 Hello and welcome, my name is Shannon Kemp and I'm the Chief Digital Manager for Data Diversity. We want to thank you for joining the latest in the Monthly Webinar Series, Data Architecture Strategies with Donna Burbank. Today Donna will discuss data as a profit driver emerging technologies to monetize data as a strategic asset. Just a couple of points to get us started. Due to the large number of people that attend these sessions, you will be muted during the webinar. We very much encourage you to chat with us and with each other throughout the webinar to do so. Click the chat icon in the top right-hand corner of your screen to activate that feature. For questions, we'll be collecting them via the Q&A section or if you'd like to tweet. We encourage you to share our questions via Twitter using Hashtag DA Strategies. As always, we will send a follow-up email within 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 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. And with that, let me get the floor to Donna to get today's webinar started. Hello and welcome. Thank you, Shannon. Always fun to do these. And as Shannon mentioned, I am a regular speaker at Data Diversity. We have a monthly series on data architecture and a question that often comes up at the end is, will this be recorded? And the answer is yes. So they are all in demand. So if any of those previous recording topics are of interest to you, they are all out on the Data Diversity website. And I believe, Shannon, quickly, if I'm wrong, I think they're up there forever. So any of the past ones this year or last are always available. And we have some other topics coming up. So this is your first time joining us. Welcome. This will hopefully be an interesting mix of topics for the rest of the year. So today's topic, as Shannon mentioned, is about data as a profit driver. And there's the buzzword of monetization. And I would say it's not a buzzword anymore because it's just become reality. Because the digital economy is here to stay and data is really transforming the way companies are doing business. And it really is the new norm. I was chatting with Shannon before the call and I have several clients right now doing looking at ways they can monetize. And there's several ways to do it. So some of you may say, isn't this what we've always been doing? But better data helps us be more profitable. And the answer is yes. But there's also some new exciting ways as well. So I'm going to talk about all of those and a little bit of definitions of what does that actually mean? Like with anything, when big data was the buzzword, what does big data mean? And we're misclassifying certain things. So as a data person, I'm a metadata person, I'm always a fan of definitions. So we'll start with that. So this is a definition from Gartner on sort of what is data monetization. So the basic definition is using data for quantifiable economic benefit, right? So that could be, as I mentioned, just improving business performance. And that might be the cynic in you saying, haven't we all been doing this? Yes, but how do we quantify that? How do you actually show ROI? That's often the elusive part of a project that you know you're doing a good job and you know you're making things more efficient. But how are we actually tracking that and showing that back up to upper management? Because that often can be hard to explain. If you've ever heard me present, I always stress that you need to market, you need to sell, you need to have your elevator pits. Because you will always be competing with other projects. And there's always the new shiny thing with management. And you want to make sure that they continue to know that data is really driving the business and that needs to be something supported, funded, understood. So we'll get more into the different ways we can monetize in this slide. So there's many ways. I think it's helpful to sort of break it into three different buckets. So that first bucket is the one we may be most familiar with. And I don't want to say it's the easiest to quantify. But it's something you could probably start doing right today with your business without having to be the next Uber, right? There's something you could do no matter what your organization. How are we using the data to better optimize revenue, minimize costs, and reduce risk? Though there's sort of the three tenets of any organization, right? We want to make as much revenue as we can by minimizing costs and reducing risk because that's always the big bugbear that can really get you in trouble. So there's also though, I think the second two columns are the most exciting and get me interested in why I'm still in data. I remember earlier in my career, I was an economics major first. And there was sort of, I love tech, but I also like business and though I have to choose, we don't have to choose anymore. A lot of the big organizations are data driven and having both skill sets in your toolkit can be very helpful. And we'll talk more about that. But there's new products and services. Either embedding analytics into products, new things we'll give some examples of companies we've worked with. Things like smart metering with Internet of Things. Are there data sets you can sell? Do you have weather data? Do you have customer data? Do you have whatever you can sell ethically? Of course, we can really get value literally monetizing your data. But I think most exciting, right? And we all sort of hope we'll have that next big billion dollar company like an Uber or a Facebook or whatever of taking data and thinking what new business opportunity can I have with this new data I have available to me? Or the new platforms and technologies. Sometimes the data was there, but we didn't have the scalability, the cloud or the interconnectedness that we've had today. So a lot of perfect storm is coming together when it comes to data to really start the profit from it in ways we could not before. So let's go into the first use case and we'll spend the most time there. Because as I mentioned, that's probably the most practical thing you can start doing today in your own home, right, in your own company. Of how do I improve my core business using data? And we'll go through each one of these. So I will just start by just saying to remember this, right? And I've done it myself. We get so tied up in the project itself, it's really interesting. We're doing a new analytics project or we're building an internet of things and integration. And you sort of forget to stop and say what's the value of this? Because whether we like it or not, part of our job is always marketing. As I mentioned in the beginning, you're always competing with another project, another shiny thing, another budget cut, and you have to continue to keep yourself top of mind. So whenever I start a project, I like to think what's the end goal? And when this is successful, and not if, hopefully. How are we gonna show the value? Are there KPIs we can show? And consider your audience. And I, as a consultant, I work with a lot of different types of companies. And consider what your audience is most interested in. Is it? You're a new startup and it's all about revenue. I had one CEO say to me, I don't care about the cost. We're just in revenue gaining mode. And no matter how much it costs, we'll spend it. That's what you wanna hear, right? How much it costs, we'll spend it to grow the revenue. Often, it's the opposite. We didn't have a good quarter. The biggest thing right now is cutting costs. And we have to show how data can help make things more efficient. Or generally, it's a combination of both of those in a mix, right? Of how do I make the most money and spend the least. Often risk is a big thing. I've been brought into companies because unfortunately they had an audit and something went wrong where they had been sued by somebody. Or depending on the industry, insurance, they live and breathe risk. That's really their algorithms of how to