foreign welcome back to re invent 2022 we're wrapping up four days well one evening and three solid days wall-to-wall of cube coverage I'm Dave vellante John furrier's birthday is today he's on a plane to London to go see his nephew get married his his great Sister Janet awesome family the furriers uh spanning the globe and uh and John I know you wanted to be here you're watching in Newark or you were waiting to uh to get in the plane so all the best to you happy birthday one year the Amazon PR people brought a cake out to celebrate John's birthday because he's always here at AWS re invented his birthday so I'm really pleased to have two really special guests uh former Cube host Cube Alum great wikibon contributor Stu miniman now with red hat still good to see you again great to be here Dave yeah I was here for that cake uh the twitterverse uh was uh really helping to celebrate John's birthday today and uh you know always great to be here with you and then with this you know Awesome event this week and friend of the cube of many time Cube often Cube contributor as here's a cube analyst this week as his own consultancy sarbj johal great to see you thanks for coming on good to see you Dave uh great to see you stu I'm always happy to participate in these discussions and um I enjoy the discussion every time so this is kind of cool because you know usually the last day is a getaway day and this is a getaway day but this place is still packed I mean it's I mean yeah it's definitely lighter you can at least walk and not get slammed but I subjit I'm going to start with you I I wanted to have you as the the tail end here because cause you participated in the analyst sessions you've been watching this event from from the first moment and now you've got four days of the Kool-Aid injection but you're also talking to customers developers Partners the ecosystem where do you want to go what's your big takeaways I think big takeaways that Amazon sort of innovation machine is chugging along they are I was listening to some of the accessions and when I was back to my room at nine so they're filling the holes in some areas but in some areas they're moving forward there's a lot to fix still it doesn't seem like that it seems like we are done with the cloud or The Innovation is done now we are building at the millisecond level so where do you go next there's a lot of room to grow on the storage side on the network side uh the improvements we need and and also making sure that the software which is you know which fits the hardware like there's a specialized software um sorry specialized hardware for certain software you know so there was a lot of talk around that and I attended some of those sessions where I asked the questions around like we have a specialized database for each kind of workload specialized processes processors for each kind of workload yeah the graviton section and actually the the one interesting before I forget that the arbitration was I asked that like why there are so many so many databases and IRS for the egress costs and all that stuff can you are you guys thinking about reducing that you know um the answer was no egress cost is not a big big sort of uh um show stopper for many of the customers but but the from all that sort of little discussion with with the folks sitting who build these products over there was that the plethora of choice is given to the customers to to make them feel that there's no vendor lock-in so if you are using some open source you know um soft software it can be on the you know platform side or can be database side you have database site you have that option at AWS so this is a lot there because I always thought that that AWS is the mother of all lock-ins but it's got an ecosystem and we're going to talk about exactly we'll talk about Stu what's working within AWS when you talk to customers and where are the challenges yeah I I got a comment on open source Dave of course there because I mean look we criticized to Amazon for years about their lack of contribution they've gotten better they're doing more in open source but is Amazon the mother of all lock-ins many times absolutely there's certain people inside Amazon I'm saying you know many of us talk Cloud native they're like well let's do Amazon native which means you're like full stack is things from Amazon and do things the way that we want to do things and you know I talk to a lot of customers they use more than one Cloud Dave and therefore certain things absolutely I want to Leverage The Innovation that Amazon has brought I do think we're past building all the main building blocks in many ways we are like in day two yes Amazon is fanatically customer focused and will always stay that way but you know there wasn't anything that jumped out at me last year or this year that was like Wow new category whole new way of thinking about something we're in a vocals last year Dave said you know we have over 200 services and if we listen to you the customer we'd have over two thousand his session this week actually got some great buzz from my friends in the serverless ecosystem they love some of the things tying together we're using data the next flywheel that we're going to see for the next 10 years Amazon's at the center of the cloud ecosystem in the IT world so you know there's a lot of good things here and to your point Dave the ecosystem one of the things I always look at is you know was there a booth that they're all going to be crying in their beer after Amazon made an announcement there was not a tech vendor that I saw this week that was like oh gosh there was an announcement and all of a sudden our business is gone where I did hear some rumbling is Amazon might be the next GSI to really move forward and we've seen all the gsis pushing really deep into supporting Cloud bringing workloads to the cloud and there's a little bit of rumbling as to that balance between what Amazon will do and their uh their go to market so a couple things so I think I think we all agree that a lot of the the announcements here today were taping seams right I call it and as it relates to the mother of all lock-in the reason why I say that it's it's obviously very much a pejorative compare Oracle company you know really well with Amazon's lock-in for Amazon's lock-in is about bringing this ecosystem together so that you actually have Choice Within the the house so you don't have to leave you know there's a there's a lot to eat at the table yeah you look at oracle's ecosystem it's like yeah you know oracle is oracle's ecosystem so so that is how I think they do lock in customers by incenting them not to leave because there's so much Choice Dave I agree with you a thousand I mean I'm here I'm a I'm a good partner of AWS and all of the partners here want to be successful with Amazon and Amazon is open to that it's not our way or get out which Oracle tries how much do you extract from the overall I.T budget you know are you a YouTube where you give the people that help you create a large sum of the money YouTube hasn't been all that profitable Amazon I think is doing a good balance of the ecosystem makes money you know we used to talk Dave about you know how much dollars does VMware make versus there um I think you know Amazon is a much bigger you know VMware 2.0 we used to think talk about all the time that VMware for every dollar spent on VMware licenses 15 or or 12 or 20 were spent in the ecosystem I would think the ratio is even higher here sarbji and an Oracle I would say it's I don't know yeah actually 1 to 0.5 maybe I don't know but I want to pick on your discussion about the the ecosystem the the partner ecosystem is so it's it's robust strong because it's wider I was I was not saying that there's no lock-in with with Amazon right AWS there's lock-in there's lock-in with everything there's lock-in with open source as well but but the point is that they're they're the the circle is so big you don't feel like locked in but they're playing smart as well they're bringing in the software the the platforms from the open source they're picking up those packages and saying we'll bring it in and cater that to you through AWS make it better perform better and also throw in their custom chips on top of that hey this MySQL runs better here so like what do you do I said oh Oracle because it's oracle's product if you will right so they are I think think they're filing or not slenders from their go to market strategy from their engineering and they listen to they're listening to customers like very closely and that has sort of side effects as well listening to customers creates a sprawl of services they have so many services and I criticized them last year for calling everything a new service I said don't call it a new service it's a feature of a existing service sure a lot of features a lot of features this is egress our egress costs a real problem or is it just the the on-prem guys picking at the the scab I mean what do you hear from customers so I mean Dave you know I I look at what Corey Quinn talks about all the time and Amazon charges on that are more expensive than any other Cloud the cloud providers and partly because Amazon is you know probably not a word they'd use they are dominant when it comes to the infrastructure space and therefore they do want to make it a little bit harder to do that they can get away with it um because um yeah you know we've seen some of the cloud providers have special Partnerships where you can actually you know leave and you're not going to be charged and Amazon they've been a little bit more flexible but absolutely I've heard customers say that they wish some good tunning and tongue-in-cheek stuff what else you got we lay it on us so do our players okay this year I think the focus was on the upside it's shifting gradually this was more focused on offside there were less talk of of developers from the main stage from from all sort of quadrants if you will from all Keynotes right so even Werner this morning he had a little bit for he was talking about he he was talking he he's job is to Rally up the builders right yeah so he talks about the go build right AWS pipes I thought was kind of cool then I said like I'm making glue easier I thought that was good you know I know some folks don't use that I I couldn't attend the whole session but but I heard in between right so it is really adopt or die you know I am Cloud Pro for last you know 10 years and I think it's the best model for a technology consumption right um because of economies of scale but more importantly because of division of labor because of specialization because you can't afford to hire the best security people the best you know the arm chip designers uh you can't you know there's one actually I came up with a bumper sticker you guys talked about bumper sticker I came up with that like last couple of weeks The Innovation favorite scale they have scale they have Innovation so that's where the Innovation is and it's it's not there again they actually say the market sets the price Market you as a customer don't set the price the vendor doesn't set the price Market sets the price so if somebody's complaining about their margins or egress and all that I think that's BS um yeah I I have a few more notes on the the partner if you you concur yeah Dave you know with just coming back to some of this commentary about like can Amazon actually enable something we used to call like Community clouds uh your companies like you know Goldman and NASDAQ and the like where Industries will actually be able to share data uh and you know expand the usage and you know Amazon's going to help drive that API economy forward some so it's good to see those things because you know we all know you know all of us are smarter than just any uh single company together so again some of that's open source but some of that is you know I think Amazon is is you know allowing Innovation to thrive I think the word you're looking for is super cloud there well yeah I mean it it's uh Dave if you want to go there with the super cloud because you know there's a metaphor for exactly what you described NASDAQ Goldman Sachs we you know and and you know a number of other companies that are few weeks at the Berkeley Sky Computing paper yeah you know that's a former supercloud Dave Linthicum calls it metacloud I'm not really careful I mean you know I go back to the the challenge we've been you know working at for a decade is the distributed architecture you know if you talk about AI architectures you know what lives in the cloud what lives at the edge where do we train things where do we do inferences um locations should matter a lot less Amazon you know I I didn't hear a lot about it this show but when they came out with like local zones and oh my gosh out you know all the things that Amazon is building to push out to the edge and also enabling that technology and software and the partner ecosystem helps expand that and Pull It in it's no longer you know Dave it was Hotel California all of the data eventually is going to end up in the public cloud and lock it in it's like I don't think that's going to be the case we know that there will be so much data out at the edge Amazon absolutely is super important um there some of those examples we're giving it's not necessarily multi-cloud but there's collaboration happening like in the healthcare world you know universities and hospitals can all share what they're doing uh regardless of you know where they live well Stephen Armstrong in the analyst session did say that you know we're going to talk about multi-cloud we're not going to lead with it necessarily but we are going to actually talk about it and that's different to your points too than in the fullness of time all the data will be in the cloud that's a new narrative but go ahead yeah actually Amazon is a leader in the cloud so if they push the cloud even if they don't say AWS or Amazon with it they benefit from it right and and the narrative is that way there's the proof is there right so again Innovation favorite scale there are chips which are being made for high scale their software being tweaked for high scale you as a Bank of America or for the Chrysler as a typical Enterprise you cannot afford to do those things in-house what cloud providers can I'm not saying just AWS Google cloud is there Azure guys are there and few others who are behind them and and you guys are there as well so IBM has IBM by the way congratulations to your red hat I know but IBM won the award um right you know very good partner and yeah but yeah people are dragging their feet people usually do on the change and they are in denial denial they they drag their feet and they came in IBM director feed the cave Den Dell drag their feed the cave in yeah you mean by Dragon vs cloud deniers cloud deniers right so server Huggers I call them but they they actually are sitting in Amazon Cloud Marketplace everybody is buying stuff from there the marketplace is the new model OKAY Amazon created the marketplace for b2c they are leading the marketplace of B2B as well on the technology side and other people are copying it so there are multiple marketplaces now so now actually it's like if you're in in a mobile app development there are two main platforms Android and Apple you first write the application for Apple right then for Android hex same here as a technology provider as and I I and and I actually you put your stuff to AWS first then you go anywhere else yeah they are later yeah the Enterprise app store is what we've wanted for a long time the question is is Amazon alone the Enterprise app store or are they partner of a of a larger portfolio because there's a lot of SAS companies out there uh that that play into yeah what we need well and this is what you're talking about the future but I just want to make a point about the past you talking about dragging their feet because the Cube's been following this and Stu you remember this in 2013 IBM actually you know got in a big fight with with Amazon over the CIA deal you know and it all became public judge wheeler eviscerated you know IBM and it ended up IBM ended up buying you know soft layer and then we know what happened there and it Joe Tucci thought the cloud was Mosey right so it's just amazing to see we have booksellers you know VMware called them books I wasn't not all of them are like talking about how great Partnerships they are it's amazing like you said sub GC and IBM uh with the the GSI you know Partnership of the year but what you guys were just talking about was the future and that's what I wanted to get to is because you know Amazon's been leading the way I I was listening to Werner this morning and that just reminded me of back in the days when we used to listen to IBM educate us give us a master class on system design and decoupled systems and and IO and everything else now Amazon is you know the master educator and it got me thinking how long will that last you know will they go the way of you know the other you know incumbents will they be disrupted or will they you know keep innovating maybe it's going to take 10 or 20 years I don't know yeah I mean Dave you actually you did some research I believe it was a year or so ago yeah but what will stop Amazon and the one thing that worries me a little bit um is the two Pizza teams when you have over 202 Pizza teams the amount of things that each one of those groups needs to take care of was more than any human could take care of people burn out they run out of people how many amazonians only last two or three years and then leave because it is tough I bumped into plenty of friends of mine that have been you know six ten years at Amazon and love it but it is a tough culture and they are driving werner's keynote I thought did look to from a product standpoint you could say tape over some of the seams some of those solutions to bring Beyond just a single product and bring them together and leverage data so there are some signs that they might be able to get past some of those limitations but I still worry structurally culturally there could be some challenges for Amazon to keep the momentum going especially with the global economic impact that we are likely to see in the next year bring us home I think the future side like we could talk about the vendors all day right to serve the community out there I think we should talk about how what's the future of technology consumption from the consumer side so from the supplier side just a quick note I think the only danger AWS has has that that you know Fred's going after them you know too big you know like we will break you up and that can cause some disruption there other than that I think they they have some more steam to go for a few more years at least before we start thinking about like oh this thing is falling apart or anything like that so they have a lot more they have momentum and it's continuing so okay from the I think game is on retail by the way is going to get disrupted before AWS yeah go ahead from the buyer's side I think um the the future of the sort of Technology consumption is based on the paper uh use and they actually are turning all their services to uh they are sort of becoming serverless behind the scenes right all analytics service they had one service left they they did that this year so every service is serverless so that means you pay exactly for the amount you use the compute the iops the the storage so all these three layers of course Network we talked about the egress stuff and that's a problem there because of the network design mainly because Google has a flatter design and they have lower cost so so they are actually squeezing the their their designing this their services in a way that you don't waste any resources as a buyer so for example very simple example when early earlier In This Cloud you will get a VM right in Cloud that's how we started so and you can get 20 use 20 percent of the VM 80 is getting wasted that's not happening now that that has been reduced to the most extent so now your VM grows as you grow the usage and if you go higher than the tier you picked they will charge you otherwise they will not charge you extra so that's why there's still a lot of instances like many different types you have to pick one I think the future is that those instances will go away the the instance will be formed for you on the fly so that is the future serverless all right give us bumper sticker Stu and then Serb G I'll give you my quick one and then we'll wrap yeah so just Dave to play off of sharp G and to wrap it up you actually wrote about it on your preview post for here uh serverless we're talking about how developers think about things um and you know Amazon in many ways you know is the new default server uh you know for the cloud um and containerization fits into the whole serverless Paradigm uh it's the space that I live in uh you know every day here and you know I was happy to see the last few years serverless and containers there's a blurring a line and you know subject we're still going to see VMS for a long time yeah yeah we will see that so give us give us your book Instagram my number six is innovation favorite scale that's my bumper sticker and and Amazon has that but also I I want everybody else to like the viewers to take a look at the the Google Cloud as well as well as IBM with others like maybe you have a better price to Performance there for certain workloads and by the way one vendor cannot do it alone we know that for sure the market is so big there's a lot of room for uh Red Hats of the world and and and Microsoft's the world to innovate so keep an eye on them they we need the competition actually and that's why competition Will Keep Us to a place where Market sets the price one vendor doesn't so the only only danger is if if AWS is a monopoly then I will be worried I think ecosystems are the Hallmark of a great Cloud company and Amazon's got the the biggest and baddest ecosystem and I think the other thing to watch for is Industries building on top of the cloud you mentioned the Goldman Sachs NASDAQ Capital One and Warner media these all these industries are building their own clouds and that's where the real money is going to be made in the latter half of the 2020s all right we're a wrap this is Dave Valente I want to first of all thank thanks to our great sponsors AWS for for having us here this is our 10th year at the cube AMD you know sponsoring as well the the the cube here Accenture sponsor to third set upstairs upstairs on the fifth floor all the ecosystem partners that came on the cube this week and supported our mission for free content our content is always free we try to give more to the community and we we take back so go to thecube.net and you'll see all these videos go to siliconangle com for all the news wikibon.com I publish weekly a breaking analysis series I want to thank our amazing crew here you guys we have probably 30 35 people unbelievable our awesome last session John Walls uh Paul Gillen Lisa Martin Savannah Peterson John Furrier who's on a plane we appreciate Andrew and Leonard in our ear and all of our our crew Palo Alto Boston and across the country thank you so much really appreciate it all right we are a wrap AWS re invent 2022 we'll see you in two weeks we'll see you two weeks at Palo Alto ignite back here in Vegas thanks for watching thecube the leader in Enterprise and emerging Tech coverage [Music]

>>Hey everyone. Welcome back to Las Vegas. It's the Cube live at AWS Reinvent 2022. This is our fourth day of coverage. Lisa Martin here with Paul Gillen. Paul, we started Monday night, we filmed and streamed for about three hours. We have had shammed pack days, Tuesday, Wednesday, Thursday. What's your takeaway? >>We're routed final turn as we, as we head into the home stretch. Yeah. This is as it has been since the beginning, this show with a lot of energy. I'm amazed for the fourth day of a conference, how many people are still here I am too. And how, and how active they are and how full the sessions are. Huge. Proud for the keynote this morning. You don't see that at most of the day four conferences. Everyone's on their way home. So, so people come here to learn and they're, and they're still >>Learning. They are still learning. And we're gonna help continue that learning path. We have an alumni back with us, Toron joins us, the CPO and co-founder of Dremeo. Tomer, it's great to have you back on the program. >>Yeah, thanks for, for having me here. And thanks for keeping the, the best session for the fourth day. >>Yeah, you're right. I like that. That's a good mojo to come into this interview with Tomer. So last year, last time I saw you was a year ago here in Vegas at Reinvent 21. We talked about the growth of data lakes and the data lake houses. We talked about the need for open data architectures as opposed to data warehouses. And the headline of the Silicon Angle's article on the interview we did with you was, Dremio Predicts 2022 will be the year open data architectures replace the data warehouse. We're almost done with 2022. Has that prediction come true? >>Yeah, I think, I think we're seeing almost every company out there, certainly in the enterprise, adopting data lake, data lakehouse technology, embracing open source kind of file and table formats. And, and so I think that's definitely happening. Of course, nothing goes away. So, you know, data warehouses don't go away in, in a year and actually don't go away ever. We still have mainframes around, but certainly the trends are, are all pointing in that direction. >>Describe the data lakehouse for anybody who may not be really familiar with that and, and what it's, what it really means for organizations. >>Yeah. I think you could think of the data lakehouse as the evolution of the data lake, right? And so, you know, for, for, you know, the last decade we've had kind of these two options, data lakes and data warehouses and, you know, warehouses, you know, having good SQL support, but, and good performance. But you had to spend a lot of time and effort getting data into the warehouse. You got locked into them, very, very expensive. That's a big problem now. And data lakes, you know, more open, more scalable, but had all sorts of kind of limitations. And what we've done now as an industry with the Lake House, and especially with, you know, technologies like Apache Iceberg, is we've unlocked all the capabilities of the warehouse directly on object storage like s3. So you can insert and update and delete individual records. You can do transactions, you can do all the things you could do with a, a database directly in kind of open formats without getting locked in at a much lower cost. >>But you're still dealing with semi-structured data as opposed to structured data. And there's, there's work that has to be done to get that into a usable form. That's where Drio excels. What, what has been happening in that area to, to make, I mean, is it formats like j s o that are, are enabling this to happen? How, how we advancing the cause of making semi-structured data usable? Yeah, >>Well, I think first of all, you know, I think that's all changed. I think that was maybe true for the original data lakes, but now with the Lake house, you know, our bread and butter is actually structured data. It's all, it's all tables with the schema. And, you know, you can, you know, create table insert records. You know, it's, it's, it's really everything you can do with a data warehouse you can now do in the lakehouse. Now, that's not to say that there aren't like very advanced capabilities when it comes to, you know, j s O and nested data and kind of sparse data. You know, we excel in that as well. But we're really seeing kind of the lakehouse take over the, the bread and butter data warehouse use cases. >>You mentioned open a minute ago. Talk about why it's, why open is important and the value that it can deliver for customers. >>Yeah, well, I think if you look back in time and you see all the challenges that companies have had with kind of traditional data architectures, right? The, the, the, a lot of that comes from the, the, the problems with data warehouses. The fact that they are, you know, they're very expensive. The data is, you have to ingest it into the data warehouse in order to query it. And then it's almost impossible to get off of these systems, right? It takes an enormous effort, tremendous cost to get off of them. And so you're kinda locked in and that's a big problem, right? You also, you're dependent on that one data warehouse vendor, right? You can only do things with that data that the warehouse vendor supports. And if you contrast that to data lakehouse and open architectures where the data is stored in entirely open formats. >>So things like par files and Apache iceberg tables, that means you can use any engine on that data. You can use s SQL Query Engine, you can use Spark, you can use flin. You know, there's a dozen different engines that you can use on that, both at the same time. But also in the future, if you ever wanted to try something new that comes out, some new open source innovation, some new startup, you just take it and point out the same data. So that data's now at the core, at the center of the architecture as opposed to some, you know, vendors logo. Yeah. >>Amazon seems to be bought into the Lakehouse concept. It has big announcements on day two about eliminating the ETL stage between RDS and Redshift. Do you see the cloud vendors as pushing this concept forward? >>Yeah, a hundred percent. I mean, I'm, I'm Amazon's a great, great partner of ours. We work with, you know, probably 10 different teams there. Everything from, you know, the S3 team, the, the glue team, the click site team, you know, everything in between. And, you know, their embracement of the, the, the lake house architecture, the fact that they adopted Iceberg as their primary table format. I think that's exciting as an industry. We're all coming together around standard, standard ways to represent data so that at the end of the day, companies have this benefit of being able to, you know, have their own data in their own S3 account in open formats and be able to use all these different engines without losing any of the functionality that they need, right? The ability to do all these interactions with data that maybe in the past you would have to move the data into a database or, or warehouse in order to do, you just don't have to do that anymore. Speaking >>Of functionality, talk about what's new this year with drio since we've seen you last. >>Yeah, there's a lot of, a lot of new things with, with Drio. So yeah, we now have full Apache iceberg support, you know, with DML commands, you can do inserts, updates, deletes, you know, copy into all, all that kind of stuff is now, you know, fully supported native part of the platform. We, we now offer kind of two flavors of dr. We have, you know, Dr. Cloud, which is our SaaS version fully hosted. You sign up with your Google or, you know, Azure account and, and, and you're up in, you're up and running in, in, in a minute. And then dral software, which you can self host usually in the cloud, but even, even even outside of the cloud. And then we're also very excited about this new idea of data as code. And so we've introduced a new product that's now in preview called Dr. >>Arctic. And the idea there is to bring the concepts of GI or GitHub to the world of data. So things like being able to create a branch and work in isolation. If you're a data scientist, you wanna experiment on your own without impacting other people, or you're a data engineer and you're ingesting data, you want to transform it and test it before you expose it to others. You can do that in a branch. So all these ideas that, you know, we take for granted now in the world of source code and software development, we're bringing to the world of data with Jamar. And when you think about data mesh, a lot of people talking about data mesh now and wanting to kind of take advantage of, of those concepts and ideas, you know, thinking of data as a product. Well, when you think about data as a product, we think you have to manage it like code, right? You have to, and that's why we call it data as code, right? The, all those reasons that we use things like GI have to build products, you know, if we wanna think of data as a product, we need all those capabilities also with data. You know, also the ability to go back in time. The ability to undo mistakes, to see who changed my data and when did they change that table. All of those are, are part of this, this new catalog that we've created. >>Are you talk about data as a product that's sort of intrinsic to the data mesh concept. Are you, what's your opinion of data mesh? Is the, is the world ready for that radically different approach to data ownership? >>You know, we are now in dozens of, dozens of our customers that are using drio for to implement enterprise-wide kind of data mesh solutions. And at the end of the day, I think it's just, you know, what most people would consider common sense, right? In a large organization, it is very hard for a centralized single team to understand every piece of data, to manage all the data themselves, to, you know, make sure the quality is correct to make it accessible. And so what data mesh is first and foremost about is being able to kind of federate the, or distribute the, the ownership of data, the governance of the data still has to happen, right? And so that is, I think at the heart of the data mesh, but thinking of data as kind of allowing different teams, different domains to own their own data to really manage it like a product with all the best practices that that we have with that super important. >>So we we're doing a lot with data mesh, you know, the way that cloud has multiple projects and the way that Jamar allows you to have multiple catalogs and different groups can kind of interact and share data among each other. You know, the fact that we can connect to all these different data sources, even outside your data lake, you know, with Redshift, Oracle SQL Server, you know, all the different databases that are out there and join across different databases in addition to your data lake, that that's all stuff that companies want with their data mesh. >>What are some of your favorite customer stories that where you've really helped them accelerate that data mesh and drive business value from it so that more people in the organization kind of access to data so they can really make those data driven decisions that everybody wants to make? >>I mean, there's, there's so many of them, but, you know, one of the largest tech companies in the world creating a, a data mesh where you have all the different departments in the company that, you know, they, they, they were a big data warehouse user and it kinda hit the wall, right? The costs were so high and the ability for people to kind of use it for just experimentation, to try new things out to collaborate, they couldn't do it because it was so prohibitively expensive and difficult to use. And so what they said, well, we need a platform that different people can, they can collaborate, they can ex, they can experiment with the data, they can share data with others. And so at a big organization like that, the, their ability to kind of have a centralized platform but allow different groups to manage their own data, you know, several of the largest banks in the world are, are also doing data meshes with Dr you know, one of them has over over a dozen different business units that are using, using Dremio and that ability to have thousands of people on a platform and to be able to collaborate and share among each other that, that's super important to these >>Guys. Can you contrast your approach to the market, the snowflakes? Cause they have some of those same concepts. >>Snowflake's >>A very closed system at the end of the day, right? Closed and very expensive. Right? I think they, if I remember seeing, you know, a quarter ago in, in, in one of their earnings reports that the average customer spends 70% more every year, right? Well that's not sustainable. If you think about that in a decade, that's your cost is gonna increase 200 x, most companies not gonna be able to swallow that, right? So companies need, first of all, they need more cost efficient solutions that are, you know, just more approachable, right? And the second thing is, you know, you know, we talked about the open data architecture. I think most companies now realize that the, if you want to build a platform for the future, you need to have the data and open formats and not be locked into one vendor, right? And so that's kind of another important aspect beyond that's ability to connect to all your data, even outside the lake to your different databases, no sequel databases, relational databases, and drs semantic layer where we can accelerate queries. And so typically what you have, what happens with data warehouses and other data lake query engines is that because you can't get the performance that you want, you end up creating lots and lots of copies of data. You, for every use case, you're creating a, you know, a pre-joy copy of that data, a pre aggregated version of that data. And you know, then you have to redirect all your data. >>You've got a >>Governance problem, individual things. It's expensive. It's expensive, it's hard to secure that cuz permissions don't travel with the data. So you have all sorts of problems with that, right? And so what we've done because of our semantic layer that makes it easy to kind of expose data in a logical way. And then our query acceleration technology, which we call reflections, which transparently accelerates queries and gives you subsecond response times without data copies and also without extracts into the BI tools. Cause if you start doing bi extracts or imports, again, you have lots of copies of data in the organization, all sorts of refresh problems, security problems, it's, it's a nightmare, right? And that just collapsing all those copies and having a, a simple solution where data's stored in open formats and we can give you fast access to any of that data that's very different from what you get with like a snowflake or, or any of these other >>Companies. Right. That, that's a great explanation. I wanna ask you, early this year you announced that your Dr. Cloud service would be a free forever, the basic DR. Cloud service. How has that offer gone over? What's been the uptake on that offer? >>Yeah, it, I mean it is, and thousands of people have signed up and, and it's, I think it's a great service. It's, you know, it's very, very simple. People can go on the website, try it out. We now have a test drive as well. If, if you want to get started with just some sample public sample data sets and like a tutorial, we've made that increasingly easy as well. But yeah, we continue to, you know, take that approach of, you know, making it, you know, making it easy, democratizing these kind of cloud data platforms and, and kinda lowering the barriers to >>Adoption. How, how effective has it been in driving sales of the enterprise version? >>Yeah, a lot of, a lot of, a lot of business with, you know, that, that we do like when it comes to, to selling is, you know, folks that, you know, have educated themselves, right? They've started off, they've followed some tutorials. I think generally developers, they prefer the first interaction to be with a product, not with a salesperson. And so that's, that's basically the reason we did that. >>Before we ask you the last question, I wanna just, can you give us a speak peek into the product roadmap as we enter 2023? What can you share with us that we should be paying attention to where Drum is concerned? >>Yeah. You know, actually a couple, couple days ago here at the conference, we, we had a press release with all sorts of new capabilities that we, we we just released. And there's a lot more for, for the coming year. You know, we will shortly be releasing a variety of different performance enhancements. So we'll be in the next quarter or two. We'll be, you know, probably twice as fast just in terms of rock qu speed, you know, that's in addition to our reflections and our career acceleration, you know, support for all the major clouds is coming. You know, just a lot of capabilities in Inre that make it easier and easier to use the platform. >>Awesome. Tomer, thank you so much for joining us. My last question to you is, if you had a billboard in your desired location and it was going to really just be like a mic drop about why customers should be looking at Drio, what would that billboard say? >>Well, DRIO is the easy and open data lake house and, you know, open architectures. It's just a lot, a lot better, a lot more f a lot more future proof, a lot easier and a lot just a much safer choice for the future for, for companies. And so hard to argue with those people to take a look. Exactly. That wasn't the best. That wasn't the best, you know, billboards. >>Okay. I think it's a great billboard. Awesome. And thank you so much for joining Poly Me on the program, sharing with us what's new, what some of the exciting things are that are coming down the pipe. Quite soon we're gonna be keeping our eye Ono. >>Awesome. Always happy to be here. >>Thank you. Right. For our guest and for Paul Gillin, I'm Lisa Martin. You're watching The Cube, the leader in live and emerging tech coverage.

