 Hello and welcome. My name is Shannon Kemp and I'm the Chief Digital Manager of Data Diversity. I'd like 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 who will be muted during the webinar. And we very much encourage you to chat with us and with each other throughout the webinar. To do so, just click the chat icon in the bottom middle of your screen to activate that feature. And for questions, we will be collecting them by the Q&A section in the bottom right hand corner of your screen. Or if you'd like to tweet, we encourage you to share highlights or questions via Twitter using hashtag DA Strategies. As always, we will send a follow-up email within two business days, continuing links to the recording of the session and additional information requested throughout the webinar. Now let me introduce to you the series speaker, Donna Burbank. She 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. She has worked with dozens of Fortune 500 companies worldwide in the Americas, Europe, Asia, and Africa, and speaks regularly at industry conferences, including the upcoming Enterprise Data World. And with that, let me give the floor to Donna to get today's webinar started. Hello and welcome. Hello. Thanks, Shannon. Thanks, everyone who joined. It's always a pleasure to do these. And so just to give a little preview into what is upcoming later this year and what we've already had. So the number one question I think Shannon might agree that we always get is, will this be recorded? And yes, DataVersion keeps all of these on demand. So if you missed our last month's webinar on emerging trends and data architecture that is all available on DataVersion website, as are all of the past ones for the past few years. And then next month, we'll be talking about a case study from one of our clients in the Environment Agency of England on data modeling, which I know is near and dear to a lot of people's hearts. So just wanted to make sure folks knew about that. And hopefully you can see us on an upcoming webinar. But today's topic is a hot one. And we always get a lot of interest when we do things on data strategy. In fact, any of you who are coming to Enterprise Data World in Boston, as Shannon mentioned, I have a workshop on data strategy where we have some similar content, but also we get into some workshops and things like that where you can do a little more hands on. So sales picked over. So but today, one of the reasons why data strategy is is popular because a more companies are becoming data driven, but that can be also very daunting. And so there's a lot of interest of how do I take such a daunting task and just break it down into something that's reasonable. So hopefully what we're trying to do in this webinar is that yes, executing a data strategy is a challenge, but it shouldn't be that complex to put together some of the very basics. So some very concrete practical steps is the goal of this webinar. We obviously cannot build a data strategy for you or with you in one hour. But hopefully a couple of light bulbs will go off. If you've heard me speak before you know, I'm a big fan of frameworks and templates and just kind of some of the building blocks you can think of as you build a strategy for your organization. So I'm a I'm a data person. We love definitions, right? So what is a data strategy? Good to think of that. But also not only do I love definitions because I am a writer and a data person, but we often get that question, just what do you mean by strategy? And is that really how is it any different than what we've been doing all along with data management? So I went to good old dictionary, Merriam-Webster. And I thought I'd just go back to the definition of those two terms. And I think the answer is in it. So if you look at a strategy, we have things like a plan towards a goal. I had to put some of these that I found a little funny, actually, so a complex adaptation for achieving evolutionary success. Well, you know, that sounds like maybe that doesn't have to do with data because we're talking about metabolism, but actually it does. In fact, one of the clients I was speaking with Shannon, one of my Latin American clients, and the name of their initiative is their data evolution strategy. And because it's not as if they hadn't been doing data before, but now they're looking to become data driven. And they're looking to have that evolution. Other sales pitch, they'll be speaking with me in Boston. So if folks want to hear more about that, we'll be at EDW. The other part I liked about that is that it is a complex adaptation is taking what you may have been doing all along. Certain things don't change, but certain things do. So how do we take advantage and why people's brains rightly hurt is that there's a lot of exciting changes in data management right now. So how do you take advantage of these new new technologies, but not not lose some of the foundations that we've done in love for years, right? So it is a how do you adapt what you've been doing? The other piece I liked about the piece on the left with strategy is a science and an art, right? So I think there's with anything we're talking strategic, we're taking big picture, we're talking vision. Yes, there's some scientific principles, there's some templates I can walk you through. But I think if some of it's the art and I will probably focus in this presentation, maybe more on that art part than maybe we're expecting you always thinking of with data management, because the more I do these and the more gray hairs I get, it is so much how you sell it and how you message it and how you jump on the right bandwagon and do the right strategy because there's a lot of things we can do with data management. But it's doing most valuable things. And so this was again, some of the things I highlighted might confuse you, but why meet the enemy under advantageous conditions? Well, there's a lot of enemies out there. There's the enemies of completing priorities. There's the enemy of budget. There's sometimes other competing groups may feel like enemies, right? So we've got the other team they're competing for the same budget. So how do we, we join with them hopefully and join forces. So yeah, I don't want to think of data strategies and military operation. But I guess sometimes those tactics can actually help with your strategy. But I think when you read through strategy, you should have get the idea its goals, its plans, its visions, its adaptations and science and art. When you look at the management, it's judicious means of accomplishing an end and the art of managing and supervising and just sort of running things, you know, which is very valuable. A lot of us want to call it probably in management. And I sort of hope people are managing our budgets and our data and things like that. The reason I put these out here, though, is I think us, we and data management tend to err on the side of management a bit too much, which is good. As I said, I'm hoping with GDPR, someone's looking at privacy roles. And I'm hoping when I'm managing customer data, someone's looking at the fact that we have a single view of customer and that we are, you know, have this in third normal form. But I think sometimes we are the bloody deadies in the room, and we always sort of come with the negative. Well, we can't do that because or did you worry about this? And yes, we need to worry, we need to manage. But I think some of us in the column myself included often go right to the techie solution or the risk or how we manage, which, of course, we have to manage this whatever in data management, right? But I think some of us, it's a good thing to think of it. Can we put our strategy head on? And how do we work with a lucky enough to work with a lot of executives around the world? And often when I speak to the sea level, almost a defining factor of that level is they're looking for opportunities and visions. And how can I make my company successful? And the more you can tie in with their vision and how you can support that rather than why we can't do something. And I just sometimes they don't want to talk to IT or data because they're going to tell me I can't do something. And you're right. It's getting the right balance