from the Hard Rock Hotel in Las Vegas it's the cube covering no joke on 2018 brought to you by osho everyone welcome back to our live coverage here in Las Vegas for Osho Khan's first industry security conference dedicated to security in the blockchain it's presented by ho show and also the industry it's an industry conference it's not necessarily a host show cause I'm John Ford's the cue for our coverage our next guest is Kenzie Crone and vice president of business development prime trust welcome to the cube thanks for joining us thanks for having me here so crowdsourcing and crowdfunding all this has been a big part of it I mean terrorists are funding through Bitcoin you've got all kinds of things going on in entrepreneurial spaces so it's clearly the money's flowing with with with crypto what do you guys do if we're getting into some of the things that we want to talk about what is prime trust to take a minute to explain your business business model value proposition absolutely so prime trust is a trust company so it's a regulated financial institution that holds funds between transactions between businesses you could also use prime trust to created a trust account for an individual as well so what our value is in this industry is that we hold crypto assets which very few qualified custodians like us exist to do that so that's a really important part of bringing in institutional funding because institutions are looking for qualified custodians as a regulated place to keep funds and they want to get into crypto so it's a it's a very important part of the puzzle so custody and custodial service has been a big topic here at O joke on controversial on the keynotes as well because you know the purists will say hey like Andreas why don't we need custody if it's working it's just it's the same old guard with new faces new business cards it's not really revolutionary and that's on one answer on the other inspection is there's so much growth in activity we've got a trusted partners to actually help us manage the risk and do these things so you have again two spectrums what's the story what should people understand about these two dynamics well what I think yeah what I think the key note you're talking about the the idea is we are just trading one type of banker for another type of banker right that's happening anyway so you are you're trading one type of financial system for another type of financial system the question is what does that look like and how can we be secure and safe in that space right personally I'm a big fan of anything that requires some kind of a license right and it's not because I think it's really fun to go through the bureaucratic process of getting a license or filling out paperwork but it's really because that once you have a license that license can be taken away from you if you misbehave right and that's really important so if you're following the laws that are set forth that are designed to protect people and then you break those laws then you're not you're not allowed to do that anymore right so that's what you get out of having regulation involved in this space is its protection and it's making sure that they're really by the way the regulation is happening anyway so that's another the regulation is happening anyway and that's why these very smart people who are managing billions of dollars are looking for that they're not saying oh cool you have a website that with technology that I don't understand you're telling me that you can safely hold something but there's no other protection there there's no liability you could just mount GOx me right and so there's got to be a way to get some sort of some sort of regulation in there and I know there's a lot of opinions in the space and obviously I'm very much on the side of regulation yeah and it also made some balance within the day those are polarized positions but I think the industry recognizes growth by recognizing the domicile problem of companies and governments so the question is you know really than a licenses legitimacy is people want legitimacy trust and growth yes at the same time but the other side says is hey you know who are those people making the laws so who's taking what away so again this is the ecosystem will solve these problems in my opinion and I believe that you know as much as I love the purist view and I think this architectural technical things that make that happen the end of the day is the self-governance of the community really is is what me happen here and so that's where the growth comes in because if real money is coming in to the sector you got to have parties that are trusted it's my opinion all right so what do you think about the conference here what's your take away so far I'll see its kind of diverse background you got you know people walking around with colorful costumes too you know buttoned up bankers and FBI agents and NSA agency folks yes we're in a really funny time in this space I think because you still have yet the Bitcoin garb and the like you know the flashing glasses and and then you've got people who spent 20 years on Wall Street and now they're in the space so I've seen that actually a lot lately in the last year at these conferences and it's very interesting I love when both sides can come in with an open mind to the other because you think there's something to be learned on both sides absolutely it's so for the people who have been in the traditional regulated space they are getting all this inspiration and the possibility of doing things differently the system that the financial system that we have now is one it's essentially you know a very old house that's just been added on to and built and there's corridors going into stairways that you know don't go anywhere right and that's that's something that needs to be fixed and and it is being fixed well Security's a driver in all this and I think one of the things I've observed you'd love to get your reaction to is you have the crypto world that's certainly changing a lot of in dynamics on the global scale you have a cyber security and then you have fin tech so you guys this is where everything I think is a melting pot which is interesting you have all these things happening but at the center of all this is security absolutely it's almost like we're all swimming out to the to the raft and whoever gets there first and wins a security model wins at all well I thought I think well I think this the conversations all threads through security so the cyber conversations we've had are like okay Cyrus security for individuals and nation-states crypto currency for protection and freedom and and you know in immutability Ledger's almost great supply-chain aspects and then you get the FinTech which is like hey people want to do business so you have the entire changeover on the financial services side all kind of happening yeah yeah I think that they're all gonna be contributing to a solution it's it's each one is going to learn we're really open-minded at prime trust we want to build and grow we know that this we're in the most embryonic stage of this and so we don't know exactly what's gonna come next or what's going to be down the road and we want to be informed by everybody that's around us at a place that makes sense do you have to work with with the industries so take me through I want to ask you a question about your job so we'll take me through the day in the life of what's going on in prime chess what are some of the things that you guys do customers and what are they asking for what's like what's the some of the issues you guys are solving what did some of the dynamics can you share some color around that sure so our main services are so we are a trust company so we do escrow services and we do compliance on all of the escrow that comes through our ICS and stos that come through so that's a ml and kyc that's really important what distinguishes us I think is a real a real game changer for our customers is that we're really a technology company and we have API stocks that allow for companies to build their businesses on top of integration so that they have customers coming in and making accounts on their their their website their dashboard their platform and that's all feeding directly and they're actually making an account so you're building your you're targeting folks saying hey we'll take care of the heavy lifting on kyc ma ml and all the stuff that needs to happens that's heavy lifting that's around DoDEA services custodial service all comes through you yes so it comes in we can hold it we can review it you're not having asset managers also holding funds which is a problem so you're not needing to touch the funds at all you can just you can just do you at you're trying to do in this space and we'll take care of that aspect that's entrepreneurial side that's the stos and the IC knows what's the alternative for the your customer build their own go with unknown shop of their other so what so if I if it's a great service sounds like a great service and takes a lot of pressure off the build out of a opportunity what's the alternative if someone doesn't go with you well there's a few I mean it's to hold your own funds right figure that out on your own in the case of many different types of funds and businesses their boards are not okay with that because it's it's too much risk and liability so in many cases the alternative is don't do it yet just keep watching and waiting and wanting to be in crypto but you can't yet so and when we're seeing that a lot that there's like a sigh of relief when we finally have this conversation and it turns out it's extremely easy to make an account with us and suddenly that major roadblock is just gone so that's what that's the career opportunity takes the risk off the table little bit and accelerates the opportunity when the sec bomb decrypt yesterday was reporting that the sec in the united states is actually going into IC OS and having them return their money because of of course they are like well of course they are that makes sense that's they were always going to do that just because they make a statement and slowly decide how to act because look last july is when they said we're going to do this and most of the crypto community said you can't because we really don't want you to and we are gonna tell ourselves all these excuses for why it's not possible for the US government to actually pursue this and why they won't really do it because they're dinosaurs and that's just not how the government works so the way the government does work is that they everything takes a long time and it's all thought through and there are a million different approval processes within the system and they don't tell you anything until they're really ready to stand by whatever same and they make so they leave you in the dark for eight months a year whatever well you guys have a good opportunity so I had to ask the question what's the business model how does someone engage with you guys sounds likely to go in and create an account is there a fee involved what's the fee can you share the engagement that somewhere would would engage with you young sure so they can visit our website which is prime trust com they can email me at Kinsey at prime trust pretty easy and we have different pricing for escort services versus custodial services and we actually pay interest on any Fiat that we held in custody and we charge a monthly basis point fee based on how much is in in custody with us and where's you guys located was the company located headquarters this here in Nevada in Las Vegas I'm based out of Los Angeles we've got some team members in San Francisco in New York as well that's awesome so it's a question how did you get into the space what's your story I got into the space I started out an equity crowdfunding so I was working with companies that were raising capital under A+ reg D and reg CF and I was in the trenches with them figuring out from like the very earliest days how what the laws were gonna look like you know launching companies the day the regulations came out barking into effect and then sort of working through that so it's been an adventure on that side and then my first experience in crypto was at an at a meet up in Santa Monica where companies were talking about raising 40 million dollars in ten seconds and that and they were also pitching in methods like I knew were not legal so it was it's kind of just dropping to me well one was how did you manage to get that many people to want to invest in you so quickly because it's a struggle for for many companies and then so that's amazing I want to learn more about that and then also did you know that there's a more legal way to do this and that you're putting yourself at a lot of risk so that made me really want to jump in and figure this out so you got totally intoxicated by the Wild West yeah there's a problem they gotta be solved in there it's kind of fun at the same time because you know all those those days are over thankfully so because you know it should be it should be more legitimize and it is getting there I think security tokens are a good sign that people are moving border security tokens at least in the u.s. the legal firms the service providers are starting to get hold up on some of the new things and that's good still expensive to run the run the process it's like own public almost as a start-up it's almost ridiculous and I kinda had the same view we're the gaps in your opinion so you now look at the crowdfunding which has been great you see all that stuff happening as essentially as a decentralized you know efficiency around disrupting venture capital and other fundraising which is great where are the gaps in your mind from a service provider standpoint from an ecosystem where's the to-do items what needs to get done faster where are the gaps I think everybody's building out their technology to make everything easier currently there's a lot that's done manually or just to manually and needs to be more automated and then I think there's also a lot of education on both sides that needs to be done that's that's I think a huge gap there's a tendency to create echo chambers and so you end up talking with people who just won't even consider the other side of it with the possibility for change in whichever area they're in and that is I think we are gonna see that come together but that tends to hold people back because you thanks for coming on and sharing your insights great to have you on the cube and good luck with prime trust thank you okay this is a cube live coverage here at hosts show con I'm John furrow your stay with us more live coverage after the short break

(upbeat music) >> Thank you and good morning or afternoon, everyone, depending on where you're coming to us from and welcome to today's breakout session, Fast Data, a retail industry business imperative. My name is Brent Biddulph, Global Managing Director of Retail and Super Bids here at Cloudera and today's hosts. Joining me today is our feature speaker Brian Kilcourse, Managing Partner from RSR. We'll be sharing insights and implications from recently completed research across retailers of all sizes in empirical segments. At the end of today's session I'll share a brief overview on what I personally learned from retailers and how Cloudera continues to support retail data analytic requirements, and specifically around streaming data ingest, analytics, automation for customers around the world. There really is the next step up in terms of what's happening with data analytics today. So let's get started. So I thought it'd be helpful to provide some background first on how Cloudera is supporting retail industry leaders specifically how they're leveraging Cloudera for leading practice data analytics use cases, primarily across four key business pillars and these will be very familiar to those in the industry. Personalize interactions of course plays heavily into e-commerce and marketing, whether that's developing customer profiles, understanding the omni-channel journey, moving into the merchandising line of business, focused on localizing sorbet, promotional planning, forecasting, demand forecast accuracy, then into supply chain where inventory visibility is becoming more and more critical today, whether it's around fulfillment or just understanding where your stuff is from a customer perspective. And obviously in and outbound route optimization, right now as retailers are taking control of actual delivery, whether it's to a physical store location or to the consumer. And then finally, which is pretty exciting to me as a former store operator, what's happening with physical brick and mortar right now, especially for traditional retailers. The whole re-imagining of stores right now is on fire in a lot of focus because frankly this is where fulfillment is happening, this is where customers steal 80% of revenue is driven through retail through physical brick and mortar. So right now store operations is getting more focused and I would say it probably is had in decades and a lot of it has to do of course with IoT data and analytics in the new technologies that really help drive benefits for retailers from a brick and mortars standpoint. And then finally, to wrap up before handing off to Brian, as you'll see, all of these lines of businesses are rogue, really experiencing the need for speed, fast data. So we're moving beyond just discovery analytics, things that happened five, six years ago with big data, et cetera and we're