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 the cube studios in Palo Alto in Boston connecting with thought leaders all around the world this is a cube conversation hi buddy welcome back to the cube this is Dave Volante and you're watching a special presentation data ops enacted made possible by IBM you know what's what's happening is the innovation engine in the IT economy is really shifted used to be Moore's Law today it's applying machine intelligence and AI to data really scaling that and operationalizing that new knowledge the challenges that is not so easy to operationalize AI and infuse it into the data pipeline but what we're doing in this program is bringing in practitioners who have actually had a great deal of success in doing just that and I'm really excited to have it Kumal a Himalayan Manali is here she's the executive head of data management or personal and business banking at Standard Bank of South Africa the tomb of length thanks so much for coming in the queue thank you for having me Dave you're very welcome and first of all how you holding up with this this bovid situation how are things in Johannesburg um things in Johannesburg are fine we've been on lockdown now I think it's day 33 if I'm not mistaken lost count and but we're really grateful for the swift action of government we we only I mean we have less than 4,000 places in the country and infection rate is is really slow so we've really I think been able to find the curve and we're grateful for being able to be protected in this way so all working from home or learning the new normal and we're all in this together that's great to hear why don't you tell us a little bit about your your role you're a data person we're really going to get into it but here with us you know how you spend your time okay well I head up a date operations function and a data management function which really is the foundational part of the data value chain that then allows other parts of the organization to monetize data and liberate it as as as the use cases apply we monetize it ourselves as well but really we're an enterprise wide organization that ensures that data quality is managed data is governed that we have the effective practices applied to the entire lineage of the data ownership and curation is in place and everything else from a regulatory as well as opportunity perspective then is able to be leveraged upon so historically you know data has been viewed as sort of this expense it's it's big it's growing it needs to be managed deleted after a certain amount of time and then you know ten years ago of the Big Data move data became an asset you had a lot of shadow I people going off and doing things that maybe didn't comply to the corporate ethics probably drove here here you're a part of the organization crazy but talk about that how what has changed but they in the last you know five years or so just in terms of how people approach data oh I mean you know the story I tell my colleague who are all bankers obviously is the fact that the banker in 1989 had to mainly just know debits credits and be able to look someone in the eye and know whether or not they'd be a credit risk or not you know if we lend you money and you pay it back the the banker of the late 90s had to then contend with the emergence of technologies that made their lives easier and allowed for automation and processes to run much more smoothly um in the early two-thousands I would say that digitization was a big focus and in fact my previous role was head of digital banking and at the time we thought digital was the panacea it is the be-all and end-all it's the thing that's gonna make organizations edit lo and behold we realized that once you've gotten all your digital platforms ready they are just the plate or the pipe and nothing is flowing through it and there's no food on the face if data is not the main photo really um it's always been an asset I think organizations just never consciously knew that data was that okay so so what sounds like once you've made that sort of initial digital transformation you really had to work it and what we're hearing from a lot of practitioners like self as challenges related to that involve different parts of the organization different skill sets of challenges and sort of getting everybody to work together on the same page it's better but maybe you could take us back to sort of when you started on this initiative around data Ops what was that like what were some of the challenges that you faced and how'd you get through them okay first and foremost Dave organizations used to believe that data was I t's problem and that's probably why you you then saw the emergence of things like chatter IP but when you really acknowledge that data is an essay just like money is an asset then you you have to then take accountability for it just the same way as you would any other asset in the organization and you will not abdicate its management to a separate function that's not cold to the business and oftentimes IT are seen as a support or an enabling but not quite the main show in most organizations right so what we we then did is first emphasize that data is a business capability the business function it presides in business makes to product management makes to marketing makes to everything else that the business needs for data management also has to be for to every role in every function to different degrees and varying bearing offense and when you take accountability as an owner of a business unit you also take accountability for the data in the systems that support the business unit for us that was the first picture um and convincing my colleagues that data was their problem and not something that we had to worry about they just kind of leave us to to it was was also a journey but that was kind of the first step into it in terms of getting the data operations journey going um you had to first acknowledge please carry on no you just had to first acknowledge that it's something you must take accountability of as a banker not just need to a different part of the organization that's a real cultural mindset you know in the game of rock-paper-scissors you know culture kinda beats everything doesn't it it's almost like a yep a trump card and so so the businesses embrace that but but what did you do to support that is there has to be trust in the data that it has to be a timeliness and so maybe you could take us through how you achieve those objectives and maybe some other objectives that business the man so the one thing I didn't mention Dave is that obviously they didn't embrace it in the beginning it wasn't a it wasn't there oh yeah that make sense they do that type of conversation um what what he had was a few very strategic people with the right mindset that I could partner with that understood the case for data management and while we had that as as an in we developed a framework for a fully matured data operations capability in the organization and what that would look like in a target date scenario and then what you do is you wait for a good crisis so we had a little bit of a challenge in that our local regulator found us a little bit wanting in terms of our date of college and from that perspective it then brought the case for data quality management so now there's a burning platform you have an appetite for people to partner with you and say okay we need this to comply to help us out and when they start seeing their opt-in action do they then buy into into the concept so sometimes you need to just wait for a good Christ and leverage it and only do that which the organization will appreciate at that time you don't have to go Big Bang data quality management was the use case at the time five years ago so we focused all our energy on that and after that it gave us leeway and license really bring to maturity all the other capabilities at the business might not well understand as well so when that crisis hit of thinking about people process in technology you probably had to turn some knobs in each of those areas can you talk about that so from a technology perspective that that's when we partnered with with IBM to implement information analyzer for us in terms of making sure that then we could profile the data effectively what was important for us is to to make strides in terms of showing the organization progress but also being able to give them access to self-service tools that will give them insight into their data from a technology perspective that was kind of I think the the genesis of of us implementing and the IBM suite in earnest from a data management perspective people wise we really then also began a data stewardship journey in which we implemented business unit stewards of data I don't like using the word steward because in my organization it's taken lightly almost like a part-time occupation so we converted them we call them data managers and and the analogy I would give is every department with a P&L any department worth its salt has a FDA or financial director and if money is important to you you have somebody helping you take accountability and execute on your responsibilities in managing that that money so if data is equally important as an asset you will have a leader a manager helping you execute on your data ownership accountability and that was the people journey so firstly I had kind of soldiers planted in each department which were data managers that would then continue building the culture maturing the data practices as as applicable to each business unit use cases so what was important is that every manager in every business unit to the Data Manager focus their energy on making that business unit happy by ensuring that they data was of the right compliance level and the right quality the right best practices from a process and management perspective and was governed and then in terms of process really it's about spreading through the entire ecosystem data management as a practice and can be quite lonely um in the sense that unless the whole business of an organization is managing data they worried about doing what they do to make money and most people in most business units will be the only unicorn relative to everybody else who does what they do and so for us it was important to have a community of practice a process where all the data managers across business as well as the technology parts and the specialists who were data management professionals coming together and making sure that we we work together on on specific you say so I wonder if I can ask you so the the industry sort of likes to market this notion of of DevOps applied to data and data op have you applied that type of mindset approach agile of continuous improvement is I'm trying to understand how much is marketing and how much actually applicable in the real world can you share well you know when I was reflecting on this before this interview I realized that our very first use case of data officers probably when we implemented information analyzer in our business unit simply because it was the first time that IT and business as well as data professionals came together to spec the use case and then we would literally in an agile fashion with a multidisciplinary team come together to make sure that we got the outcomes that we required I mean for you to to firstly get a data quality management paradigm where we moved from 6% quality at some point from our client data now we're sitting at 99 percent and that 1% literally is just the timing issue to get from from 6 to 99 you have to make sure that the entire value chain is engaged so our business partners will the fundamental determinant of the business rules apply in terms of what does quality mean what are the criteria of quality and then what we do is translate that into what we put in the catalog and ensure that the profiling rules that we run are against those business rules that were defined at first so you'd have upfront determination of the outcome with business and then the team would go into an agile cycle of maybe two-week sprints where we develop certain things have stand-ups come together and then the output would be - boarded in a prototype in a fashion where business then gets to go double check that out so that was the first iterate and I would say we've become much more mature at it and we've got many more use cases now and there's actually one that it's quite exciting that we we recently achieved over the end of of 2019 into the beginning of this year so what we did was they I'm worried about the sunlight I mean through the window you look creative to me like sunset in South Africa we've been on the we've been on CubeSat sometimes it's so bright we have to put on sunglasses but so the most recent one which was in in mates 2019 coming in too early this year we we had long kind of achieved the the compliance and regulatory burning platform issues and now we are in a place of I think opportunity and luxury where we can now find use cases that are pertinent to business execution and business productivity um the one that comes to mind is we're a hundred and fifty eight years old as an organization right so so this Bank was born before technology it was also born in the days of light no no no integration because every branch was a standalone entity you'd have these big ledges that transactions were documented in and I think once every six months or so these Ledger's would be taken by horse-drawn carriage to a central place to get go reconcile between branches and paper but the point is if that is your legacy the initial kind of ERP implementations would have been focused on process efficiency based on old ways of accounting for transactions and allocating information so it was not optimized for the 21st century our architecture had has had huge legacy burden on it and so going into a place where you can be agile with data is something that we constantly working toward so we get to a place where we have hundreds of branches across the country and all of them obviously telling to client servicing clients as usual and and not being able for any person needing sales teams or executional teams they were not able in a short space of time to see the impact of the tactic from a database fee from a reporting history and we were in a place where in some cases based on how our Ledger's roll up and the reconciliation between various systems and accounts work it would take you