 Hello and welcome. My name is Shannon Kemp, and I'm the Chief Digital Manager for DataVersity. We'd like to thank you for joining the second and the latest in the new monthly webinar series, Data Architecture Strategies with Donna Burbank. Today, Donna will be joined by, we'll be discussing building an enterprise data strategy where to start. And just a couple of points to get us started. Due to a large number of people that attend these sessions, you 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 upper right hand corner of your screen for 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 with in two business days, containing links to the recording of this session and additional information requested throughout the webinar. Now, let me introduce to you our speaker of the series, Donna Burbank. She's 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 to regular and industry conferences. And joining today is John Pinkleton from DataWatch. And we're just going to have a quick word from our sponsor. Let me turn it over to John. You there, John? Yep, there you go. Can you hear me? There we go. There we go. Okay, great. Hi, my name is John Pinkleton. I'm the Chief Products Officer at DataWatch. DataWatch is an organization focused on solving the problem of making sure data analysts have an immense amount of agility to get the data in the right shape and right form to put their data either through an operational process or an analytic process. In short, we're a data preparation company. It's been around for the better part of 30 years. And we're making a very big impact in this space by providing tools or organizations to be able to get any data anywhere, anytime. What I'd like to do is take you through a bit of understanding about where to start with a strategy. So where do you start? You start with the analysts. The analysts really need to be an overall part of how you're going to structure and piece your overall strategy together. For the simple fact that they're the ones who are getting the data out. They're the ones who are getting the most efficiency out of their information. So you need to consider the analysts, the use cases they work on, what type of agility they need, but also on the IT side what's the level of governance that they need to have. But the problem with most strategies is they haven't considered the analysts well enough. They go down a path where we've locked information down where we've put governance well over agility. And we started to see self-service analytics start to make a shift towards that and give a bit more power and a bit more availability to the business side of the house. So when we look at these issues and the problems that have plagued analysts for years, they come into a couple different categories. And some of them are simple. I've been in the BI industry since before who's called BI and I haven't seen these problems solve for years. In many cases, business users, business analysts have no idea what any of their peers are doing on any given day. So there's a lot of misused or unmodels and data sets that aren't reused that a domain expert would have worked on. What I mean by that is a person in marketing may not know that a peripheral partner in the finance team is actually working on something that would complement what they work on. They have no visibility into this. They've never even had the opportunity to see that anything was worked on. If you can raise that visibility by leveraging social aspects such as like and follow and subscribe to other users, the ability to reuse models that have been built to extract data and blend data from disparate sources and still maintain that governance and agility, you have the opportunity to start breaking down some of the barriers that have plagued this analytic industry forever. We also see the challenge that there's an issue of trust when data starts to be shared. So why isn't data trusted? Well, typically we don't necessarily know who worked on it. We don't necessarily know the version. There's a lack of curation and stewardship in an organization which could be broken down departmentally. And this lack of trust can be solved once again by providing social aspects. Is it curated? It's got a high level of trust. Is it liked by 50 people? Okay, great. That gives me a little bit of flavor for it. But what models or what data sets and where are they being used in other applications? So if I can go in as an HR person and I sort the data and I find out that Frank from Finance has actually built this and that it's a curated data set and it's being used in maybe some of our financial reporting or a board deck, it raises that level of visibility. So being able to create a data strategy where people can see the models that other people are building, when they can start seeing who the other people are that contribute models, they can find those other people in the other departments. When they start leveraging those models and repurposing them for themselves, they're getting the domain value that the other person created that they simply don't have, because that person in HR doesn't know the finance system and doesn't have the tribal knowledge that that person in finance has. So they'll never be as good at it and most likely, as you've probably all seen, when those people sit in a room together, their numbers don't match and they don't match because of that lack of tribal knowledge. So being able to reuse and repurpose many of those data sets is a big play for people. From a perspective of how the organization needs to bring this together, we believe that providing a socialization strategy and a sharing and collaborative strategy that lets people work cross-functionally, we start giving analysts the ability to share their work and ensure an easy way that their information is trusted through the rating, ranking, and curation. It also gives them a chance to showcase their own work. I mean, how many people are out there saying, I've worked and built these models or I've built these reports and I don't know who uses them. Being able to get feedback as to who's following you and where the data ends up is a big contributor to understanding your role in the organization and what your contribution is and bringing back more employment satisfaction. And then being able to leverage other people's work is simple. You know, it's if I can get it done and I can get it done faster, that's a great thing. But in doing all this, we need to make sure that IT has the oversight of it. So when you look at self-service analytics, the biggest problem that's plagued self-service analytics are all of these loose files, the Excel files, the Access files, the CSVs, PDF documents. All of that information out there is the Wild West. Nobody knows where it's been, how it got there. Nothing worse than looking at a visualization and asking a person, where did this data come from and you can't get an answer. They can't prove it, nor can they repeat it. So by centralizing these models and as well as these files, we start creating a concept of a data marketplace. And a data marketplace provides the analyst that collaborative ability gives them the agility they need, but it gives IT an opportunity and the ability to control and manage what's there. They can control the connectivity to the backend systems and build the views that people want, but they can also allow users to contribute loose datasets into the models to pretty much satisfy the results that they need. It reduces the burden and a big time on the domain experts to manage the data because the models are built and they get repurposed. And then really from an organizational perspective, it's about the awareness of the data. It's about how users can start working and collaborating together. It's about agility and speed and the opportunity to simply get your job done and get it done easier because somebody else has already paved that path for you up front. And most importantly, it's about trust and better quality data. Better quality data comes from people and the domains that build it. Repeating those models repeats the quality of information as it comes across and gets blended across these disparate systems. So really the goals for the operational analytics pieces we see it, it's about