 Hello and welcome, my name is Shannon Kemp and I'm the Chief Digital Manager of Data Diversity. We'd like to thank you for joining the latest Data Diversity Webinar, Data Monetization. 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. If you'd like to chat with us or with each other, we certainly encourage you to do so. Just click the chat icon in the upper right-hand corner for that feature. Third questions, we'll be collecting them by the Q&A in the bottom right-hand corner of your screen. If you'd like to tweet, we encourage you to share your questions via Twitter using hashtag dataversity. As always, we will send a follow-up email within two business days containing links to the slides, the recording of the session, and additional information requested throughout the Webinar. Now let me introduce to you our speakers for today, Michelin Casey and Q. McCallum. Michelin served as the Federal Reserve Board's first Chief Data Officer. She is a senior executive with over 20 years' experience helping organizations use data and analytics for growth, transformation, and achievement of strategic objectives. Her focus areas include data strategy and innovation, data monetization, and building high-performance data teams. Q works as a professional services consultant with a focus on strategic matters around data and technology. He is especially interested in helping companies build and shape their internal analytics practice. Among other publications, he is the editor of the Bad Data Handbook. And with that, I will turn it over to Michelin and Q to get us started. Hello and welcome. Thank you so much, Shannon. And thank you for everyone out there who's joining us today. I want to give a big thanks and shout out to Shannon and the Dataversity team for pulling this together. I've had the pleasure of speaking here multiple times, and it's always a pleasure to come back. It feels like coming home with this community. So we're going to talk today about turning your corporate data into revenue and really focus in on a variety of aspects of data monetization. So just one key thought to start us off with, there are lots of different ways to generate revenue from your data, but it takes planning and it takes effort. And we're going to go into this over the course of the next several minutes here in the slides that we have. So we all know the story. Data has exploded and companies are sitting on piles of data that are going unused. But beyond just using data to drive internal growth, we've seen an interest in data monetization trending up over the past year or two. And I think we both think that over the next few years, particularly as organizational data analytics capabilities continue to mature, we're really going to see data monetization come into its own. So we're really pleased you can join us for the next 50 minutes. So just want to talk a little bit about who we are and what we do. So as Shannon said, I've been in the data space for a really long time. I've been a Chief Data Officer twice, once in the Governor's Office in Colorado and once with the Federal Reserve Board. And I've been a lot of consulting with companies and industries spanning from financial services to healthcare to consumer packaged goods to help them leverage their data and analytics for growth. Q, do you want to introduce yourself? Sure, thanks. And also a huge thanks to Shannon for the intro earlier. I guess I'll just add on to that a little bit. Similar to Micheline, I've been in this technology field, whatever we're calling it these days, data science, big data, some flavor of data for the past 20 years. And seeing that number makes me feel a bit old. I try not to think about it too much. As far as where I've been, similar to Micheline, I've spent quite a bit of time in the financial services space. That's actually where I started my career. And at the time, I didn't realize it, but I was learning a lot about what it meant to work in a data-driven business, what it meant to focus on turning data into smart decisions and into revenue. I don't work in the financial space as much anymore. I still do have some ties there. One reason I bring this up is because, and related to the subject of data monetization, a couple of years ago I published a short paper with a friend who still works in the trading space, Ken Gleason, called Business Models for a Data Economy. When Shannon sends out the link in a couple of days, we'll be sure to include that link as well. Might have some additional insights based on what we talk about today. That's about us. I guess a little bit about you. Shannon has given us sort of a high-level breakdown of what sort of people are joining today. And we know that you're all a group of leaders in technical architects and so on. And we know that you've seen all the media pieces, as well as the vendor solicitations about the power of data. We also know that you can have all the fancy tools and data scientists you want, but there are no use unless you can actually use your data to generate revenue. And that's why we're here today, because monetizing data is a core part of our mission. Thank you. So, as far as what we do, Michigan and I came together a short while ago to create a new business. We call it CDO LLC. And the focus was to really fill that strategic leadership gap in the space of data. So, our work is focused on four pillars, the first being data strategy. In other words, that's helping a company develop a roadmap on which to execute their data activities. The next one related to today's webinar is data monetization, how to make money from all the data you collect. Third one, very important pillar of these days, I think it's going to grow in importance over time, is data ethics. In other words, yes, we're all in this to make money off of our data, but let's do it in a way that keeps us out of trouble. And last but not least, we also offer what we call data science assessments, which is a way for us to come in, evaluate your shop, help you identify problems and also opportunities. So with that, I'll turn this back over to Michelleine, and she can kick us off. Thank you. So, data analytics, leading to new revenue streams. I think that's all what we're here to talk about and what we're interested in bringing back to our organization. So, as we talked about earlier, and you're all familiar with this, there's simply more data than ever, which gives you potentially unlimited opportunities to create value from it. This isn't new. Commercializing data has been around for years now. My first job in the data space was with a data broker that no longer exists, but was a spin-off of Equifax, the credit reporting company, and is mostly now part of less of its nexus, but also comes in Reuters. Companies like this have been around for 20, 30, 40, 50 years, aggregating massive amounts of data to package up and sell for risk mitigation, for credit scoring, and for broad customer segmentation. Back in the day, I think that was a little bit broader, lifestyle and behavioral, but it certainly didn't go into the level of detail that organizations are able to go to today in terms of micro-personalization services. But where we're at today, the monetization efforts and the commercializing data efforts certainly look different. We've got very different engines of data today. We've got interactive data, interactions data, so you know, use social network relationships. We've got, of course, transactional data, and more and more of that is going online. And then exploding, particularly in the last five years, have been automation data with the Internet of Things, being able to pull logs off the servers and things along those lines. So all of these are really driving demand for more interesting data sets across lots of different ecosystems out there, which is allowing companies to do everything from fine-tuning micro-personalization services, including geolocation-based services or digital companies developing data