manufacturing companies, very similar. And one, basically, and they both were actually in the kind of the construction industry and sold construction projects. One was definitely on the left. We've been around for 100 years. We know what we're doing. We want to be able to do it better. We'd like to use data to optimize our business, but we don't need to be the next Google, right? We just want to do what we want to do and we want to optimize that, right? The other company was basically saying, well, because we're in the construction industry, that is kind of cyclical, right? When the economy is good, people are building, when they're not, they're not and we kind of are affected by that. So they were trying to have new business models or basically startups within their own company. Using the data they had, can we have new products? Can we sell our data for anything, right? Monetize anything? And here's one, they did many, many examples, but here's one that's kind of something we can all relate to. They did a lot of trucks that deliver their product around to a lot of rural areas where things were being built. And they had all the, they were doing everything on the left, the business optimization, they had censored data on the trucks to see how fast when they needed to be serviced, all of that. And they said, we have this data. And this is really helpful because if we're trying to go over big bridges in the mountains or certain roads you can't take with heavy materials, that's kind of unique. So they basically made like a Google Maps for other folks carrying big, heavy loads and sold that to their other other folks in the industry who really need to know what's what are good routes for driving trucks and shipping. And so that was a great example of they had based on the data they had created a whole new kind of sub area of their business. It wasn't you know, gonna that particular one wasn't gonna you know, make them billions of dollars a year. But it was a great example of taking data they had and they're creating a whole new company from it. That said, you know your company. Generally, the companies we work with are on the left. I want to do what we do better. Sometimes though you really are other opportunities to also say, do we really want to become a data company and more and more I've seen, you know, you go to the office and the poster on the wall is we are a data company. We're data first, because like all the examples I showed you from Wall Street Journal and Forbes and World Economic Forum, businesses driven by data and more and more companies do want to become a data first company. So you know, you know better than I do your own company. But that's a great way to kind of engage where we're headed. The other thing that's really interesting and fun and why I'm still in the industry is that, you know, obviously, I think you've been hopefully convinced that your data strategy should be driven by your business strategy. It's what you want to do as a business informs what you want to do with data. But more and more it's the data and the insights you find from data that inform and maybe guide your business strategy. Could it be like the example I just gave? There's a whole new business model from the data we have, we could monetize and there's a whole new business we have. Or is it looking at our data and saying, you know what? We're suddenly selling a lot of data in this new region. Maybe we create a new office there, right? The whole area is that your data can inform to drive your data in a different way. Or hey, this product that we never thought was very hot actually has a whole lot of interest or whatever it is, right? It's a cyclical area. And when I find a lot of fun because I'm a data person. But you know, my first degree was economics and I very business focused and what's nice is you don't have to choose it or if you're a business driven person and you like data, the world's your oyster. That's really where data strategy sings, right? That you're the person who can understand data and understand the business. That's, you know, that's where we get a lot of our business coming, business is coming to us and saying, how do I optimize my business through data? And could you explain this data stuff to me and create a roadmap for us in a way that makes sense and in a way. Many of you I'm sure on this call or in that have the opportunity to be in that same boat of can you be the strategic advisor to your C level folks on how they can use data to drive their business, right? That's really what makes it a strategy. So this is again a framework we use. I'm not going to go through every box here, but I think it shows in this will we will walk through these areas in the presentation, because these are all the things you need to do in a strategy, but not all in the same level, right? So that's what makes it an art in the science. Absolutely. You want to start top down. Why are we doing what we're doing? What is the business telling us? And how does that inform our data strategy and vice versa, right? You also need to look bottom up. If you think of that first slide where we had, where are you now and where do you want to be? Absolutely. Look at where you look at where you are, right? Your your management could say we need to be AI driven, completely automated machine learning and everything's out of spreadsheet. Extreme example, right? You can get there, but it's going to take a little longer. My analogy is, you know, running a marathon, anyone