>>from the bellagio Hotel >>in Las Vegas. >>It's the >>cube covering >>Ui Path forward. >>Four brought to >>you by Ui Path. >>Welcome back to Las Vegas. The cube is here. We've been here for two days covering Ui Path Forward for lisa martin here with David Monty. We've talked about automation and many industries. Now this segment is going to focus on automation and healthcare. We've got two guests joining us Jim Petrosea Cto of Blue Cross, Blue Shield and Gadget. Dhaliwal. The global C. I. O. Industry lead at you. I pass guys welcome to the program. Thank you. So let's start unpacking from the CTO level and the ceo level the agenda for automation. Jim let's start with you. What does that look like >>for us. It's actually pretty strategic and part of as we think about digital and what digital transformation means, it actually plays a pretty key role. Um There are a lot of processes that can be very manual within a big organization like Blue cross and Blue shield and to be able to streamline that and take away kind of what I would call the mundane work. Right? The the you know, going through a spreadsheet and then typing it into the screen, there are a lot of processes like that that are legacy. But what if you could take that away um and actually create a better work experience for the people that work there right? And and focus on higher value type uh type things and it's really key. And it really It goes down to our our business folks right? There are a lot of things we can drive with automation. We started a program um in 2019. Um that's been quite successful. We now have 250 box, we measure what we call annualized efficiency gains. So how much efficiency are we getting by these bots? So the bots are doing um this repetitive work that people would do. Um And what we're finding is, you know, we've got about $11 million in any wise efficiency gain through the process and we're just getting started. Um But we're all we're not stopping there too though, we're enabling citizen developers. So we're saying, hey business, if you want to automate, you know, parts of your job, we're gonna help you do that. So we've got about 60 people that were training. Um We run bad Ethan's where they come together and they actually create bots uh And it's really really creating some some impact and buzz in our business >>anywhere from your lens, where does automation fit within the C. I. O. S. Agenda? And how do you work together in unison with the C. T. O. To help roll this out across the enterprise? >>Yeah, no, definitely. And in fact as a part of introduction, I can actually share that. How I'm wearing a Ceo had within your path since I'm just joining join path and I'm actually now helping a client ceos in their automation strategy but I was a deputy ceo in my prior role at L. A. County where actually I ran the automation strategy. So if we look at from our organization perspective B complex as L. A County which is such a Federated organization. From a Ceo perspective, the way we look at the strategy is it's always driven by the business goals of the city or a county and we typically drive into three different areas. One is how we can transform our operational processes so that we can save the tax dollars. It's all about doing more with the less dollars. And then second is about how we can transform our residents experience because end of the day it is all about how we can improve the quality of life for our residents. So we've got 10 million people for L. A. County, the largest populous county in us. So it was an uphill task to serve that such a diverse population need and that the third area is about how to transform the new business models because as we are moving away from a government centric approach to the residents centric approach, you really need to come up with a new digital solutions. And Ceo is in the center of all these three elements when you look at it. So it's a very appear to us to keep keep improving your efficiency and then at a time keep adding the new digital solutions and that's where automation strategy is kind of a horizontal strategy which enables all these components. So what I hear from >>that is alignment with the business. Yeah. Right. Change management. Absolutely. That's like really fundamental and then see IOS this this agent of transformation uh you can see or she has a horizontal purview across the organization now now jim the cto role is the automation at blue cross blue shield lead by you or you there to make sure the technology plugs into your enterprise architecture. What's your shoulder? >>You know? Uh my my role is really to drive uh what I'll call technology enabled business change. Right. So I actually uh started our our automation journey uh at hc sc and I did that by partnering with our business. Um There was actually a lot of buzz around automation and there were actually some small pockets of it, none of it was enterprise scale. Um Right. And we really wanted to go big in this and and working with the business sponsors, they saw value in it. Um and we've you know, we've generated um a lot of uh efficiency, better quality of work because of it but but I very closely had a partner with our business, we have a committee that is lead of business folks that I facilitate. So I view my role as an enabler, um we have to communicate the change management pieces is huge. Uh the education just having a common vernacular on what is automation mean, Right, because everybody interpreted it differently um and then being able to do it at an enterprise scale is quite challenging. Um You know, I I really enjoyed um one of the key notes, I don't know if you had a chance to see shankar by Duncan from the hidden brain, right? But he talked a lot