promoting cooperation, getting everybody in the organization from the Excel user all the way up to data scientists to share their models and share some of the appropriate levels of data. Well, always having that governance and control of the backend system of what you can see and what you can't see. Controlling groups and controlling classes of users, but giving that agility for everybody to share their data. That visibility that comes into play simply improves data. I've watched it for years. A data warehouse is great. An operational report is better because operational reports, everybody's eyes are on it every single day. So by operationalizing the data and putting it in a specific area where everybody can look and see other people's work, you improve the overall quality of the data, improves the visibility, and you can get better measurement from your data because the quality of it's that much better. And lastly, it's about improving productivity, which I think is straightforward. Get insights much faster than you could get them before. Improve the analytics cycle. Move data from point A to point B in a much faster way. So we see is a great place to get started. Consider the analysts. Consider getting people to collaborate and work more cross-functionally. Take advantage of the work that they've done and be able to create an environment that is strong and rich and the ability to solve the problems of the analysts. Thank you so much. And we're going to continue on. And actually, we're going to touch on some of those same themes that John's just covered as we kind of step back and look at the more broad strategy. And you'll see here from sort of the abstract of what we'll cover today. I mean, the real message that you should get by the end is really how do we leverage data and inform a strategic advantage. And we talk, I've had a lot of questions in the past. What is the data strategy? Is that just a fancy word for data management and haven't we been doing that for years? And yes and no, we've done data strategy for many years, data management for many years, but I think the difference in terms of strategy that we'll talk about is it really has that business focus, almost the pure definition of what a strategy is, right? You're sticking strategically to look for new innovations and new ideas where management is just that. You can manage data and you can do that very well, but it's so what? And what business drivers are for that? So how do you really leverage technology from business success? And so many companies now are trying to be that data-driven company. It's just really the crux of what's more strategic now about data. And Shannon, can you hear me okay? Just before I do a sound check before I ramble on. Are things good? I will assume all of it. Yes, you sound great. You said a little quieter than John, but you sound good. All right, I will lean closer to my laptop. So the next slide is really talking to this idea, and many of you have joined our webcast in the past, have seen this. And I keep using it as sort of the Zachman framework for data strategy if I could be so bold, but it just seems to resonate with a lot of people because it really hits on all the aspects of strategy and it really is inclusive. And one of the things we do at Global Data Strategy is really always start with this idea that I just mentioned about a business strategy because I love data. Data is fun, but it's only relevant when it's doing something for the business. You can find the latest, coolest technology, but who cares except for your other tech geek friends if you're not doing something. But at the same time, I'm the first person to say that at that sort of bottom up, so that top down is the business, that bottom up would be more of what technologies do you have and how can we leverage that? And it really is sort of a synergistic relationship that we'll talk throughout this presentation of from a top down, what business drivers are you trying to look at? And then from the bottom up, what data do we have? So it could be just relational databases. It could be legacy systems. It could be something like we now have internet of things data or streaming data and if we collect this and harness it in the right way, we can have an entirely new business model. So that's the beauty of it is getting that integration between the two. And then there's sort of everything in between, right? So even just doing the basics of that, what data do we have is not a simple task as many of you know. What formats that it is, is there rationalization across it? How do we integrate these data sources? How do we get the metadata about these data sources that everyone knows metadata is near and dear to my heart, but both on the technical side in terms of how is this data structured as well as the business side of what this data means. And that ladder is probably the more interesting side. And then the next layer up is what I, I guess, would call that what do we do with it and what do we care, right? So it may be that I'm trying to do a great new customer data management strategy, but the data quality is bad and you can't get any further other than that. Or I would love to do some data warehousing at BI, right? So that's sort of what you do to it. I can't do that until I have great master data around customer and product. I can't do any of that without an architecture. So this area is a bit of a laundry list of things, but they do fit together. And I always look at this holistically when I go into a client because it always sort of like when you go to the doctor and you say my hip hurts and they say, well, actually it's because your shoulder is out of alignment and everything's mess. So it's sort of that same way with the dead strands. You really need to look at all of it. If you forget one, you're going to be lopsided. And then governance as John sort of hit on as well, that's that people process policy and culture around data, right? So are we doing this in silos? Do we all agree on what we even mean by some of these core marketing metrics? And more importantly, we'll talk a lot about it, and John hit on it as well, is that idea of culture and getting people working on the same hymn sheet, as they say, or on the same page and really care and become that data-driven culture. And that's sometimes the hardest part, getting the hurting cats, right? I enter data because this is sort of a lot easier to get data working. But the more I work in data, you've probably heard me say that before, I realize it is all about the people. I think I have the best technology in the world, but unless it's aligned with that business strategy, again, it's not going to get that buy-in. And then I spent a long time on this slide, but this is really sort of the crux of a lot of things. And I think I've mentioned another webcast. I'm always flattered. I went into a client a few weeks ago, and she should have had the different areas circled that she knew she needed help with, right? So, yeah, I know we're really strong in data warehousing, but we really haven't mastered our, you know, conformed dimensions or whatever. So it's kind of a nice sort of checklist to see where you are. But the real interesting part, as I mentioned, is how do we transform our business through data? And the old folks like me on the call might say, yeah, hello, that's not a new thing. We've been doing that for years, and we have. But at the same time, there is a lot more opportunity now with how fast the technology is changing and how fast data sources are growing. So for many years, we've had this idea of optimizing your business through data. We're becoming a data-driven company. And companies are still getting better at that. Some folks are not there yet. Some people have been there for years, right? So how can we use data to make better business decisions? If we had a better view of customer, could we have better marketing campaigns? Could we build better products by understanding how customers are using them, et cetera, et cetera? But really what's new and honestly has kept me in data this long is this new opportunity of becoming a data company. And I'm going to spend a bit of time in the beginning with some case studies, because I think that, as I mentioned before, is the cool