>> Sue: Hi, everybody. Thank you for joining us today, for the Virtual Vertica BDC 2020. This breakout session is entitled "Vertica @ Uber Scale" My name is Sue LeClaire, Director of Marketing at Vertica. And I'll be your host for this webinar. Joining me is Girish Baliga, Director I'm sorry, user, Uber Engineering Manager of Big Data at Uber. Before we begin, I encourage you to submit questions or comments during the virtual session. You don't have to wait, just type your question or comment in the question box below the slides and click Submit. There will be a Q and A session, at the end of the presentation. We'll answer as many questions as we're able to during that time. Any questions that we don't address, we'll do our best to answer offline. Alternately, you can also Vertica forums to post your questions there after the session. Our engineering team is planning to join the forums to keep the conversation going. And as a reminder, you can maximize your screen by clicking the double arrow button, in the lower right corner of the slides. And yet, this virtual session is being recorded, and you'll be able to view on demand this week. We'll send you a notification as soon as it's ready. So let's get started. Girish over to you. >> Girish: Thanks a lot Sue. Good afternoon, everyone. Thanks a lot for joining this session. My name is Girish Baliga. And as Sue mentioned, I manage interactive and real time analytics teams at Uber. Vertica is one of the main platforms that we support, and Vertica powers a lot of core business use cases. In today's talk, I wanted to cover two main things. First, how Vertica is powering critical business use cases, across a variety of orgs in the company. And second, how we are able to do this at scale and with reliability, using some of the additional functionalities and systems that we have built into the Vertica ecosystem at Uber. And towards the end, I also have a little extra bonus for all of you. I will be sharing an easy way for you to take advantage of, many of the ideas and solutions that I'm going to present today, that you can apply to your own Vertica deployments in your companies. So stick around and put on your seat belts, and let's go start on the ride. At Uber, our mission is to ignite opportunity by setting the world in motion. So we are focused on solving mobility problems, and enabling people all over the world to solve their local problems, their local needs, their local issues, in a manner that's efficient, fast and reliable. As our CEO Dara has said, we want to become the mobile operating system of local cities and communities throughout the world. As of today, Uber is operational in over 10,000 cities around the world. So, across our various business lines, we have over 110 million monthly users, who use our rides, services, or eat services, and a whole bunch of other services that we provide to Uber. And just to give you a scale of our daily operations, we in the ride business, have over 20 million trips per day. And that each business is also catching up, particularly during the recent times that we've been having. And so, I hope these numbers give you a scale of the amount of data, that we process each and every day. And support our users in their analytical and business reporting needs. So who are these users at Uber? Let's take a quick look. So, Uber to describe it very briefly, is a lot like Amazon. We are largely an operation and logistics company. And employee work based reflects that. So over 70% of our employees work in teams, which come under the umbrella of Community Operations and Centers of Excellence. So these are all folks working in various cities and towns that we operate around the world, and run the Uber businesses, as somewhat local businesses responding to local needs, local market conditions, local regulation and so forth. And Vertica is one of the most important tools, that these folks use in their day to day business activities. So they use Vertica to get insights into how their businesses are going, to deeply into any issues that they want to triage , to generate reports, to plan for the future, a whole lot of use cases. The second big class of users, are in our marketplace team. So marketplace is the engineering team, that backs our ride shared business. And as part of this, running this business, a key problem that they have to solve, is how to determine what prices to set, for particular rides, so that we have a good match between supply and demand. So obviously the real time pricing decisions they're made by serving systems, with very detailed and well crafted machine learning models. However, the training data that goes into this models, the historical trends, the insights that go into building these models, a lot of these things are powered by the data that we store, and serve out of Vertica. Similarly, in each business, we have use cases spanning all the way from engineering and back-end systems, to support operations, incentives, growth, and a whole bunch of other domains. So the big class of applications that we support across a lot of these business lines, is dashboards and reporting. So we have a lot of dashboards, which are built by core data analysts teams and shared with a whole bunch of our operations and other teams. So these are dashboards and reports that run, periodically say once a week or once a day even, depending on the frequency of data that they need. And many of these are powered by the data, and the analytics support that we provide on our Vertica platform. Another big category of use cases is for growth marketing. So this is to understand historical trends, figure out what are various business lines, various customer segments, various geographical areas, doing in terms of growth, where it is necessary for us to reinvest or provide some additional incentives, or marketing support, and so forth. So the analysis that backs a lot of these decisions, is powered by queries running on Vertica. And finally, the heart and soul of Uber is data science. So data science is, how we provide best in class algorithms, pricing, and matching. And a lot of the analysis that goes into, figuring out how to build these systems, how to build the models, how to build the various coefficients and parameters that go into making real time decisions, are based on analysis that data scientists run on Vertica systems. So as you can see, Vertica usage spans a whole bunch of organizations and users, all across the different Uber teams and ecosystems. Just to give you some quick numbers, we have over 5000 weekly active, people who run queries at least once a week, to do some critical business role or problem to solve, that they have in their day to day operations. So next, let's see how Vertica fits into the Uber data ecosystem. So when users open up their apps, and request for a ride or order food delivery on each platform, the apps are talking to our serving systems. And the serving systems use online storage systems, to store the data as the trips and eat orders are getting processed in real time. So for this, we primarily use an in house built, key value storage system called Schemaless, and an open source system called Cassandra. We also have other systems like MySQL and Redis, which we use for storing various bits of data to support serving systems. So all of this operations generates a lot of data, that we then want to process and analyze, and use for our operational improvements. So, we have ingestion systems that periodically pull in data from our serving systems and land them in our data lake. So at Uber a data lake is powered by Hadoop, with files stored on HDFS clusters. So once the raw data lines on the data lake, we then have ETL jobs that process these raw datasets, and generate, modeled and customize datasets which we then use for further analysis. So once these model datasets are available, we load them into our data warehouse, which is entirely powered by Vertica. So then we have a business intelligence layer. So with internal tools, like QueryBuilder, which is a UI interface to write queries, and look at results. And it read over the front-end sites, and Dashbuilder, which is a dash, board building tool, and report management tool. So these are all various tools that we have built within Uber. And these can talk to Vertica and run SQL queries to power, whatever, dashboards and reports that they are supporting. So this is what the data ecosystem looks like at Uber. So why Vertica and what does it really do for us? So it powers insights, that we show on dashboards as folks use, and it also powers reports that we run periodically. But more importantly, we have some core, properties and core feature sets that Vertica provides, which allows us to support many of these use cases, very well and at scale. So let me take a brief tour of what these are. So as I mentioned, Vertica powers Uber's data warehouse. So what this means is that we load our core fact and dimension tables onto Vertica. The core fact tables are all the trips, all the each orders and all these other line items for various businesses from Uber, stored as partitioned tables. So think of having one partition per day, as well as dimension tables like cities, users, riders, career partners and so forth. So we have both these two kinds of datasets, which will load into Vertica. And we have full historical data, all the way since we launched these businesses to today. So that folks can do deeper longitudinal analysis, so they can look at patterns, like how the business has grown from month to month, year to year, the same month, over a year, over multiple years, and so forth. And, the really powerful thing about Vertica, is that most of these queries, you run the deep longitudinal queries, run very, very fast. And that's really why we love Vertica. Because we see query latency P90s. That is 90 percentile of all queries that we run on our platform, typically finish in under a minute. So that's very important for us because Vertica is used, primarily for interactive analytics use cases. And providing SQL query execution times under a minute, is critical for our users and business owners to get the most out of analytics and Big Data platforms. Vertica also provides a few advanced features that we use very heavily. So as you might imagine, at Uber, one of the most important set of use cases we have is around geospatial analytics. In particular, we have some critical internal dashboards, that rely very heavily on being able to restrict datasets by geographic areas, cities, source destination pairs, heat maps, and so forth. And Vertica has a rich array of functions that we use very heavily. We also have, support for custom projections in Vertica. And this really helps us, have very good performance for critical datasets. So for instance, in some of our core fact tables, we have done a lot of query and analysis to figure out, how users run their queries, what kind of columns they use, what combination of