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 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.  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.  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.  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?  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.  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.  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.  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.  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 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.  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.  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.  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.  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.


There have been about 10 news articles recently pushing an anti-animal foods agenda. In this case, red meat and processed meat. About two months ago we saw something similar happen with eggs. I guess we have to keep the general population afraid of meat and animal foods in general. God forbid we have healthy children with a normal IQ level. All of these articles are pushing the exact same study. Titled Association of Changes in Red Meat Consumption with Total and Cause Specific Mortality Among US Women and Men. Two prospective cohort studies. Sounds official, right? The first thing we have to look at is the authors and see if there is any affiliation with the Seventh Day Adventist Church. The Seventh Day Adventists are a religious group with a strong belief in the removal of meat and really animal foods in general from the diet. The media blitz on eggs that I mentioned happened two months ago was associated with them solely. So if you haven't seen my video debunking that, eggs and cholesterol, definitely check it out. Now let's take a further look at what these authors have done in the past. And by authors of the study, we simply mean the PhDs, the doctors, the medical professionals whoever reviewed the study and approved it. I don't want to say that Frankie Boy is always right, but if you look up any of the names in the study, you will find that every single person is bought and paid for, or they are not conducting research without modern bias. Look at any study, any single one of these authors is involved with. They only find positive associations with plant foods and negative associations with meat. In every single study they've reviewed, bit odd, isn't it? You can take a look at Yan Zeng's published work, Yan Ping Li, Ambicca Satia, same with Mercedes Soto's Prieto, Eric Rym literally works for General Mills Food Company, Walter C. Woolett has done his own studies on the Seventh Day Adventists, yet he says the author had no conflicts of interest. Yeah, and I'm six foot five. Frank Hu is on the dietary board with the Lancet, a heavily vegan affiliated association. So every single one of these people has a prior conflict of interest based on either the other studies they are affiliated with or the actual organizations they are involved with. It is far too much political, social, and religious influence in health and nutrition, and it's compromising our health, and that's an understatement, it's very obvious.  The first thing to understand here is relative risk. These studies apply the relative risk, which is actually only to be used in the context of a study, and then they take this and apply it as a blanket term to disease risk rates. So if they find a relative risk of 1.5, that would be a 50% increased risk for the disease rate in the context and method of the study. 1.0 means there is no change in the disease risk rate. 1.5 means 50%, but we see relative risk of 2, 3, 4, 10, 100. 100 relative risk means a 10,000% increased risk for the disease, but not every single person gets the disease even when they find relative risks that high. If a relative risk is not above 2, maybe even 3, the study is pretty much dismissed. However, in nutrition science, this basic research principle of dismissing studies with a relative risk below 2 or 3 is not used simply because the methodology and data collection we have access to is horrible. So you would literally have to dismiss every single nutrition study that has ever done and people really like grasping at straws and pushing their dietary agenda. As a reference point, the relative risk of cigarettes is 100 and there have been nutrition studies that actually show relative risks that are credible. One was the India Railway Study with a relative risk of disease over 7.0. Indians who consumed more grains and vegetable-based foods instead of animal foods had higher rates of heart disease, but this doesn't fit what we've been peddling our entire lives so that study was swept under the rug. There can be many issues with studies, it's not just about relative risk. The methodology could be wrong. They actually took information from questionnaires, which aren't really reliable, then they manipulate this data from the questionnaires. They alter the percentage of meat consumed, mortality rates, etc., based on confounding factors. This could be exercise, smoking, alcohol, maybe the person tripped on their staircase every morning, maybe the person had to walk up two flights of stairs. The point is they can pick and choose what variables they want to put in the study and that can shift the results drastically. Here's the thing. All 10 of these news articles said a similar thing. Meat is bad, increases your risk of disease, pure nonsense. You know what vegans always hide from? Statistics. Hong Kong has the highest meat consumption per capita