in the planet can run a marathon. Some people are already elite runners. Some people are sitting on the couch. It's just you can both get there. It just might take different steps, right? So that's knowing where you are and then kind of moving up the stack from the bottom. How do we integrate these data sources? I'm sure if you were any company in the planet, you have disparate data sources that need to be integrated. You need to understand the metadata, the meaning, the lineage of your data security, the life cycle, right? And then moving up a stack again, are we trying to do advanced analytics and AI? Are we doing business intelligence? Do we need master data, that single view of our customer product, student, citizen, you know, et cetera? And do we have the right data quality and the right data architecture to support that? And then that that second row down, data governance and collaboration. We often get questions around why do you have collaboration with data governance and I may be naive, but very few times I've been proven wrong on this. Most people coming to work are adults and they're trying to do the right thing. They may be incented to work in silos because that's what their job is. But generally when data is explained to them of, hey, when you put this data in it affects someone downstream, could you make sure it's right? They do. If they understand the why, right, that all goes back to that business strategy. I'm the first person if you give me a form to fill in and I don't know how the data is used. I'm going to put in garbage data because I don't really care. But if you explain to me I want to download a white paper and the email address I put in is going to mean, do I get the white paper or not? Well, I'm going to put in the right email address, right? So from a business perspective, do I know how this data is going to be used? If I know that it's going to help me or help someone else downstream, I'm probably going to pay attention to it, right? Or do I understand the risk around using citizens data and things like that? So that's why I call it collaboration. Generally when people understand the why and are working together towards a goal, it makes data management and governance a whole lot easier. So that's the framework. The magic sauce of a strategy is you can't and shouldn't do all of this to the same level. You know, is it the biggest thing we need to focus on? Everything's fine. Our data is perfect. We just need better analytics on it, maybe. Or do we have a great data scientists team? They're brilliant. But the data quality isn't good enough to let them do their job. Maybe, likely. Or, hey, we have all the information. We have all the analytics, but I'm not really sure about privacy and security and I'm nervous about who we even use this data. Maybe. So you know by your business strategy and where you are today, which one of these you need to focus on the most and maybe the other ones are a bit lighter. And that's part of what's putting it all together in the roadmap. So another thing to think of when you're creating the strategy, and this is classic. I didn't make this upgrade. Are we looking offense or defensively? So offense is, and often when I do workshops and things and maybe think of this yourself. Now, if I were to describe my company and I'm selling my data strategy, who am I selling? And I know it changes within the org. You know, maybe sales is very offense and legal is very defense and who are you talking to? But within that, every company has a culture and a, you know, which I'm going to call it, a way of thinking, right? So think as again extreme. I'm a Silicon Valley startup and I'm all about growth and excitement and that's clear offense. We're going to sell more. We're going to be big. We're going to be awesome, right? Defense might be, I'm a 200-year-old insurance company and I'm all about real wealth management or something, right? And I have regulation. Any mistake is going to be in the news. Yes, we want to help our customers, but we're really on risk reduction or medical device testing software or something, right? Super important on the defense side. And then that purple middle, most companies are a bit of both, right? Where, yes, we want to sell a lot and be growth, but we also have HIPAA regulations or FERPA or, you know, CCPA or Consumer Privacy Act, et cetera, you know, GDPR. So you know kind of what spectrum you're on, but not that I've ever made a mistake. But sometimes when I make a mistake, it's not considering the audience and, you know, being all offense and growth with a company that's super on the defensive side of a vice versa, you know, talking, I think as an industry, we in data management sometimes are more in the defense. We're so, you know, Debbie Downer sometimes on, yeah, I know you've wanted all this great growth, but have you thought about your metadata? Have you thought, we're right, but sometimes just think of the audience. Are we too much thinking on the defense and not enough on the opportunity of where data can break, right? And that's really what makes it a strategy of how do you get that balance? So something to think through is your messaging, your strategy. So how I like to think when you're looking at your data, because there's a lot of opportunity, what are these business value levers, levers? However, if you say that word, you know, is this a new cool thing that's gonna be a high, you know, supporting a high visibility marketing campaign or I had a boss that