products to crowdsourcing information, right, with your, like, ELF reviews or four-square data or even as far as understanding commodity prices in developing countries. And this is all backed, of course, by computational power, cloud, mobile, IoT, et cetera. But to add to that, right, we've got this really hyper-competitive business environment now where disruption can come from any corner, and hopefully you're going to be walking away from this also thinking about how to disrupt some of your own business models with some new revenue streams, or leveraging data truly becomes a competitive advantage. So the types of data that we're going to be talking about today and providing examples on include everything from pricing data and credit data, which is sort of traditional and, of course, customer transaction data, but also automation data, again, IoT or geolocation or event detection, app usage and web traffic, and reviews and ratings and how that can tie into your ability to actually do more with your data and whether it's developing new business models or just bulk sale of your data. Keele? Exactly. And I know it's somewhat cliche to start these sorts of talks off with a definition, but I think it's very important in this case. When we talk about data monetization, we're really talking about the act of turning corporate data into currency, right? And the reason we give the definition is because we're not just talking about bulk sales of data, right? You can just collect a bunch of data. You can sell it that's one way. There are so many other things you can do. You can create actual data products. You can use data to barter for something else that you want. Something that's also very interesting and a lot of people don't think about when they think about data monetization is turning that data back inward to focus on product or service enhancements, otherwise known as customer experience. Initially when I talk about the sort of thing a lot, I think a lot of our ideas are inspired by and supported by the work of Doug Laney at Gartner. If you haven't seen his material, I think you should check it out if you're interested in data monetization. But zooming out, it's really about getting a competitive advantage. What can you actually get from your data? There's an increasing demand for interesting data sets across a variety of industries, variety of ecosystems. Every company out there wants to better understand their customers, their businesses, their patterns, both their customers' patterns internally, macroeconomic conditions, everything to drive a decision. Point being, it's about being creative when you're trying to find ways to make money off of your data. Especially when you consider the point we made just a moment ago about improving the customer experience. I can give you a quick story about a missed opportunity that could have been very easily data-driven in a very primitive way, which would be Amazon Prime. I can't see the room, but I'm guessing if I had to ask how many people here had Amazon Prime, it's probably quite a few people. And if you got Amazon Prime when it first launched, I think a few years ago, you might recall what I saw at the time, which was you're about to check out from Amazon and you get this sort of display off to the side that says, hey, you should try Prime. It's great. And I think, okay, yeah, I'm sure Prime is great, but I'm in the middle of checking out. I don't have nearly enough information to make a decision right now. Point being, had Amazon actually just shown me what I had spent on shipping for the past year, it would have been an easy win. I could have just signed up for Prime right then and there. Instead, I had to go back and go through my past shipping history myself in order to realize, oh, wow, Amazon Prime would have saved me tons of money on shipping compared to the annual membership cost. Point being, Amazon missed an opportunity with just a little bit of math summing up my shipping costs for the past year. They could have converted me to a Prime customer several months sooner. And that's just one of many such stories around missed opportunity. Micheline? Yeah, so, and I'm sure all of you can think of examples like that. I certainly have some one-off shares. I've been, I've had a loyalty card with a national clothing chain for at least 15 years now. So they've got 15 years at least of product history for me, products I bought in the store, products I bought online, how many, what prices I paid, how much I bought from sale versus full retail, what the combination of those products was, et cetera, et cetera. Day and time of week that I'd like to shop. But with them, my customer experience is anything but highly personalized. So I'm constantly being offered products I would never buy from them. When they go to the counter, there's no actual acknowledgement that I've ever shopped in a store, one of their stores previously in their national chain. So they're not doing anything creative to leverage the information that they have actually about me. So in terms of improving the customer experience with me and upselling the additional products, they've dropped the ball. Whereas American Express, for example, specifically curates their ads directly to my pace. And sometimes I'm quite surprised how much they know when I don't actually use my American Express card that much for general day-to-day sort of purchases. So when you add it up, it's about building a data set. Once you have the data, you can place and dice it any way you want to use it to drive your internal decisions. And you should be using that data to drive value to your stakeholders and to your customers. One of the questions we're going to ask you now, we're going to do a couple of audience polls for this and Shannon can tally things up and kind of get back to us in the end. But which of you today, who can raise their hand or pull into Shannon and say, who's successfully monetized their data today? And we're going to now dive into sort of the heart of our presentation and ask you another poll at the end of this. So I'm going to move to the next slide and turn it over to you. Sounds great. Thank you. And I apologize to everyone, my voice is getting a bit raspy. It's a little dry in here. Hopefully you can still hear me. So when you're talking about monetization opportunities, you really want to think, in a broad sense, be creative in looking at opportunities and go across the spectrum. When I think about turning data into dollars or euro or pound or whatever currency we want to use today, I think of forming pillars. You're either adding to your revenue somehow and you can do that by selling something related to your data. Another would be to reduce your losses. Somewhat related to reducing your losses would also be using data to mitigate risk. In other words, to find potential problems and cap them off before they grow. And the last one would be finding new areas of opportunity. So try to keep those in mind as Micheline and I walk through a number of examples. I know we're in a little short on time. Today we have a ton of examples. We're going to try to keep these short to leave time for questions at the end. But we've broken our examples of monetization opportunities into four or five main areas. The first one that comes to mind would be selling bulk data products. This is just collecting some form of data and selling it out to others. And in fact, when Ken and I wrote business models for the data economy, we just refer to this simply as collect and sell, get some data, package it up, advertise and sell it to someone else. The thing is, selling raw data isn't quite as lucrative as some people would think. And outside of Wall Street Quant, there's just not that much demand for pure raw data. Another concern is if you're just selling someone a bulk data set like that, you can also run into some potential ethical issues. In other words, asking, what's this buyer going to do with this