and the longest life expectancy. These vegans would be shaken in their boots if they actually acknowledged statistics. It's very simple. Look at meat consumption per capita versus life expectancy. Look at processed food consumption, sugar consumption in the diets as well. Meat should be the last thing to blame if you look at statistics. There's a chart that is commonly pushed around in the keto carnivore zero carb community. If you look at meat consumption per capita since like 1970, beef has shot down drastically, chicken has gone up, we've shot away from pork as well. Pork and beef and red meat and processed meat have gone down by half, less than half. I believe it's like we eat one third of the beef we used to eat and things aren't getting better. People have it here to the nutrition guidelines. Vegans always like to say, oh, well, no one listens to the nutrition guidelines. People still eat meat and that's why they're sick. No. Look at statistics. Look at beef sales. Look at cattle sales. We consume less meat and we are getting sick and unhealthy and everyone is ignoring it. It's as clear as day. There's two problems with our modern diets.  Vegetable oils, refined foods, sugar. If you can't procure it in the forest, you probably shouldn't be eating it. Our ancestors lived off of animal foods, wild plants, wild fruits and more recently grain. Can you be healthy on a grain-based diet? Yes, but what constitutes a grain-based diet? Americans already consume 70% of their calories from plant foods. Guess who else consumes 70% of their calories from plant foods? Blue Zones. So if Americans and blue zones have the same ratio of animal foods to plant foods in the diet, what's the difference? It's food quality. I did a video on blue zones I think last year. You guys want to check that out on my channel, but we're looking at the wrong thing here. You can consume 40% to 50% of your calories from wheat bread if the food is high quality. If you have heirloom varieties of wheat naturally fermented into sourdough, then maybe if you do that for 10,000 years, you'll be as funny as us Italians. If not, I mean, most important thing, guys, increase quality animal foods in the diet, reduce inflammation, vitamin D3, water. There's so many elements to health, but it's really disheartening how even someone like me who's aware of a lot of issues that goes on, reality is all of us have our own day-to-day lives where we have priorities and have to make a living. So I mean, it's safe to say it was much simpler when people used to live in tribes and small villages and as hunter-gatherers societies and we were all happy and healthy and doing what humans were meant to do, you know, live off the land, hunt animals and make babies and be happy. It doesn't happen anymore. So thank you guys for joining me today. If you guys can please like the video, subscribe, hit that bell icon and share it. If you can, it is 12.15 in the morning and I started working 6.30. So I'm on like an 18 or 19 hour day. You guys can spend three seconds clicking the like button. Outside of that, if you guys would like to support me further, check out some of the videos I mentioned. We did, I mean, I've done so many news covering videos. We did mention the Blue Zone video and we did mention the eggs and cholesterol video. I will link those at the end here. I have also launched Frankie's Free-Range Meat, my goal being to provide you guys with nutrient dense animal foods at an affordable price. If you want to know why Frankie Boy is killing himself for 18 hours a day, check out frankiesfreerangemeat.com and get Frankie's Meat in your mouth. In addition to Frankie's Free-Range Meat, I also have Frankie's Naturals, Minimal Ingredients Minimally Processed Hygiene and Cosmetic Products at frankiesnaturals.com.


My smoke alarm keeps chirping. It's driving me crazy. If your smoke alarm is chirping, that's a sign that the battery needs replacing. Remember, only working smoke alarms save lives.


We are at a defining moment in our community. Will we meet the challenges that face our community in terms of housing affordability, homelessness, environmental protection, and quality of life? The next few years, both in policy and approach, will set the stage either way. Affordable housing is at the root of many of our challenges. Traffic is linked to a job, housing imbalance, where people have to live further away from work in order to find an affordable place to live. And unsheltered homeless rates continue to rise as finding safe and secure housing options are difficult and road conditions vex many in our district due to deferred maintenance. But we can reverse these trends. With the greater willingness to say yes to affordable housing options, a stronger focus on encouraging major local employers to have work sites in the South County, and with new road funding that's come online, we can significantly improve housing options, traffic issues, and even the quality of life. In the next year, areas of Seacliff, Pleasant Valley, La Selva, and Coralitos will receive road repairs as part of a new road funding that I work to secure. Areas that haven't seen repairs in decades, and new parks are being built, homeless navigation centers, and new affordable housing is being planned and more. These improvements over the next four years are a result of my work over the last seven, but they require an ability to say yes, to work with other electives to get the votes to make it happen, advocacy at the state and federal level, and non-stop persistence. It's been a privilege to serve you on the Board of Supervisors, and I'd be honored to serve you again for another term.