loved this phrase, you know, you're just rearranging the deck chairs in the Titanic, right? There, you could be doing a lot of things. And I, unfortunately, I see this a lot. Teams that, you know, I've got my data catalog and it's populated and I have all the lineage. And you say, great, who's using this or why or what data have you focused on? I don't know, we just have everything in there, right? Is that the best use of your time, right? Or do we get those critical data elements that are really affecting something in the business or the organization that's really gonna help drive? So just something to think of as you prioritize. Because you should always be making the business case and the business case is going to be different, obviously, with every organization. But I would say generally, things fall into these four categories. You're either decreasing costs. And I think, and then think of this early on your strategy. And again, not that I've ever made a mistake, but I often do this. You're so focused on doing the cool stuff. Did we do a before and after, right? Did we do a time study of how much time people are spending managing data manually and then after how much we've saved or whatever, right? So think of this ahead of time as you're even selling this strategy or designing this strategy. What are we trying to achieve? And think of it early, A, so you can help align what we're doing, but also so you can literally track it with ROI, right? So decreasing costs, I would say, is often the easiest one because so much of data management is fixing efficiencies or driving efficiencies and fixing wasted labor. So not only the time spent cleaning up data, which is not as exciting because does that really help the business? But Gus, the number of projects we're doing is something as simple as product master data, right? I'm trying to sell my product and I get it. You're growing a company and you're just trying to sell, sell, sell. And then you're scaling to a multi-billion dollar company. And the stuff we did when we were a $3 million company with spreadsheets or someone's access database doesn't scale when you're selling millions of products around the globe. So how do we manage that better, right? Or, et cetera, et cetera. Increasing revenue is a little more exciting. How do we have better price optimization or improve marketing campaigns or, again, nonprofits, better grant writing through the data we have, et cetera. Often a little harder sometimes to quantify but kind of the more exciting part. And it isn't always harder. I'll talk a little later about getting advocates in the organization. I've had strategies, the best strategy presentations I've given, I haven't given. What do I mean by that? Once we had marketing get up and say, if you commit to doing this stuff in the strategy, we'll commit to a 2% increase in revenue because we are so confident that our campaigns will be better, we'll do that, right? Or sales will say, or price optimization will be better with this, right? So you can show that I can help grow the business through better data. Reducing risk, I think a lot of us, it's a known thing. Sometimes I think data management's too much associated with data governance, right? Yes, I need to be careful with GDPR but that isn't the only reason we're doing it. Can we take all that customer data we've managed through GDPR and make it a positive? Can we better target our customers and market to them in the way they wanna be marketed to, et cetera, et cetera, right? But I think we're all very, we don't wanna be the people in the news through a data breach or an audit or food traceability for menus and things like that. You don't wanna say that this product doesn't have peanuts in it and it doesn't kill somebody, right? So across any industry, risk is a big part of it. And then just protecting reputation, right? How many of us have not, there's a woman in the industry who has a whole book on crimes against data with kind of funny examples that aren't so funny, but you know, I get a letter, dear important customer here, you know? We care about you as a customer and they literally have the words important customer here, you know, or the wrong name or, you know, your bank that doesn't know I have one, I have a credit card and that bank sends me every month, please apply to our credit card. I've had it for 10 years and they don't seem to know that, right? I haven't gotten rid of the credit card, so it's not, they're losing revenue, but it doesn't really give you a good reputation, right? I don't trust that brand as much as I had. And today in the days of social media, are you using data to get the pulse of your customer and really understanding the metrics around customer satisfaction, right? So that's another big one. But again, think of these four categories, you probably have something in all four and I often, I actually steal this slide and use it, right? And say, you know, in my organization, what is my strategy gonna do to help decrease cross-increase revenue, reduce risk or protect reputation or others if you come up with them, but generally I think they kind of fall into these four buckets. So another thing as you're thinking of the ROI, the risk, don't forget to include the risk of doing nothing because I often will overtly ask people, what makes you nervous about this or what do you think the biggest barrier to success is? And I've had people be slightly snarky and just say, apathy or I can't think of the word right now, but I can't