data? Are they going to use it for ill intent? Something to consider. You can also focus on developing data products for consumption by others. By that, I mean, people typically want some sort of insights or action. And if you can sell them that, they don't really care about the data underneath. Even if they want the actual data, what they really want is some subset or refinement thereof. And let's face it, businesses that are usually in a hurry, they have some sort of goal to meet, they will happily pay someone to hand them some sort of data or some sort of insights so that they don't have to collect it themselves. So related to that, if you're going to focus on just selling data of some sort, set on the actual data itself, also consider offering some sort of API development and partner integration. Providing an API gives you a chance to, first of all, just to see what data people are actually looking for on your platform, which we'll get to monetizing that in a bit. You can also bill people per use or by request volume or that sort of thing. It just lets people get the data they need when they need it. That would be the first of the five categories. The next one, and this gets back to the customer experience stories we were telling earlier, would be focusing your data inward. In other words, using your data to make some sort of internal decision and go beyond what you're doing today. The big one, the one I'm sure we all think about, would be analyzing your sales data that can help you determine which new markets to enter or even leave or what sort of products to sell. You can also determine where to place your stores. If you manage a large chain, the physical location of your stores is very important. In fact, we've read that certain fast food chains and drug store chains put so much research into where to place stores they're actually concerned about which side of the streets and which corner. In order to maximize foot traffic, in order to maximize people coming through the drive-through and not having to make lots of crazy turns to get in, that sort of thing. It's also very data-driven. Also, somewhat related to chain stores would be logistics, right? If you run any sort of retail system, you understand that you have to track your store's inventory. But if you're in charge of the entire chain, you can also track all of the store's inventories at once. And when you look at this from a logistics perspective, Walmart really made a name for itself. It must have been two or three decades ago. It's been a while. By resupplying stores' stock point to point. In other words, they would have a truck that would go from store to store dropping off goods to replenish the stock. And if one store had too much of one item and another store along the routes didn't have enough of that item, they would pick it up and take it along. It sounds very simple and very primitive, and it is, but Walmart was able to use that data to make those stores superbly efficient. And last but not least, related to those internal improvements you can make with your data, analyzing purchase patterns to uncover fraud. This would be an example of an indirect source of revenue in that you're not really using it to generate revenue, but you're actually using it to protect revenue and shield yourself from losses. Third, on our list of monetization opportunities would be using your data to shape new products and services and business models. Going back to selling bulk data we're talking about before. If you have an API, you can very quickly see that a bunch of people are just querying this one particular data set or just this one particular slice of something. Okay, well, if that's the case, why make them go through and click through and select and querying all the sudden nonsense. You can package up the data set that clearly a lot of people want, sell it off as a data product and you're home free. Micheline? Thanks, Q. I think one other example that I really love in the product service business model category is the example of Square Capital. Square has been around for a while and they started with just the piece of hardware that they were selling to small businesses, but soon realized that they had so much information on the small businesses and money coming in and payments and accounts receivable and all those things that they realized they could actually set up a credit arm. And over the past few years have actually built up a really successful model where they provide loans without the customer even reaching out to them. They will provide unfulfited loan offers and because they know and can predict exactly how much cash flow that that business is going to have. So it's a completely new business model, but Square started out thinking about when they came out with that hardware and tried to support small businesses but it has become quite successful for them, one of their main product drivers. So let's talk about, next about increasing customer lifetime value. And traditionally, this has been a lot about targeted ads and customization and personalization, whether it's ads on Google, ads on Facebook or just flyer ads coming to your home, which if you're like me, you don't even look at them, it just ends up in the trash can. But there's been much more sophisticated ways over the last five to 10 years in terms of improving product recommendations, product service penetration, et cetera. I'm sure probably two of the most famous examples and most of you out there probably use both of these products are Netflix and Amazon recommendation engines. So everyone understands those two models and there's a reason why they're so successful. They really understand their customer base in a way that nobody else does. They're able to provide really strong recommendations. And Netflix, for example, was able to leverage this data to give them our recommendation to actually produce, go into the production business and produce House of Cards. And there's a whole case study that you can read about how they took their swath of information and used it to identify Kevin Spacey as one of the main actors that people really love and the producer that they found as someone that people want to see his movies and then the theme of the story and actually came up with this model. But it's a fabulous model and Netflix is certainly one of the stars in this space. And Amazon, of course, we all use those products, right? But both companies have been able to really turn their data science efforts inwards, right? So instead of just, as we talked previously in the first category, packaging up their data and selling it off, they are able to take that data and continually use it to reinforce the customer's relationships with them and really understand what's going to add value and continue to add value to the customers. I worked at the Financial Services Company a year or two ago that we did a data science project where we were analyzing cash flow information, both in and out of their business customer accounts to be able to understand and predict customer events and improve sales of services to clients, right? So if you're a business to business client, maybe if you do a big ad purchase, that might indicate you've got a new product launch or rebranding about to happen. Or the other thing we were looking at was providing industry benchmarks back to the clients. So for example, you know, if you... other companies of your size or industry spend X percent on HR benefits, it looks like you're spending more than that. You know, how can we help you adjust that? So different things like that can really help not only improve your connection to that customer and the customer experience with you, but keep them wanting to come back for more and more and more of what you have to offer. And then finally, we're going to talk about improving profit margins. So one way to think about this is to be able to take your operational data and use it to manage systems or processes