about the brain aspect and how do you get people to change? And and that's a large part of it. There's a lot about technology, but there's really a lot about being a change agent um and and really working very closely with your business, >>how does one measure? I'm hearing a lot time saved. Our saved. How does one measure that and quantify the dollar impact, which by the way, I'm on record as saying the soft dollars are way bigger. And but when you're talking to the, you know, the bottom line CFO and it's all about, you know, the cash flow, whatever is, how do you measure that? >>I can take it. So we, what we do is as we define these use cases right? We we go through an actual structure product process where we we gather them. Um we then rate them and we actually prioritize them based on those that are going to have the greatest impact. Um and we can tell based on, you know, what is the manual effort today. So we understand there are X number of people that do this X number of days and we think this body can take that some load off of them. Right? Um So we we go in with the business case. Um And then the Ui Path platform actually allows us to measure well, how much is that pot running? Right. So we can actually sit there and say, well we wanted that thing to run 10 hours a day and it did and it's generated this kind of efficiency because otherwise the human would have had to do that work. >>So the business case is kind of redeploying >>human. It really is is really maximizing human capital and make and and you know really using because the bots do repetitive stuff really well. They don't do higher level thinking and and we don't view it as replacing people, we view it as augmenting and actually making them more efficient and more effective at what, how do you get the dollars out of that? Well, a couple of ways. Right. And so one of the things we've we've done is we we create and measure the efficiency our business users and financed by the way is one of our bigger ones. And the CFO is one of the sponsors of the program, um can decide how to reinvest it in a lot of cases it is actually cost avoidance as we grow, literally being able to grow without adding staff. I mean that's very measurable. Um in some cases it is actually taking, you know cost out um in in certain cases, but a lot of times that's just through attrition, right? You don't back fill positions, you let it happen naturally. Um and and then there's just things that happen to your business that you have to respond to give you a great example, state of texas, um passes what's the equivalent of the no surprise attack. But they did it there before the federal government did it. Um but it requires a lot of processes to be put in place, because now you have providers and payers having to deal with disputes, right? It actually generates a boatload of work. And we thought there might be, you know, 5000 of these in the first year, where there were 21,000 in the first year. And so far this year we're doubling that amount, right. We were able to use automation to respond to that without having to add a bunch of stuff. If we had to add staff for that, it would have literally been, you know, maybe hundreds of people, right? And but now, you know, there's, you can clearly put a value on it and it's millions of dollars a year, that we would have otherwise had to expect. >>The reason I'm harping on this lease is because I've been through a lot of cycles, as you know, and after the dot com boom, the the cost avoidance meant not writing the check to the software company, right? And that's what nick Carr wrote this, i. T matter. And then, and then, you know, post the financial crisis, we've entered uh a decade plus of awareness on the impact of technology. And I wonder if it's, I think this, I think this the cycle is changing I think. And I wonder if you have an opinion here where people, I think organizations are going to look at Technology completely different than they did like in the early 2000s when it was just easy to cut. >>No, I think the other point I will add to it. I agree with the gym. So we typically look at our away but it doesn't always have to be the cost. Right? If you look from the outcomes of the value, there are other measures also right? If you look at the how automation was able to help in the Covid generate. It was never about costs at that time. It was about a human lives. So you always may not be able to quantify it what you look at. Okay. What how are we maximizing the value or what kind of situations where we are and where we may not even have a human power to do that work. And we are running against the time. It could be the compliance needs. I'll give example of our covid use case which was pretty big success uh within L. A. County we deployed bots for the covid contact tracing program. So we were actually interviewing all the people who were testing positive so that we actually can keep track of them and then bring back that data within our HR so that our criminologists actually can look at the trends and see how we are doing as a county as compared to other counties and nationally. And we were in the peak, we were interviewing about 5000 people a day And we had to process that data manually into our nature and we deployed 15 members to do that. And they were doing like about 600 interviews a day. So every day we had a backlog of 2500 interviews. So it is not about a cost saving or a dollar value here because nobody planned for these unplanned events and now we don't have a time and money to find more data entry operators and parts were able to actually clear up all the backlog. So the value which we were able to bring it is way beyond the cost element. >>I I believe that 100% and