really moving into real time capabilities because that's really where the difference makers are, that's where the competitive differentiation is across all of these lines of business and these four key pillars within retail. The dependency on fast data is evident, it's something that we all read in terms of those that are students of the industry if you will, that we're all focused on in terms of bringing value to the individual lines of business but more importantly to the overall enterprise. So without further ado, I really want to have Brian speak here as a third party analyst. He's close in touch with what's going on retail talking to all the solution providers, all the key retailers about what's important, what's on their plate, what are they focusing on right now in terms of fast data and how that could potentially make a difference for them going forward. So Brian off to you. >> Well, thanks, Brent. I appreciate the introduction. And I was thinking as you were talking, what is fast data? Well, fast data is fast data, it's stuff that comes at you very quickly. When I think about the decision cycles in retail, they were time phased and there was a time when we could only make a decision perhaps once a month and then met once a week and then once a day, and then intraday. Fast data is data that's coming at you in something approaching real time and we'll explain why that's important in just a second. But first I want to share with you just a little bit about RSR. We've been in business now for 14 years and what we do is we studied the business use cases that drive the adoption of technology in retail. We come from the retail industry. I was a retail technologist my entire working life and so we started this company. So I have a built-in bias of course, and that is that the difference between the winners in the retail world and in fact in the entire business world and everybody else is how they value the strategic importance of information, and really that's where the battle is being fought today. We'll talk a little bit about that. So anyway, one other thing about RSR Research, our research is free to the entire world. We don't have a paywall that you have to get behind, all you have to do is sign into our website, identify yourself and all of our research, including these two reports that we're showing on the screen now are available to you and we'd love to hear your comments. So when we talk about data, there's a lot of business implications to what we're trying to do with fast data and is being driven by the real world. We saw a lot of evidence of that during the COVID pandemic in 2020, when people had to make many decisions very, very quickly, for example, a simple one, do I redirect my replenishments to store B because store A is impacted by the pandemic, those kinds of things. These two drawings are actually from a book that came out in 1997 and it was a really important book for me personally is by a guy named Steven Hegel and the name of the book was "The Adaptive Enterprise." When you think about your business model and you think about the retail business model, most of those businesses are what you see on the left. First of all, the mission of the business doesn't change much at all, it changes once in a generation or maybe once in a lifetime, but it's established quite early. And then from that point on, it's basically a wash, rinse and repeat cycle. You do the things that you do over and over and over again, year in and year out, season in and season out and the most important pieces of information that you have is the transaction data from the last cycle. So Brent knows this from his experience as a retailer, the baseline for next year's forecast is last year's performance. And this is transactional in nature, it's typically pulled from your ERP or from your best of breed solution set. On the right is where the world is really going, and before we get into the details of this, I'll just use a real example. I'm sure like me, you've watched the path of hurricanes as they go up to the Florida Coast. And one of the things you might've noticed is that there are several different possible paths. These are models and you'll hear a lot about models when you talk to people in the AI world. These are models based on lots and lots of information that they're getting from Noah and from the oceanographic people and all those kinds of folks to understand the likely path of the hurricane. Based on their analysis, the people who watch these things will choose the most likely paths and they will warn communities to lock down and do whatever they need to do. And then they see as the real hurricane progresses, they will see if it's following that path or if it's varying, it's going down a different path and based on that they will adapt to a new model. And that is what I'm talking about here. Not everything is of course is life and death as a hurricane but it's basically the same concept. What's happening is you have your internal data that you've had since this command and control model that we've mentioned on the left and you're taking an external data from the world around you and you're using that to make snap decisions or quick decisions based on what you see, what's observable on the outside. Back to my COVID example, when people were tracking the path of the pandemic through communities, they learned that customers or consumers would favor certain stores to pick up what they needed to get. So they would avoid some stores and they would favor other stores and that would cause smart retailers to redirect the replenishments on very fast cycles to those stores where the consumers are most likely to be. They also did the same thing for employees, they wanted to know where they could get their employees to service these customers, how far away were they, were they in a community that was impacted or were they relatively safe. These are the decisions that were being made in real time based on the information that they were getting from the marketplace around them. So first of all, there's a context for these decisions, there's a purpose and the bounds of the adaptive structure, and then there's a coordination of capabilities in real time and that creates an internal feedback loop, but there's also an external feedback loop. This is more of an ecosystem view and based on those two inputs what's happening internally, where your performance is internally and how your community around you is reacting to what you're providing. You make adjustments as necessary and this is the essence of the adaptive enterprise. Engineers might call this a sense and respond model, and that's where retail is going. But what's essential to that is information and information, not just about the products that you sell or the stores that you sell it in or the employees that you have on the sales floor or the number of market baskets you've completed in the day, but something much, much more. If you will, a twin, a digital twin of the physical assets of your business, all of your physical assets, the people, the products, the customers, the buildings, the rolling stock, everything, everything. And if you can create a digital equivalent of a physical thing, you can then analyze it. And if you can analyze it, you can make decisions much, much more quickly. So this is what's happening with the predict pivot based on what you see and then because it's an intrinsically more complicated model to automate decision-making where it makes sense to do so. That's pretty complicated and I talk about new data and as I said earlier, the old data is all transactional in nature, mostly about sales. Retail has been a wash in sales data for as long as I can remember, they throw most of it away but they do keep enough to create the forecast for the next business cycle. But there's all kinds of new information that they need to be thinking about and a lot of this is from the outside world and a lot of this is non-transactional in nature. So let's just take a look at some of them. Competitive information. Retailers are always interested in what the competitor is up to, what are they promoting? How well are they doing? Where are they? What kind of traffic are they generating? Sudden and significant changes in customer behaviors and sentiment, COVID is a perfect example of something that would cause this, consumers changing their behaviors very quickly. And we have the ability to observe this because in a great majority of cases nowadays, retailers have observed that customers start their shopping journey in the digital space. As a matter of fact, Google recently came out and said that 63% of all sales transactions begin in the digital domain, even if many of them end up in the store. So we have the ability to observe changes in consumer behavior, what are they looking at? When are they looking at it? How long do they spend looking at it? What else are they looking at while they're doing that? What is the outcome of them looking? Market metrics certainly, what's going on in the marketplace around you? A good example of this might be something related to a sporting event. If you've planned based on normal demand and for your store and there's a big sporting event, like a football match or a baseball game, suddenly you're going to see a spike in demand, so understanding what's going on in the market is really important. Location, demographics and psychographics. Demographics have always been important to retailers, but now we're talking about dynamic demographics. What customers or what consumers are in your market in something approaching real time. Psychographics has more to do with their attitudes, what kind of folks are in a particular marketplace, what do they think about, what do they favor, and all those kinds of interesting details. Real time environmental and social incidents, of course, I mentioned hurricanes and so that's fairly self-evident. Disruptive events, sporting events, et cetera, these are all real. And then we get the real time Internet-of-Things, these are RFID sensors, beacons, video, et cetera. There's all kinds of stuff. And this is where it really gets interesting, this is where the supply chain people will start talking about the digital twin to their physical world. If you can't say something you can't manage it and retailers want to be able to manage things in real time. So IoT along with AI analytics and the data that's generated is really, really important for them going forward. Community health, we've been talking a lot about that, the progression of the flu, et cetera, et cetera. Business schedules, commute patterns, school schedules, and weather, these are all external data that are interesting to retailers and can help them to make better operational decisions in something approaching real time. I mentioned the automation of decision-making, this is a chart from Gardner and I'd love to share with you. It's a really good one because it describes very simply what we're talking about and it also describes where the inflection of new technology happens. If you look on the left there's data, we have lots and lots of data, we're getting more data all the time. Retailers for a long time now since certainly since the seventies or eighties have been using data to describe what happened, this is the retrospective analysis that we're all very familiar with, data cubes and those kinds of things. And based on that, the human makes some decisions about what they're going to do going forward. Sometime in the not-too-distant past this data was started to be used to make diagnostic decisions, not only what happened but why did it happen? And we might think of this as, for example, if sales were depressed and for a certain product, was it because we had another product on sale that day, that's a good example of fairly straightforward diagnostics. We then move forward to what we might think of as predictive analytics and this was based on what happened in the past and why it happened in the past. This is what's likely to happen in the future. You might think of this as, for example, halo effect or the cannibalization effect of your category plans if you happen to be a grocer. And based on that, the human will make a decision as to what they need to do next. Then came along AI, and I don't want to oversell AI here. AI is a new way for us to examine lots and lots of data, particularly unstructured data. AI if I could simplify it to the next maximum extent, it essentially is a data tool that allows you to see patterns in data which might be interesting. It's very good at sifting through huge data sets of unstructured data and detecting statistically significant patterns. It gets deeper than that of course, because it uses math instead of rules. So instead of an if then or else statement that we might've used with our structured data, we use the math to detect these patterns in unstructured data and based on those we can make some models. For example, my guy in my (chuckles) just turned 70. My 70 year old man, I'm a white guy, I live in California, I have a certain income and a certain educational level. I'm likely to behave in this way based on a model, that's pretty simplistic but based on that, you can see that when another person who meets my psychographics, my demographics, my age group, my income level and all the rest, they might be expected to make a certain action. And so this is where prescriptive really comes into play. AI makes that possible. And then finally, when you start to think about moving closer to the customer or something approaching a personalized level, a one-to-one level, you suddenly find yourself in the situation of having to make not thousands of decisions but tens of millions of decisions and that's when the automation of decision-making really gets to be pretty important. So this is all interesting stuff, and I don't want to oversell it. It's exciting and it's new, it's just the latest turn of the technology screw and it allows us to use this new data to basically automate decision-making in the business in something approaching real time so that we can be much, much more responsive to real-time conditions in the marketplace. Very exciting. So I hope this is interesting. This is a piece of data from one of our recent pieces of research. This happens to be from a location analytics study we just published last week, and we asked retailers, what are the big challenges? What's been going on in the last 12 months for them, and what's likely to be happening for them in the next few years and it's just fascinating because it speaks to the need for faster decision-making. The challenges in the last 12 months are all related to COVID. First of all, fulfilling growing online demand, this is a very real time issue that we all had to deal with. But the next one was keeping forecasts in sync with changing demand and this is one of those areas where retailers are now finding themselves needing to look at that exogenous or that external data that I mentioned to you. Last year sales were not a good predictor of next year sales, they needed to look at sentiment, they needed to look at the path of the disease, they needed to look at the availability of products, alternate sourcing, global political issues, all of these things get to be pretty important and they affect the forecast. And then finally, managing the movement of the supply through the supply chain so that they could identify bottlenecks. Now, point to one of them which we can all laugh at now because it's kind of funny, it wasn't funny at the time. We ran out of toilet paper (laughs) toilet paper was a big problem. Now there is nothing quite as predictable as toilet paper, it's tied directly to the size of the population and yet we ran out. And the thing we didn't expect when the COVID pandemic hit was that people would panic and when people panic they do funny things. One of the things I do is buy up all the available toilet paper, I'm not quite sure why that happen but it did happen and it drained the supply chain. So retailers needed to be able to see that, they needed to be able to find alternative sources, they needed to be able to do those kinds of things. This gets to the issue of visibility, real-time data, fast data. Tomorrow's challenge is kind of interesting because one of the things that retailers put at the top of their list is improve inventory productivity. The reason that they are interested in this is because they will never spend as much money on anything as they will on inventory and they want the inventory to be targeted to those places where it is most likely to be consumed and not to places where it's least likely to be consumed. So this is trying to solve the issue of getting the right product at the right place at the right time to the right consumer and retailers want to improve this because the dollars are just so big. But in this complex, fast moving world that we live in today is this requires something approaching real-time visibility. They want to be able to monitor the supply chain, the DCs and the warehouses and their picking capacity. We're talking about Echo's, we're talking about Echo's level of decision-making about what's flowing through the supply chain all the way from the manufacturing door to the manufacturer through to consumption. There's two sides of the supply chain and retailers want to look at it. You'll hear retailers and people like me talk about the digital twin, this is where this really becomes important. And again, the digital twin is