internally and ask what other parts of the business that can be applied to. You know, how do you connect one part of the organization with another to create self-educating feedback loops? One very simple example is funnel optimization. You know, get your sales leader, whoever your head of sales is, and convert your ten-year feeble sales data into Amazon-like recommendation algorithms to improve, you know, funnel flow and to predict what customer is actually going to buy next. In terms of price optimization, you know, certainly the airline and hotel industries have been doing this for years to improve their revenue streams. Cable companies now are leveraging their data to manage field technicians more efficiently to identify efficiency lags and reassign technicians to create optimal routes and scheduling within their system to help them optimize both the prices that they're able to charge for field service assignments but also be able to maximize and save revenue on the back side. Uber does this with surge pricing. You know, they factor in time and weather data and other events that might be going on in the area. GE, in terms of supply and demand predictions, I think, you know, Dell was certainly the very early model for how to do this really well but there's a lot of companies today being able to leverage their data internally to improve this, right? Utility companies leverage meter information to predict demand more importantly now as renewables are coming more and more online. How do we leverage the information that we have to ensure that we have enough renewables during the day, some, for example, because we know we're not going to have solar power overnight or how do we incent people not to run their dishwasher and their laundry during the day but instead run those machines overnight when there's less demand and so we can drop the price for those customers when they're utilizing the energy from the grid. GE Aviation and GE overall has had some really interesting wins over the past several years as they've rolled out their previous platform to leverage their data science capabilities to change their business model and to provide guarantees around uptot and longevity of service length of their parts. Amazon certainly leverages transaction patterns to know how to stock warehouses and manage delivery channels. So there's obviously tons of examples out there but these are similarly key ways for you to think about how to leverage both your historical data and real-time data to improve your company's ability to either sell new products or services, develop new products or services, improve the customer experience or to improve profit margins and keep costs down on the back side. So the low-hanging fruit is certainly mining the data that you're sitting on. And then start to think about how you use the additional real-time data coming into your organization or how you can supplement your existing data with external data sources to do really new and interesting things for your organization. So with that, and before we go to the next slide, I'm going to take the next audience poll and hopefully Shannon can help us out with this. So just let us know, like who's aware of all of your firm's monetizable data sets? You guys actually have inventories of your data that could actually be leveraged for monetization activities which is a really important starting point to go down the path. So with that, I'm going to transition over to Q. All right. Thanks, Micheline. I would also add to your poll question there. You know, there's a difference between we are pretty sure we think we collect this versus, no, we have been intentionally collecting these data assets for the purpose of monetization. I bring that up because I've been in startups before where people thought they had something or at least one wing of the company thought they had some data. The other wing of the company didn't. And, well, sometimes people aren't so happy about that. It happens. But moving on to strategic questions around monetizing your data, I know we just walloped you with a ton of examples. And we, I'll have, you know, we actually cut back on a list of examples we had earlier for time. But the reason we're costing out so many examples is because this is about helping you get the wheels turning on your own, right? This is about helping you see what ideas you can draw from what other companies have done, what other angles you can take and what you're already doing at your company in order to use your data to generate that revenue. So one other key point I'd like to make, though, is when you look back through all those examples, yeah, some of them used some of the higher-end predictive data science, machine learning, AI, whatever we're calling it this week. But a lot of them don't. And I think that's another key takeaway, right? There is a ton of value in traditional business intelligence. This idea of just looking at where we are now versus our end where we're at as opposed to where can we be in the future. And that's a big part of why I mentioned starting my career working in training early on, because that's less new to learn early on the trading floor. Everything boils down to... I mean, yes, we will use complex models if we need to, but if we can make a profit, if we can figure out where the market is moving with a very simple system, we're going to do it with that. That's all we really care about. So with that in mind, as we said, hopefully your gears are turning as far as ways you can monetize your own company's data. And we can walk through just a couple of ideas to inspire you, hopefully. I mean, one big question you should ask yourself is, if you're looking to make money off of this data, what is the actual value proposition? In other words, if you're just going to sell either raw data or a data product of some sort, ask yourself what businesses or what partners might need it and why. And really, that's less a point of about data. That's more a point of about sales. Anytime you're in a sales organization, there's a lot of questions they run through when it comes to a product because that helps to determine where is the market, is there sufficient markets, can we make enough money off of this market to make it worth a while? Similarly, ask yourself, why is your data better? And better can be both better than some other source of data. In other words, they can perhaps buy from the competition. Or also better than someone having to collect it themselves. Now, to the point I made earlier, companies don't really want to be in the business of collecting data. They want to be in the business of moving their business forward somehow. And if they can get the data from someone else without having to collect it on their own, then they win. Especially when you consider that some data sets, for example, Michelin mentioned a number of financial transactions data sets, those can take years to accrue enough data to be meaningful for any sort of decision-making. If they can buy that from someone else, then they don't have to collect it themselves and they can move to market much faster. Similarly, you can ask yourself, what sort of business models and use cases would maximize return? In other words, if you're going to focus inward, what sort of value is at stake in terms of revenue and profits? What's the expected timing to develop the market? These are the sorts of questions you want to ask yourself as you're starting to monetize your data sets. Yeah, and just a few more. So that's, you know, the first two points are really about the data. The second two points are about how are you going to deliver it, how are you going to get there, right? And so, the third question is, what technology enablers need to be put in place? One of the things that Q talked about when we talked about selling raw data was APIs and making APIs available. So, you know, question number one for you is do you actually have the necessary APIs to drive the