I've been fighting this battle for a long time and it's easier to fight now because we're in this economic cycle even despite the pandemic, but I think it can be quantified. I honestly believe it can be tied to the income statement or in the case of a public sector, it could be tied to the budget and the mission how that budget supports the mission of the company. But I really believe it. And and I've always said that those soft factors are dwarf the cost savings, but sometimes, you know, sometimes the CFO doesn't listen, you know, because he or she has to cut. I think automation could change that >>for public sector. We look at how we can do more about it. So it's because we don't look at bottom line, it's about the tax dollars, we have limited dollars, but how we can maximize the value which we are giving to residents, it is not about a profit for us. We look at the different lens when it comes to the commercial >>Side, it's similar for us. So as a as a health care pair, because we're a mutual right? Our members and we have 17 million of them are really the folks that own the company and we're very purpose driven. Our our purpose is to do everything in our power to stand by members in sickness and in health. So how do you get the highest quality, cost effective health care for them? So if automation allows you to be more effective and actually keep that cost down, that means you can cover more people and provide higher quality care to our members. So that's really the driver for mission driven, >>I was gonna ask you as a member as one of your 17 million members, what are some of the ways in which automation is benefiting me? >>Um you know, a number of different ways. First off, you know, um it lowers our administrative costs, right? So that means we can actually lower our rights as as we go out and and and work with folks? That's probably the the the the bottom line impact, but we're also automating processes uh to to make it easier for the member. Right? Uh the example I used earlier was the equivalent of no surprises. Right. How do we take the member out of the middle of this dispute between, you know, out of network providers and the payer and just make it go away. Right, and we take care of it. Um but that that creates potentially administrative burden on our side, but we want to keep their costs down and we do it efficiently using it. So there's a number of use cases that we've we've done across, you know, different parts of our business. We automate a lot of our customer service, right? When you call um there's bots in the background that are helping that that agent do their job. And what that means is you're on the show, you're on the phone a lot shorter of a period of time. And that agent can be more concise and more accurate in answering your question. >>So your employee experience is dramatically improved, as is the member experience? >>Yes, they go hand in hand. They do go hand, unhappy members means unhappy employees, 100% >>mentioned scale before, you said you can't scale in this particular, the departmental pockets. Talk about scale a little bit. I'm curious as to how important cloud is to scale. Is it not matter. Can you scale without cloud? What are the other dimensions of scale? >>Well, you know, especially with my CTO had, we're we're pushing very heavily to cloud. We view ourselves as a cloud first. We want to do things in a cloud versus our own data centers, partially because of the scale that it gives us. But because we're healthcare, we have to do it very securely. So. We are very meticulous about guarding our data, how we encrypt information um, not only in our data center but in the cloud and controlling the keys and having all the controls in place. You know, the C. So and I are probably the best friends right now in the company because we have to do it together and you have to take that that security mind set up front. Right cloud first. Put security first with it. Um, so we're moving what we can to the cloud because we think it's just going to give us better scale as we grow and better economics overall, >>Any thoughts on that? I think a similar thoughts but if we look from L. A. county because of the sheer volume itself because the data which we are talking about. We had 40 departments within the county. Each department is serving a different business purpose for the resident beit voting or B justice or being social services and all and the amount of data which we are generating for 10 million residents and the amount of duplicate asi which it comes out because it's a very government centering model. You have a different systems and they may not be talking to each other. The amount of diplomacy and identity delicacy which we are creating and as we are enabling the interoperability between these functions to give us seamless experience keeping security in mind so fully agree on that because the end of the day we have to ensure that customer guarantee but it's a sheer volume that as and when we are adding these data sets and the patient's data as well as the residents data and now we have started adding a machine data because we have deployed so many IOT solutions so the data which is coming from those machines, the logs and all its exponential so that's where the scale comes into picture and how we can ensure that we are future ready for the upscale which we need and that's where cloud ability definitely helps a lot. >>What do you mean by future ready? >>So if you look at from a future smart city or a smart community perspective, imagine when machines are everywhere machines and IOT solutions are deployed, beat even healthcare, your bad information, you're even patient information, everything is interconnected and amount of