enabled by IoT and AI analytics. And finally, they need to increase their profitability for online fulfillment. This is a huge issue, for some grocers the volume of online orders went from less than 10% to somewhere north of 40%. And retailers did in 2020 what they needed to do to fulfill those customer orders in the year of the pandemic, that now the expectation that consumers have have been raised significantly. They now expect those features to be available to them all the time and many people really like them. Now retailers need to find out how to do it profitably and one of the first things they need to do is they need to be able to observe the process so that they can find places to optimize. This is out of our recent research and I encourage you to read it. Now when we think about the hard one wisdom that retailers have come up with we think about these things, better visibility has led to better understanding which increases their reaction time which increases their profitability. So what are the opportunities? This is the first place that you'll see something that's very common and in our research, we separate over-performers, who we call retail winners from everybody else, average and under-performers. And we've noticed throughout the life of our company that retail winners don't just do all the same things that others do, they tend to do other things and this shows up in this particular graph. This again is from the same study. So what are the opportunities to address these challenges I mentioned to you in the last slide? First of all, strategic placement of inventory throughout the supply chain to better fulfill customer needs. This is all about being able to observe the supply chain, get the inventory into a position where it can be moved quickly to fast changing demand on the consumer side. A better understanding and reacting to unplanned events that can drive a dramatic change in customer behavior. Again, this is about studying the data, analyzing the data and reacting to the data that comes before the sales transaction. So this is observing the path to purchase, observing things that are happening in the marketplace around the retailer so that they can respond very quickly, a better understanding of the dramatic changes in customer preference and path to purchase as they engage with us. One of the things we all know about consumers now is that they are in control and literally the entire planet is the assortment that's available to them. If they don't like the way they're interacting with you, they will drop you like a hot potato and go to somebody else. And what retailers fear justifiably is the default response to that is to just see if they can find it on Amazon. You don't want this to happen if you're a retailer. So we want to observe how we are interacting with consumers and how well we are meeting their needs. Optimizing omni-channel order fulfillment to improve profitability. We've already mentioned this, retailers did what they needed to do to offer new fulfillment options to consumers. Things like buy online pickup curbside, buy online pickup in-store, buy online pick up at a locker, a direct to consumer, all of those things. Retailers offer those in 2020 because the consumers demand it and needed it. So when retailers are trying to do now is to understand how to do that profitably. And finally, this is important and never goes away is the reduction of waste, shrink within the supply chain. I'm embarrassed to say that when I was a retail executive in the nineties, we were no more certain of consumer demand than anybody else was but we wanted to commit to very high service levels for some of our key categories somewhere approaching 95% and we found the best way to do that was to flood the supply chain with inventory. It sounds irresponsible now, but in those days that was a sure-fire way to make sure that the customer had what she was looking for when she looked for it. You can't do that in today's world, money is too tight and we can't have that inventory sitting around and move to the right places once we discover what the right places. We have to be able to predict, observe, and respond in something much closer to real time. Onto the next slide, the simple message here, again a difference between winners and everybody else. The messages, if you can't see it you can't manage it. And so we asked retailers to identify to what extent an AI enabled supply chain can help their company address some issues. Look at the differences here, they're shocking. Identifying network bottlenecks, this is the toilet paper story I told you about. Over half of retail winners feel that that's very important, only 19% of average and under-performers, no surprise that they're average and under-performers. Visibility into available to sell inventory anywhere within the enterprise, 58% of winners and only 32% of everybody else. And you can go on down the list but you get the just, retail winners understand that they need to be able to see their assets and something approaching real time so that they can make the best decisions possible going forward in something approaching real time. This is the world that we live in today and in order to do that you need to be able to number one, see it and number two, you need to be able to analyze it, and number three, you have to be able to make decisions based on what you saw. Just some closing observations and I hope this was interesting for you. I love talking about this stuff, you can probably tell I'm very passionate about it. But the rapid pace of change in the world today is really underscoring the importance, for example, of location intelligence as a key component of helping businesses to achieve sustainable growth, greater operational effectiveness and resilience, and ultimately your success. So this is really, really critical for retailers to understand and successfully evolving businesses need to accommodate these new consumer shopping behaviors and changes and how products are brought to the market. And in order to do that they need to be able to see people, they need to be able to see their assets, and they need to be able to see their processes in something approaching real time, and then they need to analyze it and based on what they've uncovered, they need to be able to make strategic and operational decision making very quickly. This is the new world we live in, it's a real-time world, it's a sense and respond world and it's the way forward. So Brent, I hope that was interesting for you. I really enjoyed talking about this as I said, we'd love to hear a little bit more. >> Hey, Brian, that was excellent. I always love hearing from RSR because you're so close to what retailers are talking about and the research that your company pulls together. One of the higher level research articles around fast data frankly, is the whole notion of IoT, right? Now many does a lot of work in this space. What I find fascinating based off the recent research is believe it or not, there's $1.2 trillion at stake in retail per year between now and 2025. Now, how's that possible? Well, part of it is because of the Kinsey captures not only traditional retail but also QSRs and entertainment venues, et cetera, that's considered all of retail. But it's a staggering number and it really plays to the effect that real time can have on individual enterprises, in this case we're talking of course about retail. So a staggering number and if you think about it, from streaming video to sensors, to beacons, RFID, robotics, autonomous vehicles retailers are asking today, even pizza delivery and autonomous vehicles. If you think about it, it shouldn't be that shocking, but when they were looking at 12 different industries, retail became like the number three out of 12 and there's a lot of other big industries that will be leveraging IoT in the next four years. So retailers in the past have been traditionally a little stodgy about their spend in data and analytics. I think retailers in general have got the religion that this is what it's going to take to compete in today's world, especially in a global economy and IoT really is the next frontier, which is kind of the definition of fast data. So I just wanted to share just a few examples or exemplars of retailers that are leveraging the Cloudera technology today. So now they pay for advertisement at the end of this, right? So what is Cloudera bringing to market here? So across all retail verticals, if we look at, for example, a well-known global mass virtual retailer, they're leveraging Cloudera data flow which is our solution to move data from point to point in wicked fast space. So it's open source technology that was originally developed by the NSA. So it is best to class movement of data from an ingest standpoint, but we're also able to help the round trip. So we'll pull up sensor data off all the refrigeration units for this particular retailer, they'll hit it up against the product lifecycle table, they'll understand temperature fluctuations of 10, 20 degrees based on fresh food products that are in the store, what adjustments might need to be made because frankly store operators, they'll never know refrigeration, they'll know if a cooler goes down and they'll have to react quickly, but they won't know that 10, 20 degree temperature changes have happened overnight. So this particular customer leverages further data flow to understand temperature fluctuations, the impact on the product life cycle and the roundtrip communication back to the individual department manager, let's say a produce department manager, deli manager, meat manager. Hey, you had a 20 degree drop in temperature, we suggest you lower the price on these products that we know are in that cooler for the next couple of days by 20%. So you don't have to worry about freshness issues and or potential shrink. The grocery with fresh product, if you don't sell it, you smell it, you throw it away, it's cost to the bottom line. So critically important and tremendous ROI opportunity that we're helping to enable there. From a leading global drugstore retailer, so this is more about data processing and we're excited of the recent partnership with the Nvidia. So fast data isn't always at the edge with IoT, it's also about workloads. And in retail, if you are processing your customer profiles or segmentation like intra day, you will never achieve personalization, you will never achieve one-on-one communications with retailers or with customers, and why is that? Because customers in many cases are touching your brand several times a week. So if taking you a week or longer to process your segmentation schemes, you've already lost and you'll never achieve personalization, in fact, you may offend customers by offers you might push out based on what they just bought yesterday you had no idea of it. So that's what we're really excited about, again with the computation speed that Nvidia brings to Cloudera. We're already doing this today, we've already been providing levels of exponential speed and processing data, but when Nvidia brings to the party is course GPUs right, which is another exponential improvement to processing workloads like demand forecast, customer profiles. These things need to happen behind the scenes in the back office much faster than retailers have been doing in the past. That's just the world we all live in today. And then finally, from a proximity marketing standpoint or just from an in-store operations standpoint, retailers are leveraging Cloudera today, not only data flow but also of course our compute and storage platform and ML, et cetera, to understand what's happening in store. It's almost like the metrics that we used to look at in the past in terms of conversion and traffic, all those metrics are now moving into the physical world. If you can leverage computer vision in streaming video, to understand how customers are traversing your store, how much time they're standing in front of the display, how much time they're standing in checkout line, you can now start to understand how to better merchandise the store, where the hotspots are, how to in real time improve your customer service. And from a proximity marketing standpoint, understand how to engage with the customer for right at the moment of truth, right, when they're right there in front of the particular department or category, upward leveraging mobile device. So that's the world of fast data in retail and just kind of a summary in just a few examples of how folks are leveraging Cloudera today. From an overall platform standpoint of course, Cloudera is an enterprise data platform, right? So we're helping to enable the entire data life cycle, so we're not a data warehouse, we're much more than that. So we have solutions to ingest data from the Edge, from IoT, leading practice solutions to bring it in. We also have experiences to help leverage the analytic capabilities of data engineering, data science, analytics and reporting. We're not encroaching upon the legacy solutions that many retailers have today, we're providing a platform that's open source that helps weave all this mess together that existed retail today from legacy systems because no retailer frankly is going to rip and replace a lot of stuff that they have today. Right. And the other thing the Cloudera brings to market is this whole notion of on-prem hybrid cloud and multicloud, right. So our whole culture has been built around open source technology as the company that provides most of the source code to the Apache network around all these open source technologies. We're kind of religious about open source and lack of vendor lock-in, maybe to our fault, but as a company we pull that together from a data platform standpoint so it's not a rip or replace situation. It's like helping to connect legacy systems, data and analytics, weaving that whole story together to be able to solve this whole data life cycle from beginning to end. And then finally, I want to thank everyone for joining today's session, I hope you found it informative. I can't thank Brian Kilcourse enough, like he's my trusted friend in terms of what's going on in the industry. He has much broader reach of course in talking to a lot of our partners in other technology companies out there as well. But I really appreciate everyone joining the session, and Brian, I'm going to kind of leave it open to you to any closing comments that you might have based on what we're talking about today in terms of fast data and retail. >> First of all, thank you, Brent. And this is an exciting time to be in this industry. And I'll just leave it with this. The reason that we are talking about these things is because we can, the technology has advanced remarkably in the last five years. Some of this data has been out there for a lot longer than that and it frankly wasn't even usable. But what we're really talking about is increasing the cycle time for decisions, making them go faster and faster so that we can respond to consumer expectations and delight them in ways that make us a trusted provider of their lifestyle needs. So this is really a good time to be a retailer, a real great time to be servicing the retail technology community and I'm glad to be a part of it and I'm glad to be working with you. So thank you, Brent. >> Yeah, of course, Brian. And one of the exciting things for me too, I've being in the industry as long as I have and being a former retailer is it's really exciting for me to see retailers actually spending money on data and IT for a change, right? (Brian laughs) They've all kind of come to this final pinnacle of this is what it's going to take to compete. You and I talked to a lot of colleagues, even salespeople within Cloudera, like, oh, retail, very stodgy, slow to move. That's not the case anymore. >> No. >> Everyone gets the religion of data and analytics and the value of that. And what's exciting for me to see as all this infusion of immense talent within the industry that we couldn't see years ago, Brian. I mean, retailers are like pulling people from some of the greatest tech companies out there, right? From a data science, data engineering standpoint, application developers. Retail is really getting its legs right now in terms of go to market and the leverage of data and analytics, which to me is very exciting. >> Well, you're right. I mean, I became a CIO around the time that point of sale and data warehouses were starting to happen, data cubes and all those kinds of things. And I never thought I would see a change that dramatic as the industry experience back in those days, 1989, 1990, this changed doors that, but the good news is again, as the technology is capable, we're talking about making technology and information available to retail decision-makers that consumers carry around in their purses and pockets as they're right now today. So the question is, are you going to utilize it to win or are you going to get beaten? That's really what it boils down to. >> Yeah, for sure. Hey, thanks everyone. We'll wrap up, I know we ran a little bit long, but appreciate everyone hanging in here with us. We hope you enjoyed the session. Our contact information is right there on the screen, feel free to reach out to either Brian and I. You can go to cloudera.com, we even have joint sponsored papers with RSR, you can download there as well as other eBooks, other assets that are available if you're interested. So thanks again, everyone for joining and really appreciate you taking the time today.