between that you don't want to overpromise and build the world and that 360 view of company customer is going to happen next week. Of course not. But I think in the balance, when we're thinking strategy, I think the difference is in that vision piece. Of course, you need to manage as part of a strategy, but it's thinking long term. It's thinking strategically, obviously, and it's thinking kind of the business value. So I know I spent a long time in that slide, but I think it's a good theme as we move through this is to me, that's the difference with strategy. And I do get that question a lot. What is data strategy? Haven't we been doing that forever? It's called data management. That's the DM box. Well, yes, but also it's more than that. So a little more and you may have seen the slide. If you've joined my presentations before, this is our framework, my company that we use to really have this document framework for data strategy, right? One of the boxes you need to fill in. And you'll see that it starts with business strategy. And again, I've said it before. I'll say it again. The reason I am in data still is partly because it's become so aligned with the business and so much of technology can help drive the business. It's just not in either or anymore. I think business people are becoming much more technical. So this idea of we have we have we have tech and we have business or that person isn't technical enough or all those kind of words. I think those lines are blurring. I also think, you know, tech people being put in a box and go code and come back later is also going away. And then if you think like a business person and you want to take initiatives forward, the doors often wide open. Because I think again, why I'm on a plane a lot is that I think a lot of executives are looking for someone that can really help explain data in a way that this practical and makes others, you know, people can understand. So I see more of that happening in my my role of the business and data being in the same room. In fact, I'm planning a workshop right now with my client where I am today and where that's exactly what we're doing. And both sides are often surprised. I think often the business is surprised how much the data people get their business. And if you've heard me speak again, you know, I'm a fan of data models. And what I love about it is I can build a model. I almost know more about that company and the people who work at it because you ask all the leading questions. So often the data people do know a lot about the details of business. And vice versa, often the data people are sort of shocked at how technical the business people really are. And they do get data. I mean, data is that kind of piece that it is not owned by the business and is not owned by it. So that's sort of the level one of we must start there. Why are we doing that? What's the top down business strategy? And then as we sort of, if we start from the bottom kind of that level five. Yes, a big part of it is how do we look at what the data estate is? What data do we have? Is everything nicely cleanly in our in a relational database, that might be great. But maybe do should we be looking at other sources should we look at social media data or video streaming or taking call logs and voice to text and doing some advanced analytics on that, right? So never sit still, obviously. Or it could be the other extreme where maybe everything is a spreadsheet or in documents. And how do we get that into a more structured format or probably all of the above. So taking a realistic view of what you have, what you should get rid of and what you should maybe add to your repertoire is always a big part and why a data strategy never stops. It's an evolving process because what can be stressful, but are either exciting or stressful, but are industry, depending on your mood or the day is things change so fast. I feel stupid every week that there's some new technology that I now have to learn to get up to speed on which is exciting. But there's a lot to keep up with. So if you're using the same technology only that you've used 20 years ago, you may start to look around. This is an exciting thing. But then everything in between, if we kind of move up and maybe go from the level four, how do you even integrate those different data sources? So how do you literally integrate? Do we want to do ETL batch processing? Do we want to virtualize? Do we want to move things to the cloud? Big decisions there. On the left, do we even have an inventory of what we have? Plenty of organizations don't. I don't if you ask me today, and maybe you are being asked for a GDPR or something, if you ask me today of where all of my customer data lives, I could not answer you. And that's okay to stay in this call because we're all friends here. I want to say both, but I guess many, many customers, companies and clients across the globe cannot answer that question today. So just getting that inventory and getting a metadata management structure to how do I get that source of what I have, how it's tagged, how it's labeled, what's the security risk? Super important before you do anything else. At level three is what I call West where you're starting to leverage data for strategic advantage. So things like master data, getting that 360 view or single view of customer BI and data warehousing and analytics, you can't do any of that if you don't have good data quality, or if you don't have a good architecture. So all of this ties together the analogy I often use is, you know, whatever an organization comes to me and needs help with any of this, I always pull up the slide and look at all of it because people might say, well, I have a problem with data quality, our addresses are wrong. Well, do we have a governance in place? Maybe that's what's causing the data quality or is there metadata to manage the definitions for data quality, sort of like you go to the doctor and you say, you know, your shoulder hurts, well, my shoulder hurts because my hip is out of alignment and I'm walking funny and therefore my everything like the human body is connected, or maybe you're stressed and you have a headache and you know, there's not always the cause that you think. So we need to look holistically. That line between governance and business, the second though the business is data governance. And that is often one of the more important pieces of people are complicated. If we think data is complicated, people are even more complicated, right? So how do we get people aligned together with similar definitions that elusive what do we even mean by customer? And again, I told the story. I remember when I was a youngster starting out in my 20s with data management, I was at I think it was a game of conference actually, the diversity conference. And someone told that joke in the room, we're trying to get a single definition for customer, everyone laughed. And I said, how does that? I think I know what a customer is. How naive I was, right? Is it a prospect? Is it a lead? Is it a, you know, gold star customer? Is it a lapsed customer? Is the customer on support, you know, anyone who's on this culture and you do that, it's very complicated. And that's a big part of governance, but it also ties with metadata architecture, etc. So we can talk about policies and data governance processes. The piece on the right culture is often the biggest piece of how do we all agree that we want to work together? Sometimes there are silos, maybe not for malicious purpose, sometimes, but office is people trying to get their job done. And do they see that when they're getting their job done as a fix? Other people? And maybe the data you're putting in, you don't use, but it's being used downstream. So I would say strategy, if you came across, if you came out of this webinar with one piece of advice, you can go back to it was often the slide like this, are you touching when you look at your strategy, are you looking at all of these pieces? Are you looking at why we're doing it with a business? Are you looking at how we're doing it with your processes? Who's doing it with governance? You know what we're managing is their metadata? And let's just do a checklist of where we are on each of