stuff, right? What are people actually doing this? We can talk all day and you know I could about data architecture and how you master data and why you need a data model and all that great stuff. But it's the so what? What a company, how are they monetizing data? And I do have many companies come to me now and say we want to be a data company. And that's before I came on site, right? So I know that data is the product. You'll think of folks like Facebook and Uber and Amazon. They're awesome because of their data and they've sort of capitalized on the data asset and created entirely new business models from cool data. And that's that idea of this previous slide of take the inventory of your data and step back and think of it strategically and say what could we do with this that we're not doing today? So of course we can become more streamlined by managing it better. We can do better at what we're doing today. But is there something completely different? And that is what gets me interested with a lot of these opportunities because it's just something completely different. And I'll show some examples of that as we go through. And again, back to definitions because we're data management people and we love our definitions. What's that difference between a business strategy and a data strategy? The strategy is strategy. And it's sort of obvious. But the strategy of the business is how we're going to grow this business with a certain aims and objectives of the business. We want to be the leader in selling widgets. We want to cure more patients than anybody else in our health care operation. To do that, you need a data strategy. So data strategy is particularly focused on data. So how do we manage that data across the business to achieve the goals of the business? But as I mentioned, it's a synergistic, interdependent relationship that the business strategy should inform what you're doing with data. If we suddenly would become an online company and not no longer a brick and mortar, that's a data-driven digital transformation strategy. You need to have data. But it could be that, hey, we've got a lot of sensor data. Could we have an Internet of Things strategy and productize that data from their products? Or could we feed that back to product management and get statistics, right? So that's what's exciting, as I mentioned, is that this is not one or the other. It's an and, which really I'll talk about later, can put data folks at a great seat at the table to really make some interesting business decisions. So getting it wrong sounds like a negative way to start. But we've all kind of worked with projects. It didn't quite make it. And so what are some of the things we've seen over the years that kind of caused folks to misstep? So one of them is, you know, this lack of a clearly articulated business strategy. It's hard to align with a business if they don't know where they're going, right? So where do you want to be? And we'll talk later about starting with the mission and vision of the company. And if that's not defined, you need to. Because how can we align with something that doesn't exist? On that note, you need both, you know, I also get the question very often, is a business data strategy a business thing or an IT thing? And I say yes, which is the annoying consultant answer, but it's both, right? So yes, you need business to understand, business people to understand and champion. And as John mentioned, even be much more hands-on with the data. But also data is a very technical IT thing. So you need IT to guide some of these ideas of how we actually execute the strategy. At the same time, business often needs to take ownership of the data. And that's what's weird about data, as we know. It's, yes, it's a technical thing. It might be stored in SQL Server, but the actual values, they are what the business is doing. So you do need that data stewardship and the folks in the field actually doing it. On that note, you know, the data strategy sometimes, again, people think, isn't that just the data management roadmap? Isn't it just the stuff we're going to buy? And, you know, tools are great, but that's not a true strategy. That's a tactic, right? That's how you get there. Almost that, you know, the pure MBA was the strategy, was the tactic, was an execution plan type of thing. And, you know, IT should be involved, but it really shouldn't be led by IT unless IT has this great business idea that's vetted with the business, right? That it really is a business strategy. And unfortunately, we'll talk more about this later. And again, I've come from tech and I've helped build and sell products as well, so I won't want to knock any vendor. But we'll all do that. We'll sort of want that hottest new technology and how folks come to me and say, shouldn't we implement Hadoop or don't we need a streaming database? Unless you have a reason for it, no, you don't. You might be very happy on your Oracle relational database. And so we all sort of, and I do it myself, there's a lot of cool technology I'd love to have a use case for, but you need a use case for it. Otherwise, it's just, you're playing with your toys in the IT toolbox, right? And you might get funding in the beginning, but you're not going to get long-term funding for that. And that's really where it becomes that collaboration between business and IT. At the same time, this is hard, right? And so a lot of the things I'll show you in this presentation look really simple. And hopefully that's the beauty of architecture and consulting templates and thinking of things strategically. It's taking complex things and making them simple. But it is complex. Really getting a business strategy and a technical strategy aligned is a lot of moving pieces. So you have to priority, you have to focus. You can't, as they say, boil the ocean. Pick those quick wins and show results and then keep going, because this stuff will take a long time. You can't get that single view of customer across all silos of the organization tomorrow, but you could get a high-profile one for a marketing campaign coming up and show some results and that's going to get the buy-in. At the same time, it's not a technical roadmap, but it is technical. So you have to make sure you have the skills and expertise. So if you are still on some technology that's 20, 30 years old and haven't learned some of the new stuff, learn it now, because it's maybe not replaced the stuff that's there, but it can augment. And stuff is changing so fast. It's actually a fun thing to do, which is probably why a lot of you run that call, because we like to learn. So more positively, what do you do to get it right? And a bit of it's the opposite of what I said, but if you get it right and you're aligned with the cool business drivers, that's sort of showing the success. And I sort of joke. And we've all worked on projects and sometimes it isn't apparent to somebody else of why that one project was cool. And I was complaining to a family member. I was home after work. I said, gosh, those backup and recovery people, they're the cool kids. They can all be attention. And I'm sick of them being the cool kids. My friends knocked me down a notch. And they said, where else on the planet is data backup and recovery cool? Because we get to... But it was cool because the thing of the business that was hot, they had aligned to. And I joke with a lot of my clients, we're going to make you the cool kids. And you can. Whatever project you are, whatever technology you're using, data warehouses are great. But is that aligned to what the business stakeholders want to see? Then you'll get the buy-in. Otherwise, you're just another thing we can cut, when budgets are cut. And it needs to be unique to you, right? So, yes, there's certain templates we can help you with. There's certain architectural artifacts that are kind of the same. There's a lot of how we model customers and orders and products. They're kind of similar across organizations. But your strategy is your strategy. And that's really if you're a unique differentiator. Your data and your strategy for that data is what's going to make you the Ubers and the Amazons and the Hezles of the world, right? Because they're looking at something new based on their data. And at the same time, strategy