six weeks to verify whether your technique were effective or not because to actually see the revenue hitting our our general ledger and our balance sheet might take that long that is an ineffective way to operate in a such a competitive environment so what you had our frontline sales agents literally manually documenting the sales that they had made but not being able to verify whether that or not is bringing revenue until six weeks later so what we did then is we sat down and defined all the requirements were reporting perspective and the objective was moved from six weeks latency to 24 hours um and even 24 hours is not perfect our ideal would be that bite rows of day you're able to see what you've done for that day but that's the next the next epoch that will go through however um we literally had the frontline teams defining what they'd want to see in a dashboard the business teams defining what the business rules behind the quality and the definitions would be and then we had an entire I'm analytics team and the data management team working around sourcing the data optimising and curating it and making sure that the latency had done that's I think only our latest use case for data art um and now we're in a place where people can look at a dashboard it's a cubed self-service they can learn at any time I see the sales they've made which is very important right now at the time of covert nineteen from a form of productivity and executional competitiveness those are two great use cases of women lying so the first one you know going from data quality 6% the 99% I mean 6% is all you do is spend time arguing about the data bills profanity and then 99% you're there and you said it's just basically a timing issue use latency in the timing and then the second one is is instead of paving the cow path with an outdated you know ledger Barret data process week you've now compressed that down to 24 hours you want to get the end of day so you've built in the agility into your data pipeline I'm going to ask you then so when gdpr hit were you able to very quickly leverage this capability and and apply and then maybe other of compliance edik as well well actually you know what we just now was post TDP our us um and and we got GDP all right about three years ago but literally all we got right was reporting for risk and compliance purposes they use cases that we have now are really around business opportunity lists so the risk so we prioritize compliance report a long time it but we're able to do real-time reporting from a single transaction perspective I'm suspicious transactions etc I'm two hours in Bank and our governor so from that perspective that was what was prioritize in the beginning which was the initial crisis so what you found is an entire engine geared towards making sure that data quality was correct for reporting and regulatory purposes but really that is not the be-all and end-all of it and if that's all we did I believe we really would not have succeeded or could have stayed dead we succeeded because Dana monetization is actually the penis' t the leveraging of data for business opportunity is is actually then what tells you whether you've got the right culture or not you're just doing it to comply then it means the hearts and minds of the rest of the business still aren't in the data game I love this story because it's me it's nirvana for so many years we've been pouring money to mitigate risk and you have no choice do it you know the general council signs off on it the the CFO but grudgingly signs off on it but it's got to be done but for years decades we've been waiting to use these these risk initiatives to actually drive business value you know it kind of happened with enterprise data warehouse but it was too slow it was complicated and it certainly didn't happen with with email archiving that was just sort of a tech balk it sounds like you know we're at that point today and I want to ask you I mean like you know you we talking earlier about you know the crisis gonna perpetuated this this cultural shift and you took advantage of that so we're out who we the the mother nature dealt up a crisis like we've never seen before how do you see your data infrastructure your data pipeline your data ops what kind of opportunities do you see in front of you today as a result of ovid 19 well I mean because of of the quality of kind data that we had now we were able to very quickly respond to to pivot nineteen in in our context where the government put us on lockdown relatively early in in the curve or in the cycle of infection and what it meant is it brought a little bit of a shock to the economy because small businesses all of a sudden didn't have a source of revenue or potentially three to six weeks and based on the data quality work that we did before it was actually relatively easy to be agile enough to do the things that we did so within the first weekend of of lockdown in South Africa we were the first bank to proactively and automatically offer small businesses and student and students with loans on our books a instant three month payment holiday assuming they were in good standing and we did that upfront though it was actually an opt-out process rather than you had to fall in and arrange for that to happen and I don't believe we would have been able to do that if our data quality was not with um we have since made many more initiatives to try and keep the economy going to try and keep our clients in in a state of of liquidity and so you know data quality at that point and that Dharma is critical to knowing who you're talking to who needs what and in which solutions would best be fitted towards various segments I think the second component is um you know working from home now brings an entirely different normal right so so if we had not been able to provide productivity dashboard and and and sales and dashboards to to management and all all the users that require it we would not be able to then validate or say what our productivity levels are now that people are working from home I mean we still have essential services workers that physically go into work but a lot of our relationship bankers are operating from home and that face the baseline and the foundation that we said productivity packing for various methods being able to be reported on in a short space of time has been really beneficial the next opportunity for us is we've been really good at doing this for the normal operational and front line and type of workers but knowledge workers have also know not necessarily been big productivity reporters historically they kind of get an output then the output might be six weeks down the line um but in a place where teams now are not locate co-located and work needs to flow in an edge of passion we need to start using the same foundation and and and data pipeline that we've laid down as a foundation for the reporting of knowledge work and agile team type of metric so in terms of developing new functionality and solutions there's a flow in a multidisciplinary team and how do those solutions get architected in a way where data assists in the flow of information so solutions can be optimally developed well it sounds like you're able to map a metric but business lines care about you know into these dashboards you usually the sort of data mapping approach if you will which makes it much more relevant for the business as you said before they own the data that's got to be a huge business benefit just in terms of again we talked about cultural we talked about speed but but the business impact of being able to do that it has to be pretty substantial it really really is um and and the use cases really are endless because every department finds their own opportunity to utilize in terms of their also I think the accountability factor has has significantly increased because as the owner of a specific domain of data you know that you're not only accountable to yourself and your own operation but people downstream to you as a product and in an outcome depend on you to ensure that the quality of the data you produces is of a high nature so so curation of data is a very important thing and business is really starting to understand that so you know the cards Department knows that they are the owners of card data right and you know the vehicle asset Department knows that they are the owners of vehicle they are linked to a client profile and all of that creates an ecosystem around the plan I mean when you come to a bank you you don't want to be known as a number and you don't want to be known just for one product you want to be known across everything that you do with that with that organization but most banks are not structured that way they still are product houses and product systems on which your data reside and if those don't act in concert then we come across extremely schizophrenic as if we don't know our clients and so that's very very important stupid like I can go on for an hour talking about this topic but unfortunately we're we're out of time thank you so much for sharing your deep knowledge and your story it's really an inspiring one and congratulations on all your success and I guess I'll leave it with you know what's next you gave us you know a glimpse of some of the things you wanted to do pressing some of the the elapsed times and the time cycle but but where do you see this going in the next you know kind of mid term and longer term currently I mean obviously AI is is a big is a big opportunity for all organizations and and you don't get automation of anything right if the foundations are not in place so you believe that this is a great foundation for anything AI to be applied in terms of the use cases that we can find the second one is really providing an API economy where certain data product can be shared with third parties I think that probably where we want to take things as well we are really utilizing external third-party data sources I'm in our data quality management suite to ensure validity of client identity and and and residents and things of that nature but going forward because been picked and banks and other organizations are probably going to partner to to be more competitive going forward we need to be able to provide data product that can then be leveraged by external parties and vice-versa to be like thanks again great having you thank you very much Dave appreciate the opportunity thank you for watching everybody that we go we are digging in the data ops we've got practitioners we've got influencers we've got experts we're going in the crowd chat it's the crowd chat net flash data ops but keep it right there way back but more coverage this is Dave Volante for the cube [Music] you

from the cube studios in Palo Alto in Boston connecting with thought leaders all around the world this is a cube conversation hi Bri welcome back this is Dave Volante and welcome to this special presentation made possible by IBM we're talking about data op data ops in Acton Steve Lucas here he's the senior vice president and director of data management at Associated Bank be great to see how are things going and in Wisconsin all safe we're doing well we're staying safe staying healthy thanks for having me Dave yeah you're very welcome so Associated Bank and regional bank Midwest to cover a lot of the territories not just Wisconsin but another number of other states around there retail commercial lending real estate offices stuff I think the largest bank in in Wisconsin but tell us a little bit about your business in your specific role sure yeah no it's a good intro we're definitely largest bank at Corvis concen and then we have branches in the in the Upper Midwest area so Minnesota Illinois Wisconsin our primary locations my role at associated I'm director data management so been with the bank a couple of years now and really just focused on defining our data strategy as an overall everything from data ingestion through consumption of data and analytics all the way through and then I'm also the data governance components and keeping the controls and the rails in place around all of our data in its usage so financial services obviously one of the more cutting-edge industries in terms of their use of technology not only are you good negotiators but you you often are early adopters you guys were on the Big Data bandwagon early a lot of financial services firms we're kind of early on in Hadoop but I wonder if you could tell us a little bit about sort of the business drivers and and where's the poor the pressure point that are informing your digital strategy your your data and data op strategy sure yeah I think that one of the key areas for us is that we're trying to shift from more of a reactive mode into more of a predictive prescriptive mode from a data and analytics perspective and using our data to infuse and drive more business decisions but also to infuse it in actual applications and customer experience etc so we have a wealth of data at our fingertips we're really focused on starting to build out that data link style strategy make sure that we're kind of ahead of the curve as far as trying to predict what our end users are going to need and some of the advanced use cases we're going to have before we even know that they actually exist right so it's really trying to prepare us for the future and what's next and and then abling and empowering the business to be able to pivot when we need to without having everything perfect that they prescribed and and ready for what if we could talk about a little bit about the data journey I know it's kind of a buzzword but in my career as a independent observer and analyst I've kind of watched the promise of whether it was decision support systems or enterprise data warehouse you know give that 360 degree view of the business the the real-time nature the the customer intimacy all that in and up until sort of the recent digital you know meme I feel as though the industry hasn't lived up to that promise so I wonder if you could take us through the journey and tell us sort of where you came from and where you are today and I really want to sort of understand some of the successes they've had sure no that's a that's a great point nice I feel like as an industry I think we're at a point now where the the people process technology have sort of all caught up to each other right I feel that that real-time streaming analytics the data service mentality