think of it. But we're not, it's just there's a big shift to move and things don't happen very easily in this company, right? But that's a risk too. So it's almost the inverse of some of those things. If we don't invest in it now, we're not gonna be able to scale, we're now a billion dollar company, we're not gonna be able to scale to two billion because our people are trying to, you know, sell, change product skews on a spreadsheet, right? And I'm giving these examples because probably five companies in the past two years have had that exact same problem. You're not alone if you're rolling your eyes saying, yes, we do that, right? It's typical, right? Because what you do to grow a company isn't always what you do to scale it. And that's often where data management comes in. So this often, A, you're fighting apathy of what we, and I've had executives say that to me, what, you think we don't want to run our business? Well, yes, you've clearly done something right to get here, but there is risk if you keep doing things the same way as you scale and as data management capabilities improve. So moving ahead, I like to use data. As you know, my company and Data Diversity put together a trends in data management report each year. Last month, we went through that. So if you're interested, that's all recorded. But I thought this finding was interesting because when we asked companies, what are your main priorities in the coming years? Number one was data governance, and number two was data strategy, which I'm not surprised with, but that's what I do a lot in my day job. But I also think the important messages or these two are related. You can't have a data strategy without good data governance. And I think often the process of putting together data governance often highlights the need of, do we have a strategy? Because governance for governance's sake is not valuable. There's generally the why and how do I prioritize what we're doing and that really needs a strategy. So I thought that was an interesting kind of data point there. So a little bit of governance. This isn't a governance webinar. There's plenty of those at Data Diversity, including my own. But I think this is a good framework to think of when I'm thinking of data strategy one and I've whacked it over the head enough. Do you know your vision and strategy? Do you know the why of doing governance? And if not, go back and ask because the rest of it is not gonna be successful. And then how do we get the right organization and people, the right roles and responsibilities and accountability is one checklist. Do we have the right processes and workflows? And this is multifaceted. It could be data governance processes. How do I know if data is PII or personal identified information? How do I log a data issue and who takes care of it and who has accountability for it? Things like that, but also business processes. I mean, what makes data complicated and also valuable is that it is part of the business. If the invoice numbers are wrong, who is putting in the invoice number and why in part of that process and why might it be wrong? We had one great governance success story. It isn't always this easy, but we were working for a big company. We did a workshop and we were kind of mapping out business process and data issues. And then the issue was, it was payment terms over for invoices, right? So is it 30 days, 60 days, 90 days? And as you can imagine, if those are wrong, it's really hard to forecast your revenue coming in. So what we found was that one group when they did the contract, it was 30 days. And then another group later kind of worked with sales or worked with a client and it turned into 60 days or 90 days or a different number. And we showed that and it was easy enough that the second group looked at it and said, oh, I didn't know. I didn't know someone had already said those. We won't do that anymore, right? It isn't always that easy, but that was a data issue that was really a business process issue. And through governance, we highlighted that and fixed it. So when governance is working well, it's so embedded in the business processes that that's what really makes it driving it, right? Data management measures is a big data management is separate from governance, but it is related. Do we have glossaries? Do we have data linings? Do we have data models? All of that really helps your governance. And then tools and platforms. I love and hate tools, right? You absolutely need tools that just don't start there. The number of times I've had customers come to me and say, I know we need to do data governance. What tool do I buy? And I always say, yes, you may need a tool. You don't always, but that's not where you start, right? Don't start with what catalog to buy. Do I understand my org and my processes? And generally it's tools. Data modeling tool is a governance tool. PowerPoint is a data governance tool. A catalog is a governance tool, right? So it's probably a suite of tools, right? And then the culture and communications. And we'll touch that at the end with a strategy, but also with governance. The people know why they're doing it. Is it so embedded in the way we work? Think of, if you're in manufacturing safety programs that safety is so embedded in everything you do, it's really part of the culture. Same thing with data governance and data. Are we truly data-driven? Again, this isn't a data governance webinar. It's a data strategy webinar and part of data strategy is governance. But