can sort of get a bad rap, because it seems so up in the cloud. So make it actionable. You need to have kind of clear milestones, deliverables and stuff you're going to do and keep showing roadmaps. And at the same time, evolutionary. So, yes, there is new technology you should look at. And that's where we get paid the big bucks in tech, right? Kidding. But maybe not, depending who you are. But technology keeps changing. And that can be frustrating, but it's also pretty darn exciting. So keep looking at the new stuff. And it doesn't mean you need to buy all the new stuff every second that comes out, but take a look. And does this make my new changing business needs? Could I have a new business model based on this new tech? So just some kind of starting points. I wanted to kind of start, as I mentioned, with some success stories that we've seen of folks that maybe you might not have thought of. Again, we all mentioned the ones everyone's heard of, the Ubers and the Amazons, when it comes to data. But it's the everyday company. And what I keep telling my friends and coworkers that's what's sort of fun right now in my consulting practice is we're working with a lot of companies that we never would have 10 and 20 years ago. It was the financial services and government and healthcare. But now it's some of the different kinds of companies. So here's a consumer energy company we worked with a couple of years ago. And they were really trying to transform. Again, it's almost a commodity energy. And the ironic thing of this company is they're actually trying to incent people to use less of their product. So we conserve energy, right? We not use so much electricity. Well, you're actually setting people to buy less of your product. So one of the things they're trying to do or are successfully doing is this idea of smart metering. And that's a completely different business model where when you think of it, someone from your cell phone can turn down your heat. You can talk about data-driven dashboards. I can see my energy usage. I can control it for my cell phone. I can see where maybe energy improvements I've made can help save money, all that data. And they were one of the folks that when I started had, we are a data company on their wall before I even came in. Who would have thought an electric energy company is going to be a data company 10 years ago? That didn't even make sense. But now they're realizing that data and the usage data and the end of things data really is their biggest asset. But they realized that to get there and to blame, we all have these things. They were still having trouble with getting the right bill to the right people with the right amount on it with the proper energy usage. So like all of us in any company, you're sort of changing the wings on a moving plane, as they say. So how do we fix that very tactical, sort of at the bottom here, this traditional business model? We need to fix the basics. Management's not going to let us go any further until we can even just get the invoices and bills right. But at the same time, we can make that more efficient as we move to this new business model. So sort of what they did, the first thing was quality. If we can't even get the address right and the billing usage right, how are we going to get to the next generation? But we can't boil that ocean. So one of the things was just prioritize these critical data elements that's going to be in the business traditional model and looking forward to that new. So that was almost your traditional data architecture, data models and critical data elements and metadata and the governance around that. How are we going to, from this course, set elements move forward and really get to that new data driven, big data was their main new goal. So this was a great case of doing the old stuff, doing the traditional stuff with the eye to getting to that new next generation business model. This is a restaurant chain where, again, this may be more on the doing what we do better. But they realized that if when you think what is our business, our business is selling food, they did not have correct data around their menus. And this was initially driven by marketing. Again, it was nothing to do with the IT team initially. And they knew that if I really want to sell better food, what am I doing? That's all about the menu and what I'm selling. And they were very innovative in changing their menu a lot. I think their snarky comment was our printer and knows more about our data than we do because they print the menus and they have the list of the menus. So we did, and we actually did a holistic, holistic, if that's a word. If anyone knows me, I'm a big old nerd and I love learning about everything. So we talked to everybody. We actually went into the test kitchens and talked to the chef. What I found interesting is that each of these people had their own version of a data diagram. So the chef talked about all the ingredients. It was sort of a process model with which data, what ingredients I have, what's the cost of that ingredient, how I put that into a recipe that then goes to marketing, which goes under the recipe, which goes supply chain to price it. And then at the end of the day, when you're actually in the restaurant using one of these kiosks and ordering it is at all priced and streamlined. Each one of the marketing had the same thing. We don't see that flow across the data. So it was a very basic data flow that actually saved them a lot of money and literally having the wrong slice of cheese linked to the kiosk when you want to cheese on your hamburger could cost them a lot of money. And it didn't link back to the slice of cheese that the chef had chosen when you built that recipe. And this is all that techy master data stuff, but it really transformed their business because it was streamlined. And they did the business process models, data models, crud makes the trees, built a master data management program. But for them, I mean, the amount of data wasn't massive. It was, you know, product and ingredient and price, but it was the governance around it. How do we get all these disparate teams? Kind of a John's point in the beginning of getting people working together on the same page. And here the motivation was clear once we connected data and trying to, you know, initially getting the CEO, we brought this up to the CEO. And it was these kind of pictures that sold her that it was. And why would I need something like master data management? We told the story of the slice of cheese literally from the chef to the kiosk and where it touched. And that was master data and light bulb went off. And everyone was bought it. Everybody across this from supply chain to menu to the marketing all had the same pain point. They just didn't have that words around it. Another one similar in terms that it was retail. This was a new retail company where they same thing. They wanted to be data driven. Their product actually was Internet of Things based. And it was sort of, you know, a lot of it had to do with, you know, think of sort of Fitbit that people can actually use their product and see their own data. And so not only is that an interesting data solution, they can actually see what people are doing with their product to feed back to product design, customer service, that sort of thing. But again, to do that is really hard from understanding when from the marketing supply chain of when the customer purchase to how that product is delivered to how I get good support to how I build better products based on that data. So they again, this was also sponsored by marketing, but very quickly we got bought in buy in from the product folks that said, yeah, if you can get that customer journey from the marketing perspective of when they buy to when they purchase to when they get it shipped to them, that getting it shipped to them was kind of hard. We're seeing some gaps and that hurts the customer experience. So again, all these conversations were all about the data, but weren't all about the data, if that made sense. It was about the business and how just small pieces of even if we could get customer email address and physical address across this chain, that would do volumes for the company. So again, some themes here of picking a