just leveraging web services and API is more throughout our organization in our industry as a whole I feel like that's really starting to take shape right now and and all the pieces of that puzzle have come together so kind of where we started from a journey perspective it was it was very much if your your legacy reporting data warehouse mindset of tell me tell me the data elements that you think you're going to need we'll figure out how do we map those in and form them we'll figure out how to get those prepared for you and that whole lifecycle that waterfall mentality of how do we get this through the funnel and get it to users quality was usually there the the enablement was still there but it was missing that that rapid turnaround it was also missing the the what's next right than what you haven't thought of and almost to a point of just discouraging people from asking for too many things because it got too expensive it got too hard to maintain there was some difficulty in that space so some of the things that we're trying to do now is build that that enablement mentality of encouraging people to ask for everything so when we bring out new systems - the bank is no longer an option as far as how much data they're going to send to us right we're getting all of the data we're going to we're going to bring that all together for people and then really starting to figure out how can this data now be used and and we almost have to push that out and infuse it within our organization as opposed to waiting for it to be asked for so I think that all of the the concepts so that bringing that people process and then now the tools and capabilities together has really started to make a move for us and in the industry I mean it's really not an uncommon story right you had a traditional data warehouse system you had you know some experts that you had to go through to get the data the business kind of felt like it didn't own the data you know it felt like it was imposing every time it made a request or maybe it was frustrated because it took so long and then by the time they got the data perhaps you know the market had shifted so it create a lot of frustration and then to your point but but it became very useful as a reporting tool and that was kind of this the sweet spot so so how did you overcome that and you know get to where you are today and you know kind of where are you today I was gonna say I think we're still overcoming that we'll see it'll see how this all goes right I think there's there's a couple of things that you know we've started to enable first off is just having that a concept of scale and enablement mentality and everything that we do so when we bring systems on we bring on everything we're starting to have those those components and pieces in place and we're starting to build more framework base reusable processes and procedures so that every ask is not brand new it's not this reinvent the wheel and resolve for for all that work so I think that's helped if expedite our time to market and really get some of the buy-in and support from around the organization and it's really just finding the right use cases and finding the different business partners to work with and partner with so that you help them through their journey as well is there I'm there on a similar roadmap and journey for for their own life cycles as well in their product element or whatever business line there so from a process standpoint that you kind of have to jettison the you mentioned waterfall before and move to a more being an agile approach did it require different different skill sets talk about the process and the people side of yeah it's been a it's been a shift we've tried to shift more towards I wouldn't call us more formal agile I would say we're a little bit more lean from a an iterative backlog type of approach right so what are you putting that work together in queues and having the queue of B reprioritized working with the business owners to help through those things has been a key success criteria for us and how we start to manage that work as opposed to opening formal project requests and and having all that work have to funnel through some of the old channels that like you mentioned earlier kind of distracted a little bit from from the way things had been done in the past and added some layers that people felt potentially wouldn't be necessary if they thought it was a small ask in their eyes you know I think it also led to a lot of some of the data silos and and components that we have in place today in the industry and I don't think our company is alone and having data silos and components of data in different locations but those are there for a reason though those were there because they're they're filling a need that has been missing or a gap in the solution so what we're trying to do is really take that to heart and evaluate what can we do to enable those mindsets and those mentalities and find out what was the gap and why did they have to go get a siloed solution or work around operations and technology and the channels that had been in place what would you say well your biggest challenges in getting from point A to point B point B being where you are today there were challenges on each of the components of the pillar right so people process technology people are hard to change right men behavioral type changes has been difficult that there's components of that that definitely has been in place same with the process side right so so changing it into that backlog style mentality and working with the users and having more that be sort of that maintenance type support work is is a different call culture for our organization and traditional project management and then the tool sets right the the tools and capabilities we had to look in and evaluate what tools do we need to Mabel this behavior in this mentality how do we enable more self-service the exploration how do we get people the data that they need when they need it and empower them to use so maybe you could share with us some of the outcomes and I know it's yeah we're never done in this business but but thinking about you know the investments that you've made in intact people in reprocessing you know the time it takes to get leadership involved what has been so far anyway the business outcome and you share any any metrics or it is sort of subjective a guidance I yeah I think from a subjective perspective the some of the biggest things for us has just been our ability to to truly start to have that very 60 degree view of the customer which we're probably never going to get they're officially right there's there everyone's striving for that but the ability to have you know all of that data available kind of at our fingertips and have that all consolidated now into one one location one platform and start to be that hub that starts to redistribute that data to our applications and infusing that out has been a key component for us I think some of the other big kind of components are differentiators for us and value that we can show from an organizational perspective we're in an M&A mode right so we're always looking from a merger and acquisition perspective our the model that we've built out from a data strategy perspective has proven itself useful over and over now in that M&A mentality of how do you rapidly ingest new data sets it had understood get it distributed to the right consumers it's fit our model exactly and and it hasn't been an exception it's been just part of our overall framework for how we get that data and it wasn't anything new that we had to do different because it was M&A just timelines were probably a little bit more expedited the other thing that's been interesting in some of the world that were in now right from a a Kovach perspective and having a pivot and start to change some of the way we do business and some of the PPP loans and and our business models sort of had to change overnight and our ability to work with our different lines of business and get them the data they need to help drive those decisions was another scenario where had we not had the foundational components there in the platform there to do some of this if we would have spun a little bit longer so your data ops approach I'm gonna use that term helped you in this in this kovat situation I mean you had the PPE you had you know of slew of businesses looking to get access to that money you had uncertainty with regard to kind of what the rules of the game were what you was the bank you had a Judah cape but you it was really kind of opaque in terms of what you had to do the volume of loans had to go through the roof in the time frame it was like within days or weeks that you had to provide these so I wonder if we could talk about that a little bit and how you're sort of approach the data helped you be prepared for that yeah no it was a race I mean the bottom line was it felt like a race right from from industry perspective as far as how how could we get this out there soon enough fast enough provide the most value to our customers our applications teams did a phenomenal job on enabling the applications to help streamline some of the application process for the loans themselves but from a data and reporting perspective behind the scenes we were there and we had some tools and capabilities and readiness to say we have the data now in our in our lake we can start to do some business driven decisions around all all of the different components of what's being processed on a daily basis from an application perspective versus what's been funded and how do those start to funnel all the way through doing some data quality checks and operational reporting checks to make sure that that data move properly and got booked in in the proper ways because of the rapid nature of how that was was all being done other covent type use cases as well we had some some different scenarios around different feed reporting and and other capabilities that the business wasn't necessarily prepared for we wouldn't have planned to have some of these types of things and reporting in place that we were able to give it because we had access to all the data because of these frameworks that we had put into place that we could pretty rapidly start to turn around some of those data some of those data points and analytics for us to make some some better decisions so given the propensity in the pace of M&A there has to be a challenge fundamentally in just in terms of data quality consistency governance give us the before and after you know before kind of before being the before the data ops mindset and after being kind of where you are today I think that's still a journey we're always trying to get better on that as well but the data ops mindset for us really has has shifted us to start to think about automation right pipelines that enablement a constant improvement and and how do we deploy faster deploy more consistently and and have the right capabilities in place when we need it so you know where some of that has come into place from an M&A perspective is it's really been around the building scale into everything that we do dezq real-time nature this scalability the rapid deployment models that we have in place is really where that starts to join forces and really become become powerful having having the ability to rapidly ingesting new data sources whether we know about it or not and then exposing that and having the tools and platforms be able to expose that to our users and enable our business lines whether it's covent whether it's M&A the use cases keep coming up right they we keep running into the same same concept which is how rapidly get people the data they need when they need it but still provide the rails and controls and make sure that it's governed and controllable on the way as well [Music] about the tech though wonder if we could spend some time on that I mean can you paint a picture of us so I thought what what what we're looking at here you've got you know some traditional IDI w's involved I'm sure you've got lots of data sources you you may be one of the zookeepers from the the Hadoop days with a lot of you know experimentation there may be some machine intelligence and they are painting a pic before us but sure no so we're kind of evolving some of the tool sets and capabilities as well we have some some generic kind of custom in-house build ingestion frameworks that we've started to build out for how to rapidly ingest and kind of script out the nature of of how we bring those data sources into play what we're what we've now started as well as is a journey down IBM compact product which is really gonna it's providing us that ability to govern and control all of our data sources and then start to enable some of that real-time ad hoc analytics and data preparation data shaping so some of the components that we're doing in there is just around that data discovery pointing that data sources rapidly running data profiles exposing that data to our users obviously very handy in the emanating space and and anytime you get new data sources in but then the concept of publishing that and leveraging some of the AI capabilities of assigning business terms in the data glossary and those components is another key component for us on the on the consumption side of the house for for data we have a couple of tools in place where Cognos shop we do a tableau from a data visualization perspective as well that what that were we're leveraging but that's where cloud pack is now starting to come into play as well from a data refinement perspective and giving the ability for users to actually go start to shape and prep their data sets all within that governed concept and then we've actually now started down the enablement path from an AI perspective with Python and R and we're using compact to be our orchestration tool to keep all that governed and controlled as well enable some some new AI models and some new technologies in that space we're actually starting to convert all of our custom-built frameworks into python now as well so we start to have some of that embedded within cloud pack and we can start to use some of the rails of those frameworks with it within them okay so you've got the ingest and ingestion side you've done a lot of automation it sounds like called the data profiling that's maybe what classification and automating that piece and then you've got the data quality