when you are thinking of governance strategically, one thing I'm very careful of, and I talked about it a little bit with the culture of the organization, is it offense, defense? Is it, there's a lot of parts, could be a whole webinar on the org culture. One is how you design your org structure for governance. And we always look very carefully at the org store. Is it very top-down and hierarchical? Is it more federated? Do people, is it very flat? And so for example, the org chart on the left is kind of very, it's that the organization on the left is kind of very process-oriented, very top-down hierarchical. The data governance structure on the right, it's almost your classic Dama Diembach approach where you have a data governance steering committee and a governance committee, maybe working groups, et cetera. But it is sort of hierarchical and top-down. Not every company likes committees. Some people need committees, right? So just be conscious of it. Does even the word committee not land right to people like the word team, right? And it's not just playing with words, but it's really understanding of the culture. Some companies, absolutely, everything needs to be run through a committee. Often the framework on the right is exactly what we build. There's a lot of value to that, but that doesn't work for every company and be careful of that, right? Or is it an evolution? You may not start with three committees, you start with one and build towards it, right? Because you don't want to have a bad taste in people's mouth. One story that I tell just because it's kind of so extreme, but it shows a good example. This is another data governance framework we built and where it was, I'll tell a story which is sort of funny to me because it was a Latin American manufacturing company and I thought manufacturing, it's probably very hierarchical and process-driven. We'll have a very boxy kind of thing like this. They pushed right back and they said it, not me, but they're like, the way you showed up top, it's very North American, that's not how we work. I don't even like your colors. And they came up with this, they called it the data governance flower, which I would not have done until very much a manufacturing company. But what they said is we're much more federated. The colors thing, that was a preference, but that worked for them. And they said, more importantly, we're a federated organization and really our driver is innovation. And we wanted data architecture as a foundation, but it's really about innovation. Can we get people together back to my tenant of we're all adults and we're trying to do the right thing? And can we get together with just the right enough things and then create agile project teams for maybe we need to clean up product data or we need a work group on something like that, right? So this worked really well for them. Just another option, but understand that because you don't wanna force something on an organization as part of your strategy that doesn't fit the culture. And there's no one size fits all, it's whatever works for you as long as you're getting that proper level of governance. So another part, I know I'm jumping all over because a strategy is very wide reaching but another part of a strategy is the architecture. I think I've seen a lot too. Everyone has a different definition of strategy which is why I had my kind of definition. Some people think just the architecture. What's your data strategy? We're moving to AWS. That's not a strategy, that's a tactic. And that's really not even an architecture, it's a tool, right? So architecture, however, is a big part of your strategy. How do I architect the data or my platforms to fit the purpose that my company wants? And I hope it doesn't look like this picture in the back which is kind of Cambridge. But without an architecture, probably does look like that. So I'm kind of doing data architectures 101 and I'll go through it quickly because this isn't a data architecture proper webinar but there's different patterns. So let's think of it that way. One, a classic one that is still around, fight me outside, nothing wrong with the warehouse. There's other options as well but warehouses still work for what they were designed to do, right? But this is a classic pattern where let's just, for example, I wanna know, I'm reporting finance revenue to the board and I wanna know how many widgets we sold by quarter, by region over time, right? Classic still valid use case. You have your operational systems, you transform them into a way that's structured in a warehouse often that's a star schema. Think of that as like a pivot table in your spreadsheet where you can slice and dice things by month, by year, by product, right? So you can have BI reporting. You absolutely should have data governance at the bottom to get those organ roles. Definitely should have some sort of metadata management to track that. And master data is sort of a special type of data for the nerds in the room. Those are your conformed dimensions in your warehouse but think of it as I wanna see revenue by product, by customer, by vendor. All those buys, that's a whole effort. How do I know I have the right product catalog and the right list of customers and the right list of suppliers and vendors? That's your master data and so that little gray box turns into the gray circle in your warehouse but also because this master data pushes back to your source systems. Once I've gotten that John Smith is the customer, is John Smith's