additional revenue through business partner channels, or do you need to hire someone to build those, whether internally or externally? Do you want to partner with someone who may have the brokering meter and payment services to get the money back off the data or do you want to build them yourself? So these are trade-offs that you need to think about. And certainly, you know, what's the optimal level of investment in technologies and services that makes it worth your while to put your data out there and partner with others to get your data. But it still makes it a winning value proposition, you know, AKA you're not losing money on your data out there and partnering with others. You don't want to just put it out there to say that you've put the data out there. You certainly want to be able to make money from it. And then finally, what capabilities and partnerships need to be put in place? This could be other stakeholders across your ecosystem really understanding the value that they could get from your data or partnering with the data broker who might actually be able to take it off your hands and package it up and sell it for you in the best ways. There's different ways to think about capabilities, partnerships. If you're going to be building data products in-house, do you actually have the team and the staff to be focused day-to-day on developing data products and monetizing the data and putting the strategy in place and thinking creatively about it and really pushing the P&L around those data products? All of those things need to be thought through. I think the key takeaways certainly are figuring out how to connect the dots in new and interesting ways for your customers or about supplying data for their part of use. But at the end of the day, being a data product provider, it takes time, it takes effort, and it takes focus resources. So if you're going to leverage it, you should start with a strong data strategy. And you want that data strategy centered around customer value. If you're having trouble selling your data to others, which we've certainly seen, it usually means your prospects, your potential customers probably also need a data strategy. So with that, we'll move on to our next slide, which is key considerations. And Q, I think you want to talk a little bit also about ethics at this point. Yeah, exactly. This is going to touch on something we mentioned at the top of the webinar, which is if you're selling data products, especially bulk data products, you have to concern yourself with ethical considerations. In other words, yes, we all want to make money off of our data. That's great. But a big part of monetizing that data is keeping yourself out of hot water, because if you make a lot of money selling a data set, but then you get some sort of PR backlash and you end up spending lots of money cleaning up the mess, you haven't really made a lot of money. And spending lots of PR time and money to clean up a mess, that's a drain on your revenues. A drain on your profit, I should say. In other words, not every opportunity is one you want to undertake. And we're starting to see this now. I know those of us who have worked in the data field for a number of years, a number of you as well, you know, you're probably a lot more data and privacy-conscious than a lot of your friends and relatives and colleagues. But I suspect over the past few years you're seeing that even they are becoming a little more wary, a little more skeptical of companies that want to install some new device in their home or want them to install some application and they're starting to ask those key questions such as, what are you actually going to do with my data? You claim that you're going to use this data to improve wildlife, which is why I'm buying your service or product, and that's great. But where else are you going to use this data? If I buy something from you, am I going to start getting tons of ads from other companies with which I don't want to be associated, that sort of thing? For those of you who have worked in IT for a while, you may be familiar with the security term honeypot. And if you're not familiar with that, it's a fancy way of saying it's a fake service used to collect information on and track hackers. In other words, you might set up a fake website, let someone think they've hacked it, figure out where they are, send in law enforcement, that sort of thing. What we're starting to see are about what we're starting to see are the number of firms who are creating effectively what we call data honeypots. They create some service or some app, the entire goal of which is to collect personal data so they can resell it. People are getting wary, I think they're starting to get frustrated, and point being, if you're going to get into this business of monetizing your data, generally speaking, try to go beyond the pure collect and resell. In other words, set up a business that's just really a data honeypot. And also, develop a strong data ethics infrastructure, and that'll help you stay out of trouble. And by developing the infrastructure, what we mean is, and again, those of you who have worked in IT security, you've seen this as well. If IT security isn't after thought, you're going to end up with a great product or service that has all sorts of holes, and you end up with all sorts of PR problems down the line. And you face the same concerns when it comes to monetizing your data. You want to bake in those ethical considerations. You want to bake in seeing things from the consumer perspective. You want to bake in this notion of thinking beyond your intended outcome, far in advance, and you want to do this along with creating data product itself. And that way, by the time this rolls off the quote-unquote assembly line, you can be somewhat assured that you're not going to ruffle too many feathers, at least not needlessly. So, given that, and I know we have just a few minutes left, we do want to leave time for questions, but Mishley and I would like to wrap up with just a few key considerations and probably a handful of more examples as you start to move forward on your journey of monetizing your data. I guess the big point for me would be about understanding the market, understanding the data, and understanding the delivery. Three main points of that would be first, is understand your market and your customers. You have to understand how well they actually use it, how well it adds value to them. And you want to think about not just the person whom you're selling, but also the customers from whom you may have collected that data. You know, we've seen far too many efforts that focus on the value to the company that's underselling or buying the data, but it often comes at customer expense. So you just want to make sure that you're not needlessly creating enemies along the way. Also, you want to consider what level of data quality, consistency, and timeliness, and certainty does your customer need. Timeliness and certainty are a big one. And again, going back to my example from working in Wall Street, sometimes you only need a market data update once a day, depending on what sort of strategies you're trading. Sometimes you need that data coming in real-time in terms of milliseconds, right? A lot of it's just based on what you're actually trying to achieve. So if you're going to be in the business of selling someone data, you have to understand what level of truth do they need and when. Can they take a lump update sometime down the line? Do they need definite facts in a real stream all the time? Another big question is ask yourself, how can this data actually be valued? Understand the quality issues and limitations and also the biases in the data. For example, let's say you've collected some series of financial data. Have you really taken a broad spectrum of the country or the population? If not, you can talk to someone who thinks they're getting a broad spectrum of the country. Maybe they're only