Hi, this is your host Sapil Bhartiya and we are here at CubeCon in Chicago and today we have with us Murali Thirumalai, GM of Portworx. Murali is great to have you on the show again. It's really good to be here Sapnir. Since we are here at the event, I would love to hear from you a bit about what has been your experience so far. We've been in many different CubeCon's now. This is my seventh or eighth one. And so I've been involved with the Kubernetes movement right from the beginning as a co-founder of Portworx. And so the difference that I see this year is really that there is an air of innovation but also an air of maturity. The industry has really come to the point where you see large companies, industrial companies, who are deploying now at scale. So there are two differences. The scale of deployment means that customers are putting mission-critical applications on Kubernetes and are doing it in a large scale. The second thing is that many large, you know, traditional companies who you think of as not being innovators, right? You always find some companies in the Silicon Valley or things like that who are early adopters. But really, this CubeCon is to me an example where we've kind of crossed into the early majority both in terms of the number of companies deploying but also the types of applications at scale of deployment. So it's very exciting time for the Kubernetes community. And if you look at this Kubernetes community, this whole ecosystem, what role do you see of Portworx and also just give a quick kind of overview of, you know, because you folks move to your storage. So talk a bit about what Portworx is today and what role you are playing in this ecosystem. Portworx's role is very simple. In the ecosystem, Portworx are responsible for ensuring that applications and their data are deployed with high resilience reliably. So we provide things like persistent storage, we provide backup, we provide disaster recovery, and data services that are all managed underneath Kubernetes. So when customers deploy containerized applications, really, there's two parts to that application. There's the app and there's the data. And Kubernetes is used to orchestrate the app. Portworx is used to orchestrate the data. So we complete Kubernetes in allowing it to be managing not just the app but also the data. And as you were saying that, you know, the exciting thing was that, you know, maturity is also, the innovation is also there. Third thing I want to add to that is adoption. Talk a bit about what kind of trends you are seeing because Kubernetes has now moved into production. So when it comes to adoption, you folks have been around for a while. So what are you seeing? What are trends? So you know, I would say there are three significant trends that are happening. One of them is really all over. You can see it everywhere. And that is the creation of platform engineering. So Swapnil platform engineering is just the maturation of DevOps, right? So what platform engineering is now, it is taking what used to be a cultural phenomenon called DevOps, like let have developers and operations work together to now that has been formalized into an organization. They have a budget, right? And think about it as shadow Kubernetes IT is being consolidated now. People were doing tiny Kubernetes projects. There were islands of Kubernetes being done. Now all of that is being consolidated into one organization with the responsibility of making sure Kubernetes is deployed centrally and deployed well. The other part of that is that platform engineering is now responsible for a curated stack. They pick the technology stack and they recommend that and they manage that technology stack. So they provide, you know, Gartner had a great phrase for it. They called it paved roads. It's a way to ensure that the Kubernetes deployment that is being done by developers is always assured to run at scale, that it's going to be reliable. It has failover. It has guardrails to ensure that people don't misuse Kubernetes beyond its limits. They also allocate resources per, you know, the requirements of the application, provide billing, provide security. So it is really an example of what used to be happening with cloud IT 10, 15 years ago is now happening with Kubernetes. No, very well said. So that is one trend. The second trend is something that everybody is familiar with now is how AI ML and the Kubernetes stack, the cloud native stack are converging. Everybody of course now, this is every place I go, I hear AI ML, AI ML largely because of generative AI. But if you think about it, AI ML has certain characteristics. The models are changing. Data scientists are changing the models all the time. So rapid change. Second thing is people when they went to training or the needs, the number of the amount of data that's being brought into the system varies and the number of users varies. So it needs a very elastic system. Third, because data scientists don't understand computer computers and storage, they need a self service model. So all of this points to why containers and the Kubernetes model is ideal, because it provides self service for developers and data scientists. It provides a elastic kind of use model. And third, it provides the ability for people not to have to make copies of data. So if you think about it, the data is constantly changing in AI ML data is all important. So one way to ensure that the data is curated and managed properly is to have one copy that is available to everybody through a virtualized model, which is what Portworx provides. So what we're seeing is the second trend is the convergence of AI ML and the use of containers and Kubernetes in AI ML. And so these are two trends that are very, very common in that we're seeing all over in KubeCon. When we look at some of these trends, these trends are sometimes driven by innovation. At the same time, sometimes these trends lead to innovation. What does it these trends mean for Portworx? I will tell you one thing about I think not just Portworx, but most companies in the Kubernetes ecosystem. You know, people think that the innovation happens only at the software companies or at startups. The reality is that to require us to be able to introduce innovative capabilities, you need innovative customers who also are willing to kind of take these technologies on. So there are two innovators in this ecosystem, right? There is the creators of the technology and there are the users of the technology, which is and so Portworx, for example, has always created new capabilities with the co-creation model. We have lead customers who help create these new technologies with us. They are the leaders in their industry. So we've had customers like T-Mobile, for example, or Ford. There are customers who have created these new capabilities