reduce risk. So when you're making your case, you don't wanna think the wrong case, right? You don't wanna go to a startup Silicon Valley CEO and be all about the risk of this new opportunity. They may need to think of that, but they're probably thinking revenue, right? So think of what the, all of these three things are good to track. But think of what the balance is that your audience wants to hear. And often those anecdotes can help. So hard numbers are always good, but sometimes it's the anecdote. And then also think of your audience, right? I had a great anecdote for a CIO of insurance companies. She wanted to see numbers. She's like, yeah, I know the gut feel, but I don't live by gut feel. I live by numbers. So again, I don't wanna overstress that. But think of the right thing that at the end of the project you can say, hey, this is what we did and this is why it was successful. So minimizing costs is often the easiest to show and I don't want to oversimplify it. But I know one of the big ones and if you've heard any of my other webinars, we've had some statistics of the average data scientist, depending on the study, can spend 50, 60, 70, 80% of their day cleaning data or finding data or trying to get access to data they don't have, right? And not only those expensive resources, but it leads to people not being interested in their job. But you want to do the cool algorithms. You want to give the results, not trying to find data or deduplicate data, right? So can you quantify that? Can you say, and I would say be conservative because this is, there's sometimes skepticism with that. People can think you're inflating. So just say, marry the data scientist. She spends just 10 hours a week cleaning data. That's probably a huge under assessment, right? And say she makes $50 an hour, 10 hours a week. Maybe she weeks 46 weeks out of the year because she has vacation and training and things like that. So you could say that's just one person we lose efficiency of $23,000 a year. So if we had better data through master data or data quality or whatever, that's really making your most valuable resources more cost effective. And I wouldn't just limit it to tech. That's often the easier one, but you could say the marketing team. They were the customer we worked with, a small nonprofit actually outside of London. And for each marketing campaign, they obviously didn't have a huge tech department, it was a small nonprofit. But their marketing team before each monthly campaign literally spent a week getting the data together. And they would do the same thing every month. It was really redundant information. So this organization actually got the budget to spend quite a bit of money on a new tool, a new tool set with data quality and metadata and things like that. Because when they actually do the hours of how much their marketing team spent a week out of every month, they should be getting ready for the campaign. They said that's something a tool could do. So that for them, that was their big justification. And that's something management didn't see. They saw the campaign, that was great. They didn't realize how many hours behind the scene once they realized that. This is how many hours just spent getting the data right. Can't re-automate that. So again, in that particular case, now I've used this one a lot with several different projects. It's often an easier one to show because you have hourly rates and you have time and that's sort of an easier one. Can you improve in efficient business processes? So I mentioned that marketing one as an inefficient process, that it just took a long time to get a campaign out. You can't stop doing campaigns. You need to, can you make it more efficient? Our supply chain is often one, we need better material, master data. We need better supply or information. And perhaps you can either get real-time efficiency or percentages and that's often an easier thing to quantify. How much actual percentage of business process improvement can we have? Another one is cost avoidance. And again, be really creative in this. Just look, think of everything with the data lens. And it may just be obvious, but we've never spent that time to actually calculate it. We just know it, but never sort of calculated that beginning and end. One of my clients, we were trying to justify a master data management tool and they want to do some data cleansing. And in this case, it was a healthcare company that actually had to send out physical mailings. I know we often do email, but they did a lot of physical US mail mailings. And a lot of the addresses are wrong and they had them returned. And they were doing mailings every week, every month. And some of them were packets of things. They were a big, heavy thing and it cost as much as a, I just picked a dollar. But they were fairly expensive marketing. And they say you had 500 return per week or 50 weeks. That can be as much as $25,000 a year just because of address cleansing, right? So again, these are just three that I've used that you could use. But just think of that. It might not be something you've thought of before of just sitting down and quantifying it. But that cost avoidance is an excellent one to show that if we were to do this right, because often if you're kicking off a project, there is a spend. And we'll talk about this. You may need training for a new tool. You need to buy a new tool. You might need consultancy to help you get kickstarted, et cetera, et cetera. Or it may be your team just taking a little longer doing things, because it's building something new. So you'll have to justify why that extra spend is gonna save more in the long run. So one cost is mitigating a longer term cost, if that makes sense. The other one, and it's sort of more fun and more, everyone likes to optimize making money rather than saving money, it's just a little more interesting. So in one sense, you can optimize revenue by reducing the inefficiencies we talked about before, right? We didn't have our marketing team cleaning up the addresses. They could actually be doing marketing, right? And I've had that across the board in almost every industry I've worked with. I had, it was in the university, and they were trying to do research. And they couldn't do research because they were cleaning the data, right? So you can sort of often kind of quantify that. Improved business performance, so I keep mentioning marketing, but marketing is good for several reasons. One, it's one of those companies, it's making money for the companies. They often get a lot of attention. They're often very numbers focused, right? Marketing's all about KPI, click through rates, net promoter score, all of that. So we'll talk later in the presentation. Can you track actual metrics that help improve marketing KPI? If we actually had 100% email addresses, our campaign effectiveness would be better. Could it be that we're adding new data sources? If we had social media sentiment analysis that would help our net promoter score, if we could get weather data and put it in our data lake, we could see why shopping patterns, when it was snowing, we shouldn't do our marketing campaign or something like that, right? So often, marketing's good because they do track metrics so closely. And if you can align your efforts with those metrics and how they improved, all the better, right? Advanced analytics, again, you may be doing this and that's often a good one to show because analytics is often showing those very KPIs. If we could do price optimization with this new analytics with this right data, we could make you this much more money because we could optimize price by customers. Customer segmentation is another oftenly used advanced analytics technique. And you can put those two together. If we could segment our customers by certain groups, we could have different, think of the airlines, they do that all the time. I know I get charged a lot. The more I fly with them, the more I get charged, right? Because they