these as well. The other part I talked to briefly, but it's worth stressing the exciting part about doing data strategies or anything with data today is that it isn't an either or. So the business strategy obviously guides your data strategy. And then we think of this carefully, are you trying to as many companies are trying to do a digital transformation? And we're doing a sea change at our company, we were working with several customers now that literally they are doing paper and pen with some of their processes. And they're doing a massive jump to switching that, you know, basically scrapping their process and moving everything straight digital. That's huge. But that's a big change for what you're going to do with data. Similarly, sometimes it's the data you have that can guide your business strategy. And some of the leading, I talked a lot about this in the previous webinar in January, a lot of the leading organizations in the world today are data companies. Think of Google, think of Amazon. Yeah, they sell stuff, but really they're a data company where they make a lot of their money is because they manage their data really well. So they both inform and guide each other, which I think is an exciting time to be in data management. Where to begin? And I often have not if you've been in my sessions on day strategy, I often have not put this in because we actually use this full disclosure in our consulting practice when we do an assessment. But it was asked so much, you don't want to seem salesy. But a lot of people found it helpful because you can do the same steps, right? So generally, and we do this fairly quickly, you know, the strategy shouldn't last forever. You know, we might take six weeks or you might be able to do in, you know, a short period of time. Don't skip it though, though, I wouldn't skip any of these pieces. So the first piece is just understanding business goals and strategy. Now we have some tools and templates, some of which I will share with you today, where you can do this yourself. What are our business motivations? Why are we doing this? And we know the motivations of each group, not just ours, but other people. How does that map to our data initiatives? What do we want to do with that? The second column is is a current state assessment. And I would say that's both from where you are in a technology, but also governance and culture. Look at that carefully. So it's not only where you are, but where you want to be. And not everybody has to do a major sea change, digital transformation. Maybe you just want to get the mailings for your marketing campaign, right? We need to get data correct. So, you know, be realistic here, because that's going to define everything else, which aligns to your future state. And we look at future state, like the previous slide, holistically, is the right, you know, we've seen all of the combinations of these in our engagements. Maybe the technology is fine. We have all the technology we need, but there's no governance structure in place. People aren't working together. We don't have the right roles and responsibilities or maybe everyone's, we have the right structure and everyone's eager to work together, but the technology doesn't support us. We need to really modernize or we need to maybe not modernize because that's sort of the first thing people jump to the new technology. Maybe we need to manage, you know, in old fashion, I sort of scoffed at management, right? We have metadata in place. We have linears. We have the definition of customer problems to the glossary. All of that. So, and then we go to a roadmap. And again, some of this does to most of this will take time. You're not going to do a sea chain digital transformation or data strategy in a day. But there's certain things you can do quickly. And I think part of it when you think of a culture change in an organization, it's things like governance and metadata, master data and all that can sound really academic, especially the business people. So I've found if you can do something quickly in three months, a month, six months, depending on what's fast in your company, because every company has a different pace, right? So show how it works. And we're working with a couple of companies now where, you know, one one company, we actually had a long time in the negotiation process. It kept saying, I want to do a pilot on MVP. We kept thinking he wanted like a dashboard thing. No, I want to governance MVP. I want to show how governance can fix something quickly. Because he's right that the show you show how doing this the right way works because we're going to face the Oh, that's too hard. I have a deadline. I can't do data right. Well, you know, old saying if you don't have time to do it right, you have time to do it again. So we know that long term, it will take longer but people have deadlines. So can we do something in three months, six months, two months to do it to show some value? You know, it's going to be different. But look at that carefully. And if you can get ROI, one of my nonprofit clients actually had a great example. What they did was clean up a donations database where they had the data quality before and after it did some address augmentation and literally they were sending out a campaign so they could do easily met easily do some metrics and they were able to calculate a $30,000 increase just with that one day to clean up effort because the people they cleaned up the addresses donated money. You know, maybe that's not a lot in your particular company for this nonprofit. It was but it was just so, you know, easy to pinpoint the cause and effect in that case. So other things like that you can look at that have our life and look at and then never forget the communication and evangelism. And so so often we have that success and we move on to the next thing. But don't forget not everybody is thinking about this every day. So how do you sell your successes? How do you market yourself as an organization? You'll see the little slide there. One of the clients we worked with was basically selling information management services. How we can help you across the organization? Did you think you're starting a project come to us? And often that's part of the job as well. So those are kind of again, the building blocks of how you might move forward on this. So if we kind of move through those levels we had in that kind of first framework slide. Again, these are just things to think of each one of these could be a full webinar. So we'll just kind of move through to give you some ideas and some light bulbs as you start to execute in your company. The first one is the idea of business motivations and that might be something you know. I'm a big fan of models and architectures and so you can architect anything. We're architecting motivation and how this works and this is obviously a fictitional company. You know, start with your do you know your company's mission and vision? Sometimes it's written on the wall. Do we ever look at it and pay attention? So everything you're doing is part of your data initiative should tie to that. Especially if you're trying to sell this up to executives. They live and breathe this every day. They probably wrote that mission and vision. So it's near and dear to their heart. And then look at business drivers both internally in your organization. What's hot? You know, is it cost savings? Is that the biggest thing? Maybe you didn't make your numbers and cost so go to them execs with cost saving. Is it innovation and cost isn't so important because we're a startup and we want to just show value and talk about innovation. So make sure you're aligned with the business drivers of your company and then also look externally. So too often where you know we go nine to five or longer every day in our company and we obviously have blinders because that's what we're looking at. What are other companies doing? You know, are we building our brick and mortar store and meanwhile everyone's gone digital and we sort of missed that way? You know, that's the last thing you want to do. And then define golden objectives very tactically for your data initiative and almost, you know, I'll say it's sort of a marketing slide. What is