>>Thank you and good morning or afternoon, everyone, depending on where you're coming to us from and welcome to today's breakout session, fast data, a retail industry business imperative. My name is Brent Bedell, global managing director of retail, consumer bids here at Cloudera and today's hosts joining today. Joining me today is our feature speaker Brian Hill course managing partner from RSR. We'll be sharing insights and implications from recently completed research across retailers of all sizes in vertical segments. At the end of today's session, I'll share a brief overview on what I personally learned from retailers and how Cloudera continues to support retail data analytic requirements, and specifically around streaming data, ingest analytics, automation for customers around the world. There really is the next step up in terms of what's happening with data analytics today. So let's get started. So I thought it'd be helpful to provide some background first on how Clare to Cloudera is supporting and retail industry leaders specifically how they're leveraging Cloudera for leading practice data analytics use cases primarily across four key business pillars. >>And these will be very familiar to, to those in the industry. Personalize interactions of course, plays heavily into e-commerce and marketing, whether that's developing customer profiles, understanding the OB omni-channel journey, moving into the merchandising line of business focused on localized promotional planning, forecasting demand, forecast accuracy, then into supply chain where inventory visibility is becoming more and more critical today, whether it's around fulfillment or just understanding where your stuff is from a customer perspective. And obviously in and outbound route optimization right now, as retailers are taking control of actual delivery, whether it's to a physical store location or to the consumer. And then finally, uh, which is pretty exciting to me as a former store operator, you know, what's happening with physical brick and mortar right now, especially for traditional retailers. Uh, the whole re-imagining of stores right now is on fire in a lot of focus because, you know, frankly, this is where fulfillment is happening. >>Um, this is where customers, you know, still 80% of revenue is driven through retail, through physical brick and mortar. So right now store operations is getting more focused and I would say it probably is had and decades. Uh, and a lot of has to do for us with IOT data and analytics in the new technologies that really help, uh, drive, uh, benefits for retailers from a brick and mortar standpoint. And then, and then finally, um, you know, to wrap up before handing off to Brian, um, as you'll see, you know, all of these, these lines of businesses are raw, really experiencing the need for speed, uh, you know, fast data. So we're, we're moving beyond just discovery analytics. You don't things that happened five, six years ago with big data, et cetera. And we're really moving into real time capabilities because that's really where the difference makers are. >>That's where the competitive differentiation as across all of these, uh, you know, lines of business and these four key pillars within retail, um, the dependency on fast data is, is evident. Um, and it's something that we all read, you know, you know, in terms of those that are students of the industry, if you will, um, you know, that we're all focused on in terms of bringing value to the individual, uh, lines of business, but more importantly to the overall enterprise. So without further ado, I, I really want to, uh, have Brian speak here as a, as a third party analyst. You know, he, he's close in touch with what's going on, retail talking to all the solution providers, all the key retailers about what's important, what's on their plate. What are they focusing on right now in terms of fast data and how that could potentially make a difference for them going forward? So, Brian, uh, off to you, >>Well, thanks, Brent. I appreciate the introduction. And I was thinking, as you were talking, what is fast data? Well, data is fast. It is fast data it's stuff that comes at you very quickly. When I think about the decision cycles in retail, they were, they were, they were time phased and there was a time when we could only make a decision perhaps once a month and then met once a week and then once a day, and then intraday fast data is data that's coming at you and something approaching real time. And we'll explain why that's important in just a second. But first I want to share with you just a little bit about RSR. We've been in business now for 14 years. And what we do is we studied the business use cases that drive the adoption of technology in retail. We come from the retail industry, I was a retail technologist, my entire working life. >>And so we started this company. So I'm, I have a built in bias, of course, and that is that the difference between the winners in the retail world and in fact, in the entire business world and everybody else is how they value the strategic importance of information, and really that's where the battle is being fought today. We'll talk a little bit about that. So anyway, uh, one other thing about RSR research, our research is free to the entire world. Um, we don't, we don't have a paywall. You have to get behind. All you have to do is sign into our website, uh, identify yourself and all of our research, including these two reports that we're showing on the screen now are available to you. And we'd love to hear your comments. So when we talk about data, there's a lot of business implications to what we're trying to do with fast data and as being driven by the real world. >>Uh, we saw a lot of evidence of that during the COVID pandemic in 2020, when people had to make many decisions very, very quickly, for example, a simple one. Uh, do I redirect my replenishments to store B because store a is impacted by the pandemic, those kinds of things. Uh, these two drawings are actually from a book that came out in 1997. It was a really important book for me personally is by a guy named Steven Hegel. And it was the name of the book was the adaptive enterprise. When you think about your business model, um, and you think about the retail business model, most of those businesses are what you see on the left. First of all, the mission of the business doesn't change much at all. It changes once in a generation or maybe once in a lifetime, um, but it it's established quite early. >>And then from that point on it's, uh, basically a wash rinse and repeat cycle. You do the things that you do over and over and over again, year in and year out season in and season out. And the most important piece of information that you have is the transaction data from the last cycle. So a Brent knows this from his experience as a, as a retailer, the baseline for next year's forecast is last year's performance. And this is transactional in nature. It's typically pulled from your ERP or from your best of breed solution set on the right is where the world is really going. And before we get into the details of this, I'll just use a real example. I'm I'm sure like, like me, you've watched the path of hurricanes as they go up to the Florida coast. And one of the things you might've noticed is that there's several different possible paths. >>These are models, and you'll hear a lot about models. When you talk to people in the AI world, these are models based on lots and lots of information that they're getting from Noah and from the oceanographic people and all those kinds of folks to understand the likely path of the hurricane, based on their analysis, the people who watch these things will choose the most likely paths and they will warn communities to lock down and do whatever they need to do. And then they see as the, as the real hurricane progresses, they will see if it's following that path, or if it's varying, it's going down a different path and based on that, they will adapt to a new model. And that is what I'm talking about here now that not everything is of course is life and death as, as a hurricane. But it's basically the same concept what's happening is you have your internal data that you've had since this, a command and control model that we've mentioned on the left, and you're taking an external data from the world around you, and you're using that to make snap decisions or quick decisions based on what you see, what's observable on the outside, back to my COVID example, um, when people were tracking the path of the pandemic through communities, they learn that customers or consumers would favor certain stores to pick up their, what they needed to get. >>So they would avoid some stores and they would favor other stores. And that would cause smart retailers to redirect the replenishments on very fast cycles to those stores where the consumers are most likely to be. They also did the same thing for employees. Uh, they wanted to know where they could get their employees to service these customers. How far away were they, were they in a community that was impacted or were they relatively safe? These are the decisions that were being made in real time based on the information that they were getting from the marketplace around them. So, first of all, there's a context for these decisions. There's a purpose and the bounds of the adaptive structure, and then there's a coordination of capabilities in real time. And that creates an internal feedback loop, but there's also an external feedback loop. This is more of an ecosystem view. >>And based on those two, those two inputs what's happening internally, what your performance is internally and how your community around you is reacting to what you're providing. You make adjustments as necessary. And this is the essence of the adaptive enterprise. Engineers might call this a sense and respond model. Um, and that's where retail is going. But what's essential to that is information and information, not just about the products that you sell or the stores that you sell it in, or the employees that you have on the sales floor or the number of market baskets you've completed in the day, but something much, much more. Um, if you will, a twin, a digital twin of the physical assets of your business, all of your physical assets, the people, the products, the customers, the buildings, the rolling stock, everything, everything. And if you can create a digital equivalent of a physical thing, you can then analyze it. >>And if you can analyze it, you can make decisions much, much more quickly. So this is what's happening with the predict pivot based on what you see, and then, because it's an intrinsically more complicated model to automate, decision-making where it makes sense to do so. That's pretty complicated. And I talk about new data. And as I said earlier, the old data is all transactional in nature. Mostly about sales. Retail has been a wash in sales data for as long as I can remember throw, they throw most of it away, but they do keep enough to create the forecast the next for the next business cycle. But there's all kinds of new information that they need to be thinking about. And a lot of this is from the outside world. And a lot of this is non-transactional nature. So let's just take a look at some of them, competitive information. >>Those are always interested in what the competitor is up to. What are they promoting? How well are they they doing, where are they? What kind of traffic are they generating sudden and stuff, significant changes in customer behaviors and sentiment COVID is a perfect example of something that would cause this consumers changing their behaviors very quickly. And we have the ability to, to observe this because in a great majority of cases, nowadays retailers have observed that customers start their, uh, shopping journey in the digital space. As a matter of fact, Google recently came out and said, 60%, 63% of all, all sales transactions begin in the digital domain. Even if many of them end up in the store. So we have the ability to observe changes in consumer behavior. What are they looking at? When are they looking at it? How long do they spend looking at it? >>What else are they looking at while they're, while they're doing that? What are the, what is the outcome of that market metrics? Certainly what's going on in the marketplace around you? A good idea. Example of this might be something related to a sporting event. If you've planned based on normal demand and for, for your store. And there's a big sporting event, like a football match or a baseball game, suddenly you're going to see a spike in demand. So understanding what's going on in the market is really important. Location, demographics and psychographics, demographics have always been important to retailers, but now we're talking about dynamic demographics, what customers, or what consumers are, are in your market, in something approaching real time, psychographics has more to do with their attitudes. What kind of folks are, are, are in them in a particular marketplace? What do they think about what do they favor? >>And all those kinds of interesting deep tales, real-time environmental and social incidents. Of course, I mentioned hurricanes. And so that's fairly, self-evident disruptive events, sporting events, et cetera. These are all real. And then we get the real time internet of things. These are, these are RFID sensors, beacons, video, et cetera. There's all kinds of stuff. And this is where, yeah, it's interesting. This is where the supply chain people will start talking about the difference, little twin to their physical world. If you can't say something, you can manage it. And retailers want to be able to manage things in real time. So IOT, along with it, the analytics and the data that's generated is really, really important for them going forward, community health. We've been talking a lot about that, the progression of the flu, et cetera, et cetera, uh, business schedules, commute patterns, school schedules, and whether these are all external data that are interesting to retailers and can help them to make better operational in something approaching real time. >>I mentioned the automation of decision making. This is a chart from Gardner, and I'd love to share with you. It's a really good one because it describes very simply what we're talking about. And it also describes where the inflection of new technology happens. If you look on the left there's data, we have lots and lots of data. We're getting more data all the time, retailers for a long time. Now, since certainly since the seventies or eighties have been using data to describe what happened, this is the retrospective analysis that we're all very familiar with, uh, data cubes and those kinds of things. And based on that, the human makes some decisions about what they're going to do going forward. Um, sometime in the not too distant past, this data was started to be used to make diagnostic decisions, not only what happened, but why did it happen? >>And me might think of this as, for example, if sales were depressed for a certain product, was it because we had another product on sale that day, that's a good example of fairly straightforward diagnostics. We then move forward to what we might think of as predictive analytics. And this was based on what happened in the past and why it happened in the past. This is what's likely to happen in the future. You might think of this as, for example, halo effect or, or the cannibalization effect of your category plans. If you're, if you happen to be a grocer and based on that, the human will make a decision as to what they need to do next then came along AI, and I don't want to oversell AI here. AI is a new way for us to examine lots and lots of data, particularly unstructured data AI. >>If I could simplify it to its maximum extent, it essentially is a data tool that allows you to see patterns in data, which might be interesting. It's very good at sifting through huge data sets of unstructured data and detecting statistically significant patterns. It gets deeper than that, of course, because it uses math instead of rules. So instead of an if then, or else a statement that we might've used with our structured data, we use the math to detect these patterns in unstructured data. And based on those, we can make some models. For example, uh, my guy in my, in my, uh, just turned 70 on my 70 year old man, I'm a white guy. I live in California. I have a certain income and a certain educational level. I'm likely to behave in this way based on a model that's pretty simplistic. But based on that, you can see that. >>And when another person who meets my psychographics, my demographics, my age group, my income level and all the rest, um, you, they might, they might be expected to make a certain action. And so this is where prescriptive really comes into play. Um, AI makes that possible. And then finally, when you start to think about moving closer to the customer on something, approaching a personalized level, a one-to-one level, you, you suddenly find yourself in this situation of having to make not thousands of decisions, but tens of millions of decisions. And that's when the automation of decision-making really gets to be pretty important. So this is all interesting stuff, and I don't want to oversell it. It's exciting. And it's new. It's just the latest turn of the technology screw. And it allows us to use this new data to basically automate decision-making in the business, in something approaching real time so that we can be much, much more responsive to real-time conditions in the marketplace. >>Very exciting. So I hope this is interesting. This is a piece of data from one of our recent pieces of research. Uh, this happens to be from a location analytics study. We just published last week and we asked retailers, what are the big challenges what's been going on in the last 12 months for them? And what's likely to be happening for them in the next few years. And it's just fascinating because it speaks to the need for faster decision-making there. The challenges in the last 12 months were all related to COVID. First of all, fulfilling growing online demand. This is a very, very real time issue that we all had to deal with. But the next one was keeping forecasts in sync with changing demand. And this is one of those areas where retailers are now finding themselves, needing to look at that exoticness for that external data that I mentioned to you last year, sales were not a good predictor of next year of sales. >>They needed to look at sentiment. They needed to look at the path of the disease. They needed to look at the availability of products, alternate sourcing, global political issues. All of these things get to be pretty important and they affect the forecast. And then finally managing a supply them the movement of the supply through the supply chain so that they could identify bottlenecks now, point to one of them, which we can all laugh at now because it's kind of funny. It wasn't funny at the time we ran out of toilet paper, toilet paper was a big problem. Now there is nothing quite as predictable as toilet paper, it's tied directly to the size of the population. And yet we ran out and the thing we didn't expect when the COVID pandemic hit was that people would panic. And when people panic, they do funny things. >>One of the things I do is buy up all the available toilet paper. I'm not quite sure why that happened. Um, but it did happen and it drained the supply chain. So retailers needed to be able to see that they needed to be able to find alternative sources. They needed to be able to do those kinds of things. This gets to the issue of visibility, real time data, fast data tomorrow's challenge. It's kind of interesting