(lively music) >> Good afternoon cloud community and welcome back to beautiful Las Vegas, Nevada. We are here at AWS re:Invent, day four of our wall to wall coverage. It is day four in the afternoon and we are holding strong. I'm Savannah Peterson, joined by my fabulous co-host Paul Gillen. Paul, how you doing? >> I'm doing well, fine Savannah. You? >> You look great. >> We're in the home stretch here. >> Yeah, (laughs) we are. >> You still look fresh as a daisy. I don't know how you do it. >> (laughs) You're too kind. You're too kind, but I'm vain enough to take that compliment. I'm very excited about the conversation that we're going to have up next. We get to get a little DevRel and we got a little swagger on the stage. Welcome, Austin. How you doing? >> Hey, great to be here. Thanks for having me. >> Savannah: Yeah, it's our pleasure. How's the show been for you so far? >> Busy, exciting. Feels a lot like, you know it used to be right? >> Yeah, I know. A little reminiscent of the before times. >> Well, before times. >> Before we dig into the technical stuff, you're the most intriguingly dressed person we've had on the show this week. >> Austin: I feel extremely underdressed. >> Well, and we were talking about developer fancy. Talk to me a little bit about your approach to fashion. Wasn't expecting to lead with this, but I like this but I like this actually. >> No, it's actually good with my PR. You're going to love it. My approach, here's the thing, I give free advice all the time about developer relations, about things that work, have worked, and don't work in community and all that stuff. I love talking about that. Someone came up to me and said, "Where do you get your fashion tips from? What's the secret Discord server that I need to go on?" I'm like, "I will never tell." >> Oh, okay. >> This is an actual trait secret. >> Top secret. Wow! Talk about. >> If someone else starts wearing the hat, then everyone's going to be like, "There's so many white guys." Look, I'm a white guy with a beard that works in technology. >> Savannah: I've never met one of those. >> Exactly, there's none of them at all. So, you have to do something to kind stand out from the crowd a little bit. >> I love it, and it's a talk trigger. We're talking about it now. Production team loved it. It's fantastic. >> It's great. >> So your DevRel for Lightstep, in case the audience isn't familiar tell us about Lightstep. >> So Lightstep is a cloud native observability platform built at planet scale, and it powers observability at some places you've heard of like Spotify, GitHub, right? We're designed to really help developers that are working in the cloud with Kubernetes, with these huge distributed systems, understand application performance and being able to find problems, fix problems. We're also part of the ServiceNow family and as we all know ServiceNow is on a mission to help the world of work work better by powering digital transformation around IT and customer experiences for their many, many, many global 2000 customers. We love them very much. >> You know, it's a big love fest here. A lot of people have talked about the collaboration, so many companies working together. You mentioned unified observability. What is unified observability? >> So if you think about a tradition, or if you've heard about this traditional idea of observability where you have three pillars, right? You have metrics, and you have logs, and you have traces. All those three things are different data sources. They're picked up by different tools. They're analyzed by different people for different purposes. What we believe and what we're working to accomplish right now is to take all that and if you think those pillars, flip 'em on their side and think of them as streams of data. If we can take those streams and integrate them together and let you treat traces and metrics and logs not as these kind of inviolate experiences where you're kind of paging between things and going between tab A to tab B to tab C, and give you a standard way to query this, a standard way to display this, and letting you kind of find the most relevant data, then it really unlocks a lot of power for like developers and SREs to spend less time like managing tools. You know, figuring out where to build their query or what dashboard to check, more just being able to like kind of ask a question, get an answer. When you have an incident or an outage that's the most important thing, right? How quickly can you get those answers that you need so that you can restore system health? >> You don't want to be looking in multiple spots to figure out what's going on. >> Absolutely. I mean, some people hear unified observability and they go to like tool consolidation, right? That's something I hear from a lot of our users and a lot of people in re:Invent. I'll talk to SREs, they're like, "Yeah, we've got like six or seven different metrics products alone, just on services that they cover." It is important to kind of consolidate that but we're really taking it a step lower. We're looking at the data layer and trying to say, "Okay, if the data is all consistent and vendor neutral then that gives you flexibility not only from a tool consolidation perspective but also you know, a consistency, reliability. You could have a single way to deploy your observability out regardless of what cloud you're on, regardless if you're using Kubernetes or Fargate or whatever else. or even just Bare Metal or EC2 Bare Metal, right? There's been so much historically in this space. There's been a lot of silos and we think that unify diversability means that we kind of break down those silos, right? The way that we're doing it primarily is through a project called OpenTelemetry which you might have heard of. You want to talk about that in a minute? . >> Savannah: Yeah, let's talk about it right now. Why don't you tell us about it? Keep going, you're great. You're on a roll. >> I am. >> Savannah: We'll just hang out over here. >> It's day four. I'm going to ask the questions and answer the questions. (Savannah laughs) >> Yes, you're right. >> I do yeah. >> Open Tele- >> OpenTelemetry . >> Explain what OpenTelemetry is first. >> OpenTelemetry is a CNCF project, Cloud Native Computing Foundation. The goal is to make telemetry data, high quality telemetry data, a builtin feature of cloud native software right? So right now if you wanted to get logging data out, depending on your application stack, depending on your application run time, depending on language, depending on your deployment environment. You might have a lot... You have to make a lot of choices, right? About like, what am I going to use? >> Savannah: So many different choices, and the players are changing all the time. >> Exactly, and a lot of times what people will do is they'll go and they'll say like, "We have to use this commercial solution because they have a proprietary agent that can do a lot of this for us." You know? And if you look at all those proprietary agents, what you find very quickly is it's very commodified right? There's no real difference in what they're doing at a code level and what's stopped the industry from really adopting a standard way to create this logs and metrics and traces, is simply just the fact that there was no standard. And so, OpenTelemetry is that standard, right? We've got dozens of companies many of them like very, many of them here right? Competitors all the same, working together to build this open standard and implementation of telemetry data for cloud native software and really any software right? Like we support over 12 languages. We support Kubernetes, Amazon. AWS is a huge contributor actually and we're doing some really exciting stuff with them on their Amazon distribution of OpenTelemetry. So it's been extremely interesting to see it over the past like couple years go from like, "Hey, here's this like new thing that we're doing over here," to really it's a generalized acceptance that this is the way of the future. This is what we should have been doing all along. >> Yeah. >> My opinion is there is a perception out there that observability is kind of a commodity now that all the players have the same set of tools, same set of 15 or 17 or whatever tools, and that there's very little distinction in functionality. Would you agree with that? >> I don't know if I would characterize it that way entirely. I do think that there's a lot of duplicated effort that happens and part of the reason is because of this telemetry data problem, right? Because you have to wind up... You know, there's this idea of table stakes monitoring that we talk about right? Table stakes monitoring is the stuff that you're having to do every single day to kind of make sure your system is healthy to be able to... When there's an alert, gets triggered, to see why it got triggered and to go fix it, right? Because everyone has the kind of work on that table stake stuff and then build all these integrations, there's very little time for innovation on top of that right? Because you're spending all your time just like working on keeping up with technology. >> Savannah: Doing the boring stuff to make sure the wheels don't fall off, basically. >> Austin: Right? What I think the real advantage of OpenTelemetry is that it really, from like a vendor perspective, like it unblocks us from having to kind of do all this repetitive commodified work. It lets us help move that out to the community level so that... Instead of having to kind of build, your Kubernetes integration for example, you can just have like, "Hey, OpenTelemetry is integrated into Kubernetes and you just have this data now." If you are a commercial product, or if you're even someone that's interested in fixing a, scratching a particular itch about observability. It's like, "I have this specific way that I'm doing Kubernetes and I need something to help me really analyze that data. Well, I've got the data now I can just go create a project. I can create an analysis tool." I think that's what you'll see over time as OpenTelemetry promulgates out into the ecosystem is more people building interesting analysis features, people using things like machine learning to analyze this large amount, large and consistent amount of OpenTelemetry data. It's going to be a big shakeup I think, but it has the potential to really unlock a lot of value for our customers. >> Well, so you're, you're a developer relations guy. What are developers asking for right now out of their observability platforms? >> Austin: That's a great question. I think there's two things. The first is that they want it to just work. It's actually the biggest thing, right? There's so many kind of... This goes back to the tool proliferation, right? People have too much data in too many different places, and getting that data out can still be really challenging. And so, the biggest thing they want is just like, "I want something that I can... I want a lot of these questions I have to ask, answered already and OpenTelemetry is going towards it." Keep in mind it's the project's only three years old, so we obviously have room to grow but there are people running it in production and it works really well for them but there's more that we can do. The second thing is, and this isn't what really is interesting to me, is it's less what they're asking for and more what they're not asking for. Because a lot of the stuff that you see people, saying around, "Oh, we need this like very specific sort of lower level telemetry data, or we need this kind of universal thing." People really just want to be able to get questions or get questions answered, right? They want tools that kind of have these workflows where you don't have to be an expert because a lot of times this tooling gets locked behind sort of is gate kept almost in a organization where there are teams that's like, "We're responsible for this and we're going to set it up and manage it for you, and we won't let you do things outside of it because that would mess up- >> Savannah: Here's your sandbox and- >> Right, this is your sandbox you can play in and a lot of times that's really useful and very tuned for the problems that you saw yesterday, but people are looking at like what are the problems I'm going to get tomorrow? We're deploying more rapidly. We have more and more intentional change happening in the system. Like it's not enough to have this reactive sort of approach where our SRE teams are kind of like or this observability team is building a platform for us. Developers want to be able to get in and have these kind of guided workflows really that say like, "Hey, here's where you're starting at. Let's get you to an answer. Let's help you find the needle in the haystack as it were, without you having to become a master of six different or seven different tools." >> Savannah: Right, and it shouldn't be that complicated. >> It shouldn't be. I mean we've certainly... We've been working on this problem for many years now, starting with a lot of our team that started at Google and helped build Google's planet scale monitoring systems. So we have a lot of experience in the field. It's actually one... An interesting story that our founder or now general manager tells BHS, Ben Sigelman, and he told me this story once and it's like... He had built this really cool thing called Dapper that was a tracing system at Google, and people weren't using it. Because they were like, "This is really cool, but I don't know how to... but it's not relevant to me." And he's like, the one thing that we did to get to increase usage 20 times over was we just put a link. So we went to the place that people were already looking for that data and we added a link that says, "Hey, go over here and look at this." It's those simple connections being able to kind of draw people from like point A to point B, take them from familiar workflows into unfamiliar ones. You know, that's how we think about these problems right? How is this becoming a daily part of someone's usage? How is this helping them solve problems faster and really improve their their life? >> Savannah: Yeah, exactly. It comes down to quality of life. >> Warner made the case this morning that computer architecture should be inherently event-driven and that we are moving toward a world where the person matters less than what the software does, right? The software is triggering events. Does this complicate observability or simplify it? >> Austin: I think that at the end of the day, it's about getting the... Observability to me in a lot of ways is about modeling your system, right? It's about you as a developer being able to say this is what I expect the system to do and I don't think the actual application architecture really matters that much, right? Because it's about you. You are building a system, right? It can be event driven, can be support request response, can be whatever it is. You have to be able to say, "This is what I expect to... For these given inputs, this is the expected output." Now maybe there's a lot of stuff that happens in the middle that you don't really care about. And then, I talk to people here and everyone's talking about serverless right? Everyone... You can see there's obviously some amazing statistics about how many people are using Lambda, and it's very exciting. There's a lot of stuff that you shouldn't have to care about as a developer, but you should care about those inputs and outputs. You will need to have that kind of intermediate information and understand like, what was the exact path that I took through this invented system? What was the actual resources that were being used? Because even if you trust that all this magic behind the scenes is just going to work forever, sometimes it's still really useful to have that sort of lower level abstraction, to say like, "Well, this is what actually happened so that I can figure out when I deployed a new change, did I make performance better or worse?" Or being able to kind of segregate your data out and say like... Doing AB testing, right? Doing canary releases, doing all of these things that you hear about as best practices or well architected applications. Observability is at the core of all that. You need observability to kind of do any of, ask any of those higher level interesting questions. >> Savannah: We are here at ReInvent. Tell us a little bit more about the partnership with AWS. >> So I would have to actually probably refer you to someone at Service Now on that. I know that we are a partner. We collaborate with them on various things. But really at Lightstep, we're very focused on kind of the open source part of this. So we work with AWS through the OpenTelemetry project, on things like the AWS distribution for OpenTelemetry which is really... It's OpenTelemetry, again is really designed to be like a neutral standard but we know that there are going to be integrators and implementers that need to package up and bundle it in a certain way to make it easy for their end users to consume it. So that's what Amazon has done with ADOT which is the shortening for it. So it's available in several different ways. You can use it as like an SDK and drop it into your application. There's Lambda layers. If you want to get Lambda observability, you just add this extension in and then suddenly you're getting OpenTelemetry data on the other side. So it's really cool. It's been a really exciting to kind of work with people on the AWS side over the past several years. >> Savannah: It's exciting, >> I've personally seen just a lot of change. I was talking to a PM earlier this week... It's like, "Hey, two years ago I came and talked to you about OpenTelemetry and here we are today. You're still talking about OpenTelemetry." And they're like, "What changes?" Our customers have started coming to us asking for OpenTelemetry and we see the same thing now. >> Savannah: Timing is right. >> Timing is right, but we see the same thing... Even talking to ServiceNow customers who are... These very big enterprises, banks, finance, healthcare, whatever, telcos, it used to be... You'd have to go to them and say like, "Let me tell you about distributed tracing. Let me tell you about OpenTelemetry. Let me tell you about observability." Now they're coming in and saying, "Yeah, so we're standard." If you think about Kubernetes and how Kubernetes, a lot of enterprises have spent the past five-six years standardizing, and Kubernetes is a way to deploy applications or manage containerized applications. They're doing the same journey now with OpenTelemetry where they're saying, "This is what we're betting on and we want partners we want people to help us go along that way." >> I love it, and they work hand in hand in all CNCF projects as well that you're talking about. >> Austin: Right, so we're integrated into Kubernetes. You can find OpenTelemetry and things like kept in which is application standards. And over time, it'll just like promulgate out from there. So it's really exciting times. >> A bunch of CNCF projects in this area right? Prometheus. >> Prometheus, yeah. Yeah, so we inter-operate with Prometheus as well. So if you have Prometheus metrics, then OpenTelemetry can read those. It's a... OpenTelemetry metrics are like a super set of Prometheus. We've been working with the Prometheus community for quite a while to make sure that there's really good compatibility because so many people use Prometheus you know? >> Yeah. All right, so last question. New tradition for us here on theCUBE. We're looking for your 32nd hot take, Instagram reel, biggest theme, biggest buzz for those not here on the show floor. >> Oh gosh. >> Savannah: It could be for you too. It could be whatever for... >> I think the two things that are really striking to me is one serverless. Like I see... I thought people were talking about servers a lot and they were talking about it more than ever. Two, I really think it is observability right? Like we've gone from observability being kind of a niche. >> Savannah: Not that you're biased. >> Huh? >> Savannah: Not that you're biased. >> Not that I'm biased. It used to be a niche. I'd have to go niche thing where I would go and explain what this is to people and nowpeople are coming up. It's like, "Yeah, yeah, we're using OpenTelemetry." It's very cool. I've been involved with OpenTelemetry since the jump, since it was started really. It's been very exciting to see and gratifying to see like how much adoption we've gotten even in a short amount of time. >> Yeah, absolutely. It's a pretty... Yeah, it's been a lot. That was great. Perfect soundbite for us. >> Austin: Thanks, I love soundbites. >> Savannah: Yeah. Awesome. We love your hat and your soundbites equally. Thank you so much for being on the show with us today. >> Thank you for having me. >> Savannah: Hey, anytime, anytime. Will we see you in Amsterdam, speaking of KubeCon? Awesome, we'll be there. >> There's some real exciting OpenTelemetry stuff coming up for KubeCon. >> Well, we'll have to get you back on theCUBE. (talking simultaneously) Love that for us. Thank you all for tuning in two hour wall to wall coverage here, day four at AWS re:Invent in fabulous Las Vegas, Nevada, with Paul Gillin. I'm Savannah Peterson and you're watching theCUBE, the leader in high tech coverage. (lively music)

(upbeat music) >> Okay, welcome back, everyone, it's CUBE's live coverage. I'm John Furrier, host of theCUBE here with Paul Gillen. Got a great guest coming up here, talking about cloud security, all things going on in the cloud. Paul, great day. How you doing? How you holding up? >> I'm about at the end of my, running on fumes, John. (John laughs) >> Let's bring it home. >> And we got another day coming up. >> Day three, let's bring it home, come on, let's go. Lot of energy. >> Lot of energy on the floor and certainly a lot of talk about security at this conference. Busy, busy market, lots of vendors. And one of the more notable ones, Cohesity, recently introduced a brand new suite, a brand new approach to security that combines data protection and security and backup. With us, to talk about that is Amith Nair, who is the Senior Vice president and General Manager of cloud at Cohesity. Welcome. >> Thank you very much. Thanks for having me, Paul and John. >> So tell us about DataHawk, your new product. >> Yeah, just to set a little bit of perspective on Cohesity, and how we think about DataHawk and security in general is, Cohesity is the leading solution for data security and management. And if you think about all the pillars that we provide in terms of solution around that data solutions, so we have data protection, data security, data access, data mobility and data insights. So the focus for us over the last many months was really to make our data security solutions really strong. So generally when customers think about security, they think about starting with security at the perimeter, on the edge. They think about firewalls, network layer, and so on and so forth. But in the end, what they're really trying to protect is the data that aligns to what they're really trying to save. Right? So DataHawk was formulated and built in order to help extend our existing solutions to provide additional security, layers of security, and also work with partners to enable doing that. Many months ago, we released this product called FortKnox, which is our cyber vaulting solution. One that customers really love and use today. >> It's an air gap solution, right? >> It's an air gap solution with forum capabilities, and so on. Extremely liked by customers, very well adopted, and we extended that to provide lots more data classification capabilities, and ransomware checks as well. So malware checks in the product itself in terms of what it is being backed up. And is there malware in the backed up data and so on? >> Maybe, we can talk about the evolution of ransomware, because ransomware is getting a lot more sophisticated. It used to start at the end point and then penetrate into the network. Increasingly, now, we're seeing it move into the backup, and actually corrupt backup files before moving into the production data. How is ransomware evolving? >> I mean, there's a ransomware attack that's happening right now as we speak, right? What is it? One in every 11 seconds or so on. And it's getting very, very sophisticated. And you're absolutely right, the target early on used to be the network, or the firewall and so on and so forth. Now, it is the backup. So you have to be very smart about how you protect your backup and if you do get attacked, which a lot of CSOs are starting to realize, it's not about just preventing. But it's also what do you do if it does happen? How can you be resilient in the case of an attack? How can you recover if something happens? And that's where we come in to play as well. >> What's some of the state of the art posture, security posture and cyber resilient techniques? Can you share your observations on what are some of the current state of the art positions? I mean, besides they buy everything, and they want everything, but we're looking at a cost reduction, slow down in the recession, customer's going to look at belt tightening. We heard that from Adam Celeste. Has that changed or enhanced the posture, and impact to the resiliency on the cyber side? >> Yeah, I think customers are getting really smart in terms of how they're adopting cloud. We saw a tremendous amount of growth from a cloud usage perspective, I think, over the last two years and through the pandemic. But now they're getting smart about, "How am I consuming that cloud?" Which is where the consumption's starting to slow down. But that does not mean they're not using cloud, right? And security from a cloud perspective is way different from the old world, which was very static. You're in a completely dynamic environment now. So everybody talks about zero trust security. You have to have that level of no trust, trust nothing, authenticate everything, in terms of how you approach what connects to your network, what services connect to your network and so on. And we follow the same approach, but we also believe that one solution cannot solve it. And which is why we had this announcement around our security advisory council, and security partnership and alliances, where we are providing data to additional solutions, or insights into other security solutions that will help the customer in the end. We talked about how some customers have anywhere between 50 to 70 vendors on their network for security. We want to reduce that noise and that clutter, especially when it comes to cost and expenses. Right? >> Awesome. I want to ask you a personal question if you don't mind. You're new, relatively new to Cohesity, SVP, Senior Vice President, General Manager of the cloud. Obviously, AWS, the biggest cloud, there's other clouds. What attracted you to Cohesity? What was the key thing that attracted you to this company to take a leadership role as this next wave comes in for cloud, and security and what Cohesity is doing? >> Yeah, there are a couple of reasons. Number one and most important was the maturity of the product and the quality of the product. Mohit Aron was our founder, you know, known as the grandfather or as the father of hyperconverge networking. >> He's a legend. >> He's a legend, right? >> (laughs) Just say it. >> And he's built a phenomenal set of technologies that really helps customers and that brings me to the second point, which is customers. We are a customer-obsessed