columns they use, and what joints they do for typical queries. And then we have laid out our custom projections to maximize performance on these particular dimensions. And the ability to do that through Vertica, is very valuable for us. So we've also had some very successful collaborations, with the Vertica engineering team. About a year and a half back, we had open-sourced a Python Client, that we had built in house to talk to Vertica. We were using this Python Client in our business intelligence layer that I'd shown on the previous slide. And we had open-sourced it after working closely with Eng team. And now Vertica formally supports the Python Client as an open-source project, which you can download to and integrate into your systems. Another more recent example of collaboration is the Vertica Eon mode on GCP. So as most of or at least some of you know, Vertica Eon mode is formally supported on AWS. And at Uber, we were also looking to see if we could run our data infrastructure on GCP. So Vertica team hustled on this, and provided us early preview version, which we've been testing out to see how performance, is impacted by running on the Cloud, and on GCP. And so far, I think things are going pretty well, but we should have some numbers about this very soon. So here I have a visualization of an internal dashboard, that is powered solely by data and queries running on Vertica. So this GIF has sequence have different visualizations supported by this tool. So for instance, here you see a heat map, downgrading heat map of source of traffic demand for ride shares. And then you will see a bunch of arrows here about source destination pairs and the trip lines. And then you can see how demand moves around. So, as the cycles through the various animations, you can basically see all the different kinds of insights, and query shapes that we send to Vertica, which powers this critical business dashboard for our operations teams. All right, so now how do we do all of this at scale? So, we started off with a single Vertica cluster, a few years back. So we had our data lake, the data would land into Vertica. So these are the core fact and dimension tables that I just spoke about. And then Vertica powers queries at our business intelligence layer, right? So this is a very simple, and effective architecture for most use cases. But at Uber scale, we ran into a few problems. So the first issue that we have is that, Uber is a pretty big company at this point, with a lot of users sending almost millions of queries every week. And at that scale, what we began to see was that a single cluster was not able to handle all the query traffic. So for those of you who have done an introductory course, on queueing theory, you will realize that basically, even though you could have all the query is processed through a single serving system. You will tend to see larger and larger queue wait times, as the number of queries pile up. And what this means in practice for end users, is that they are basically just seeing longer and longer query latencies. But even though the actual query execution time on Vertica itself, is probably less than a minute, their query sitting in the queue for a bunch of minutes, and that's the end user perceived latency. So this was a huge problem for us. The second problem we had was that the cluster becomes a single point of failure. Now Vertica can handle single node failures very gracefully, and it can probably also handle like two or three node failures depending on your cluster size and your application. But very soon, you will see that, when you basically have beyond a certain number of failures or nodes in maintenance, then your cluster will probably need to be restarted or you will start seeing some down times due to other issues. So another example of why you would have to have a downtime, is when you're upgrading software in your clusters. So, essentially we're a global company, and we have users all around the world, we really cannot afford to have downtime, even for one hour slot. So that turned out to be a big problem for us. And as I mentioned, we could have hardware issues. So we we might need to upgrade our machines, or we might need to replace storage or memory due to issues with the hardware in there, due to normal wear and tear, or due to abnormal issues. And so because of all of these things, having a single point of failure, having a single cluster was not really practical for us. So the next thing we did, was we set up multiple clusters, right? So we had a bunch of identities clusters, all of which have the same datasets. So then we would basically load data using ingestion pipelines from our data lake, onto each of these clusters. And then the business intelligence layer would be able to query any of these clusters. So this actually solved most of the issues that I pointed out in the previous slide. So we no longer had a single point of failure. Anytime we had to do version upgrades, we would just take off one cluster offline, upgrade the software on it. If we had node failures, we would probably just take out one cluster, if we had to, or we would just have some spare nodes, which would rotate into our production clusters and so forth. However, having multiple clusters, led to a new set of issues. So the first problem was that since we have multiple clusters, you would end up with inconsistent schema. So one of the things to understand about our platform, is that we are an infrastructure team. So we don't actually own or manage any of the data that is served on Vertica clusters. So we have dataset owners and publishers, who manage their own datasets. Now exposing multiple clusters to these dataset owners. Turns out, it's not a great idea, right? Because they are not really aware of, the importance of having consistency of schemas and datasets across different clusters. So over time, what we saw was that the schema for the same tables would basically get out of order, because they were all the updates are not consistently applied on all clusters. Or maybe they were just experimenting some new columns or some new tables in one cluster, but they forgot to delete it, whatever the case might be. We basically ended up in a situation where, we saw a lot of inconsistent schemas, even across some of our core tables in our different clusters. A second issue was, since we had ingestion pipelines that were ingesting data independently into all these clusters, these pipelines could fail independently as well. So what this meant is that if, for instance, the ingestion pipeline into cluster B failed, then the data there would be older than clusters A and C. So, when a query comes in from the BI layer, and if it happens to hit B, you would probably see different results, than you would if you went to a or C. And this was obviously not an ideal situation for our end users, because they would end up seeing slightly inconsistent, slightly different counts. But then that would lead to a bad situation for them where they would not able to fully trust the data that was, and the results and insights that were being returned by the SQL queries and Vertica systems. And then the third problem was, we had a lot of extra replication. So the 20/80 Rule, or maybe even the 90/10 Rule, applies to datasets on our clusters as well. So less than 10% of our datasets, for instance, in 90% of the queries, right? And so it doesn't really make sense for us to replicate all of our data on all the clusters. And so having this set up where we had to do that, was obviously very suboptimal for us. So then what we did, was we basically built some additional systems to solve these problems. So this brings us to our Vertica ecosystem that we have in production today. So on the ingestion side, we built a system called Vertica Data Manager, which basically manages all the ingestion into various clusters. So at this point, people who are managing datasets or dataset owners and publishers, they no longer have to be aware of individual clusters. They just set up their ingestion pipelines with an endpoint in Vertica Data Manager. And the Vertica Data Manager ensures that, all the schemas and data is consistent across all our clusters. And on the query side, we built a proxy layer. So what this ensures is that, when queries come in from the BI layer, the query was forwarded, smartly and with knowledge and data about which cluster up, which clusters are down, which clusters are available, which clusters are loaded, and so forth. So with these two layers of abstraction between our ingestion and our query, we were able to have a very consistent, almost single system view of our entire Vertica deployment. And the third bit, we had put in place, was the data manifest, which were the communication mechanism between ingestion and proxy. So the data manifest basically is a listing of, which tables are available on which clusters, which clusters are up to date, and so forth. So with this ecosystem in place, we were also able to solve the extra replication problem. So now we basically have some big clusters, where all the core tables, and all the tables, in fact, are served. So any query that hits 90%, less so tables, goes to the big clusters. And most of the queries which hit 10% heavily queried important tables, can also be served by many other small clusters, so much more efficient use of resources. So this basically is the view that we have today, of Vertica within Uber, so external to our team, folks, just have an endpoint, where they basically set up their ingestion jobs, and another endpoint where they can forward their Vertica SQL queries. And they are so to a proxy layer. So let's get a little more into details, about each of these layers. So, on the data management side, as I mentioned, we have two kinds of tables. So we have dimension tables. So these tables are updated every cycle, so the list of cities list of drivers, the list of users and so forth. So these change not so frequently, maybe once a day or so. And so we are able to, and since these datasets are not very big, we basically swap them out on every single cycle. Whereas the fact tables, so these are tables which have information about our trips or each orders and so forth. So these are partition. So we have one partition roughly per day, for the last couple of years, and then we have more of a hierarchical partitions set up for older data. So what we do is we load the partitions for the last three days on every cycle. The reason we do that, is because not all our data comes in at the same time. So we have updates for trips, going over the past two or three days, for instance, where people add ratings to their trips, or provide feedback for drivers and so forth. So we want to capture them all in the row corresponding to that particular trip. And so we upload partitions for the last few days to make sure we capture all those updates. And we also update older partitions, if for instance, records were deleted for retention