because one of the things that they've retailers put at the top of their list is improved inventory productivity. Uh, the reason that they are interested in this is because then we'll never spend as much money, anything as they will on inventory. And they want the inventory to be targeted to those places where it is most likely to be consumed and not to places where it's least likely to be consumed. >>So this is trying to solve the issue of getting the right product at the right place at the right time to the right consumer and retailers want to improve this because the dollars are just so big, but in this complex, fast moving world that we live in today, it's this requires something approaching real-time visibility. They want to be able to monitor the supply chain, the DCS and the warehouses. And they're picking capacity. We're talking about each of us, we're talking about each his level. Decision-making about what's flowing through the supply chain all the way from the, from the manufacturing doctor, the manufacturer through to consumption. There's two sides of the supply chain and retailers want to look at it, you'll hear retailers and, and people like me talk about the digital twin. This is where this really becomes important. And again, the digital twin is, is enabled by IOT and AI analytics. >>And finally they need to re to increase their profitability for online fulfillment. Uh, this is a huge issue, uh, for some grocers, the volume of online orders went from less than 10% to somewhere north of 40%. And retailers did in 2020, what they needed to do to fulfill those customer orders in the, in the year of the pandemic, that now the expectation that consumers have have been raised significantly. They now expect those, those features to be available to them all the time. And many people really liked them. Now retailers need to find out how to do it profitably. And one of the first things they need to do is they need to be able to observe the process so that they can find places to optimize. This is out of our recent research and I encourage you to read it. >>And when we think about the hard one wisdoms that retailers have come up with, we think about these things better visibility has led to better understanding, which increases their reaction time, which increases their profitability. So what are the opportunities? This is the first place that you'll see something that's very common. And in our research, we separate over performers, who we call retail winners from everybody else, average and under-performers, and we've noticed throughout the life of our company, that retail winners, don't just do all the same things that others do. They tend to do other things. And this shows up in this particular graph, this again is from the same study. So what are the opportunities to, to address these challenges? I mentioned to you in the last slide, first of all, strategic placement of inventory throughout the supply chain to better fulfill customer needs. This is all about being able to observe the supply chain, get the inventory into a position where it can be moved quickly to fast changing demand. >>And on the consumer side, a better understanding and reacting to unplanned events that can drive a dramatic change in customer behavior. Again, this is about studying the data, analyzing the data and reacting to the data that comes before the sales transaction. So this is observing the path to purchase observing things that are happening in the marketplace around the retailer, so that they can respond very quickly, a better understanding of the dramatic changes in customer preference and path to purchase. As they engage with us. One of the things we, all we all know about consumers now is that they are in control and the literally the entire planet is the assortment that's available to them. If they don't like the way they're interacting with you, they will drop you like a hot potato and go to somebody else. And what retailers fear justifiably is the default response to that is to just see if they can find it on Amazon. >>You don't want this to happen if you're a retailer. So we want to observe how we are interacting with consumers and how well we are meeting their needs, optimizing omni-channel order fulfillment to improve profitability. We've already mentioned this, uh, retailers did what they needed to do to offer new fulfillment options to consumers. Things like buy online pickup curbside, buy online pickup in store, buy online, pick up at a locker, a direct to consumer all of those things. Retailers offer those in 2020 because the consumers demand it and needed it. So when retailers are trying to do now is to understand how to do that profitably. And finally, this is important. It never goes away. Is the reduction of waste shrink within the supply chain? Um, I'm embarrassed to say that when I was a retail executive in the nineties, uh, we were no more certain of consumer demand than anybody else was, but we, we wanted to commit to very high service levels for some of our key county categories somewhere approaching 95%. >>And we found the best way to do that was to flood the supply chain with inventory. Uh, it sounds irresponsible now, but in those days, that was a sure-fire way to make sure that the customer had what she was looking for when she looked for it. You can't do that in today's world. Money is too tight and we can't have that, uh, inventory sitting around and move to the right places. Once we discovered what the right place is, we have to be able to predict, observe and respond in something much closer to your time. One of the next slide, um, the simple message here, again, a difference between winners and everybody else, the messages, if you can't see it, you can't manage it. And so we asked retailers to identify, to what extent an AI enabled supply chain can help their company address some issues. >>Look at the differences here. They're shocking identifying network bottlenecks. This is the toilet paper story I told you about over half of retail winners, uh, feel that that's very important. Only 19% of average and under performers, no surprise that their average and under-performers visibility into available to sell inventory anywhere within the enterprise, 58% of winners and only 32% of everybody else. And you can go on down the list, but you get the just retail winners, understand that they need to be able to see their assets and something approaching real time so that they can make the best decisions possible going forward in something approaching real time. This is the world that we live in today. And in order to do that, you need to be able to number one, see it. And number two, you need to be able to analyze it. And number three, you have to be able to make decisions based on what you saw, just some closing observations on. >>And I hope this was interesting for you. I love talking about this stuff. You can probably tell I'm very passionate about it, but the rapid pace of change in the world today is really underscoring the importance. For example, of location intelligence, as a key component of helping businesses to achieve sustainable growth, greater operational effectiveness and resilience, and ultimately your success. So this is really, really critical for retailers to understand and successfully evolving businesses need to accommodate these new consumer shopping behaviors and changes in how products are brought to the market. So that, and in order to do that, they need to be able to see people. They need to be able to see their assets, and they need to be able to see their processes in something approaching real time, and then they need to analyze it. And based on what they've uncovered, they need to be able to make strategic and operational decision making very quickly. This is the new world we live in. It's a real-time world. It's a, it's a sense and respond world and it's the way forward. So, Brent, I hope that was interesting for you. I really enjoyed talking about this, as I said, we'd love to hear a little bit more. >>Hey, Brian, that was excellent. You know, I always love me love hearing from RSR because you're so close to what retailers are talking about and the research that your company pulls together. Um, you know, one of the higher level research articles around, uh, fast data frankly, is the whole notion of IOT, right? And he does a lot of work in this space. Um, what I find fascinating based off the recent research is believe it or not, there's $1.2 trillion at stake in retail per year, between now and 2025. Now, how is that possible? Well, part of it is because the Kinsey captures not only traditional retail, but also QSRs and entertainment then use et cetera. That's considered all of retail, but it's a staggering number. And it really plays to the effect that real-time can have on individual enterprises. In this case, we're talking of course, about retail. >>So a staggering number. And if you think about it from streaming video to sensors, to beacons, RFID robotics, autonomous vehicles, retailers are testing today, even pizza delivery, you know, autonomous vehicle. Well, if you think about it, it shouldn't be that shocking. Um, but when they were looking at 12 different industries, retail became like the number three out of 12, and there's a lot of other big industries that will be leveraging IOT in the next four years. So, um, so retailers in the past have been traditionally a little stodgy about their spend in data and analytics. Um, I think retailers in general have got the religion that this is what it's going to take to compete in today's world, especially in a global economy. And in IOT really is the next frontier, which is kind of the definition of fast data. Um, so I, I just wanted to share just a few examples or exemplars of, of retailers that are leveraging Cloudera technology today. >>So now, so now the paid for advertisement at the end of this, right? So, so, you know, so what bringing to market here. So, you know, across all retail, uh, verticals, you know, if we look at, you know, for example, a well-known global mass virtual retailer, you know, they're leveraging Cloudera data flow, which is our solution to move data from point to point in wicked fast space. So it's open source technology that was originally developed by the NSA. So, um, it is best to class movement of data from an ingest standpoint, but we're also able to help the roundtrip. So we'll pull the sensor data off all the refrigeration units for this particular retailer. They'll hit it up against the product lifecycle table. They'll understand, you know, temperature fluctuations of 10, 20 degrees based on, you know, fresh food products that are in the store, what adjustments might need to be made because frankly store operators, they'll never know refrigeration don't know if a cooler goes down and they'll have to react quickly, but they won't know that 10, 20 degree temperature changes have happened overnight. >>So this particular customer leverages father a data flow understand temperature, fluctuations the impact on the product life cycle and the round trip communication back to the individual department manager, let's say a produce department manager, deli manager, meat manager, Hey, you had, you know, a 20 degree drop in temperature. We suggest you lower the price on these products that we know are in that cooler, um, for the next couple of days by 20%. So you don't have to worry, tell me about freshness issues and or potential shrink. So, you know, the grocery with fresh product, if you don't sell it, you smell it, you throw it away. It's lost to the bottom line. So, you know, critically important and, you know, tremendous ROI opportunity that we're helping to enable there, uh, from a, a leading global drugstore retailer. So this is more about data processing and, you know, we're excited to, you know, the recent partnership with the Vidia. >>So fast data, isn't always at the edge of IOT. It's also about workloads. And in retail, if you are processing your customer profiles or segmentation like intra day, you will ever achieve personalization. You will never achieve one-on-one communications with readers killers or with customers. And why is that? Because customers in many cases are touching your brand several times a week. So taking you a week or longer to process your segmentation schemes, you've already lost and you'll never achieve personalization in frack. In fact, you may offend customers by offering. You might push out based on what they just bought yesterday. You had no idea of it. So, you know, that's what we're really excited about. Uh, again, with, with the computation speed, then the video brings to, to Cloudera, we're already doing this today already, you know, been providing levels, exponential speed and processing data. But when the video brings to the party is course GPU's right, which is another exponential improvement, uh, to processing workloads like demand forecast, customer profiles. >>These things need to happen behind the scenes in the back office, much faster than retailers have been doing in the past. Um, that's just the world we all live in today. And then finally, um, you know, proximity marketing standpoint, or just from an in-store operation standpoint, you know, retailers are leveraging Cloudera today, not only data flow, but also of course our compute and storage platform and ML, et cetera, uh, to understand what's happening in store. It's almost like the metrics that we used to look at in the past in terms of conversion and traffic, all those metrics are now moving into the physical world. If you can leverage computer vision in streaming video, to understand how customers are traversing your store, how much time they're standing in front of the display, how much time they're standing in checkout line. Um, you can now start to understand how to better merchandise the store, um, where the hotspots are, how to in real time improve your customer service. >>And from a proximity marketing standpoint, understand how to engage with the customer right at the moment of truth, right? When they're right there, um, in front of a particular department or category, you know, of course leveraging mobile devices. So that's the world of fast data in retail and just kind of a summary in just a few examples of how folks are leveraging Cloudera today. Um, you know, from an overall platform standpoint, of course, father as an enterprise data platform, right? So, you know, we're, we're helping to the entire data life cycle. So we're not a data warehouse. Um, we're much more than that. So we have solutions to ingest data from the edge from IOT leading practice solutions to bring it in. We also have experiences to help, you know, leverage the analytic capabilities of, uh, data engineering, data science, um, analytics and reporting. Uh, we're not, uh, you know, we're not, we're not encroaching upon the legacy solutions that many retailers have today. >>We're providing a platform, this open source that helps weave all of this mess together that existed retail today from legacy systems because no retailer, frankly, is going to rip and replace a lot of stuff that they have today. Right. And the other thing that Cloudera brings to market is this whole notion of on-prem hybrid cloud and multi-cloud right. So our whole, our whole culture has been built around open source technology as the company that provides most of the source code to the Apache network around all these open source technologies. Um, we're kind of religious about open source and lack of vendor lock-in, uh, maybe to our fault. Uh, but as a company, we pull that together from a data platform standpoint. So it's not a rip and replace situation. It's like helping to connect legacy systems, data and analytics, um, you know, weaving that whole story together to be able to solve this whole data life cycle from beginning to end. >>And then finally, you know, just, you know, I want to thank everyone for joining today's session. I hope you found it informative. I can't say Brian killed course enough. Um, you know, he's my trusted friend in terms of what's going on in the industry. He has much broader reach of course, uh, in talking to a lot of our partners in, in, in, in other, uh, technology companies out there as well. But I really appreciate everyone joining the session and Brian, I'm going to kind of leave it open to you to, you know, any closing comments that you might have based on, you know, what we're talking about today in terms of fast data and retail. >>First of all, thank you, Brent. Um, and this is an exciting time to be in this industry. Um, and I'll just leave it with this. The reason that we are talking about these things is because we can, the technology has advanced remarkably in the last five years. Some of this data has been out there for a lot longer than that in it, frankly wasn't even usable. Um, but what we're really talking about is increasing the cycle time for decisions, making them go faster and faster so that we can respond to consumer expectations and delight them in ways that that make us a trusted provider of their life, their lifestyle needs. So this is really a good time to be a retailer, a real great time to be servicing the retail technology community. And I'm glad to be a part of it. And I was glad to be working with you. So thank you, Brian. >>Yeah, of course, Brian, and one of the exciting things for me to not being in the industry, as long as I have and being a former retailer is it's really exciting for me to see retailers actually spending money on data and it for a change, right? They've all kind of come to this final pinnacle of this is what it's going to take to compete. Um, you know, you know, and I talked to, you know, a lot of colleagues, even, even salespeople within Cloudera, I like, oh, retail, very stodgy, you know, slow to move. That's not the case anymore. Um, you know, religion is everyone's, everyone gets the religion of data and analytics and the value of that. And what's exciting for me to see as all this infusion of immense talent within the industry years ago, Brian, I mean, you know, retailers are like, you know, pulling people from some of the, you know, the greatest, uh, tech companies out there, right? From a data science data engineering standpoint, application developers, um, retail is really getting this legs right now in terms of, you know, go to market and in the leverage of data and analytics, which to me is very exciting. Well, >>You're right. I mean, I, I became a CIO around the time that, uh, point of sale and data warehouses were starting to happen data cubes and all those kinds of things. And I never thought I would see a change that dramatic, uh, as the industry experience back in those days, 19 89, 19 90, this changed doors that, but the good news is again, as the technology is capable, uh, it's, it's, we're talking about making technology and information available to, to retail decision-makers that consumers carry around in their pocket purses and pockets is there right now today. Um, so the, the, the question is, are you going to utilize it to win or are you going to get beaten? That's really what it boils down to. Yeah, >>For sure. Uh, Hey, thanks everyone. We'll wrap up. I know we ran a little bit long, but, uh, appreciate, uh, everyone, uh, hanging in there with us. We hope you enjoyed the session. The archive contact information is right there on the screen. Feel free to reach out to either Brian and I. You can go to cloudera.com. Uh, we even have, you know, joint sponsored papers with RSR. You can download there as well as eBooks and other assets that are available if you're interested. So thanks again, everyone for joining and really appreciate you taking the time. >>Hello everyone. And thanks for joining us today. My name is Brent