information the same in the order management and the registration and all of the systems, right? Even this is a big deal and it's still valid, right? So there's some pieces but things have evolved since when this was the only option where we now have this idea of a data lake house. So some things are the same. You still have operational data but not everything has to be designed up front. You can have this idea of a lake or raw landing. I like to think and there's also not everything is in structured systems. You can have log files, video, social media, streaming data. You still may have your structured data source but you may also have, maybe you do need to look at the raw data for things like your advanced analytics et cetera but never forget your master data. You still wanna single view the customer whether you're doing AI especially if you're doing AI or you're doing a warehouse, right? So these building blocks of data management are gonna be valuable no matter what kind of analytics you're doing which is why if you think back to that framework in the beginning of all those blocks whether I'm doing AI or I'm doing business intelligence I still need my customer data to be right and I still need a good product list. So again, these are all building blocks, right? Another term and again this isn't a whole webinar on architectures it's just thinking what are some patterns I need to meet my use case, right? Do I have social media data? Do I have video streaming? Just as an example, and again, I'm a techie I love to do cool things. We had one customer they did a whole social media analytics thing but it was a utility company. We said has anyone tweeted or put on Facebook anything about the company? Like well, three people in the past six months like it wasn't a big deal. They just thought it was fun, right? But if I'm a product company maybe that's a huge part of my business, right? So you know as part of that business strategy what pieces of these are important, right? Another term that you may have heard a lot of this idea of a data fabric a lot of the same components above but there's this idea of a data virtualization layer there's other components of a fabric but I would say the big differentiator just to keep things in your brain or you don't always have to move data in order to access it through a semantic layer maybe a good example we had was a university where each department already had a warehouse. So do we move it again and create it yet another warehouse and they didn't they just did a virtualization layer on top of that. It doesn't mean because you're virtualized you skip all the hard stuff you don't have metadata and you don't have data quality you're still in to do that it's just you don't always have to move data. Enough said on that these are just some options we could do a whole webinar on it but just again it's where your brain should be thinking now that I know the business drivers and I know what data I need to manage how do I architect it is a big part of your strategy don't skip that part it's huge right. Well it makes it complicated so I feel for you data strategies are big but what's exciting about it too is that architectures are really becoming ecosystems and again don't start with a tool but a lot of the vendors have platforms now where it's and right I can have a warehouse and a data lake and virtualization and some real-time streaming right. So whereas before you kind of had I need to build a warehouse or an ODS or a lake now it's really a more colorful interrelated group but you have a lot more choices just make sure it's architected right. So I do want to get to questions so I'll go I don't want to skip though how I turn this into a roadmap and how I work on the change right. So you don't want your roadmap looks like the thing in the picture behind. A big part of a roadmap again of all the pieces we've talked about the who, what, where, why and when don't forget the why that's the often I've seen roadmaps of what are we getting at each phase and how do we put this together. So when we think of the when a big part of a strategy is storytelling right. Think of climbing Everest it's hard there's a lot of phases but you need to have that excitement where the first data strategy team making it to the top of Everest and these are the steps to get there. First you go to base camping you do the second step right. So we know the steps along the way you know everybody's role you know I'm the guide I'm the Sherpa I'm the you know the eyes that plays music at the camp at night to keep everyone happy right whatever it is everybody knows their place not too different from a strategy. So think of that. We talked about organization maturity there's a lot of maturity assessments out there there on the right is ours but think across all of these what are your strengths and what are your gaps even if it's a finger in the wind ours is fairly formal but you know even just to start do we have BI do we even have master data or is that a gap we need to fill in case until we can do what we want to do right. And then find quick wins and I get careful using this term because again it's a strategy and you need to look several years out but you want to do it I think of these as building blocks what how can I do what I need to do in three years but through a series of successes and iterate over time right. A quick win is not a quick fix right you're not putting duct tape on something your is to think of it as your first brick in your