know I'm a captive customer because I travel a lot for work. But that type of thing you can do for your own customer segmentation. The other thing, and these three buckets, whether it's a new application, whether it's improving your current business, you can argue about what bucket it falls in, so this may be in the other buckets. But new data-driven applications, so chatbots, right? How many people have gone to customer service and you have married the chatbot talking to you instead of a human? Sometimes that's annoying. Sometimes that's really nice. I work with a university and they actually had, who loves to work with financial aid, nobody, right? So they had the study that students were actually not able to come to school because they weren't able to get their aid. They weren't able to get their aid because calling financial aid was difficult or they weren't embarrassed. So they actually preferred going to a chatbot. They could just talk to this person that isn't a person and get the right answers. Or, you know, I just want to call my cell phone company and get a question about a service. I don't really need to call who talks on the phone anymore, right? And chatbots are great because you can embed things like AI and they can learn better over time just like humans. But you need good data to do that. My one customer who were working on this type of technology and they didn't have the right product codes, so it was hard if someone called in and asked for a part. This was their manual support team. But even a human, they couldn't find the right part because the part codes were wrong, right? So, again, often that's hard for management to understand. Can you try to monetize that? We could save your valuable support reps. Have them focused on the things they need a human being for. And save money by having a chatbot to do the typical stuff that these people don't want to have to answer the same question 100 times a day. So again, that could be something that can be monetized. Recommendation engines that's often excellent, you know, think of Amazon.com, purchase this, would you like this? That can sort of enhance the sales cycle and help monetize revenue streams. Also, can your data actually be sold? Can you sell some different data? Or can you get new revenue streams? We'll talk more about that later. But if you're a non-profit or a university, often to get your grants, there's a whole lot of data-driven analysis. I have one non-profit customer. And I think when we were trying to, again, trying to buy some software, and I think the line that sold it is that data is what's keeping the money coming in. If we don't show how we're meeting our grant objectives and how many people we've served in this non-profit and how we're improving, we're not going to get the grants, so we need that better data. And I'm sure you have something similar, especially if you are a grant-driven organization. Is there a data your company owns? We'll talk more about this. Could we monetize? For who else might be interested in this data we could have? As always, think of ethics, right? You don't want to sell customer and PII and things like that or health information. But is it something that could be anonymized or is fairly non-threatening that somebody else would be interested in that we use every day? And again, we'll talk more about that, but that's often when we think of monetization, you're literally selling your data sets to another group that might be interested. Oh, I'm going, did I just go to the wrong slide? It's been one of those days. Where'd my slide go? I'm sorry, I have not had this happen before. Here we go, reducing risk, reducing risk of fat-figuring your own slide. So this is often something that is not, I don't think, some people love this sort of stuff. They sort of live on compliance. They don't know what gets me up every day. It's sort of, I would rather not be brought in to have us not be sued as a company to do data, but it is something you can't forget. So GDPR, if you're not compliant, you should be, if you have customers in Europe or you have anything in Europe, you want to start thinking of that. You can get sued 4% of your global revenue, right? Or if you're a healthcare company, I'm sure you're aware of HIPAA or BCBA. These should be familiar to you. And that's often what helps drive data because you can't not do lineage, you can't not have data if you're in any of these industries. But those are kind of the big ones. We don't want to get audited, but even in a company, even without regulation, you probably want to reduce your risk. So product traceability is an interesting one, especially with consumers being more interested in the food they're eating and where it came from. I had one company I worked with and they had, they sort of sold food, right? And fish, and a lot of people were interested in where that fish came from. So they actually had the lineage of when you bought a piece of fish or a canned product of fish, you could trace it back to the ship it came on. That's all data-driven, right? And you can't do that without good data of where my food came from. Or if there's a health risk and you're selling a food and there was somehow food poisoning, how do I track that back? So often that kind of product traceability. Health and safety sort of ties into what we have. So it could be employee health and safety. One of the manufacturing companies I've worked with is really interesting. They're doing sort of a driver tracking. They have truck drivers that are driving along and health and safety with any manufacturing company is always top of mind. But they actually had an app that if that driver was speeding, they would immediately know it was a real-time data-driven app. And A, they could track over time where there are certain drivers that were always speeding. Or could we alert that driver real-time, hey, slow down, we're watching you, which I'm sure the driver loved, but it is a big piece of their business where there are certain areas where drivers were always speeding. And we'll talk more about that when we talk about data monetization because they had a lot of great GPS-type data that they were able to monetize because they could see where drivers were going in road conditions. But again, that was one area that actually helped employee health and safety, which was a big part of reducing risk. Your whole business is drivers and trucks, you don't want them speeding and driving off the road. Or could be customer information. A restaurant chain I worked with, and I'm sure you're familiar with this, a lot of restaurants now have nutritional information on the menu, either calorie or allergens, and it's actually some federal requirements there. So if I'm allergic to nuts and this menu did not say that this had nuts in it, and I have an issue, then there's some lawsuits there, right? Or if it's, you know, I'm vegan and it has meat. So a lot of that is data-driven. Then they had to track each of their supply chain food items and where it came from and what the nutritional things were. And that was a big risk for them. You know, audit and fine. So again, that's sort of with the regulation went up front. But unfortunately, again, when you're telling the story of success, were there fines that already happened? Often that's what kind of sales. Remember we had that $50,000 fine last year? We don't want that to happen again. Again, that's, I don't love to start with that kind of stuff, but it often gets people's attention. Litigation is similar. You know, was there ever a lawsuit based on data? Could there be a lawsuit? And that often gets people's attention as well. So often it's a combination of all of those things that actually puts together and tells the story. And I would say, you know, we just talked about part of your job is marketing. Part of your job is also finance. Some people, nerds like me, find that sort of fun and interesting. Some of you may cringe. That's not why you went into