piece the governance you got visualization with with tableau and and this kind of all fits together in a in an open quote unquote open framework is that right yeah I exactly I mean the the framework itself from our perspective where we're trying to keep the tools as as consistent as we can we really want to enable our users to have the tools that they need in the toolbox and and keep all that open what we're trying to focus on is making sure that they get the same data the same experience through whatever tool and mechanism that they're consuming from so that's where that platform mentality comes into place having compact in the middle to help govern all that and and reprovision some of those data sources out for us has it has been a key component for us well see if it sounds like you're you know making a lot of progress or you know so the days of the data temple or the high priest of data or the sort of keepers of that data really to more of a data culture where the businesses kind of feel ownership for their own data you believe self-service I think you've got confidence much more confident than the in the compliance and governance piece but bring us home just in terms of that notion of data culture and where you are and where you're headed no definitely I think that's that's been a key for us too as as part of our strategy is really helping we put in a strategy that helps define and dictate some of those structures and ownership and make that more clear some of the of the failures of the past if you will from an overall my monster data warehouse was around nobody ever owned it there was there wasn't you always ran that that risk of either the loudest consumer actually owned it or no one actually owned it what we've started to do with this is that Lake mentality and and having all that data ingested into our our frameworks the data owners are clear-cut it's who sends that data in what is the book record system for that source data we don't want a ability we don't touch it we don't transform it as we load it it sits there and available you own it we're doing the same mentality on the consumer side so we have we have a series of structures from a consumption perspective that all of our users are consuming our data if it's represented exactly how they want to consume it so again that ownership we're trying to take out a lot of that gray area and I'm enabling them to say yeah I own this I understand what I'm what I'm going after and and I can put the the ownership and the rule and rules and the stewardship around that as opposed to having that gray model in the middle that that that we never we never get but I guess to kind of close it out really the the concept for us is enabling people and end-users right giving them the data that they need when they need it and it's it's really about providing the framework and then the rails around around doing that and it's not about building out a formal bill warehouse model or a formal lessor like you mentioned before some of the you know the ivory tower type concepts right it's really about purpose-built data sets getting the giving our users empowered with the data they need when they need it all the way through and fusing that into our applications so that the applications and provide the best user experiences and and use the data to our advantage all about enabling the business I got a shove all I have you how's that IBM doing you know as a as a partner what do you like what could they be doing better to make your life easier sure I think I think they've been a great partner for us as far as that that enablement mentality the cloud pack platform has been a key for us we wouldn't be where we are without that tool said I our journey originally when we started looking at tools and modernization of our staff was around data quality data governance type components and tools we now because of the platform have released our first Python I models into the environment we have our studio capabilities natively because of the way that that's all container is now within cloud back so we've been able to enable new use cases and really advance us where we would have a time or a lot a lot more technologies and capabilities and then integrate those ourselves so the ability to have that all done has or and be able to leverage that platform has been a key to helping us get some of these roles out of this as quickly as we have as far as a partnership perspective they've been great as far as listening to what what the next steps are for us where we're headed what can we what do we need more of what can they do to help us get there so it's it's really been an encouraging encouraging environment I think they as far as what can they do better I think it's just keep keep delivering write it delivery is ping so keep keep releasing the new functionality and features and keeping the quality of the product intact well see it was great having you on the cube we always love to get the practitioner angle sounds like you've made a lot of progress and as I said when we're never finished in this industry so best of luck to you stay safe then and thanks so much for for sharing appreciate it thank you all right and thank you for watching everybody this is Dave Volante for the cube data ops in action we got the crowd chat a little bit later get right there but right back right of this short break [Music] [Music]

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 this special digital presentation where we're covering the topic of data ops and specifically how IBM is really operationalizing and automating the data pipeline with data ops and with me is Interpol Bhandari who is the global chief data officer at IBM in Nepal has always great to see you thanks for coming on my pleasure you know the standard throw away question from guys like me is you know what keeps the chief data officer up at night well I know what's keeping you up at night it's kovat 19 how are you doing it's keeping keeping all of us yeah for sure so how you guys making out as a leader I'm interested in you know how you have responded with whether it's you know communications obviously you're doing much more stuff you know remotely you're not on airplanes certainly like you used to be but but what was your first move when you actually realized this was going to require a shift well I think one of the first things that I did was to test the ability of my organization who worked remotely this was well before the the recommendations came in from the government but just so that we wanted you know to be sure that this is something that we could pull off if there were extreme circumstances where even everybody was good and so that was one of the first things we did along with that I think another major activity that we embarked on is even that we had created this central data and AI platform for IBM using our hybrid multi cloud approach how could that be adapting very very quickly you helped with the covert situation but those were the two big items that my team embarked on very quickly and again like I said this is well before there was any recommendations from the government or even internally within IBM any recommendations but B we decided that we wanted to run ahead and make sure that we were ready to ready to operate in that fashion and I believe a lot of my colleagues did the same yeah there's a there's a conversation going on right now just around productivity hits that people may be taking because they really weren't prepared it sounds like you're pretty comfortable with the productivity impact that you're achieving oh I'm totally comfortable with the productivity in fact I will tell you that while we've gone down this spot we've realized that in some cases the productivity is actually going to be better when people are working from home and they're able to focus a lot more on the work aspect you know and this could this runs the gamut from the nature of the job where you know somebody who basically needs to be in the front of a computer and is remotely taking care of operations you know if they don't have to come in their productivity is gonna go up somebody like myself who had a long drive into work you know which I would use on phone calls but now that entire time is can be used a lot more productivity but not maybe in a lot more productive manner so there is a we realize that that there's going to be some aspects of productivity that will actually be helped by the situation provided you're able to deliver the services that you deliver with the same level of quality and satisfaction that you've always done now there were certain other aspects where you know productivity is going to be affected so you know my team there's a lot of whiteboarding that gets done there are lots of informal conversations that spark creativity but those things are much harder to replicate in a remote in life so we've got a sense of you know where we have to do some work what things together versus where we were actually going to be more productive but all in all they are very comfortable that we can pull this off no that's great I want to stay on Kovac for a moment and in the context of just data and data ops and you know why now obviously with a crisis like this it increases the imperative to really have your data act together but I want to ask you both specifically as it relates to Co vid why data ops is so important and then just generally why at this this point in our time so I mean you know the journey we've been on they you know when I joined our data strategy centered around the cloud data and AI mainly because IBM's business strategy was around that and because there wasn't the notion of ái in enterprise right there was everybody understood what AI means for the consumer but for the enterprise people don't really understand what it meant so our data strategy became one of actually making IBM itself into an AI and a BA and then using that as a showcase for our clients and customers who look a lot like us to make them into a eye on the prize and in a nutshell what that translated to was that one had to in few AI into the workflow of the key business processes of enterprise so if you think about that workflow is very demanding why do you have to be able to deliver data and insights on time just when it's needed otherwise you can essentially slow down the whole workflow of a major process with but to be able to pull all that off you need to have your own data very very streamlined so that a lot of it is automated and you're able to deliver those insights as the people who are involved in the workflow needed so we've spent a lot of time while we were making IBM into an AI enterprise and infusing AI into our keepers and thus processes into essentially a data ops pipeline that was very very streamlined which then allowed us to very quickly adapt to the covert 19 situation and I'll give you one specific example that we'll go to you know how one would say one could essentially leverage that capability that I just talked about to do this so one of the key business processes that we had taken aim at was our supply chain you know we're a global company and our supply chain is critical we have lots of suppliers and they are all over the globe and we have different types of products so that you know it has a multiplicative fact is we go from each of those you have other additional suppliers and you have events you have other events you have calamities you have political events so we have to be able to very quickly understand the risk associated with any of those events with regard to our supply chain and make appropriate adjustments on the fly so that was one of the key applications that we built on our central data and the Aqua and as part of a data ops pipeline that meant he ingested the ingestion of the several hundred sources of data had to be blazingly fast and also refreshed very very quickly also we had to then aggregate data from the outside from external sources that had to do with weather related events that had to do with political events social media feeds etcetera and overlay that on top of our map of interest with regard to our supply chain sites and also where they were supposed to deliver we'd also weaved in our capabilities here to track those shipments as they flowed and have that data flow back as well so that we would know exactly where where things were this is only possible because we had a streamlined data ops capability and we had built this central data Nai platform for IBM now you flip over to the covert 19 situation when go with 19 you know emerged and we began to realize that this was going to be a significant significant pandemic what we were able to do very quickly was to overlay the Kovach 19 incidents on top of our sites of interest as well as pick up what was being reported about those sites of interest and provide that over to our business continuity so this became an immediate exercise that we embarked but it wouldn't have been possible if you didn't have the foundation of the data ops pipeline as well as that central data Nai platform in place to help you do that very very quickly and adapt so so what I really like about this story and something that I want to drill into is it essentially a lot of organizations have a real tough time operationalizing AI and fusing it to use your word and the fact that you're doing it is really a good proof point that I want to explore a little bit so you're essentially there was a number of aspects of what you just described there was the data quality piece with your data quality in theory anyway is gonna go up with more data if you can handle it and the other was speed time to insight so you can respond more quickly if it's think about this Kovan situation if your days behind or weeks behind which is not uncommon you know sometimes even worse you just can't respond I mean these things change daily sometimes certainly within the day so is that right that's kind of the the business outcome and objective that you guys were after yes you know so trauma from an infused AI into your business processes by the overarching outcome metric that one focuses on is end to end cycle so you take that process the end-to-end process and you're trying to reduce the end-to-end cycle time by you know several factors several orders of magnitude we did for instance in my organization that have to do with the generation of metadata is data about data and that's usually