data which is getting generated in that your automobile they're going to start talking to entertainment or we have to potentially track a single resident might be going same person going to the justice or maybe same person might be having a mental health issues, A same person might be looking for a social services, how we're going to connect those dots and what all systems they are touching. So all that interconnections needs to happen. So that exponential increase of data is a future readiness, which I'm talking about. Are we future ready from a technology perspective? Are we future ready from the other ecosystem perspective and how and how we're gonna manage those situations? Uh, so those are the things which we >>look at it and it's a it's a multiplier to, right? We all have this influx of information and you need to figure out what to do with it. Right. This is where artificial intelligence, machine learning is so important. But you also have interoperability standards that are coming. So now we're we have this massive data that each of our organizations have. But now you have interoperability which is a good thing for the member saying now I need to be able to share that data. Yeah, I wanted to ask you about >>that because a lot of changes in health care, um, are meaningful use. You have to show that to get paid but the standards weren't mature. Right? And so now that's changing what role does automation play in facilitating those standards. >>So, you know, we're big, big supporters of the fire standard that's out there um to in order to be able to support the standards and and create a P. I. S. And and pull together the information. What what will happen sometimes in the background is there's actually um artificial intelligence, machine learning models that create algorithms right? The output of that though often has to be active. Now a person can do something with that information or a vodka. Right? So when you start taking the ideal of artificial intelligence and now you have a robotic process that can use that to pull together the information and assimilated in a way to make it higher quality. But now it's available. It's kind of in the background. You don't see it but it's there helping. >>What are some of the things that you see? I know we're out of time but I just have a couple more questions. Some of the things that you see here we are you I path forward for we're in person. This is a bold company that's growing very quickly. Some of the announcements that were made, what are what are some of your reaction to that? And how do you see it helping move blue crush blue shield forward even >>faster. Well you know a lot of the announcements in terms of some of the features that that they've added around their robotics processing are great right? The fact that they're in the cloud and and some of the capabilities and and and better ability to to support that the process mining is key. Right. In order for abouts to be effective, you have to understand your process and you just don't want to necessarily automate the bad practices. Right? So you want to take a look at those processes to figure out how you can automate things smartly. Um and some of their capabilities around that are very interesting. We're going to explore that quite a bit but but I think they're the ambition here is beyond robotics. Right. It's actually creating um you know, applications that actually are using bots in the background which is very intriguing and has a lot of potential potentially to drive even more digital transformation. This can really affect all of our workers and allow us to take digital solutions out to the market a lot faster >>and to see what was going to ask you, you are here for four weeks at UI Path, you got to meet a lot of your colleagues, which is great. But what about this company attracted you to leave your former role and come over here to the technology vendor side. >>Well, I think I was able to achieve the similar role within L. A. County, able to establish the automation practice and achieve the maturity, able to stand up things and I feel that this is the same practitioner activity which I can actually take it back to the other clients ceos because of one thing which I really like about your hypothesis. RP is just a small component of it. I really want to change that mindset that we have to start looking ui path as an end to end full automation enterprise solution and it is not only the business automation, it's the idea automation and it's a plus combination and whether we are developing a new industry solutions with our partners to help the different industry segments and we actually helping Ceo in the center of it because Ceo is the one who is driving the automation, enabling the business automation and actually managing the automation ceo and the governess. So CEO is in left and center of it and my role is to ensure that I actually help those Ceos to make successful and get that maturity and you will path as a platform is giving that ability of length and breath and that's what is really fascinating me and I'm really looking forward that how that spectrum is changing that we are getting matured in a process mining area and how we are expanding our horizons to look at the whole automation suit, not just the R. P. Product and that's something which I'm really looking forward and seeing that how we're going to continue expanding other magic quadrants and we're actually going to give the seamless experience so the client doesn't have to worry about okay for this, I have to pick this and further, I have to pick something else >>that's seamless experience is absolutely table stakes these days. Guys, we're out of time. But thank you so much for joining. David me, talking about automation and health care. Your recommendations for best practices, how to go about doing that and and the change management piece. That's a critical piece. We appreciate your time. >>Thanks for having. Thank >>you. Our pleasure for day Volonte. I'm lisa martin live in las Vegas. The cubes coverage of you a path forward for continues next. Mhm. Mhm mm.