IT. That's why you didn't go into finance. But it's good to at least be a little bit literate in what people are looking for. So some of those items you might want to put in the back of the envelope calculation. Maybe just name drop or put it in a PowerPoint presentation. But I've had luck just doing a little extra credit and actually putting it in a full financial spreadsheet. You may want to get help from finance if this isn't your strain. But often they're happy to help you because this is helping them, right? Is there a projection you use that you would like us to fill and of how we can best show you the ROI from our project? I had one finance team, they sat down next to me and we did this together and it actually was very helpful for me because there were things they looked at that I had not thought of. And we'll talk a little bit about that. So it's often good. Some of those things we talked about again is benefits and costs. Often it comes down to that. So could it be a one-time benefit? You know, we got a research grant as a result of this data or we would get a research grant. If we could get the data about how we served our members, we could potentially get this $50,000 grant or whatever it is, or a million dollar grant. Could there be recurring benefits? So the fact that Mary's more productive because she does 10 hours a day cleaning data, that could be quantified over time. Could we reduce the mailing costs again over time? So just like benefits and costs, you might have a one-time purchase of software. You might have to do initial training but there's also maybe recurring. So it could be subscription costs over time or maintenance. That's often when people forget about the software but if you're doing that kind of software structure, there's also other costs over time. You might also want to consider CAPEX versus OPEC and just dropping those names makes you sound like a finance person, right? But that is often why some people are going to the cloud because capital operational expenditure is easier to justify. But that's not always the case. So again, speak to finance. I had some clients that they actually wanted it to be a capital expenditure because of the way they were doing their books. That's what they wanted to see. So again, if you're really going to put together the case, it's probably good to know what the goals are. Again, I had one customer that said it does not matter costs. We're only looking at revenue in this quarter because we're in startup mode. Other companies are very cost avoidant and so you want to, when you're making the case, make sure you're making that right case. Another good thing to think of is kind of a realistic breakeven point and even quantifying that. So often, and it's just the reality, to start making money or saving money, you often have to spend money. And so often your chart that you put together sort of looks like the one on the right, that there's going to be an initial period where you're being less efficient because we have to buy some software. We have to train our people. We have to get some consulting. The people we're working with are spending time setting this up. But at a certain point, you start being more profitable. So it might be a good thing to throw in. Hey, there's a certain break point that we know it's going to be more expensive but by August, we really should be able to show you some value. And that's good to level set too because you could have sold this great story in a month later, they say, okay, we're not making money yet. So you're going to be clear how long it's going to take and level set with your management that's the right initiative to show. Another thing we should have talked to but I wanted to show an example of I'm a big fan of data quality KPIs. If you have any data governance council meets, I always love to show these them each council meeting, how are we improving over time with data? It's often good to actually link this with business KPIs where you can. So again, marketing is always a popular one because they look at that so closely. If we had better, if we had complete email addresses and 90% of them were accurate, we augmented some data, we can increase campaign effectiveness by X. And if you're in the column, you're in IT, it might be good to get a sponsor with you from someone like marketing that will help you commit to that. Yes, this is really important to us. And start looking at, I've had some companies that would start email campaigns and historically, they've been in business for 50 years. Both of their customers had physical addresses but didn't have email addresses. That wasn't part of their business process. So you might wanna start looking at that as well, that what is the best way to start monetizing information and what data is important. And so maybe we have new data sets we should be looking at that we hadn't before. Maybe it's email, I mean, I'm sorry, maybe it's social media. If we're looking at the promoter score and wanna start tracking social media activity, maybe that's something we need to track. So again, trying to, and here I think the examples, we just have some numbers in terms of what's important, but you can actually try to link this actually to try to get commitment of actual physical revenue numbers. And this can be done. So some of you may be rolling your eyes and say, yeah, that sounds great, but really AI don't time for this. Can you really justify these numbers? And yes, and here's a case study. It's a little bit dated now, but we use this partly because one of the people on our team, if you're familiar with Nigel Turner over in the UK, he helped lead this project, the project initially and kick it off. And he spent a lot of time at BT British, I guess it was called British Telecom in the past. So again, they were multinational telecommunications company. And as any big company, they had troubles with business agility and regulatory compliance and customer satisfaction and all of the regular things. This is that perfect case of just business as usual activity. And part of the problem, they were able to quantify it was poor customer data, poor supplier data, inaccurate inventory, just things like trying to get a bill out to people. Well, they weren't able to quantify it. And you can see the number there should have jumped right out to, they were able to quantify over $800 million directly from their data quality improvement. So it was again, part of all of the things we actually talked about of avoiding some costs by having better data. Actually, they were able to kind of target it, they had improved some revenue and productivity games. It was a big one for them rather than wasting time kind of trying to get the data together. So this was well documented. It was actually published in Gartner. We have the publication number there if you want to read more about it. But if there were any nice errors in the call, this proves that it can be done and actually spending the time. So of course they probably on the project, this took time to do. It might have been a little inefficient. They didn't get maybe as many reports out that week because they were tracking some of this, but in the long run it certainly paid off in terms of paying off the data quality. But also they were able to show results from their work. And this is what we always want to do. So kind of back to my point in the beginning it's so easy to say I'm too busy to do this. And the most important thing people care about is the deliverable I'm going to give. And that's true. And they may not even realize it, but just take the extra time of where we today in terms of productivity, revenue, risk and then where are we in six months or and keep tracking that especially again, I'm a big fan of things like dashboards and data quality and add profitability to that. So hopefully this gives you a little bit of hope that yes this can be done and these numbers can actually realistically be achieved. So more into maybe the traditional