business problem that everybody can agree on and champion, picking a small slice of that and picking why we care because we could have picked anything in this company. We could have picked product ID codes, but those were all set and that would only affect a certain amount of things. So picking that even just something similar, like simple like customer contact data, had a big business benefit and everyone could agree. So some of those are fairly, you know, you'd think customer product. We've had some fun with some different types of things where you might not think of data driven. One of our flagship customers over in Europe is a UK, is a UK environment agency. And again, their product is managing the environment and they actually have a complete digital transformation and open data initiative. And to do that, they realize unless we have standards for our data, we need to manage it. So I have to say this is a whole lot of fun. We're modeling fish populations and things like that. But think of it like the fishermen now, they have their fishing license. They can get that online. So you can just have it on your cell phone when someone asks for your license. But maybe more interesting than some folks in this call, they're publishing open data sets. You know, what are the fish populations in a region? How do we define regions as a county different than a attachment for, you know, certain areas of water bodies and everyone had their own different view. And very quickly, you know, these quote business stakeholders were scientists and we're all trying to get similar environmental measurements from we're looking at a lake. What are the organisms in that lake? What are the water measurements in that lake? And if we all get together, we can actually share this data and make new environmental decisions. So again, we tend to think very business driven. We talk about business models, but this was a government agency and about the environment, which was kind of fun. And fish, which is not something you can get to have data models for every day. I found that very interesting. But a big part of that with all of these, again, the theme is how do we tell the story? Because a strategy is basically a story for the organization on how we're going to get there. So if anyone has seen me present these before, I'm a big fan of literally telling a story through pictures. And especially when you're talking to business stakeholders, you're trying to get buy-in from senior management or anybody. This is really the hearts and minds piece of it. So this is what I did for a managed care organization. And often it's hard for people to understand why this is complicated. So in this case, we have a member or a patient. They might have a healthcare provider. They might have internal staff helping them. They might be in a certain hospital. They might have had an incident with the police when they were in a crisis situation, et cetera, et cetera. But it was sort of showing that story around how we can better help the patient to win hearts and minds. And then if anyone thinks like me, and I feel sorry for you, no, I'm just kidding. We data people put everything in terms of data. This is a data model. And really how I started doing this when I would do an architecture for a customer, I needed to get, visually understand a customer, and I would draw out pictures. And then I thought, well, let's show it back to them and see, did I understand your business correctly? Got a lot of positive response from this. But to me, this is a data model, right? So a member can be associated with more than one provider or a provider has certain credentials, et cetera, et cetera. But it's a way for people to kind of internalize what a data model is and why it's important. A totally off track comment, which I want to do. So I'm actually here. I'm laughing about this. But my father just had heart surgery yesterday. And the night before, we were trying to keep our minds off of heart surgery the next day and he would ask me about work. And I said, I was working for health care and trying to get the data together. And he said, well, how do you get the data in for all these to know the provider and the patient? So literally when he's going into the surgery, they're asking him all those standard questions. What's your birth date? Do you take any medications? When was your last? And then they took this form and we're going to put it in the computer. And I said, dad, this is the data entry procedure I was just telling you about. And I started to explain it. And I said, only Donna Burbank as her father is going through major heart surgery brings up the analogy to data models and data quality initiatives. He didn't really laugh. He didn't think that was funny. But that is the crux of that. Because I still can't not think of that. The person who had that birthday match with what's in the birthday for the EHR electronic health record does the medication that he mentioned he had a different provider in a different state. Can they get that same information? But that's sort of the crux. So one of the nice things about these stories as we mentioned and John mentioned as well is getting that common motivation. And you may have heard this joke before, but I repeat them. I'm sorry, but they're good. No, but it is my sort of analogy with the cruise ship and the life raft. Because we have silos in any organization and we tend to do this. And my analogy is you're on a cruise where you've finally taken a vacation from doing data and you're on the, you're on the, you're on your lounge chair and you're reading a book. And you see this guy get up every morning and he's jogging around the yacht. And you just, we tend to judge people. So don't say you do because we all do. I think, well, I'm sitting here on the lounge chair. Why is he getting, he's on vacation. Why does he get up at 6 a.m. every morning and run? He's always running and he looks great. But you know, could you just chill? None of your business, but you're resting so everybody else has to. And then you see this guy in the laptop a little while later. And you're like, dude, you're on vacation. Get off the laptop. You don't even know what he's doing. You can be chatting with his friends. But you should have internally judged. Why is this geek on his laptop? Because I am derging a marterita on my chair, right? And then another lady on the cell phone and she's always talking to her stage voice. And we're, you know, we're a cruise to Greece. And she's, you know, calling all her friends in Europe and just bragging that, you know, they're, you know, she's speaking French and then she speaks Italian and then she's speaking Greek in a stage voice. You can all hear like, would you just shut up, lady? Even though it doesn't have anything to do with you, right? So you're implicitly judging this person and then you hit a, well, I guess you wouldn't hit a glacier. You hit a rock right on your way to Greece and you start to sink and you all go into the life raft. And now you have a clear motivation that you want to survive. So that guy that was running around the ship, dude, you're strong. Why don't you start rowing? And guy on the laptop, could you find out our GPS coordinates? And lady on the cell phone, we're off the coast of Italy. Could you talk to the Coast Guard in Italian? And again, these things that annoyed you when everything's fine are suddenly motivations. And again, maybe only me would relate this to data. But it's like when you're on a project, because we all do that, oh, I don't want to talk to marketing. Those, I don't know why people do that. I don't want to talk to marketing because they have all the Chosh keys and they can't be serious. Well, I don't know, you've got a data governance program. Could you work with marketing and have them do posters and have them help you market your governance campaign? Could you work with marketing and see what their pain points are? Or a different group, I don't talk to the programmers. They're doing that agile stuff and I don't do agile. Well, maybe you should. Maybe they could help you integrate your APIs with your data. So we create these silos. And if we don't, and we're all on that same page, then things become easy because those things are annoyed. You just say, how