purposes, or GDPR purposes, for instance, or other regulatory reasons. So we do this less frequently, but these are also updated if necessary. So there are endpoints which allow dataset owners to specify what partitions they want to update. And as I mentioned, data is typically managed using a hierarchical partitioning scheme. So in this way, we are able to make sure that, we take advantage of the data being clustered by day, so that we don't have to update all the data at once. So when we are recovering from an cluster event, like a version upgrade or software upgrade, or hardware fix or failure handling, or even when we are adding a new cluster to the system, the data manager takes care of updating the tables, and copying all the new partitions, making sure the schemas are all right. And then we update the data and schema consistency and make sure everything is up to date before we, add this cluster to our serving pool, and the proxy starts sending traffic to it. The second thing that the data manager provides is consistency. So the main thing we do here, is we do atomic updates of our tables and partitions for fact tables using a two-phase commit scheme. So what we do is we load all the new data in temp tables, in all the clusters in phase one. And then when all the clusters give us access signals, then we basically promote them to primary and set them as the main serving tables for incoming queries. We also optimize the load, using Vertica Data Copy. So what this means is earlier, in a parallel pipelines scheme, we had to ingest data individually from HDFS clusters into each of the Vertica clusters. That took a lot of HDFS bandwidth. But using this nice feature that Vertica provides called Vertica Data Copy, we just load it data into one cluster and then much more efficiently copy it, to the other clusters. So this has significantly reduced our ingestion overheads, and speed it up our load process. And as I mentioned as the second phase of the commit, all data is promoted at the same time. Finally, we make sure that all the data is up to date, by doing some checks around the number of rows and various other key signals for freshness and correctness, which we compare with the data in the data lake. So in terms of schema changes, VDM automatically applies these consistently across all the clusters. So first, what we do is we stage these changes to make sure that these are correct. So this catches errors that are trying to do, an incompatible update, like changing a column type or something like that. So we make sure that schema changes are validated. And then we apply them to all clusters atomically again for consistency. And provide a overall consistent view of our data to all our users. So on the proxy side, we have transparent support for, replicated clusters to all our users. So the way we handle that is, as I mentioned, the cluster to table mapping is maintained in the manifest database. And when we have an incoming query, the proxy is able to see which cluster has all the tables in that query, and route the query to the appropriate cluster based on the manifest information. Also the proxy is aware of the health of individual clusters. So if for some reason a cluster is down for maintenance or upgrades, the proxy is aware of this information. And it does the monitoring based on query response and execution times as well. And it uses this information to route queries to healthy clusters, and do some load balancing to ensure that we award hotspots on various clusters. So the key takeaways that I have from the stock, are primarily these. So we started off with single cluster mode on Vertica, and we ran into a bunch of issues around scaling and availability due to cluster downtime. We had then set up a bunch of replicated clusters to handle the scaling and availability issues. Then we run into issues around schema consistency, data staleness, and data replication. So we built an entire ecosystem around Vertica, with abstraction layers around data management and ingestion, and proxy. And with this setup, we were able to enforce consistency and improve storage utilization. So, hopefully this gives you all a brief idea of how we have been able to scale Vertica usage at Uber, and power some of our most business critical and important use cases. So as I mentioned at the beginning, I have a interesting and simple extra update for you. So an easy way in which you all can take advantage of many of the features that we have built into our ecosystem, is to use the Vertica Eon mode. So the Vertica Eon mode, allows you to set up multiple clusters with consistent data updates, and set them up at various different sizes to handle different query loads. And it automatically handles many of these issues that I mentioned in our ecosystem. So do check it out. We've also been, trying it out on DCP, and initial results look very, very promising. So thank you all for joining me on this talk today. I hope you guys learned something new. And hopefully you took away something that you can also apply to your systems. We have a few more time for some questions. So I'll pause for now and take any questions.

>>Live from Las Vegas. It's the cube covering UI path forward Americas 2019 brought to you by UI path. >>We're back in Las Vegas. UI path forward three. You're watching the cube, the leader in live tech coverage. Bobby Patrick is here. He's the COO of UI path. Welcome. Hi Dave. Good to see it to be here. Wow. Great to have the cube here again. Right? Q loves these hot shows like this. I mean this is, you've said Gardner hasn't done the fastest growing software segment you've seen in the data that we share from ETR. You guys are off the chart in terms of net score. It's happening. I hanging onto the rocket ship. How's it feel? Well it's crazy. I mean it's great. You all have seen some of the growth along the way too, right? I mean we had our first forward event less than two years ago and you know about 500 plus plus non UI path and people then go year later. It was Miami USY. >>There's probably a lot. Cube I think was Miami right yet and a, and that was a great event, but that was more in the 13 1400 range. This one's almost 3000 and the most amazing part about it was we had 8% attrition from the registrations. Yeah. That's never seen that we're averaging 18% of 20% for all of our, most of our events worldwide. But 8% the commitment is unbelievable. Even 18 to to 20% is very good. I mean normally you'll see 25 to sometimes as high as 50% yeah. It just underscores the heat. >> Well I think what's also great, other stats that you might find interesting. So over 50% of the attendees here are exec. Our senior executives, like for the first time we actually had S you know, C level executive CHRs and CEOs on stage. Right. You could feel the interest level. Now of course we want RPA developers at events too, right? >>But this show really does speak, I think to the bigger value propositions and the bigger business transformation opportunity from RPA. And I mean, you've come so far where no one knew RPA two years ago to the CIO of Morgan Stanley on stage, just warning raving about it. That's, we've come a long way in two years. >> Well, and I saw a lot of the banks here hovering around, you know, knocking on your door so they, they know they are like heat seeking missiles, you know, so, but the growth has been amazing. I mean I think ARR in 2017 was what, 25 million at this time. Uh, at the end of 17 it was 43 and 43 and 25 and now you're at 12 times higher now 1212 X solve X growth, which is the fastest growing software company. I think in that we know from one to 100 we were, we did that in 21 months and all that. >>And we had banks who now we're not really counting anymore and we're kind of, you know, now focus more on customer expansion. Even though we hit 5,000 customers, which we started the year at 2050 ish. We just crossed 5,000. I mean, so the number of customers is great, but there's no question. This conference is focused on scaling, helping them grow at enterprise wide with, with, with RPA. So I think our focus will be in to shift a bit, you know, to really customer expansion. Uh, and that's a lot of what this announcements, the product announcements were about a lot of what the theme here is about. We had four dozen customers on, on stage, you know, the Uber's of the world, the Amazons of the world. It's all about how they've been scaling. So that's the story now. Well, you know, we do a lot of these events and I go back to some of the, uh, when the cube first started, companies like Tablo, Dallas Blunck great service. >>Now, I mean, these you can, and when you talk to customers, first of all, it's easy to get customers to come talk about RPA. Yeah. And they're, they're all saying the same thing. I mean, Jeanne younger said she's never been more excited in her career from security benefit. But the thing is, Bobby, it's, I feel like they're, they're really just getting started. Yeah. I mean most of the use cases that you see are again, automating mundane task. We had one which was the American fidelity, which is a really bringing in AI. Right. But they're really just getting started. It's like one to 3% penetration. So what are your thoughts on that to kind of land and expand, if you will? I think, you know, look, last year we announced our vision of a robot for every person. At that point we had SNBC on stage and they were the one behind it. >>And they are an amazing story. Now we have a dozen or so that are onstage talking about a robot for every person like st and others. And so, but that, that, that's a pretty, pretty, pretty bold vision I think. Look, I think it's important to look at it both ways. Um, there's huge gold and applying RPA to solve real problems. There's a big opportunity, enterprise wide, no question. We've got that. But I look New York Foundling was on stage yesterday. We have New York Foundling is a 150 year old associate. Our charity in New York focused on child welfare, started by three fishers of charity. They focused on infants. And anyway, it's an amazing firm. Just the passion that New York family had on stage with Daniel yesterday was amazing. But what they flew here because for once they found a technology that actually makes a huge difference for them and what in their mission. >>So their first RPA operation was they have 850 clinicians every week. They spend four hours a week moving their contact, uh, a new contact data associate with child child issues from system to system to spreadsheet and paper to system, right? They use RPA and they now say for a 200,000 hours a year. But more importantly, those clinicians spend those four hours every week with children not moving. So I'm still taking, I think Daniel had a bit of a tear in his eye, hearing them talk about it on stage, but I'm still taken by, by the, by the sheer massive opportunity for RPA in, in a particular to solve some really amazing things. Now on a mass scale, a