data monetization what new things can we do with data? What new products and services can we offer? So I like to call this kind of using data for strategic advantage. So there's some. One I've seen several of my customers use this idea of embedded analytics is sort of a hot topic. One is it was a big distribution company and they were actually able to sell analytics back to their customers. So several things. One was for their very large customer think of a big retail chain. They actually had thousands of sites across the US and they weren't able themselves to even see the buying patterns of the efficiencies across those companies. So this distributor was actually able to sell some of that back to them because they had better visibility into that than the customers themselves. Also they had some data sharing agreements and it was anonymized. They were able to share with the customer how they were comparing against industry average. So you are a US wide company. It looks like your stores in Massachusetts are a little less efficient than your ones in California. You may want to look at that but compared to industry average you're actually doing better than most. That's something that these companies had never actually seen and they were willing to pay for that. Things like smart metering. We'll have a use case actually in the next slide we'll talk more about that. Think of internet of things and data that was never available before could be a new service of actually metering energy usage and things like that. And again the one we mentioned before actually getting revenue from data sets and these are all real cases. So think of a weather company meteorology. I live in Boulder, Colorado that I have a lot of weather nerd friends and two of them own their own company just selling weather data because why is that important? You're a retailer. You want to know what weather patterns for sales cycle. You're an engineering company. You're a drilling company. You're almost so much depends on shipping company. So much depends on weather either macro weather patterns across the globe or macro weather patterns of a certain region. So that's something again, you might think I do weather for a living. Who cares? A lot of people care. And that's not new. They've been doing that for a while. But again, think of that with your own company. What do we have that someone else might be interested in? The nonprofit I mentioned is actually very, very data savvy. They have a very unique demographic that they're trying to help. And of course they anonymized it but a lot of universities were very interested in some of this data for their own research and they were actually able to get a large chunk of money for some local universities for doing research on that data because nobody had been in business for I think over 30 years and no one had sort of time sequence data for that type of demographic for that long of time. So again, who would have thought that the small nonprofit actually was able to monetize their data but they were very unique? Can you probably have something unique? The shipping company I mentioned that again was Kingett's Drivers. Again, I think Drivers probably weren't a fan of that but because they had this GPS enabled technology a lot of their shipping routes were in very rural areas where yes, we have Waze and we have MapQuest and we have a lot of Waze we can now get geo location data on our cell phone but not in these rural areas. And it was a very unique kind of big truck go on this road and there are any gutches that you can't cross this river because your truck is too heavy or whatever, right? Nobody else had that really unique kind of information. So they're now investigating, creating their own kind of ways or MapQuest or whatever for shipping in rural areas because again, nobody else has that. They're the only ones that really have that data. So again, what did your company have that you could either sell out right and just anonymize and here's my data set or is there some sort of new product or service either to your own customers or new customers that maybe you hadn't thought of? Here's an example and my next use case is a company we worked with in the UK that really did both and so I'm a big fan, is it either or or could it be an and? If you wanna do some of this new sexy stuff you often are also helping your core business that you've been doing for a long time because the beauty of data is that it helps a lot of different areas. So this was a UK energy company we worked with in a way this is a really strange use case and that energy is something that's decreasing and they're actually trying to incent their customers to use less energy. So what product company does that? I sell Coca-Cola, I want you to buy less of it. That's just weird, you don't normally do that, right? But this was a type of industry that you do that. So yes, they could keep getting more efficient and more and more efficient and more and more efficient but unless you're getting more revenue that's kind of a never end, that's not a winning game. So they had several issues with that and what they wanted to do is really get into the smart metering and smart home products and we're all sort of used to this now but this was sort of several years ago where I wanna seek my energy home usage from my cell phone. I see that it suddenly got cold and I wanna turn down the heat because I'm spending too much money during the day when I'm not home. I wanna see my energy tracking over time. I wanna see that this window is leaking and your heat and I could maybe buy some weather stripping from the company, right? All of that and they wanted to head to that and they did and they were moving all from traditional databases to Hadoop and Internet of Things and all this great stuff but they also had trouble with just their core data just getting the bills out and the payments correct. Getting actually it was sort of funny one of our consultants on site. He had first have a technician come to his house to fix his connection and they couldn't find his house because he had the wrong address which was exactly and he was late to work and all this big problem and it was funny because that's exactly what we were trying to fix. And so they knew to get to this next generation of data and data monetization and they had the signs on the wall that we wanna be a data-driven company they had to start with a basic. So they had some basic data architecture. Do we even know what the critical data elements are? Should we start with just customer name address, billing information? Do we have the governance around that? What is data quality? As I just mentioned, it was not good. So they needed to improve that as well as things like that platform schedule. Lily, I can't do this on a relational database we need to move to more of a big data platform. So they did all that and the beauty of that is that that was both of those pillars. It was strengthening their traditional data model and it also set the foundation to really transform their business into new smart meeting homes, smart products and I've been sort of following them. We're not there on site anymore and they keep watching new products because once you have the new data, I mean your data in a nice format you can do these new innovative things much more quickly but you have to get that foundation. So moving ahead, another company we worked with and actually this was in the UK as well. Another one that kind of leads into that second of how do we have a whole new business model? Again, Telco is sort of a, I don't know, it's a commodity, right? A lot of it is just the network and you should have expected your phone company is gonna have a good network. So they were able to use their data to improve their core business. They could kind of track the phones and see where performance outages were. They could see how customers were using their product to see how it was working. So that was good but the bigger part