company. And as I was talking to Mohit and Sanjay was our CEO, and Lynn was our CMO and others in the company, it was very evident to me that the core DNA of the company is really helping our customers be successful. Those two things put together. And the third thing, really, I am very culturally-obsessed when it comes to how organizations are run. We have a very strong culture in terms of how we treat employees, how we build the right set of products, and how we go to market. Right? Those three things put together, helped me really make a decision. Obviously, the leadership team within Cohesity was top notch as well. So every one of them that I spoke to had that same core belief system. That had helped a lot. >> Sanjay's a good friend of theCUBE, we've interviewed him many times with VMware. Paul, you know Sanjay's, he loves to get on cam. We hope to have him on tomorrow, if we can get him on the calendar. But you know, Sanjay told me one time, "I never missed a quarter." In his SAP, VMware, he's proud. We'll see, Paul, we're- >> Well, I'm going to hold him to that. >> We better not miss a quarter, I'm going to hold him to that. How's business? How's it, healthy? >> It's been great. We are seeing consistent demand for all of our products. As you can see, we continue to release new products into the market that customers are asking for. We are listening to what customers really want. Our roadmap is really based on two things, customer demand and market and where the market is growing. We have to stay on top of how the market is evolving based on the new challenges that customers are facing. Right? So markets, we are doing really good, company continues to grow and Sanjay has been fantastic in terms of driving that leadership. >> Yeah, he's a good driver. And again, he's Mr. Quarter for a reason, he's disciplined. >> (laughs) Very disciplined. >> Another reason, initiative, Cohesity's is the data security alliance. You put together a group of about a dozen security companies. Getting security companies to work with each other is always a challenge. How did you convince them to join with you? >> Well, one, we aligned on a mission. I mean, in the end, all the partners that we are talking about, they all care about what customers want. And we talked earlier about having that, you know, what is that single pane of glass when it comes to security? Is there one? Probably not. But if you can reduce the chatter, and the noise amongst all these companies, that helps. The other thing is they also understood our mission was really around the security, around data. We talked earlier about how security used to be very parameter or centric, but what you're really trying to save and secure is your data, which is your Queen Bee. And so a couple of months ago at our customer advisory council, I talked about moving and shifting the focus of security to be very data centric. And what we do in this partnership and alliance is a true integration. So there's a lot of engineering work that goes in, is us providing insights around the data to the security partners who can then leverage that to help customers be protected early on. Conversely, they can provide insights into an attack that's emanating possibly, to let us know that there's something happening, so we can lock up the data. So it's a bidirectional, symbiotic relationship between these partners and they all believe in that common cause of making sure the customers get protected. As we talked about earlier, lots of cyber attacks happening even as we speak, if we can collectively do something good in terms of making customers secure and successful, let's do it. >> So what will result from this alliance other than a press release? >> Customers will be successful, hopefully, not just protect customers from ransomware attacks, but also respond and recover if something does happen. We also announce our security council led by Kevin Mandia, and then we have some other big security advisors in that council as well. And that's been very helpful. So it's not just about the product itself, but it's also the collective experience of all these folks who can help and advise and coach CSOs, and other organizations on, what are the best practices? What are the things you're not really considering? What is the vision for you from an architecture standpoint? How is security threats starting to get more, and more mature? And how can you account for that? How can you reduce cost, to your point, right? How can you reduce cost when it comes to managing all these security solutions? >> No, there's no industry where working, it's more important for vendors to work together than in this one. >> Absolutely. I mean, especially for security, I don't think there's a one size fits all solution. So we have to work together. Right? >> What's your state of the union? You were at HashiCorp before you came here, you've been in the industry for a while, you've seen a few cycles of innovation. We're in a really weird time right now, because AWS wasn't really as powerful in 2008, when the last recession was hard too. They weren't really that big then. Now, they're a big part of the economic equation. So agility means fast speed. Can they help us get out of the pandemic? Customer's going to tighten their belts? Is there going to be a pullback? Is there tech spending? All these questions are looming. What are your customers seeing? What do you think is going to happen given the history? 'Cause I don't see the building stopping. I think you'll see more cloud, more savings. So is there fine-tuning solutions? What are customers thinking like now? >> I mean, if you think back to the last recession, the last major one, 2009, that's really about the time when you saw customers thinking about that whole digital transformation, because they started understanding that the way to connect with customers is through a digital engagement. Right? Now, as we've gone through a 10, 15 year period where there has been a lot of digital transformation, there's been a lot of investment in the cloud. Cloud is no longer seen with suspicion. Now, it's about getting smart on how to use it, how to build the right applications. Are there the right set of applications that need to stay in the cloud? And there might be others that need to stay on-prem. Right? I've talked to customers and CIOs who've mentioned to me in the past, that they would go a hundred percent in the cloud, and six months later they come back and they're like, "Nope, you're not going a hundred percent in the cloud. Maybe it's 10% or 15%." >> So they're moving. So what's your plan? You're the GM, you're in charge, you've got to take that next hill. Is it a tailwind, headwind? You've got to navigate the waters here, so to speak, mixed metaphors, but for the most part, you got a business opportunity. >> Absolutely. >> What's the outlook look like? What's your vision? What's the plan? >> Yeah. When it comes to cloud, there are certain things that are a common denominator. Right? One is how do you enable not just applications that are completely on cloud, but also that's on-prem? So for us, that hybrid movement is extremely important. But to create a single seamless UI and experience from an end-customer perspective. So for me, maintaining that and more at team, the R and D team at Cohesity have done a phenomenal job around that. For me, it's to maintain that, and then build additional workloads that make sense from a customer standpoint. There's a lot of investment customers are making. We also have to make sure that they're utilized correctly, and their stored, backed up data, recovered in a way that makes sense for them. And then if things do go south in terms of attacks or other issues, how can we help them get back up to speed, and make sure their business does not suffer? Right? So all of those combined, I think from a cloud perspective, it's the agility, the scalability, and the speed and swiftness that we can work with. >> Well, it sounds like he's ready for the Instagram Real Challenge, our new format on theCUBE. We're going to do a little segment where you can deliver a YouTube Short, Instagram Reel, TikTok or CUBE Gem. More of a thought leadership soundbite for 30 seconds around your view of why is cloud important right now. What's going on at this event that people should pay attention to? What's Cohesity doing? If you can put together a reel, a sizzle reel, or a thought leadership statement. What would that be? >> It would be that cloud is important for any business to be successful. And that's a given right now. I mean, digital transformation is an overused term, but the reality is it's here to stay. And it is the reason why everybody has a mobile phone. Half the people walking on the floor right now is looking at their phone and walking around. And that's your engagement method. So if you don't transform yourself to be able to connect with your end-user, your customer, you will not be successful. And Cohesity can help you by making sure that all of that data that you have, everything that you need in order to be successful to drive that engagement with your customers secure is backed up. No matter what, we will get you back up and running, and you will be successful. And we are in the success journey with you. >> Amith Nair, Senior Vice President, General Manager, Cohesity, the Cloud. Thanks for coming on theCUBE. For Paul Gillen, my co-host. I'm John Furrier here, live on the floor, wrapping up day two, few more segments, stay with us. We got a lot of action coming. We'll be right back with more after the short break. theCUBE, the leader in tech coverage. (bright music)

>>We are back in the final stretch at Supercomputing 22 here in Dallas. I'm your host Paul Gillum with my co-host Dave Nicholson, and we've been talking to so many smart people this week. It just, it makes, boggles my mind are next guest. J Poso is the HPC and AI technology strategist at Dell. Jay also has a PhD in astronomy from the University of Texas. And I'm guessing you were up watching the Artemis launch the other night? >>I, I wasn't. I really should have been, but, but I wasn't, I was in full super computing conference mode. So that means discussions at, you know, various venues with people into the wee hours. >>How did you make the transition from a PhD in astronomy to an HPC expert? >>It was actually really straightforward. I did theoretical astrophysics and I was modeling what white dwarfs look like when they create matter and then explode as type one A super Novi, which is a class of stars that blow up. And it's a very important class because they blow up almost exactly the same way. So if you know how bright they are physically, not just how bright they appear in the sky, but if you can determine from first principles how bright they're, then you have a standard ruler for the universe when they go off in a galaxy, you know how far the galaxy is about how faint it is. So to model these though, you had to understand equations of physics, including electron degeneracy pressure, as well as normal fluid dynamics kinds of of things. And so you were solving for an explosive burning front, ripping through something. And that required a supercomputer to have anywhere close to the fat fidelity to get a reasonable answer and, and hopefully some understanding. >>So I've always said electrons are degenerate. I've always said it and I, and I mentioned to Paul earlier, I said, finally we're gonna get a guest to consort through this whole dark energy dark matter thing for us. We'll do that after, after, after the segment. >>That's a whole different, >>So, well I guess super computing being a natural tool that you would use. What is, what do you do in your role as a strategist? >>So I'm in the product management team. I spend a lot of time talking to customers about what they want to do next. HPC customers are always trying to be maximally productive of what they've got, but always wanting to know what's coming next. Because if you think about it, we can't simulate the entire human body cell for cell on any supercomputer day. We can simulate parts of it, cell for cell or the whole body with macroscopic physics, but not at the, you know, atomic level, the entire organism. So we're always trying to build more powerful computers to solve larger problems with more fidelity and less approximations in it. And so I help people try to understand which technologies for their next system might give them the best advance in capabilities for their simulation work, their data analytics work, their AI work, et cetera. Another part of it is talking to our great technology partner ecosystem and learning about which technologies they have. Cause it feeds the first thing, right? So understanding what's coming, and Dell has a, we're very proud of our large partner ecosystem. We embrace many different partners in that with different capabilities. So understanding those helps understand what your future systems might be. That those are two of the major roles in it. Strategic customers and strategic technologies. >>So you've had four days to wander the, this massive floor here and lots of startups, lots of established companies doing interesting things. What have you seen this week that really excites you? >>So I'm gonna tell you a dirty little secret here. If you are working for someone who makes super computers, you don't get as much time to wander the floor as you would think because you get lots of meetings with people who really want to understand in an NDA way, not just in the public way that's on the floor, but what's, what are you not telling us on the floor? What's coming next? And so I've been in a large number of customer meetings as well as being on the floor. And while I can't obviously share the everything, that's a non-disclosure topic in those, some things that we're hearing a lot about, people are really concerned with power because they see the TDP on the roadmaps for all the silicon providers going way up. And so people with power comes heat as waste. And so that means cooling. >>So power and cooling has been a big topic here. Obviously accelerators are, are increasing in importance in hpc not just for AI calculations, but now also for simulation calculations. And we are very proud of the three new accelerator platforms we launched here at the show that are coming out in a quarter or so. Those are two of the big topics we've seen. You know, there's, as you walk the floor here, you see lots of interesting storage vendors. HPC community's been do doing storage the same way for roughly 20 years. But now we see a lot of interesting players in that space. We have some great things in storage now and some great things that, you know, are coming in a year or two as well. So it's, it's interesting to see that diversity of that space. And then there's always the fun, exciting topics like quantum computing. We unveiled our first hybrid classical quantum computing system here with I on Q and I can't say what the future holds in this, in this format, but I can say we believe strongly in the future of quantum computing and that this, that future will be integrated with the kind of classical computing infrastructure that we make and that will help make quantum computing more powerful downstream. >>Well, let's go down that rabbit hole because, oh boy, boy, quantum computing has been talked about for a long time. There was a lot of excitement about it four or five years ago, some of the major vendors were announcing quantum computers in the cloud. Excitement has kind of died down. We don't see a lot of activity around, no, not a lot of talk around commercial quantum computers, yet you're deep into this. How close are we to have having a true quantum computer or is it a, is it a hybrid? More >>Likely? So there are probably more than 20 and I think close to 40 companies trying different approaches to make quantum computers. So, you know, Microsoft's pursuing a topol topological approach, do a photonics based approach. I, on Q and i on trap approach. These are all different ways of trying to leverage the quantum properties of nature. We know the properties exist, we use 'em in other technologies. We know the physics, but trying the engineering is very difficult. It's very difficult. I mean, just like it was difficult at one point to split the atom. It's very difficult to build technologies that leverage quantum properties of nature in a consistent and reliable and durable way, right? So I, you know, I wouldn't wanna make a prediction, but I will tell you I'm an optimist. I believe that when a tremendous capability with, with tremendous monetary gain potential lines up with another incentive, national security engineering seems to evolve faster when those things line up, when there's plenty of investment and plenty of incentive things happen. >>So I think a lot of my, my friends in the office of the CTO at Dell Technologies, when they're really leading this effort for us, you know, they would say a few to several years probably I'm an optimist, so I believe that, you know, I, I believe that we will sell some of the solution we announced here in the next year for people that are trying to get their feet wet with quantum. And I believe we'll be selling multiple quantum hybrid classical Dell quantum computing systems multiple a year in a year or two. And then of course you hope it goes to tens and hundreds of, you know, by the end of the decade >>When people talk about, I'm talking about people writ large, super leaders in supercomputing, I would say Dell's name doesn't come up in conversations I have. What would you like them to know that they don't know? >>You know, I, I hope that's not true, but I, I, I guess I understand it. We are so good at making the products from which people make clusters that we're number one in servers, we're number one in enterprise storage. We're number one in so many areas of enterprise technology that I, I think in some ways being number one in those things detracts a little bit from a subset of the market that is a solution subset as opposed to a product subset. But, you know, depending on which analyst you talk to and how they count, we're number one or number two in the world in supercomputing revenue. We don't always do the biggest splashy systems. We do the, the frontier system at t, the HPC five system at ENI in Europe. That's the largest academic supercomputer in the world and the largest industrial super >>That's based the world on Dell. Dell >>On Dell hardware. Yep. But we, I think our vision is really that we want to help more people use HPC to solve more problems than any vendor in the world. And those problems are various scales. So we are really concerned about the more we're democratizing HPC to make it easier for more people to get in at any scale that their budget and workloads require, we're optimizing it to make sure that it's not just some parts they're getting, that they are validated to work together with maximum scalability and performance. And we have a great HPC and AI innovation lab that does this engineering work. Cuz you know, one of the myths is, oh, I can just go buy a bunch of servers from company X and a network from company Y and a storage system from company Z and then it'll all work as an equivalent cluster. Right? Not true. It'll probably work, but it won't be the highest performance, highest scalability, highest reliability. So we spend a lot of time optimizing and then we are doing things to try to advance the state of HPC as well. What our future systems look like in the second half of this decade might be very different than what they look like right. Now. >>You mentioned a great example of a limitation that we're running up against right now. You mentioned an entire human body as a, as a, as an organism >>Or any large system that you try to model at the atomic level, but it's a huge macro system, >>Right? So will we be able to reach milestones where we can get our arms entirely around something like an entire human organism with simply quantitative advances as opposed to qualitative advances? Right now, as an example, let's just, let's go down to the basics from a Dell perspective. You're in a season where microprocessor vendors are coming out with next gen stuff and those next NextGen microprocessors, GPUs and CPUs are gonna be plugged into NextGen motherboards, PCI e gen five, gen six coming faster memory, bigger memory, faster networking, whether it's NS or InfiniBand storage controllers, all bigger, better, faster, stronger. And I suspect that systems like Frontera, I don't know, but I suspect that a lot of the systems that are out there are not on necessarily what we would think of as current generation technology, but maybe they're n minus one as a practical matter. So, >>But yeah, I mean they have a lifetime, so Exactly. >>The >>Lifetime is longer than the evolution. >>That's the normal technologies. Yeah. So, so what some people miss is this is, this is the reality that when, when we move forward with the latest things that are being talked about here, it's often a two generation move for an individual, for an individual organization. Yep. >>So now some organizations will have multiple systems and they, the system's leapfrog and technology generations, even if one is their real large system, their next one might be newer technology, but smaller, the next one might be a larger one with newer technology and such. Yeah. So the, the biggest super computing sites are, are often running more than one HPC system that have been specifically designed with the latest technologies and, and designed and configured for maybe a different subset of their >>Workloads. Yeah. So, so the, the, to go back to kinda the, the core question, in your opinion, do we need that qualitative leap to something like quantum computing in order to get to the point, or is it simply a question of scale and power at the, at the, at the individual node level to get us to the point where we can in fact gain insight from a digital model of an entire human body, not just looking at a, not, not just looking at an at, at an organ. And to your point, it's not just about human body, any system that we would characterize as being chaotic today, so a weather system, whatever. Do you, are there any milestones that you're thinking of where you're like, wow, you know, I have, I, I understand everything that's going on, and I think we're, we're a year away. We're a, we're, we're a, we're a compute generation away from being able to gain insight out of systems that right now we can't simply because of scale. It's a very, very long question that I just asked you, but I think I, but hopefully, hopefully you're tracking it. What, what are your, what are your thoughts? What are these, what are these inflection points that we, that you've, in your mind? >>So I, I'll I'll start simple. Remember when we used to buy laptops and we worried about what gigahertz the clock speed was Exactly. Everybody knew the gigahertz of it, right? There's some tasks at which we're so good at making the hardware that now the primary issues are how great is the screen? How light is it, what's the battery life like, et cetera. Because for the set of applications on there, we we have enough compute power. We don't, you don't really need your laptop. Most people don't need their laptop to have twice as powerful a processor that actually rather up twice the battery life on it or whatnot, right? We make great laptops. We, we design for all of those, configure those parameters now. And what, you know, we, we see some customers want more of x, somewhat more of y but the, the general point is that the amazing progress in, in microprocessors, it's sufficient for most of the workloads at that level. Now let's go to HPC level or scientific and technical level. And when it needs hpc, if you're trying to model the orbit of the moon around the earth, you don't really need a super computer for that. You can get a highly accurate model on a, on a workstation, on a server, no problem. It won't even really make it break a sweat. >>I had to do it with a slide rule >>That, >>That >>Might make you break a sweat. Yeah. But to do it with a, you know, a single body orbiting with another body, I say orbiting around, but we both know it's really, they're, they're both ordering the center of mass. It's just that if one is much larger, it seems like one's going entirely around the other. So that's, that's not a super computing problem. What about the stars in a galaxy trying to understand how galaxies form spiral arms and how they spur star formation. Right now you're talking a hundred billion stars plus a massive amount of inter stellar medium in there. So can you solve that on that server? Absolutely not. Not even close. Can you solve it on the largest super computer in the world today? Yes and no. You can solve it with approximations on the largest super computer in the world today. But there's a lot of approximations that go into even that. >>The good news is the simulations produce things that we see through our great telescopes. So we know the approximations are sufficient to get good fidelity, but until you really are doing direct numerical simulation of every particle, right? Right. Which is impossible to do. You need a computer as big as the universe to do that. But the approximations and the science in the science as well as the known parts of the science are good enough to give fidelity. So, and answer your question, there's tremendous number of problem scales. There are problems in every field of science and study that exceed the der direct numerical simulation capabilities of systems today. And so we always want more computing power. It's not macho flops, it's real, we need it, we need exo flops and we will need zeta flops beyond that and yada flops beyond that. But an increasing number of problems will be solved as we keep working to solve problems that are farther out there. So in terms of qualitative steps, I do think technologies like quantum computing, to be clear as part of a hybrid classical quantum system, because they're really just accelerators for certain kinds of algorithms, not for general purpose algorithms. I do think advances like that are gonna be necessary to solve some of the very hardest problem. It's easy to actually formulate an optimization problem that is absolutely intractable by the larger systems in the world today, but quantum systems happen to be in theory when they're big and stable enough, great at that kind of problem. >>I, that should be understood. Quantum is not a cure all for absolutely. For the, for the shortage of computing power. It's very good for certain, certain >>Problems. And as you said at this super computing, we see some quantum, but it's a little bit quieter than I probably expected. I think we're in a period now of everybody saying, okay, there's been a lot of buzz. We know it's gonna be real, but let's calm down a little bit and figure out what the right solutions are. And I'm very proud that we offered one of those >>At the show. We, we have barely scratched the surface of what we could talk about as we get into intergalactic space, but unfortunately we only have so many minutes and, and we're out of them. Oh, >>I'm >>J Poso, HPC and AI technology strategist at Dell. Thanks for a fascinating conversation. >>Thanks for having me. Happy to do it anytime. >>We'll be back with our last interview of Supercomputing 22 in Dallas. This is Paul Gillen with Dave Nicholson. Stay with us.