getting, let's say, at least coasters and that sort of thing. It can really skew their decisions when they buy the data from you and that can in turn impact whether they will be a repeat customer of your business. Similarly, ask yourself about the depth of your data. In other words, how far back in time does this data set go? Again, going back to Wall Street, sometimes I just want to test a trading strategy and I want maybe the last few weeks' data. Maybe it's something simple. Maybe I'm really, really worried about my strategy on the last four years' worth of data. Something else to consider because some customers are going to want a lot more data than others, which is related to younger companies, especially startups. They haven't been around that long. They don't have a lot of data to sell in the first place because they just don't have that history. Last but not least, at least from my point, we can also talk about product delivery. We've developed this product, whether it's actual data product, whether it's bulk delivery, whether it's some sort of in-sites service. You really want to focus on pricing and intellectual property. Rules and regulations around who can use your data when, under what terms. Whether to provide that data by API, whether it's just bulk downloads, try to factor in the cost of actually delivering that data. If you're going to set up an API, that's going to involve some developer time. It may involve bringing in a third-party service to help you manage keys or that sort of thing. You want to develop the entire picture of what it's going to mean to sell and provide the service, otherwise you can find yourself digging a hole before you realize it and you can end up with maybe a lot of revenue, but negative profit, which kind of defeats the point of data monetization. So I'm going to talk about the last two points, which are probably the least fun here, but we'll keep you out of this. We'll keep you out of trouble. So this is the legal stuff, the regulatory stuff, the things that, you know, the compliance and governance side of the equation that, you know, hopefully leads you from, or prevents you from landing on the front page of the Law Street Journal or the New York Times or whatever your local publication is. So let's talk about regulatory laws, data privacy laws in terms and conditions. So I would guess that the majority of folks on this call are in some sort of regulated business. There is some sort of federal, state or international law that governs what you can and can't do with data, whether it is healthcare data or financial services data or education data or just, you know, straight up consumer data, but, you know, your state doesn't allow you to do some things. You need to know what those laws are and these laws vary, particularly at the state level. There are some federal laws, there are certainly some international laws, but when you're dealing at the state level, if you're a multi-state provider, those state laws could vary from state to state in terms of what you can and can't do and that could include breach notifications and things along those lines. So you'll want to make sure that you're partnering with your legal team, your privacy team, and you've got an inventory of what those regulations are that you've crossed, walked them, because failure to do so can get you really into a lot of trouble. We've spent a bunch of time already talking about ethics, so I'm not going to go into that again, but certainly I think one thing to keep in mind is just because you can do something with the data doesn't mean that you should spend some time thinking about whether or not what you're trying to do from the data productization and monetization perspective is actually the right thing. Terms and conditions, right? So these are the agreements that you've got, whether you're a mobile app company or a product company or a service company. You've got some sort of terms and conditions that your customers are agreeing to that hopefully are very, very specific about what you do with the data, how you manage the data, who gets to see the data and use the data, what resale restrictions you've got around that data. There's certainly more and more of a push for transparency around the terms and conditions that data used and reused, because as you know, very few people actually read those terms and conditions. So if you want to be a data-friendly company, you'll think about these things in advance and try to make the terms and conditions as user-friendly as possible, fill out in as much explicit detail as you can to your customers what your intents are around the data. Customers, you know, don't necessarily get angry that you use data about them. They don't like it when they find out that you've been doing something that you didn't tell them whether it's selling their address, selling their email address, selling their IP address, something along those lines. Certainly, if you do want to get into the bulk data sale, your data should be strictly anonymized. De-anonymization is much easier today than it's ever been, and it doesn't take that many data elements to actually de-anonymize the dataset. As I'm sure many of you know, but you'll want to make sure that if you are selling your data, that you've anonymized it to the best extent that you possibly could. And so as you're working with the product teams, the data product teams should certainly be thinking about having folks from the legal privacy and security teams as part of the review processes to stay again on both the right side of the law as well as the right side of your terms and conditions. And certainly, from a terms and conditions perspective, you should give the customers the choice of what to share and for what purposes. It may take some extra effort and time to do this, but certainly we've all seen press stories, whether it's Wells Fargo or Uber or others, where it never ends really well for those companies that have done bad things with the data that the customers didn't know were actually going on. Certainly from a legal and intellectual property perspective, you'll want to make sure that you've got the right protections for you, the company. It's your intellectual property that you're putting out there. And the best way to have control of the intellectual property obviously is to make sure you own the data. So if you are actually selling data or putting it out there, make sure you own it and make sure you can actually do that. That's number one. Second of all, put in intellectual property provisions to protect yourselves. And contractually, make sure you've got policies in your contracts on the use of the data, the reuse of the data and other limitations. For example, if you're selling the data, you might not want the customers who are buying that data to be able to resell that data. And you might want to put retention and destruction terms in. Maybe they can only use it for six months. Maybe they can use it for three years. And then after that, you'll want them to either return it or destroy it, something along those lines. Because that's really, certainly an important thing. You don't want your data just sitting out with somebody's servers for, you know, in perpetuity because you've gotten to put those things into the contract. Certainly from a feature control perspective, if you're thinking about, you know, how do we incorporate data real-time into new products and services that you're using to drive customer experience, you should allow people to enable or disable those new features that you're rolling out. For example, Twitter lets you disable its algorithmic timeline. Facebook does not. So, you know, if it doesn't get in a way, and if it doesn't matter how, if it gets in a way and it doesn't matter how novel the idea is, it's a bad user experience if they're not able to control it the way that they'd like to control it. And then finally, certainly, you know, don't misuse and don't allow people who