was really new data monetization opportunities. And this is where the creep factor comes in and this company actually was very good at anonymizing but your cell phone company knows a lot of what you're doing. It knows where you're going, it knows who you're talking to. There's a lot of data in there but that can be very valuable. So one of the many things, they had a long list of things they were doing because they do have that valuable data. And that's a perfect example of our core business is interesting enough and that we're making profit but we have really interesting data and how can we make profit off the data itself? So they anonymized all this data. They knew everywhere people were going across the city. And they were actually able to do some things. They were able to sell some of that, they called it footfall analytics back to city planners to say, okay at rush hour, where are people going? Should we build a new train platform? Should we build a new pedestrian walkway? Because they could actually see realistically where people were going in the city. They did some things with the retailers. So I think we're all familiar with kind of loyalty programs and they're great and you get a discount but the other thing they're doing is they know what products you're buying and your sort of patterns and things like that. So yes, there's a benefit that they're also getting value from your data as well. But several of the large retailers in the UK didn't have that program and they weren't set up for it. So they actually used the footfall traffic from the cell phone to see where people were buying food and actually how were their store layouts. So I go to the grocery store, I bill by milk and then I walk all the way across the store to buy cheese. Maybe we should buy milk and cheese together. So they were actually able to see some great retail patterns from cell phone data which was kind of interesting. They also did it internally. I did not opt in for this because I am a day paranoid, I'll say that right. But it was neat because you had your cell phone and they could do patterns of, they were actually looking to expand their campus and how employees were traveling between buildings. Do we need a new lunch room because everybody from all six buildings is going to this one lunch room we have. Do we should we buy another? How are office, conference rooms being used? When do people leave every morning and in the evening? So they actually, and they showed some interesting graphics of how their own employees were moving back and forth from that cell phone data. So again, cell phone was their, telco was their business, but data was their real business, their augmented business because they were actually get a lot of revenue from that as well. So again, hopefully some of these are like bulbs in your own mind of where you might be able to have some new ideas. So the third bucket is, is I think what we all often we think of with new business models, who can be the next Uber or the next Facebook or the next whatever. And I've said this before and I'll say it again, it is sort of no surprise that some of the largest companies on the planet and the most successful companies are data-driven, right? Think of Amazon, Uber, Lyft, all the big ones we all talk about are all data-driven and we're driven kind of from data, ways to use data we hadn't before. So to go more deeply into that, and I think this is what I think is just sort of fun to think of new ideas. So a lot of cities are doing some smart city initiatives with IoT, smart parking, this is Wednesday I had years ago and it makes so much sense of there are sort of an internet of things sensors and can you see where the parking is available from your smartphone? Football analytics, I already talked about. There's been some fun things actually, a city near where I used to live in Italy, Switzerland, they actually had trash cans and I tried to find the picture of it for this presentation that I couldn't it was a couple of years ago. They had trash cans that would tell the trash collectors when they were full because they had sensors on them. That was a wealthy town in Switzerland, Italy border but they could have sort of afford to spend the money on telling when the trash cans were full. But it's interesting to think of all the other things you could be doing with things like smart city and internet of things and pedestrian tracking and things like that. The one we're all, it sounds funny when we say peer-to-peer ride sharing is sort of like saying facial tissue when everyone says clean effects, right? We just say we're gonna take an Uber or they're so ubiquitous now. It's even funny to see that word, but that's what it is, right? And that's all data driven. And I have an example on the next slide about that. It's kind of how they use data. Social networking is another huge one. And that's basically graph type databases with large datasets and relations between people. Again, they've had some negative press some of those in the news. So ethical use is always, but there's always some really great opportunities. And I will never be a billionaire because I always seem to miss. I still remember when they had cell phones out and I said, why would you put a camera on a cell phone? I still sort of wonder but maybe for some of the next new trends. But I have a friend here, Boulder, Colorado was near where I live. And it's a startup central like a lot of cities are. And I had one friend that I used to go rock climbing with and he was always talking about this cool. He had a startup, but you know, so many people do it. And I asked him what it was and it was basically social. It was way, it was about seven years ago. And it was an API that kind of connects social media sites to get usage data and you know, whatever he sold to about $150 million a few years ago. And now I see him on Facebook traveling around India and he basically found a clever way to use data from social network that was valuable and ever company that he sold it for about $150 million. So again, that's all data driven and so many people now are saying, okay, there's data. What can I build from that? Smart buildings, one of the manufacturing company I'm working with are actually looking to put sensors in some of the building material to see sources of failure. You know, think of a big high rise. You know, is the cement in that going to fail or the window panes maybe wearing out and that talk about risk, right? If something goes wrong, there's a big lawsuit but it's also for maintenance costs and things like that. I mean, I just, I get a kick out of reading the news and how many industries have become data driven farming is a big one, right? You think of that, you know, if you're going to be a stereotype just a very difficult farmer and is, you know, weeding the fields, right? Those are, that they're probably the most technologically advanced with things like things in drone and technical connected devices and that makes so much sense because the scale of these farms can be massive, et cetera, et cetera, et cetera, right? And I think that's where a lot of the excitement, at least for me, an opportunity is in the business. And again, so many of the big companies that are making news in a positive way with revenue is data driven. So let's go to Uber because that's almost the classic and this is the one I did not work with Uber. The other ones we did, but this one can't take any credit for it. I did go to an interesting presentation. There's an Uber office here and I'm a big fan if you haven't gone to the meetups. Yes, I go to data meetups on that much of a nerd but hopefully there's other nerds like me on this call because they can be really interesting. And I had the engineers from Uber actually showing, they were very open with what they were doing. It was fascinating of how they actually linked all this data and the algorithms they used and the platforms they used, really, really interesting. But that's a