can that person help me? How can whatever skills they have join in? And too often we do build these silos. So one of the things we use in our practice is this idea of a business motivation model. And again, I'm a techie person. So all these techie-feely things, I was almost embarrassed to show in the beginning because that's people's stuff. There's my silo. I just want to do the tech. Yeah, that's never going to work. That's not a strategy. That's an implementation. You need to think big picture. So we'll put these together of, what's your corporate mission and vision? And then how does your data program align with that? And so what's the corporate mission? What are the drivers of the company? Maybe it's something like self-service, like John mentioned. Maybe it's I'm doing a marketing campaign. Maybe I'm trying to help patients get better health care, both internally and externally. Because when we talk about silos, you can often just look at what you're doing. So maybe you're a coffee shop and you're spending so much time in your brick-and-mortar store. You forgot that now they're ordering coffee online with drones and they deliver it to people's office. You missed that whole thing because you're so focused on your own internal silo. And that is where you can really see where new technologies. And again, think about marketing. This was for a data governance type thing. What's data governance? Accountability, quality, and culture. What are your buzzwords? What are your taglines for your data management? So think of that. And I have had teams when they're arguing say, can we go back to the motivation model? And that's a nice way to be neutral. I'm not arguing with you because it's you. I'm arguing because we're worried about data quality and this process you're implementing might hurt quality. So it's just that even playing field, this is why we're doing it. It ties nicely into these stories. This is the gut feel of why. And these are some of the more tactical of why. And that's really an exciting time to be in data management because we now can have it to the table because we do have a lot of good ideas. We're seeing that data. So if we can put our business hat on, people want to hear. I mean, I have a lot of success with clients because I can actually look at the data and speak their business language. And could someone please explain this technology to me in a way that I understand for my business? Not just, I don't care about Hadoop. I don't even know what that is. How is it going to help me? So speaking that way will help. And my advice is you may have seen this similar slide. But again, I saw someone print this on their desk. So don't ever encourage me because I'll keep showing the slides. But I think I've used this little guy on the left for like 15 years now. But it's true. We're often seen of kind of those geeks in the organization and I'm proud to be a geek, but we kind of do it to ourselves. And I have a whole presentation on different personalities and how that applies to IT. But we love what we do. A data architect or data management person is often very passionate. And we're also one of the things that makes an architect we think big picture. We love to talk, right? And we want everyone to look at our data model and really understand it. But no one cares. And I often say that to myself, no one cares about your data model. But they do if it's in their terms because everybody has the what's in it for me. So if you can be the less, I'm talking about, you know, world is okay. So what is this guy on the left? We're kind of that crazy guy on the, on the, or gal on the sidewalks in the world is going to end. If the model is not in third normal form, beware. You might be right, but people think you're kind of weird and you're Birkenstocks and you're funny sign there, right? So just chill a little and maybe don't use words like third normal form and say, hey, if you could link your customer data with project user stats, we could do both, you know, think of their business and say it in that way. And that might be obvious, but we all do it. I do it all the time. The other thing and just a little side note here, I just noticed personalities, a business person, an entrepreneur, almost by definition is optimistic. It's all going to work. It's all awesome. And we're going to have huge success and what's the opportunity. Technical folks kind of because it's our job is to find problems tend to be the opposite. Oh, there's all these problems. And yes, you can sell success with, you know, we need to fix this because it's a problem, even better if you can say how awesome it's going to be. If we did this, we could have this new business opportunity. And then you become that data advisor in West State Architect, which is a great opportunity. Similarly, find that balance. Often architecture has that negative connotation. It's going to take too long. It's too academic. It's too hard. It doesn't have to be. So you don't want to be seeing this thinker thinking forever and nothing getting done. But at the same time, if you don't do any architecture, you're going to, this is going to be chaos. So, you know, it's kind of like you all have that friend. We're going to go on a trip. Could I turn on my GPS? We don't have time to turn on the GPS. We don't need to find. Let's just drive. You know, three hours later, you're still driving around, not knowing where you are. And that's sort of the analogy here. Yes, you can do some planning. And some of these templates I'm showing you, just enough architecture. Just map it out. At least understand what elements you're talking about. And then run and do quick wins. But don't do analysis paralysis. And don't do wild at West. No analysis. There is that balance. And the balance is what's going to be the most valuable thing in your business. I'm a big fan of seeing where you are. And my analogy, and there's an article at the end, I'll refer to a diversity published on data strategies and how you build them. And my analogy is, you know, I'm a runner. Anyone doesn't know me. And, you know, I want to run a marathon. Somebody wants to run a marathon. Can I do it? Well, sure. Why do you want to do it? You know, I had one friend say that. And she's like, oh, I think I met 10K. I'm like, yeah, there's a big difference. Why do you want to do it? Do you want to lose weight? You want to go from a three hour marathon to two and a half, or you're running your first, you know, where are you today? You wouldn't say no to anybody. Anyone can run a marathon with one leg. You can hop a marathon, right? But where are you today and what do you need to be? Maybe if you've never run a marathon, a two hour marathon is humanly impossible yet. But, you know, it's too hard for you. But we can start with 10K and then go to a 12K or whatever. So go across the aisle. We have our own maturity assessment. Of course, every consultant, she does, but there's a theme on my, there's a lot of maturity assessments out there. I like ours, of course, but it's ours. But we go much more detailed than some of those because there's a lot of subtlety to that. If you have a strategy, it isn't just binary, yes or no. But anyway, we go through where are you today and then where do you want to be? And that gets back to that, do you really need to run a marathon for every one of these? Or can I just walk down and get the mail and it'll make me healthier? So it's just being realistic. And what's the difference for my business strategy? Where do I need to be? What's going to have the biggest value and realistically where I am today? And then look at that. A lot of folks are fan of spider charts. They hurt my brain. I like kind of red, yellow, green. I almost, and that's something we all do. I was just watching the Olympics, right? And there's these great skaters and they have little thing up in the corner. It's green, that's good. Like for my small brain, that's awesome. Anything, again, making it simple for folks. But this is one way to kind of see gaps. So here, for example, for metadata management, we're kind of on track. If current state is green and future maturity, we're basically where we want to be. Don't touch it. Don't over engineer if you don't have to. Governance, on the other hand, we're kind of here and we