are buying your data or using your data to misuse that data in any way. And certainly, there's a number of examples out there. I'll just leave you with one more, which is the LinkedIn and IQ chat. We can provide you some links to that information or you can just go Google it. I don't have time here if I won't get into it. So, those are the key things that you need to think about. Again, you know, I think the question isn't should you try to monetize your data but when and how? What's your strategy? And certainly, how will you stay out of hot water if you try to monetize it? And what's the missed cost? What's the cost in missing any opportunities if the organization isn't willing to monetize the data? Do you under-optimize in the high end because you're not analyzing the data or where can you lean in because, you know, which customers or partners are most valuable? At the end of the day, this is really about deepening the moat to keep your competitors at bay. And with that, and I think we're closing in on, I think a few minutes, few minutes left in the presentation. We'll turn it back over for Q&A. Ms. Lening, thank you for this great presentation. Sorry, I was working with my mute button there. For this great presentation, we've got a lot of questions coming in. If you have questions, submit them in the bottom right-hand corner for the Q&A section. And just a reminder to answer the most popular and most commonly asked question, we will be sending a follow-up email to all registrants by end of day Thursday with links to the slides, the recording of this presentation and anything else coming through. And so just jumping right into the Q&A here, defining monetization as turning data into dollars is great. Too many people go around talking about monetization when they mean doing a better job of analyzing our own internal data. Do you have any additional comments on that? So we covered that a bit. I don't think there's anything wrong, certainly, and Q will certainly jump in. I don't think there's anything wrong in analyzing the data that you're currently sitting on and figuring out what else can you do with it, you know, whether it's being more interactive with customers, being able to identify gaps in your product service offering, all of those things drive additional revenue, and certainly it's a really good place to start. I think generally in my experience, people, companies immediately turn to, oh, we need to package up some data and throw it over our walls outside to the rest of the world, and that's how we're going to make money. When in fact, you're sitting on a goldmine that you probably haven't dug into nearly enough. And in particular, as other parts of the organization are actually able to dive into data sets that they perhaps never had access to, that's where light bulbs can really start to go off for people, and people can get really creative about what else to do with data that, well, it may not be particularly of interest to your business unit, maybe really valuable and provide tons of insight to another business unit. And Q, anything else you want to add to that? Yeah, just a couple. I mean, I agree with all of the points you made there. I would just add that, yeah, looking internally to your data is, I'd say it's under form of monetization. I mean, I think most of not all of us work in some form of for-profit business, which means in the end, if I can be crass, we're here to make money. Anything we can do to improve the bottom line in a way that keeps us out of hot water, we're here to make money in the game. To Michelin's point about some people, just wanted to package up data and kick it over the wall, that's perfectly valid, I mean, that sells. But we are just really inspired by, especially the examples of Amazon and Netflix for recommendation engines, because one could argue that these are indirect sources, but you can say that they're looking inward, they're analyzing their data, they're determining, oh, these end people who bought this also bought these other things. Let's present this to someone else. This is the customer relationship, which leads to further revenue, because what is evident is by the fact that people keep going back to Amazon and they keep going back to Netflix. Clearly, they're doing something right. I mean, if memory serves, I think even the, don't quote me on this, but I think the, what do they call it? It's the feature that really makes binge-watching so much easier, the auto start of the next episode. I mean, come on. That's just dead simple use of data, and I had to say that counts as a monetization effort just as much as anything else. Great, so many companies' data assets are not well organized or easy to look at at the entire picture, so how can we do this in order to then drive monetization insights? Yeah, so that's a great question, and in fact, Shannon, I think that's tied into both of our poll questions or at least our second poll question. Did you get any responses on the second one which is about, like, do you actually know what data that you have to be able to monetize? No? No. Okay, there you go. Okay, so can you just repeat the question one more time? Sure. Let me go back. So many companies' data assets are not well organized or easy to look at for the entire picture, so how can we do this in order to drive monetization insights? Yeah, so I think one of the points that we've tried to make is certainly starting with the strategy, right? Thinking about the potential customer value, the market use, the potential business use cases, either internally or externally, and developing a strategy around how you can develop data products and take it to market. But one of the foundational items in that is really understanding the data that you have, knowing where that quality it is, how accessible it is, you know, all the challenges with the data, the biases, et cetera, with it. That's kind of your starting point out of the gate. Certainly, if you don't have a complete inventory and most organizations don't, you probably know at least your, you know, top 10 most valuable data sets because those are the ones that are being used to drive the business, right? And so certainly that's, you know, an easy way to get started as you work in parallel with any sort of inventory and effort that you might want to take across the organization, whether, you know, in a formal way or a crowd-sourced way to develop more insights about the actual data sets that you might have in order to be able to monetize them. Yeah, I think I'll just add, oh, go ahead. Go ahead. I'm just going to say I would also add briefly to that answer, which is what we're talking about here, it's very similar to the questions we get from companies who are just getting started with data science. In other words, you know, a lot of people, they're just really eager to dive right in, but there's just a lot of legwork, a lot of really unsexy legwork that goes in before you can even have that first data science effort or before you can even start to truly monetize your data. You know, I guess they're really, they're two big approaches you can take, two main branches, if you will. One would be you can start small, excuse me, start small. You can pick just one or two data sets that you know you have and think about business use cases around those, think about other businesses with which you're in contact and just to determine who might be interested in these. You can start small. And then over time you'll start to uncover more data sets as you go along, and that also gives you a chance to practice being some sort of data vendor, whether you're selling the raw data or whether you're selling products or what have you. Another approach would be what I call the Big Bang Theory, and that's a bit more around you're just going to go through and hunt every possible data set you have, every piece of data you've collected, and everything you could collect, especially if you have lots of custom internal