Bedell, managing director retail, consumer goods here at Cloudera. Cloudera is very proud to be partnering with companies like three soft to provide data and analytic capabilities for over 200 retailers across the world and understanding why demand forecasting could be considered the heartbeat of retail. And what's at stake is really no mystery to most, to most retailers. And really just a quick level set before handing this over to my good friend, uh, Camille three soft, um, you know, IDC Gartner. Um, many other analysts have kind of summed up an average, uh, here that I thought would be important to share just to level set the importance of demand forecasting or retail. And what's at stake. I mean the combined business value for retailers leveraging AI and IOT. So this is above and beyond. What demand forecasting has been in the past is a $371 billion opportunity. >>And what's critically important to understand about demand forecasting. Is it directly impacts both the top line and the bottom line of retail. So how does it affect the top line retailers that leverage AI and IOT for demand forecasting are seeing average revenue increases of 2% and think of that as addressing the in stock or out of stock issue in retail and retail is become much more complex now, and that is no longer just brick and mortar, of course, but it's fulfillment centers driven by e-commerce. So inventory is now having to be spread over multiple channels. Being able to leverage AI and IOT is driving 2% average revenue increases. Now, if you think about the size of most retailers or the average retailer that on its face is worth millions of dollars of improvement for any individual retailer on top of that is balancing your inventory, getting the right product in the right place and having productive inventory. >>And that is the bottom line. So the average inventory reduction, leveraging AI and IOT as the analyst have found, and frankly, having spent time in this space myself in the past a 15% average inventory reduction is significant for retailers not being overstocked on product in the wrong place at the wrong time. And it touches everything from replenishment to out-of-stocks labor planning and customer engagement for purposes of today's conversation. We're going to focus on inventory and inventory optimization and reducing out-of-stocks. And of course, even small incremental improvements. I mentioned before in demand forecast accuracy have millions of dollars of direct business impact, especially when it comes to inventory optimization. Okay. So without further ado, I would like to now introduce Dr. Camille Volker to share with you what his team has been up to. And some of the amazing things that are driving at top retailers today. So over to you, Camille, >>Uh, I'm happy to be here and I'm happy to speak to you, uh, about, uh, what we, uh, deliver to our customers. But let me first, uh, introduce three soft. We are a 100 person company based in Europe, in Southern Poland. Uh, and we, uh, with 18 years of experience specialized in providing what we call a data driven business approach, uh, to our customers, our roots are in the solutions in the services. We originally started as a software house. And on top of that, we build our solutions. We've been automation that you get the software for biggest enterprises in Poland, further, we understood the meaning of data and, and data management and how it can be translated into business profits. Adding artificial intelligence on top of that, um, makes our solutions portfolio holistic, which enables us to realize very complex projects, which, uh, leverage all of those three pillars of our business. However, in the recent time, we also understood that services is something which only the best and biggest companies can afford at scale. And we believe that the future of retail, uh, demon forecasting is in the product solutions. So that's why we created occupy our AI platform for data driven retail. That also covers this area that we talked about today. >>I'm personally proud to be responsible for our technology partnerships with other on Microsoft. Uh, it's a great pleasure to work with such great companies and to be able to, uh, delivered a solution store customers together based on the common trust and understanding of the business, uh, which cumulates at customer success at the end. So why, why should you analyze data at retail? Why is it so important? Um, it's kind of obvious that there is a lot of potential in the data per se, but also understanding the different areas where it can be used in retail is very important. We believe that thanks to using data, it's basically easier to the right, uh, the good decisions for the business based on the facts and not intuition anymore. Those four areas that we observe in retail, uh, our online data analysis, that's the fastest growing sector, let's say for those, for those data analytics services, um, which is of course based on the econ and, uh, online channels, uh, availability to the customer. >>Pandemic only speeds up this process of engagement of the customers in that channel, of course, but traditional offline, um, let's say brick and mortar shops. Uh, they still play the biggest role for most of the retailers, especially from the FMCG sector. However, it's also very important to remember that there is plenty of business, uh, related questions that meet that need to be answered from the headquarter perspective. So is it actually, um, good idea to open a store in a certain place? Is it a good idea to optimize a stock with Saturday in producer? Is it a good idea to allocate the goods to online channel in specific way, those kinds of questions they are, they need to be answered in retail every day. And with that massive amount of factors coming into that question, it's really not, not that easy to base, only on the intuition and expert knowledge, of course, uh, as Brent mentioned at the beginning, the supply chain and everything who's relates to that is also super important. We observe our customers to seek for the huge improvements in the revenue, just from that one single area as well. Okay. >>So let me present you a case study of one of our solutions, and that was the lever to a leading global grocery retailer. Uh, the project started with the challenge set of challenges that we had to conquer. And of course the most important was how to limit overstocks and out of stocks. Uh, that's like the holy grail in of course, uh, how to do it without flooding the stores with the goods and in the same time, how to avoid empty shelves, um, from the perspective of the customer, it was obvious that we need to provide a very well, um, a very high quality of sales forecast to be able to ask for, uh, what will be the actual sales of the individual product in each store, uh, every day, um, considering huge role of the perishable goods in the specific grocery retailer, it was a huge challenge, uh, to provide a solution that was able to analyze and provide meaningful information about what's there in the sales data and the other factors we analyzed on daily basis at scale, however, uh, our holistic approach implementing AI with data management, uh, background, and these automation solutions all together created a platform that was able to significantly increase, uh, the sales for our customer just by minimizing out of stocks. >>In the same time we managed to not overflow the stock, the shops with the goods, which actually decreased losses significantly, especially on the fresh fruit. >>Having said that this results of course translate into the increase in revenue, which can be calculated in hundreds of millions of dollars per year. So how the solution actually works well in its principle, it's quite simple. We just collect the data. We do it online. We put that in our data lake, based on the cloud, there are technology, we implement our artificial intelligence models on top of it. And then based on the aggregated information, we create the forecast and we do it every day or every night for every single product in every single store. This information is sent to the warehouses and then the automated replenishment based on the forecast is on the way the huge and most important aspect of that is the use of the good tools to do the right job. Uh, having said that you can be sure that there is too many information in this data, and there is actually two-minute forecast created every night that any expert could ever check. >>This means our solution needs to be, uh, very robust. It needs to provide information with high quality and high porosity. There is plenty of different business process, which is on our forecast, which need to be delivered on time for every product in each individual shop observing the success of this project and having the huge market potential in mind, we decided to create our QB, which can be used by many retailers who don't want to create a dedicated software for that. We'll be solving this kind of problem. Occupy is, uh, our software service offering, which is enabling retailers to go data driven path management. >>We create occupant with retailers, for retailers, uh, implementing artificial intelligence, uh, on top of data science models created by our experts, uh, having data, data analysis in place based on data management tools that we use we've written first, um, attitude. The uncertain times of pandemic clearly shows that it's very important to apply correction factors, which are sometimes required because we need to respond quickly to the changes in the sales characteristics. That's why occupy B is open box solution, which means that you basically can implement that in your organization. We have without changing the process internally, it's all about mapping your process into this into the system, not the other way around the fast trends and products, collection possibilities allow the retailers to react to any changes, which are pure in the sales every day. >>Also, it's worth to mention that really it's not only FMCG. And we believe that different use cases, which we observed in fashion health and beauty, common garden pharmacies and electronics, flavors of retail are also very meaningful. They also have one common thread. That's the growing importance of e-commerce. That's why we didn't want to leave that aside of occupant. And we made everything we can to implement a solution, which covers all of the needs. When you think about the factors that affect sales, there is actually huge variety of data and that we can analyze, of course, the transactional data that every dealer possesses like sales data from sale from, from e-commerce channel also, uh, averaging numbers from weeks, months, and years makes sense, but it's also worth to mention that using the right tool that allows you to collect that data from also internal and external sources makes perfect sense for retail. Uh, it's very hard to imagine a competitive retailer that is not analyzing the competitor's activity, uh, changes in weather or information about some seasonal stores, which can be very important during the summer during the holidays, for example. Uh, but on the other hand, um, having that information in one place makes the actual benefit and environment for the customer. >>Okay. Demon forecasting seems to be like the most important and promising use case. We can talk about when I think about retail, but it's also their whole process of replenishment that can cover with different sets of machine learning models. And they done management tools. We believe that analyzing data from different parts of the retail, uh, replenishment process, uh, can be achieved with implementing a data management solution based on caldera products and with adding some AI on top of it, it makes perfect sense to focus on not only demand forecasting, but also further use cases down the line when it comes to the actual benefits from implementing solutions for demand management, we believe it's really important to analyze them holistically. First is of course, out of stocks, memorization, which can be provided by simply better sales focus, but also reducing overstocks by better inventory management can be achieved in, in the same time. Having said that we believe that analyzing data without any specific new equipment required in point of sales is the low hanging fruit that can be easily achieved in almost every industry in almost every regular customer. >>Hey, thanks, Camille, having worked with retailers in this space for a couple of decades, myself, I was really impressed by a couple of things and they might've been understated, frankly. Um, the results of course, I mean, you, you know, as I kind of set up this session, you doubled the numbers on the statistics that the analysts found. So obviously in customers you're working with, um, you know, you're, you're doubling average numbers that the industry is having and, and most notably how the use of AI or occupy has automated so many manual tasks of the past, like tour tuning, item profiles, adding new items, et cetera. Uh, and also how quickly it felt like, and this is my, my core question. Your team can cover, um, or, or provide the solution to, to not only core center store, for example, in grocery, but you're covering fresh products. >>And frankly, there are, there are solutions out on the market today that only focus on center store non-perishable department. So I was really impressed by the coverage that you're able to provide as well. So can you articulate kind of what it takes to get up and running and your overall process to roll out the solution? I feel like based on what you talked about, um, and how you were approaching this in leveraging AI, um, that you're, you're streamlining processes of legacy demand, forecasting solutions that required more manual intervention, um, how quickly can you get people set up and what is the overall process like to get started with soft? >>Yeah, it's usually it takes three to six months, uh, to onboard a new customer to that kind of solution. And frankly it depends on the data that the customer, uh, has. Uh, usually it's different, uh, for smaller, bigger companies, of course. Uh, but we believe that it's very important to start with a good foundation. The platform needs to be there, the platform that is able to, uh, basically analyze or process different types of data, structured, unstructured, internal, external, and so on. But when you have this platform set, it's all about starting ingesting data there. And usually for a smaller companies, it's easier to start with those, let's say, low hanging fruits. So the internal data, which is there, this data has the highest veracity is already easy to start with, to work with them because everyone in the organization understands this data for the bigger companies. It might be important to ingest also kind of more unstructured data, some kind of external data that need to be acquired. So that may, that may influence the length of the process. But we usually start with the customers. We have, uh, workshops. That's very important to understand their business because not every deal is the same. Of course, we believe that the success of our customers comes also due to the fact that we train those models, those AI models individually to the needs of our >>Totally understand and POS data, every retailer has right in, in one way shape or form. And it is the fundamental, uh, data point, whether it's e-comm or the brick and mortar data, uh, every retailer has that data. So that, that totally makes sense. But what you just described was bunts. Um, there are, there are legacy and other solutions out there that this could be a, a year or longer process to roll out to the number of stores, for example, that you're scaling to. So that's highly impressive. And my guess is a lot of the barriers that have been knocked down with your solution are the fact that you're running this in the cloud, um, you know, on, from a compute standpoint on Cloudera from a public cloud stamp point on Microsoft. So there's, there's no, it intervention, if you will, or hurdles in preparation to get the database set up and in all of the work, I would imagine that part of the time-savings to getting started, would that be an accurate description? >>Yeah, absolutely. Uh, in the same time, this actually lowering the business risks, because we simply take data and put that into the data lake, which is in the cloud. We do not interfere with the existing processes, which are processing this data in the combined. So we just use the same data. We just already in the company, we ask some external data if needed, but it's all aside of the current customers infrastructure. So this is also a huge gain, as you said, right? >>And you're meeting customers where they are. Right. So, as I said, foundationally, every retailer POS data, if they want to add weather data or calendar event data or, you know, want incorporate a course online data with offline data. Um, you have a roadmap and the ability to do that. So it is a building block process. So getting started with, for data, uh, as, as with POS online or offline is the foundational component, which obviously you're very good at. Um, and then having that ability to then incorporate other data sets is critically important because that just improves demand, forecast accuracy, right. By being able to pull in those, those other data sources, if you will. So Camille, I just have one final question for you. Um, you know, there, there are plenty of not plenty, but I mean, there's enough demand forecasting solutions out on the market today for retailers. One of the things that really caught my eye, especially being a former retailer and talking with retailers was the fact that you're, you're promoting an open box solution. And that is a key challenge for a lot of retailers that have, have seen black box solutions come and go. Um, and especially in this space where you really need direct input from the, to continue to fine tune and improve forecast accuracy. Could you give just a little bit more of a description or response to your approach to open box versus black box? >>Yeah, of course. So, you know, we've seen in the past the failures of the projects, um, based on the black box approach, uh, and we believe that this is not the way to go, especially with this kind of, uh, let's say, uh, specialized services that we provide in meaning of understanding the customer's business first and then applying the solution, because what stands behind our concept in occupy is the, basically your process in the organization as a retailer, they have been optimized for years already. That's where retailers put their, uh, focus for many years. We don't want to change that. We are not able to optimize it properly. For sure as it combined, we are able to provide you a tool which can then be used for mapping those very well optimized process and not to change them. That's our idea. And the open box means that in every process that you will map in the solution, you can then in real time monitor the execution of those processes and see what is the result of every step. That way we create truly explainable experience for our customers, then okay, then can easily go for the whole process and see how the forecast, uh, was calculated. And what is the reason for a specific number to be there at the end of the day? >>I think that is, um, invaluable. Um, can be, I really think that is a differentiator and what three soft is bringing to market with that. Thanks. Thanks everyone for joining us today, let's stay in touch. I want to make sure to leave, uh, uh, Camille's information here. Uh, so reach out to him directly or feel free at any, any point in time, obviously to reach out to me, um, again, so glad everyone was able to join today, look forward to talking to you soon.