(gentle music) >> From the cube studios in Palo Alto and Boston, connecting with thought leaders all around the world. This is a Cube conversation. >> With Snowflake's, enormously successful IPO, it's clear that data warehousing in the cloud has come of age and a few companies know more about data and analytics than Google. Hi, I'm Paul Gillen. This is a cube conversation. And today we're going to talk about data warehousing and data analytics in the cloud. Google BigQuery, of course, is a popular, fully managed server less data warehouse that enables rapid SQL queries and interactive analysis of massive data sets. This summer, Google previewed BigQuery Omni, which essentially brings the capabilities of BigQuery to additional platforms including Amazon web services and soon Microsoft Azure. It's all part of Google's multicloud strategy. No one knows more about this strategy than Debanjan Saha, General Manager and Vice President of engineering for data analytics and Google cloud. And he joins me today. Debanjan, thanks so much for joining me. >> Paul, nice to meet you and thank you for having me today. >> So it's clear the data warehousing is now part of many enterprise data strategies. How has the rise of cloud change the way organizations are using data science in your view? >> Well, I mean, you know, the cloud definitely is a big enabler of data warehousing and data science, as you mentioned. I mean, it has enabled things that people couldn't do on-prem, for example, if you think about data science, the key ingredient of data science, before you can start anything is access to data and you need massive amount of data in order to build the right model that you want to use. And this was a big problem on-prem because people are always thinking about what data to keep, what to discard. That's not an issue in cloud. You can keep as much of data as you want, and that has been a big boon for data science. And it's not only your data, you can also have access to other data your, for example, your partner's data, public data sets and many other things that people have access to right? That's number one, number two of course, it's a very compute intensive operation and you know, large enterprises of course can afford them build a large data center and bring in lots of tens of thousands of CPU codes, GPU codes, TPU codes whatever have you, but it is difficult especially for smaller enterprises to have access to that amount of computing power which is very very important for data science. Cloud makes it easy. I mean, you know, it has in many ways democratize the use of data science and not only the big enterprises everyone can take advantage of the power of the computing power that various different cloud vendors make it available on their platform. And the third, not to overlook that, cloud also makes it available to customers and users, lots of various different data science platform, for example, Google's own TensorFlow and you have many other platforms Spark being one example of that, right? Both a cloud native platform as well as open source platforms, which is very very useful for people using data science and managed to open source, Spark also makes it very very affordable. And all of these things have contributed to massive boon in data science in the cloud and from my perspective. >> Now, of course we've seen over the last seven months a rush to the cloud triggered by the COVID-19 pandemic. How has that played out in the analytics field? Do you see any longterm changes to, to the landscape? The way customers are using analytics as a result of what's happened these last seven months? >> You know, I think as you know about kind of a digitization of our business is happening over a long period of time, right? And people are using AIML analytics in increasing numbers. What I've seen because of COVID-19 that trend has accelerated both in terms of people moving to cloud, and in terms of they're using advanced analytics and AIML and they have to do that, right? Pretty much every business is kind of leaning heavily on their data infrastructure in order to gain insight of what's coming next. A lot of the models that people are used to, is no longer valid things are changing very very rapidly right? So in order to survive and thrive people have to lean on data, lean on analytics to figure out what's coming around the corner. And that trend in my view is only going to accelerate. It's not going to go the other way round. >> One of the problems with cloud databases, We often hear complaints about is that there's so many of them. Do you see any resolution to that proliferation? >> Well, you know, I do think a one size does not fit all right. So it is important to have choice. It's important to have specialization. And that's why you see a lot of cloud databases. I don't think the number of cloud databases is going to go down. What I do expect to happen. People are going to use interoperable data formats. They are going to use open API so that it's very, very portable as people want to move from one database to another. The way I think the convergence is going to come is two ways, One, you know, a lot of databases, for example, use Federation. If you look at BigQuery, for example, you can start with BigQuery, but with BigQuery, you can have also access to data in other databases, not only in GCP or Google cloud but also in AWS with BigQuery Omni, for example, right? So that provides a layer of Federation, which kind of create convergence with respect, to weighing various different data assets people may have. I have also seen with, for example, with Looker, you know creation of enterprise wide data models and data API is gives people a platform so that they can build their custom data app and data solutions on top up and even from data API. Those I believe are going to be the points of convergence. I think data is probably going to be in different databases because different databases do different things well, that does not mean people wouldn't have access to all their data through one API or one set of models. >> Well, since we're on the subject of BigQuery. Now this summer, you introduced BigQuery Omni which is a database data warehouse, essentially a version of BigQuery that can query data in other cloud platforms, what, what is the strategy there? And what is the customer reaction been so far? >> Well, I mean, you know as you probably have seen talking to customers more than 80% of the customers that we talk to use multiple clouds and that trend is probably not going to change. I mean, it happens for various different reasons sometime because of compliance sometimes because they want to have different tools and different platform sometime because of M and a, we are a big believer of multi-cloud strategy and that's what we are trying to do with BigQuery Omni. We do realize people have choices. Customers will have their data in various different places and we will take our analytics wherever the data is. So customers won't have to worry about moving data from one place to another., and that's what we are trying to do with BigQuery Omni you know, going to see, you know for example, with Anthos, we have created a platform over which you can build this video as different data stacks and applications, which spans multiple clouds. I believe we are going to see more of that. And BigQuery Omni is just the beginning. >> And how have your customers reacted to that announcement. >> Oh deep! They reacted very, very positively. This is the first time they have a major cloud vendor offering a fully managed server less data warehouse platform on multiple clouds. And as I mentioned, I mean we have many customers who have some of their data assets for example, in GCP, they really love BigQuery. And they also have for example, applications running on AWS and Azure. And today the only option they have is to essentially shuttle their data between various different clouds in order to gain insight across the collective pool of data sets that they have, with BigQuery, Omni, they all tended to do that. They can keep their data wherever it is. They can still join across that data and get insights irrespective of which cloud their data is. >> You recently wrote on Forbes about the shortage of data scientists and the need to make data analytics more accessible to the average business user. What is Google doing in that respect? >> So we strongly, I mean, you know one of our goals is to make the data and insight from data available to everybody in the business right? That is the way you can democratize the use of analytics and AIML. And you know, one way to do that is to teach everybody R or Python or some specific tools but that's going to take a long time. So our approach is make the power of data analytics and AI AML available to our users, no matter what tools they're comfortable with. So for example, if you look at a B Q ML BigQuery ML, we have made it possible for our users who like SQL very much to use the power of ML without having to learn anything else or without having to move their data anywhere else. We have a lot of business users for example, who prefer X prefer spreadsheets and, you know, we've connected sheets. We have made the spreadsheet interface available on top of BigQuery, and they can use the power of BigQuery without having to learn anything else. Better yet we recently launched a BigQuery Q and A. And what Q and A allows you to do is to use natural language on top of big query data, right? So the goal, I mean, if you can do that that I think is the Nevada where people, anyone for example, somebody working in a call center talking to a customer can use a simple query to figure out what's going on with the bill, for example, right? And we believe that if we can democratize the use of data, insight and analytics that not only going to accelerate the digital transformation of the businesses, it's also going to grow consumption. And that's good for both the users, as well as business. >> Now you bought Looker last year, what would you say is different about the way Google is coming out the data analytics market from the way other cloud vendors are doing it. >> So Looker is a great addition to already strong portfolio of products that we have but you know, a lot of people think about Looker as a business intelligence platform. It's actually much more than that. What is unique about Looker is the semantic model that Looker can build on top of data assets, govern semantic model Looker can build on top of data assets, which may be in BigQuery maybe in cloud SQL maybe, you know, in other cloud for example, in Redshift or SQL data warehouse. And once you have the data model, you can create a data API and essentially an ID or integrated development environment on top of which you can build your custom workflows. You can build your custom dashboard you can build your custom data application. And that is, I think, where we are moving. I don't think people want the old dashboards anymore. They want their data experience to be immersive within the workflow and within the context in which they are using the data. And that's where I see Lot of customers are now using the power of Looker and BigQuery and other platform that we have and building this custom data apps. And what again, like BigQuery, Looker is also multi-platform it supports multiple data warehouses and databases and that kind of aligns very well with our philosophy of having an open platform that is multicloud as well as hybrid. >> Certainly, with Anthos and with BigQuery Omni, you demonstrated your commitment on P cloud, but not all cloud vendors have an interest in being multicloud. Do you see any, any change that standoff and are you really in a position to influence it? >> Absolutely. I think more than us it's a customer who is going to influence that, right? And almost every customer I talk to, they don't want to be in a walled garden. They want to be an open platform where they have the choice they have the flexibility and I believe these customers are going to push essentially the adoption of platforms, which are open and multicloud. And, you know, I believe over time the successful platforms have to be open platform. And the closed platform if you look at history has never been very successful, right? And you know, I sincerely think that we are on the right path and we are on the side of customers in this philosophy. >> Final question. What's your most important priority right now? >> You know, I wake up everyday thinking about how can you make our customer successful? And the best way to make our customer successful is to make sure that they can get business outcome out of the data that they have. And that's what we are trying to do. We want to accelerate time to value to data, you know, so that people can keep their data in a governed way. They can gain insight by using the tools that we can provide them. A lot of them, we have used internally for many years and those tools are now available to our customers. We also believe we need to democratize the use of analytics and AIML. And that's why we are trying to give customers tools where they don't have to learn a lot of new things and new skills in order to use them. And if we can do them successfully I think we are going to help our customers get more value out of their data and create businesses which can use that value. I'll give you a couple of quick examples. I mean, for example, if you look at Home Depot, they use our platform to improve the predictability of the inventory by two X. If you look at, for example HSBC, they have been able to use our platform to detect financial fraud 10 X faster. If you look at, for example Juan Perez, who's the CIO of UPS, they have used our AIML and analytics to do better logistics and route planning. And they have been able to save 10 million gallons of fuel every year which amounts to 400 million in cost savings. Those are the kind of business outcome we would like to drive with the power of our platform. >> Powerful stuff, democratize data multicloud data in any cloud who can argue with that. Debanjan Saha, General Manager and Vice President of engineering for data analytics at Google cloud. Thanks so much for joining me today. >> Paul, thank you thank you for inviting me. >> I'm Paul Gillen. This has been a cube conversation. >> Debanjan: Thank you. (soft music)

>> Announcer: Live from Boston, Massachusetts, it's theCube. Covering IFS World Conference 2019. Brought to you by IFS. >> Welcome back to Boston everybody you're watching theCube, the leader in live tech coverage. This is day one of the IFS World Conference. I'm Dave Vallante with my co-host Paul Gillen. Melissa Di Donato is here, she's the CEO of SUSE and Jon Roskill is the CEO of Acumatica. Folks, welcome to theCube. >> Thank you so much. >> So you guys had the power panel today? Talking about digital transformation. I got a question for all of you. What's the difference between a business and a digital business? Melissa, I'll give you first crack. >> Before a regular old business and a digital business? Everyone's digital these days, aren't they? I was interviewing the, one of the leaders in Expedia and I said, "Are you a travel company "or are you a digital company? "Like where do you lead with?" And she said to me, "No no, we're a travel company "but we use digital." So it seems like the more and more we think about what the future means how we service our customers, customers being at the core everyone's a digital business. The way you service, the way you communicate the way you support. So whether you're a business or none you're always got to be a digital business. >> You better be a digital business and so-- >> I'm going to take a slightly different tact on that which is, we talk about digital and analog businesses and analog businesses are ones that are data silos they have a lot of systems, so they think they're digital but they're disconnected. And, you know, part of a transformation is connecting all the systems together and getting them to work like one. >> But I think the confict other common thread is data, right? A digital business maybe puts data at the core and that's how they get competitive advantage but, I want to ask you guys about your respective businesses. So SUSE, obviously you compete with the big whale RedHat, you know, the big news last year IBM $34 billion. How did that or will that in your view affect your business? >> It's already affecting our business. We've seen a big big uptake in interest in SUSE and what we're doing. You know, they say that a big part of the install based customers that RedHat and IBM currently have are unhappy about the decision to be acquired by IBM. Whether they're in conflict because we're a very big heavily channel business, right? So a lot of the channel partners are not quite happy about having one of their closest competitors now be, you know, part of the inner circle if you will. And other customers are just not happy. I mean, RedHat had fast innovation, fast pace and thought leadership and now all of a sudden they're going to be buried inside of a large conglomerate and they're not happy about that. So when we look at what's been happening for us particularly since March, we became an independent company now one of the world's largest independent open source company in the world. Since IBM has been taking over from RedHat. And, you know, big big uptake. Since March we became independent we've been getting a lot of questioning. "Where are we, where are we going, what are we doing?" And, " Hey, you know, I haven't heard about SUSE a while "what are you doing now?" So it's been really good news for us really, really good news. >> I mean, we're huge fans of RedHat. We do a lot of their events and-- >> Melissa: I'm a huge fan myself. >> But I tell you, I mean, we know from first hand IBM has this nasty habit of buying companies tripling the price. Now they say they're going to leave RedHat alone, we'll see. >> Yeah, like they said they'd leave Lotus alone and all the others. >> SPSS, you saw that, Ustream, you know one of our platforms. >> What's your view, how do you think it's going to go? >> I don't think it's about cloud I think it's about services and I think that's the piece that we don't really have great visibility on. Can IBM kind of jam OpenShift into its customers you know, businesses without them even really knowing it and that's the near-term cash flow play that they're trying to, you know, effect. >> Yeah, but it's not working for them, isn't though? Because when you look at the install base 90% of their business it's been Linux open source environment and OpenShift is a tag-along. I don't know if that's a real enabler for the future rather than, you know, an afterthought from the past. >> Well, for $34 billion it better be. >> I want to ask you about the cost of shifting because historically, you know if you were IBM, you were stuck with IBM forever. What is involved in customers moving from RedHat to SUSE presumably you're doing some of those migrations style. >> We are, we are doing them more and more in fact, we're even offering migration services ourself in some applications. It depends on the application layer. >> How simple is that? >> It depends on the application. So, we've got some telco companies is very very complex 24/7, you know, high pays, big fat enterprise applications around billing, for example. They're harder to move. >> A lot of custom code. >> A lot of custom code, really deep, really rich they need, you know, constant operation because it's billing, right? Big, fat transactions, those are a little bit more complex than say, the other applications are. Nonetheless, there is a migration path and in fact, we're one of the only open source companies in the world that provides support for not just SUSE, but actually for RedHat. So, if you're a RedHat, for or a well customer that want to get off an unsupported version of RedHat you can come over to SUSE. We'll not just support your RedHat system but actually come up with a migration plan to get you into a supported version of SUSE. >> If it's a package set of apps and you have to freeze the code it's actually not that bad-- >> It's not that bad, no. >> To migrate. All right, Jon I got to ask you, so help us understand Acumatica and IFS and the relationship you're like sister companies, you both the ERP providers. How do you work together or? >> Yeah, so we're both owned by a private equity firm called EQT. IFS is generally focused on $500 million and above company so more enterprise and we're focused on core mid-market. So say, $20 million to $500 million. And so very complementary in that way. IFS is largely direct selling we're a 100% through channels. IFS is stronger in Europe, we're stronger in North America and so they see these as very complementary assets and rather than to, perhaps what's going on with the IBM, RedHat discussion here. Slam these big things together and screw them up they're trying to actually keep us independent. So they put us in a holding company but we're trying to leverage much of each other's goodness as we can. >> Is there a migration path? I mean, for customers who reach the top end of your market can they smoothly get to IFS? >> Yeah, it's not going to be like a smooth you know, turn a switch and go. But it absolutely is a migration option for customers and we do have a set of customers that are outgrowing us you know, we have a number of customers now over a billion dollars running on Acumatica and you know, for a company, we've got one that we're actually talking to about this right now operating in 41 countries global, they need 24/7 support we're not the right company to be running their ERP system. >> On your panel today guys you were talking about, a lot about digital transformations kind of lessons learned. What are the big mistakes you see companies making and kind of what's your roadmap for success? >> I think doing too much too fast. Everyone talks about the digital innovation digital transformation. It's really a business transformation with digital being the underpinning the push forward that carries the business forward, right? And I think that we make too many mistakes with regards to doing too much, too fast, too soon, that's one. Doing and adopting technology for technology's sake. "Oh, it's ML, it's AI." And everyone loves these big buzz words, right? All the code words for what technology is? So they tend to bring it on but they don't really know the outcome. Really really important at SUSE were absolutely obsessed with our customers and during a digital transformation if you remain absolutely sick of anything about your customer at the core of every decision you make and everything you do. Particularly with regards to digital transformation you want to make sure that business outcome is focused on them. Having a clear roadmap with milestones along the journey is really important and ensuring it's really collaborative. We talked this morning about digital natives you know, we're all young, aren't we? Me in particular, but, you know I think the younger generation of digital natives think a little bit differently perhaps than we were originally thinking when we were their age. You know, I depend on that thinking I depend on that integration of that thought leadership infused into companies to help really reach customers in different ways. Our customers are buying differently our customers have different expectations they have different deliverables they require and they expect to be supported in different way. And those digital natives, that young talent can really aid in that delivery of good thought leadership for our businesses. >> So Jon, we're seeing IT spending at the macro slow down a little bit. You know, a lot of different factors going on it's not a disaster, it's not falling off the cliff but definitely pre-2018 levels and one of the theories is that you had this kind of spray-and-pray kind of like Melissa was say, deal was going too fast trying everything and now we're seeing more of a narrow focus on things that are going to give a return. Do you see that happening out there? >> Yeah, definitely some, I mean people are looking for returns even in what's been a really vibrant economy but, you know, I agree with Melissa's point there's a lot of ready, shoot, aim projects out there and, you know, the biggest thing I see is the ones that aren't, the fail that aren't the ones that aren't led by the leadership. They're sort of given off to some side team often the IT team and said, "Go lead digital transformation of the company." And digital transformation you know, Melissa said this morning it's business transformation. You've got to bring the business part of it to the table and you've got to think about, it's got to be led by the CEO or the entire senior leadership team has to be on board and if not, it's not going to be successful. >> So, pragmatism would say, okay, you get some quick hits get some wins and then you got kind of the, you know, Bezos, Michael Dell mindset go big or go home, so what's your philosophy? Moonshots or, you know, quick hits? >> I always think starting you know, you've got to understand your team's capabilities. So starting is something that you can get a gauge of that you know, particularly if you're new and you're walking into an organization, you know. Melissa, I don't know how long you've been in your role now? >> Melissa: 65 days. >> Right, so there you go. So it's probably a good person to ask what, you know, what you're finding out there but I think, you know, getting a gauge of what your resources are. I mean, one of the things you see around here is there are, you know, dozens of partner firms that are, or can be brought into, you know supplement the resources you have in your own team. So being thoughtful in that is part of the approach. And then having a roadmap for what you're trying to do. Like we talked this morning about a customer that Linda had been talking about. Have been working on for six or seven years, right? And you're saying, for an enterprise a very large enterprise company taking six or seven years to turn the battleship maybe isn't that long. >> Okay, so you got the sister company going on. Do you have a commercial relationship with IFS or you just here as kind of an outside speaker and a thought leader? >> I'm here as an outside speaker thought leader. There is talk that perhaps we can you know, work together in the future we're trying to work that out right now. >> I want to ask you about open source business models. We still see companies sort of struggling to come up with, not profitable but, you know, insanely profitable business models based on open source software. What do you see coming out of all this? Is there a model that you think is going to work in the long term? >> I think the future is open source for sure and this is coming from a person who spent 25 years in proprietary software having worked for the larger piece here in vendors. 100% of my life has been dedicated to proprietary software. So whilst that's true I came at SUSE and the open source environment in a very different way as a customer running my proprietary applications on open source Linux based systems. So I come with a little bit different of a, you know, of an approach I would say. The future's open source for sure the way that we collaborate, the innovation the borderless means of which we deliver you know, leadership within our business is much much different than proprietary software. You would think as well that, you know the wall that we hide behind an open source being able to access software anywhere in a community and be able to provide thought leadership masks and hides who the developers and engineers are and instead exacerbates the thought leadership that comes out of them. So it provides for a naturally inclusive and diverse environment which leads to really good business results. We all know the importance of diversity and inclusion. I think there is definitely a place for open source in the world it's a matter providing it in such a way that creates business value that does enable and foster that growth of the community because nothing is better than having two or three or four or five million developers hacking away at my software to deliver better business value to my customers. The commercial side is going to be around the support, right? The enterprise customers would want to know that when bump goes in the night I've got someone I can pay to support my systems. And that's really what SUSE is about protecting our install base. Ensuring that we get them live, all the time every day and keep them running frictionlessly across their IT department. >> Now there's another model, the so-called open core model that holds that, the future is actually proprietary on top of an open base. So are you saying that you don't think that's a good model? >> I don't know, jury's out. Next time that you come to our event which is going to be in March, in Dublin. We're doing our SUSECON conference. Leave that question for me and I'll have an answer for you. I'm pontificating. >> Well I did and-- >> It's a date. The 12th of March. >> It's certainly working for Amazon. I mean, you know, Amazon's criticized for bogarting open source but Redshift is built on open source I think Aurora is built on open source. They're obviously making a lot of money. Your open core model failed for cloud era. Hortonworks was pure, Hortonworks had a model like, you know, you guys and RedHat and that didn't work and now that was kind of profitless prosperity of Hadoop and maybe that was sort of an over head-- >> I think our model, the future's open-source no question. It's just what level of open source within the sack do we keep proprietary or not, it's the case maybe, right? Do we allow open source in the bottom or the top or do we put some proprietary components on top to preserve and protect like an umbrella the core of which is open source. I don't know, we're thinking about that right now. We're trynna think what our future looks like. What the model should look like in the future for the industry. How can we service our customers best. At the end of the day, it's satisfying customer needs and solving business problems. If that's going to be, pure open source or open source with a little bit of proprietary to service the customer best that's what we're all going to be after, aren't we? >> So, there's no question that the innovation model is open source. I mean, I don't think that's a debate, the hard part is. Okay, how do you make money? A bit of open source for you guys. I mean, are you using open source technologies presumable you are, everybody is but-- >> So we're very open API's, who joined three years ago. We joined openapi.org. And so we've been one of the the leading ERP companies in the industry on publishing open API's and then we do a lot of customization work with our community and all of that's going on in GitHub. And so it's all open source, it's all out there for people who want it. Not everybody wants to be messing around in the core of a transaction engine and that's where you get into you know, the sort of the core argument of, you know which pieces should be people modifying? Do you want people in the kernel? Maybe, maybe not. And, you know, this is not my area of expertise so I'll defer to Melissa. Having people would be able to extend things in an open source model. Having people be able to find a library of customizations and components that can extend Acumatica, that's obviously a good thing. >> I mean, I think you hit on it with developers. I mean, that to me is the key lever. I mean, if I were a VM where I'd hire you know, 1000, 2000 open source software developers and say, "Go build next-generation apps and tools "and give it away." And then I'd say, "Okay, Michael Dell make you a hardware "run better in our software." That's a business model, you can make a lot of money-- >> 100% and we're, you know, we're going to be very acquisitive right now, we're looking for our future, right? We're looking to make a mark right now and where do we go next? How can we help predict the outcome next step in the marketplace when it pertains to, you know, the core of applications and the delivery mechanism in which we want to offer. The ease of being able to get thousands of mainframe customers with complex enterprise applications. Let's say, for example to the cloud. And a part of that is going to be the developer network. I mean, that's a really really big important segment for us and we're looking at companies. Who can we acquire? What's the business outcome? And what the developer networks look like. >> So Cloud and Edge, here got to be two huge opportunities for you, right? Again, it's all about developers. I think that's the right strategy at the Edge. You see a lot of Edge activity where somebody trying to throw a box at the Edge with the top down, in a traditional IT model. It's really the devs up, where I think-- >> It is, it is the dev ups, you're exactly right. Exactly right. >> Yeah, I mean, Edge is fascinating. That's going to be amazing what happens in the next 10 years and we don't even know, but we ship a construction edition we've got a customer that we're working with that's instrumenting all of their construction machinery on something like a thousand construction sites and feeding the sensor data into a Acumatica and so it's a way to keep track of all the machines and what's going on with them. You know, obviously shipping logistics the opportunity to start putting things like, you know, RFID tags on everything an instrument to all of that, out at the Edge. And then the issue is you get this huge amount of data and how do you process that and get the intelligence out of it and make the right decisions. >> Well, how do you? When data is plentiful, insights, you know, aren't is-- >> Yeah, well I think that's where the machine learning breakthroughs are going to happen. I mean, we've built out a team in the last three years on machine learning, all the guys who've been talking about Amazon, Microsoft, Google are all putting out machine learning engines that companies can pick up and start building models around. So we're doing one's around, you know inventory, logistics, shipping. We just release one on expense reports. You know, that really is where the innovation is happening right now. >> Okay, so you're not an inventor of AI you're going to take those technologies apply 'em to your business. >> Yeah, we don't want to be the engine builder we want to be the guys that are building the models and putting the insight for the industry on top that's our job. >> All right Melissa, we'll give you the final word and IFS World 2019, I think, is this your first one? >> It's my first one, yeah-- >> We say bumper sticker say when your truck's are pulling away or-- (laughs) >> A bumper sticker would say, "When you think about the future of open source "think about SUSE." (laughing) >> Dave: I love it. >> I'd say in the event, I mean, I'm super-impressed I think it's the group that's here is great the customers are really enthused and you know, I have zero bias so I'm just giving you my perspective. >> Yeah, I mean the ecosystem is robust here, I have to say. I think they said 400 partners and I was pleasantly surprised when I was walking around last-- >> This is your second one, isn't it? >> It's theCubes second one, my first. >> Oh your first, all right, well done. And so what do you think? Coming back? >> I would love to come back. Especially overseas, I know you guys do a bunch of stuff over seas. >> There you go, he wants to travel. >> Dublin in March? >> March the 12th. >> Dublin is a good place for sure so you're doing at the big conference? >> Yep, the big conference center and it's-- >> That is a great venue. >> And not just because the green thing but it's actually because (laughs). >> No, that's a really nice venue, it's modern It's got, I think three or four floors. >> It does, yeah yeah, we're looking forward to it. >> And then evening events at the, you know, the Guinness Storehouse. >> There you go. >> Exactly right. So we'll look forward to hosting you there. >> All right, great, see you there. >> We'll come with our tough questions for you. (laughing) >> Thanks you guys, I really appreciate your time. >> Thanks very much. >> Thank you for watching but right back, right after this short break you're watching theCube from IFS World in Boston be right back. (upbeat music)