software development, you've probably created a number of apps that are just throwing off all sorts of really insanely crazy useful data, and you can just start to tap into that data, start to collect it more. So in the second case, that's more of let's try to gather everything first and then try to build some use cases around it. I would say there's no right or wrong answer here, but frankly, if I'm in a situation where I know there are tons of data sets to scatter all over the place, I'm probably going to go for that first stop and just pick a couple that I know we have and see what we can do to monetize those, and that gives us a bit more practice in just being someone who monetizes our data. All righty. So, you know, the Laney's monetization models are so pretty theoretical with subjective variables, so how can we monetize, how can monetization be done with more precision? Do you want to start off? I'm actually going to have to think this over for a second. I guess I'm not trying to understand the point of, I don't understand the meaning of more precision. Could you please clarify? I will look for the... Let me throw in another question here. We have just a couple of minutes left and I'll give the questioner a moment to specify, and then if we don't get a chance to answer it, I'll get it in the follow-up email. So, can you explain a bit more why you think it's hard to sell the raw data direct and which sort of product partners are the best options to consider using monetization? You either want to start and then I'll add in. Yeah, there you go, and I'll actually take these in reverse order. So, which sort of product partners are the best options to consider using to monetize? That goes back to the point we were making earlier around someone who needs data in a hurry. They don't have the time to collect it themselves. They're usually a good target for both data sales, but what you're also looking for would be someone who actually, and this I think gets to the heart of your question, actually wants the raw data. This is probably someone who... I don't know how to phrase this. Your target customer inside that company, it's probably someone who works in some sort of data-related function. In other words, maybe they work in marketing and it's for a data-driven or they work in finance for a data-driven. Someone who has their own internal data science team, someone who can actually take that raw data and make use of it. When it comes to raw data, I suspect that those are going to be the folks. At least that's a good starting point for those sorts of folks who would be good consumers simply because unlike the other people we mentioned earlier during the webinar, these are the sorts of companies that actually have data science teams that aren't afraid to roll up their hands and start working with the data. They probably have their own data sets with which they're going to want to blend the data you're selling them. So I think that's where I'd start as far as best partners. Micheline, which I'd like to talk about, I guess before Micheline dives in on why it's hard to sell direct, one point I'll mention, and it's one a lot of people selling direct probably don't think about yet, but I suspect they're going to over time, is just the ethical issue. In other words, it's tough in the sense that you're going to put the brakes on more often when you're asking, well, I'm about to hand over several years worth of someone's financial history, personal contact information, maybe information that could be used to learn about their children and that sort of thing. You have to ask yourself, is this really data I want to sell or is this a company to whom I want to sell it? And what makes it even more difficult if this gets back to the terms and conditions issue we talked about before, which is, can we put the right terms of service around this and protect ourselves and protect the people from whom we've collected this data to make sure that they're in a good spot if we sell this data to this other party? So I think I've covered a couple of angles. Micheline, do you have anything else you'd like to add? Yeah, so the first question was about raw data and the thing with raw data, we're all in the data space. We all understand how dirty and messy data is. And for lots of people, that's great. They want to dive in. They want to get their hands dirty. They don't want the high-level information. They want to be able to play around with it, have a sandbox, mix it with other data, analyze it the way they want to analyze it with the tools that they want to use. In reality, there aren't that many people in the world. And that's why we talked about quants, right? And certainly heavy data science teams, quants, economists, they'll want to do that. But you're sort of more general business end-users. You're a business analyst in marketing or sales or product. Doesn't necessarily want to get into that level of detail. Nor do they have the skills to. And lots of companies don't necessarily have enough resources within their teams to actually undertake the messiness that comes with raw data. So unless your contract table is going to really clean up this data, which takes a lot of work on your end as a data provider if you're going to sell really clean data, in my experience as both a provider of false data and a receiver of false data, the data is typically really, really messy. It is raw files. It is missing stuff. It is all over the place. And it just takes a lot of work to massage it into the shape that's actually usable within the organization for the use cases that you actually want to use it for, which is why we go back to it can actually be really difficult. If you're, you know, four square, for instance, to sell your raw data to huge numbers of organizations that aren't hedge funds and investment banks and financial services organizations, because it just takes a lot of resources, which is why insights and higher level data sales, whether they're industries, metrics, things like that, are often easier places and starting points for organizations as they embark along this journey. The best sort of customers for your data, I think was the second question. I think Q addressed part of that, but I would also say like map out your ecosystem. Will we get a sense for your ecosystem and potential compliments to your ecosystem to understand where there might be some low hanging fruit for your data. You know, if you've got sales data that might be an interesting compliment that a business partner might need, that's a really good place to start. Again, I think to Q's point, you don't need to stand up a whole data product shop here. Start small, play around with a few data sets, and start to experiment. Reach out to companies that you either do business with or could potentially do business with and ask them, query them, have conversations with them about where they might be, where they might have gaps and if you might have some data that they could be interested in. Those are some really easy ways to just get conversations going and get your productization and monetization efforts off the ground. All right. I'm afraid that brings us past the top of the hour there, so I just want to wrap it up here. We did have some clarifying questions from the previous questioner, so I will get any questions that are unanswered to both you and Micheline and Q today. If you've got additional questions, feel free to keep submitting them. We'll get the answers written out in the follow-up email that will go out by end of Thursday, also containing links to the slides and the recording. Micheline and Q, thank you so much for this great presentation today. It's such a great topic that we get requests for all the time. Thanks to our attendees for being so engaged in everything we do. We just love the questions that are coming in still. I hope everyone has a great day and thanks as always. Thanks, Shannon. Thanks, everyone. Thanks. Bye-bye.