governance? In this case, it was a governance slide. Accountability, quality and culture. Something people can get their brains around because starting with, hey, we want to create a data governance framework probably isn't going to get people all that excited except for nerds like us. And nerds is a good thing. But not everybody thinks so. So you know, how do you tie it back to the business? And just on that note, I mentioned this in the beginning, but when you do that successfully, it's a great time for data professionals to have a seat at a table. And because so many people want to be David driven and someone that can speak data and someone that can speak business and corporate culture, your career will work. And they often I hear some people complain, oh, how come my cost, my project didn't get funding in theirs did? Well, I'm sure when you ask that question in that tone of voice, sound like my mother now, you weren't expecting an answer, but I will answer it. You think of that, think of the reason of why they got the funding and you did they tie to the marketing campaign that's coming up or the new product launch or so much of it. You know, you can say politics in a negative way, but it's politics in a positive way of go where the company's going. And the funding will follow. So I talked about this already, but it is good to think of, you know, what are those levers or levers of your pronounce that word depending on where you live? What a lot of things we can do a data management, we could work 12 hours a day managing data, we will never be done this job security, right? But better job security is finding something that you can have that value quickly and I see data as that fulcrum. If we had that, think of that nonprofit, if we had better data for our marketing campaign, we will get more donors and it came true. And now when they're trying to look more funding for other things, people get that and they can see it might be small, it might be big. We had one customer that actually had gotten some fines of our use over a million US dollars and they're able to solve that. So that was a huge win, right? We can't all see those. So they might be smaller one you can find. So another kind of framework you may want to use, and again, these are all kind of like laundry lists, none of these is brain surgery, but kind of putting them together in an easy way, kind of you're the pilot and the cockpit and yes, you've flown the plane a million times, but there's similar checklists you go through so you don't crash this time. So hopefully some of the things we've put together to help you not crash can help you. So you just think of it as three obvious columns or what are our business drivers? Often I find these from things like the company report and the annual report or a company strategy. And if you don't have a company strategy, you can do your best guess. What are you thinking in the company's whereas they had it, you know, you've been in the business, you probably have a good idea. So what are the key business drivers? And then what might be challenges for supporting those drivers, right? Is it because we can't get a single view of customer or their process and efficiency? We're working with a big manufacturing company. For them, it's all about process. It's a manufacturing process and how can data be a part of process? So again, the challenges will be unique to your business. A lot of them are similar. Some of the ones either all hypothetical sort of taken from random customers and put into something generic to protect the innocent. But I'm sure you recognize some of these, right? And some of these will happen everywhere. We always have information silos. We always just how we sort of break those down. And then very specifically, what are your objectives around data? We want to get a single view of customer. We love that better lineage. You know, again, think more strategically. We want to collaborate and have discovery. My new way I'm often doing governance is less about the governance telling people what to do and just getting the right brains in the room and collaborating. Because the things of what the definition of customer is that'll come out in the wash as you brainstorm and you'll find out that you're trying to do a project. You have a different definition of customer. So yes, you need rules and regulations, but if we can get people at the board with on at the table through collaboration, it just works a lot better. We're all human beings would rather be excited about something than told what to do. So hopefully that's kind of a format that might help you that we do this in a lot of different ways, you know, visually. But the basic when it comes down to is what are we trying to do? Why can't we do it? And what are we going to do with data to fix that piece of the note? So again, we're running through all of this at a quick pace. But just again, things you can go back to and in the other question we always get is are the slides available? And they are. And again, this is all recorded. So don't feel you like you have to take screens on to or detailed notes. It'll all be available after the fact. But these would just be things you should go back to later when you're building your strategy of check some of these boxes off. So as we get to culture and people and process and governance, again, I think this is the most important part, maybe not because your architecture crash is right, that's not going to work. But maybe the part people don't think enough about in the data world is this part of the stakeholders and this the cartoon from one of my books. But I often feel like a data therapist and people will say at the end of the day, they've been nine to six back to back interviews about data. Are you tired? Probably one of the few people on the planet that loves to hear your data stories, right? So you often feel like a therapy session. Tell me about your data issues, right? What you get in these interviews and do them? I mean, we do that because we're a consultancy. We come in, you can do that in your own company. And don't just talk to the people you know, because you're doing several things. And you're getting information about their goals, maybe why there's silos, what their, you know, what their initiatives are. And so you're also probably getting champions. We always find someone when we do these interviews that no one realized that they were just psyched about product and component data, you know, and think your governance, those are your future data stewards or data owners, they're there. And they're often very excited to finally have a voice. Thank you. We've been wondering for someone to come get this. But, you know, on the, as those in the white hat, the black hats, sometimes you'll find people body languages, you know, key arms crossed, they're not so excited. Well, that's good to know, you need to sort of work with them. And generally, maybe one in my whole career have just been difficult people to work with generally their arms across because they have another initiative they're being, you know, held accountable for. So can you tie into that? Again, most people don't wake up in the morning and say, I want to, you know, be difficult for data management that people don't really think about data is probably the worst that you get. So it's a big part of making sure a you covered all the bases and understanding people's motivations and getting allies. And often again, some of these people will turn into your biggest stewards and owners. So again, when you look at governance, just like, again, this canon is a whole webinar of itself, this frameworks for that. So some of them are similar. What are your business goals and objectives? We don't just do governance for governance sake. Often, when we're brought in to help fix a failed governance, it's because they did just that well, we've got the committee and we have all the meetings and we've got what what what are you trying to fix? What are you trying to do? So we talked a lot about that already. But do you have the right organization structure in place? Do you have the right processes in place, data management culture and then the tool. So it's all of the we take away any of the pieces of