>>Bly from Boston, Massachusetts. It's the cube covering ifs world conference 2019 brought to you by ifs. >>Okay. We're back in Boston, Massachusetts ifs world day one. You walked into cube Dave Vellante with Paul Gillen boss Devoss is here. He's the director of ISF I F S labs and Bob Dico who's the vice president of AI and RPA at ifs jets. Welcome. Good to see you again. Good morning bossy. We're on last year. I'm talking about innovation ifs labs. First of all, tell us about ifs labs and what you've been up to in the last 12 months. Well, I have has Lapsis a functioning as the new technology incubator. Fire Fest writes over continuously looking at opportunities to bring innovation into, into product and help our customers take advantage of all the new things out there to yeah. To, to create better businesses. And one of the things I talked about last year is how we want to be close to our customers. And I think, uh, that's what we have been doing over the pasta pasta year. Really be close to our customers. So Bob, you got, you got the cool title, AI, RPA, all the hot cool topics. So help us understand what role you guys play as ifs. As a software developer, are you building AI? Are you building RPA? Are you integrating it? Yes, yes. Get your paint. >>I mean, our value to our customers comes from wrapping up the technology, the AI, the RPA, the IOT into product in a way that it's going to help their business. So it's going to be easy to use. They're not going to need to be a technical specialist to take advantage of it. It's going to be embedded in the product in a way they can take advantage of very easily that that's the key for us as a software developer. We don't want to offer them a platform that they can just go and do their own thing. We want to sort of control it, make it easier for them. >>So I presume it's not a coincidence that you guys are on together. So this stuff starts in the labs and then your job is to commercialize it. Right? So, so take machine intelligence for example. I mean it can be so many things to so many different people. Take us back to sort of, you know, the starting point, you know, within reason of your work on machine intelligence, what you were thinking at the time, maybe some of the experiments that you did and how it ends up in the product. Oh, very good question. Right? So I think we start at a, Oh, well first of all, I think ifs has been using a machine learning at, at various points in our products for many, many years of Trumbull in our dynamic scheduling engine. We have been using neural networks to optimize fuel serve scheduling for quite some many years. >>But I think, um, if we go back like two years, what we sold is that, uh, there, there's a real potential, um, in our products that if you will take machine learning algorithms inside of the product to actually, um, help ultimately certain decisions in there, um, that could potentially help our business quite a bit. And the role of ifs lapse back in the day as that we just started experimenting, right? So we went out to different customers. Uh, we started engaging with them to see, okay, what kind of data do we have, what kind of use cases are there? And basically based on that, we sort of developed a vision around AI and a division back in the day was based on on three important aspects, human machine interaction optimization and automation. And that kind of really lended well with our customer use case. We talked quite a bit about that or the previous world conference. >>So at that point we basically decided, okay, you know what, we need to make serious work of this, uh, experimenting as boots. But at a certain point you have to conclude that the experiments were successful, which we did. And at that point we decided to look at, okay, how can we make this into a product and how to normally go system. We started engaging with them more intensively and starting to hand over in this guys, we decided the most also a good moment to bring somebody on board that actually has even more experience and knowledge in AI and what we already had as hive as labs. But that could basically take over the Baton. And say, okay, now I am going to run with it and actually start commercializing and productizing that still in collaboration with IVIS laps. But yeah, taking that next step in the road and then then Bob came onboard. >>Christian Pedersen made the point during the keynote this morning that you have to avoid the, the appeal of technology for technology's sake. You have to have it. I start with the business use case. You are both very technology, very deep into the technology. How do you keep disciplined to avoid letting the technology lead your, your activities? >>Well, both. Yeah. So, so I think a good example is what we see this world's going fronts as well. It is staying closer to customer and, and, and accepting and realizing that there is no, um, there's no use in just creating technology for sake of technology as you say yourself. So what we did here for example, is that we showcase collaboration projects with, with customers. So, for example, we show showcase a woman chair pack, which um, as a, as a manufacturing of spouting pouches down here in Massachusetts actually, uh, and they wanted to invest in robotics to get our widows. So what we basically did is actually wind into their factory literally on the factory floor and start innovating there. So instead of just thinking about, okay, how do robotics and AI for subrogations or one of our older products work together, we set, let's experiment on the shop floor off a customer instead of inside of the ivory towers. Sometimes our competitors to them, they'll start to answer your question. >>Sure. I can pick up a little, a little feasible. Yeah. Well, so in, I think the really important thing, and again, Christian touched on it this morning is not the individual technologies themselves. It's how they work together. Um, we see a lot of the underlying technologies becoming more commoditized. That's not where companies are really starting to differentiate algorithms after a while become algorithms. There's a good way of doing things. They might evolve slightly over time, but effectively you can open source a lot of these things. You can take advantage, the value comes from that next layer up. How you take those technologies together, how you can create end to end processes. So if we take something like predictive, we would have an asset. We would have sensors on that asset that would be providing real time data, uh, to an IOT system. We can combine that with historical maintenance data stored within a classic ERP system. >>We can pull that together, use machine learning on it to make a prediction for when that machine is gonna break down. And based on that prediction, we can raise a work order and if we do that over enough assets, we can then optimize our technicians. So instead of having to wait for it to break down, we can know in advance, we can plan for people to be in the right the right place. It's that end to end process where the value is. We have to bring that together in a way that we can offer it to our customers. There's certainly, you know, a lot of talk in the press about machines replacing humans. Machine of all machines have always replaced humans. But for the first time in history, it's with cognitive functions. Now it's, people get freaked out. A little bit about that. I'm hearing a theme of, of augmentation, you know, at this event. >>But I wonder if you could share your thoughts with regard to things like AI automation, robotic process automation. How are customers, you know, adopting them? Is there sort of concern up front? I mean we've talked to a number of RPA customers that, you know, initially maybe are hesitant but then say, wow, I'm automating all those tasks that I hate and sort of lean in. But at the same time, you know, it's clear that this could have an effect on people's jobs and lives. What are your thoughts? Sure. Do you want to kick off on them? Yeah, I'll know. Yeah, absolutely. That's fine. So I think in terms of the, the automation, the low level tasks, as you say, that can free up people to focus on higher value activities. Something like RPA, those bots, they can work 24, seven, they can do it error free. >>Um, it's often doing work that people don't enjoy anyway. So that tends to actually raise morale, raise productivity, and allow you to do tasks faster. And the augmentation, I think is where it gets very interesting because you need to, you often don't want to automate all your decisions. You want people to have the final say, but you want to provide them more information, better, more pertinent ways of making that decision. And so it's very important. If you can do that, then you've got to build the trust with them. If you're going to give them an AI decision that's just out of a black box and just say, there's a 70% chance of this happening. And what I founded in my career is that people don't tend to believe that or they start questioning it and that's where you have difficulty. So this is where explainable AI comes in. >>I do to be able to state clearly why that prediction is being made, what are the key drivers going into it? Or if that's not possible, at least giving them the confidence to see, well, you're not sure about this prediction. You can play around with it. You can see I'm right, but I'm going to make you more comfortable and then hopefully you're going to understand and, and sort of move with it. And then it starts sort of finding its way more naturally into the workplace. So that's, I think the key to building up successful open sexually. What it is is it's sort of giving a human the, the, the parameters the and saying, okay, now you can make the call as to whether or not you want to place that bet or make a different decision or hold off and get more data. Is that right? >>Uh, yeah. I think a lot of it is about setting the threshold and the parameters with within which you want to operate. Often if a model is very confident, either you know, a yes or a no, you probably be quite happy to let it automate. Take that three, it's the borderline decision where it gets interesting. You probably would still want someone to look over it, but you want them to do it consistently. You want them to do it using all the information to hand and say that's what you do. You're presented to them. And to add to that, um, I think we also should not forget they said a lot of our customers, a lot of companies are, are actually struggling finding quality stuff, right? I mean aging of the workforce riots, we're, we're old. I'm retiring eventually. Right? So aging of the workforce is a potential issue. >>Funding, lack of quality. Stop. So if I go back to the chair pack example I was just talking about, um, and, and, and some of the benefits they get out of that robotics projects, um, um, is of course they're saving money right there. They're saving about one point $5 million a year on money on that project, but their most important benefits for them, it's actually the fact that I have been able to move the people from the work floor doing that into higher scope positions, effectively countering the labor shortage today. They were limited in their operations, but in fact, I had two few quality stuff. And by putting the robots in, they were able to reposition those people and that's for them the most important benefits. So I think there's always a little bit of a balance. Um, but I also think we eventually need robots. >>We need ultimation to also keep up with the work that needs to be done. Maybe you can speak to Bobby, you can speak to software robots. We've, Pete with people think of robots, they tend to think of machines, but in fact software robots are, where are the a, the real growth is right now, the greatest growth is right now. How pervasive will software robots be in the workplace do you think in the three to five years? >> I think the software robots as they are now within the RPA space, um, they fulfill a sort of part of the Avril automation picture, but they're never going to be the whole thing. I see them very much as bringing different systems together, moving data between systems, allowing them to interact more effectively. But, um, within systems themselves, uh, you know, the bots can only really scratched the surface. >>They're interacting with software in the same way a human would on the whole by clicking buttons going through, et cetera, beneath the surface. Uh, you know, for example, within the ifs products we have got data understanding how people interact with our products. We can use machine learning on that data to learn, to make recommendations to do things that our software but wouldn't be able to see. So I think it's a combination. There's software bots, they're kind of on the outside looking in, but they're very good at bringing things together. And then insight you've got that sort of deeper automation to take real advantage of the individual pieces of software. >> This may be a little out there, but you guys >>are, you guys are deep into, into the next generation lot to talk right now about quantum and how we could see workable quantum computers within the next two to two to three years. How, what do you think the, the outlook is there? How is that going to shake things up? So >>let me answer this. We were actually a having an active project and I for slabs currently could looking at quantum computing, right? Um, there's a lot of promise in it. Uh, there's also a lot of unfilled, unfulfilled problems in that, right? But if you look at the, the potential, I think where it really starts playing, um, into, uh, into benefits is if the larger the, the, the optimization problems, the larger the algorithms are that we have to run, the more benefits it actually starts bringing us. So if you're asking me for an for an outlook, I say there is potential definitely, especially in optimization problems. Right. Um, but I also think that the realistic outlook is quite far out. Uh, yes, we're all experimenting it and I think it's our responsibility as ifs or ciphers laps to also look on what it could potentially mean for applications as we FSI Fs. >>But my personal opinion is the odd Lucas. Yeah. So what comes five to 10 years out? What comes first? Quantum computing or fully autonomous driverless vehicles? Oh, that's a tricky question. I mean, I would say in terms of the practical commercial application, it's going to be the latter in that much so that's quite a ways off. Yeah, I think so. Of course. Question back on on RPA, what are you guys exactly doing on RPA? Are you developing your own robotic process automation software or are you integrating, doing both say within the products? We, you know, if we think of RPA as, as this means of interacting with the graphical user interface in a way that a human would within the product. Um, we, we're thinking more in terms of automating processes using the machine learning as I mentioned, to learn from experience, et cetera. Uh, in a way that will take advantage of things like our API eighth, an API APIs that are discussed on main stage today. >>RPA is very much our way of interacting with other systems, allowing other systems when trapped with ifs, allowing us to, to send messages out. So we need to make it as easy as possible for those bots to call us. Uh, you know, that can be by making our screens nice and accessible and easy to use. But I think the way that RPA is going, a lot of the major vendors are becoming orchestrators really. They're creating these, these studios where you can drag and drop different components into to do ACR, provide cognitive services and you know, elements that you could drag and drop in would be to say, ah, take data from a file and load it into ifs and put it in a purchase order. And you can just drag that in and then it doesn't really matter how it connects to YFS. It can do that via the API. And I think it probably will say it's creating the ability to talk to ifs. That's the most important thing for us. So you're making your products a RPA ready, friendly >>you, it sounds like you're using it for your own purposes, but you're not an RPA vendor per se. You know what I'm saying? Okay. Here's how you do an automation. You're gonna integrate that with other RPA leadership product. I think we would really take a more firm partner approach to it. Right? So if a customer, I mean, there's different ways of integrating systems to get our RPA as a Google on there. There's other ways as well, right? That if a customer actually, um, wants to integrate the systems together using RPA, very good choice, we make sure that our products are as ready as much for that as possible. Of course we will look at the partner ecosystem to make sure that we have sufficient and the right partners in there that a customer has as a choice in what we recommends. But basically we say where we want to be agnostic to what kind of RPA feminists sits in there that was standing there was obviously a lot of geopolitical stuff going on with tariffs and the like. >>So not withstanding that, do you feel as though things like automation, RPA, AI will swing the pendulum back to onshore manufacturing, whether it's Europe or, or U S or is the costs still so dramatically advantageous to, you know, manufacture in China? Well, that pendulum swing in your opinion as a result of automation? Um, I have a good, good question. Um, I'm not sure it's will completely swing, but it will definitely be influenced. Right. One of the examples I've seen in the RPA space ride wire a company before we would actually have an outsourcing project in India where people would just type over D uh, DDD, the purchase orders right now. Now in RPA bolts scans. I didn't, so they don't need the Indian North shore anymore. But it's always a balance between, you know, what's the benefit of what's the cost of developing technology and that's, and it's, and, and it's almost like a macro economical sort of discussion. >>One of the discussions I had with my colleagues in Sri Lanka, um, and, and maybe completely off topic example, we were talking about carwash, right? So us in the, in the Western world we have car wash where you drive your car through, right? They don't have them in Sri Lankan. All the car washes are by hands. But the difference is because labor is cheaper there that it's actually cheaper to have people washing your car while we'd also in the us for example, that's more expensive than actually having a machine doing it. Right. So it is a, it's a macro economical sort of question that is quite interesting to see how that develops over the next couple of years. All right, Jess. Well thanks very much for coming on the cube. Great discussion. Really appreciate it. Thank you very much. You're welcome. All right. I'll keep it right there, but he gave a latte. Paul Gillen moved back. Ifs world from Boston. You watch in the queue.

>> Announcer: Live from Boston, Massachusetts. It's theCUBE, covering IFS World Conference 2019. Brought to you by IFS. >> We're back at IFS World 2019 from the Hynes Convention Center in Boston. I'm Dave Volonte, with my co-host, Paul Gillen. You're watching theCUBE, the leader in live tech coverage. We go out to the events, we extract the signal from the noise, get the best guest, Christian Peterson is here. He's the chief product officer at IFS. Christian, great to see you. >> All right, thank you very much. Happy to be here. >> Your first IFS World Conference, so ... >> It is mine ... >> Mine too, so ... >> Yeah, I'm happy to be here. It's just like getting an injection of customer input and feedback in a very short amount of time So, that's uh, that's awesome. I really love it. >> Yeah, these events are great to connect with customers its one to many conversations. But, give us a sense of your background and why you were attracted to IFS. Why did you join? >> Well from a background perspective, I've always been in the effects of business and technology and uh, you know my passion has always been what we can actually do with technology for businesses to innovate, to differentiate, to do new things to automate things. Really, really a strong believer in the promise of software. Because that's what software is all about. Um, so, um, I have a past with Starbucks, I've started ELP companies, I've been with Microsoft. Uh, for fifteen, sixteen years. Um, have been with SAP for a number of years. So I joined, I joined IFS last year, um, really because of the transformation and the uh, the journey I just was on and the passion that IFS has always had for the customers. And the outcomes we've created for customers. It's just a perfect environment to, to uh to realize the dream of providing value to customers, outcomes for customers, and leveraging technology in the process. >> Yeah, so see you're a challenger, hashtag for the challenger. A hashtag is started. >> Really, really I mean you were at the giant uh, SAP and going to a smaller, not much smaller, but a smaller company, What were they doing that you thought that excited you so much? >> Well the exciting thing again is the focus on the customer and the close proximity to customers in everything I.. >> Wouldn't SAP, sorry to interrupt, wouldn't SAP be the same thing though? >> Let me just, let me put it this way, I went to IFS because I (intelligible) really, really brilliantly. So, is that a, is that a nice way of saying it. (laughter) >> (laughing) Okay. >> So were here for your keynote today you sort of laid out a roadmap, a little vision uh, talked a little bit about digital transformation. But, I wanted to talk about, the, you made a big big emphasis on your API platform. Open API's, embracing that, uh its been somewhat a criticism of you guys in the past. And so, maybe it's a response to that or a response to customers, but why the platform, why, to explain it, its importance and how it fits into your roadmap going forward. >> Well the API enablement is important for many different perspectives. First of all, we use API's ourselves. To create user experiences and drive a lot of the innovation where they are merging technology and so forth. That's one aspect of it. So just for our own, our own level of innovation and the pace at which we can innovate with, going forward on the API platform, is, is, is is dramatic. The second area is really again back to the digital transformation that customers are really driving out there um, a lot of that involves, um, really most companies becoming software companies themselves. So now we have a lot of our customers that actually have developers, they're writing software they're driving new offerings to their customers. And to get value out of these offerings for their customers They really need to get access to a a lot of the capabilites that lives inside of the IFS models. They need to get access to data, to get access to processes because, on of the keys in digital transformation regardless in what shape or form it comes is, you need data, you need massive amounts of data. And you need data from within your firewall you need data from third party, and you need structure data all structure data. And participating in that world is absolutely essential that you have that open API philosophy where you expose yourself and your own data and API's. But, also so we can turn the other way and we can consume data and API's from others so we can create similar scenarios. So it's really about being apart of the ecosystem of, uh, of technologies and solutions that customers rely on. And that's why we joined also, the open API foundation. >> You also demonstrated this morning, uh Orena, your new customer experience platform. Talk about what that is and why it's important. >> Well, so it's, it's important of course again because we, um, um, we have this generational shift in people that are coming into the workforce that expect and want to work differently. And, um, if you think about how people actually work, to do and get things done today, or think about ourselves. Now, we're no spring chickens anymore, right, we've been around... >> Speak for yourself. >> We've seen DOS, we've seen DOS systems. >> Yeah my hand went up in the 3.1 question. >> When the three point, did you put the mouse on the screen as well? (laughing) I've literally seen that. So we've been through that, but the people we are getting into the workforce now they have a different mentality. They are not thinking about what they do. Like, we are thinking about, "how does the system work?" "Where do I click? Where do I go next?" The intuition that people now apply to the system when they start working with them, the systems just have to reflect that intuition. It has to be intuitive, it has to be immersive as well. And the immersive part is really based on what the users see, what they do. The contextual information, the contextual intelligence they get in the context of what they do should want them to do more. Because they can, so they get dragged in and the new type of users, they just have that natural intuition, because that's how you browse the web. You go to one place on the web, go to the next thing, You get inspired by this, you go there. And there's no reason why the systems that you get your work done, why they shouldn't be the exact same thing. Orena is a huge step in that direction, together with our mobile enablement on multiple form factors and devices. >> So you, you mentioned you know saw everybody's becoming a software company, every company is becoming, you've been in the software business for awhile you work for a software company now. You're talking about Orena, you're talking about API integration, I showed you our software. My point is, software is hard. (laughs) There's a talent war for employees, we talked about that off camera. Um, so, as you see these companies digitally transforming, becoming software companies, Mark Endrese's, "software is eating the world", Mark Beneoff, "Everybody is becoming a software company", How are they doing? And what role can you play, IFS, in terms of helping them become a software company. Because it's, it's so damn difficult. >> Yeah, I think that the role of being a software company I think the absolute differentiation they want to create through software and differentiate the offerings or other things that they really want to do, We can't really help them there, because they're differentiated. Like if you're differentiated, you can't find something standard and use for that. But we can enable it and um, as we're looking at it, a lot of the emerging technologies that we can enable them with to achieve it, that's a number of things we can do. And, we are introducing a notion of an application, of application services here, where we really, enable these emerging technologies in the context of what we do. So, while you hear about technologies or augmented realities, mixed realities, artificial intelligence and robotics and IOT and artificial intelligence, all the stuff that you have, we take that and put into context of the focus industries that we focus on and the solution categories that we focus on. So EAP, enterprise asset management, service management. And in that way our customers can focus on what they actually need to do with it, versus focus on the, on the technologies. >> And the API platform allows those customers to, whatever the build to integrate to their ERP system if in fact... >> That's correct, that's correct. And as I mentioned, we also use API's not only on the front end of what we provide and expose all we have, but we also consume on the back end. So the way we actually consume the application services and drag them in and embed them is through API, these application services. >> I understand you're working on an entirely new architecture that you will be debuting in the spring of 2020. How is that going to change the game? >> We don't really think about it as a new architecture. We think about it as a natural evolution that includes some of these things. Uh, so for instance, the introducing, uh the introduction of the application services layer that I mentioned, is more a new layer in our architecture that we introduced. So we don't think about it as a new architecture, we're just evolving what we have. And because of that evolution, that is something that our entire product portfolio will benefit from. Um, and, I already mentioned today how we are aligning the product portfolio from an experience perspective. We are bringing the arena experience through our FSM product to our um, PSO product, to our customer engagement product and so forth. So we are aligning that front end experience on the same design patterns, so forth, because you know, a good experience is a good user experience. >> You talk about Orena bot and this, this gentleman here, who's given us this talk, just through out a gardner status. That, that by, I don't know, by whatever year 2023, uh, more money will be spent on bots than mobile integration. Which is, you know, quite a prediction. Your thoughts. >> Well, I, you know, there's, there's always all kinds of interesting predictions. I think actually, um, I actually think, um, there, amount of money may go down but I think the number of bots will go up dramatically. And, I think we will actually get to a situation where, bots will be creating bots. (laughs) Right? So, That's when you get, when we talk about intelligent and autonomous systems, I really believe it. Because there is no reason why we should not begin to see autonomy in software. >> Dave: Right. >> Um, we see it, uh, I use the example this morning, that we put our lives in the hands of technology everyday, when you go in your car and you use adaptor to cruise to control, you're trusting technology. Like, when you are driving your Tesla. I mean there was an example in San Francisco, uh, I think, uh, in December last year, where the police had been following a driver for 17 miles. And the car wouldn't stop because it was driving itself, and the driver was sleeping. So, they had to, they had to, you know, call up Tesla and say like how can we manipulate this technology so the car actually stops, so the police gradually got the car to stop. And, uh, you know, finally the guy woke up and uh, he'd probably had one too many. But he claimed he wasn't driving, so they shouldn't charge him, but, they did. (laughter) >> Of course, yes. Well bots are getting better, but I still, I still often know when I'm talking to a bot, but it's getting better, wouldn't you say? >> Christian: Yeah, it's getting reallly good. >> Paul: I know, last year I was completely fooled by a fundraising bot. But, I got a phone call from a bot that I spoke to for ninety seconds before realizing it was a bot. (laughter) So it's, its getting pretty good. As you look at, at the technology that excites you, about what you're bringing with your product, you talked a lot this morning about different kinds of technology and how you want to be a leader. What technologies excite you the most about the markets you are serving? >> I tell you what excites me the most is to work through the different levels of, of, uh, digital transformation that I talked about. I'm excited about the reflection between businesses and technology. I'm excited about the reflections between people and experiences, and I'm excited about the reflections between automation and efficiency. We have a lot of technology at our hands, That can help us achieve these different things. But, at the end of the day, it's the outcomes that matter. The technologies are exciting and you know, I can get super geeky about a lot of different technologies. But if it doesn't relate to any, any, not technical vision of product, but any business vision you have on what you actually want to do with it as a business, then I think it becomes dangerous. But, of course we have our geek sessions, where we geek out on all these different things. But, we try to separate that from when we actually, uh, you know, designing and building things directly into the product. But we need the geek sessions to get inspired. And understand what is available, so we can put it in the context of what our customers need today and also what they'll be needing in the future. >> Since you have some decent observation space and digital transformation, I want to ask a question. Uh, uh, our partner ETR, they have a data platform. And I was down in New York last week just talking to them and, one of the theories is, is so spending is starting to slow down a little bit overall on the macro. One of the theories is that digital transformation in the last two years, there's been a lot of experimentation. So a lot of try and, you know, everything. And now they're going into the production with, with what they, what they feel will delivery business value. And two things are happening is their premise. One is, they're narrowing down the focus on new technologies and make, making bets for all the disruptive technologies. The other is, a lot of the legacy stuff, they are pulling out. Saying, "okay, we're moving on." Um, are you seeing that, are you seeing this sort of... That, the bell weathers anyway going heavy now into production with digital transformation. What are you seeing? >> I think its a progression. >> Dave: Uh huh. >> I think it's scenario based. I don't see, I don't see companies making like, an all out bet from one day to another. >> Dave: Just mixed. >> It's mixed and I think you need to take a cautious approach because, you know, you don't, you... When you're in the technology world, you don't always get it right in the first go, we certainly don't get it right, the first time all the time, right? So, often times its important to get something out there. Learn from it, innovate, fail fast sometimes. Um, the worst thing you can do is not acknowledge when you have mad a mistake, And I think that is a risk that some companies also, bear with digital transformation is... If you need to adjust what you, what you thought was the right thing to do, make the adjustment as quickly as possible. >> Dave: You talked in your keynote about tailoring solutions and I want to understand your philosophy. How dogmatic are you, uh, uh, about, uh, not making customizations versus allowing your customers to make those, those tailored? And, and how do you manage that from a, you know cloud and SaaS delivery, evergreen, I think you call it stand point? >> Christian: We, we, absolutely believe that customers should have solutions that match exactly what they need and so forth. We also heard from stage today that, a good philosophy, I really subscribe to that philosophy, that if you're doing things that, you know, is not really differentiating you as a company or something just use a standard process. Why do something custom if it doesn't mean anything. Then you can adjust your processes to that. But if you have things that really differentiate you as a company, you obviously want to have the technology that supports that. And since that is differentiated, you're not likely to have a standard package file. So in that process, what we need to enable is, we need to enable these scenarios where you can extend, uh, we call it extend on the inside, extend on the outside, but you can achieve what you want but, do it in a way where, you do it in a declarative way. Not by creating or modifying code. So instead we want to make sure that our, the code that we have, that is part of the standard product, can actually interpret declarative code. And that means when we have upgrades and all that stuff, we upgrade the core but the declarative code that the customer has that is, specific to them, remains there and stays there. >> Dave: And that's why the API platform is critical. >> Paul: Right. >> You said no product will be announced or shipped without API enablement, period the end. >> That's correct, We can not because, we can not create a use of front end to anything that doesn't, that isn't API enabled. So, it's very simple. >> Paul: That's a modern architecture. I am curious about you said that one of the reasons that you're at IFS is because it's so customer focused. What is it that this company does differently from companies you've worked at in the past, that exemplifies that customer focus? >> Christian: I think it goes deep um, not only into the culture but also how we actually have people in, all the way in to the individual development teams. Um, I've been in other software companies and the development teams you have developers, you have QA's, you have, you know...testers, you have, you know... Programming just to write the specifications, so forth. We actually have industry solution specialists embedded into the development teams. So, we are, we are, probably our own, you know, worst critic um, and of course then working hand and hand with customers in their processes is essential. But again, if we don't provide the out...if we don't provide the value and the output from what we create for our customers, then it's worth nothing. And that's really the philosophy. If we do not provide value, technology means nothing. >> Dave: So the intersection of domain expertise and software development. Uh Chris, the last question is sort of, what do you hope to get out of this event? Things that you hope to, to take away, or learn or convey to your customers? >> Well I always, I always, look to get feedback. I'm a sucker for feedback and input and learning. Uh, so first of all, I can't wait to walk the expo floor here and really see what all our partners are bringing to the table of innovation. Because they're doing amazing things, so I always enjoy spending a few hours on the, on the expo floor. In the process, get to meet a lot of people, uh and then during the sessions if we can or I'll always end any presentation with an email address. Any, anybody, any customer, any partner will always be able to email me, uh directly, and I, you know... Sometimes a little hard to keep up, but I will respond to every single request. >> Dave: Feedback is a gift. Christian, thanks so much for coming on theCUBE, it was great to see ya. >> Thank you. >> Alright, thank you very much. >> Alright, thank you for watching everybody. Keep it right there, we'll be back with our next guest. We're at IFS World, Boston. You're watching theCUBE. (upbeat music)