classic where a whole company was built off data. So GPS data is sort of the one you think of, that's how you know. And I get a kick out of riding one and seeing where my little car is going on the picture, if nothing else, you don't have to pay, right? It's all sort of data driven payment systems, a lot of that. The user rating system, I can say you can see if this driver has a good rating and all that kind of stuff but they do a lot more than that. They can link with things like airline arrival to kind of do trends of what demand is going to be and volume pricing and having the cars available to see. I was actually talking with, again, I'm a nerd. So when I have an Uber, I kind of ask kind of how the experience was and that the Uber drivers were saying that was actually very helpful. They could kind of get a sense of when people were gonna arrive and where to kind of head. And then the algorithm. So there's the data itself that they're getting, et cetera, et cetera. There's a lot of really interesting data they use but then the algorithms to do, setting the pricing, how to match the drivers, all of that. So again, yes, there's cars involved but they don't buy them, right? They basically own the data. So really cool example of someone was thinking outside the box and using a lot of data that was out there to create this and left to and there's others. I don't wanna just sit in the sales pitch for Uber but since that is so ubiquitous, I thought I would put that out there. And sort of my, the excitement to my voice didn't show. I'm a big fan of these type of ideas and I've used this slide in other presentations but this idea that if you are a business savvy person and you are in data, this is a great time for you. So either you're gonna be the next Uber person or like my friend traveling India because he sold his great API idea to a large company, all data driven. Or even as we spent a lot of time in the beginning with your regular job, right? Are you showing the ROI from your project? And are you thinking of ways to benefit the business that maybe hadn't been done with business? Could you have a more efficient marketing campaign? Do you have a great new analytics algorithm that could really help with product pricing or whatever it is? So put your business hat on and that, everyone loves to talk about that, that elusive data scientist that is the sexiest job of the 21st century from Harvard University. That's their famous quote, right? What makes a data scientist more than just a data person? A lot of it is that business savvy, right? Knowing how to use data for strategic business advantage. And a lot of it, let's talk quickly about that last bullet supporting organizational chains and that's a big thing to think of as well. So there's data, but if you're gonna change the business model and the process of how things are going to work, one of the companies I'm working with is a big billion dollar multinational and they wanna do a lot more data driven and very wisely, they're spending as much time on the organizational chains and the business process chains as anything else. So yes, you can, you know, how we do use an Uber and how we use a taxi is very different. That's just a personal, non consumer example. This could happen in the organization. If you're changing how people are doing business, don't forget that there's an organizational and a training and a business process change as well. So in summary, the data driven economy is not a buzzword. It's a real thing and companies that are embracing that really are doing better, not only with their core business, things like reducing costs, increasing revenue, minimizing risk. And hopefully I gave me some tools in your toolkit that at least maybe one of them was new that you hadn't thought of a way to kind of monetize your data. And then this idea of new products and services. Could it be something like analytics sold back to your customers or a new product, you can data driven product? Or are you that unicorn that's really gonna have that brand new business model with data that is publicly available or you can purchase or you can leverage with some of the new scalable data platforms. That's, you know, as I mentioned, I've got some weather data nerd friends. And one of my friends who's in weather was actually the son of a father who was in weather. And these are their dinner conversations. He said, son, the amount of data you're able to get. Now we had a building that housed even just a fraction of that data. They had a whole up in Wyoming, a whole data center and they still do. But the amount of processing power that's available either in the cloud or on laptop or on a lot of these big data systems, you can do a lot of these analytics and store a lot of data that you just couldn't before. So you do have data at your fingertips that you didn't have. So this is us, we do it for a living if you're interested more importantly, there's some upcoming webinars. So again, if you can join us next month on Data Lakes, which actually ties into what I just said, there's a lot more scalable platforms out there that weren't available before. Catch some of the, this will be on demand after the session. And I'm going to pass it back to Shannon to open it up for Q and A. Hi Donna, thank you so much for another great presentation and just to answer the most commonly asked questions, I will be sending a follow-up email for this particular webinar by end of day Monday with links to the slides and links to the recording and anything else requested throughout. If you've got questions, go ahead and submit them in the bottom right-hand corner in the Q and A section of your screen for Donna. I see a couple questions coming, that haven't gone through in the chat for a bit more. So what would have been the ROI on that BT program? I did not see what he meant. Well, the actual, the benefits were $800 million so that they were able to quantify. I did not see what the percentage was. That was the money that they had quantified from that data quality improvement program. That's incredible. It is incredible. Yeah, either way, no matter what percentage that is, I'd say that's a nice little savings there. Yeah. No questions, everybody. If it's summer, is it too hot or is it too cold in the Southern Hemisphere for y'all? Like, Yeah. Well, there's one that's just the same. I've got questions submitted in the Q and A section, but I see how to monetize data, I'm doing that today, but I don't see how the architecture is being leveraged. The architecture is being leveraged. Well, the architecture can be leveraged in several ways. So in one sense, there's the data architecture. So actually, if we go back to the telco example we mentioned, their whole effort started with, I'm sorry, the energy company, it started with a data architecture project, basically even looking at what types of data, the big buckets of data, and how, which data we start with. So that was more of an enterprise architecture, architecture, but also the physical architecture. So think of Uber and Lyft, they can only do that because of the processing power of a lot of the systems that aren't available before. So, or the energy company, they move from relational databases to Hadoop. The shipping company mentioned they're doing kind of real-time data streaming on a whole new data hub platform. So part of it, and that's a good point, part of it is knowing what to do, and then part of it is making sure you have that data architecture foundation to be able to scale, because a lot of these ROI, or the data monetization is kind of built on some of the scale, and that was different than what was that we didn't have before. Yes, we've had weather data for a lot, but we were able to scale it and make it real-time and pass it out to folks. The next one I see in my answer, because I'm excited to answer it. The question was, data governance is often driven by taking out costs and reducing risk. How do you see it driving revenue? Love that