a very time-consuming process and we've reduced that by over 95% by using AI you actually help in the metadata generation itself and that's applied now across the board for many different business processes that you know iBM has that's the same kind of principle that was you you'll be able to do that so that foundation essentially enables you to go after that cycle time reduction right off the bat so when you get to a situation like of open 19 situation which demands urgent action your foundation is already geared to deliver on that so I think actually we might have a graphic and then the second graphic guys if you bring up this second one I think this is Interpol what you're talking about here that sort of 95 percent reduction guys if you could bring that up would take a look at it so this is maybe not a co vid use case yeah here it is so that 95 percent reduction in in cycle time improving and data quality what we talked about there's actually some productivity metrics right this is what you're talking about here in this metadata example correct yeah yes the middle do that right it's so central to everything that one does with data I mean it's basically data about data and this is really the business metadata that we're talking about which is once you have data in your data Lee if you don't have business metadata describing what that data is then it's very hard for people who are trying to do things to determine whether they can even whether they even have access to the right data and typically this process has been done manually because somebody looks at the data they looks at the fields and they describe it and it could easily take months and what we did was we essentially use a deep learning and a natural language processing approach looked at all the data that we've had historically over an idea and we've automated the metadata generation so whether it was you know you were talking about both the data relevant for probit team or for supply chain or for a receivable process any one of our business processes this is one of those fundamental steps that one must go through to be able to get your data ready for action and if you were able to take that cycle time for that step and reduce it by 95% you can imagine the acceleration yeah and I liked it we were saying before you talk about the end to end a concept you're applying system thinking here which is very very important because you know a lot of a lot of points that I talked you'll they'll be they're so focused on one metric may be optimizing one component of that end to end but it's really the overall outcome that you're trying to achieve you you may sometimes you know be optimizing one piece but not the whole so that systems thinking is is very very important isn't it the system's thinking is extremely important overall no matter you know where you're involved in the process of designing the system but if you're the data guy it's incredibly important because not only does that give you an insight into the cycle time reduction but it also gives it clues you in into what standardization is necessary in the data so that you're able to support an eventual out you know a lot of people will go down the path of data governance and creation of data standard and you can easily boil the ocean trying to do that but if you actually start with an end-to-end view of your key processes and that by extension the outcomes associated with those processes as well as the user experience at the end of those processes and kind of then work backwards as to what are the standards that you need for the data that's going to feed into all that that's how you arrive at you know a viable practical data standards effort that you can essentially push forward with so there's there are multiple aspects when you take that end-to-end system you that helps the chief later one of the other tenets of data ops is really the ability across the organization for everybody to have visibility communications it's very key we've got another graphic that I want to show around the organizational you know in the right regime and this is a complicated situation for a lot of people but it's imperative guys if you bring up the first graphic it's imperative that organizations you know fine bring in the right stakeholders and actually identify those individuals that are going to participate so that there's full visibility everybody understands what their their roles are they're not in in silos so a guys if you could show us that first graphic that would be great but talk about the organization and the right regime they're Interpol yes yes I believe you're going to what you're gonna show up is actually my organization but I think it's yes it's very very illustrative of what one has to set up to be able to pull off the kind of impact you know so let's say we talked about that central data and AI platform that's driving the entire enterprise and you're infusing AI into key business processes like the supply chain you then create applications like the operational risk insights that we talked about and then extend it over to a faster merging and changing situation like the overt nineteen you need an organization that obviously reflects the technical aspects of the plan right so you have to have the data engineering arm and in my case there's a lot of emphasis around because that's one of those skill set areas that's really quite rare and but also very very powerful so they're the major technology arms of that there's also the governance arm that I talked about where you have to produce a set of standards and implement them and enforce them so that you're able to make this end-to-end impact but then there's also there's a there's an adoption where there's a there's a group that reports in to me very very you know empowered which essentially has to convince the rest of the organization to adopt but the key to their success has been in power in the sense that they are empowered to find like-minded individuals in our key business processes who are also empowered and if they agree they just move forward and go ahead and do it because you know we've already provided the central capabilities by central I don't mean they're all in one location we're completely global and you know it's it's it's a hybrid multi-cloud set up but it's central in the sense that it's one source to come for for trusted data as well as the expertise that you need from an AI standpoint to be able to move forward and deliver the business outcome so when these business schemes come together with the adoption that's where the magic hand so that's another another aspect of the organization that's critical and then we've also got a data officer council that I chair and that has to do with the people who are the chief data officer z' of the individual business units that we have and they're kind of my extended team into the rest of the organization and we leverage that bolt from a adoption of the platform standpoint but also in terms of defining and enforcing standard it helps us do want to come back the Ovid talked a little bit about business resiliency people I think you've probably seen the news that IBM's you know providing super computer resources to the government to fight coronavirus you've also just announced that some some RTP folks are helping first responders and nonprofits and providing capabilities for no charge which is awesome I mean it's the kind of thing look I'm sensitive the companies like IBM you know you don't want to appear to be ambulance-chasing in these times however IBM and other big tech companies you're in a position to help and that's what you're doing here so maybe you could talk a little bit about what you're doing in this regard and then we'll tie it up with just business resiliency and the importance of data right right so you know I'd explained the operational risk insights application that we had which we were using internally and be covert nineteen even be using it we were using it primarily to assess the risk to our supply chain from various events and then essentially react very very quickly to those through those events so you could manage the situation well we realize that this is something that you know several non government NGOs that big they could essentially use the ability because they have to manage many of these situations like natural disasters and so we've given that same capability to the NGOs to you and to help them to help them streamline their planning and their thinking by the same token but you talked about Oh with nineteen that same capability with the poet mine team data overlaid on top of them essentially becomes a business continuity planning and resilience because let's say I'm a supply chambers right now I can look the incidence of probe ignite and I can and I know where my suppliers are and I can see the incidence and I can say oh yes know this supplier and I can see that the incidence is going up this is likely to be affected let me move ahead and start making plans backup plans just in case it reaches a crisis level then on the other hand if you're somebody in our revenue planning you know on the finance side and you know where your keep clients and customers are located again by having that information overlaid with those sites you can make your own judgments and you can make your own assessment to do that so that's how it translates over into a business continuity and resilient resilience planning - we are internally doing that now - every department you know that's something that we are actually providing them this capability because we could build rapidly on what we had already done and to be able to do that and then as we get inside into what each of those departments do with that data because you know once they see that data once they overlay it to their sites of interest and this is you know anybody and everybody in IBM because no matter what department they're in there are going to be sites of interest that are going to be affected and they have an understanding of what those sites of interest mean in the context of the planning that they're doing and so they'll be able to make judgments but as we gain a better understanding of that we will automate those capabilities more and more for each of those specific areas and now you're talking about a comprehensive approach an AI approach to business continuity and resilience planning in the context of a large complicated organization like IBM which obviously will be of great interest to enterprise clients and customers right one of the things that we're researching now is trying to understand you know what about this crisis is gonna be permanent some things won't be but but we think many things will be there's a lot of learnings do you think that organizations will rethink business resiliency in this context that they might sub optimize profitability for example to be more prepared for crises like this with better business resiliency and what role would data play in that so no it's a very good question and timely question Dave so I mean clearly people have understood that with regard to such a pandemic the first line of beef right is it is it's not going to be so much on the medicine side because the vaccine is not even we won't be available for a period of time it has to go to development so the first line of defense is actually to take a quarantine like a pro like we've seen play out across the world and then that in effect results in an impact on the businesses right in the economic climate and the businesses there's an impact I think people have realized this now they will obviously factor this in into their into how they do business will become one of those things from if this is time talking about how this becomes permanent I think it's going to become one of those things that if you're a responsible enterprise you are going to be planning for you're going to know how to implement this on the second go-around so obviously you put those frameworks and structures in place and there will be a certain cost associated with them and one could argue that that could eat into the profitability on the other hand what I would say is because these two points really that these are fast emerging fluid situations you have to respond very very quickly to those you will end up laying out a foundation pretty much like we did which enables you to really accelerate your pipeline right so the data ops pipelines we talked about there there's a lot of automation so that you can react very quickly you know data ingestion very very rapidly that you're able to you know do that kind of thing the metadata generation just the entire pipeline that we're talking about that you're able to respond and very quickly bring in new data and then aggregated at the right levels infuse it into the workflows and then deliver it to the right people at the right time I will you know that will become a must now but once you do that you could argue that there is a cost associated with doing that but we know that the cycle time reductions on things like that they can run you know I mean I gave you the example of 95 percent you know on average we see like a 70% end to end cycle time era where we've implemented the approach that's been pretty pervasive with an idea across a business process so that in a sense in in essence then actually becomes a driver for profitability so yes it might you know this might back people into doing that but I would argue that that's probably something that's going to be very good long term for the enterprises involved and they'll be able to leverage that in their in their business and I think that just the competitive pressure of having to do that will force everybody down that path mean but I think it'll be eventually a good that end and cycle time compression is huge and I like what you're saying because it's it's not just a reduction in the expected loss during a crisis there's other residual benefits to the organization Interpol thanks so much for coming on the cube and sharing this really interesting and deep case study I know there's a lot more information out there so really appreciate your time all right take care buddy thanks for watching and this is Dave Allante for the cube and we will see you next time [Music]