>> from Cambridge, Massachusetts. It's the Cube covering M I T. Chief data officer and information quality Symposium 2019. Brought to you by Silicon Angle Media. >> Welcome back to Cambridge, Massachusetts. Everybody, You're watching the Cube, the leader in live tech coverage, and we're covering the M I t CDO conference M I t. CDO. My name is David Monty in here with my co host, Paul Galen. Mike Stone breakers here. The legend is founder CTO of Of Tamer, as well as many other companies. Inventor Michael. Thanks for coming back in the Cube. Good to see again. Nice to be here. So this is kind of ah, repeat pattern for all of us. We kind of gather here in August that the CDO conference You're always the highlight of the show. You gave a talk this week on the top 10. Big data mistakes. You and I are one of the few. You were the few people who still use the term big data. I happen to like it. Sad that it's out of vogue already, but people associated with the doo doop it's kind of waning, but regardless, so welcome. How'd the talk go? What were you talking about. >> So I talked to a lot of people who were doing analytics. We're doing operation Offer operational day of data at scale, and they always make most of them make a collection of bad mistakes. And so the talk waas a litany of the blunders that I've seen people make, and so the audience could relate to the blunders about most. Most of the enterprise is represented. Make a bunch of the blunders. So I think no. One blunder is not planning on moving most everything to the cloud. >> So that's interesting, because a lot of people would would would love to debate that, but and I would imagine you probably could have done this 10 years ago in a lot of the blunders would be the same, but that's one that wouldn't have been there. But so I tend to agree. I was one of the two hands that went up this morning, and vocalist talk when he asked, Is the cloud cheaper for us? It is anyway. But so what? Why should everybody move everything? The cloud aren't there laws of physics, laws of economics, laws of the land that suggest maybe you >> shouldn't? Well, I guess 22 things and then a comment. First thing is James Hamilton, who's no techies. Techie works for Amazon. We know James. So he claims that he could stand up a server for 25% of your cost. I have no reason to disbelieve him. That number has been pretty constant for a few years, so his cost is 1/4 of your cost. Sooner or later, prices are gonna reflect costs as there's a race to the bottom of cloud servers. So >> So can I just stop you there for a second? Because you're some other date on that. All you have to do is look at a W S is operating margin and you'll see how profitable they are. They have software like economics. Now we're deploying servers. So sorry to interrupt, but so carry. So >> anyway, sooner or later, they're gonna have their gonna be wildly cheaper than you are. The second, then yet is from Dave DeWitt, whose database wizard. And here's the current technology that that Microsoft Azure is using. As of 18 months ago, it's shipping containers and parking lots, chilled water in power in Internet, Ian otherwise sealed roof and walls optional. So if you're doing raised flooring in Cambridge versus I'm doing shipping containers in the Columbia River Valley, who's gonna be a lot cheaper? And so you know the economies of scale? I mean, that, uh, big, big cloud guys are building data centers as fast as they can, using the cheapest technology around. You put up the data center every 10 years on dhe. You do it on raised flooring in Cambridge. So sooner or later, the cloud guys are gonna be a lot cheaper. And the only thing that isn't gonna the only thing that will change that equation is For example, my lab is up the street with Frank Gehry building, and we have we have an I t i t department who runs servers in Cambridge. Uh, and they claim they're cheaper than the cloud. And they don't pay rent for square footage and they don't pay for electricity. So yeah, if if think externalities, If there are no externalities, the cloud is assuredly going to be cheaper. And then the other thing is that most everybody tonight that I talk thio including me, has very skewed resource demands. So in the cloud finding three servers, except for the last day of the month on the last day of the month. I need 20 servers. I just do it. If I'm doing on Prem, I've got a provision for peak load. And so again, I'm just way more expensive. So I think sooner or later these combinations of effects was going to send everybody to the cloud for most everything, >> and my point about the operating margins is difference in price and cost. I think James Hamilton's right on it. If he If you look at the actual cost of deploying, it's even lower than the price with the market allows them to their growing at 40 plus percent a year and a 35 $40,000,000,000 run rate company sooner, Sooner or >> later, it's gonna be a race to the lot of you >> and the only guys are gonna win. You have guys have the best cost structure. A >> couple other highlights from your talk. >> Sure, I think 2nd 2nd thing like Thio Thio, no stress is that machine learning is going to be a game is going to be a game changer for essentially everybody. And not only is it going to be autonomous vehicles. It's gonna be automatic. Check out. It's going to