company can drive, you know, 10, 15, 20% productivity by every employee having a robot. Yes, that's true on a mass scale. They can completely transform their business, your transform customer experience, transform the workplace on a mass scale. >>And that, that is, that's a sea level GFC level goal and that's a big deal. But I love the stories that are very real. Um, and, and I think those are important to still do plug some great tech for good story. Look, tech gives, you know, the whole Facebook stuff and the fake news got beat up and it had Benny come out recently say, Hey, it's, it's not just about increasing the value to shareholders, you know, it's about tech for good and doing other things affecting lifestyle's life changing. And Michael Dell is another one. Now I've, I've, I've kind of said tongue in cheek, you know, show me the CEO misses is four quarters in a row and see if that holds up. But nonetheless, you love to see successful companies giving back. It seems to be, it's part of your, well look I've been part of hardware companies and I met you all through a few of them and others they have good noble causes but it was hard to really connect the dots. >>Yes there CPS underneath a number of these things. But I think judging by the emotional connection that these customers have on stage, right and these are the Walmarts and Uber's and others in the world judging by the employee and job satisfaction that they talk about the benefits there. I just, I my career, I have not seen that kind of real direct impact from you know, from B2B software for example on the lives of people both everyday at work but also just solving the solving, you know, help accelerate human achievement. Right. And so many amazing ways. We had the CEO of the U N I T shared services group on stage yesterday and they have a real challenge with, you know, with the growth of refugees worldwide and he would express them and they can't hit keep up. They don't have the funding, which is, you know, with everybody and, and Trump and others trying to hold back money. >>But they had this massive charter for of good, the only way they get there is through digital. The new CEO, the new head of the U N is a technology engineer. He came in and said, the way we solve this is with templates, with technology. And they decided, they said on stage yesterday that RPA and RPA has the path to AI and the greater, the greater new technologies and that's how they're going to do it. And it's just a, it's a really, it's, I think it's, it feels really great. You know, it's funny too, one of the things we've been talking about this week is people might be somewhat surprised that there's so much head room left for automation because the boy, 50 years of tech, Kevin, we automated everything. That's the other, but, and Daniel put forth the premise last night, it actually, technology is created more process problems or inefficiencies. >>So it's almost like tech has created this new problem. Can tech get us out of the problem? Well, essentially you think about all the applications we use in our lives, right? Um, you know, although people do have, you know, a Salesforce stack and sometimes in this SAP, the reality is they have a mix of a bunch of systems and then we add Slack to it and we add other tools and we add all the tools alone, have some great value. But from a process perspective of how we work everyday, right? How a business user might work at a call center, they have to interact then. And the reality is they're often interacting with old systems too because moving them is not easy, right? So now you've got old systems, new systems and, and really the only way to do that is to put a layer on top of the systems of engagement and the systems of record, right? >>A layer on top that's easy to actually build an application that goes between all of these different, these different applications, outlook, Excel, legacy systems and salesforce.com and so on and so on and, and build an app that solves a real problem, have it have outcomes quickly. And this is why, Dave, we unveiled the vision here that we believe that automation is the application. And when you begin to think about I could solve a problem now without requiring a bunch of it engineers who already are maxed out, right? Uh, I can solve a problem that can directly impact the businesses or directly impact customers. And I can do that on top of these old technologies by just dragging and dropping and using a designer tool like studio or studio X in a business user can do that. That's, that's a game changer. I think what's amazing is when you go to talk to a CIO who says, I've been automating for 20 years, you know, take up the ROI. >>Once they realize this is different, the light bulb goes off. We call it the automation first mindset. A light bulb goes off and you realize, okay, this is a very different whole different way of creating value for, for an organization. I think about how people weigh the way that people work today. You're constantly context switching. You're in different systems. Like you said, Slack, you're getting texts and you want to be responsive. You want to be real time. I know Jeff Frick who was the GM of the cube has got two giant screens right on his desk. I myself, I always have 1520 tabs open if I go, Oh you got so many tabs on my, yeah. Cause I'm constantly context switching, pulling things out of email, going back and forth and so and so. I'm starting to grok this notion of the automation is the app. >>At first I thought, okay, it's the killer app, but it's not about stitching things together with through API APIs. It's really about bringing an automation perspective across the organization. We heard it from Pepsi yesterday. Yeah, right. Sort of the fabric, the automation fabric throughout the organization. Now that's aspirational for most companies today, but that really is the vision. Well, I think you had Layla from Coca-Cola also on, right. And her V their vision there and they actually took the CDO role of the CIO and put them together. And they're realizing now that that transformation is driven by this new way of thinking. Yeah, I think, you know, look, we introduced a whole set of new brand new products and capabilities around scaling around helping build these applications quicker. I, I think, you know, fast forward one year from now, the, you know, the vision we outlined will be very obvious the way people interact with, you know, via UI path to build applications, assault come, the speed to the operate will be transformational and, and so, you know, and you see this conference hear me walk around. >>I mean you saw last year in the year before you see the year before, but it's, it's a whole, the speed at which we're evolving here, I think it's unprecedented. And so I'll talk a little bit about the market for has Crigler killer was awesome this morning. He really knows his stuff now. Last year I saw some data from him and said the market by 2020 4 billion, and I said, no way. It's going to be much larger than that. Gonna be 10 billion by 2020 I did Dave Volante fork, Becca napkin by old IDC day forecast. Now what he, what he showed today is data. It actually was 10 billion by 2020 because he was including services, the services, which is what I was including in my number as well, but the of it, which was so good for him now, but the only thing is he had this kind of linear growth and that's not how these rocket ship Marcus grow. >>They're more like an old guy for an S curve. You're going to get some steep part now, so I'd love to see like a longer term forecast because that it feels like that's how this is going to evolve. Right now it's like you've seated the base and you can just feel the momentum building and then I would expect you're going to see massive steep sort of exponential growth. Steeper. There may be, you know, nonlinear because that's how these markets go >> to come from the expansion potential, right? And none of our customers are more than 1% audit automated from an RPA perspective. So that shows you the massive opportunity. But back to the market site, data size, Craig and I and the other analysts, we talk often about this. I think the Tam views are very low and you'll look at our market share, let's just get some real data out there, right? >>Our market share in 2017 was 5% let's use Craig's linear data for now. You know, our market share this year is over 20% our market share applying, and I don't want to give the exact numbers as you don't provide guidance anymore, is substantially we're substantially gaining share now. I believe that's the reality of the market. I think because we know blue prisms numbers, we go four times faster than the every quarter automation. The world won't share their numbers. But you know, I can make some guesses, but either way I think, you know, I think we're gaining share on them significantly. I think, you know, Craig's not gonna want us to be 50% of the market two years, he's just not. And so he's going to have to figure out how to identify how to think. That brought more broadly about, about that market trend. He talked about it on stage today about how does he calculate the AI impact and the other pieces now the process mining now that now that we are integrating process mining into RPA, right? >>It's strategic component of that. How does that also involve the market? So I think you have both the expansion and the plot product portfolio, which drives it. And then you have the fact that customers are going to add more automations at faster pace and more robots and that's where the expansion really kicks in. And we often say, you know, look as a, as a, as a, as a company that, you know, one day we'll be public company, our ARR numbers. Very important. We do openly transparently share that. But you know, the other big metric will be, you know, dollar based net expansion rate that shows really how customers are expanding. I think that, I know it, our numbers, we haven't shared it yet. I know all the SAS companies, the top 10 I can tell you, you know we're higher than all of them. >>The market projections are low. And I think he knows it well. >> Speaking of Tam, and when we, I saw this with, with service now, now service now the core was it right? So the, the ROI was not as obvious with, with, with you guys, you're touching business process. And so, so in David Flory are way, way back, did an analysis of service and now he said, wow, the Tam is way being way under counted by everybody. That wall street analyst Gardner, it feels like the same here because there are so many adjacencies and just talk to the customers and you're seeing that the Tam could be enormous, much bigger than the whatever 16 billion a Daniel show, the other Danielson tangles, the guy's balls. He said, Oh that's 16 billion. That's you. I pass this data. And you know, we laugh, but I'm, I'm like listening. Say I wonder if he's serious cause this guy thinks big. >>I mean, who would've thought