from the cube studios in Palo Alto in Boston connecting with thought leaders all around the world this is a cube conversation hi buddy welcome to this special noob digital event where we're focusing in on data ops data ops in Acton with generous support from friends at IBM let me set up the situation here there's a real problem going on in the industry and that's that people are not getting the most out of their data data is plentiful but insights perhaps aren't what's the reason for that well it's really a pretty complicated situation for a lot of organizations there's data silos there's challenges with skill sets and lack of skills there's tons of tools out there sort of a tools brief the data pipeline is not automated the business lines oftentimes don't feel as though they own the data so that creates some real concerns around data quality and a lot of finger-point quality the opportunity here is to really operationalize the data pipeline and infuse AI into that equation and really attack their cost-cutting and revenue generation opportunities that are there in front of you think about this virtually every application this decade is going to be infused with AI if it's not it's not going to be competitive and so we have organized a panel of great practitioners to really dig in to these issues first I want to introduce Victoria Stassi with who's an industry expert in a top at Northwestern you two'll very great to see you again thanks for coming on excellent nice to see you as well and Caitlin Alfre is the director of AI a vai accelerator and also part of the peak data officers organization at IBM who has actually eaten some of it his own practice what a creep let me say it that way Caitlin great to see you again and Steve Lewis good to see you again see vice president director of management associated a bank and Thompson thanks for coming on thanks Dave make speaker alright guys so you heard my authority with in terms of operationalizing getting the most insight hey data is wonderful insights aren't but getting insight in real time is critical in this decade each of you is a sense as to where you are on that journey or Victoria your taste because you're brand new to Northwestern Mutual but you have a lot of deep expertise in in health care and manufacturing financial services but where you see just the general industry climate and we'll talk about the journeys that you are on both personally and professionally so it's all fair sure I think right now right again just me going is you need to have speech insight right so as I experienced going through many organizations are all facing the same challenges today and a lot of those pounds is hard where do my to live is my data trust meaning has a bank curated has been Clinton's visit qualified has a big a lot of that is ready what we see often happen is businesses right they know their KPIs they know their business metrics but they can't find where that data Linda Barragan asked there's abundant data disparity all over the place but it is replicated because it's not well managed it's a lot of what governance in the platform of pools that governance to speak right offer fact it organizations pay is just that piece of it I can tell you where data is I can tell you what's trusted that when you can quickly access information and bring back answers to business questions that is one answer not many answers leaving the business to question what's the right path right which is the correct answer which which way do I go at the executive level that's the biggest challenge where we want the industry to go moving forward right is one breaking that down along that information to be published quickly and to an emailing data virtualization a lot of what you see today is most businesses right it takes time to build out large warehouses at an enterprise level we need to pivot quicker so a lot of what businesses are doing is we're leaning them towards taking advantage of data virtualization allowing them to connect to these data sources right to bring that information back quickly so they don't have to replicate that information across different systems or different applications right and then to be able to provide that those answers back quickly also allowing for seamless access to from the analysts that are running running full speed right try and find the answers as quickly as they find great okay and I want to get into that sort of how news Steve let me go to you one of the things that we talked about earlier was just infusing this this mindset of a data cult and thinking about data as a service so talk a little bit about how you got started what was the starting NICUs through that sure I think the biggest thing for us there is to change that mindset from data being just for reporting or things that have happened in the past to do some insights on us and some data that already existed well we've tried to shift the mentality there is to start to use data and use that into our actual applications so that we're providing those insight in real time through the applications as they're consumed helping with customer experience helping with our personalization and an optimization of our application the way we've started down that path or kind of the journey that we're still on was to get the foundation laid birch so part of that has been making sure we have access to all that data whether it's through virtualization like vic talked about or whether it's through having more of the the data selected in a data like that that where we have all of that foundational data available as opposed to waiting for people to ask for it that's been the biggest culture shift for us is having that availability of data to be ready to be able to provide those insights as opposed to having to make the businesses or the application or asked for that day Oh Kailyn when I first met into pulp andari the idea wobble he paid up there yeah I was asking him okay where does a what's the role of that at CBO and and he mentioned a number of things but two of the things that stood out is you got to understand how data affect the monetization of your company that doesn't mean you know selling the data what role does it play and help cut cost or ink revenue or productivity or no customer service etc the other thing he said was you've got a align with the lines of piss a little sounded good and this is several years ago and IBM took it upon itself Greek its own champagne I was gonna say you know dogfooding whatever but it's not easy just flip a switch and an infuse a I and automate the data pipeline you guys had to go you know some real of pain to get there and you did you were early on you took some arrows and now you're helping your customers better on thin debt but talk about some of the use cases that where you guys have applied this obviously the biggest organization you know one of the biggest in the world the real challenge is they're sure I'm happy today you know we've been on this journey for about four years now so we stood up our first book to get office 2016 and you're right it was all about getting what data strategy offered and executed internally and we want to be very transparent because as you've mentioned you know a lot of challenges possible think differently about the value and so as we wrote that data strategy at that time about coming to enterprise and then we quickly of pivoted to see the real opportunity and value of infusing AI across all of our needs were close to your question on a couple of specific use cases I'd say you know we invested that time getting that platform built and implemented and then we were able to take advantage of that one particular example that I've been really excited about I have a practitioner on my team who's a supply chain expert and a couple of years ago he started building out supply chain solution so that we can better mitigate our risk in the event of a natural disaster like the earthquake hurricane anywhere around the world and be cuz we invest at the time and getting the date of pipelines right getting that all of that were created and cleaned and the quality of it we were able to recently in recent weeks add the really critical Kovach 19 data and deliver that out to our employees internally for their preparation purposes make that available to our nonprofit partners and now we're starting to see our first customers take advantage too with the health and well-being of their employees mine so that's you know an example I think where and I'm seeing a lot of you know my clients I work with they invest in the data and AI readiness and then they're able to take advantage of all of that work work very quickly in an agile fashion just spin up those out well I think one of the keys there who Kaelin is that you know we can talk about that in a covet 19 contact but it's that's gonna carry through that that notion of of business resiliency is it's gonna live on you know in this post pivot world isn't it absolutely I think for all of us the importance of investing in the business continuity and resiliency type work so that we know what to do in the event of either natural disaster or something beyond you know it'll be grounded in that and I think it'll only become more important for us to be able to act quickly and so the investment in those platforms and approach that we're taking and you know I see many of us taking will really be grounded in that resiliency so Vic and Steve I want to dig into this a little bit because you know we use this concept of data op we're stealing from DevOps and there are similarities but there are also differences now let's talk about the data pipeline if you think about the data pipeline as a sort of quasi linear process where you're investing data and you might be using you know tools but whether it's Kafka or you know we have a favorite who will you have and then you're transforming that that data and then you got a you know discovery you got to do some some exploration you got to figure out your metadata catalog and then you're trying to analyze that data to get some insights and then you ultimately you want to operationalize it so you know and and you could come up with your own data pipeline but generally that sort of concept is is I think