>> Narrator: From around the globe. It's theCUBE with digital coverage of PagerDuty summit 2020, brought to you by PagerDuty. >> Welcome to theCUBES coverage of PagerDuty summit 20, I'm Lisa Martin. Very pleased to welcome back to theCUBE, one of our alumna, distinguished alumna, the CEO of PagerDuty, Jennifer Jehada. Jennifer it's great to be talking with you today. >> Thanks Lisa, it's great to be here with theCUBE again and great to see you. >> Yeah, so lots happened in the last six months alone with that whiplash from all that, but you've been fifth year of the PagerDuty summit. The first year virtual, lot of things have changed. Talk to us about the evolution of PagerDuty over the last few years in particularly the last six months. >> Well, let's start with the last six months. I mean, I think we have all seen a society go through a big transformation with a global pandemic, kind of underpinning a volatile economic environment, a very difficult jobs environment. But in many cases, we've also seen tremendous acceleration. We've seen companies pull forward 10 years of transformation into a matter of months. And we saw that recently in some Kinsey research. And this is really been driven by the compulsory need for brands to meet their consumers online, for companies to enable and empower their employees online and for children to be able to learn online. And so, as we've moved, made this shift to doing everything in the digital world, it means that all of our customers, the biggest brands in the fortune 500, the most innovative tech companies that you're aware of. They've all had to really transform quickly to deliver an entire, nearly perfect customer experience online. And the stakes are higher, because they can't depend on their bricks and mortar revenue for business success. And that's meant that IT teams and developer teams have become the frontline of the digital default era because digital really truly is, the new operating system. That kind of fits squarely into how PagerDuty is evolve. Because we started out as a platform that served developers and helping them manage on-call notifications and alerting. So, engineers who wanted to be alerted when something went wrong and make sure they could address an issue in a service they were responsible for, before it had customer impact. Over the last five years, we've really evolved the platform, leveraging over a decade of proprietary data, about events, about incidents, about people, responder behavior, with machine learning, to really help our customers and engineering and IT, and IT ops and security and in customer support, truly manage what is an increasingly complex digital tech ecosystem. And this means that we're using software and automation to detect issues. We're then intelligently routing those issues in that work, that unplanned spontaneous work to the right people in the right moments. So that a customer and employee doesn't even feel any pain. There is no issue with availability. They can continue to engage with a brand or a service the way they want to. And that's become increasingly important because that's where all the revenue is today. >> It's essential, it's like, we've been talking for months about essential frontline workers and we think right away of healthcare, fire police, things like that. But, the digital default that you talked about, there's new digital frontline. I know PagerDuty has over 13,000 customers and some of the new sort of digital frontline that are enabling people to do everything from work, shop, learn, zoom, Netflix for example, Peloton helping us, keep fit in this time of such isolation, are now considered essential and depending on PagerDuty to help them be able to do that. To meet those increasing customer demands. >> Sure, all of these are PagerDuty customers. And the thing about the digital frontline is they can be invisible. You don't necessarily see them because they're behind the scenes trying to manage all the complex technology that makes that on demand Peloton class efficient and amazing for you. And when that class doesn't work, you're unhappy with Peloton. It really directly impacts the brand. Luckily Peloton is very reliable. I'm a big Peloton fun myself. And I really like to acknowledge and just let the frontline know that we do see them. We know that digital workers have been putting in on average, an extra 10 to 15 hours a week. During this environment, many of them are also either living in isolation on their own because of shelter in place rules, or they're trying to manage their own children's schooling. And, we all ask ourselves this question, are we working from home or are we living at work? It's sometimes those lines are blurred. So, anything that we can do as a platform to automate more and more of this work for the digital frontline, is really our focus. And this year at summit, we're going to be talking in particular about freeing our users from complexity about helping them orchestrate and automate work more effectively. And about leveraging machine learning and analytics to improve the cost efficiency, the productivity and the team, the health of their digital teams and their digital operations. >> So, in your keynote, you're going to be talking about digital ops. That's kind of dig into that. Cause we've shifted from this very structured way of working to sort of this chaotic approach, the last six months. Digital ops, what does it mean from PagerDuty's perspective and how is it going to impact every business? >> Well, I think when we look forward in a couple of years, we won't even use the word digital. It'll just be the operations of a company of a modern organization. How do you bring together all the application technology, the infrastructure technology, the networking, the Wi-Fi connectivity, the customer engagement data. How do you bring all of that together, to deliver these wonderful experiences that we've become reliant? You use the word essential, right? Well, PagerDuty essentially become the critical foundation or infrastructure that helps companies manage all this technology. And the problem is, with architecture becoming more distributed with powerful tools like the cloud, that's actually proliferated the complexity. It's actually increased the speed of the number of applications and services that an organization has mattered. And so, adopting the cloud can be very powerful for a company. It can be very freeing. It can allow you to innovate much faster. But it also, is not an easy thing to do. There's a lot of change management associated with it. And you have to make sure, that your team is ready for it. PagerDuty really facilitates a cultural shift, leveraging DevOps, which really, in a DevOps culture really in methodology allows companies to empower people closest to the action, to make better decisions. If you think about this digital world, we're living in, a consumer wait a nanosecond, a microsecond, maybe a couple of seconds. If you don't get that experience to be perfect for them. And yet traditional ways of solving technology problems, or ticketing systems and command and control environments that would take hours, maybe days to resolve issues. We don't have that time anymore. And so, digital operations is all about instantly detecting an issue, being able to run correlation and consolidate those issues to start to become more proactive, to predict whether or not, this small issue could become a major incident. And address it, resolve it, leveraging automation, before customers feel any pain before you see any impact to the business, the bottom line or brand reputation. >> All of those, are absolutely critical for every type of company, every size, every industry, because as you talked about, customers are demanding, we're also ready to, if something doesn't happen right away, we're going to go find the next service that's going to be able to deliver it. And the cost of that to a business, is I saw some numbers that you shared that if that costs you a hundred, a second of a minute, rather of downtime. A year ago, costs you a $100,000. That's now 4 to 5X. So, that costs can actually put a company adding up out of business. And we're in this. Let's not just survive, but thrive mode. And, to be able to have that immediate response. And as you say, shift from being reactive to proactive is I think absolutely business critical. >> Lisa, you should come work for us. >> You have this down pat. >> (laughs) And you're exactly right. I mean, I remember back in the day when I used to work in an office and walk out onto the street before I went home, you would see employees standing outside, switching back and forth between their rideshare app, their food delivery app, maybe their dating app, or their movie entertainment app. And if one thing is not serving them fast enough, they just switched to the other one. And, consumers are very fickle. They've got become increasingly more demanding, which means there are more demands on our teams and that digital frontline and our technology. And in fact, to your point, because all of that revenue has shifted online over the last six months. We've seen the cost of a minute and that cost is really calculated based on loss, labor productivity, but also lost revenue. We've seen that cost go up, from if you lost a $100,000 during disruption last year, you're maybe losing half a million dollars a minute when your app is disrupted. And, these apps and websites don't really go down very often anymore, but small disruptions, when you're trying to close out your shopping cart, when you're trying to select something, when you're trying to do some research. It can be very frustrating, when all of those little pieces backed by very complex technology, don't come together beautifully. And, that's where PagerDuty brings the power of automation, the power of data and intelligence and increasingly orchestrates all this work. We don't start our day anymore by coming into an office, having a very structured well laid out calendar and environment. We often are interrupted constantly throughout the day. And PagerDuty was designed and architected to serve unpredictable, spontaneous, but emergent, meaning time critical and mission critical work. And I think that's really important because that digital environment is how companies and brands build trust with their consumers or their employees. PagerDuty essentially operationalizes that trust. The challenge with trust, is it can take years to build trust up and you can destroy it in a matter of seconds. And so, that's become really important for our customers. >> Absolutely, another thing that obviously has gone on, in the last six months is, you talked about those digital frontline workers working an extra 10 to 15 hours a week, living at work basically, but also the number of incidents has gone up. But how has PagerDuty helping those folks respond to and reduce the incidents faster? >> Well, this is something that I'm very proud of, and PagerDuty's entire product and engineering team should be extremely proud of. I mean, we were held to a very high standard. Because we're the platform that is expected to be up, when everything else is having a bad day. And in this particular environment, we've seen a number of our customers experience unprecedented demand and scale, like zoom and Netflix, who you mentioned earlier. And when that happens, that puts a lot of pressure, events transiting across our platform on PagerDuty. PagerDuty has not only held up extremely well. Seeing some customers experiencing 50 times the number of incidents and other customers experiencing maybe 12 times the number of incidents they used to. Those customers are actually seeing an improvement in their time to resolve an incident by about 20%. So, I love the fact that, not only have we scaled almost seamlessly in this environment with the customers of ours that are seeing the most demand and the most change. And at the same time, we've helped all of our customers improve their time to resolve these incidents, to improve their overall business outcomes. >> One of the things I saw Jennifer recently, I think it was from McKinsey, was that 92% of this, is the survey before the pandemics. That, yeah, we've got to shift to a digital business. So, I'm curious customers that were on that cussing. We're not there yet, but we need to go. When this happened six months ago, when they came to PagerDuty, how did you advise them to be able to do this when time was of the essence? >> Well, first of all, one of our first company value, is champion the customer. So, I think our initial response to what we saw happen as COVID started to impact many industries was to listen. Was to lean in with empathy and try and understand the position our customers were in. Because just like our employees, every person is affected differently by this environment. And every customer has had a different experience. Some industries have done very well, and we hear a lot about that on the news, but many industries are really having a very difficult time and have had to massively transform their business model just to survive, much less to thrive. And so, PagerDuty has really worked with those customers to help them manage the challenge of trying to transform and accelerate their digital offerings and at the same time, reduce their overall costs. And we do that very effectively. We did a study with IDC about a year ago, and found that, most of our enterprise customers experience a 730% return on investment in four months. And that's because we automate what has traditionally been a lot of manual work, instead of just alerting someone there's a problem. We orchestrate that problem across cross-functional teams, who otherwise might not be able to find each other and are now distributed. So, there's even more complicated. You can't just sit in a room and solve these problems together anymore. We actually capture all of the data that is created in the process of resolving an incident. And now, we're using machine learning and AI to make recommendations, to suggest ways to resolve an incident, to leverage past incident experiences and experts within the platform to do that. And that means that we're continually consolidating the time that it takes to resolve an incident from detection all the way through to being back to recovery, but also reducing the amount of manual work that people have to do, which also reduces their stress when they're under fire and under time constraints. Because they know these types of incidents can have a public and a financial impact on their companies. We also help them learn from every incident that runs on the platform. And we're really bringing a more power to the table on that front, with some of the new releases. I'll be talking about later on this morning with analytics and our analytics lab. >> As we look at the future, the future of life is online, right? The future of work is online, but also distributed teams. Cause we know that things are going to come back to normal, but a lot isn't. So, being able to empower organizations to make that pivot so quickly, you brought up a great point about it's not just the end-user customer who can churn and then go blast about it to social media and cause even more churn. But it's also the digital frontline worker who totally needs to be cared for, because of burnout happens. That's a big issue that every company has to deal with. How is PagerDuty kind of really focused on, you mentioned culture on helping that digital frontline worker not feel burnout or those teams collaborate better? >> Well, we look at operations through the lens