by working with us in helping to deploy them, helping to improve them and create that product market fit. So this is a wonderful ecosystem with both suppliers and users coming together to kind of create the new technologies of the future. I want to talk about people for a while, which means cultural changes. With these trends or the whole evolution of Kubernetes landscape, of course, we have been talking about DevOps, DevSecOps, sorry, platform you touch upon. Do you see the next wave of cultural change or do you see that platform engineering is where we have finished for a while? But we'll see in a bit. In the early days of Kubernetes, the technologies were evolving very fast and they were not necessarily as stable. Now, six, seven years later, the technology has stabilized. There's a lot of innovation going on, but there's also a lot of stability. The APIs are well defined. There's been consolidation in the industry. A lot of products have matured to the point where you can deploy them reliably. So what does this mean? Well, one thing that it means is that you don't have to be as much of an expert to be able to deploy some of these new Kubernetes stacks. The stacks are mature. They work pretty well. So the ability of customers to be able to develop applications and deploy these applications has gotten a lot better because their expertise needed is a little bit less. The second thing that has happened, which is very significant, is a lot of these technologies are being offered as a service. And when something is consumed as a service, Swapnil, you know this, then you need less expertise. A service is meant for ready-made use. It's off the shelf. And you can just use it without necessarily having to go under the covers. And so that's the other sign of maturity in the industry. A lot of our services have been offered as a service, including, for example, Portworx now is offering a lot of our storage DR backup as a service. The third thing that I would say is that more and more what I see happening is that like most mature technologies, it will become more and more invisible. People will use Kubernetes without knowing they're using Kubernetes in the future. It reminds me that in the past, people would talk about transistors and ICs. Now we have these things, you know, there's, you know, thousands of chips inside a car. There's probably 50 microprocessors and we don't even think about it, right? Because it's all invisible. So the goal is really to make Kubernetes something that is so powerful, but also more and more invisible so that people get the benefits of it without having to kind of having to know each aspect about it. We hear a lot of time that Kubernetes is too complicated, too complex, which is, you know, it was not designed to be, but there is a discussion that, you know, there was a Twitter thread a few days ago, you may have seen it too, was more about, you know, it's just too complicated for developers. What's next to make it easier? So when you do say it will become, what are you seeing that is there will be a successor, a new technology, or it just vendors will make it easier and invisible? I think it is about vendors making it easier and also operating much more with a, for example, a GUI interface or, you know, you don't really need to know all about the the APIs that Kubernetes offers to be able to kind of use the APIs, right? So a perfect example is in the in the early days of porkworks and for Kubernetes, people used to operate on the CLI. But today we provide, you know, use a user interface where people can just point and click drag and drop, right? All of these things are going to make it so that you it just becomes more invisible. I actually don't see Kubernetes being supplanted by something new for quite a while, right? Because it is really still on a very steep ramp of use. And the maturity of the technology means that more and more people are using it and are not asking us for new fundamental technologies. They're asking for more, you know, more of an ecosystem around Kubernetes.  So Kubernetes is almost now a sub kind of met up technology now for other technologies to be built on top of it. I look at it as like the Linux kernel, you know, it is 30 40 years. And it's so it's not about the success, you know, that is so it has become a very foundational technology. Exactly. So yes, of course, there are vendors who are making it easy to use Linux. Same thing is there. One last question before we wrap this up is chat gpd or generative AI, you know, what role do you see of generative AI in the Kubernetes ecosystem or for porkworks? There are so many different ways that generative AI or any AI actually is going to become part of our ecosystem, right? The first and most simple obvious thing is for us to use it in our business, right? So as an example, porkworks is beginning to use generative AI to be able to improve our documentation, right? Very simple, improve our documentation, improve our test plans, improve the way that we actually create examples and demos for our products. So one, just using it inside of our business, second way to do that we're beginning to use it is use it in our products. So copilot is a very common version that we actually have now, for example, when you create and scripts within a porkworks product, you can now create a setup by just telling in common natural NLP, natural language, what kind of goal you're trying to accomplish with your setup and the copilot generates the code that is used by our product. So it's built in as an interface to our product. The third thing is obviously helping our industry, helping to enable chat gpd or other applications, right? And there again, I just talked about a little bit earlier how the nature of containers and Kubernetes, the fact that it is very elastic, the fact that it is very programmable and, you know, very, very resilient in terms of being able to kind of deal with various workloads, the fact that it is a self-service model, the fact that you can, by using technology like porkworks that virtualizes the underlying data, not have to make multiple copies of data, data curation and ensuring the provenance of the data is very important in AI. So all of these things are reasons why you would use a container and Kubernetes stack to be able to deploy, you know, LLM models, use just AI models and even ML models. So I think there's a natural convergence of how these things come together in our industry. Murli, thank you so much for taking time out today and I'm a great insights there, you know, and I love the way you talk about those trends and the role of culture and, of course, the role of porkworks in this ecosystem. Thanks for all those great insights and I would love to chat with you again. Thank you. Absolutely a pleasure. Thank you so much.