that he'd be at this point by now? And you're just getting started? Well, I think, you know, one thing I think is, you know, we're, we're, you know, we were a little bit kind of over a little less humble when we talked about things like valuation over the last few years. We were trying to show this market's real, you know, we want to now focus more on outcomes and things get a little less from around those numbers. And I think that shows the evolution of a company's maturity, um, that we, I think we're going through right now. Uh, you know, the outcomes of, you know, Walmart on stage saying, you know, their first robot that was, this was, this was two years ago, delivered 360,000 hours of capacity for them in, in, in, in, in HR, right? That, you know, I think those, that's where we're gonna be focused because the reality is if we can deliver these big outcomes and continue them and we can go company-wide deliver on the robot for every, every, every, every person, then you know, the numbers follow along with it. >>Well we saw some M and a this week as well, which again leads me to the larger Tam cause we had PD on, um, with Rudy and you can start to see how, okay now we're going to actually move into that vision that the guy from PepsiCo laid out this, this fabric of this automation fabric across the organization. So M and a is, is a part of that as well. That starts to open up new Tam. Opportunity does. And I think, you know, a process mind is a great example of a market that is pretty well known in Europe, not so much in the U S um, and there are really only a few players in that, in that market today. Look, we're going to do what we did in RPA. We're going to do the same thing. You're process mining. We're going to just say anything we're doing in it, not as democratization, you'll our strategy will be to go mass market with these technologies, make it very easy for accessibility for every single person in the case of process mining, every business analyst to be able to mind their processes for them and, and ultimately that flows through to drive faster implementations and then faster, faster outcomes. >>I think our approach, again, our approach to the business users, our approach to democratization, um, you know it's very different than our competitors. A lot of these low code companies, I won't name a number cause I don't remember our partners here at our conference. They're IT-focused their services heavy and, and you know, their growth rates I'll be at okay are 30% year over year in this market. That shouldn't be the case at all. I mean we're a 200 plus a year. We are still and we've got big numbers and we have a whole different approach to the market. I don't think people have figured it out yet, Dave. Exactly, exactly. The strategy behind which is, which is when you have business users, subject matter experts and citizen developers that can access our technology and build automations quickly and deliver value proof for their company. And you do that in mass scale. >>Right. And then you will now allow with our apps for your end users, I get a call center to engage with a robot as part of their daily operation that none of the other it vendors who are all kind of conventional thinking and that's not, our models are very different, which I think shows in our numbers and and, and the growth rates. Yeah. Well you bet on simplicity early on. In fact, when you join you iPad, you challenged me so you have some of your Wiki bond analysts go out. I remember head download our stuff and then try to download the competitors and they'll tell us, you know how easy it as well we were able to download UI path. We, we built some simple automations. We couldn't get ahold of the other other, other companies products we tried. We were told we'll go to the reseller or how much did you have to spend and okay so you bet on simplicity, which was interesting because Daniel last night kind of admitted, look, he tracked the audience. >>He said thank you for taking a chance on us because frankly a couple of years ago this wasn't fully baked right and and so, so I want to talk about last, the last topic is sort of one of the things Craig talked about was consolidation and I've been saying that all week and said this, this market is going to consolidate. You guys are a leader now you've got to get escape velocity cause the leader makes a lot of money and becomes, gets big. The number two does. Okay, number three man, everybody else and the big guys are starting to jump in as well. You saw SAP, you know, makes an announcement and you guys are specialists and so your thoughts on hitting escape velocity, I wouldn't say you're quite there yet. I want to see more on the ecosystem. There's maybe, who knows, maybe there's an IPO coming. I've predicted that there is, but your thoughts on achieving escape velocity and some of the metrics around there, whether it's customer adoption penetration, what are your thoughts? >>Yeah, I mean we definitely don't have a timetable on an IPO, but we have investors, public investors and VCs that at some point are going to want, this is the reality of how, of how it works. Right. Um, you know, I think the, uh, you know, I think the numbers to focus right now are on around, you know, customer outcomes. I think the ecosystem is a good one. Right? You know, we have, I'd say the biggest ecosystem for us to date has been the SAP ecosystem. When we look at our advisory board members, for others, that's really where, where the action is. Supply chain management, ERP, you know, certainly CRM and others, we don't have a view that, so our competitors have, but we have chosen not to take money from our, from ecosystem companies because we don't, our customers here are building processes, all the automation across ecosystems. >>Right? So you know, we don't want to go bet on say just one like Salesforce or Workday. We want to help them across all the ecosystem now. So I think it's a little bit of a different strategy there. Look, I think the interesting thing is the SAP is the world. They bought a small company in France called contexture. They're trying to do this themselves. Microsoft, Microsoft didn't in Mark Benioff and Salesforce are asked on every earnings call now what are you doing for RPA? So they've got pressure. So maybe they invest in one of our competitors or maybe they, you'll take flow in Microsoft and expanded. I think we can't move fast enough because you know, I don't know if Microsoft has, I mean they're a great sponsor by the way. So I don't want to only be careful we swept with what I say. But you know, strategically speaking, these larger companies operate in 18 months, 12 1824 months kind of planning cycles. >>If he did that, he will never keep up with us. There's no one at any of our traditional large enterprise software companies that ever would have bet that we would come out and say that the best way to build applications right to solve problems will be through RPA. Either there'll be a layer on top of all their technologies that makes it easier than ever for business users to build applications and solve problems, that's going to scare them to death. Why? Because you don't have to move all your legacy systems anymore. Yes, you've got tons of databases, but guess what? Don't worry about it. Leave him alone. Stop spending money on ridiculous upgrades right now. Just build a new layer and I'm telling you I there. As they figured this out, they're going to keep looking back and say, Oh my God, why didn't we know? >>Why did we know there's it looked I hopefully we could all partner. We're going to try to go down that route, but there's something much bigger going on here and they haven't figured it out. Well, the SAP data is very interesting to me that I'm starting to connect the dots. I just did a piece on my breaking analysis and SAP, they thank you. They, they've acquired 31 companies over the last nine years, right? And they've not bit the bullet on integration the way Oracle had to with fusion. Right? And so as a result, there's this, they say throw everything into HANA. It's a memory that's not going to work from an integration standpoint, right? Automation is actually a way to connect, you know, the glue across all those disparate systems, right? And so that makes a lot of sense that you're having success inside SAP and there's no reason that can't continue. >>Why there's, you know, there's a number of major kind of trends we've outlined here. One of, uh, we call human in the loop. And you know, today, you know, when each, when an unattended robot could actually stop a process and instead of sending the exception to a, an it person who monitoring, say, orchestrator actually go to an inbox, a task and box of that business user in a call center or wherever, and that robot can go do something else because it's so, so efficient and productive. But once that human has to solve that problem, right, that robot or a robot will take that back on and keep going. This human and robot interaction, it doesn't exist today and we know we're rolling that out in our UI path apps. I think you know that that's kind of mind blowing and then when you add a, I can't go too far into our roadmap and strategy or when you added the app programming layer and you add data science, that's a little bit of a hint into where we're going because we're open and transparent. >>Our data science connection, it's, it's this platform here, this kind of, I'd like to still call it all RPA. I think that that's a good thing, but the reality is this platform does Tam. What it can do is nothing like it was a year ago and it won't be like where it is today. A year from now you've got the tiger by the tail, Bobby, you got work to do, but congratulations on all the success. It's really been great to be able to document this and cover it, so thanks for coming on the cube. Thank you. All right. Thank you for watching everybody back with our next guest. Right after this short break, you're watching the cube live from UI path forward three from Bellagio in Vegas right back.