of sort of humanity. And we think about what's the impact of the operational environment today on what we call team health. And in our analytics solution, we can heat map your team for you and help you understand who in your team is experiencing the most incident response stress. they're having to take on work during dinner time, after hours on weekends, in the middle of the night. Cause these big incidents, for some reason, don't seem to happen at one on a Tuesday. They tend to happen at 4:00 AM on a Saturday. And oftentimes what happens is what I call the hero syndrome. You have a particularly great developer who becomes the subject matter expert, who gets pulled into every major difficult puzzle or incident to solve. And the next thing, that person's spending 50% of their time on unplanned, unpredictable high stress work. And we can see that, before it becomes that challenging and alert leaders that they potentially have a problem. We also, in our analytics products can help managers benchmark their teams in terms of their overall productivity, how much their services are costing them to run and manage. And also looking after the health of those folks. And, we've often said PagerDuty is for people. We really build everything from design to architecture, in service of helping our users be more efficient, helping our users get to the work that matters the most to them. And helping our users to learn. Like I said, with every incident or problem or challenge that runs on the platform. And likewise, I believe culture is a business imperative. Likewise is diversity and equality and PagerDuty as a platform from a technology perspective that doesn't discriminate. And we're also a company that is really focused on unbalanced, on belonging, on inclusion, diversity and equality in everything that we do. And I'm really excited that at summit, we have Derek Johnson who is the president of the NAACP, speaking with us to talk about how we get out the vote, how we support individuals in having a say in leveraging their voices at a time when I think it's more important than ever. >> And that was one of the things that really struck me Jennifer, when I was looking at, Hey, what's going on with PagerDuty summit 20. And just even scanning the website with the photographs of the speakers from keynotes and general session to break out influencers, the amount of representation of women and people of color and diversity, really struck me. Because we just don't see that enough. And I just wanted to say, congratulations as a woman who's been in tech for 15 years. That is so important, but it's not easy to achieve. >> Well, thank you for saying that. I mean, honestly, I think that when you look on that summit website and at those speakers, it really is a great picture or snapshot of the richly diverse community that PagerDuty serves and engages in partners in. Sometimes you just have to be more intentional about identifying some of those phenomenal speakers, who are maybe not like the obvious person to have on a topic because we become accustomed used to having the same types of speakers over and over again. So, this started with intent, but to be honest, like these people are out there and I think we have to give them a stage. We have to give them a spotlight. And it's not about whether you're a man or a woman at our stage. It's making sure that the entire summit environment really brings a diverse and I think rich collection of expertise of experience to the table, so that we all benefit. And I'm really excited. There are just so many fantastic folks joining us from Brett Taylor, who is the president and CEO of Salesforce and was the founding CTO of Facebook to Andy Jassy, who is leading Amazon web services right now. There's Ebony Beckwith who's going to speak about some of the great things that we're doing with pagerduty.org and the list goes on and on. I could spend, all morning talking about the people I'm excited to hear from and learn from. But I would encourage everybody who's putting an event together, to have a strategy and be intentional and be insistent about making sure that your content and the people providing that content, the experts that you're bringing to bear really do reflect the community that we're all trying to serve. >> That is outstanding and congratulations on PagerDuty summit by the first virtual, but you're going to have the opportunity to influence and educate so many more people. Jennifer, it's been such a pleasure talking to you and having you back on theCUBE. I look forward to seeing you again soon. >> Thank you so much, Lisa. It's been great to be with you. >> All right, for Jennifer Tejada. I'm Lisa Martin. You're watching theCUBE conversation. (upbeat music)

>> live from Orlando, Florida It's the que covering Accelerate nineteen. Brought to you by Fortunate >> Welcome back to the Cube. Lisa Martin with Peter Burgers. We are coming to you Live from Orlando, Florida We've been at forty nine. Accelerate twenty nineteen all day. Peter, What a day our third year co hosting the Cuba Forty and accelerate. We heard a lot about industry leadership, product, leadership in innovation, partner. Success fourteen and accelerate. What? Some of the things that really stuck with you from the keynote all the way to the end of our interviews. >> Well, I was going to say first put a fork in May. Um, uh, Here's one of the things that I've observed. I've been doing the analyst thing and been a practitioner I t for over thirty years now on DH. Uh, it's amazing the degree to its security. People are often some of the smartest people you meet and some of the most straightforward people you meet, and partly that's because they are paid to ferret out nonsense. It's very, very difficult to fake security on. Uh, it just is, if there's one thing that even more than the last couple of years just struck me today. Perhaps it's because we're coming more familiar. Affording it is how smart these guys are, how smart they are, how informed they are, how well spoken they are. I mean, the interviews have been a breeze. I learned something from every single one of these for Jeanette interviews. So that's probably the first thing I'd say. The second thing I'd say is, um, the Ford. It has taken a different tack. We talked about this in the open, they have acknowledged, or they believe that having a degree of control over the underlying hardware is going to be a source of benefit to the customer on a source of advantage to Ford in it. And they continue to push that, and it appears pretty clear that they made a good bet that regard. We heard a lot about how a lot of new products are being placed on top of that platform and top those appliances a lot of additional functionality. But it also is pretty obvious that the ecosystem is growing faster, even in many respects and fortunate is in terms of the number of the amount of invention and innovation it's happening, and that's in part made possible by having a platform that's just higher performance. Oh, and if there's one last thing that I'd say is the degree to which Fortunate has made talked about this a second ago but made good bets and it appears clear that they're going to continue to make good bets bringing full circle Smart people that get stuff done in a domain that's absolutely essential to business are in a position to really shape the way that all this digital business transformation of digital business evolves. And Ford Net is punching above their weight in terms of how they're influencing the directions of the industry. >> They are punching up that there way. I think you mentioned that during one of our interview segment. I think they're proud of that. I think their confidence in what they're delivering and their history of being able to be pretty good at predicting what's going to happen was evident from the keynote this morning, where they showed a number of times where they are from an industry leadership in a market share perspective, calling out the names of their competitors, showing how much how far they've come, how much their customers are benefiting how much their business is growing as a result. So that confidence on pride was evident from the first time CEO Kinsey stepped on stage this morning. And I think we heard that throughout every interview segment today that you and I did with their leaders and some of their partners as well that there's since there that they know what they're doing. To your point, I agree. There was a lot of clarity of message. It's a very it's Security's a very interesting topic of conversation because it's pervasive across every industry. >> There wasn't the interviews weren't interchangeable. Each of them bought their expertise to bear on DH had something really interesting and useful to say, But it's at the core. You could see that the culture is thriving, that obviously it's a great Tam's great total addressable market that's growing. There's a lot of excitement inside the fortunate employee base about the possibilities and the role that they're likely to play, and I are playing on, you know, they talked a lot about Canada, Dabo's and some of the new. Some of the new alliance isn't even able to put together and influence. I mean, it's just It's a very good story in a market that is increasingly important. That's a potent combination for the Cube and for customers overall. >> And they did a great job on the education piece. Education was you mentioned Davis. That was an interesting kind of nod back to what they talked about last year's Accelerate twenty eighteen Educate education Ecosystem technology knows of the three pillars that were discussed in Davos is being essential components for safe and secure digital transformation, which they even set of Davos. Hey, there's the potential here in the next ten years for digital transformation to unlock. Ten can't be million. Maybe it is a huge value for businesses for society, and they said, Hey, fortunate, we've talked about these three tenants last year. We talked to John both just a little bit ago about how they are actively educating the channel from their bars to help them become msp sto. MSS peas their distributors how they're really educating, helping to mitigate some of the ostensible cybersecurity skills gap that we've talked about a very long time. But that's a a dedicated business model for them that hey, they want to drive preference with their partners. Everybody has. His customers have toys. Partners have choice. They've put a very strategic and evolutionary focus on evolving that. So customers in any industry have the opportunity to leverage security as as a best practice it as a benefit to their business. >> And there's a degree of altruism for why they did it, because they recognize that there's three and a half million open cybersecurity positions in the world. But they also demonstrated how smart and practical it is. Try to take that leadership. They want to become more competency based. How? Okay, great. Now, what does that allow you to do? It allows you to have your partners, your partner, network, connect independent of you to create solutions independent of you still based in your technology and basing your capabilities and services, but to engage customers in faster ways that may not necessarily involve you. Okay, so competency leads to new partner arrangements. Well, that also leads to more complex kinds of customer relations that generate greater value, greater service, all with the certainty of trust behind it, because you've done a better job of articulating what constitutes competency in an extremely complex domain. So it's a It's a It's a really interesting story. They've. They've clearly taken some best practices that we've seen emerge in the industry over the last few years and applied them anew. In a company that's going quite fast and a market that's growing faster than any other in Tech, >> this is largely this event accelerated. Think Derek Banky. I mention this is his seven. So around the seventh or eighth forty nine accelerate event that started its history wise as a partner conference. Obviously, it's grown tremendously, but there's a lot of partners here I would love to hear next year from the voice of the customer, a customer who has faced these challenges. We were speaking with one of their partners. It'LL come to me with Siemens, who was talking about Hey together. Seaman's from an O. T. Challenge and Opportunity, Perspective and Fortunate can help a customer transform and converge, and ot and thirty days in a harsh type of environment that's huge would love to hear more stories like showed the impact that customers can make by addressing these challenges and leveraging these technologies to not just react to threats as they come all the time. But she eventually become proactive and predictive. >> Well, the the the world economic form Dabo's uh, sport that put up a couple charts that showed how the World Economic Forum is basically putting cyber security at the center of a lot of the new economic activity associated with digital business on way would tend to agree with that. That's a very, very important feature, if for no other reason than just this notion of trust becomes so very essential. And so you know, for Net is in a position to make some crucial to really have a strong influence on how this industry plays out to make some pretty decent money. This they're generating more patents, then eighty percent. I mean, I don't know what the number is, but three times as many patents in the segment that they're operating in as anybody else. Lot of innovation, lot of dedication to doing that kind of stuff. But I think it is important for them to take on Maura the customer. You and I were talking about this earlier. They did it, you know, this conference and the keynotes and the conversations spoke to network administrators, network pros, security prose partners. We would weigh. Both believe that digital business outcomes are going to be tied into a CZ moral economic form. Does that core cybersecurity capability of abyss that of his says? And so it would be nice to have them feature more customers, but also to do eh clear job of taking a pull on that thread from outcome all the way to technology because the market needs that. It's not clear to a lot of people what really is the relationship between investment in cyber security and how that translates into new classes of business value that are gonna have a long term implications on how markets operate. >> Yeah, and it's going to be We gotta hear more than scalability, flexibility and speed those air obvious. But how our industry's being and business is being transformed. I know they >> are >> so waken boy, a lot of that down to that, that simple word trust. I mean, we heard a lot here. If there has been an erosion of trust and a lot of the most important institutions that we operate under, and if that continues, that's going to create a whole bunch of problems looking forward and so having a brand have trust associated with it in a physical as well as the digital world is going to be a major determinant of whether or not a company is going to be able to transform and take advantage of some of the new technologies and approaches to doing business in the future. >> That's a great point. Well, Peter, I enjoyed co hosting the Cube with you at our third ported. Accelerate. Appreciate all your insights and your time. >> You too. >> Thank you so much. We want to thank you for watching the queue began. We've been live here. Fortinet Accelerate twenty nineteen from Florida, Orlando, Florida for Peter Bourjos. Lisa Martin, You're watching the Cube?