be drone delivery of most everything. Uh, and so you can, either. And it's gonna affect essentially everybody gonna concert of, say, categorically. Any job that is easy to understand is going to get automated. And I think that's it's gonna be majorly impactful to most everybody. So if you're in Enterprise, you have two choices. You can be a disrupt or or you could be a disruptive. And so you can either be a taxi company or you can be you over, and it's gonna be a I machine learning that's going going to be determined which side of that equation you're on. So I was a big blunder that I see people not taking ml incredibly seriously. >> Do you see that? In fact, everyone I talked who seems to be bought in that this is we've got to get on the bandwagon. Yeah, >> I'm just pointing out the obvious. Yeah, yeah, I think, But one that's not quite so obvious you're is a lot of a lot of people I talked to say, uh, I'm on top of data science. I've hired a group of of 10 data scientists, and they're doing great. And when I talked, one vignette that's kind of fun is I talked to a data scientist from iRobot, which is the guys that have the vacuum cleaner that runs around your living room. So, uh, she said, I spend 90% of my time locating the data. I want to analyze getting my hands on it and cleaning it, leaving the 10% to do data science job for which I was hired. Of the 10% I spend 90% fixing the data cleaning errors in my data so that my models work. So she spends 99% of her time on what you call data preparation 1% of her time doing the job for which he was hired. So data science is not about data science. It's about data integration, data cleaning, data, discovery. >> But your new latest venture, >> so tamer does that sort of stuff. And so that's But that's the rial data science problem. And a lot of people don't realize that yet, And, uh, you know they will. I >> want to ask you because you've been involved in this by my count and starting up at least a dozen companies. Um, 99 Okay, It's a lot. >> It's not overstated. You estimated high fall. How do you How >> do you >> decide what challenge to move on? Because they're really not. You're not solving the same problems. You're You're moving on to new problems. How do you decide? What's the next thing that interests you? Enough to actually start a company. Okay, >> that's really easy. You know, I'm on the faculty of M i t. My job is to think of news new ship and investigate it, and I come up. No, I'm paid to come up with new ideas, some of which have commercial value, some of which don't and the ones that have commercial value, like, commercialized on. So it's whatever I'm doing at the time on. And that's why all the things I've commercialized, you're different >> s so going back to tamer data integration platform is a lot of companies out there claim to do it day to get integration right now. What did you see? What? That was the deficit in the market that you could address. >> Okay, great question. So there's the traditional data. Integration is extract transforming load systems and so called Master Data management systems brought to you by IBM in from Attica. Talent that class of folks. So a dirty little secret is that that technology does not scale Okay, in the following sense that it's all well, e t l doesn't scale for a different reason with an m d l e t l doesn't scale because e t. L is based on the premise that somebody really smart comes up with a global data model For all the data sources you want put together. You then send a human out to interview each business unit to figure out exactly what data they've got and then how to transform it into the global data model. How to load it into your data warehouse. That's very human intensive. And it doesn't scale because it's so human intensive. So I've never talked to a data warehouse operator who who says I integrate the average I talk to says they they integrate less than 10 data sources. Some people 20. If you twist my arm hard, I'll give you 50. So a Here. Here's a real world problem, which is Toyota Motor Europe. I want you right now. They have a distributor in Spain, another distributor in France. They have a country by country distributor, sometimes canton by Canton. Distribute distribution. So if you buy a Toyota and Spain and move to France, Toyota develops amnesia. The French French guys know nothing about you. So they've got 250 separate customer databases with 40,000,000 total records in 50 languages. And they're in the process of integrating that. It was single customer database so that they can Duke custom. They could do the customer service we expect when you cross cross and you boundary. I've never seen an e t l system capable of dealing with that kind of scale. E t l dozen scale to this level of problem. >> So how do you solve that problem? >> I'll tell you that they're a tamer customer. I'll tell you all about it. Let me first tell you why MGM doesn't scare. >> Okay. Great. >> So e t l says I now have all your data in one place in the same format, but now you've got following problems. You've got a d duplicated because if if I if I bought it, I bought a Toyota in Spain, I bought another Toyota in France. I'm both databases. So if you want to avoid double counting customers, you got a dupe. Uh, you know, got Duke 30,000,000 records. And so MGM says Okay, you write some rules. It's a rule based technology. So you write a rule. That's so, for example, my favorite example of a rule. I don't know if you guys like to downhill downhill skiing, All right? I love downhill skiing. So ski areas, Aaron, all kinds of public databases assemble those all together. Now you gotta figure out which ones are the same the same ski area, and they're called different