really need to fill in this gap to get where we want to be. We maybe want to spend time there. But it's also got the other way. Architecture, we're kind of overdoing it. We don't need completely full conceptual, logical, physical for the entire enterprise to start doing anything. Maybe we just start with a customer area and do a conceptual to get buy in and then build the logical and do a reverse engineer for physical, right? Don't overdo it. And that's your choice. There's no magic bullet. And then pick those quick wins. There's no simple answer. One of the things I like to do as a template is kind of put all those things we just talked about together. So what are your business drivers? I'm a modeler. Everyone knows right now. Model out what you need to do. Why can't you do it? We want to do digital self-service. We can't because we did terrible data quality. And then kind of heat map. What's going to fix that, right? So in this one, we need better data governance, all these sort of, you know, if these are the numbers, what's your heat map? If we just did better data governance and metadata, that's going to get us really far. Might not be. And one of the nice things about this, it helps prioritize. So maybe right now we don't need to worry about inventory. We kind of know that it's not going to help us. The better thing would be fixed on the governance of that inventory, right? But it's going to be hard for a business person to jump from, I want to do digital self-service for customers to why we need metadata management than be what, you know? So this is kind of a nice way for you to see the priorities and for business people to kind of see that connection. The other part of alignment is talking to a lot of people. And this is a picture from one of my books, a cartoon. Yes, we have data cartoons. I often feel like this when I'm doing a lot of things. Tell me about your data. I'm one of the few people who, and you will be amazed. People will vent. I have spent days and days locked in a room with people venting about their data. And people you might not have thought of would just say, tell me about your data. And a non-technical person would get it right away and talk for about an hour. So you can see what people's challenges are. You can review two allies and maybe folks that you still need to convince, right? But that's just the most important thing. And again, I'm a big fan of matrices and checklists and all of that. So I mark it up. Who did I speak to? What groups are they in? Is it the finance department? Is it the data architecture team? I do the old-fashioned race. Who's responsible? Who's accountable? All that will kind of influence. And maybe you don't show this to people, but the CEO is really upset about something versus a low-level analyst. They might be right, but don't forget to pay attention to the CEO because they have the budget. Similarly, kind of that data asset inventory. Again, these checklists can be, they're super simple, but they can be helpful. So what are we using today? And of course, yes, huge fan of, you can reverse engineer some of this for data models. You can use metadata discovery tools to see the lineage. But even just something simple like this of, we have Oracle and everybody's using Oracle. Or maybe that's a priority. Maybe it's not. Maybe the fact that leadership is looking for some agricultural trend analysis and open data. Maybe we should look at that because they're leadership. But again, it at least lets you make that decision. Huge fan of checklists and templates and things like that. This is a trend that I showed earlier this year. We did a, we have a link in the back, data architecture survey. What people are using and what you need to use. And I thought this is very valuable and the fact that as I mentioned earlier, awesome new cool technologies out there. You don't need them all, right? You might be a biker and there's this great new fancy new racing bike, but you're really just going to ride your bike to work. You don't need that yet. You'd like it. Maybe something to play with, right? So what I found interesting, what people are actually using today to run their business, a lot of relational databases. Yes, there's kind of a move to the cloud. There's also still a whole lot of mainframe out there, right? So not that you should plan your data strategy for mainframe, but be realistic. You'll also see that there's a lot of spreadsheets that ubiquitous managing by spreadsheet. And I've worked with a lot of big companies that are still using a lot of spreadsheets. So that's kind of that I want to run a marathon and you're still sitting on the couch. So you're managing on spreadsheets. Maybe just going to a relational database for that. I mean, they were built for what they're good for, and I'm trying to get better data quality and better linkage of data. That's relational databases. I mean, they work for a lot of good things. So don't discount them. And that might be a big jump from where you are. I'm on spreadsheet and I'm sitting on the couch and I'm going to go run a 5K, right? Or it could be you skip all that and make a jump. Maybe I'm skipping and I want to go to the news and there's some new semantics technologies or any other things, right? It doesn't mean you have to take step by step. There's people in rural countries going right to an iPhone and they never had any technology. But just do it wisely and don't discount stuff that's been working for you. It doesn't mean it's bad. I mean, my January webinar, we talked to a cartoon about not reinventing the wheel. The wheel has been around for centuries. It doesn't mean it's bad, but let's put it on a Tesla and not a cart, you know? So use the pieces that make sense. I thought it was interesting because it's trends. So yes, it makes sense to look at big data. Do you need it? Not everybody does, you know? It doesn't necessarily make sense. Some cloud. A lot of folks are looking at cloud. I'm a big fan of graph when you're kind of trying to see interconnected things and see relationships between things. But a lot of folks are uncertain when you look at the amount of folks that I don't know what I'm going to be using in the future. And that's a good thing. I think that's a great thing because it means people are thinking you shouldn't know yet. You shouldn't just say, everyone's using a graph. I need to use graph. Or everyone's doing internet of things. That may make nothing to do with your business. So yes, look at the tech. Look at the buzzwords. Don't let that make change your decision of where you want to go. Your business drivers should be where you go. I know that's obvious, but it's worth saying. A few things, too, because John kind of talked about it with governance. Don't overdo it with governance. So I do disagree with John a little bit. I wouldn't say that an operational is better than a warehouse. It's a better place. Warehouses, that common core enterprise data and reference data and mastery, it doesn't go away. That has to be closely governed. That's your traditional governance. But be wise about it. Know what you should for your highly governed financial data and your customers and products. Yes, have formal governance. Lock it down. Have metadata. Focus on those for that. But if you're doing some exploratory analytics and you're doing some social media, sentiment analysis, don't lock people down so much that you quell innovation. And there is a balance. And think back to the energy company. This is where they started. Let's filter and focus. Let's just pick the stuff we have to govern closely and let people play with the rest. And it's just, again, a synergistic thing. Some of the stuff that's exploratory may become core down the road. And that idea of the crowdsourcing and the method that John mentioned is awesome. But you also need that encyclopedia version. So finding that balance of what needs to be closely vetted if it's going to be your corporate reference data set and what is more analyst crowdsourcing and understanding and doing different models and understanding. And a lot of that, like everything we've mentioned here is finding that balance. What's standards-based and what's collaboration-based and how you get those two working together. Like anything, I'm a fan of a