well accepted there's different roles and unlike DevOps where it might be the same developer who's actually implementing security policies picking it the operations in in data ops there might be different roles and fact very often are there's data science there's may be an IT role there's data engineering there's analysts etc so Vic I wonder if you could you could talk about the challenges in in managing and automating that data pipeline applying data ops and how practitioners can overcome them yeah I would say a perfect example would be a client that I was just recently working for where we actually took a team and we built up a team using agile methodologies that framework right we're rapidly ingesting data and then proving out data's fit for purpose right so often now we talk a lot about big data and that is really where a lot of industries are going they're trying to add an enrichment to their own data sources so what they're doing is they're purchasing these third-party data sets so in doing so right you make that initial purchase but what many companies are doing today is they have no real way to vet that so they'll purchase the information they aren't going to vet it upfront they're going to bring it into an environment there it's going to take them time to understand if the data is of quality or not and by the time they do typically the sales gone and done and they're not going to ask for anything back but we were able to do it the most recent claim was use an instructure data source right bring that and ingest that with modelers using this agile team right and within two weeks we were able to bring the data in from the third-party vendor what we considered rapid prototyping right be able to profile the data understand if the data is of quality or not and then quickly figure out that you know what the data's not so in doing that we were able to then contact the vendor back tell them you know it sorry the data set up to snuff we'd like our money back we're not gonna go forward with it that's enabling businesses to be smarter with what they're doing with 30 new purchases today as many businesses right now um as much as they want to rely on their own data right they actually want to rely on cross the data from third-party sources and that's really what data Ops is allowing us to do it's allowing us to think at a broader a higher level right what to bring the information what structures can we store them in that they don't necessarily have to be modeled because a modeler is great right but if we have to take time to model all the information before we even know we want to use it that's gonna slow the process now and that's slowing the business down the business is looking for us to speed up all of our processes a lot of what we heard in the past raised that IP tends to slow us down and that's where we're trying to change that perception in the industry is no we're actually here to speed you up we have all the tools and technologies to do so and they're only getting better I would say also on data scientists right that's another piece of the pie for us if we can bring the information in and we can quickly catalog it in a metadata and burn it bring in the information in the backend data data assets right and then supply that information back to scientists gone are the days where scientists are going and asking for connections to all these different data sources waiting days for access requests to be approved just to find out that once they figure out how it with them the relationship diagram right the design looks like in that back-end database how to get to it write the code to get to it and then figure out this is not the information I need that Sally next to me right fold me the wrong information that's where the catalog comes in that's where due to absent data governance having that catalog that metadata management platform available to you they can go into a catalog without having to request access to anything quickly and within five minutes they can see the structures what if the tables look like what did the fields look like are these are these the metrics I need to bring back answers to the business that's data apps it's allowing us to speed up all of that information you know taking stuff that took months now down two weeks down two days down two hours so Steve I wonder if you could pick up on that and just help us understand what data means you we talked about earlier in our previous conversation I mentioned it upfront is this notion of you know the demand for for data access is it was through the roof and and you've gone from that to sort of more of a self-service environment where it's not IT owning the data it's really the businesses owning the data but what what is what is all this data op stuff meaning in your world sure I think it's very similar it's it's how do we enable and get access to that clicker showing the right controls showing the right processes and and building that scalability and agility and into all of it so that we're we're doing this at scale it's much more rapidly available we can discover new data separately determine if it's right or or more importantly if it's wrong similar to what what Vic described it's it's how do we enable the business to make those right decisions on whether or not they're going down the right path whether they're not the catalog is a big part of that we've also introduced a lot of frameworks around scale so just the ability to rapidly ingest data and make that available has been a key for us we've also focused on a prototyping environment so that sandbox mentality of how do we rapidly stand those up for users and and still provide some controls but have provide that ability for people to do that that exploration what we're finding is that by providing the platform and and the foundational layers that were we're getting the use cases to sort of evolve and come out of that as opposed to having the use cases prior to then go build things from we're shifting the mentality within the organization to say we don't know what we need yet let's let's start to explore that's kind of that data scientist mentality and culture it more of a way of thinking as opposed to you know an actual project or implement well I think that that cultural aspect is important of course Caitlin you guys are an AI company or at least that you know part of what you do but you know you've you for four decades maybe centuries you've been organized around different things by factoring plant but sales channel or whatever it is but-but-but-but how has the chief data officer organization within IBM been able to transform itself and and really infuse a data culture across the entire company one of the approaches you know we've taken and we talk about sort of the blueprint to drive AI transformation so that we can achieve and deliver these really high value use cases we talked about the data the technology which we've just pressed on with organizational piece of it duration are so important the change management enabling and equipping our data stewards I'll give one a civic example that I've been really excited about when we were building our platform and starting to pull districting structured unstructured pull it in our ADA stewards are spending a lot of time manually tagging and creating business metadata about that data and we identified that that was a real pain point costing us a lot of money valuable resources so we started to automate the metadata and doing that in partnership with our deep learning practitioners and some of the models that they were able to build that capability we pushed out into our contacts our product last year and one of the really exciting things for me to see is our data stewards who be so value exporters and the skills that they bring have reported that you know it's really changed the way they're able to work it's really sped up their process it's enabled them to then move on to higher value to abilities and and business benefits so they're very happy from an organizational you know completion point of view so I think there's ways to identify those use cases particularly for taste you know we drove some significant productivity savings we also really empowered and hold our data stewards we really value to make their job you know easier more efficient and and help them move on to things that they are more you know excited about doing so I think that's that you know another example of approaching taken yes so the cultural piece the people piece is key we talked a little bit about the process I want to get into a little bit into the tech Steve I wonder if you could tell us you know what's it what's the tech we have this bevy of tools I mentioned a number of them upfront you've got different data stores you've got open source pooling you've got IBM tooling what are the critical components of the technology that people should be thinking about tapping in architecture from ingestion perspective we're trying to do a lot of and a Python framework and scaleable ingestion pipe frameworks on the catalog side I think what we've done is gone with IBM PAC which provides a platform for a lot of these tools to stay integrated together so things from the discovery of data sources the cataloging the documentation of those data sources and then all the way through the actual advanced analytics and Python models and our our models and the open source ID combined with the ability to do some data prep and refinery work having that all in an integrated platform was a key to us for us that the rollout and of more of these tools in bulk as opposed to having the point solutions so that's been a big focus area for us and then on the analytic side and the web versus IDE there's a lot of different components you can go into whether it's meal soft whether it's AWS and some of the native functionalities out there you mentioned before Kafka and Anissa streams and different streaming technologies those are all the ones that are kind of in our Ketil box that we're starting to look at so and one of the keys here is we're trying to make decisions in as close to real time as possible as opposed to the business having to wait you know weeks or months and then by the time they get insights it's late