>>from the Cube Studios in Palo Alto and Boston connecting with thought leaders all around the world. This is a cube conversation. >>Hi, everybody. This is Dave Volante with Cuban. Welcome to the special digital presentation. We're really digging into how IBM is operational izing and automating the AI and data pipeline not only for its clients, but also for itself. And with me is Julie Lockner, who looks after offering management and IBM Data and AI portfolio really great to see you again. >>Great, great to be here. Thank you. Talk a >>little bit about the role you have here at IBM. >>Sure, so my responsibility in offering >>management and the data and AI organization is >>really twofold. One is I lead a team that implements all of the back end processes, really the operations behind any time we deliver a product from the Data and AI team to the market. So think about all of the release cycle management are seeing product management discipline, etcetera. The other role that I play is really making sure that I'm We are working with our customers and making sure they have the best customer experience and a big part of that is developing the data ops methodology. It's something that I needed internally >>from my own line of business execution. But it's now something that our customers are looking for to implement in their shops as well. >>Well, good. I really want to get into that. So let's let's start with data ops. I mean, I think you know, a lot of people are familiar with Dev Ops. Not maybe not everybody's familiar with data ops. What do we need to know about data? >>Well, I mean, you bring up the point that everyone knows Dev ops. And in fact, I think you know what data ops really >>does is bring a lot of the benefits that Dev Ops did for application >>development to the data management organizations. So when we look at what is data ops, it's a data management. Uh, it is a data management set of principles that helps organizations bring business ready data to their consumers. Quickly. It takes it borrows from Dev ops. Similarly, where you have a data pipeline that associates a business value requirement. I have this business initiative. It's >>going to drive this much revenue or this must cost >>savings. This is the data that I need to be able to deliver it. How do I develop that pipeline and map to the data sources Know what data it is? Know that I can trust it. So ensuring >>that it has the right quality that I'm actually using, the data that it was meant >>for and then put it to use. So in in history, most data management practices deployed a waterfall like methodology. Our implementation methodology and what that meant is all the data pipeline >>projects were implemented serially, and it was done based on potentially a first in first out program management office >>with a Dev Ops mental model and the idea of being able to slice through all of the different silos that's required to collect the data, to organize it, to integrate it, the validate its quality to create those data integration >>pipelines and then present it to the dashboard like if it's a Cognos dashboard >>or a operational process or even a data science team, that whole end to end process >>gets streamlined through what we're pulling data ops methodology. >>So I mean, as you well know, we've been following this market since the early days of Hadoop people struggle with their data pipelines. It's complicated for them, there's a a raft of tools and and and they spend most of their time wrangling data preparing data moving data quality, different roles within the organization. So it sounds like, you know, to borrow from from Dev Ops Data offices is all about streamlining that data pipeline, helping people really understand and communicate across. End the end, as you're saying, But but what's the ultimate business outcome that you're trying to drive? >>So when you think about projects that require data to again cut costs Teoh Artemia >>business process or drive new revenue initiatives, >>how long does it take to get from having access to the data to making it available? That duration for every time delay that is spent wasted trying to connect to data sources, trying to find subject matter experts that understand what the data means and can verify? It's quality, like all of those steps along those different teams and different disciplines introduces delay in delivering high quality data fat, though the business value of data ops is always associated with something that the business is trying to achieve but with a time element so if it's for every day, we don't have this data to make a decision where either making money or losing money, that's the value proposition of data ops. So it's about taking things that people are already doing today and figuring out the quickest way to do it through automation or work flows and just cutting through all the political barriers >>that often happens when these data's cross different organizational boundaries. >>Yes, sir, speed, Time to insights is critical. But in, you know, with Dev Ops, you really bringing together of the skill sets into, sort of, you know, one Super Dev or one Super ops. It sounds with data ops. It's really more about everybody understanding their role and having communication and line of sight across the entire organization. It's not trying to make everybody else, Ah, superhuman data person. It's the whole It's the group. It's the team effort, Really. It's really a team game here, isn't it? >>Well, that's a big part of it. So just like any type of practice, there's people, aspects, process, aspects and technology, right? So people process technology, and while you're you're describing it, like having that super team that knows everything about the data. The only way that's possible is if you have a common foundation of metadata. So we've seen a surgeons in the data catalog market in the last, you know, 67 years. And what what the what? That the innovation in the data catalog market has actually enabled us to be able >>to drive more data ops pipelines. >>Meaning as you identify data assets you captured the metadata capture its meaning. You capture information that can be shared, whether they're stakeholders, it really then becomes more of a essential repository for people don't really quickly know what data they have really quickly understand what it means in its quality and very quickly with the right proper authority, like privacy rules included. Put it to use >>for models, um, dashboards, operational processes. >>Okay. And we're gonna talk about some examples. And one of them, of course, is IBM's own internal example. But help us understand where you advise clients to start. I want to get into it. Where do I get started? >>Yeah, I mean, so traditionally, what we've seen with these large data management data governance programs is that sometimes our customers feel like this is a big pill to swallow. And what we've said is, Look, there's an operator. There's an opportunity here to quickly define a small project, align into high value business initiative, target something that you can quickly gain access to the data, map out these pipelines and create a squad of skills. So it includes a person with Dev ops type programming skills to automate an instrument. A lot of the technology. A subject matter expert who understands the data sources in it's meeting the line of business executive who translate bringing that information to the business project and associating with business value. So when we say How do you get started? We've developed A I would call it a pretty basic maturity model to help organizations figure out. Where are they in terms of the technology, where are they in terms of organizationally knowing who the right people should be involved in these projects? And then, from a process perspective, we've developed some pretty prescriptive project plans. They help you nail down. What are the data elements that are critical for this business business initiative? And then we have for each role what their jobs are to consolidate the data sets map them together and present them to the consumer. We find that six week projects, typically three sprints, are perfect times to be able to a timeline to create one of these very short, quick win projects. Take that as an opportunity to figure out where your bottlenecks are in your own organization, where your skill shortages are, and then use the outcome of that six week sprint to then focus on billing and gaps. Kick off the next project and iterating celebrate the success and promote the success because >>it's typically tied to a business value to help them create momentum for the next one. >>That's awesome. I want to get into some examples, I mean, or we're both Massachusetts based. Normally you'd be in our studio and we'd be sitting here for face to face of obviously with Kobe. 19. In this crisis world sheltering in place, you're up somewhere in New England. I happened to be in my studio, but I'm the only one here, so relate this to cove it. How would data ops, or maybe you have a, ah, a concrete example in terms of how it's helped, inform or actually anticipate and keep up to date with what's happening with both. >>Yeah, well, I mean, we're all experiencing it. I don't think there's a person >>on the planet who hasn't been impacted by what's been going on with this Cupid pandemic prices. >>So we started. We started down this data obscurity a year ago. I mean, this isn't something that we just decided to implement a few weeks ago. We've been working on developing the methodology, getting our own organization in place so that we could respond the next time we needed to be able todo act upon a data driven decision. So part of the step one of our journey has really been working with our global chief data officer, Interpol, who I believe you have had an opportunity to meet with an interview. So part of this year Journey has been working with with our corporate organization. I'm in a line of business organization where we've established the roles and responsibilities we've established the technology >>stack based on our cloud pack for data and Watson knowledge padlock. >>So I use that as the context. For now, we're faced with a pandemic prices, and I'm being asked in my business unit to respond very quickly. How can we prioritize the offerings that are going to help those in critical need so that we can get those products out to market? We can offer a 90 day free use for governments and hospital agencies. So in order for me to do that as a operations lead or our team, I needed to be able to have access to our financial data. I needed to have access to our product portfolio information. I needed to understand our cloud capacity. So in order for me to be able to respond with the offers that we recently announced and you'll you can take a look at some of the examples with our Watson Citizen Assistant program, where I was able to provide the financial information required for >>us to make those products available from governments, hospitals, state agencies, etcetera, >>that's a That's a perfect example. Now, to set the stage back to the corporate global, uh, the chief data office organization, they implemented some technology that allowed us to, in just data, automatically classify it, automatically assign metadata, automatically associate data quality so that when my team started using that data, we knew what the status of that information >>was when we started to build our own predictive models. >>And so that's a great example of how we've been partnered with a corporate central organization and took advantage of the automated, uh, set of capabilities without having to invest in any additional resources or head count and be able to release >>products within a matter of a couple of weeks. >>And in that automation is a function of machine intelligence. Is that right? And obviously, some experience. But you couldn't you and I when we were consultants doing this by hand, we couldn't have done this. We could have done it at scale anyway. It is it is it Machine intelligence and AI that allows us to do this. >>That's exactly right. And you know, our organization is data and AI, so we happen to have the research and innovation teams that are building a lot of this technology, so we have somewhat of an advantage there, but you're right. The alternative to what I've described is manual spreadsheets. It's querying databases. It's sending emails to subject matter experts asking them what this data means if they're out sick or on vacation. You have to wait for them to come back, and all of this was a manual process. And in the last five years, we've seen this data catalog market really become this augmented data catalog, and the augmentation means it's automation through AI. So with years of experience and natural language understanding, we can home through a lot of the metadata that's available electronically. We can calm for unstructured data, but we can categorize it. And if you have a set of business terms that have industry standard definitions through machine learning, we can automate what you and I did as a consultant manually in a matter of seconds. That's the impact that AI is have in our organization, and now we're bringing this to the market, and >>it's a It's a big >>part of where I'm investing. My time, both internally and externally, is bringing these types >>of concepts and ideas to the market. >>So I'm hearing. First of all, one of the things that strikes me is you've got multiple data, sources and data that lives everywhere. You might have your supply chain data in your er p. Maybe that sits on Prem. You might have some sales data that's sitting in a sas in a cloud somewhere. Um, you might have, you know, weather data that you want to bring in in theory. Anyway, the more data that you have, the better insights that you could gather assuming you've got the right data quality. But so let me start with, like, where the data is, right? So So it's it's anywhere you don't know where it's going to be, but you know you need it. So that's part of this right? Is being able >>to get >>to the data quickly. >>Yeah, it's funny. You bring it up that way. I actually look a little differently. It's when you start these projects. The data was in one place, and then by the time you get through the end of a project, you >>find out that it's moved to the cloud, >>so the data location actually changes. While we're in the middle of projects, we have many or even during this this pandemic crisis. We have many organizations that are using this is an opportunity to move to SAS. So what was on Prem is now