that house, and it'll fall. So they're all important. Something that you'll be relieved to know I'm not going to read through in detail. But again, it's something you can if you download the slides and listen to a little helpful checklist. What does each of these mean? You know, what does it mean to have a clear vision and strategy? You kind of covered that. Is it aligned with your business goals? What does it mean for the organization of people? Yes, we need an org structure. But our people with their in their HR reviews held accountable for data. One of my clients working with it right now that actually they're clinical psychologists and they're they're actually being held accountable for the data metrics they're putting in about their clients. So again, not typically what you think is governance. But that's their day to day governance. Again, the manufacturing company I talked about their governance quote is data is now a stage gate in their manufacturing process, right? So all of that can make sense. So processes and workflows could be two things. It can be the data management processes and the business processes. You think about both data management measures. Are you looking at the right data? Are you focusing on the right data? And do you have data quality statistics? So you know how much better you're getting to give data models in place? You know, this is probably the biggest column we're most familiar with in this call. Pulturing communications huge. You can do everything else right. But if people aren't bought in, it's not going to be again, not bought in because they're hateful to you. Probably they have other goals and everyone is a day job and it's generally not data unless data is my title. Or it is data, but they haven't really thought of that. What they're doing every day data. So actually, I have to get off track. So the customer I met today, we will be speaking at EDW and embrace data world. So if you're there, we just we just finalized this morning a video and it's a manufacturing company or they're selling building materials products. And basically that was one of the themes of the video is no matter what you're doing from loading the truck to selling the products to doing the invoicing that data. And because people didn't get that they actually have internet of things from the trucks. You're a truck driver delivering cement. You're doing data whether you thought about it or not. So, you know, think of that in your culture. And then the tools and technology and we talked a lot about that. So again, this is governance specific. Do I have data models? Do I have a metadata catalog? Do I have metadata repository? What's the difference? You know, think of all of that. And you can't have again, take away any of these pillars, probably not going to be successful. And generally, when we do a strategy, we come in and look at that. Yep, you've got the organization in place, but do they have a vision? Yes, you have the organization in place, do they have the right tools to manage data? Yes, you have the right tools, but is there a culture for knowing how to use them? Right. So, kind of look when you're doing your strategy, do you have all these other gaps in the ones that one of these I didn't think of often it's culture. And then actually, Len Silverstone, I think he's going to be to be to also a Colorado like me. And one of the he also is another data person that talks a lot about culture building. And he gave me a good piece of advice on one of his presentations. And it was, have we thought of our own motivations? What's Michael, are we enforcing our thoughts? So what we tend to do? I've got these great new projects. How come everyone isn't excited? You know, well, because are you the only one, you know, really think back and answer that question. You are one of your own stakeholders as you go through and do this assessment. Because we all have our blinders. So when we do a strategy, you do a strategy. I haven't mentioned it yet, but I should if you're not familiar with the Dama DMBocker data management body of knowledge, great resource. So a lot of the things in the previous slide, you know, what is a data governance organization structure? What does that mean? People have done this before, right? So that's a great resource. But I will be the first person to say, even though I was a contributor to that book, look at it and then throw it away because where I've also seen failed governance is they read the book and they say, it says we need a steering committee. And that says we need, well, I mean, that's a guideline. You need some sort of governance organization that steers governance, right? But if you again, if you take that to literally maybe steering committees often have a that's not your corporate culture, right? So one of the companies we work with, they have governance as part of their product launch life as a state's gate and product launch is the data right to support this product from the bill of materials to analytics afterwards, etc. So be creative here because again, governance can be funny, dirty, anyone and don't add to it by creating another yet another committee. So think strategically and all those stakeholders I talked to, how do we tie that into their day job? Is it a top down culture where they need people need executive support before they can do anything? Is it more of a federated culture where the fact that someone's telling them what to do will raise personal people's attitudes. So you think of that again, that where things tend to fail and trying to give you a quick of things that we've run into before this is a big one and why thinking of this carefully can be good. The other piece of that was the piece on the right. The piece on the left is just from an organizational planning point of view. You can't do everything with data. So where does data map to your organizational capabilities? So this is again, anonymized protecting the innocent view. In this case, it was more of a manufacturing company and they had the different from plan to make to move to sell to serve their customers, right? So highlighting where is customer data used to cost it? Where's product? Where are the pain points in the red? Where are we okay in green? Because as you start to build your governance organization or your data management or your anything linking back to what organizational structure is key. So these are sort of enterprise architecture artifacts that one on the left. But as you've heard me again, talk before I'm a big fan of that blurring between enterprise architecture and data architecture, especially when you're looking at a strategy because how do you not look at the enterprise? I mean, you need to look at the business capabilities as well when you're looking at data governance and stewardship, thinking of how you do the stewards. So if the previous slide was more about how we set up the organization structure, do we have a committee? Do we jump on the bandwagon of another committee? Do we, etc, etc? These are the kind of people in that picture. How do we set up governance owners or stewards or custodians or whatever you name them in your organization? This again, some industry standards in the DM box, but often that's what we customize. The name is the last thing we should worry about. It's what makes sense to you. So how we do this makes sense. It's important of again, what is your cultural way of doing things? So process centric would be maybe I'm the steward for the claims process or the billing process, the manufacturing process. Again, manufacturing is one where process is a very important part of the organization and aligning data to that process can make a lot of sense. System centric, it could be that you are the steward for the CRM system or the billing system or the data warehouse. I will caveat all of these that none of these is ideal, because you're I think true governance has pieces of all of these, but together with I'm jumping to my punchline that generally is some sort of blended model. But I don't want people to sort of say why you should not build governance around systems and you shouldn't each have a business for you. That said often, I often have you know, kind of a business level, maybe the