and really rearview mirror so Vic your focus you know in your career has been a lot on data data quality governance master data management data from a data quality standpoint as well what are some of the key tools that you're familiar with that you've used that really have enabled you operationalize that data pipeline you know I would say I'm definitely the IBM tools I have the most experience with that also informatica though as well those are to me the two top players IBM definitely has come to the table with a suite right like Steve said cloud pack for data is really a one-stop shop so that's allowing that quick seamless access for business user versus them having to go into some of the previous versions that IBM had rolled out where you're going into different user interfaces right to find your information and that can become clunky it can add the process it can also create almost like a bad taste and if in most people's mouths because they don't want to navigate from system to system to system just to get their information so cloud pack to me definitely brings everything to the table in one in a one-stop shop type of environment in for me also though is working on the same thing and I would tell you that they haven't come up with a solution that really comes close to what IBM is done with cloud pack for data I'd be interested to see if they can bring that on the horizon but really IBM suite of tools allows for profiling follow the analytics write metadata management access to db2 warehouse on cloud those are the tools that I've worked in my past to implement as well as cloud object store to bring all that together to provide that one stop that at Northwestern right we're working right now with belieber I think calibra is a great set it pool are great garments catalog right but that's really what it's truly made for is it's a governance catalog you have to bring some other pieces to the table in order for it to serve up all the cloud pack does today which is the advanced profiling the data virtualization that cloud pack enables today the machine learning at the level where you can actually work with our and Python code and you put our notebooks inside of pack that's some of this the pieces right that are missing in some of the under vent other vendor schools today so one of the things that you're hearing here is the theme of openness others addition we've talked about a lot of tools and not IBM tools all IBM tools there there are many but but people want to use what they want to use so Kaitlin from an IBM perspective what's your commitment the openness number one but also to you know we talked a lot about cloud packs but to simplify the experience for your client well and I thank Stephen Victoria for you know speaking to their experience I really appreciate feedback and part of our approach has been to really take one the challenges that we've had I mentioned some of the capabilities that we brought forward in our cloud platform data product one being you know automating metadata generation and that was something we had to solve for our own data challenges in need so we will continue to source you know our use cases from and grounded from a practitioner perspective of what we're trying to do and solve and build and the approach we've really been taking is co-creation line and that we roll these capability about the product and work with our customers like Stephen light victorious you really solicit feedback to product route our dev teams push that out and just be very open and transparent I mean we want to deliver a seamless experience we want to do it in partnership and continue to solicit feedback and improve and roll out so no I think that will that has been our approach will continue to be and really appreciate the partnerships that we've been able to foster so we don't have a ton of time but I want to go to practitioners on the panel and ask you about key key performance indicators when I think about DevOps one of the things that we're measuring is the elapsed time the deploy applications start finished where we're measuring the amount of rework that has to be done the the quality of the deliverable what are the KPIs Victoria that are indicators of success in operationalizing date the data pipeline well I would definitely say your ability to deliver quickly right so how fast can you deliver is that is that quicker than what you've been able to do in the past right what is the user experience like right so have you been able to measure what what the amount of time was right that users are spending to bring information to the table in the past versus have you been able to reduce that time to delivery right of information business answers to business questions those are the key performance indicators to me that tell you that the suite that we've put in place today right it's providing information quickly I can get my business answers quickly but quicker than I could before and the information is accurate so being able to measure is it quality that I've been giving that I've given back or is this not is it the wrong information and yet I've got to go back to the table and find where I need to gather that from from somewhere else that to me tells us okay you know what the tools we've put in place today my teams are working quicker they're answering the questions they need to accurately that is when we know we're on the right path Steve anything you add to that I think she covered a lot of the people components the around the data quality scoring right for all the different data attributes coming up with a metric around how to measure that and and then showing that trend over time to show that it's getting better the other one that we're doing is just around overall date availability how how much data are we providing to our users and and showing that trend so when I first started you know we had somewhere in the neighborhood of 500 files that had been brought into the warehouse and and had been published and available in the neighborhood of a couple thousand fields we've grown that into weave we have thousands of cables now available so it's it's been you know hundreds of percent in scale as far as just the availability of that data how much is out there how much is is ready and available for for people to just dig in and put into their their analytics and their models and get those back into the other application so that's another key metric that we're starting to track as well so last question so I said at the top that every application is gonna need to be infused with AI this decade otherwise that application not going to be as competitive as it could be and so for those that are maybe stuck in their journey don't really know where to get started I'll start with with Caitlin and go to Victoria and then and then even bring us home what advice would you give the people that need to get going on this my advice is I think you pull the folks that are either producing or accessing your data and figure out what the rate is between I mentioned some of the data management challenges we were seeing this these processes were taking weeks and prone to error highly manual so part was ripe for AI project so identifying those use cases I think that are really causing you know the most free work and and manual effort you can move really quickly and as you build this platform out you're able to spin those up on an accelerated fashion I think identifying that and figuring out the business impact are able to drive very early on you can get going and start really seeing the value great yeah I would actually say kids I hit it on the head but I would probably add to that right is the first and foremost in my opinion right the importance around this is data governance you need to implement a data governance at an enterprise level many organizations will do it but they'll have silos of governance you really need an interface I did a government's platform that consists of a true framework of an operational model model charters right you have data domain owners data domain stewards data custodians all that needs to be defined and while that may take some work in in the beginning right the payoff down the line is that much more it's it it's allowing your business to truly own the data once they own the data and they take part in classifying the data assets for technologists and for analysts right you can start to eliminate some of the technical debt that most organizations have acquired today they can start to look at what are some of the systems that we can turn off what are some of the systems that we see valium truly build out a capability matrix we can start mapping systems right to capabilities and start to say where do we have wares or redundancy right what can we get rid of that's the first piece of it and then the second piece of it is really leveraging the tools that are out there today the IBM tools some of the other tools out there as well that enable some of the newer next-generation capabilities like unit nai right for example allowing automation for automation which right for all of us means that a lot of the analysts that are in place today they can access the information quicker they can deliver the information accurately like we've been talking about because it's been classified that pre works being done it's never too late to start but once you start that it just really acts as a domino effect to everything else where you start to see everything else fall into place all right thank you and Steve bring us on but advice for your your peers that want to get started sure I think the key for me too is like like those guys have talked about I think all everything they said is valid and accurate thing I would add is is from a starting perspective if you haven't started start right don't don't try to overthink that over plan it it started just do something and and and start the show that progress and value the use cases will come even if you think you're not there yet it's amazing once you have the national components there how some of these things start to come out of the woodwork so so it started it going may have it have that iterative approach to this and an open mindset it's encourage exploration and enablement look your organization in the eye to say why are their silos why do these things like this what are our problem what are the things getting in our way and and focus and tackle those those areas as opposed to trying to put up more rails and more boundaries and kind of encourage that silo mentality really really look at how do you how do you focus on that enablement and then the last comment would just be on scale everything should be focused on scale what you think is a one-time process today you're gonna do it again we've all been there you're gonna do it a thousand times again so prepare for that prepare forever that you're gonna do everything a thousand times and and start to instill that culture within your organization a great advice guys data bringing machine intelligence an AI to really drive insights and scaling with a cloud operating model no matter where that data live it's really great to have have three such knowledgeable practitioners Caitlyn Toria and Steve thanks so much for coming on the cube and helping support this panel all right and thank you for watching everybody now remember this panel was part of the raw material that went into a crowd chat that we hosted on May 27th Crouch at net slash data ops so go check that out this is Dave Volante for the cube thanks for watching [Music]