>> Live from San Francisco, California, it's the theCUBE, covering the IBM Chief Data Officer Summit, brought to you by IBM. >> Welcome back to San Francisco everybody. You're watching theCUBE, the leader in live tech coverage. We go out to the events, we extract the signal from the noise and we're here at the IBM Chief Data Officer Summit, 10th anniversary. Seth Dobrin is here, he's the Vice President and Chief Data Officer of the IBM Analytics Group. Seth, always a pleasure to have you on. Good to see you again. >> Yeah, thanks for having me back Dave. >> You're very welcome. So I love these events you get a chance to interact with chief data officers, guys like yourself. We've been talking a lot today about IBM's internal transformation, how IBM itself is operationalizing AI and maybe we can talk about that, but I'm most interested in how you're pointing that at customers. What have you learned from your internal experiences and what are you bringing to customers? >> Yeah, so, you know, I was hired at IBM to lead part of our internal transformation, so I spent a lot of time doing that. >> Right. >> I've also, you know, when I came over to IBM I had just left Monsanto where I led part of their transformation. So I spent the better part of the first year or so at IBM not only focusing on our internal efforts, but helping our clients transform. And out of that I found that many of our clients needed help and guidance on how to do this. And so I started a team we call, The Data Science an AI Elite Team, and really what we do is we sit down with clients, we share not only our experience, but the methodology that we use internally at IBM so leveraging things like design thinking, DevOps, Agile, and how you implement that in the context of data science and AI. >> I've got a question, so Monsanto, obviously completely different business than IBM-- >> Yeah. >> But when we talk about digital transformation and then talk about the difference between a business and a digital business, it comes down to the data. And you've seen a lot of examples where you see companies traversing industries which never used to happen before. You know, Apple getting into music, there are many, many examples, and the theory is, well, it's 'cause it's data. So when you think about your experiences of a completely different industry bringing now the expertise to IBM, were there similarities that you're able to draw upon, or was it a completely different experience? >> No, I think there's tons of similarities which is, which is part of why I was excited about this and I think IBM was excited to have me. >> Because the chances for success were quite high in your mind? >> Yeah, yeah, because the chance for success were quite high, and also, you know, if you think about it there's on the, how you implement, how you execute, the differences are really cultural more than they're anything to do with the business, right? So it's, the whole role of a Chief Data Officer, or Chief Digital Officer, or a Chief Analytics Officer, is to drive fundamental change in the business, right? So it's how do you manage that cultural change, how do you build bridges, how do you make people, how do you make people a little uncomfortable, but at the same time get them excited about how to leverage things like data, and analytics, and AI, to change how they do business. And really this concept of a digital transformation is about moving away from traditional products and services, more towards outcome-based services and not selling things, but selling, as a Service, right? And it's the same whether it's IBM, you know, moving away from fully transactional to Cloud and subscription-based offerings. Or it's a bank reimagining how they interact with their customers, or it's oil and gas company, or it's a company like Monsanto really thinking about how do we provide outcomes. >> But how do you make sure that every, as a Service, is not a snowflake and it can scale so that you can actually, you know, make it a business? >> So underneath the, as a Service, is a few things. One is, data, one is, machine learning and AI, the other is really understanding your customer, right, because truly digital companies do everything through the eyes of their customer and so every company has many, many versions of their customer until they go through an exercise of creating a single version, right, a customer or a Client 360, if you will, and we went through that exercise at IBM. And those are all very consistent things, right? They're all pieces that kind of happen the same way in every company regardless of the industry and then you get into understanding what the desires of your customer are to do business with you differently. >> So you were talking before about the Chief Digital Officer, a Chief Data Officer, Chief Analytics Officer, as a change agent making people feel a little bit uncomfortable, explore that a little bit what's that, asking them questions that intuitively they, they know they need to have the answer to, but they don't through data? What did you mean by that? >> Yeah so here's the conversations that usually happen, right? You go and you talk to you peers in the organization and you start having conversations with them about what decisions are they trying to make, right? And you're the Chief Data Officer, you're responsible for that, and inevitably the conversation goes something like this, and I'm going to paraphrase. Give me the data I need to support my preconceived notions. >> (laughing) Yeah. >> Right? >> Right. >> And that's what they want to (voice covers voice). >> Here's the answer give me the data that-- >> That's right. So I want a Dashboard that helps me support this. And the uncomfortableness comes in a couple of things in that. It's getting them to let go of that and allow the data to provide some inkling of things that they didn't know were going on, that's one piece. The other is, then you start leveraging machine learning, or AI, to actually help start driving some decisions, so limiting the scope from infinity down to two or three things and surfacing those two or three things and telling people in your business your choices are one of these three things, right? That starts to make people feel uncomfortable and really is a challenge for that cultural change getting people used to trusting the machine, or in some instances even, trusting the machine to make the decision for you, or part of the decision for you. >> That's got to be one of the biggest cultural challenges because you've got somebody who's, let's say they run a big business, it's a profitable business, it's the engine of cashflow at the company, and you're saying, well, that's not what the data says. And you're, say okay, here's a future path-- >> Yeah. >> For success, but it's going to be disruptive, there's going to be a change and I can see people not wanting to go there. >> Yeah, and if you look at, to the point about, even businesses that are making the most money, or parts of a business that are making the most money, if you look at what the business journals say you start leveraging data and AI, you get double-digit increases in your productivity, in your, you know, in differentiation from your competitors. That happens inside of businesses too. So the conversation even with the most profitable parts of the business, or highly, contributing the most revenue is really what we could do better, right? You could get better margins on this revenue you're driving, you could, you know, that's the whole point is to get better leveraging data and AI to increase your margins, increase your revenue, all through data and AI. And then things like moving to, as a Service, from single point to transaction, that's a whole different business model and that leads from once every two or three or five years, getting revenue, to you get revenue every month, right? That's highly profitable for companies because you don't have to go in and send your sales force in every time to sell something, they buy something once, and they continue to pay as long as you keep 'em happy. >> But I can see that scaring people because if the incentives don't shift to go from a, you know, pay all up front, right, there's so many parts of the organization that have to align with that in order for that culture to actually occur. So can you give some examples of how you've, I mean obviously you ran through that at IBM, you saw-- >> Yeah. >> I'm sure a lot of that, got a lot of learnings and then took that to clients. Maybe some examples of client successes that you've had, or even not so successes that you've learned from. >> Yeah, so in terms of client success, I think many of our clients are just beginning this journey, certainly the ones I work with are beginning their journey so it's hard for me to say, client X has successfully done this. But I can certainly talk about how we've gone in, and some of the use cases we've done-- >> Great. >> With certain clients to think about how they transformed their business. So maybe the biggest bang for the buck one is in the oil and gas industry. So ExxonMobile was on stage with me at, Think, talking about-- >> Great. >> Some of the work that we've done with them in their upstream business, right? So every time they drop a well it costs them not thousands of dollars, but hundreds of millions of dollars. And in the oil and gas industry you're talking massive data, right, tens or hundreds of petabytes of data that constantly changes. And no one in that industry really had a data platform that could handle this dynamically. And it takes them months to get, to even start to be able to make a decision. So they really want us to help them figure out, well, how do we build a data platform on this massive scale that enables us to be able to make decisions more rapidly? And so the aim was really to cut this down from 90 days to less than a month. And through leveraging some of our tools, as well as some open-source technology, and teaching them new ways of working, we were able to lay down this foundation. Now this is before, we haven't even started thinking about helping them with AI, oil and gas industry has been doing this type of thing for decades, but they really were struggling with this platform. So that's a big success where, at least for the pilot, which was a small subset of their fields, we were able to help them reduce that timeframe by a lot to be able to start making a decision. >> So an example of a decision might be where to drill next? >> That's exactly the decision they're trying to make. >> Because for years, in that industry, it was boop, oh, no oil, boop, oh, no oil. >> Yeah, well. >> And they got more sophisticated, they started to use data, but I think what you're saying is, the time it took for that analysis was quite long. >> So the time it took to even overlay things like seismic data, topography data, what's happened in wells, and core as they've drilled around that, was really protracted just to pull the data together, right? And then once they got the data together there were some really, really smart people looking at it going, well, my experience says here, and it was driven by the data, but it was not driven by an algorithm. >> A little bit of art. >> True, a lot of art, right, and it still is. So now they want some AI, or some machine learning, to help guide those geophysicists to help determine where, based on the data, they should be dropping wells. And these are hundred million and billion dollar decisions they're making so it's really about how do we help them. >> And that's just one example, I mean-- >> Yeah. >> Every industry has it's own use cases, or-- >> Yeah, and so that's on the front end, right, about the data foundation, and then if you go to a company that was really advanced in leveraging analytics, or machine learning, JPMorgan Chase, in their, they have a division, and also