Oh, oh man. How many times did you ask her if it's okay to stay in touch? You're the master, bro. Like, I think you asked her and she said no like four times and somehow like you circle back to it again and again. What's up, everybody? Welcome back to another live cold calling session. Yes, sir. Yes, sir. Excited about this one. I got my guy JR one month real estate agent up in Philly. What's up, buddy? What's happening, Rick? You know, I'm excited to do this. Yeah. You sound excited, man. Yeah, a little nervous.  It wouldn't be natural for at least a little nervous, but I'm proud of you now for, you know, about a month. I did that 28 day, 30 day talent. I got my first listing by cold calling. So you've already got one listing one month in as a new agent, just circle prospecting? Or what is it? Yep.  That's incredible, bro. What's the listing? Like what, uh, what price range? 230. Nice. Three bath, two and a half bath, townhome. Okay. Sweet, man. So take me through that story real quickly, man. Like you called them kind of what happened just like the real quick story, like how did this come to be? Sure, my approach. So hopefully this can help a few of you. I mean, I don't like to utilize, Hey, is this Mr. Smith? Like it's hard enough to get someone to pick up the phone, right? So I don't even like go into that because I don't want to lose someone by not getting their name right. And we'll get into kind of you'll see my scripting. My scripting is just generic. And actually this particular phone call was, I was trying to set appointments for appraisals and just say, Hey, you know, would you like to know the value of your own? And the lady's like, sure. And I didn't think anything of them like, Oh, she might know, you know, what's your house work? But the back of my head, I was like, look, let's envision this click going through. So cold called her Thursday, said, Hey, can I meet you? Not am Saturday. She said, yeah, I'll be up. I said, okay, no problem. I'll meet you Saturday. You know, talk to her. I wasn't even sure she was like, ready to sell. She's like, look, I owe 155. Like, you know what I mean? How much do you think I'm going to lose on the sale? Like 20% of the house. I'm like, no, you're not going to lose 20% of the sale. I was like, maybe like 8%. She's like, well, I owe 155 on the house. So where do you think we can list it? I was like, probably this 30 probably get that. Okay, buddy. What are we looking like over here, man? I want to hear some phones ringing like some, I want to hear some, some dialing some, uh, so I mean, I'm calling the community. It could be some renters in there. It's like a, it's amazing. You get a human being on the phone, bro. Human being on the phone. That's what we want humans. Hey, this is JR from teller Williams. How are you? Good. Thanks for asking. I just listed a home for sale in your area and I was calling to see if there's anything I can do for you. Okay. Well, in the event you want to buy, sell or rent real estate, who would help you? Oh, well, I'm not thinking about right now. In the event that you need to help, would it be okay just to keep in touch? Yeah, I could tell she hung up. You're, you're long winded there. You're like, Oh, well, in the event, right? In the event, you're talking about like it, like it's a monsoon, right? Like in the event that a hurricane or an earthquake hits and you decide you want to not only buy, but also possibly rent or sell or lease or borrow or he started naming off the different ways you can buy. It's the different things you can do with real estate, bro. That was hilarious. Hey, this is JR with teller Williams. How are you? I just listed a home for sale in your area and I was calling to see if there's anything I can do for you. So I just listed a home for sale in your area and I was calling to see if there's anything I could do for you. Yeah, I have a home for sale in your area. So I was calling you today to see if there's anything I could do for you. Okay. Well, is there an agent that you would work with if you needed to buy or sell? Yeah, so I know a home did sell in your neighborhood. I have a listing up the street as well. So I'm just making calls out, you know, let the people know that I'll be here to assist if they needed a real estate need. So I was just calling you specifically to see if you were looking to, you know, make a move. I'm sure you're probably going to do something in the future. What do you think? So in the event that you were going to buy and sell, would it be okay just to keep in touch so maybe I could help you? No, I think we need to be, I will look for people if I want to. That's fine. I'll just keep in touch while I had you on the phone. It doesn't look like you'd be working with anyone. Can I just get your email address? We could just keep in touch that way. All right. Have a good day. What am I supposed to do there, Ricky? Oh, man. How many times did you ask her if it's okay to stay in touch? You're the master, bro. Like I think you asked her and she said no like four times and somehow like you circle back to it again and again. I'm not making fun of you.  