>> from our studios in the heart of Silicon Valley. PALO ALTO, California It is a cute conversation, >> living welcome to this special Cuba conversation in Palo Alto, California We're here with our friends on Twitter and influences in the cloud computing edge and open source game. We have our distinguished power panel here talking about if every tech company, every company should be a tech company. And what does it mean in the air of a modern infrastructure? Police to have my kale with ct of everest dot org's from most Gatto's California Rob Hirschfeld, founder and CEO of Rock n Calling in From Where You Calling in from >> Austin, Texas. >> Austin, Texas. Good to have you and Mark Theo Who's with EJ Gravity brand New opportunity. Congratulations calling in Las Vegas. Thanks for coming in, guys. Thanks for spending the time on this cube power panel from the influencers. Always great to see you guys on Twitter with this morning. I woke up, was very active at a Crouch said earlier this morning. And Mark, you wrote a post that got my attention. So I think you hit a nerve that has been sparking around the Internets around the role of technology as couples, they're starting to rethink and building out there enterprise architectures in their businesses. And we're seeing some signals around cybersecurity. Dev Ops certainly has been kind of banging on this drum with cloud computing, and that is that the role of technology plays as a percentage of the business part of the business. And your tweet was simply put, you said every bit. If every business needs to become a tech business, it business has to decide to own its own infrastructure something of that effect, which which triggered me because it's like That's a good question. It isn't just a part of an organization supporting it. Tech is becoming much more instrumental. So I want to get your reaction. What was the motivation behind that tweet? What's your what's your What was your point around it? >> Yeah, I mean, like many of my tweets, they're poorly worded and rushed out, so you know, it's not as clear as it could have been. But the real point of the message wasn't Thio highlight that a technology company has to be all in the cloud or has to own its infrastructure, but rather as a company makes a change towards becoming a technology company. I mean, if we go back Thio you know, 1995 or 1996 when we wanted a library, we went to the library. But now we have Google. We didn't know that Google was gonna become an online the equivalent of a library. But it became a digital company before anybody asked for that solution or anybody was running that kind of solution in some sort of company format and then changed it over. But, you know, Google Facebook, Microsoft's into it. Adobe PayPal. We could go down the long list there. All I t cos in the end, whether you call the technology that they built to run their businesses engineering with a CTO or I t. Is the material. They are in fact, large giant I t organizations that do what they do to make money. And so, as more companies look to make the change as digital transformation takes hold as more efforts are presented to try to get a closer handle on customers to build loyalty with customers, create new engagement models, maybe at the edge, even in traditional application environments, then companies have to make a decision about how they're going toe oh, nightie and whether they're goingto own any portion of the infrastructure of I T. And if they're going to do that, then I don't think that there's any question that they have to own it. Atleast following a model of the way the large providers and the facebooks, et cetera have provided for us cannot continue. In other words, what I've been known to say before, we can't continue to throw more hardware and people at the problem. >> My mike, I want to get your thoughts on this because one of the things that I know you have been involved a lot with security on dhe I t. As well in security, which which is a canary in the coal mine. For a lot of these architectural decisions are all kind of looking at how they hire and build on premise in house around tech stacks. And one of the things that became apparent to me at Amazon Aws reinforce, which is their Amazons first cloud security conference, was most of the ceases. When I talk privately was saying, we don't really believe in multi cloud. We have multiple clouds, but We're investing in people on certain stacks that fit our guiding principles of what we're building as a company. And they said we then go to the suppliers and saying, Here's the AP eyes we want you to support So you start to see the shift from being hiring the general purpose software vendors to come in and supply them with I t stuff Were hardware. As Mark pointed out, too much more, the customer saying No, no, this is our spec build that we built it. And so the trend that points to the trend of a reinvestment of building tech at the core of the business, which would imply to Mark's point around their tech companies. What's your thoughts on this? >> So a nuance. My answer. I think their tech enabled companies more than tech companies like Tech is enabling, whether it's Google or into it or pay power of the other companies. Mark mentioned technologies the base of their companies stack, um, then to go into your security portion, security has to be architected and embedded into the core solutions not bolted on after the fact with vendor solutions like it is today, and I think we've proven time and time again, including the capital one issue as a day or two ago that the current approaches are not working. And, uh, I agree with whomever See says you've been talking thio like being driving a P I integrations and be consumptive of them and telling what you need to build is a much better approach. Would you want to build a custom house with that actually talking to your builder and finding out later? What? What features and pictures have been installed in your home. But what do you wanna have a hand in that from the ground up? I think that's the mischief. >> Well, I want to come back to the capital. One point that's gonna be a separate talk track. So let's hold that thought. Rob, I want to go to you. Because StarBeat Joel, whose prolific on these threads you know, posting is nice Twitter cards on their um, he said, If you know, talk about leasing out extra capacity in a private data centers question Mark, you know, teasing out the question. And then Ben Haines responded and said, Why the hell would you want to be in that business when you have a real business to run again to what Mark was saying about, You know, Tech is going to be everywhere. Why should I even be in the data center? Because I don't want to be in that business. I gotta figure out Tech for the business. So Ben kind of brings that practitioner perspective. What's your thought? Because you're in the middle of this with the devil's movement. Bare metal, big part of it, Your thoughts. >> Yeah, And that's why we really focus on fixing the bear mental problem. Andi, I want to come back to where a bear metal fits with all this because you really can't get away from bare metal. I think the first question is really is every day to send is every business in I t business. And you know, not every business is a Google and strictly a nighty business. But what we're seeing with machine learning and Internet of things and just extension of what was traditionally siloed I t or data center, I t into everyday operations. You can't get away from the fact that if you're not able to take in the data, work with the data, manipulate and understand what your customers were doing. Then you are going to be behind. That's That's how you're gonna lose. You're gonna be out of business on. So I think that what we're doing is we're redefining business into not just a product that you're selling, but understanding how your customers air interacting with that product, what value they're getting from it. We really redefined supply chain in a very transformative way compared to anything else. And that's an I T enabled transformation. >> Ben brings up a good point, but the Brent wanted Friends Point is essentially teasing out mark and yourself a bare metal. All this stuff is complicated. Cut and make investments. Ben's teasing as What the hell business do you want to be in? I think that becomes a lot of this digital transformation. Conversation is Hey, Cloud is an easy decision. We were start up 10 years ago. We don't have I t. We have 50 plus people on growing. We're all in the cloud. That's fine for us. Dropbox started in the cloud. All these guys started class. It's easy as hell to do it. No, no debate there. But as you start thinking, Maurin Maur integration as a big enterprise which wasn't born in the cloud. This is where the transformations happening is what business? What the hell they doing? What's what's the purpose of their >> visit? Yeah, but the reality