foundation that you're planning ahead for right you build it brick by brick but someone has a design of the house and that's the the art and the science of a strategy plan ahead but also build those building block blocks and those quick wins would have something like your glossary and your warehouse and your BI but put all in your governance right it isn't just all the flashy stuff up front it's starting to build that foundation right. And the other thing when you build a roadmap and I think I just talked to it but it's not a laundry list of things you need to do that's data management. What are the themes or I think of climbing Everest we're a base camp and this is exciting where the first data strategy team at base camp where we're getting acclimated right everyone knows why and the what's in it for me which leads me to this last point which is probably maybe I should have started with because it's the most important well actually before I get there planning out in a roadmap with different themes right do we have the right stance training you can't pick everything so what are some of the big rocks we need to move for this particular company one was getting the strategy together and in planning that over time but getting your governance and then analytics but to do analytics that need master data management and then one of the steps along the way and do we have the right people for it right. So as we go through the roadmap a big part of it this is the part I should have started with is that culture eats strategy for breakfast and I know this is a data strategy webinar but I'm the first to say and I'm techie and I'm a data person but unless you bring the people along for the ride it's not gonna be successful everyone should feel that it's there so think of your organizational culture I will say full disclosure this idea of organizational change management was new to me as much as five years ago right I thought of change management is like techie change management you know how do I do my packages in my development cycle no this is people right how do I change the culture if we're not a data driven culture today how do I do that how do I get people as excited about data as I am right. When you think about it this was a big aha for me people come to anything new in a journey first of it what is it is this thing I hear data should I care is that something that affects me that the what's in it for me or does this I never even thought about data right and then am I motivated is this gonna help me and then oh yeah maybe it does I'd love to learn how to do my own dashboards and then wow this becomes so much that reinforcement so much of what I'm doing how did I ever forget it and I've just noticed a lot of us as data people we jump right to that gray box the knowledge I love data I'm gonna set up a bunch of training courses to make people understand data and become literate I don't really love that term data literacy you know people aren't illiterate do they even care do they even know what you're talking about so start backwards what are people's motivations and how is this gonna help them I always say you know embrace your inner teenager who rolls their eyes and like gosh why do I care like rather than get frustrated by that answer it why does that I'm going to finance and telling them maybe not finance they're generally convinced going to marketing they're probably convinced too but anyway I'm going to marketing and saying you need to now become data literate why because gosh the data is going to help you drive campaigns and make more money right so think of their motivation and are they already aware and before you jump right into training courses and then keep building it over time right think of anything we do HR finance you hear about that all the time security training you don't just do it once and then forget about it right so think of that and that was a big aha for me I tend to do the same thing I'm just going to teach people more then they'll be excited no they need to step back a little bit understand the why the other part of this and some of these things you can go back to and kind of think but data strategy or culture change happens at the individual level the project level and then the organizational level or all at the same time right you're convincing individual people to care you may enter a certain project I'm the digital transformation team why do I need you well we can help you buy X answer those questions the more you become embedded and other projects the better and then the org itself how do that goes back to the ROI at the beginning right all you have to address all of these as you go and the drivers of each one and you know we all love the marketing side but don't forget I mean we often do we're so busy building the next thing we forget to tell everybody else about the cool thing we already built right and part of its education lunch and learn training sometime you know your culture I had one client he's like until the t-shirt happens no one thinks it's a thing so we made t-shirts right maybe your company people roll their eyes at the t-shirts right or is it the poster on the wall is it the video from the CEO you know but how do you launch this as a thing and do it in business language don't just say hey we now have a new data lake platform it's hey we now have you know self-service analytics for your marketing campaign so you can do your job better that's more exciting right so think of that also think of resistance