claims process person is the business data owner of the steward. And then you often have a system data steward for the billing system for the claims. Because both are the reality. You do have systems with certain business rules and you certain have a business with certain business rules. And often they don't match. And that's where these problems come up. And so you need someone who knows both sort of the ideal if you read the textbook in the perfect world is the data domain centric and actually argued with people at Damon conferences on this because so much written is just yeah, well, you have a student data owner or a customer data owner. Well, good luck finding one owner for customer, I say from the real world with many battle scars. And that doesn't necessarily make sense. You know, I'm working with a big hospital right now and you who owns a patient. Gosh, I hope no one person owns the patient, right? There's the billing for that patient. There's the medical record for that patient. There's the, you know, the follow up in the home care for that patient. There's the legal for that patient. So there's pieces of patient. I think also to do this well, you need a very good data architecture. So you better have a data model and you better have your metadata around customer or patient or student or any of those to do that well. I do think that would be the ideal in a perfect world. And even when you break it up that there's different elements for customer, I often do that by attribute or by data entity around customer and you can't do that. So again, that's sort of I would say in my my world that would be the preferred but the house has to be realistic. And the other piece of that is the orientation of the business organization. Is it finance or is it marketing or is it clinical, depending on your organization? It could be geographic reasons. It could be here's the European and you're already seeing the complexity of this. So that's where things like these committees come in. And I'm doing something around customer. Wow, that's going to be billing and sales and regional because we handle customers differently in every region. It's going to be so that's where kind of that blended model comes in that you may have ownership by one of these and then the committee looks at all of them or maybe there's one owner with both of these. Again, think of this carefully, but this is again a checklist that kind of can help you think of some of the ways of doing this. Okay, on the running along in the super super fast strategy in this general level of kind of leveraging data for strategic advantage, that's kind of your data management would be a easier way to think of that. I'm a big fan of using a maturity model. So other question that always comes up is where does this come from? This is ours and we sort of start with things like the DMBock and the CMMI is kind of guidelines, but we customize it for our battle scars or experience of things that really work. So again, again, yes, you need a governance committee, but what do you do in that committee and what this is how our decisions made? And so we get nerdy detail, a couple hundred questions, but there are others out there. And I think I think CMMI is one that they have a lower cost one, you know, to have them come in and do it. Obviously they're experts can be more, but there are some ones you can download yourself, you know, if you're a gardener client, they have some tools you can look at. Or you read the DMBock and you kind of look through and say, am I doing these things? Right? So you can have an expert come in, you can get some things online, you can sort of DIY do it yourself, DIY, I guess. But in any case, look or you can use that template I have in the beginning and just say, are we doing these things? So however detailed you want to get on this, do it. So when you do it, where am I today? Realistically, no one's looking at this, but you so don't lie. And then where you want to be. And both of those are important. Because, you know, maybe I don't need to be the next Amazon. I'm just trying to get my campaigns out. You don't need to be if the scale then one to five, you don't need to be a one and, you know, a five and everything. And sometimes and we advise clients. In some cases, we're actually saying you're you're overdoing it, you know, yours. Okay, Donna Burbank is actually saying this that some people might do too much data architecture and data modeling. It does happen. Not usually. And because I love it. But usually that's where there's gaps. But in some cases, you know, you don't need a third normal form detailed physical logical conceptual model with metadata lineage and data stewards for every piece of data in the entire organization, you will get nothing done. So in some cases, you may be overdoing it. You have so much process in place, there's too much governance or maybe technology on buying I have six different bi tools and they're all cool. That's too much. It's not helping you, right? So for our cases, we may want to back off. And the guide is what you want to do as a company. So, you know, that that should be what you think of when you do a maturity assessment. And again, that has to do the balance. And if you've been in one of my works up, I always get interesting answers of this because neither is good. Are you so academic? And you know, and we all can if you're in data, I could look all day at a data model. They're kind of fun to do. But you know, there's a lot of things I like to skate to I'd rather do more of that data model. So you can get over academic and then nothing gets done. Because you argue. Actually, I, if you've ever worked with me, I tend to be just joke around. And one of my clients, we were just arguing over whether there was something with a hierarchy or relationship. And I'm like, Mike, why do you care? And we got to stop to get why do I care? We don't care. We just we're getting up. And it was sort of the dope slap of we're just arguing about a model. Nobody else on the planet care move on. And so something that's a good thing to do. And then Wild West is you're not doing anything. And I've worked in those environments to get the product out now stat, you know, and or maybe stat, you were, you know, we work with one organization that had a kind of a 911 type help desk with people's lives were at order. And those people had a pass. You didn't have to have a valid address. If someone's dying at the other end of the line, we'll get that later, you know, so but if you don't have a plan in place and nothing gets done, it's going to be chaotic. And so what's the right balance of that? That's the business value, you know, do is it a, I don't know, a clinical pharmaceutical company where yes, people are going to die if we don't have data right unless we should be more in the academic side. Or are we just doing a proof of concept for a new visit we're developing, we're going to throw it away. We just want to do some testing. Maybe that's more on the wild west side. So you again, that's that you're your guide on your business value, but just give that one some thought of where you need to be on your spectrum. And again, I can't go through all of this, but one of the things I do when I look at a strategy, I look with the customer, are we using the right tools for the right job? And this often when I'm seeing a strategy, is where things fail. So the obvious one in the strategy strategy, am I doing each piece in the right area that I need to? Am I doing advanced analytics or are I only doing descriptive analytics? Do I have, you know, data models in place? Do I have a golden record for possible of that? But also, am I using a hammer to put in the screw or a screwdriver to bang in the nail, right? So I too often, and you can't really blame business people who read the news and everyone has a day to like, what do you need a day like for, you know, well, we want to say, you know, total sales by region. Well, that's a warehouse, you really don't need to like, right, or we're trying to use your wrong tool for the wrong job. So often in a strategy is making sure you have the right things and you but you're also using the right things in the right way. I know that's obvious. A