of you, a cloud infrastructure and how cloud infrastructure is structured does not really take you away from owning how you operate and run that infrastructure, right Amazons than an amazing marketing job of telling everybody that they're not smart enough to run their own infrastructure. And it's just not true way definitely let operations get very lax. We built up a lot of technical debt that we we need to be able to fix. An Amazon walked in and said, This is too hard for you. Let us take it off your plate. But the reality is people using Amazon still have toe owned their operations of that infrastructure. The capital one didn't doesn't get to just get a pass and say, I used Amazon. Oh, well, Too bad. Talk to them. You still own your infrastructure. >> Technically, it wasn't Amazons fall, so let's get the capital. One is this brings up a good point. Converged infrastructure was the Holy Grail, savior for the I t If you go back when we started doing Cuba interviews, stupidity and I would talk about converged is awesome. You got Nutanix kicked ass and grew like crazy. And so then you have the converge kind of meat's maker. When it sees the cloud, it's like, OK, I got great converged infrastructure, but yet the breach on capital one had nothing to do with a W s. It was basically an s three bucket that the firewall Miss configured. So it was really Amazon was a victim of its simplicity there. I mean, there's a >> I mean, this is this is what we're talking about with. To me with this tweet is that we need to look, we need to be better at operating the infrastructure we have, whether it's Amazon or physical assets on your premises. What we've really done is we've eroded our ability to manage those pieces well and do it in a way that builds on itself. And so as soon as we can get on improvement there, I mean, this this is where I went with this threat is if we can really improve our operational efficiency with the infrastructure we have, whether it's in the cloud on premises. You create benefits there than everything you build on top of that is gonna have a nim prove mint, right. We're gonna change the way we look at infrastructure. Amazons already done that on. We think about infrastructure in cloud terms, but I don't think that what they've done is the end destination. They just taught us how to be better running infrastructure. >> Well, it brings up that it brings up the point, and I have so Mike shaking his head to get his thought and mark on this. If I is that I tease problem our operational technologies problem because the world's not as simple as it used to be. It was not. It wasn't. It's not simple. You got edge. You get externally incest cloud players now multi cloud. So information technology teams and operational technology teams whose fault is it? Who is responsible thing? Could you just had a AI bots managing the the filtering and access to history buckets that could have been automated away? What, Whose problem was it? Operations, technology or I t. >> So that I think, to touch upon what Rob was talking about. There's my chain and technology, uh, from the classic sound byte is people process and technology. The core cause of literally every security breach, including capital one is a lack of sophisticated process and the root cause being people, and there's no amount of a I currently that can fix that. So you have to start focusing on your operational supply chain processes, which has, Rob said. Amazon has really solidified, and the company should look to emulate that forces trying to emulate the cloud infrastructure and some of your processed and your people challenges first. And then you can leverage the technology. >> Great point. Totally agree with you on that one >> market. Yeah, I would agree with everything that both Mike and Rob just said, and I would just add that we we don't have any choice but to face the future. That is, I t. And in order to provide the best possible service to our customers for our applications that even haven't been built yet, we have to look at the service is that are available to us and utilize them the best way possible and then find appropriate management and, like so correctly put it supply chain processes for managing them. So I've talked to people who are building unique cloud platforms internally to solve a specific business problem in ways that the individual clouds offered by the Big Three is an example can't do or can't do as well or can't do is cheaply. And the same thing applies to customers who are just using more than one of the big cloud providers. Even for some in some cases, for workloads. That might seem similar because each of the clouds provide a different opportunity associated with that specific set of requirements. And so we don't have any choice but to manage it better. And whether it's we make a choice to use it in our data center because it's more cost effective long term. And that's our single most important driver. Or whether we decide to leverage every tool in our tool belt, which includes a handful of cloud providers. And some we do our own, um, or we put it all in one cloud. It doesn't change our responsibility for owning it correctly, right? And my simple message really was that you have to figure out how to own and I'll steal from Mike again. You have to figure out how to own that supply chain. But more lower down more base is ifs. Part of that supply chain is delivering compute into a data center or environment that you own. Then you have to find the tools capabilities to ensure that you're not making the kind of mistakes that were made with capital or >> or, if you have tools are networks and tools you don't know and look at the quotes. So called scare with the China hack from Super Micro. That's a silly why chain problems? Well, it's on the silicon. So again, back to the process, people an equation. I think that's right on this brings us kind of through the next talking track. I want to get your thoughts on, which is cloud two point. Oh, I mean, I'm putting that term out there on Lee is a provocative way. Remember, Web to point. It works so well in debating about what it what it was. If one if cloud one data was Amazon Web service is, thank you very much. Public cloud. You could say cloud two point. Oh, our second inning would be just what happens next because you're seeing now a confluence of different dynamics edge, um, security, industrial edge. And then you know this all coming into on premises, which is hybrid and public, all working together. And then you throw multi cloud in there from a complexity standpoint. Do you wanna have support Microsoft's Stack, Azure Stack, Google and Amazon? This is this is the fundamental 2.0 question. Because things are more real time. Things are data specific. This costs involved. There's really network innovation needed what you guys thoughts on cloud to point out. >> I think the basic cloud 2.0, is moving to the shared responsibility model. And we should stop blaming people for teams for breaches as architectures become much more complex, including network computing, storage and in service orchestration layers like kubernetes, no one team or individual, individual or one team and manage all of that. So you're all responsible for infrastructure, scalability, performance and security. So I think it's the cultural movement more than the technology movement at the base of >> Rob. What's your definition? Cloud 2.0, from your perspective. >> Oh boy, I've been calling it Post Cloud Is my feeling on this? Yeah, it to me. It's it's about rethinking the way we automate. Um, you know, we really learned that we had to interact with infrastructure via automation and eliminate the human risk elements of. This doesn't mean that we have an automation is foolproof either It's not, but what? What I think we've seen is that people have really understood that we have to bring the type of automation and power that we're seeing in clouding the benefits because they're very riel. But back into everything that we do. There's no doubt in my mind that infrastructure is moving back into the environment. Where is what? Which is EJ from my perspective, and we'll see computing in a much more distributed way and those benefits and getting that right in the automation. Is this necessary to run autonomous zero touch infrastructure in environmental situations. That is gonna be justice transformative, freighted that that environment makes the cloud look easy. Frankly, >> Mark, what's your take? I want to get because, you know, security houses, one element get self driving cars. You got kind of a new front end of of EJ devices, whether it's a Serie Buy Me a song on iTunes, which has to go out to a traditional system and purchase a song. But that that Siri priest is different than what? The back end? Does this simply database, Get it? Moving over self driving cars, You're seeing all kinds of EJ industrial activity. You know, the debate of moving compute to the data. You got Amazon with ground station, all these new infrastructure physical activities going on that needs software to power it. What, you're in cloud to point. It seems to be a nice place not just for analytics, but for operational thing. Your thoughts on cloud to point out >> Well, I mean you you describe the opportunity relatively well. I could certainly go in. I've spent a lot of time going into detail about what EJ might mean and what might populate edge and why people would use it. But I think from if we just look at it from a cloud 2.0, standpoint, maybe I'm oversimplifying. But I would say, you know, if you add on to what Mike and Rob already so well pointed out is that it's best fit right, it's best fit from compute location, Thio CPU type Thio platform on, and historically, for I t they've always had to make pragmatic choice is that I believe, limit their ability on Helped to create Maur you know, legacy Tech that they have to manage, um on and create overhead tech debt, as they call it on DSO. I think judo. And in my book the best case for two Dato is that I can put best fit work where I need it when I need it for as long as I need it. >> That's that's really kind of gasp originals. Well, people got to get the software stood up. That's where I think Kubernetes