management and plan it as a first order thing why might people not be excited then at what level at executive mental management employee maybe they're just busy they don't not like it they just have other things to do maybe they had a different kind of change they already happened and they're already changed you know saturated nothing to do with data they just gosh we had a whole HR transformation last year and here's another one right just think about it write it down and plan around it and one thing to kind of leave yourself with to simplify is just if I am now launching a strategy or change management maybe just answer these two questions like on a piece of paper or on your in your head what do you think the fear of change and I would say fear again it's like even my iPhone I update and I freak out every time because it's different and eventually I may like it but it's just different I don't need this right now right so what is the fear of change that people will face and what you think people will really be excited about and that might just help frame the whole rest of your strategy why are we doing this so who cares again just to summarize we kind of walk through it at lightning pace but hopefully it gave you some ideas of why are we doing it where are we today where are we headed how do we get there it's kind of a nice just framework to think about it right so again think of the business goals think of governance architecture but most importantly I'm sure you're doing all those things if not get on it but those last two telling the story through your roadmap and thinking of that culture change that's what's gonna make it stick that everybody feels that it's theirs and not just this thing that's done to them they're building it with you right again next month is a master data management if you can join us please do it'll be great we do this for a living at global data chat strategy so if you need help in building data strategies it is in our DNA would be happy to help you and with that I'm gonna pass it over to Shannon to open it up for Q&A Donna thank you so much for another great presentation as always if you have questions feel free to put them in the Q&A section and just a reminder to answer the most commonly asked question I will be sending a follow up email to all registrants by the end of day Monday with links to the slides recording and anything else requested here so diving in Donna what is the difference between data strategy and data strategic plan? Well, I don't think they're very similar I would think the strategic plan might be more of a focus on that last part of what I was talking about of really what are the steps we're going to take to do that and what does the next year or two, three years look like? I would think though even if you're doing that plan hopefully already you would have had some of the things answered right why are we doing this? Who's involved? What's the architecture? Cause that's gonna help you build the plan but to me that from my perspective not knowing the context of who asked in your organization that would be my answer to that one. Thank you and on slide 11, let me second there where do goals and objectives of the business come in here? Should the above not informed business strategy? Oh dear, I'm gonna have to move my slides to figure out which one was my slide 11. Everyone's busy now, sorry. Now I ask the question again cause my brain isn't big enough. Yeah, where do goals and objectives of the business come in here? Should the above not informed business strategy? I think that is the point of this that the goals are the business strategy. You know, what are we trying to do as an org? What are our goals that are going to work? And then that should drive your data strategy that have absolutely some things from your data strategy might give you some new goals of realizing that hey, we didn't understand our customer let's do a customer success campaign or something, right? But yeah, I would put the goals and that thing on the left. Those are what you're trying to do. Yeah Thank you, we just got a couple of minutes here. So making the business case about the decreasing cost bucket your three bullets really focus on reducing data costs. How about reducing business operational costs for the use of better data and data capabilities? Absolutely, yeah, yeah. So this is easy to track data costs but that's not so example. I think I'm not going to make people dizzy going back to that slide but one example was products data and yes, we may in the data world think of that as data but that's really business, right? Do you have a good product catalog to sell on your website, right? Or do I understand my customers or can we go to market faster, right? That might be a good one with product. So I absolutely agree, it's both, right? We're inefficient because our data management processes are bad but more importantly, we're inefficient as a business that takes us six months to go to market instead of three weeks because we had good master data, something like that. So yeah, I agree. Donna, thank you so much. Well, I'm afraid that does bring us to the top of the hour and that is all the time that we have for this session. Thanks to all of our attendees for being so engaged in everything we do. Again, just a reminder, I will send a follow-up email by end of day Monday with links to the slides and links to the recording of this webinar. Thanks, Donna. Thank you, everybody. Hope you all have a great day. Thank you so much.