framework. So when you think of governance, you can't do a strategy without governments. That is not only the tools and technologies around governance. There's also a Good Day Diversity article. It came out the other day on that. One is an architecture and one is a governance. And there is an architecture behind governance that makes it work. But there's also steering committees and processes around that and culture. That's a huge part of it. So getting that framework in place is key to making that work. Don't worry, I'm not going to read it all through, but I get a lot of questions. And this topic wasn't on governance. It was on strategy, but you can't do strategy without governance. So I put this in because I get so many questions about these boxes. What do you mean by process and workflow? Why do I mean by culture? So again, in that checklist model, these are some good questions to ask yourself. Are individuals accountable for data? Do they know they are? Have we mapped business processes? Or just kind of ask yourself, this is almost your own mini maturity model. Kind of ask these questions, and what are you doing and what are you not? And that'll help you kind of create your red, yellow, green for your organization. Again, just quickly, for data governance and data quality, I go to a lot of companies that are doing a lot of data governance and data quality, what do you call it? Metrics. The difference is, I've talked to a lot of people that have built these great dashboards and no one looks, but have you tied it back to a business definition? Why would we care about duplicate records? Why would we care about having your email campaign? Because it's going to hurt our brand or it's going to have cost savings. So just something simple like this, what's important, what's red, yellow, green, and why do we care on the current metrics and KPIs you already have is huge. And that's the idea of finding these business lovers or leavers wherever you live. What are the quick wins you can build with all the cool stuff you need to do? Is it supporting something really cool? Are you the cool kids or are you rearranging the deck there in the Titanic? And we all do this. We're data folks. I organize my laundry lists and my shopping lists. I like to organize. Is that valuable or am I doing it as an academic exercise? And that's the idea of creating this actionable roadmap where you're doing the hard stuff and I present a lot to the CEOs and one lady actually called me out and she's like, oh, you put the shiny thing in for me. And I said, oh crap, I messed up. She's like, no, I like shiny things. And think of anything you're trying to learn that you don't know. You want to just broken down. Think of the Olympics. Do they get a red or do they get a gold medal or a silver medal? Explaining that you need glossary and warehouse and analytics and all that. But what are you going to get at the end? You're going to get an integrated customer view. Big shiny yellow thing. And this is who cares, right? So think of those quick wins that are still going to get you to that architecture. And you can't have both. That's one of the beauties of this. A few things just to wrap it up at the end. Some steps. And we went through these, but again, we've had a lot of good feedback on these. Just kind of quick check. Where do I start? That was the port of this webinar. Get the buy in. Define your business. Build the business case. And then, yes, assess where you are. Where you need to be for IT capabilities. And then not only create the people and process around it, but get those quick wins. The last bit of advice. Don't forget to communicate. We tend to build it and run away. It's like, I was in marketing for a while. They say you have to tell people at least three times before they'll remember. Keep selling your project. It's got it ongoing and ongoing. So in summary, where to start? Start with the business, if that wasn't obvious. And then align that with your architecture. It's just nice synergistic relationship. And build those quick wins and they'll keep coming back. Don't worry that if you do a quick win, it's going to stop. It's going to keep going. That's the beauty of a quick win. They keep building on each other. We do this for a living. If you do have any questions, just quickly, this is the article I mentioned that's on data strategy and data management. And the white paper I mentioned is free for download on our website. So sorry, Shannon. We don't have tons of time for questions, but we can open up for questions. And this is the lineup for the next year. Your next month is on metadata. If you're interested. Donna, thank you so much. And John, thank you so much. Just a reminder to answer the most popular question is, just a reminder we will send a follow-up email by end of day Monday with links to the slides and the recording of this presentation. And it's been some great chat going on there as well, which I love. And jumping right into the questions for you both here. So what training or resources do you recommend and do you recommend a mentorship or do you mentor specifically in data governance and analytics? To answer that question, yes, we do training. We do mentors. One thing we offer is kind of a data strategy quick start. And again, that almost hurts people's brains because some of the bigger consultancies, you can only do a strategy for so long. So we kind of have all of these wrapped up in a nice, where are you, where do you want to be? There's a lot of, I can't not say, data diversity has some online training. If you want to, that's a nice place to start to kind of get some of the basics. Yes, I mentor. There's some good online training with data diversity. And we also offer training classes on either training or consultancy. We kind of have a four-day data strategy course if that's something you'd be interested in. Here's where to find me if you want to. That feels awfully salesy, but you asked. Well, and I do want to address some of the chat as well. Which I love that was going on. Somebody mentioned that, you know, you started as a data archeologist and then moved to a data architect and then become a data advisor. Any comments on that from both you and John? I'll jump in quickly and then let John talk a little. But I think that's actually right. Our archeologist is finding out what those are. That's that discovery. You have to sort of define what those problems are. Take it back to an architect and really understand how that fits and then communicate that in a way. In fact, we're doing a project right now. It's really complex architecture. How do we explain that to marketing and just call out the right things and the right way to explain the complexity? So I think that was an excellent comment of it that really is the evolution in a way. John, your thoughts? I couldn't agree more. We always joke internally, you know, from an analyst's perspective that good analysts are great foragers. Great foragers, when they build teams, become great hunters because they can hunt in packs. And then the best organization really becomes shoppers. They just go and pick this stuff out. So it's right along the same continuum. It's just a different lens with the analysts and how they get there. Yeah. Totally agree. I love it. I think we need an infographic on that. That would be great. Just back to the comments, I think Gail McCollis had mentioned, we should have said we're remiss. EDW has a lot of the diversity conferences. In fact, I'm giving a data strategy workshop at Enterprise Data World in April. There's a lot of other good ones on governance. On all these pieces I mentioned, there's somebody talking about it in either a workshop or a lecture. So that might be good for you. It's in San Diego, San April. I love it. That's perfect. And that does bring us right to the top of the hour. Thanks again to DataWatch for sponsoring and to help making this all happen. John, thank you so much for joining us. And Donna, thank you as always. And thanks to our attendees for being so engaged in everything we do. We just love all the chat and everything going on. And as I mentioned, we hope you'll see you next month. And I hope everyone has a great day. Thank you so much. Enjoy. Thank you, everybody. Thanks, Shannon. Cheers. Thanks.