question, because I'm a big fan of data governance. There's two parts, there's the carrot and there's the stick, and the stick is often don't do this, don't do that, and it's something that you need, right? I don't want to share PI. We have this new great data platform in this cloud where we can do exploratory analytics and please don't put customer PII out there, and I've seen customers get that wrong and had fines and it was horrible. But what I like to do with governance, it's all about collaboration, because most people, all people generally in the company are in adults, right? And it's more about, do we have visibility into what other people are doing it, what other people are doing? And I often call kind of your steering committee a collaboration team. And so one of the companies I'm working with now, again, they're actually looking at to do data monetization. And it's actually driven from their data governance team where they have all the teams across the globe and they get together and say, what are we working on? And are we doing, do we have redundant efforts? Are we prioritizing efforts in the same way? So that's sort of your typical governance. But the reason they do that is, and then what's the aha moment? How can we work together of, oh, I didn't know you had this information could we use it this way? And that's how I see governance driving revenue because it gets to those new ideas that people hadn't thought of. And also with governance is often by definition cross functional. And that's where you have marketing in the room with sales, with development, with data folks. There was one retail company I worked with, one of my favorite quotes in data governance ever. And I was on the phone, so I couldn't hug the man, which is probably good because they would have freaked him out. But we had done basically a pure data architecture. It was sponsored by marketing initially, but basically of customer email address and how it cascaded across the systems or how it didn't. And we were able to see that it didn't cascade to the loyalty program. So someone had changed their email and the most loyal customers, their email wasn't changed in their loyalty program. And it was frustrating. They couldn't ship information correctly because information was wrong. And we were able to link just email address and physical address back to real, that was that first phase of how the data monetization, we had the head of sales thing. Shouldn't we kind of sponsor some sort of data cleanup and shouldn't we govern our sales people better? Like I said, yeah, who is the head of sales ever said that, right? But he said that because he was able to see the impact and he was bought in and he was the biggest fan of governance because he saw how that would solve his problem. So long winded answer to two, one, it can help justify why we need to do these things. That was the example of sales or it could get the right people in the right room to have those new aha moment of engineering and could say, I didn't know we had data on customer uses, passion patterns. We could build new product X as a result of that. So it's both, is that collaboration part of governance rather than just the slapping people on the risk part of governance? Sorry, I got excited about that one but I'm passionate about that topic because I've seen it work in a bunch of companies. So starting back in here. So what is the best way to overcome the business reluctance to spend the money needed to actually get a program properly launched? So I would say going back to that first section we had on the, or let's say I can find my own slides which you've seen I'm not had luck with. If you can quantify ways that this can optimize revenue, minimize costs and reduce risk, that's often how you can overcome the reluctance. I'm also a big fan if you've seen some of my other presentations I'll literally tell the story and draw a picture. Here's your customer. Here's their customer journey. I often link data issues right back to the customer journey. And if anyone's doing any design thinking or agile development, big fan of, here's my customer from data. I'm just talking retail. I don't know what the requester of business was. They try to find our product on the website and they want to purchase and that isn't linked or we're not giving them enough information at the marketing funnel or whatever. And often in addition to the numbers you often have to have the numbers too but that kind of wins the hearts and minds because data can be very difficult to understand. And I myself, if it's not my project and someone's talking about why they need HANA or Hadoop or whatever, even my eyes glazed over but telling that story, if we could get all of the customer pipeline information and link that to our loyalty program we could do great analytics on why from source to target we have the best customers or whatever it is or we could do new campaign acts or we could have new product A. And I would also focus on, especially if you're selling to business people some of the revenue opportunities and not only the risk and cost. They just, these are people to get excited about the shiny things. So tie it as well as those three buckets of optimize, minimize and reduce and show them realistically, that I kind of showed the beginning, show them that yes, it's gonna cost more in the beginning but bear with us all those cool things I just said give us six months and then you'll start seeing the value and you may have to take a little risk in committing to that but that often kind of telling that story helps them understand that yes, there is not front cost but it will pay off, believe me, that kind of thing. So along those same lines, kind of what these last few questions have been would you comment on building executive sponsorship? We get that question a lot on a lot of different ways and a lot of different webinars. Yes. And I'm always a big fan of any of my workshops. I always start with just kind of what's your elevator pitch? And I think you have to have that for any, you know, what's the elevator pitch is the classic you're in the elevator with the CEO and he asks you what you're working on and how are you gonna explain that over two floors? And so why is the project you're doing exciting? So often we'd love to sell it to the CEO but what other groups so when you put together these pain points who would be your advocates? Is it marketing? Could they be bought in? Often come to the meetings with someone like that. Is it supply chain that would love the better data and have them do some speaking for you? I'm a big fan of that. So you can tell the story and you should do interviews. Go talk to some people in the field. Can you talk to? I actually went to a retail store and talked to one of the sales guys. I surprised the heck out of them asking them questions about data but what surprised me, he immediately got it. He said, yes, if I had data on this YV I could sell more and I brought that story back to the C level. And so I went to one of your stores and I asked one of your sales guys and he said he could sell more product if he had Y. And so again, just be kind of creative and you have to do the numbers but it's often those stories you tell that really sell it and have other people on your team not just yourself saying it, get some advocates. Well Donna, thank you so much but I'm afraid that does bring us to the top of the hour here. Thank you for another great presentation and thanks to our attendees for being so engaged in everything we do and all the great questions. Again, just a reminder, I will send a follow-up email by end of day Monday for this presentation with links to the slides, links to the recording and so anything else. Again, Donna, thank you so much. Really appreciate it as you've got posted up there. We hope to see you all next August, August 23rd on Data Lake Architecture. Oh, a really great topic and that's a hot topic. Thanks everybody. Thanks Donna. Thank you. Bye.