cloud. But that shouldn't change the definition of the data. It shouldn't change. It's meaning it might change how you connect to it. It might also change your security policies or privacy laws. Now, all of a sudden, you have to worry about where is that data physically located? And am I allowed to share it across national boundaries right before we knew physically where it waas. So when you think about data ops, data ops is a process that sits on top of where the data physically resides. And because we're mapping metadata and we're looking at these data pipelines and automated work flows, part of the design principles are to set it up so that it's independent of where it resides. However, you have to have placeholders in your metadata and in your tool chain, where we're automating these work flows so that you can accommodate when the data decides to move. Because the corporate policy change >>from on prem to cloud. >>And that's a big part of what Data ops offers is the same thing. By the way, for Dev ops, they've had to accommodate building in, you know, platforms as a service versus on from the development environments. It's the same for data ops, >>and you know, the other part that strikes me and listening to you is scale, and it's not just about, you know, scale with the cloud operating model. It's also about what you were talking about is you know, the auto classification, the automated metadata. You can't do that manually. You've got to be able to do that. Um, in order to scale with automation, That's another key part of data office, is it not? >>It's a well, it's a big part of >>the value proposition and a lot of the part of the business case. >>Right then you and I started in this business, you know, and big data became the thing. People just move all sorts of data sets to these Hadoop clusters without capturing the metadata. And so as a result, you know, in the last 10 years, this information is out there. But nobody knows what it means anymore. So you can't go back with the army of people and have them were these data sets because a lot of the contact was lost. But you can use automated technology. You can use automated machine learning with natural, understand natural language, understanding to do a lot of the heavy lifting for you and a big part of data ops, work flows and building these pipelines is to do what we call management by exception. So if your algorithms say 80% confident that this is a phone number and your organization has a low risk tolerance, that probably will go to an exception. But if you have a you know, a match algorithm that comes back and says it's 99% sure this is an email address, right, and you have a threshold that's 98%. It will automate much of the work that we used to have to do manually. So that's an example of how you can automate, eliminate manual work and have some human interaction based on your risk threshold. >>That's awesome. I mean, you're right, the no schema on write said. I throw it into a data lake. Data Lake becomes a data swamp. We all know that joke. Okay, I want to understand a little bit, and maybe you have some other examples of some of the use cases here, but there's some of the maturity of where customers are. It seems like you've got to start by just understanding what data you have, cataloging it. You're getting your metadata act in order. But then you've got you've got a data quality component before you can actually implement and get yet to insight. So, you know, where are customers on the maturity model? Do you have any other examples that you can share? >>Yeah. So when we look at our data ops maturity model, we tried to simplify, and I mentioned this earlier that we try to simplify it so that really anybody can get started. They don't have to have a full governance framework implemented to to take advantage of the benefits data ops delivers. So what we did is we said if you can categorize your data ops programs into really three things one is how well do you know your data? Do you even know what data you have? The 2nd 1 is, and you trust it like, can you trust it's quality? Can you trust it's meeting? And the 3rd 1 is Can you put it to use? So if you really think about it when you begin with what data do you know, write? The first step is you know, how are you determining what data? You know? The first step is if you are using spreadsheets. Replace it with a data catalog. If you have a department line of business catalog and you need to start sharing information with the department's, then start expanding to an enterprise level data catalog. Now you mentioned data quality. So the first step is do you even have a data quality program, right. Have you even established what your criteria are for high quality data? Have you considered what your data quality score is comprised of? Have you mapped out what your critical data elements are to run your business? Most companies have done that for there. They're governed processes. But for these new initiatives And when you identify, I'm in my example with the covert prices, what products are we gonna help bring to market quickly? I need to be able to >>find out what the critical data elements are. And can I trust it? >>Have I even done a quality scan and have teams commented on it's trustworthiness to be used in this case, If you haven't done anything like that in your organization, that might be the first place to start. Pick the critical data elements for this initiative, assess its quality, and then start to implement the work flows to re mediate. And then when you get to putting it to use, there's several methods for making data available. One is simply making a gate, um, are available to a small set of users. That's what most people do Well, first, they make us spreadsheet of the data available, But then, if they need to have multiple people access it, that's when, like a Data Mart might make sense. Technology like data virtualization eliminates the need for you to move data as you're in this prototyping phase, and that's a great way to get started. It doesn't cost a lot of money to get a virtual query set up to see if this is the right join or the right combination of fields that are required for this use case. Eventually, you'll get to the need to use a high performance CTL tool for data integration. But Nirvana is when you really get to that self service data prep, where users can weary a catalog and say these are the data sets I need. It presents you a list of data assets that are available. I can point and click at these columns I want as part of my data pipeline and I hit go and automatically generates that output or data science use cases for it. Bad news, Dashboard. Right? That's the most mature model and being able to iterate on that so quickly that as soon as you get feedback that that data elements are wrong or you need to add something, you can do it. Push button. And that's where data obscurity should should bring organizations too. >>Well, Julie, I think there's no question that this covert crisis is accentuated the importance of digital. You know, we talk about digital transformation a lot, and it's it's certainly riel, although I would say a lot of people that we talk to we'll say, Well, you know, not on my watch. Er, I'll be retired before that all happens. Well, this crisis is accelerating. That transformation and data is at the heart of it. You know, digital means data. And if you don't have data, you know, story together and your act together, then you're gonna you're not gonna be able to compete. And data ops really is a key aspect of that. So give us a parting word. >>Yeah, I think This is a great opportunity for us to really assess how well we're leveraging data to make strategic decisions. And if there hasn't been a more pressing time to do it, it's when our entire engagement becomes virtual like. This interview is virtual right. Everything now creates a digital footprint that we can leverage to understand where our customers are having problems where they're having successes. You know, let's use the data that's available and use data ops to make sure that we can generate access. That data? No, it trust it, Put it to use so that we can respond to >>those in need when they need it. >>Julie Lockner, your incredible practitioner. Really? Hands on really appreciate you coming on the Cube and sharing your knowledge with us. Thank you. >>Thank you very much. It was a pleasure to be here. >>Alright? And thank you for watching everybody. This is Dave Volante for the Cube. And we will see you next time. >>Yeah, yeah, yeah, yeah, yeah

from the cube studios in Palo Alto in Boston connecting with thought leaders all around the world this is a cube conversation hi everybody this is Dave Volante and welcome to this special digital cube presentation sponsored by IBM we're going to focus in on data op data ops in action a lot of practitioners tell us that they really have challenges operationalizing in infusing AI into the data pipeline we're going to talk to some practitioners and really understand how they're solving this problem and really pleased to bring Victoria stayshia vich who's the Global Information Systems Manager for information management at harley-davidson Vik thanks for coming to the cube great to see you wish we were face to face but really appreciate your coming on in this manner that's okay that's why technology's great right so you you are steeped in a data role at harley-davidson can you describe a little bit about what you're doing and what that role is like definitely so obviously a manager of information management >> governance at harley-davidson and what my team is charged with is building out data governance at an enterprise level as well as supporting the AI and machine learning technologies within my function right so I have a portfolio that portfolio really includes DNA I and governance and also our master data and reference data and data quality function if you're familiar with the dama wheel of course what I can tell you is that my team did an excellent job within this last year in 2019 standing up the infrastructure so those technologies right specific to governance as well as their newer more modern warehouse on cloud technologies and cloud objects tour which also included Watson Studio and Watson Explorer so many of the IBM errs of the world might hear about obviously IBM ISEE or work on it directly we stood that up in the cloud as well as db2 warehouse and cloud like I said in cloud object store we spent about the first five months of last year standing that infrastructure up working on the workflow ensuring that access security management was all set up and can within the platform and what we did the last half of the year right was really start to collect that metadata as well as the data itself and bring the metadata into our metadata repository which is rx metadata base without a tie FCE and then also bring that into our db2 warehouse on cloud environment so we were able to start with what we would consider our dealer domain for harley-davidson and bring those dimensions within to db2 warehouse on cloud which was never done before a lot of the information that we were collecting and bringing together for the analytics team lived in disparate data sources throughout the enterprise so the goal right was to stop with redundant data across the enterprise eliminate some of those disparity to source data resources right and bring it into a centralized repository for reporting okay Wow we got a lot to unpack here Victoria so but let me start with sort of the macro picture I mean years ago you see the data was this thing that had to be managed and it still does but it was a cost was largely a liability you know governance was sort of front and center sometimes you know it was the tail that wagged the value dog and then the whole Big Data movement comes in and everybody wants to be data-driven and so you saw some pretty big changes in just the way in which people looked at data they wanted to you know mine that data and make it an asset versus just a straight liability so what what are the changes that you discerned in in data and in your organization over the last let's say half a decade we to tell you the truth we started looking at access management and the ability to allow some of our users to do some rapid prototyping that they could never do before so what more and more we're seeing as far as data citizens or data scientists right or even analysts throughout most enterprises is it well they want access to the information they want it now they want speed to insight at this moment using pretty much minimal Viable Product they may not need the entire data set and they don't want to have to go through leaps and bounds right to just get access to that information or to bring that information into necessarily a centralized location so while I talk about our db2 warehouse on cloud and that's an excellent example of one we actually need to model data we know that this is data that we trust right that's going to be called upon many many times from many many analysts right there's other information out there that people are collecting because there's so much big data right there's so many ways to enrich your data within your organization for your customer reporting the people are really trying to tap into those third-party datasets so what my team has done what we're seeing right change throughout the industry is that a lot of teams and a lot of enterprises are looking at s technologists how can we enable our scientists and our analysts right the ability to access data virtually so instead of repeating right recuperating redundant data sources we're actually ambling data virtualization at harley-davidson and we've been doing that first working with our db2 warehouse on cloud and connecting to some of our other trusted versions of data warehouses that we have throughout the enterprise that being our dealer warehouse as well to enable obviously analysts to do some quick reporting without having to bring all that data together that is a big change I see the fact that we were able to tackle that that's allowed technology to get back ahead because most backup Furnish say most organizations right have given IT the bad rap wrap up it takes too long to get what we need my technologists cannot give me my data at my fingertips in a timely manner to not allow for speed to insight and answers the business questions at point of time of