lot of this is, but I'm often putting it together in a framework. But what are we doing? And is this, is this the right tool or are there other things we need to do to have that? Just quickly again, and this is also the data integration piece. There's a lot of way this again, Canon is a whole webinar. But how do I integrate? This is master data, but this could be any type of data integration. Do I need to centralize all my data on place when I say I need a single view of customer? Or can it be more of a virtualized later layer? Either way, I often see too many companies say, I'm just going to virtualize it as an excuse for not doing the hard work. He's still going to virtualize it or point to point integration or whatever you still need that hard stuff, which is the semantic meeting and the data quality standards and all of that. So you still need to look at that. And good integration or MDM or anything is that mix of people, process, culture, everything up and saying all along, but particularly master data. And often, you know, when a master data problem fail, a project fails, it's generally not the tech, it's usually the governance around it and the process. And do we talk to the people and how they're entering the data? A lot of that. So Gartner has a good report on that as well. The main reasons that MDM particularly fails generally around governance and process and less on the data architecture. So moving on again in the sprint, how we coordinate and integrate some of these sources to me is sort of that metadata layer integration layer. But think again, I guess a lot of us in the call are also often thinking about the technical integration. Do I put it in a lake or a warehouse? Do I have API's that access the apps or do I do? But don't forget that a lot of the reasons for integration is on a business. Is it a merger and acquisition? And it's not just about the data, it's also about the organization. We did a big merger between and we helped support a big merger between an insurance company that was in the UK and the US. And half of that effort was doing the organization structure mapping to the data. And yes, we had to also do the data mapping. But first, and I showed you an example of that type of model earlier, we had to start with the org because it's, you know, is there finance the same as our finance and is there actuarial doing things in the same way. So now look at that as well. Metadata near and there in my heart. We have presentation was also a course on a diversity on metadata. Lots of different ways to do it just do it. So in some ways and think carefully about this. There are enterprise metadata catalogs, metadata repositories. They can be great, especially in the world of GDPR. Definitely take a look at this and there's a lot of great out of the box tools you should not be doing metadata by hand unless you need to because there's a lot of tools that can automate this. But you may not need I for many years worked for one of the vendors for a big metadata repository. And often what people were doing in that big over a million dollar implementation, they could have done a data modeling tool. What they were just doing was relational databases and structures. And, you know, there's so much overlap between these tools nowadays that, you know, what a data modeling tool is sort of they have metadata capabilities and some of the catalogs have kind of, you know, modeling type structures and this glossaries in each. So again, with this, just kind of look at the various pieces and see am I doing it and then think in what tool, but don't go for the tool first just because there's a lot of tools out there. You don't always have to go for the most expensive one. Just quickly to the new sort of metadata repository was the traditional way of doing it. A lot of the new tools out there are more of a catalog and I see a catalog is more of that sort of collaborative way of doing things, you know, the usage ranking and crowd sourcing and things like that. Definitely take a look at that because it is a nice way to get kind of their realistic view of the organization. And my analogy there is if the metadata repository in psychopedia, that is where we have the vetted source of truth and the more sometimes the catalog approach can be more of a Wikipedia where people are doing brainstorming. Both are valid with different use cases and but think of that carefully when you look at a tool. I've seen customers go the wrong way in both ways. They're trying to get a single GDPR lineage and more of a collaborative tool and they weren't able to lock it down well enough. And some people were trying to get collaboration and a repository that was just too rigid. So give some thought to that. And then just quickly on that bottom up of that has been of inventories. Definitely these metadata repositories that they just talked about one of the big advantages is it helps you give that inventory because if you don't know where things are and a lot of them have automated scanners where you can say, wow, this is my one of my customers have this legacy system, the db2 or something or other that nobody had used and they didn't even know and then when they were able to reverse engineer with a metadata tool, you know, eyes were open to what was actually in there. So they can be super helpful. I would say the business side of that I would this is obviously spreadsheet type mapping, but do a map of who is using it. Obviously, there can be some I openers to of can we and again, this is a reality, often it's the finance system that yep, it's using the legacy system, but there's so much financial logic in that and they haven't maintained and funded and we're just going to not touch that. Sometimes politics from other things. Or in terms of prioritization of funding, you know, who's using these. So in this case, you'll see that Oracle is used by pretty much everyone across the board. And then leadership using click, maybe need to keep that because it's a leadership team. We often have to be realistic or do we need to convince the leadership team that maybe everyone else is using whatever template in this case. These are just examples, not favoring any product over the other, but just take a look at kind of usage patterns as well. Data models I mentioned can be a nice way to not only have the inventory like a metadata repository, but they can be active when you make a change in the model you can also forward engineer onto the database platforms as well. This I will just go through quickly through but the takeaway and we're we are, I've said this in other other presentations, but I mean it this time, I think it's going to go out, can I correct my next week or so, the new survey. But this kind of shows the trends of what people are using today. A lot of unfortunately still some spreadsheets, a lot of relational databases and a lot of legacy still. And of course, big data. As you look to the future, not only is more moving to the cloud and gladly less fewer spreadsheets. But I think you'll see that there's just more diversity isn't all just some peaks is spreading out and I think that's actually a good thing. So when you are looking at your data strategy, are you look just take a look at these types of lists? Is everything in relational or do I need to use a graph? Those are great sources for time to get some knowledge making across the company. Am I using big data when I haven't other internet of things? Even though you're, you know, any sort of company can probably you know, insurance is using internet of things to get feedback on their customers. It isn't just things like manufacturing and tech. So take a look at that, that the takeaway isn't to go detailed into this, but just as a big part of your strategy that have kind of downplayed the focus on the business side. But this is huge. Are you looking at the new technologies that are out there? And if not, maybe start to do some proof of the concept. So building a roadmap, putting it all together, the quick takeaway is, and I talked with us already of pick a business when is it something like an integrated customer view? Take that list of people you talk to, who cares? With integrated customer view, probably a lot of people