names in different addresses and so forth. However, a vertical drop from bottom to the top is the same. Chances are they're the same ski area. So that's a rule that says how to how to put how to put data together in clusters. And so I now have a cluster for mount sanity, and I have a problem which is, uh, one address says something rather another address as something else. Which one is right or both? Right, so now you want. Now you have a gold. Let's call the golden Record problem to basically decide which, which, which data elements among a variety that maybe all associated with the same entity are in fact correct. So again, MDM, that's a rule's a rule based system. So it's a rule based technology and rule systems don't scale the best example I can give you for why Rules systems don't scale. His tamer has another customer. General Electric probably heard of them, and G wanted to do spend analytics, and so they had 20,000,000 spend transactions. Frank the year before last and spend transaction is I paid $12 to take a cab from here here to the airport, and I charged it to cost center X Y Z 20,000,000 of those so G has a pre built classification system for spend, so they have parts and underneath parts or computers underneath computers and memory and so forth. So pre existing preexisting class classifications for spend they want to simply classified 20,000,000 spent transactions into this pre existing hierarchy. So the traditional technology is, well, let's write some rules. So G wrote 500 rules, which is about the most any single human I can get there, their arms around so that classified 2,000,000 of the 20,000,000 transactions. You've now got 18 to go and another 500 rules is not going to give you 2,000,000 more. It's gonna give you love diminishing returns, right? So you have to write a huge number of rules and no one can possibly understand. So the technology simply doesn't scale, right? So in the case of G, uh, they had tamer health. Um, solve this. Solved this classification problem. Tamer used their 2,000,000 rule based, uh, tag records as training data. They used an ML model, then work off the training data classifies remaining 18,000,000. So the answer is machine learning. If you don't use machine learning, you're absolutely toast. So the answer to MDM the answer to MGM doesn't scale. You've got to use them. L The answer to each yell doesn't scale. You gotta You're putting together disparate records can. The answer is ml So you've got to replace humans by machine learning. And so that's that seems, at least in this conference, that seems to be resonating, which is people are understanding that at scale tradition, traditional data integration, technology's just don't work >> well and you got you got a great shot out on yesterday from the former G S K Mark Grams, a leader Mark Ramsay. Exactly. Guys. And how they solve their problem. He basically laid it out. BTW didn't work and GM didn't work, All right. I mean, kick it, kick the can top down data modelling, didn't work, kicked the candid governance That's not going to solve the problem. And But Tamer did, along with some other tooling. Obviously, of course, >> the Well, the other thing is No. One technology. There's no silver bullet here. It's going to be a bunch of technologies working together, right? Mark Ramsay is a great example. He used his stream sets and a bunch of other a bunch of other startup technology operating together and that traditional guys >> Okay, we're good >> question. I want to show we have time. >> So with traditional vendors by and large or 10 years behind the times, And if you want cutting edge stuff, you've got to go to start ups. >> I want to jump. It's a different topic, but I know that you in the past were critic of know of the no sequel movement, and no sequel isn't going away. It seems to be a uh uh, it seems to be actually gaining steam right now. What what are the flaws in no sequel? It has your opinion changed >> all? No. So so no sequel originally meant no sequel. Don't use it then. Then the marketing message changed to not only sequel, So sequel is fine, but no sequel does others. >> Now it's all sequel, right? >> And my point of view is now. No sequel means not yet sequel because high level language, high level data languages, air good. Mongo is inventing one Cassandra's inventing one. Those unless you squint, look like sequel. And so I think the answer is no sequel. Guys are drifting towards sequel. Meanwhile, Jason is That's a great idea. If you've got your regular data sequel, guys were saying, Sure, let's have Jason is the data type, and I think the only place where this a fair amount of argument is schema later versus schema first, and I pretty much think schema later is a bad idea because schema later really means you're creating a data swamp exactly on. So if you >> have to fix it and then you get a feel of >> salary, so you're storing employees and salaries. So, Paul salaries recorded as dollars per month. Uh, Dave, salary is in euros per week with a lunch allowance minds. So if you if you don't, If you don't deal with irregularities up front on data that you care about, you're gonna create a mess. >> No scheme on right. Was convenient of larger store, a lot of data cheaply. But then what? Hard to get value out of it created. >> So So I think the I'm not opposed to scheme later. As long as you realize that you were kicking the can down the road and you're just you're just going to give your successor a big mess. >> Yeah, right. Michael, we gotta jump. But thank you so much. Sure appreciate it. All right. Keep it right there, everybody. We'll be back with our next guest right into the short break. You watching the cue from M i t cdo Ike, you right back