they were on stage with me at, Think, that they had, basically everything is driven by a model, so they give traders a series of models and they make decisions. And now they need to monitor those models, those hundreds of models they have for misuse of those models, right? And so they needed to build a series of models to manage, to monitor their models. >> Right. >> And this was a tremendous deep-learning use case and they had just bought a power AI box from us so they wanted to start leveraging GPUs. And we really helped them figure out how do you navigate and what's the difference between building a model leveraging GPUs, compared to CPUs? How do you use it to accelerate the output, and again, this was really a cost-avoidance play because if people misuse these models they can get in a lot of trouble. But they also need to make these decisions very quickly because a trader goes to make a trade they need to make a decision, was this used properly or not before that trade is kicked off and milliseconds make a difference in the stock market so they needed a model. And one of the things about, you know, when you start leveraging GPUs and deep learning is sometimes you need these GPUs to do training and sometimes you need 'em to do training and scoring. And this was a case where you need to also build a pipeline that can leverage the GPUs for scoring as well which is actually quite complicated and not as straight forward as you might think. In near real time, in real time. >> Pretty close to real time. >> You can't get much more real time then those things, potentially to stop a trade before it occurs to protect the firm. >> Yeah. >> Right, or RELug it. >> Yeah, and don't quote, I think this is right, I think they actually don't do trades until it's confirmed and so-- >> Right. >> Or that's the desire as to not (voice covers voice). >> Well, and then now you're in a competitive situation where, you know. >> Yeah, I mean people put these trading floors as close to the stock exchange as they can-- >> Physically. >> Physically to (voice covers voice)-- >> To the speed of light right? >> Right, so every millisecond counts. >> Yeah, read Flash Boys-- >> Right, yeah. >> So, what's the biggest challenge you're finding, both at IBM and in your clients, in terms of operationalizing AI. Is it technology? Is it culture? Is it process? Is it-- >> Yeah, so culture is always hard, but I think as we start getting to really think about integrating AI and data into our operations, right? As you look at what software development did with this whole concept of DevOps, right, and really rapidly iterating, but getting things into a production-ready pipeline, looking at continuous integration, continuous development, what does that mean for data and AI? And these concept of DataOps and AIOps, right? And I think DataOps is very similar to DevOps in that things don't change that rapidly, right? You build your data pipeline, you build your data assets, you integrate them. They may change on the weeks, or months timeframe, but they're not changing on the hours, or days timeframe. As you get into some of these AI models some of them need to be retrained within a day, right, because the data changes, they fall out of parameters, or the parameters are very narrow and you need to keep 'em in there, what does that mean? How do you integrate this for your, into your CI/CD pipeline? How do you know when you need to do regression testing on the whole thing again? Does your data science and AI pipeline even allow for you to integrate into your current CI/CD pipeline? So this is actually an IBM-wide effort that my team is leading to start thinking about, how do we incorporate what we're doing into people's CI/CD pipeline so we can enable AIOps, if you will, or MLOps, and really, really IBM is the only company that's positioned to do that for so many reasons. One is, we're the only one with an end-to-end toolchain. So we do everything from data, feature development, feature engineering, generating models, whether selecting models, whether it's auto AI, or hand coding or visual modeling into things like trust and transparency. And so we're the only one with that entire toolchain. Secondly, we've got IBM research, we've got decades of industry experience, we've got our IBM Services Organization, all of us have been tackling with this with large enterprises so we're uniquely positioned to really be able to tackle this in a very enterprised-grade manner. >> Well, and the leverage that you can get within IBM and for your customers. >> And leveraging our clients, right? >> It's off the charts. >> We have six clients that are our most advanced clients that are working with us on this so it's not just us in a box, it's us with our clients working on this. >> So what are you hoping to have happen today? We're just about to get started with the keynotes. >> Yeah. >> We're going to take a break and then come back after the keynotes and we've got some great guests, but what are you hoping to get out of today? >> Yeah, so I've been with IBM for 2 1/2 years and I, and this is my eighth CEO Summit, so I've been to many more of these than I've been at IBM. And I went to these religiously before I joined IBM really for two reasons. One, there's no sales pitch, right, it's not a trade show. The second is it's the only place where I get the opportunity to listen to my peers and really have open and candid conversations about the challenges they're facing and how they're addressing them and really giving me insights into what other industries are doing and being able to benchmark me and my organization against the leading edge of what's going on in this space. >> I love it and that's why I love coming to these events. It's practitioners talking to practitioners. Seth Dobrin thanks so much for coming to theCUBE. >> Yeah, thanks always, Dave. >> Always a pleasure. All right, keep it right there everybody we'll be right back right after this short break. You're watching, theCUBE, live from San Francisco. Be right back.

>> Announcer: Live from Las Vegas, it's theCUBE! Covering Informatica World 2018. Brought to you by Informatica. >> Hey, welcome back everyone, it's theCUBE's exclusive coverage here at the Venetian, Live in Las Vegas CUBE coverage. I'm John Furrier the co-host of theCUBE with Peter Burris my co-host for the next two days of wall-to-wall coverage. Our next guest is Andrew Joss, who's the Head of Solutions and Data Governance for Europe, Middle East and Africa, and Latin America for Informatica. Great to have you on, thanks for joining us. >> Thank you! >> I could not stop waiting for this question because your anemia, Europe, Middle East and Europe. GDPR is kicking in this Friday. >> Andrew: Absolutely. >> So we're in May 2018. The big release of the law that kicks into place for GDPR in effect. Two things, what's the mood and then what does it mean? I mean, it's a shot across the bow of the industry. But we know what it means for people but like, what's the impact of this, what's going on? >> So I think we're seeing it at a couple of different levels. I think at a very individual level. I think the awareness of what GDPR potentially means for people, I think we're starting to feel that as individuals, in EMEA. We're seeing increasingly organizations reaching out to us, you know, we want permission to use your information or consensus in coding GDPR. You're customers of ours, here's our new privacy policy. We see lots of this and it's happening from lots of different organizations that we work with. So I think people are starting to see it and feel it, are starting to feel like it's real now, not just something we've been talking about for a long period of time. But I think also in terms of potentially what the impact of this will be, that I think organizations are starting to look at some of the major tenants that sit underneath GDPR, how are they going to address those, and what does that mean for the data subject? Like people like me, for example, I'm a data subject. What does it all mean for me? And I think they realize-- >> John: As an individual you have data rights. >> Exactly. Absolutely. >> The concern I have, I had a big rant on Facebook, it was good conversation, but here's the thing. It's like, you know, when laws came out, like it's so hard being so obviously, when your public, you're ready to go public, you have all the infrastructure to comply with all those regulations, a lot of people aren't prepared for GDPR because, where their, they might not even know where their data is, >> Absolutely. >> what's the format, what's the schema, they don't have mechanisms in place, 'cause there's IT legacy involved. (laughing) I mean GDPR, great on paper, everyone's got their own rights it sounds good, you know, but when you have to get under the hood and saying, hey Enterprise, you know that stuff you've been putting in drives, and the storage administrator quit 10 years ago and, you don't know what's going on and, guess what? You're now liable. >> Andrew: Major issue. >> People were scared. So, this is a problem, how can someone get ready, 'cause just like when people go public they have to be ready hire all these, process stuff, what do you guys see that, I mean Informatica has some solutions, I'm sure, but, what's the client relationship like for you guys as you talk to customers? >> I think it kind of varies, some industries seem to be a little bit more advanced in their thinking so, regulated industries for example, they're kind of used to regulation and compliance, they kind of get a lot of these things, so I think some of those have found this process a little bit easier. I think some industries, this is generally quite new, some of the ideas, some of the practices that come with GDPR I think are also quite new, for some of these industries. >> Internet companies, fast and loose, if you're fast and loose you're going to be doing a lot of work. >> But ultimately, when you think about, a lot of what GDPR brings to the data subject, that people like me and my colleagues, then, a lot of that then is about these rights, and the ability for us to be able to actually take back more control of our data, 'cause fundamentally it is our data. So if we have more control, then it's about how organizations help us with those rights, and help us move along that journey of what we can now do with our data, and what GDPR gives us. >> And just to be clear too, we reported on this in depth with Wikibon, SiliconANGLE, and theCUBE, GDPR it's been clear it's going to be a process. They're going to look for compliance, they're not lookin' for everyone to be like, they want to see directional progress, right, so it's not like the hammer's going to come down tomorrow, but people now, data subjects now can bring claims against companies, so. >> Actually John, I think it's, you're right but, we have a client, the Chief Privacy Officer of one of our clients, made the observation that had the Equifax breach