marketing sales, customer support, the exact good thing to start with. And then what initiatives can that support? It's going to help with our strategy is going to help with governance is going to help with both legacy things and things you need to do. And also some shiny things. Can we throw in some open data in there and social media to get people excited? So it's a mix. Do the foundational also do some of the, as I like to call shiny things or innovative things. And then also get the right people involved and then don't forget to continually communicate because if you don't skip one thing, don't skip the communication. You start small and talk a lot about it because people take a lot of time to adopt these things. It was kind of ties into this the long term success not just gave you launched this now we're on to the next thing continue continue training, lunch and learns, speak at conferences, you know, get the word out even beyond your company talk to other people. This example is an example of a service catalog that show in the beginning of selling the information management services across your organization, how we can help you. Right. So have a newsletter, jump on to the company newsletter, etc. So to sum up the key steps we kind of talk through but again, this can be sort of a checklist. Do we have the right support in the executive, either talking to the executives and or aligning with their vision? Have we talked to everybody in interviews? Do we have the right business case? Are we focused on the right data? Do we have an understanding of our own maturity? And then what we need to fix to that maturity to map to what we want to do as a business? Is the organization in place for my governance and execute some perspective? And then what can we do quickly? But also in this to me, I would say the key. What can we do quickly that's also building towards an architecture? Not, you know, everyone can move quickly nowadays. It's frustratingly fast. How do we do it the right way and show that that is valuable and communicate? And just before we open it up for questions, which you can either type in the Q&A and Shannon will read out. Next month, as I mentioned, is a nice case study at the Environment Agency. There is an article that's an oldie but goodie who's got a lot of hits on which talks about this at the university that did with me in September 2017. But it is out there for your reference if you want to know more. And this is us we do it for living if you need help. And I'll open up for questions, Shannon. Donna, thank you so much for another fantastic presentation. And as you said, let me answer the most commonly asked questions. As a reminder, I will send a follow-up email to everybody by end of day Monday with links to the slides and links to the recording from this presentation. And we've had comments and questions coming in, Donna, even just as we started. So I love it. We got lots of questions to get to here. And the contemporary days of Data Lake is if a medium size enterprise would like to start their journey on what is the advice, what is the advice for them? For Data Lake, I would say initially make sure you need one, right? So generally, I would say a Data Lake is great when you're trying to explain it to your analytics or you have unstructured data sources. I think often two people quickly go to a lake when they don't need one. Nothing wrong with a warehouse when you need a warehouse. I would think there's a lot of cloud-based solutions out there where you can try to easily it's great because you can a throat away, it's not working for you. You can scale up very easily and then you can start small and if it works scale out so you don't have to buy the servers and do it all yourself. Definitely look at some of the cloud-based initiatives and they have some also great training online. The vendors are getting really good now with their training. They can really help you get up to speed with that. And Donna, how is a data strategy different from data management? Oh, go back to slide two or maybe I hadn't covered that one when they asked the question. So yeah, that's probably the best way strategy is more I would say kind of business driven and big picture and vision where management is almost like management of everything. I'm making sure the ducks are in a row and I'm keeping things managed. But I see that it's sort of less visionary and less business led. So with two high payment levels, do you start from level one or level five? Or do you do both? Sure. And I'm just that last question. I really I should have just gone back to the slide. So we did kind of talk to that in the beginning. And then the other now that I know how to move my slides for the level question of a level one to level five, excellent question, you definitely do both. And I generally start with the top down and then I sort of do the bottom up and then you sort of meet in the middle. I mean, you need to look holistically, isn't it either or so you can also do them in parallel. It's not like you have to do step one, step two. But if you were to start with any step first, definitely it's the one because that's the why it could be, you know, we have no business need for data, then don't go any further. I doubt that's going to happen. But it helps kind of set the focus for everything else. That makes sense. So I was going to go one, five, and then two, four, three, maybe be able to think of it. For a large, for a large global company, how much time is realistic to develop a data strategy and roadmap? Oh, that's a tough one. I mean, we, we, when we come in, we can, but we're very focused and we come externally and that's all we do for, you know, a large company, maybe eight weeks or something. I think is, is, you know, I wouldn't go too long, which longer than a few months. I know some of the, I'll slam them to big consultancy will do six months. I'm like, how much, how long can you do a strategy for, you know, so I think you have to keep it small enough. And then I would say revise it. So here's my advice on that. So you can do anything fast. Let it breathe long enough that you talk to the right amount of people. I think that is the part that's going to take the longest. Actually, one of my larger clients that that did actually, I just knocked those people who did take a six months because it was, we have to do so much selling globally to all the and we'd be had to be very careful politically. And it took us six months just to get buy in. I think that's an extreme case. I would say, make sure you talked to everybody and do it in a few months and then iterate. Just so that you're showing value, but don't rush it and try, I know all the stuff. I could do it in a day. I could probably write up a strategy in a day where I wouldn't get things right. Is did I, did I take the time to look everywhere? Is there a database I didn't know that existed or the people I hadn't talked to, right? So I would say a few months is probably a nice average. You can do it faster. We've done them in a few weeks. If everything's already built, I think that's the other part. Maybe you've got an excellent business strategy and you already have an excellent documentation of everything else. So I hope that's a good answer with some caveats that makes sense. I love it. So but that does bring us to the top of the hour. Unfortunately, there's a lot of additional questions, but I'll send those over to you, Don, if you want to take a look at those additional questions that have come in. And just a reminder again, I will send a follow up email by end of day Monday for with links to the slides and the recording for everybody. Also, as you mentioned, we are we will be opening the survey actually tomorrow for the new research paper trends and data management and it'll be part of what we call March data education month. We declare March the whole month data education month is celebration of. So we'll do those be lots of good things going. You'll be able to find the link to the survey on our website tomorrow. So all right, all well done. Thank you again so much. Thanks to everybody. I love all the questions coming in. Well, I get those over to Donna and I hope you all have a great day. Thanks to you. Bye.