>> 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 watching the Cuban leader live tech coverage. We here covering the M I t CDO conference M I t CEO Day to wrapping up. Bob Parr is here. He's a partner in principle at KPMG, and he's joined by Streetcar Krishna, who is the managing director of data science. Aye, aye. And innovation at KPMG. Gents, welcome to the Cube. Thank >> thank you. Let's start with your >> roles. So, Bob, where do you focus >> my focus? Ah, within KPMG, we've got three main business lines audit tax, an advisory. And so I'm the advisory chief date officer. So I'm more focused on how we use data competitively in the market. More the offense side of our focus. So, you know, how do we make sure that our teams have the data they need to deliver value? Uh, much as possible working concert with the enterprise? CDO uh, who's more focused on our infrastructure, Our standards, security, privacy and those >> you've focused on making KPMG better A >> supposed exactly clients. OK, >> I also have a second hat, and I also serve financial service is si Dios as well. So Okay, so >> get her out of a dual role. I got sales guys in >> streetcar. What was your role? >> Yeah, You know, I focus a lot on data science, artificial intelligence and overall innovation s o my reaction. I actually represent a centre of >> excellence within KPMG that focuses on the I machine learning natural language processing. And I work with Bob's Division to actually advance the data site off the store because all the eye needs data. And without data, there's no algorithms, So we're focusing a lot on How do we use a I to make data Better think about their equality. Think about data lineage. Think about all of the problems that data has. How can we make it better using algorithms? And I focused a lot on that working with Bob, But no, it's it's customers and internal. I mean, you know, I were a horizontal within the form, So we help customers. We help internal, we focus a lot on the market. >> So, Bob, you mentioned used data offensively. So 10 12 years ago, it was data was a liability. You had to get rid of it. Keep it no longer than you had to, because you're gonna get soon. So email archives came in and obviously thinks flipped after the big data. But so what do you What are you seeing in terms of that shift from From the defense data to the offensive? >> Yeah, and it's it's really you know, when you think about it and let me define sort of offense versus defense. Who on the defense side, historically, that's where most of CEOs have played. That's risk regulatory reporting, privacy, um, even litigation support those types of activities today. Uh, and really, until about a year and 1/2 ago, we really saw most CEOs still really anchored in that I run a forum with a number of studios and financial service is, and every year we get them together and asked him the same set of questions. This was the first year where they said that you know what my primary focus now is. Growth. It's bringing efficiency is trying to generate value on the offensive side. It's not like the regulatory work's going away, certainly in the face of some of the pending privacy regulation. But you know, it's It's a sign that the volume of use cases as the investments in their digital transformations are starting to kick out, as well as the volumes of data that are available. The raw material that's available to them in terms of third party data in terms of the the just the general volumes that that exist that are streaming into the organization and the overall literacy in the business units are creating this, this massive demand. And so they're having to >> respond because of getting a handle on the data they're actually finding. Word is, they're categorizing it there, there, >> yeah, organizing that. That is still still a challenge. Um, I think it's better with when you have a very narrow scope of critical data elements going back to the structure data that we're talking it with the regulatory reporting when you start to get into the three offense, the generating value, getting the customer experience, you know, really exploring. You know that side of it. There's there's a ton of new muscle that has to be built new muscle in terms of data quality, new muscle in terms of um, really more scalable operating model. I think that's a big issue right now with Si Dios is, you know, we've got ah, we're used to that limited swath of CDs and they've got Stewardship Network. That's very labor intensive. A lot of manual processes still, um, and and they have some good basic technology, but it's a lot of its rules based. And when you do you think about those how that constraints going to scale when you have all of this demand. You know, when you look at the customer experience analytics that they want to do when you look at, you know, just a I applied to things like operations. The demand on the focus there is is is gonna start to create a fundamental shift >> this week are one of things that I >> have scene, and maybe it's just my small observation space. But I wonder, if you could comment Is that seems like many CBO's air not directly involved in the aye aye initiatives. Clearly, the chief digital officer is involved, but the CDO zehr kind of, you know, in the background still, you see that? >> That's a fantastic question, and I think this is where we're seeing some off the cutting it change that is happening in the industry. And when Barbara presenter idea that we can often civilly look at data, this is what it is that studios for a long time have become more reactive in their roles. And that is that is starting to come forefront now. So a lot of institutions were working with are asking What's the next generation Roll off a CDO and why are they in the background and why are they not in the foreground? And this is when you become more often they were proactive with data and the digital officers are obviously focused on, you know, the transformation that has to happen. But the studios are their backbone in order to make the transformation. Really. And if the CDO started, think about their data as an asset did as a product did us a service. The judicial officers are right there because those are the real, you know, like the data data they're living so CDO can really become from my back office to really become a business line. We've >> seen taking the reins in machine learning in machine learning projects and cos you work with. Who >> was driving that? Yeah. Great question. So we are seeing, like, you know, different. I would put them in buckets, right? There is no one mortal fits all. We're seeing different generations within the company's. Some off. The ones were just testing out the market. There's two keeping it in their technology space in their back office. Take idea and, you know, in in forward I d let me call them where they are starting to experiment with this. But you see, the mature organizations on the other end of the spectrum, they are integrating action, learning and a I right into the business line because they want to see ex souls having the technology right by their side so they can lead leverage. Aye, aye. And machine learning spot right for the business right there. And that is where we're seeing know some of the new models. Come on. >> I think the big shift from a CDO perspective is using a i to prep data for a That's that's fundamentally where you know, where the data science was distributed. Some of that data science has to come back and free the integration for equality for data prepping because you've got all this data third party and other from customer streaming into the organization. And you know, the work that you're doing around, um, anomaly detection is it transcends developing the rules, doing the profiling, doing the rules. You know, the very manual, the very labor intensive process you've got to get away from that >> is used in order for this to be scale goes and a I to figure out which out goes to apply t >> clean to prepare the data toe, see what algorithms we can use. So it's basically what we're calling a eye for data rather than just data leading into a I. So it's I mean, you know, you developed a technology for one off our clients and pretty large financial service. They were getting closer, like 1,000,000,000 data points every day. And there was no way manually, you could go through the same quality controls and all of those processes. So we automated it through algorithms, and these algorithms are learning the behavior of data as they flow into the organization, and they're able to proactively tell their problems are starting very much. And this is the new face that we see in in the industry, you cannot scale the traditional data governance using manual processes, we have to go to the next generation where a i natural language processing and think about on structure data, right? I mean, that is, like 90% off. The organization is unstructured data, and we have not talked about data quality. We have not talked about data governance. For a lot of these sources of information, now is the time. Hey, I can do it. >> And I think that raised a great question. If you look at unstructured and a lot of the data sources, as you start to take more of an offensive stance will be unstructured. And the data quality, what it means to apply data quality isn't the the profiling and the rules generation the way you would with standard data. So the teams, the skills that CEOs have in their organizations, have to change. You have to start to, and, you know, it's a great example where, you know, you guys were ingesting documents and there was handwriting all over the documents, you know, and >> yeah, you know, you're a great example, Bob. Like you no way would ask the client, like, you know, is this document gonna scanned into the system so my algorithm can run and they're like, Yeah, everything is good. I mean, the deal is there, but when you then start scanning it, you realize there's handwriting and the information is in the handwriting. So all the algorithms breakdown now >> tribal knowledge striving Exactly. >> Exactly. So that's what we're seeing. You know, if I if we talk about the digital transformation in data in the city organization, it is this idea dart. Nothing is left unseen. Some algorithm or some technology, has seen everything that is coming into. The organization has has has a para 500. So you can tell you where the problems are. And this is what algorithms do. This scale beautifully. >> So the data quality approaches are evolving, sort of changing. So rather than heavy, heavy emphasis on masking or duplication and things like that, you would traditionally think of participating the difficult not that that goes away. But it's got to evolve to use machine >> intelligence. Exactly what kind of >> skill sets people need thio achieve that Is it Is it the same people or do we need to retrain them or bring in new skills. >> Yeah, great question. And I can talk from the inspector off. Where is disrupting every industry now that we know, right? But we knew when you look at what skills are >> required, all of the eye, including natural language processing, machine learning, still require human in the loop. And >> that is the training that goes in there. And who do you who are the >> people who have that knowledge? It is the business analyst. It's the data analyst who are the knowledge betters the C suite and the studios. They are able to make decisions. But the day today is still with the data analyst. >> Those s Emmys. Those sm >> means So we have to obscure them to really start >> interacting with these new technologies where they are the leaders, rather than just waiting for answers to come through. And >> when that happens now being as a data scientist, my job is easy because they're Siamese, are there? I deploy the technology. They're semi's trained algorithms on a regular basis. Then it is a fully fungible model which is evolving with the business. And no longer am I spending time re architect ing my rules. And like my, you know, what are the masking capabilities I need to have? It is evolving us. >> Does that change the >> number one problem that you hear from data scientists, which is the 80% of the time >> spent on wrangling cleaning data 10 15 20% run into sm. He's being concerned that they're gonna be replaced by the machine. Their training. >> I actually see them being really enabled now where they're spending 80% of the time doing boring job off, looking at data. Now they're spending 90% of their time looking at the elements future creative in which requires human intelligence to say, Hey, this is different because off X, >> y and Z so let's let's go out. It sounds like a lot of what machine learning is being used for now in your domain is clean things up its plumbing. It's basic foundation work. So go out. Three years after all that work has been done and the data is clean. Where are your clients talking about going next with machine learning? Bob, did you want? >> I mean, it's a whole. It varies by by industry, obviously, but, um but it covers the gamut from, you know, and it's generally tied to what's driving their strategies. So if you look at a financial service is organization as an example today, you're gonna have, you know, really a I driving a lot of the behind the scenes on the customer experience. It's, you know, today with your credit card company. It's behind the scenes doing fraud detection. You know, that's that's going to continue. So it's take the critical functions that were more data. It makes better models that, you know, that that's just going to explode. And I think they're really you can look across all the functions, from finance to to marketing to operations. I mean, it's it's gonna be pervasive across, you know all of that. >> So if I may, I don't top award. While Bob was saying, I think what's gonna what What our clients are asking is, how can I exhilarate the decision making? Because at the end of the day on Lee, all our leaders are focused on making decisions, and all of this data science is leading up to their decision, and today you see like you know what you brought up, like 80% of the time is wasted in cleaning the data. So only 20% time was spent in riel experimentation and analytics. So your decision making time was reduced to 20% off the effort that I put in the pipeline. What if now I can make it 80% of the time? They're I put in the pipeline, better decisions are gonna come on the train. So when I go into a meeting and I'm saying like, Hey, can you show me what happened in this particular region or in this particular part of the country? Previously, it would have been like, Oh, can you come back in two weeks? I will have the data ready, and I will tell you the answer. But in two weeks, the business has ran away and the CDO know or the C Street doesn't require the same answer. But where we're headed as as the data quality improves, you can get to really time questions and decisions. >> So decision, sport, business, intelligence. Well, we're getting better. Isn't interesting to me. Six months to build a cube, we'd still still not good enough. Moving too fast. As the saying goes, data is plentiful. Insights aren't Yes, you know, in your view, well, machine intelligence. Finally, close that gap. Get us closer to real time decision >> making. It will eventually. But there's there's so much that we need to. Our industry needs to understand first, and it really ingrained. And, you know, today there is still a fundamental trust issues with a I you know, it's we've done a lot of work >> watch Black box or a part of >> it. Part of it. I think you know, the research we've done. And some of this is nine countries, 2400 senior executives. And we asked some, ah, a lot of questions around their data and trusted analytics, and 92% of them came back with. They have some fundamental trust issues with their data and their analytics and and they feel like there's reputational risk material reputational risk. This isn't getting one little number wrong on one of the >> reports about some more of an >> issue, you know, we also do a CEO study, and we've done this many years in a row going back to 2017. We started asked them okay, making a lot of companies their data driven right. When it comes to >> what they say they're doing well, They say they're day driven. That's the >> point. At the end of the day, they making strategic decisions where you have an insight that's not intuitive. Do you trust your gut? Go with the analytics back then. You know, 67% said they go with their gut, So okay, this is 2017. This industry's moving quickly. There's tons and tons of investment. Look at it. 2018 go down. No, went up 78%. So it's not aware this issue there is something We're fundamentally wrong and you hit it on. It's a part of its black box, and part of it's the date equality and part of its bias. And there's there's all of these things flowing around it. And so when we dug into that, we said, Well, okay, if that exists, how are we going to help organizations get their arms around this issue and start digging into that that trust issue and really it's the front part is, is exactly what we're talking about in terms of data quality, both structured more traditional approaches and unstructured, using the handwriting example in those types of techniques. But then you get into the models themselves, and it's, you know, the critical thing she had to worry about is, you know, lineage. So from an integrity perspective, where's the data coming from? Whether the sources for the change controls on some of that, they need to look at explain ability, gain at the black box part where you can you tell me the inferences decisions are those documented. And this is important for this me, the human in the loop to get confidence in the algorithm as well as you know, that executive group. So they understand there's a structure set of processes around >> Moneyball. Problem is actually pretty confined. It's pretty straightforward. Dono 32 teams are throwing minor leagues, but the data models pretty consistent through the problem with organizations is I didn't know data model is consistent with the organization you mentioned, Risk Bob. The >> other problem is organizational inertia. If they don't trust it, what is it? What is a P and l manage to do when he or she wants to preserve? Yeah, you know, their exit position. They attacked the data. You know, I don't believe that well, which which is >> a fundamental point, which is culture. Yes. I mean, you can you can have all the data, science and all the governance that you want. But if you don't work culture in parallel with all this, it's it's not gonna stick. And and that's, I think the lot of the leading organisations, they're starting to really dig into this. We hear a lot of it literacy. We hear a lot about, you know, top down support. What does that really mean? It means, you know, senior executives are placing bats around and linking demonstrably linking the data and the role of data days an asset into their strategies and then messaging it out and being specific around the types of investments that are going to reinforce that business strategy. So that's absolutely critical. And then literacy absolutely fundamental is well, because it's not just the executives and the data scientists that have to get this. It's the guy in ops that you're trying to get you. They need to understand, you know, not only tools, but it's less about the tools. But it's the techniques, so it's not. The approach is being used, are more transparent and and that you know they're starting to also understand, you know, the issues of privacy and data usage rights. That's that's also something that we can't leave it the curb. With all this >> innovation, it's also believing that there's an imperative. I mean, there's a lot of for all the talk about digital transformation hear it everywhere. Everybody's trying to get digital, right? But there's still a lot of complacency in the organization in the lines of business in operation to save. We're actually doing really well. You know, we're in financial service is health care really hasn't been disrupted. This is Oh, it's coming, it's coming. But there's still a lot of I'll be retired by then or hanging. Actually, it's >> also it's also the fact that, you know, like in the previous generation, like, you know, if I had to go to a shopping, I would go into a shop and if I wanted by an insurance product, I would call my insurance agent. But today the New world, it's just a top off my screen. I have to go from Amazon, so some other some other app, and this is really this is what is happening to all of our kind. Previously that they start their customers, pocketed them in different experience. Buckets. It's not anymore that's real in front of them. So if you don't get into their digital transformation, a customer is not going to discount you by saying, Oh, you're not Amazon. So I'm not going to expect that you're still on my phone and you're only two types of here, so you have to become really digital >> little surprises that you said you see the next. The next stage is being decision support rather than customer experience, because we hear that for CEOs, customer experience is top of mind right now. >> No natural profile. There are two differences, right? One is external facing is absolutely the customer internal facing. It's absolutely the decision making, because that's how they're separating. The internal were, says the external, and you know most of the meetings that we goto Customer insight is the first place where analytics is starting where data is being cleaned up. Their questions are being asked about. Can I master my customer records? Can I do a good master off my vendor list? That is where they start. But all of that leads to good decision making to support the customers. So it's like that external towards internal view well, back >> to the offense versus defense and the shift. I mean, it absolutely is on the offense side. So it is with the customer, and that's a more directly to the business strategy. So it's get That's the area that's getting the money, the support and people feel like it's they're making an impact with it there. When it's it's down here in some admin area, it's below the water line, and, you know, even though it's important and it flows up here, it doesn't get the VIN visibility. So >> that's great conversation. You coming on? You got to leave it there. Thank you for watching right back with our next guest, Dave Lot. Paul Gillen from M I t CDO I Q Right back. You're watching the Cube

>> from Cambridge, Massachusetts. It's three Cube covering M. I T. Chief data officer and information quality Symposium 2019. Brought to you by Silicon Angle Media. >> Welcome back to M I. T. CDO I Q everybody, you're watching the cube we got. We go out to the events we extract the signal from the noise is day one of this conference. Chief Data Officer event. I'm Dave, along with my co host, Paul Gillen. Stuart Bond is here is a research director of International Data Corporation I DC Stewart. Welcome to the Cube. Thanks for coming on. Thank you for having me. You're very welcome. So your space data intelligence tell us about your swim lane? Sure. >> So my role it I. D. C is a ZAY. Follow the data integration and data intelligence software market. So I follow all the different vendors in the market. I look at what kinds of solutions they're bringing to market, what kinds of problems. They're solving both business and technical for their clients. And so I can then report on the trends and market sizes, forecasts and such, And within that part of what I what I cover is everything from data integration which is more than traditionally E T l change data capture data movements, data, virtualization types of technologies as well as what we call date integrity of one. And I'm calling data intelligence, which is all of the Tell the metadata about the data. It's the data catalogs meditating management's data lineage. It's the data quality data profiling, master data intelligence. It's all of the data about the data and understanding really answering what I call a entering the five W's and h of data. It's the who, what, where, when, why and how. Data. So that's the market that I'm covering and following, and that's why I'm >> here. Were you here this morning for Mark Ramsey's Yes, I talk. So he kind of went to you. Heard it started with the D W kind of through E T L under the bus. Well, MGM, then the Enterprise data model said all that failed. But that stuff's not going away, and I'm sure they're black. So still using, you know, all those all that tooling today. So what was your reaction to that you were not in your head and yeah, it's true or saying, Well, maybe there's a little we'll have what we've been saying. The mainframe is gonna go away for years and >> still around, so I think they're obviously there's still those technologies out there and they're still being used. You can look at any of the major dtl vendors and there's new ones coming to the market, so that's still alive and well. There's no doubt that it's out there and its biggest segment of the market that I followed. So there's no source tooling, right? Yes, >> there's no doubt that it's still >> there. But Mark's vision of where things are going, where things are heading with, with data intelligence really being at the Cory talk about those spiders talked about that central depository of information about knowledge of the data. That's where things are heading to, whether you call it a data hub, whether you call it a date, a platform, not really a one big, huge data pop for one big, huge data depository, but one a place where you can go to get the information but natives you can find out where the data is. You could find out what it means, both the business context as well as the technical information you find out who's using that data. You can find out when it's being used, Why it's being used in. Why do we even have it and how it should >> be used? So it's being used >> appropriately. So you would say that his vision, actually what he implemented was visionary skating. They skated to the puck, so to speak, and that's we're going >> to see more of that. Where are seeing more of that? That's why we've seen such a jump in the number of vendors that air providing data catalogue solutions. I did, Uh, I d. C has this work product calling market glance. I did that >> beginning of 2018. >> I just did it again. In the middle of this year, the number of vendors that offer data catalogue solutions has significantly interest 240% increase in the number of vendors that offer that now itself of a small base. These air, not exhaustive studies. It may be that I didn't know about all those data catalogue vendors a year and 1/2 ago, but may also be that people are now saying that we've got a data catalogue, >> but you've really got a >> peel back the layers a little bit. Understand what these different data catalysts are and what they're doing because not all of them are crediting. >> We'll hear Radar. You don't know about it. 99% of the world mark talked this morning about some interesting new technologies. They were using Spider Ring to find the data bots to classify the data tools wrangle the data. I mean, there's a lot of new technology being applied to this area. What? Which of those technologies do you think has the greatest promise right now? And how? How how automated can this process become? >> It's the spider ring, and it's the cataloging of the data. It's understanding what you've got out there that is growing crazy. Just started to track that it's growing a lot that has the most promised. And as I said, I think that's going to be the data platform in the future. Is the intelligence knowing about where your data is? You men go on, get it. You know it's not a matter of all. The data is one place anymore. Data's everywhere Date is in hybrid cloud. It's in on premise. It's in private. Cloud isn't hosted. It's everywhere. I just did a survey. I got the results back in June 2019 just a month ago, and the data is all over the place. So really having that knowledge having that intelligence about where your data is, that has the most promise. As faras, the automation is concerned. Next step there. It's not just about collecting the information about where your data is, but it's actually applying the analytics, the machine learning and the artificial intelligence to that metadata collection that you've got so that you can then start to create those bots to create those pipelines to start to automate those tasks. We're starting to see some vendors move in that area, moving that direction. There's a lot of promise there >> you guys, at least when I remember. You see, the software is pretty robust taxonomy. I'm sure it's evolved over the years. So how do you sort of define your space? I'm interested in How big is that space, you know, in terms of market size and is a growing and where do you see it going? >> Right. So my my coverage of data integration and data intelligence is fairly small. It's a small, little marketed. I D. C. I'm part of a larger team that looks a data management, the analytics and information management. So we've got people on our team like a damn vessel. Who covers the analytics? Advanced Analytics show Nautical Palo Carlson. He's been on the cable covers, innovative technologies, those I apologize. I don't have that number off the top. >> Okay, No, But your space, my space is it. That's that Software market is so fragmented. And what I d. C has always done well, as you put people on those fragments and you know, deep in there. So So how you've been ableto not make your eyes bleed when you do that, challenging so the data and put it all together. >> It's important. Integration markets about 66 and 1/2 1,000,000,000 >> dollars. Substantial size. Yeah, but again, a lot of vendors Growing number of events in the markets growing, >> the market continues to grow as the data is becoming more distributed, more dispersed. There's no need to continue to integrate that data. There's also that need that growing >> need for that date intelligence. It's not >> just, you know, we've had a lot of enquiries lately about data being fed into machine learning artificial intelligence and people realizing our data isn't clean. We have to clean up our data because we're garbage in garbage. Out is probably more important now than ever before because you don't have someone saying, I don't think that day is right. You've got machines were looking at data instead. The technology that's out there and the problem with data quality. It's on a new problem. It's the same problem we've had for years. All of the technology is there to clean that data up, and that's a part of what I saw. I look at the data quality vendors experience here, sink sort in all of the other data quality capabilities that you get from in from Attica, from Tahoe or from a click podium. Metal is there, and so that part is growing. And there's a lot of more interest in that data quality and that data intelligence side again so the right data can be used. Good data can be used to trust in that data. Can the increase we used for the right reasons as well That's adding that context. Understand that Samantha having all that metadata that goes around that data so that could be used. Most of >> it is one of those markets that you may be relatively small. It's not 100,000,000,000 but it it enables a lot of larger markets. So okay, so it's 66 and 1/2 1,000,000,000 it's growing. It is a growing single digits, double digits. It's growing. It's hovering around the double dip double. It is okay, it's 10%. And then and then who were the, You know, big players who was driving the shares there? Is there a dominant player there? Bunch of >> so infirm. Atticus Number one in the market. Okay, followed by IBM. And I say peas right up there. Sass is there. Tell End is making a good Uh, okay, they're making a nice with Yeah, but there there's a number of different players. There's There's a lot of different players in that market. >> And in the leading market share player has what, 10%? 