occurred after Friday in Europe, it would have cost that company 160 billion dollars, My guess is what's going to happen is, they're going to look for that direction unless a company has a serious problem, then they're going to use GDPR to levy fines, and generate some, and remunerate back to the people affected, some real relief would you agree with that? >> Actually I see GDPR in a slightly different way, maybe that's just because it feels quite personal to me, because I feel it's something that's going to be a part of my life. And actually I think it's about organizations really respecting my data, and therefore respecting me. So, you know, when we talk about fines, yes I'm sure there's probably going to be some of those. A lot of the customers I talk to are actually, they're worried about reputational damage. You know, what's going to happen to their brand, what's going to happen to their image if something happens? And that, for many organizations, is far more serious and significant than any kind of fine potentially may be, so it's actually-- >> And there's a mega trend goin' on, you're seeing with blockchain and decentralized applications where people who create the value should capture it, hence the personal relationship to your data, and we all look at Facebook and say, hey I signed up for a free app so I could meet my high school friends and see them, do some things on Facebook, but I didn't sign a contract to give you my data to, have the election be thrown in the US, (laughing) so it's kind of like, wait a minute, what're you doin' with my data? >> Talk about blockchain and immutability of the data, if you have, does GDPR make it more difficult to use technologies like blockchain? >> I think organizations just have to look at GDPR and say, you know, it's a principles-based regulation, so it's not going to tell you, you know, the details of how you should do things, but it's tryna take you on a journey around kind of how you can then start to bring a lot more respect to the data subject, because of the data that you're managing and processing for them. The organizations are going to have to look at that and say, how do we take all of this, and how do we start to move it into our environment, whether it be blockchain, or any other technology, how does it apply, and do we have to make some changes, do we have to think tactically or strategically, I think organizations are going to have to look at this and say what does this mean longer term? Because I don't think anyone really knows right now. >> Well I want to get your thoughts on this, as Head of Solutions and Governance we chatted with the Deloitte guys came on earlier, and they kind of laid out, I mean, I'm just paraphrasing the playbook, data engineering, data governance, data enablement, so they're kind of looking at it, you know, as kind of a playbook. Got to do the engineering work to figure out where the data is, throw the catalog in there, MDM, there's a variety of solutions out there, and tools for other things, and then the governance piece is super critical. Then the enablement is where, then you're in an ideal state for a GDPR, or wherever where, everything's foundationally built and engineered and governed, ideally you could have things like consensus, you could have some security, do you see it the same way, and how are you guys at Informatica talking to customers? Does that jive with some of the things that you guys--? >> Yeah, it does, it resonates quite well, so, I think because it's a principles based regulation then, actually that has some potentially quite interesting and beneficial impacts for some of our customers, so a lot of our customers are going through some kind of transformation, mostly digital transformation, and you think about the principles that GDPR gives you, I look at that and I think, but actually some of these are just good data management practices and principles, it happens to be around personal data for GDPR right now but those principles are just valued for probably kind of any kind of data. So if you're on a digital transformation journey, with all the change and with all the opportunity that brings actually these practices and principles for GDPR they should be helping drive things like your digital transformation, and for a lot of our customers change is the only constant they've got. So actually managing all this, whilst everything is changing around you, it's tough for a lot of them. >> Opensource has been a big driver in our industry, we've seen some there, open always beats closed, and having all the open data's key, have you seen any GDPR impact around being open, is there like, opensource groups that are out there helping companies, you guys obviously can get called on, but what dose the customer do, I mean like, Peter and I say hey, maybe we're impacted by GDPR, who do we call? Is there an opensource community that can help with, you know, terms of service, if they want to go down the right roads of data hygiene or data setup cataloging, what do they do, I mean what's the? (laughing) I mean it's the shock, and people going well we're not really kind of where we should be, what do they do? With any movement? >> Yeah, I've seen quite a bit of movement, so, I think probably one of the biggest single challenges that I've seen is, for many organize--many of our customers, they'll be saying to us, okay, so what should we do in this circumstance? And actually that's really tough for us to answer, because it's a principles-based regulation than actually somebody needs to look at that, that's probably the legal or the privacy teams, say well what does that mean for us? How do we take that, and then come up with a set of requirements that says this is what we need to do for our organization, in our markets, in our territory, for example? So there's probably no one-size-fits-all answer, so, there's legal aspects to this, there's privacy aspects, data management, risk, compliance, opensource groups they can give opinion, but it's nothing more than that. >> And they might not have the talent internally to actually understand culturally what the principle is, so they got to call in the consultant, so our integrators, Deloitte-- >> Exactly, exactly. >> But fundamentally, it seems that one of the things GDPR is going to do, is it's going to force companies, force enterprises, to be very explicit and declare what attributes of that personal data they make money with. And be very, and effectively open that up, and be much more, as you said, what'd you call it private subject or? >> Data subjects. >> Data subjects, they're going to have to be more explicit declaring to data subjects, in simple terms, how they're making money off of data, or how they're avoiding that problem. >> Yeah, I think organizations, and I think about some of the privacy notices I've received, recently for example, I think, what organizations are doing, I think they're trying to explain to people, this is the kind of data we have, these are types things that we have to do with it, sometimes it's maybe regulatory, but actually other times it's about, these other types of business activities, so they're starting to be a lot more transparent, I think, in what they're doing with the data. Is it transparent enough? I guess time will tell. And the reaction of data subjects will also be the indicator whether people think that's acceptable or not, I don't think we know yet, it's early days, but actually that change, I think over time what we'll start to see is organizations are going to be looking at the way that they manage data, I think transparency, I think will be a huge topic for a lot of industries, I think that the notion of kind of having a respect for people and their data, and how it then leads to trust. So lots of industries have kind of lost the trust of people around the ability to manage their data, so how do they get that back? Well potentially GDPR might be a way of helping people access to that. >> Many of these guys, they got to get their act together and build up a quality data policy around it. Okay, final question for ya, I know we're tight on time, but I want to get it out there, what do you guys have for solutions for customers, what are you guys offering, specifically for products, that helps them with the compliance, any gap analysis, I mean what do you guys do for customers, what's the solution? >> It's, it's in a couple of different areas, so I'm going to tackle a couple quite specific things, then something slightly a little bit broader, so, organizations, I think you were mentioning earlier, just kind of knowing what their data is. Well actually we have some fantastic technology to go and discover, you know, or to make the discovery of that data, that's great for organizations 'cause that, today, a lot of them are doing it by hand, they're doing it manually, so discovery of data really important, so we have technology in that space. The ability to go and mask an archive, get rid of data, if you don't have a legitimate reason for having data, then why have you got it? So technology to help you, you know, get rid of that data. Other types of technology about being able to connect what you have in terms of your physical data assets, to actually your interpretation of what GDPR means to you and your business, that's fantastic, the ability to connect those together, that's our governance environment, and then technologies around, kind of, building that view of the data subject, so we can then enact all these rights that people like me have now got, but also then too, can sense that we may potentially have to give, how do you associate that with all the complexity of the data? So we have technologies in our massive data management space to do that. But I think probably the one thing that I hear fairly consistently for customers, it's not necessarily about those isolated kind of views, of the technology and how it solves specific problems, I think they're looking at it quite wholistically, and they're looking at solutions that can really automate a lot of this, as much as possible, they're looking for solutions that scale, some of these are very large, complex organizations, it's not small amounts of data, in cases, some cases, it's huge amounts of data, so they're tying to cope with this scale, but they're also looking to solve some very specific problems. So I think there's kind of a combination of things, which I think plays really well, through Informatica's core strengths. >> And it also creates awareness for companies to put data as a strategic centerpiece, not as a side thing, bring it right to the front and center. >> Andrew, thanks for sharing the insight on theCUBE, appreciate your time. theCUBE, live coverage here in Las Vegas at the Venetian, this is exclusive coverage of Informatica World 2018, I'm John Furrier with Peter Burris, stay with us for more, here on day one of two days of coverage. We'll be right back, after this short break. (bubbly music)