has shown a nice position. I want to extend this track to another thought, another topic around networking. So if you look at the three pillars of computing computing mean industry, compute storage and networking, cloud one daughter, you can say pretty much compute storage did a good job. Amazon has a C two as three. Everything went great. Networking always got taken to the wood shed. You know, networking was getting, you know, people were pissing and moaning about networking. But if you look at kind of things were just talking about networking seems to be an area that this cloud 2.0, could innovate on. So wanna get each of your thoughts on? If you could throw the magic wand out there around the network doesn't take the same track as Dev ops that gets abstracted away because you see VM wear now doing deals. All the cloud providers they got they're going after Cisco with the networking PCC Cisco trying to be relevant. The big guys you got edge, which is power and network connection. You need those things. So what is the role of the network? And two point If you guys could wave the magic wand and have something magically happen or innovate, what would it be? >> Oh, wait, it's part complaining. It's your world. You know, it's ironic that I said this Thio competitors to my most previous company. Ericsson Company was away. They asked me after an event in San everything was a cloud expo. I just got off stage and the gentleman came up to me and asked me So mark you the way you talked about Cloud. I appreciate the comments you made yada, yada, yada. But what do you think about networking? And I said Well, network big problem right now is that you can't follow cloud assumptions as faras usage characteristics and deployment characteristics with networking. When that problem is solved, will have moved light years ahead in how people can use and deploy i t. Because it doesn't matter if you can define workload opportunity in 30 minutes on an edge device somewhere or on a new set of data centers belonging to Google or 10 Cent or anybody else. If you can't treat the network with same functionality and flexibility and speed to value that, you can the cloud then, um, it's Unfortunately, you're really reducing your opportunity and needlessly lengthening the time to value for whatever activity it is. You're really >> so network, certainly critical in 2.0, terms have absolutely that Mike any any thoughts there? >> So I think you know, there's there's easy answers to this that are actually the answer. You know, I P v six was the answer from a couple years ago, and that hasn't solved in the fantasy of the solved. All the problems, just like five G is not gonna magically transform our edge infrastructure into this brilliant network. The reality is, networking is hard and it's hard because there's a ton of legacy embedded stuff that still has to keep working. You can't just, you know, install a new container on container system and say, I've now fixed networking. You have to deal with the globally interconnected MASH insistence. I think when we look at networking, we have to do it in a way that respects the legacy and figures out migration strategies. One of the biggest problems I see that a lot of our technology stacks here is that they just assume we're gonna pave over the problems of yesteryear, nor them and with network, when you don't get that benefit, what you described with cloud networking, never living up the potential, it's because cloud networking isn't club networking. It's it's, you know, early days of the Internet. Networking is still what we use today. It's not. It's not something you can just snap your fingers and disrupt. >> Well, I mean, networking had two major things that were big parts of a networking and who build networks knows you provisioned them and you have policy stuff that runs on them, right? You moving paintings from A to B, then you got networks you don't own right so that's kind of pedestrian, old thinking. But if you want to make networks programmable to me, it just seems like they just seem to be so much more there that needs to be developed, not just moving package. Well, >> you just said it's traditional. Networks were built first, and the infrastructure was then built around them or leveraging them, so you need to take like in zero. Trust paper. When Bugsy Siegel built Las Vegas, he built the town first and then put the roads around the infrastructure. So you need to take that approach with networking. You need to have the core infrastructure of first and then lay down the networking around to support it. And, as Mark said, that needs to be much more real time or programmable. So moving from ah, hardware to find to a software to find model, I think, is how you fix networking. It's not gonna be fixed by a new protocol or set of protocols or adding more policies or complexity to it, >> so you see a lot of change then, based on that, I'd take away that you see change coming to networking in a big way because Vegas we're gonna build >> our if it has to happen. The current way is not working. And that's why we need the bottlenecks. Wherever >> Mark you live in is the traffic's brutal. But, you know, still e gotta figure out, You know, they got some more roads. The bill change coming. What are your thoughts on the change coming with this networking paradigm >> show? I mean, there are a few companies in the space already. I'm going to refuse to name anyway at this point because one of them is a partner of my new company, not my new company, but the new company I work for and I don't want to leave them out of the discussion. But there are several companies in the space right now that are attempting to do just then just that from centralized locations, helping customers to more rapidly deploy network services to and from cloud or two and from other data centers in a chain of data centers. Programmatically as we've talked about. But in the long run, your ability to lay down networking from your office without having to create new firewall rules and spend months on on contract language and things like that on being able to take a slice of the network you already have and deploy it on DDE, not have to go through the complex Mpls or Or VPN set ups that are common today on defectively reroute destinations when you want to or make new connections when you need to. Is far as I'm concerned, that's vital to the success of anything we would call a cloud two point. Oh, >> well, we're gonna try tracks when he's hot startups. So you guys see anyone around this area? I love this topic. I think it's worth talking a lot more about love. Love to continue on with you guys on that another. Another time. Final five minutes. I'd love to spend with you guys talking about the the digital transformation paradox. Rob, we're talking before we came on camera. He loved this paradox because it's simply not as easy to saying Kill the old man, bringing the new and everything's gonna be hunky dorey. It's not that simple, but but it also brings up the fact that in all these major waves, the hype outlives the reality, too. So you're seeing so I want to get your thoughts on digital transformation. Each of you share your thoughts on what's come home to be realistic in digital transformation, which what hasn't showed up yet in terms of benefits and capability. >> I mean, this is this to me is one of the things that we see happen in every wave. They people jump on that bandwagon really hard, and then they tell everybody who's doing the current stuff, that they're doing it wrong. Um, and that that to me, actually does a lot more heart. What we what we've seen in places where people said, burn the boats, you know, we don't care. They have actually not managed to get traction and not create the long term sustainability that you would get if you created ways to bring things forward. Networking is a good example for that, right? Automating a firewall configuration and creating a soft firewall or virtual network function is just taking something that people understand and moving it into a much more control perspective in a lot of ways. That's what we saw with Cloud Cloud took working I t infrastructure that people understood added some change but also kept things that people 1% and so the paradox. Is that you? Is it the more you tell people, they just have to completely disrupt and break everything they've done and walk away from their no nighty infrastructure, the less actually you create these long term values. And I know there are people who really know you got totally changed everything that disrupted value. But a lot of the disrupted value comes from creating these incremental changes and then building something on top of that. So what? So >> what did what Indigenous in digital transformation, what has happened? That's positive and what hasn't happened that was supposed to happen. >> So when I look att Dev ops on what people thought we were going to do, just automate all things that turned out to be a much bigger lift than people expected. But when we started looking at pipelines and deployment pipelines and something very concrete for that which let people start in one or two places and then expand, I think I think, uh, pipelines and build deploy pipelines are transformative, right? Going from a continuously integrated system all the way to a continuously integrated data center. Yeah, that's transformative. And it's very concrete just telling people automate everything is not been as effective >> guys. Other thoughts there on the digital >> transformation dream. I agree with everything that Rob just said, and I would just add just because, you know, it's the boarding piece that someone always has to say, and nobody in Tech everyone is he here? But you know, every corporation at one point or another in its Kurt in its life span faces a transformative period of time because of product change or a new competitor that's doing things differently, or has figured out a way to do it cheaper or whatever it is. And they usually make or break that transformation not because of technology, not because of whether they have smart people, not because of whether they implemented the newest solution, but because of culture and organizational motivation and the