15%? 50%? Is it like a dominant divine spot? That's tough to say. You got a big It's over 1,000,000,000,000,000,000 right? So they've got maybe 1/6 of the market. Okay, so but it's not like Cisco as 2/3 of the networking market or anything like that. And what about the cloud guys? A participating in this guy's deal with >> the cloud guys? Yeah, the ClA got so there are some pure cloud solutions. There's a relative, for example. Pure cloud MBM mastered a management there. There's I'd say there's less pure cloud than there used to be. But, you know, but someone like an infra matic is really pushing that clouds presence in that cloud >> running these tools, this tooling in in the cloud But the cloud guys directly or not competing at this >> point. So Amazon Google? Yes, Those cloud guys. Yes. Okay, there, there. Google announced data flow back in our data. Sorry. Data fusion back. Google. >> Yeah, that's right. >> And so there they've got an e t l two on the cloud now. Ah, Amazon has blue yet which is both a catalog and an e t l tool. Microsoft course has data factory in azure. >> So those guys are coming on. I'm guessing if you talk to in dramatic and they said, Well, they're not as robust as we are. And we got a big install base and we go multi cloud is that kind of posturing of the incumbents or yeah, that's posturing. And maybe that's I don't mean it is a pejorative. If I were, those guys would be doing the same thing. You know, we were talking earlier about how the cloud guys essentially killed the Duke. All right, do you Do you see the same thing happening here, or is it well, the will the tool vendors be able to stay ahead in your view, >> depends on how they execute. If they're there and they're available in the cloud along with along with those clapper viers, they're able to provide solutions in the same same way the same elasticity, the same type of consumption based pricing models that pod vendors air offering. They can compete with that. They still have a better solution. Easton What >> in multi cloud in hybrid is a big part of their value problems that the cloud guys aren't really going hard after. I mean, this sort of dangling your toe in the water, some of them some of the >> cloud guys they have. They have the hybrid capabilities because they've got some of what they're what they built comes from on premises, worlds as well. So they've got that ability. Microsoft in particular >> on Google, >> Google that the data fusion came out of >> You're saying, But it's part of the Antos initiative. Er, >> um, I apologize. Folks are watching, >> but soup of acronyms notices We're starting a little bit. What tools have you seen or technology? Have you seen making governance of unstructured data? That looks promising? Uh, so I don't really cover >> the instructor data space that much. What I can say is Justus in the structure data world. It's about the metadata. It's about having the proper tags about that unstructured data. It's about getting the information of that unstructured data so that it can then be governed appropriately, making structure out of that, that is, I can't really say, because I don't cover that market explicitly. But I think again it comes back to the same type of data intelligence having that intelligence about that data by understanding what's in there. >> What advice are you giving to, you know, the buyers in your community and the sellers in your community, >> So the buyer's within the market. I talk a lot about that. The need for that data intelligence, so data governance to me is not a technology you can't go by data governance data governance is an organizational disappoint. Technology is a part of that. To me, the data intelligence technology is a part of that. So, really, organizations, if they really want a good handle, get a good handle on what data they have, how to use that, how to be enabled by that data. They need to have that date intelligence into go look for solutions that can help him pull that data intelligence out. But the other part of that is measurement. It's critical to measure because you can't improve what you're not measuring. So you know that type of approach to it is critical Eve, and you've got to be able to have people in the organization. You've got to be able to have cooperation collaboration across the business. I t. The the gifted office chief Officer office. You've gotta have that collaboration. You've gotta have accountability and for in order for that, to really be successful. For the vendors in the space hybrid is the new reality. In my survey data, it shows clearly that hybrid is where things are. It's not just cloud, it's not just on promise Tiebreak. That's where the future is. They've got to be able to have solutions that work in that environment. Working that hybrid cloud ability has got to be able to have solutions that can be purchased and used again in the same sort of elastic type of method that they're able to get consumers able to get. Service is from other vendors in that same >> height, so we gotta run. Thank you so much for sharing your insights and your data. And I know we were fired. I was firing a lot of questions. Did pretty well, not having the report in front of me. I know what that's like. So thank you for sharing and good luck with your challenges in the future. You got You got a lot of a lot of data to collect and a lot of fast moving markets. So come back any time. Share with you right now, Okay? And thank you for watching Paul and I will be back with our next guest right after this short break from M I t cdo. Right back

>> from Cambridge, Massachusetts. It's three Cube covering M I T. Chief data officer and information quality Symposium 2019. Brought to you by Silicon Angle Media. >> Hi already. We're back in Cambridge, Massachusetts at the M I t. Chief data officer Information quality event. Hashtag m i t cdo i Q. And I'm David Dante. He's Paul Gillen. Day one of our two day coverage of this event. This is the Cube, the leader in live tech coverage. Joe Caserta is here is the president of Caserta and Doug Laney, who is principal data strategist at Caserta, both Cube alarm guys. Great to see you again, Joe. What? Did you pick up this guy? How did that all came on here a couple of years ago? We had a great conversation. I read the book, Loved it. So congratulations. A nice pickup. >> We're very fortunate to have. >> Thanks. So I'm fortunate to be here, >> so Okay, well, what attracted you to Cassard? Oh, >> it's Joe's got a tremendous reputation. His his team of consultants has a great reputation. We both felt there was an opportunity to build some data strategy competency on top of that and leverage some of those in Phanom. Its ideas that I've been working on over the years. >> Great. Well, congratulations. And so, Joe, you and I have talked many times. And the reason I like talking because you know what's going on in the market place? You could you could siphon. What's riel? What's hype? So what do you see? It is the big trends in this data space, and then we'll get into it. Yeah, sure. Um, trends >> are chief data officer has been evolving over the last couple of years. You know, when we started doing this several years ago, there was just a handful of people, maybe 30 40 people. Now, there's 450 people here today, and it's been evolving. People are still trying to find their feet. Exactly what the chief date officers should be doing where they are in the hierarchy. Should they report to the c e o the C I O u the other CDO, which is a digital officer. So I think you know, hierarchically. That's still figuring it out politically. They're figuring it out, but technically also, they're still trying to figure it out. You know what's been happening over the past three years is the evolution of data going from traditional data warehousing and business intelligence. To get inside out of data just isn't working anymore. Eso evolving that moving it forward to more modern data engineering we've been doing for the past couple of years with quote unquote big data on That's not working anymore either, right? Because it's been evolving so fast. So now we're on, like, maybe Data three dato. And now we're talking about just pure automate everything. We have to automate everything. And we have to change your mindset from from having output of a data solution to an outcome to date a solution. And that's why I hired Doug, because way have to figure out not only had to get this data and look at it and analyze really had to monetize it, right? It's becoming a revenue stream for your business if you're doing it right and Doug is the leader in the industry, how to figure that >> you keep keep premise of your book was you gotta start valuing data and its fundamental you put forth a number of approaches and techniques and examples of companies doing that. Since you've published in phenomena Microsoft Apple, Amazon, Google and Facebook. Of the top five market value cos they've surpassed all the financial service is guys all ExxonMobil's and any manufacturer? Automobile makers? And what of a data companies, right? Absolutely. But intrinsically we know there's value their way any closer to the prescription that you put forth. >> Yeah, it's really no surprise and extra. We found that data companies have, ah, market to book value. That's nearly 33 times the market average, so Apple and others are much higher than that. But on average, if you look at the data product companies, they're valued much higher than other companies, probably because data can be reused in multiple ways. That's one of the core tenets of intra nomics is that Data's is non depleted ble regenerative, reusable asset and that companies that get that an architect of businesses based on those economics of information, um, can really perform well and not just data companies, but >> any company. That was a key takeaway of the book. The data doesn't conform to the laws of scarcity. Every says data is the new oil. It's like, No, it's not more valuable. So what are some examples in writing your book and customers that you work with. Where do you see Cos outside of these big data driven firms, breaking new ground and uses of data? I >> think the biggest opportunity is really not with the big giant Cos it's really with. Most of our most valuable clients are small companies with large volumes of data. You know if and the reason why they can remain small companies with large volumes of data is the thing that holds back the big giant enterprises is they have so much technical. Dad, it's very hard. They're like trying to, you know, raise the Titanic, right? You can't really. It's not agile enough. You need something that small and agile in order to pivot because it is changing so fast every time there's a solution created, it's obsolete. We have to greet the new solution on dhe when you have a big old processes. Big old technologies, big old mind sets on big old cultures. It's very hard to be agile. >> So is there no hope? I mean, the reason I ask the question was, What hope can you give some of these smokestack companies that they can become data centric? Yeah, What you >> see is that there was a There was a move to build big, monolithic data warehouses years ago and even Data Lakes. And what we find is that through the wealth of examples of companies that have benefited in significant ways from data and analytics, most of those solutions are very vocational. They're very functionally specific. They're not enterprise class, yada, yada, kind of kind of projects. They're focused on a particular business problem or monetizing or leveraging data in a very specific way, and they're generating millions of dollars of value. But again they tend to be very, very functionally specific. >> The other trend that we're seeing is also that the technology and the and the end result of what you're doing with your data is one thing. But really, in order to make that shift, if your big enterprises culture to really change all of the people within the organization to migrate from being a conventional wisdom run company to be a data really analytics driven company, and that takes a lot of change management, a lot of what we call data therapy way actually launched a new practice within the organization that Doug is actually and I are collaborating on to really mature because that is the next wave is really we figured out the data part. We figured out the technology part, but now it's the people part people. Part is really why we're not way ahead of where we even though we're way ahead of where we were a couple of years ago, we should be even further. Culturally, it's very, very challenging, and we need to address that head on. >> And that zeta skills issue that they're sort of locked into their existing skill sets and processes. Or is it? It's fear of the unknown what we're doing, you know? What about foam? Oh, yeah, Well, I mean, there are people >> jumping into bed to do this, right? So there is that part in an exciting part of it. But there's also just fear, you know, and fear of the unknown and, you know, part of what we're trying to do. And why were you trying Thio push Doug's book not for sales, but really just to share the knowledge and remove the mystery and let people see what they can actually do with this data? >> Yeah, it's more >> than just date illiteracy. So there's a lot of talk of the industry about data literacy programs and educating business people on the data and educating data people on the business. And that's obviously important. But what Joe is talking about is something bigger than that. It's really cultural, and it's something that is changed to the company's DNA. >> So where do you attack that problem? It doesn't have to go from the top down. You go into the middle. It has to >> be from the top down. It has to be. It has to be because my boss said to do it all right. >> Well, otherwise they well, they might do it. But the organization's because if you do, it >> is a grassroots movement on Lee. The folks who are excited, right? The foam of people, right? They're the ones who are gonna be excited. But they're going to evolve in adopt anyway, right? But it's the rest of the organization, and that needs to be a top down, Um, approach. >> It was interesting hearing this morning keynote speakers. You scored a throw on top down under the bus, but I had the same reaction is you can't do it without that executive buying. And of course, we defined, I guess in the session what that was. Amazon has an interesting concept for for any initiative, like every initiative that's funded has to have what they call a threaded leader. Another was some kind of And if they don't, if they don't have a threat of leader, there's like an incentive system tau dime on initiative. Kill it. It kind of forces top down. Yeah, you know, So >> when we interview our clients, we have a litmus test and the limits. It's kind of a ready in this test. Do you have the executive leadership to actually make this project successful? And in a lot of cases, they don't And you know, we'll have to say will call us when you're ready, you know, or because one of the challenges another part of the litmus test is this IittIe driven. If it's I t driven is gonna be very tough to get embraced by the rest of the business. So way need to really be able to have that executive leadership from the business to say this is something that we need >> to do to survive. Yeah, and, you know, with without the top down support. You could play small ball. But if you're playing the Yankees, you're gonna win one >> of the reasons why when it's I t driven, it's very challenging is because the people part right is a different budget from the i T budget. And when we start talking about data therapy, right and human resource is and training and education of just culture and data literacy, which is not necessary technical, that that becomes a challenge internally figuring out, like how to pay for Andi how to get it done with a corporate politics. >> So So the CDO crowd definitely parts of your book that they should be adopting because to me, there their main job is okay. How does data support the monetization of my organization? Raising revenue, cutting costs, improving productivity, saving lives. You call it value. And so that seems to be the starting point. At the same time. In this conference, you grew out of the ashes of back room information quality of the big data height, but exploded and have kind of gone full circle. So But I wonder, I mean, is the CDO crowd still focused on that monetization? Certainly I think we all agree they should be, but they're getting sucked back into a governance role. Can they do both, I guess, is >> my question. Well, governance has been, has been a big issue the past few years with all of the new compliance regulation and focus on on on ensuring compliance with them. But there's often a just a pendulum swing back, and I think there's a swing back to adding business value. And so we're seeing a lot of opportunities to help companies monetize their data broadly in a variety of ways. A CZ you mentioned not just in one way and, um, again those you need to be driven from the top. We have a process that we go through to generate ideas, and that's wonderful. Generating ideas. No is fairly straightforward enough. But then running them through kind of a feasibility government, starting with you have the executive support for that is a technology technologically feasible, managerially feasible, ethically feasible and so forth. So we kind of run them through that gauntlet next. >> One of my concerns is that chief data officer, the level of involvement that year he has in these digital initiatives again is digital initiative of Field of Dreams. Maybe it is. But everywhere you go the CEO is trying to get digital right, and it seems like the chief data officer is not necessarily front and center in those. Certainly a I projects, which are skunk works. But it's the chief digital officer that's driving it. So how how do you see in those roles playoff >> In the less panel that I've just spoken, very similar question was asked. And again, we're trying to figure out the hierarchy of where the CDO should live in an organization. Um, I find that the biggest place it fails typically is if it rolls up to a C I. O. Right. If you think the data is a technical issue, you're wrong, Right? Data is a business issue, Andi. I also think for any company to survive today, they have to have a digital presence. And so digital presence is so tightly coupled to data that I find the best success is when the chief date officer reports directly to the chief digital officer. Chief Digital officer has a vision for the user experience for the customer customers Ella to figure out. How do we get that customer engaged and that directly is dependent on insight. Right on analytics. You know, if the four of us were to open up, any application on our phone, even for the same product, would have four different experiences based on who we are, who are peers are what we bought in the past, that's all based on analytics. So the business application of the digital presence is tightly couple tow Analytics, which is driven by the chief state officer. >> That's the first time I've heard that. I think that's the right organizational structure. Did see did. JJ is going to be sort of the driver, right? The strategy. That's where the budget's gonna go and the chief date office is gonna have that supporting role that's vital. The enabler. Yeah, I think the chief data officer is a long term play. Well, we have a lot of cheap date officers. Still, 10 years from now, I think that >> data is not a fad. I think Data's just become more and more important. And will they ultimately leapfrog the chief digital officer and report to the CEO? Maybe someday, but for now, I think that's where they belong. >> You know what's company started managing their labor and workforce is as an actual asset, even though it's not a balance sheet. Asked for obvious reasons in the 19 sixties that gave rise to the chief human resource officer, which we still see today and his company start to recognize information as an asset, you need an executive leader to oversee and be responsible for that asset. >> Conceptually, it's always been data is an asset and a liability. And, you know, we've always thought about balancing terms. Your book sort of put forth a formula for actually formalizing. That's right. Do you think it's gonna happen our lifetime? What exactly clear on it, what you put forth in your book in terms of organizations actually valuing data specifically on the balance sheet. So that's >> an accounting question and one that you know that you leave to the accounting professionals. But there have been discussion papers published by the accounting standards bodies to discuss that issue. We're probably at least 10 years away, but I think respective weather data is that about what she'd asked or not. It's an imperative organizations to behave as if it is one >> that was your point it's probably not gonna happen, but you got a finger in terms that you can understand the value because it comes >> back to you can't manage what you don't measure and measuring the value of potential value or quality of your information. Or what day do you have your in a poor position to manage it like one. And if you're not manage like an asset, then you're really not probably able to leverage it like one. >> Give us a little commercial for I do want to say that I do >> think in our lifetime we will see it become an asset. There are lots of intangible assets that are on the books, intellectual property contracts. I think data that supports both of those things are equally is important. And they will they will see the light. >> Why are those five companies huge market cap winners, where they've surpassed all the evaluation >> of a business that the data that they have is considered right? So it should be part of >> the assets in the books. All right, we gotta wraps, But give us Give us the The Caserta Commercial. Well, concert is >> a consultancy that does essentially three things. We do data advisory work, which, which Doug is heading up. We do data architecture and strategy, and we also do just implementation of solutions. Everything from data engineering gate architecture and data science. >> Well, you made a good bet on data. Thanks for coming on, you guys. Great to see you again. Thank you. That's a wrap on day one, Paul. And I'll be back tomorrow for day two with the M I t cdo m I t cdo like you. Thanks for watching. We'll see them all.

>> From the SiliconANGLE Media office in Boston, Massachusetts, it's theCube! (funky electronic music) Now, here's your host, Dave Vellante! >> Welcome, everybody, to this Cube conversation here in our Marlborough offices. I am very excited today, I spent a number of years at IDC, which, of course, is owned by IDG. And there's a new book out, relatively new, called Future Forward: Leadership Lessons from Patrick McGovern, the Visionary Who Circled the Globe and Built a Technology Media Empire. And it's a great book, lotta stories that I didn't know, many that I did know, and the author of that book, Glenn Rifkin, is here to talk about not only Pat McGovern but also some of the lessons that he put forth to help us as entrepreneurs and leaders apply to create better businesses and change the world. Glenn, thanks so much for comin' on theCube. >> Thank you, Dave, great to see ya. >> So let me start with, why did you write this book? >> Well, a couple reasons. The main reason was Patrick McGovern III, Pat's son, came to me at the end of 2016 and said, "My father had died in 2014 and I feel like his legacy deserves a book, and many people told me you were the guy to do it." So the background on that I, myself, worked at IDG back in the 1980s, I was an editor at Computerworld, got to know Pat during that time, did some work for him after I left Computerworld, on a one-on-one basis. Then I would see him over the years, interview him for the New York Times or other magazines, and every time I'd see Pat, I'd end our conversation by saying, "Pat, when are we gonna do your book?" And he would laugh, and he would say, "I'm not ready to do that yet, there's just still too much to do." And so it became sort of an inside joke for us, but I always really did wanna write this book about him because I felt he deserved a book. He was just one of these game-changing pioneers in the tech industry. >> He really was, of course, the book was even more meaningful for me, we, you and I started right in the same time, 1983-- >> Yeah. >> And by that time, IDG was almost 20 years old and it was quite a powerhouse then, but boy, we saw, really the ascendancy of IDG as a brand and, you know, the book reviews on, you know, the back covers are tech elite: Benioff wrote the forward, Mark Benioff, you had Bill Gates in there, Walter Isaacson was in there, Guy Kawasaki, Bob Metcalfe, George Colony-- >> Right. >> Who actually worked for a little stint at IDC for a while. John Markoff of The New York Times, so, you know, the elite of tech really sort of blessed this book and it was really a lot to do with Pat McGovern, right? >> Oh, absolutely, I think that the people on the inside understood how important he was to the history of the tech industry. He was not, you know, a household name, first of all, you didn't think of Steve Jobs, Bill Gates, and then Pat McGovern, however, those who are in the know realize that he was as important in his own way as they were. Because somebody had to chronicle this story, somebody had to share the story of the evolution of this amazing information technology and how it changed the world. And Pat was never a front-of-the-TV-camera guy-- >> Right. >> He was a guy who put his people forward, he put his products forward, for sure, which is why IDG, as a corporate name, you know, most people don't know what that means, but people did know Macworld, people did know PCWorld, they knew IDC, they knew Computerworld for sure. So that was Pat's view of the world, he didn't care whether he had the spotlight on him or not. >> When you listen to leaders like Reed Hoffman or Eric Schmidt talk about, you know, great companies and how to build great companies, they always come back to culture. >> Yup. >> The book opens with a scene of, and we all, that I usually remember this, well, we're just hangin' around, waitin' for Pat to come in and hand out what was then called the Christmas bonus-- >> Right. >> Back when that wasn't politically incorrect to say. Now, of course, it's the holiday bonus. But it was, it was the Christmas bonus time and Pat was coming around and he was gonna personally hand a bonus, which was a substantial bonus, to every single employee at the company. I mean, and he did that, really, literally, forever. >> Forever, yeah. >> Throughout his career. >> Yeah, it was unheard of, CEOs just didn't do that and still don't do that, you were lucky, you got a message on the, you know, in the lunchroom from the CEO, "Good work, troops! Keep up the good work!" Pat just had a really different view of the culture of this company, as you know from having been there, and I know. It was very familial, there was a sense that we were all in this together, and it really was important for him to let every employee know that. The idea that he went to every desk in every office for IDG around the United States, when we were there in the '80s there were probably 5,000 employees in the US, he had to devote substantial amount-- >> Weeks and weeks! >> Weeks at a time to come to every building and do this, but year after year he insisted on doing it, his assistant at the time, Mary Dolaher told me she wanted to sign the cards, the Christmas cards, and he insisted that he ensign every one of them personally. This was the kind of view he had of how you keep employees happy, if your employees are happy, the customers are gonna be happy, and you're gonna make a lot of money. And that's what he did. >> And it wasn't just that. He had this awesome holiday party that you described, which was epic, and during the party, they would actually take pictures of every single person at the party and then they would load the carousel, you remember the 35-mm. carousel, and then, you know, toward the end of the evening, they would play that and everybody was transfixed 'cause they wanted to see their, the picture of themselves! >> Yeah, yeah. (laughs) >> I mean, it was ge-- and to actually pull that off in the 1980s was not trivial! Today, it would be a piece of cake. And then there was the IDG update, you know, the Good News memos, there was the 10-year lunch, the 20-year trips around the world, there were a lot of really rich benefits that, you know, in and of themselves maybe not a huge deal, but that was the culture that he set. >> Yeah, there was no question that if you talked to anybody who worked in this company over, say, the last 50 years, you were gonna get the same kind of stories. I've been kind of amazed, I'm going around, you know, marketing the book, talking about the book at various events, and the deep affection for this guy that still holds five years after he died, it's just remarkable. You don't really see that with the CEO class, there's a couple, you know, Steve Jobs left a great legacy of creativity, he was not a wonderful guy to his employees, but Pat McGovern, people loved this guy, and they st-- I would be signing books and somebody'd say, "Oh, I've been at IDG for 27 years and I remember all of this," and "I've been there 33 years," and there's a real longevity to this impact that he had on people. >> Now, the book was just, it was not just sort of a biography on McGovern, it was really about lessons from a leader and an entrepreneur and a media mogul who grew this great company in this culture that we can apply, you know, as business people and business leaders. Just to give you a sense of what Pat McGovern did, he really didn't take any outside capital, he did a little bit of, you know, public offering with IDG Books, but, really, you know, no outside capital, it was completely self-funded. He built a $3.8 billion empire, 300 publications, 280 million readers, and I think it was almost 100 or maybe even more, 100 countries. And so, that's an-- like you were, used the word remarkable, that is a remarkable achievement for a self-funded company. >> Yeah, Pat had a very clear vision of how, first of all, Pat had a photographic memory and if you were a manager in the company, you got a chance to sit in meetings with Pat and if you didn't know the numbers better than he did, which was a tough challenge, you were in trouble! 'Cause he knew everything, and so, he was really a numbers-focused guy and he understood that, you know, his best way to make profit was to not be looking for outside funding, not to have to share the wealth with investors, that you could do this yourself if you ran it tightly, you know, I called it in the book a 'loose-tight organization,' loose meaning he was a deep believer in decentralization, that every market needed its own leadership because they knew the market, you know, in Austria or in Russia or wherever, better than you would know it from a headquarters in Boston, but you also needed that tightness, a firm grip on the finances, you needed to know what was going on with each of the budgets or you were gonna end up in big trouble, which a lot of companies find themselves in. >> Well, and, you know, having worked there, I mean, essentially, if you made your numbers and did so ethically, and if you just kind of followed some of the corporate rules, which we'll talk about, he kind of left you alone. You know, you could, you could pretty much do whatever you wanted, you could stay in any hotel, you really couldn't fly first class, and we'll maybe talk about that-- >> Right. >> But he was a complex man, I mean, he was obviously wealthy, he was a billionaire, he was very generous, but at the same time he was frugal, you know, he drove, you know, a little, a car that was, you know, unremarkable, and we had buy him a car. He flew coach, and I remember one time, I was at a United flight, and I was, I had upgraded, you know, using my miles, and I sat down and right there was Lore McGovern, and we both looked at each other and said right at the same time, "I upgraded!" (laughs) Because Pat never flew up front, but he would always fly with a stack of newspapers in the seat next to him. >> Yeah, well, woe to, you were lucky he wasn't on the plane and spotted you as he was walking past you into coach, because he was not real forgiving when he saw people, people would hide and, you know, try to avoid him at all cost. And, I mean, he was a big man, Pat was 6'3", you know, 250 lbs. at least, built like a linebacker, so he didn't fit into coach that well, and he wasn't flying, you know, the shuttle to New York, he was flyin' to Beijing, he was flyin' to Moscow, he was going all over the world, squeezing himself into these seats. Now, you know, full disclosure, as he got older and had, like, probably 10 million air miles at his disposal, he would upgrade too, occasionally, for those long-haul flights, just 'cause he wanted to be fresh when he would get off the plane. But, yeah, these are legends about Pat that his frugality was just pure legend in the company, he owned this, you know, several versions of that dark blue suit, and that's what you would see him in. He would never deviate from that. And, but, he had his patterns, but he understood the impact those patterns had on his employees and on his customers. >> I wanna get into some of the lessons, because, really, this is what the book is all about, the heart of it. And you mentioned, you know, one, and we're gonna tell from others, but you really gotta stay close to the customer, that was one of the 10 corporate values, and you remember, he used to go to the meetings and he'd sometimes randomly ask people to recite, "What's number eight?" (laughs) And you'd be like, oh, you'd have your cheat sheet there. And so, so, just to give you a sense, this man was an entrepreneur, he started the company in 1964 with a database that he kind of pre-sold, he was kind of the sell, design, build type of mentality, he would pre-sold this thing, and then he started Computerworld in 1967, so it was really only a few years after he launched the company that he started the Computerworld, and other than Data Nation, there was nothing there, huge pent-up demand for that type of publication, and he caught lightning in a bottle, and that's really how he funded, you know, the growth. >> Yeah, oh, no question. Computerworld became, you know, the bible of the industry, it became a cash cow for IDG, you know, but at the time, it's so easy to look in hindsight and say, oh, well, obviously. But when Pat was doing this, one little-known fact is he was an editor at a publication called Computers and Automation that was based in Newton, Massachusetts and he kept that job even after he started IDC, which was the original company in 1964. It was gonna be a research company, and it was doing great, he was seeing the build-up, but it wasn't 'til '67 when he started Computerworld, that he said, "Okay, now this is gonna be a full-time gig for me," and he left the other publication for good. But, you know, he was sorta hedging his bets there for a little while. >> And that's where he really gained respect for what we'll call the 'Chinese Wallet,' the, you know, editorial versus advertising. We're gonna talk about that some more. So I mentioned, 1967, Computerworld. So he launched in 1964, by 1971, he was goin' to Japan, we're gonna talk about the China Stories as well, so, he named the company International Data Corp, where he was at a little spot in Newton, Mass.