No, no. That was hilarious. No, that was great on your part. Yeah. That was incredible. Hey, this is JR with Tell Williams. How are you? I'm fine. How are you? I'm happy. I appreciate that. I just listed a home for sale in your area. So I was calling to see if there's anything I could do for you. I don't know. We're okay, perhaps. In the event you were looking to buy or sell, make and move, do you have someone you'd work with? Oh, yeah. Okay. Is that an agent with Tell Williams? Uh, okay. Well, in the event you couldn't reach them, would it be all right? Maybe I could just get an email address from you, keep you on my monthly email list and, you know, something comes up. You can't reach them. Maybe I could assist. Yeah. I think I'm okay. Thanks, bro. All right. I appreciate that. Well, if you need anything, this is my cell phone. Feel free to reach out. All right. Thank you, Carl. Oh, you're welcome. A couple of tips, man. A couple of tips. Okay. Couple of tips. Number one, you sound very scripty, which is totally normal right now. You're one month in. Okay. What you need to do to try to break that up is something, not necessarily the weather, but something right there. You know how I do that weather thing? Yeah. Right. Something just a small talky thing just to try to make everybody feel comfortable. Okay. Yeah, I could have talked to you, obviously. Right. Because you're kind of like going right to the next thing and it sounds like you're just reading in a book. You know, it's just like sort of thing and you're like, oh, it just sounds so like you're ready for it and you've got it memorized and you're going for it. It's not like a natural organic conversation. Okay. Which is fine. Totally fine right now. Totally fine. You just, there's something to work on. But you never tell somebody that you're going to put them on a list.  Somebody on your huge list. And you never tell anybody you're going to send them a market report. They're immediately going to say no. Right. They get plenty of market reports and stuff. Okay. So what you do is, is you say, hey, would it be okay if I stayed in touch with you? Right. And then they say, sure. Then you say, cool, what's a good email for you? I'm not telling them I'm going to put them on a list. I'm not telling them I'm going to send them any market information. I'm just saying, hey, I just want to stay in touch with you. See what I'm saying? And I get them to commit, to commit to being okay for me to stay in touch with them before I ask for the email. So it's like, is it okay if I stay in touch with you? And then I wait for them to say yes. Then I say, cool, what's a good email for you? Yo, we got great rate. I can't believe so many people picked up so far. You got good sessions and bad sessions, man. You know, that's just the way it works.  That's how it's going to go, man. That's how it's going to go. Hey, this is JR with Keller Williams. How are you? Great. I'm happy. I appreciate you asking. I just listed a home for sale right down the road from you. And I was calling to see if you needed anything. What was it again, I'm sorry? My name's JR. I'm a realtor with Keller Williams. Oh, I just got a house. Oh, did you? Congratulations. Where'd you buy? At the University Park. Okay. All right, great. I'm familiar with that area. Hey, would there be an opportunity in case like you needed help around the house? I also have like a good vendor list. I assume this is your cell phone? Yeah. All right. Maybe instruction on that? Yeah, yeah. I can help you out. Is there a good email address I could keep in touch with you? Can you just sign up over here? Yeah. Yeah. I guess is this your cell phone? I'm about to try it right here. Okay. Yeah. What's a good email address for you? Can you like sign up and put it up there? Oh, yeah, yeah, yeah. Yeah, I'll sign you a text. You can just send me your email address. It's just probably easier to keep in touch that way. All right, I appreciate it. All right, man. There you go, bro. That was good. That was good. The tonality was good. The speed was good. The delivery was good.