vast majority of like Everything, Rob said doesn't just apply to I. T. A lot of the best I T frameworks around Agile and Dev ops apply to how the rest of the organization can and should react to opportunity so that if I t can be and should be really time, then it only makes sense that the business should be able to be real time in responding to what is being created through I t systems. And right now I would argue that the vast majority of the 80% of transformations that don't see the benefit that they're looking for have nothing to do with whether they could have gotten the right technology or done the technology correctly. But it has to do with institutional culture and motivation. And if you can fix that, then the only piece all add on to that. That again I vociferously, really agree with Robin is that if you want to lower the barrier to entry and you want to get more people into this market, you won't get more people to buy more of your stuff and grow what they own. Then you have to be able to show them a path to taking, getting the most value out of what they already have. There is no doubt in my mind that that's the only way forward, and that's where some of the tools that we're talking about and what we're talking about today on Twitter or so important >> Mike final stops on the >> docks >> on your thoughts on the transmission paradox, >> so the paradox that Robb describe think is set, the contact is set incorrectly by calling it digital transformation should be digital revolution, where the evolution process doesn't end. Transformation makes people think that there's some end state, which means let's burn the votes. That's let's get rid of all over all on prime infrastructure moved to cloud and we're done. And really, that's only the beginning. Which is why we're talking about Cloud two point. Oh, do you have to take that approach that you want to have continuous evolution and improvements, which Segways into what Rob said about de box and automating all the things you don't automate your tasks and processes and you're done? You want to keep improving upon them. Figuring out how to improve the process is and then change the automation five that the is, Mark said. It's a cultural and mental shift versus trying to get to this Holy Grail and state of transforming transformation. >> Awesome. Well, why I got you guys here first off. Thanks for spending the time and unpacking these big issue. Well, two more of it. I'd >> love to just get >> your thoughts real quick on just your opinion of Capital One. The breach, survivability and impact of the industry. Since it's still in the news, who wants to jump for us? We'll start with Mike. Mike, start with you will go down the line. Mike, Robin Mara. >> I mean, the good news for Capital One is I don't think any personal information was breached that hasn't already been exposed by the various other massive reaches. Like I do my so security number as a throw away at this point which never should have been used for identity. But I want All >> right, So there were Do you think >> it's recoverable is not gonna be as critical, say, Equifax, which was brutal. >> It doesn't sound like there was negligence where Equifax seemed like it was Maura negligent driven than just ah ah, bad process or bad hygiene around a user or roll account and access to a certain subset of data. >> I mean, this was someone who stumbled upon open history bucket and said, >> Well, well, look at this >> bragging about it on Twitter and the user groups. I mean, this >> was like from from what the press said, I think there's other companies that may or may not be affected by this as well, so that it's just capital one, which will probably defuse the attention on them and lessen the severity or backlash. >> Rob your thoughts on Capital One. >> Yeah, I wish it would move the needle. I think that we have become so used to the security of breach of the week or the hardware. Very. You know, it is we We need to really think through what it's really gonna take toe treat security as a primary thing, which means actually treating operations and infrastructure and the human processes piece of this, um, and slowed down a little bit. Um, and I always saw >> 11 lawmaker, one congressman's woman said, More regulation. >> Yeah, they don't want this. I don't think regulation is the right is the right thing. I don't know exactly what it is because I think >> regularly, we don't understand. That's Washington, DC, >> But but we're building a very, very, very fragile I T infrastructure. And so this is not a security problem. It's a It's a fact that we've built this Jenga tower of I t infrastructure, and we don't actually understand how it's built, Um, and that I don't see that slowing down. Unfortunately, >> unlike Las Vegas is, Mike pointed out, it's was built with purpose. They built the roads around the town. Mark, you live there now What's your thoughts on this capital? One piece ends and >> I have been said I would say that what I'm hoping sort of like when you have, ah, a lack of employees for a specific job type. Like right now in United States, it's incredibly difficult to find a truck driver if you're a trucking company, So what does that mean? But that means it's gonna accelerate automation and truck driving because that's the best alternative, right? If you can't solve it the old way, then you find a new way to solve it. And we have an enormous number of opportunity. He's from a process standpoint, but also, from a technology standpoint, did not build on this. Pardon my French crap that we have already >> they were digital. Then, when I ruled by the FCC, >> had build it the right way from the start. >> Well, you know what was soon? How about self driving security? We needed guys. Thanks for spending the time this cube talk. Keep conversation. Appreciate time. Mike, Rob mark. Thanks for kicking it off. Thanks. >> Thank you. >> You're watching Cute conversation with promote guests. Panel discussion Breaking down. How businesses should look at technology as part of their business. Cloud 2.0, security hacks and digital transformation Digital evolution. I'm John free. Thanks for watching.

>> Announcer: Live from San Jose, California, it's theCUBE. Covering Big Data Silicon Valley 2017. >> Okay, welcome back everyone. Here live in Silicon Valley this is theCUBE. I'm John Furrier, covering our Big Data SV event, #BigDataSV. Our companion event to Big Data NYC, all in conjunction Strata Hadoop, the Big Data World comes together, and great to have guests come by. Donna Prlich, who's the senior VP of products and solutions at Pentaho, a Hitachi company who we've been following before Hitachi had acquired you guys. But you guys are unique in the sense that you're a company within Hitachi left alone after the acquisition. You're now running all the products. Congratulations, welcome back, great to see you. >> Yeah, thank you, good to be back. It's been a little while, but I think you've had some of our other friends on here, as well. >> Yep, and we'll be at Pentaho World, you have Orlando, I think is October. >> Yeah, October, so I'm excited about that, too, so. >> I'm sure the agenda is not yet baked for that because it's early in the year. But what's going on with Hitachi? Give us the update, because you're now, your purview into the product roadmap. The Big Data World, you guys have been very, very successful taking this approach to big data. It's been different and unique to others. >> [Donna} Yep. What's the update? >> Yeah, so, very exciting, actually. So, we've seen, especially at the show that the Big Data World, we all know that it's here. It's monetizable, it's where we, actually, where we shifted five years ago, and it's been a lot of what Pentaho's success has been based on. We're excited because the Hitachi acquisition, as you mentioned, sets us up for the next bit thing, which is IOT. And I've been hearing non-stop about machine learning, but that's the other component of it that's exciting for us. So, yeah, Hitachi, we're-- >> You guys doing a lot of machine learning, a lot of machine learning? >> So we, announced our own kind of own orchestration capabilities that really target how do you, it's less about building models, and how do you enable the data scientists and data preparers to leverage the actual kind of intellectual properties that companies have in those models they've built to transform their business. So we have our own, and then the other exciting piece on the Hitachi side is, on the products, we're now at the point where we're running as Pentaho, but we have access to these amazing labs, which there's about 25 to 50 depending on where you are, whether you're here or in Japan. And those data scientists are working on really interesting things on the R & D side, when you apply those to the kind of use cases we're solving for, that's just like a kid in a candy store with technology, so that's a great-- >> Yeah, you had a built-in customer there. But before I get into Pentaho focusing on what's unique, really happening within you guys with the product, especially with machine learning and AI, as it starts to really get some great momentum. But I want to get your take on what you see happening in the marketplace. Because you've seen the early days and as it's now, hitting a whole another step function as we approach machine learning and AI. Autonomous vehicles, sensors, everything's coming. How are enterprises in these new businesses, whether they're people supporting smart cities or a smart home or automotive, autonomous vehicles. What's the trends you are seeing that are really hitting the pavement here. >> Yeah, I think what we're seeing is, and it's been kind of Pentaho's focus for a long time now, which is it's always about the data. You know, what's the data challenge? Some of the amounts of data which everybody talks about from IOT, and then what's interesting is, it's not about kind of the concepts around AI that have been around forever, but when you start to apply some of those AI concepts to a data pipeline, for instance. We always talk about that 6data pipeline. The reason it's important is because you're really bringing together the data and the analytics. You can't separate those two