-- >> Right, right. >> So, he had a vision. You said in your book, you mention, how did this gentleman get it so right for so long? And that really leads to some of the leadership lessons, and one of them in the book was, sort of, have a mission, have a vision, and really, Pat was always talking about information, about information technology, in fact, when Wine for Dummies came out, it kind of created a little friction, that was really off the center. >> Or Wine for Dummies, or Sex for Dummies! >> Yeah, Sex for Dummies, boy, yeah! >> With, that's right, Ruth Westheimer-- >> Dr. Ruth Westheimer. >> But generally speaking, Glenn, he was on that mark, he really didn't deviate from that vision. >> Yeah, no, it was very crucial to the development of the company that he got people to, you know, buy into that mission, because the mission was everything. And he understood, you know, he had the numbers, but he also saw what was happening out there, from the 1960s, when IBM mainframes filled a room, and, you know, only the high priests of data centers could touch them. He had a vision for, you know, what was coming next and he started to understand that there would be many facets to this information about information technology, it wasn't gonna be boring, if anything, it was gonna be the story of our age and he was gonna stick to it and sell it. >> And, you know, timing is everything, but so is, you know, Pat was a workaholic and had an amazing mind, but one of the things I learned from the book, and you said this, Pat Kenealy mentioned it, all American industrial and social revolutions have had a media company linked to them, Crane and automobiles, Penton and energy, McGraw-Hill and aerospace, Annenberg, of course, and TV, and in technology, it was IDG. >> Yeah, he, like I said earlier, he really was a key figure in the development of this industry and it was, you know, one of the key things about that, a lot publications that came and went made the mistake of being platform or, you know, vertical market specific. And if that market changed, and it was inevitably gonna change in high tech, you were done. He never, you know, he never married himself to some specific technology cycle. His idea was the audience was not gonna change, the audience was gonna have to roll with this, so, the company, IDG, would produce publications that got that, you know, Computerworld was actually a little bit late to the PC game, but eventually got into it and we tracked the different cycles, you know, things in tech move in sine waves, they come and go. And Pat never was, you know, flustered by that, he could handle any kind of changes from the mainframes down to the smartphone when it came. And so, that kind of flexibility, and ability to adjust to markets, really was unprecedented in that particular part of the market. >> One of the other lessons in the book, I call it 'nation-building,' and Pat shared with you that, look, that you shared, actually, with your readers, if you wanna do it right, you've gotta be on the ground, you've gotta be there. And the China story is one that I didn't know about how Pat kind of talked his way into China, tell us, give us a little summary of that story. >> Sure, I love that story because it's so Pat. It was 1978, Pat was in Tokyo on a business trip, one of his many business trips, and he was gonna be flying to Moscow for a trade show. And he got a flight that was gonna make a stopover in Beijing, which in those days was called Peking, and was not open to Americans. There were no US and China diplomatic relations then. But Pat had it in mind that he was going to get off that plane in Beijing and see what he could see. So that meant that he had to leave the flight when it landed in Beijing and talk his way through the customs as they were in China at the time with folks in the, wherever, the Quonset hut that served for the airport, speaking no English, and him speaking no Chinese, he somehow convinced these folks to give him a day pass, 'cause he kept saying to them, "I'm only in transit, it's okay!" (laughs) Like, he wasn't coming, you know, to spy on them on them or anything. So here's this massive American businessman in his dark suit, and he somehow gets into downtown Beijing, which at the time was mostly bicycles, very few cars, there were camels walking down the street, they'd come with traders from Mongolia. The people were still wearing the drab outfits from the Mao era, and Pat just spent the whole day wandering around the city, just soaking it in. He was that kind of a world traveler. He loved different cultures, mostly eastern cultures, and he would pop his head into bookstores. And what he saw were people just clamoring to get their hands on anything, a newspaper, a magazine, and it just, it didn't take long for the light bulb to go on and said, this is a market we need to play in. >> He was fascinated with China, I, you know, as an employee and a business P&L manager, I never understood it, I said, you know, the per capita spending on IT in China was like a dollar, you know? >> Right. >> And I remember my lunch with him, my 10-year lunch, he said, "Yeah, but, you know, there's gonna be a huge opportunity there, and yeah, I don't know how we're gonna get the money out, maybe we'll buy a bunch of tea and ship it over, but I'm not worried about that." And, of course, he meets Hugo Shong, which is a huge player in the book, and the home run out of China was, of course, the venture capital, which he started before there was even a stock market, really, to exit in China. >> Right, yeah. No, he was really a visionary, I mean, that word gets tossed around maybe more than it should, but Pat was a bonafide visionary and he saw things in China that were developing that others didn't see, including, for example, his own board, who told him he was crazy because in 1980, he went back to China without telling them and within days he had a meeting with the ministry of technology and set up a joint venture, cost IDG $250,000, and six months later, the first issue of China Computerworld was being published and within a couple of years it was the biggest publication in China. He said, told me at some point that $250,0000 investment turned into $85 million and when he got home, that first trip, the board was furious, they said, "How can you do business with the commies? You're gonna ruin our brand!" And Pat said, "Just, you know, stick with me on this one, you're gonna see." And the venture capital story was just an offshoot, he saw the opportunity in the early '90s, that venture in China could in fact be a huge market, why not help build it? And that's what he did. >> What's your take on, so, IDG sold to, basically, Chinese investors. >> Yeah. >> It's kind of bittersweet, but in the same time, it's symbolic given Pat's love for China and the Chinese people. There's been a little bit of criticism about that, I know that the US government required IDC to spin out its supercomputer division because of concerns there. I'm always teasing Michael Dow that at the next IDG board meeting, those Lenovo numbers, they're gonna look kinda law. (laughs) But what are your, what's your, what are your thoughts on that, in terms of, you know, people criticize China in terms of IP protections, etc. What would Pat have said to that, do you think? >> You know, Pat made 130 trips to China in his life, that's, we calculated at some point that just the air time in planes would have been something like three and a half to four years of his life on planes going to China and back. I think Pat would, today, acknowledge, as he did then, that China has issues, there's not, you can't be that naive. He got that. But he also understood that these were people, at the end of the day, who were thirsty and hungry for information and that they were gonna be a player in the world economy at some point, and that it was crucial for IDG to be at the forefront of that, not just play later, but let's get in early, let's lead the parade. And I think that, you know, some part of him would have been okay with the sale of the company to this conglomerate there, called China Oceanwide. Clearly controversial, I mean, but once Pat died, everyone knew that the company was never gonna be the same with the leader who had been at the helm for 50 years, it was gonna be a tough transition for whoever took over. And I think, you know, it's hard to say, certainly there's criticism of things going on with China. China's gonna be the hot topic page one of the New York Times almost every single day for a long time to come. I think Pat would have said, this was appropriate given my love of China, the kind of return on investment he got from China, I think he would have been okay with it. >> Yeah, and to invoke the Ben Franklin maxim, "Trading partners seldom wage war," and so, you know, I think Pat would have probably looked at it that way, but, huge home run, I mean, I think he was early on into Baidu and Alibaba and Tencent and amazing story. I wanna talk about decentralization because that was always something that was just on our minds as employees of IDG, it was keep the corporate staff lean, have a flat organization, if you had eight, 10, 12 direct reports, that was okay, Pat really meant it when he said, "You're the CEO of your own business!" Whether that business was, you know, IDC, big company, or a manager at IDC, where you might have, you know, done tens of millions of dollars, but you felt like a CEO, you were encouraged to try new things, you were encouraged to fail, and fail fast. Their arch nemesis of IDG was Ziff Davis, they were a command and control, sort of Bill Ziff, CMP to a certain extent was kind of the same way out of Manhasset, totally different philosophies and I think Pat never, ever even came close to wavering from that decentralization philosophy, did he? >> No, no, I mean, I think that the story that he told me that I found fascinating was, he didn't have an epiphany that decentralization would be the mechanism for success, it was more that he had started traveling, and when he'd come back to his office, the memos and requests and papers to sign were stacked up two feet high. And he realized that he was holding up the company because he wasn't there to do this and that at some point, he couldn't do it all, it was gonna be too big for that, and that's when the light came on and said this decentralization concept really makes sense for us, if we're gonna be an international company, which clearly was his mission from the beginning, we have to say the people on the ground in those markets are the people who are gonna make the decisions because we can't make 'em from Boston. And I talked to many people who, were, you know, did a trip to Europe, met the folks in London, met the folks in Munich, and they said to a person, you know, it was so ahead of its time, today it just seems obvious, but in the 1960s, early '70s, it was really not a, you know, a regular leadership tenet in most companies. The command and control that you talked about was the way that you did business. >> And, you know, they both worked, but, you know, from a cultural standpoint, clearly IDG and IDC have had staying power, and he had the three-quarter rule, you talked about it in your book, if you missed your numbers three quarters in a row, you were in trouble. >> Right. >> You know, one quarter, hey, let's talk, two quarters, we maybe make some changes, three quarters, you're gone. >> Right. >> And so, as I said, if you were makin' your numbers, you had wide latitude. One of the things you didn't have latitude on was I'll call it 'pay to play,' you know, crossing that line between editorial and advertising. And Pat would, I remember I was at a meeting one time, I'm sorry to tell these stories, but-- >> That's okay. (laughs) >> But we were at an offsite meeting at a woods meeting and, you know, they give you a exercise, go off and tell us what the customer wants. Bill Laberis, who's the editor-in-chief at Computerworld at the time, said, "Who's the customer?" And Pat said, "That's a great question! To the publisher, it's the advertiser. To you, Bill, and the editorial staff, it's the reader. And both are equally important." And Pat would never allow the editorial to be compromised by the advertiser. >> Yeah, no, he, there was a clear barrier between church and state in that company and he, you know, consistently backed editorial on that issue because, you know, keep in mind when we started then, and I was, you know, a journalist hoping to, you know, change the world, the trade press then was considered, like, a little below the mainstream business press. The trade press had a reputation for being a little too cozy with the advertisers, so, and Pat said early on, "We can't do that, because everything we have, our product is built, the brand is built on integrity. And if the reader doesn't believe that what we're reporting is actually true and factual and unbiased, we're gonna lose to the advertisers in the long run anyway." So he was clear that that had to be the case and time and again, there would be conflict that would come up, it was just, as you just described it, the publishers, the sales guys, they wanted to bring in money, and if it, you know, occasionally, hey, we could nudge the editor of this particular publication, "Take it a little bit easier on this vendor because they're gonna advertise big with us," Pat just would always back the editor and say, "That's not gonna happen." And it caused, you know, friction for sure, but he was unwavering in his support. >> Well, it's interesting because, you know, Macworld, I think, is an interesting case study because there were sort of some backroom dealings and Pat maneuvered to be able to get the Macworld, you know, brand, the license for that. >> Right. >> But it caused friction between Steve Jobs and the writers of Macworld, they would write something that Steve Jobs, who was a control freak, couldn't control! >> Yeah. (laughs) >> And he regretted giving IDG the license. >> Yeah, yeah, he once said that was the worst decision he ever made was to give the license to Pat to, you know, Macworlld was published on the day that Mac was introduced in 1984, that was the deal that they had and it was, what Jobs forgot was how important it was to the development of that product to have a whole magazine devoted to it on day one, and a really good magazine that, you know, a lot of people still lament the glory days of Macworld. But yeah, he was, he and Steve Jobs did not get along, and I think that almost says a lot more about Jobs because Pat pretty much got along with everybody. >> That church and state dynamic seems to be changing, across the industry, I mean, in tech journalism, there aren't any more tech journalists in the United States, I mean, I'm overstating that, but there are far fewer than there were when we were at IDG. You're seeing all kinds of publications and media companies struggling, you know, Kara Swisher, who's the greatest journalist, and Walt Mossberg, in the tech industry, try to make it, you know, on their own, and they couldn't. So, those lines are somewhat blurring, not that Kara Swisher is blurring those lines, she's, you know, I think, very, very solid in that regard, but it seems like the business model is changing. As an observer of the markets, what do you think's happening in the publishing world? >> Well, I, you know, as a journalist, I'm sort of aghast at what's goin' on these days, a lot of my, I've been around a long time, and seeing former colleagues who are no longer in journalism because the jobs just started drying up is, it's a scary prospect, you know, unlike being the enemy of the people, the first amendment is pretty important to the future of the democracy, so to see these, you know, cutbacks and newspapers going out of business is difficult. At the same time, the internet was inevitable and it was going to change that dynamic dramatically, so how does that play out? Well, the problem is, anybody can post anything they want on social media and call it news, and the challenge is to maintain some level of integrity in the kind of reporting that you do, and it's more important now than ever, so I think that, you know, somebody like Pat would be an important figure if he was still around, in trying to keep that going. >> Well, Facebook and Google have cut the heart out of, you know, a lot of the business models of many media companies, and you're seeing sort of a pendulum swing back to nonprofits, which, I understand, speaking of folks back in the mid to early 1900s, nonprofits were the way in which, you know, journalism got funded, you know, maybe it's billionaires buying things like the Washington Post that help fund it, but clearly the model's shifting and it's somewhat unclear, you know, what's happening there. I wanted to talk about another lesson, which, Pat was the head cheerleader. So, I remember, it was kind of just after we started, the Computerworld's 20th anniversary, and they hired the marching band and they walked Pat and Mary Dolaher walked from 5 Speen Street, you know, IDG headquarters, they walked to Computerworld, which was up Old, I guess Old Connecticut Path, or maybe it was-- >> It was actually on Route 30-- >> Route 30 at the time, yeah. And Pat was dressed up as the drum major and Mary as well, (laughs) and he would do crazy things like that, he'd jump out of a plane with IDG is number one again, he'd post a, you know, a flag in Antarctica, IDG is number one again! It was just a, it was an amazing dynamic that he had, always cheering people on. >> Yeah, he was, he was, when he called himself the CEO, the Chief Encouragement Officer, you mentioned earlier the Good News notes. Everyone who worked there, at some point received this 8x10" piece of paper with a rainbow logo on it and it said, "Good News!" And there was a personal note from Pat McGovern, out of the blue, totally unexpected, to thank you and congratulate you on some bit of work, whatever it was, if you were a reporter, some article you wrote, if you were a sales guy, a sale that you made, and people all over the world would get these from him and put them up in their cubicles because it was like a badge of honor to have them, and people, I still have 'em, (laughs) you know, in a folder somewhere. And he was just unrelenting in supporting the people who worked there, and it was, the impact of that is something you can't put a price tag on, it's just, it stays with people for all their lives, people who have left there and gone on to four or five different jobs always think fondly back to the days at IDG and having, knowing that the CEO had your back in that manner. >> The legend of, and the legacy of Patrick J. McGovern is not just in IDG and IDC, which you were interested in in your book, I mean, you weren't at IDC, I was, and I was started when I saw the sort of downturn and then now it's very, very successful company, you know, whatever, $3-400 million, throwin' off a lot of profits, just to decide, I worked for every single CEO at IDC with the exception of Pat McGovern, and now, Kirk Campbell, the current CEO, is moving on Crawford del Prete's moving into the role of president, it's just a matter of time before he gets CEO, so I will, and I hired Crawford-- >> Oh, you did? (laughs) >> So, I've worked for and/or hired every CEO of IDC except for Pat McGovern, so, but, the legacy goes beyond IDG and IDC, great brands. The McGovern Brain Institute, 350 million, is that right? >> That's right. >> He dedicated to studying, you know, the human brain, he and Lore, very much involved. >> Yup. >> Typical of Pat, he wasn't just, "Hey, here's the check," and disappear. He was goin' in, "Hey, I have some ideas"-- >> Oh yeah. >> Talk about that a little. >> Yeah, well, this was a guy who spent his whole life fascinated by the human brain and the impact technology would have on the human brain, so when he had enough money, he and Lore, in 2000, gave a $350 million gift to MIT to create the McGovern Institute for Brain Research. At the time, the largest academic gift ever given to any university. And, as you said, Pat wasn't a guy who was gonna write a check and leave and wave goodbye. Pat was involved from day one. He and Lore would come and sit in day-long seminars listening to researchers talk about about the most esoteric research going on, and he would take notes, and he wasn't a brain scientist, but he wanted to know more, and he would talk to researchers, he would send Good News notes to them, just like he did with IDG, and it had same impact. People said, "This guy is a serious supporter here, he's not just showin' up with a checkbook." Bob Desimone, who's the director of the Brain Institute, just marveled at this guy's energy level, that he would come in and for days, just sit there and listen and take it all in. And it just, it was an indicator of what kind of person he was, this insatiable curiosity to learn more and more about the world. And he wanted his legacy to be this intersection of technology and brain research, he felt that this institute could cure all sorts of brain-related diseases, Alzheimer's, Parkinson's, etc. And it would then just make a better future for mankind, and as corny as that might sound, that was really the motivator for Pat McGovern. >> Well, it's funny that you mention the word corny, 'cause a lot of people saw Pat as somewhat corny, but, as you got to know him, you're like, wow, he really means this, he loves his company, the company was his extended family. When Pat met his untimely demise, we held a crowd chat, crowdchat.net/thankspat, and there's a voting mechanism in there, and the number one vote was from Paul Gillen, who posted, "Leo Durocher said that nice guys finish last, Pat McGovern proved that wrong." >> Yeah. >> And I think that's very true and, again, awesome legacy. What number book is this for you? You've written a lot of books. >> This is number 13. >> 13, well, congratulations, lucky 13. >> Thank you. >> The book is Fast Forward-- >> Future Forward. >> I'm sorry, Future Forward! (laughs) Future Forward by Glenn Rifkin. Check out, there's a link in the YouTube down below, check that out and there's some additional information there. Glenn, congratulations on getting the book done, and thanks so much for-- >> Thank you for having me, this is great, really enjoyed it. It's always good to chat with another former IDGer who gets it. (laughs) >> Brought back a lot of memories, so, again, thanks for writing the book. All right, thanks for watching, everybody, we'll see you next time. This is Dave Vellante. You're watchin' theCube. (electronic music)

(upbeat music) >> Live from Boston. It's theCUBE. Covering IBM Chief Data Officer's Summit. Brought to you by IBM. >> Welcome back to theCUBE's live coverage of IBM's CDO here in beautiful Boston, Massachusetts. I'm your host, Rebecca Knight, along with my co-host Paul Gillen. We're joined by Beth Rudden. She is distinguished engineer, analytics at IBM. Thanks so much for coming on theCUBE, Beth. >> Thank you. >> So your background, you have a master's in anthropology and in classics and Greek. You're a former archeologist. And now here you are, distinguished engineer. There's only 672 in all of IBM. How did you, what are you doing here? (laughs) >> I think that I love data. I love that data represents human behavior and I think that understanding that puzzle and being able to tell that story is something that we need to do more of. And we need to understand how all of the data fits together and how all of the information is created and all the wisdom is created. And that takes a lot of effort from a lot of people and it involves storytelling. I think that 75,000 years of human history, we are always understanding conflict and resolution through storytelling. And I think that if we can have evidence for that using our data, and looking at our data in the business world, as it reflects our strategy, instead of force-fitting our data into our strategy. So I think that that's part of the change that we need to look at it, and I think with the, I would say, second or third hype curve of AI and what we're doing with AI and cognitive today, it really is being able to bind philosophy and psychology and look at it from a computer science perspective. And that's new. >> That's interesting. And we were just speaking with Inderpal earlier about the background that he sees CDOs coming from, and he talked very little about technology. It was all about human so-called soft skills. Are you finding that the CDO role is evolving in a less technical direction over time? >> Absolutely. And I think that, you know, when you're starting to look for outcomes, and as outcomes as they relate to our business, and as they relate to our clients and our customers, we have to be able to have a very diverse and inclusive viewpoint. And we have to be incredibly transparent. I think that is something that we are continuing to do within IBM, where we are really looking at how do we differentiate ourselves based on our expertise and based on our human capital. >> So it's differentiating yourself as an employer of choice. >> Absolutely. >> And attracting, recruiting or training the talent. >> 100%, yes. >> But then also being the expert that your clients come to. So yeah, just... >> So IBM has, you know, we have career frameworks. We have career paths. I was part of a team that created the data science profession at IBM and one of the things that we're looking at, as a differentiating feature, is that we really want people to continuously learn, continuously adapt, develop themselves, develop their skills, because that is our differentiating feature. And I think that, you know, when our clients meet our people they love our people. And this is such an amazing company to be a part of. We have a long history. 107 year history of being one of the most diverse and inclusive companies. 1899, we hired the first black and female person, and in 1953 we had equal opportunity rights 10 years before the Civil Rights Act. So I think that all of these things, you know, lead up to a company that shows that we can adapt and transform. And being an acting CDO for the largest IT system in IBM right now, we are doing amazing things because we are really investing in our people. We are investing in giving them that guidance, that career track. And allowing people to be themselves. Their true selves. >> You're speaking of Global Technology Services Operation, which is currently undergoing a transformation. >> That's right. >> What does the outcome look like? How do you envision the end point? >> I think that I envision the end point, we are in the process of developing our IBM Services Academy and it is a continuous learning platform for Bands Two through general managers. And, you know, one of the stories I like to tell is my general manager who I'm working for on this. You know, she believes so strongly in making sure that everybody has access to all of the available education and everybody is using that type of education and we are looking at transforming how we are measuring what we are doing to incentivize the behaviors that we want to see. And the behaviors that we are looking for are people who are helping other people, and making sure that we are continually being the premier leader of the intellectual brainchild of what we are doing for keeping us in the AI game, in the cognitive game, and making sure that we are understanding every single aspect of that as it relates to our transformation. >> So you're actually tracking and measuring how colleagues collaborate with each other. >> 100%. >> How do you do that? >> You look for words like we and team and you look for people who are enabling other people. And that's something that we can see in the data. Data are artifacts of human behavior. We can see that in our data. We are looking at unstructured data, we are looking at structured data. We are taking this in and we're taking it to what I would call a new level, so that we can see what we are doing, who our people are, and we are able to look at how many of the people are enabling other people and empowering other people. And sometimes this is called the glue, or glue work. I think that there is even a baseball reference for like a glue man. I think that we need to champion people who are enabling and empowering everybody to succeed. >> And are those typically the unsung heroes, would you say? >> Yes. 100%. And I want to sing the hero's song for those unsung heroes. And I want to make sure that those people are noticed and recognized, but I also want to make sure that people know that IBM is this amazing company with a very long history of making sure that we are singing the unsung hero's song. >> But how do you measure the outcome of that? There's got to be a big business bottom line benefit. What does that look like? >> Absolutely. I think that it always starts with our clients. Everything that we do starts with our clients. And in GTS, we have people, we have five to seven year relationships with our clients and customers. These are deep relationships, and they interact with our humans every single day. And we are the men and women who, you know, design and create and run and manage the foundational systems of the world. And every single person, like you cannot book an airline, you cannot pay your bill, you cannot do that, anything, without touching somebody in IBM. We are investing in those people, because those are who is interfacing with our clients and customers, and that is the most important thing to us right now. >> One of the things we were talking about earlier is bringing more women and underrepresented minorities and men into IBM, and into other industries too. So how, we know the technology industry has a very bad reputation, deservedly so, for being a bro culture. How are you personally combating it, and then how do you do it from an institutional perspective? >> Yeah. We have so many programs that are really looking at how we can take and champion diversity. I was very honored to walk into the Best of IBM with my husband a couple years ago, and he looks around and he goes, this is like a UN convention. He's like, you guys are so global. You have so much diversity. And you know, that viewpoint is something that, it's why I work for IBM. It's why I love IBM. I have the ability to understand different cultures, I have the ability to travel around the world. We have, you can work day and night. (laughs) You know, you can talk to India in morning and Australia in the afternoon. It is just, to me, you know, IBM operates in 172 different countries. We have the global infrastructure to be able to handle the type of global teams that we are building. >> When you look at the skills that will be needed in the future as organizations, big data becomes infused into the organization, how will the skill needs change? >> To me, I think that the skill needs are always going to continuously transform. We're always going to get new technology. Most of my data scientists, you know, I really push Python, I really push R, but I think that it's the will more than the skill. I think that it is how people have the attitude and how people collaborate. And that is more important, I think, than some of the skills. And a lot of people, you know, when they are performing data science or performing data engineering, they need to believe that they are doing something that is going to succeed. And that is will. And that's what we, we have seen a huge surge in oral and written communications which is not a hard skill. It's a soft skill. But to me, there's nothing soft about those skills. It takes courage and we have built resiliency because we have had the courage to really enable and empower people to get those types of skills. And that's a lot of where our education is going. >> So that's really an interesting point here. So are you hiring a self-selected group of people, or are you bringing in super smart people who maybe are not as skilled in those areas and bringing them into the culture. I mean, what's coming first here? >> Yeah. I think that we are, our culture is strong. IBM's brand is strong. Our culture is strong. We are investing in the people that we have. And we are investing in, you know, our humans in order to make sure that the people who already have that culture have the skills that they need in order to learn. And that understanding of going from disequilibrium to equilibrium to disequilibrium to learn, that's what we want to teach, so that any of the new technology, any of the new skills, or any of the new platforms that we need to learn, it's something that's inherent with people being able to learn how to learn. >> Beth, thank you so much for coming on theCUBE. >> Yes. >> It was great to have you. >> Thank you. >> I'm Rebecca Knight, for Paul Gillen, we will have more of theCUBE's live coverage of IBM's CDO coming up in just a little bit. (light music)