things, and that's been kind of not only a Pentaho-specific, sort of bent that I've had for years, but a personal one, as well. That, hey, when you start separating it, it makes it really hard to get to any kind of value. So I think what we're doing, and what we're going to be seeing going forward, is applying AI to some of the things that, in a way, will close the gaps between the process and the people, and the data and the analytics that have been around for years. And we see those gaps closing with some of the tools that are emerging around preparing data. But really, when you start to bring some of that machine learning into that picture, and you start applying math to preparing data, that's where it gets really interesting. And I think we'll see some of that automation start to happen. >> So I got to ask you, what is unique about Pentaho? Take a minute to share with the audience some of the unique things that you guys are doing that's different in this sea of people trying to figure out big data. You guys are doing well, an6d you wrote a blog post that I referenced earlier yesterday, around these gaps. How, what's unique about Pentaho and what are you guys doing with examples that you could share? >> Yeah, so I think the big thing about Pentaho that's unique is that it's solving that analytics workflow from the data side. Always from the data. We've always believed that those two things go together. When you build a platform that's really flexible, it's based on open source technology, and you go into a world where a customer says, "I not only want to manage and have a data lake available," for instance, "I want to be able to have that thing extend over the years to support different groups of users. I don't want to deliver it to a tool, I want to deliver it to an application, I want to embed analytics." That's where having a complete end-to-end platform that can orchestrate the data and the analytics across the board is really unique. And what's happened is, it's like, the time has come. Where all we're hearing is, hey, I used to think it was throw some data over and, "here you go, here's the tools." The tools are really easy, so that's great. Now we have all kinds of people that can do analytics, but who's minding the data? With that end-to-end platform, we've always been able to solve for that. And when you move in the open source piece, that just makes it much easier when things like Spark emerge, right. Spark's amazing, right? But we know there's other things on the horizon. Flink, Beam, how are you going to deal with that without being kind of open source, so this is-- >> You guys made a good bet there, and your blog post got my attention because of the title. It wasn't click bait either, it was actually a great article, and I just shared it on Twitter. The Holy Grail of analytics is the value between data and insight. And this is interesting, it's about the data, it's in bold, data, data, data. Data's the hardest part. I get that. But I got to ask you, with cloud computing, you can see the trends of commoditization. You're renting stuff, and you got tools like Kinesis, Redshift on Amazon, and Azure's got tools, so you don't really own that, but the data, you own, right? >> Yeah, that's your intellectual property, right? >> But that's the heart of your piece here, isn't it, the Holy Grail. >> Yes, it is. >> What is that Holy Grail? >> Yeah, that Holy Grail is when you can bring those two things together. The analytics and the data, and you've got some governance, you've got the control. But you're allowing the access that lets the business derive value. For instance, we just had a customer, I think Eric might have mentioned it, but they're a really interesting customer. They're one of the largest community colleges in the country, Ivy Tech, and they won an award, actually, for their data excellence. But what's interesting about them is, they said we're going to create a data democracy. We want data to be available because we know that we see students dropping out, we can't be efficient, people can't get the data that they need, we have old school reporting. So they took Pentaho, and they really transformed the way they think about running their organization and their community colleges. Now they're adding predictive to that. So they've got this data democracy, but now they're looking at things like, "Okay we an see where certain classes are over capacity, but what if we could predict, next year, not only which classes are over capacity, what's the tendency of a particular student to drop out?" "What could we do to intervene?" That's where the kind of cool machine learning starts to apply. Well, Pentaho is what enables that data democracy across the board. I think that's where, when I look at it from a customer perspective, it's really kind of, it's only going to get more interesting. >> And with RFID and smart phones, you could have attendance tracking, too. You know, who's not showing up. >> Yeah absolutely. And you bring Hitachi into the picture, and you think about, for instance, from an IOT perspective, you might be capturing data from devices, and you've got a digital twin, right? And then you bring that data in with data that might be in a data lake, and you can set a threshold, and say, "Okay, not only do we want to be able to know where that student is," or whatever, "we want to trigger something back to that device," and say, "hey, here's a workshop for you to login to right away, so that you don't end up not passing a class." Or whatever it is, it's a simplistic model, but you can imagine where that starts to really become transformative. >> So I asked Eric a question yest6erday. It was from Dave Valante, who's in Boston, stuck in the snowstorm, but he was watching, and I'll ask you and see how it matches. He wrote it differently on Crouch, it was public, but this is in my chat, "HDS is known for main frames, historically, and storage, but Hitachi is an industrial giant. How is Pentaho leveraging the Hitachi monster?" >> Yes, that's a great way to put it. >> Or Godzilla, because it's Japan. >> We were just comparing notes. We were like, "Well, is it an $88 billion company or $90 billion. According to the yen today, it's 88. We usually say 90, but close enough, right? But yeah, it's a huge company. They're in every industry. Make all kinds of things. Pretty much, they've got the OT of the world under their belt. How we're leveraging it is number one, what that brings to the table, in terms of the transformations from a software perspective and data that we can bring to the table and the expertise. The other piece is, we've got a huge opportunity, via the Hitachi channel, which is what's seeing for us the growth that we've had over the last couple of years. It's been really significant since we were acquired. And then the next piece is how do we become part of that bigger Hitachi IOT strategy. And what's been starting to happen there is, as I mentioned before, you can kind of probably put the math together without giving anything away. But you think about capturing, being able to capture device data, being able to bring it into the digital twin, all of that. And then you think about, "Okay, and what if I added Pentaho to the mix?" That's pretty exciting. You bring those things together, and then you add a whole bunch of expertise and machine learning and you're like, okay. You could start to do, you could start to see where the IOT piece of it is where we're really going to-- >> IOT is a forcing function, would you agree? >> Yes, absolutely. >> It's really forcing IT to go, "Whoa, this is coming down fast." And AI and machine learning, and cloud, is just forcing everyone. >> Yeah, exactly. And when we came into the big data market, whatever it was, five years ago, in the early market it's always hard to kind of get in there. But one of the things that we were able to do, when it was sort of, people were still just talking about BI would say, "Have you heard about this stuff called big data, it's going to be hard." You are going to have to take advantage of this. And the same thing is happening with IOT. So the fact that we can be in these environments where customers are starting to see the value of the machine generated data, that's going to be-- >> And it's transformative for the business, like the community college example. >> Totally transformative, yeah. The other one was, I think Eric might have mentioned, the IMS, where all the sudden you're transforming the insurance industry. There's always looking at charts of, "I'm a 17-year-old kid," "Okay, you're rate should be this because you're a 17-year-old boy." And now they're starting to track the driving, and say, "Well, actually, maybe not, maybe you get a discount." >> Time for the self-driving car. >> Transforming, yeah. >> Well, Donna, I appreciate it. Give us a quick tease here, on Pentaho World coming in October. I know it's super early, but you have a roadmap on the product side, so you can see a little bit around the corner. >> Donna: Yeah. >> What is coming down the pike for Pentaho? What are the things that you guys are beavering away at inside the product group? >> Yeah, I think you're going to see some really cool innovations we're doing. I won't, on the Spark side, but with execution engines, in general, we're going to have some really interesting kind of innovative stuff coming. More on the machine learning coming out, and if you think about, if data is, you know what, is the hard part, just think about applying machine learning to the data, and I think you can think of some really cool things, we're going to come up with. >> We're going to need algorithms for the algorithms, machine learning for the machine learning, and, of course, humans to be smarter. Donna, thanks so much for sharing here inside theCUBE, appreciate it. >> Thank you. >> Pentaho, check them out. Going to be at Pentaho World in October, as well, in theCUBE, and hopefully we can get some more deep dives on, with their analyst group, for what's going on with the engines of innovation there. More CUBE coverage live from Silicon Valley for Big Data SV, in conjunction with Strata Hadoop, I'm John Furrier. Be right back with more after this short break. (techno music)