delivery most and we've supplied data to our analysts right they're able to calculate aggregate brief the reporting metrics to get those answers back to the business but they're a week two weeks too late the information is no longer relevant so data virtualization through data Ops is one of the ways and we've been able to speed that up and act as a catalyst for data delivery but we've also done though and I see this quite a bit is well that's excellent we still need to start classifying our information and labeling that at the system level we've seen most most enterprises right I worked at Blue Cross as well with IBM tool had the same struggle they were trying to eliminate their technology debt reduce their spend reduce the time it takes for resources working on technologies to maintain technologies they want to reduce their their IT portfolio of assets and capabilities that they license today so what do they do to do that it's time to start taking a look at what systems should be classified as essential systems versus those systems that are disparate and could be eliminated and that starts with data governance right so okay so your your main focus is on governance and you talked about real people want answers now they don't want to have to wait they don't want to go big waterfall process so what was what would you say was sort of some of the top challenges in terms of just operationalizing your data pipelining getting to the point that you are today you know I have to be quite honest um standing up the governance framework the methodology behind it right to get it data owners data stewards at a catalog established that was not necessarily the heavy lifting the heavy lifting really came with I'm setting up a brand new infrastructure in the cloud for us to be quite honest um we with IBM partnered and said you know what we're going to the cloud and these tools had never been implemented in the cloud before we were kind of the first do it so some of the struggles that we aren't they or took on and we're actually um standing up the infrastructure security and access management network pipeline access right VPN issues things of that nature I would say is some of the initial roadblocks we went through but after we overcame those challenges with the help of IBM and the patience of both the Harley and IBM team it became quite easy to roll out these technologies to other users the nice thing is right we at harley-davidson have been taking the time to educate our users today up for example we had what we call the data bytes a Lunch and Learn and so in that Lunch and Learn what we did is we took our entire GIS team our global information services team which is all of IT through these new technologies it was a form of over 250 people with our CIO and CTO on and taking them through how do we use these tools what are the purpose of schools why do we need governance to maintain these pools why is metadata management important to the organization that piece of it seems to be much easier than just our initial scanning it up so it's good enough to start letting users in well sounds like you had real sponsorship from from leadership and input from leadership and they were kind of leaning into the whole process first of all is that true and how important is that for success oh it's essential we often said when we were first standing up the tools to be quite honest is our CIO really understand what it is that were for standing up as our CIO really understand governance because we didn't have the time to really get that face-to-face interaction with our leadership so I myself made it a mandate having done this previously at Blue Cross to get in front of my CIO and my CTO and educate them on what it is we are exactly standing up and once we did that it was very easy to get at an executive steering committee as well as an executive membership Council right I'm boarded with our governance council and now they're the champions of that it's never easy that was selling governance to leadership and the ROI is never easy because it's not something that you can easily calculate it's something that has to show its return on investment over time and that means that you're bringing dashboards you're educating your CIO and CTO and how you're bringing people together how groups are now talking about solutions and technologies in a domain like environment right where you have people from at an international level we have people from Asia from Europe from China that join calls every Thursday to talk about the data quality issue specific to dealer for example what systems were using what solutions on there are on the horizon to solve them so that now instead of having people from other countries that work for Harley as well as just even within the US right creating one-off solutions that are answering the same business questions using the same data but creating multiple solutions right to solve the same problem we're now bringing them together and we're solving together and we're prioritizing those as well so that return on investment necessarily down the line you can show that is you know what instead of this printing into five projects we've now turned this into one and instead of implementing four systems we've now implemented one and guess what we have the business rules and we have the classification I to this system so that you CIO or CTO right you now go in and reference this information a glossary a user interface something that a c-level can read interpret understand quickly write dissect the information for their own need without having to take the long lengthy time to talk to a technologist about what does this information mean and how do i how do I use it you know what's interesting is take away based on what you just said is you know harley-davidson is an iconic brand cool company with fuckin motorcycles right and but you came out of an insurance background which is a regulated industry where you know governance is sort of de rigueur right I mean it's it's a table steak so how are you able that arleigh to balance the sort of tension between governance and the sort of business flexibility so there's different there's different lovers I would call them right obviously within healthcare in insurance the importance becomes compliance and risk and regulatory right they're big pushes gosh I don't want to pay millions of dollars for fines start classifying this information enabling security reducing risk all that good stuff right for Harley Davidson it was much different it was more or less we have a mission right we want to invest in our technologies yet we want to save money how do we cut down the technologies that we have today reduce our technology spend yet and able our users have access to more information in a timely manner that's not an easy that's not an easy pass right um so what we did is I took that my married governance part-time model and our time model is specific worried they're gonna tolerate an application we're going to invest in an application we're gonna migrate an application or we're gonna eliminate that so I'm talking to my CIO said you know we can use governance the classifier system help act as a catalyst when we start to implement what it is we're doing with our technologies which technologies are we going to eliminate tomorrow we as IG cannot do that unless we discuss some sort of business impact unless you look at a system and say how many users are using us what reports are essential the business teams do they need this system is this something that's critical for users today to eat is this duplicate 'iv right we have many systems that are solving the same capability that is how I sold that off my CIO and it made it important to the rest of the organization they knew we had a mandate in front of us we had to reduce technology spend and that really for me made it quite easy and talking to other technologists as well as business users on why if governance is important why it's going to help harley-davidson and their mission to save money going forward I will tell you though that the businesses of biggest value right is the fact that they now owns the data they're more likely right to use your master data management systems like I said I'm the owner of our MDM services today as well as our customer knowledge center today they're more likely to access and reference those systems if they feel that they built the rule and they own the rules in those systems so that's another big value add to write as many business users will say ok you know you think I need access to this system I don't know I'm not sure I don't know what the data looks like within it is it easily accessible is it gonna give me the reporting metrics that I need that's where governance will help them for example like our state a scientist beam using a catalog right you can browse your metadata you can look at your server your database your tables your fields understand what those mean understand the classifications the formulas within them right they're all documented in a glossary versus having to go and ask for access to six different systems throughout the enterprise hoping right that's Sally next few that told you you needed access to these systems was right just to find out that you don't need the access and hence it took you three days to get the access anyway that's why a glossary is really a catalyst a lot of that well it's really interesting what you just said about you went through essentially an application rationalization exercise which which saved your organization money that's not always easy because you know businesses even though the you know IIT may be spending money on these systems businesses don't want to give them up but you were able to use it sounds like you're able to use data to actually inform which applications you should invest in versus you know sunset as well you'd sounds like you were giving the business a real incentive to go through this exercise because they ended up as you said owning the data well then what's great right who wants pepper what's using the old power and driving a new car if they can buy the I'm sorry bull owning the old car right driving the old park if they can truly own a new car for a cheaper price nobody wants to do that I've even looked at Tesla's right I can buy a Tesla for the same prices I can buy a minivan these days I think I might buy the Tesla but what I will say is that we also use that we built out a capabilities model with our enterprise architecture team and building that capabilities model we started to bucket our technologies within those capabilities models right like AI machine learning warehouse on cloud technologies are even warehousing technologies governance technologies you know those types of classifications today integrations technologies reporting technologies by kind of grouping all those into a capabilities matrix right and was Eve it was easy for us to then start identifying alright we're the system owners for these when it comes to technologies who are the business users for these based on that right let's go talk to this team the dealer management team about access to this new profiling capability with an IBM or this new catalog with an IBM right that they can use stay versus this sharepoint excel spreadsheets they were using for their metadata management right or the profiling tools that were old you know ten years old some of our sa peoples that they were using before right let's sell them on the noodles and start migrating them that becomes pretty easy because I mean unless you're buying some really old technology when you give people a purview into those new tools and those new capabilities especially with some of the IBM's new tools we have today there the buy-in is pretty quick it's pretty easy to sell somebody on something shiny and it's much easier to use than some of the older technologies let's talk about the business impact in my understanding is you were trying to increase the improve the effectiveness of the dealers not not just go out and brute force sign up more dealers were you able to achieve that outcome and what does it meant for your business yes actually we were so right now what we did is we slipped something called a CDR and that's our consumer dealer and development repository right that's where a lot of our dealer information resides today it's actually argue ler warehouse we had some other systems that we're collecting that information Kalinin like speed for example we were able to bring all that reporting man to one location sunset some of those other technologies but then also enable for that centralized reporting layer which we've also used data virtualization to start to marry submit information to db2 warehouse on cloud for users so we're allowing basically those that want to access CDR and our db2 warehouse and called dealer information to do that within one reporting layer um in doing so we were able to create something called a dealer harmonized ID really which is our version of we have so many dealers today right and some of those dealers actually sell bytes some of those dealers sell just apparel material some of those dealers just sell parts of those dealers right can we have certain you IDs kind of a golden record mastered information if you will right bought back in reporting so that we can accurately assess the dealer performance up to two years ago right it was really hard to do that we had information spread out all over it was really hard to get a good handle on what dealers were performing and what dealers weren't because was it was tough right for our analysts to wrangle that information and bring it together it took time many times we you would get multiple answers to one business question which is never good right one one question should have one answer if it's accurate um that is what we worked on within us last year and that's where really our CEO so the value at is now we can start to act on what dealers are performing at an optimal level versus what dealers are struggling and that's allowed even our account reps or field steel fields that right to go work with those struggling dealers and start to share with them the information of you know these are what some of our stronger dealer performing dealers are doing today that is making them more affecting it inside sorry effective is selling bikes you know these are some of the best practices you can implement that's where we make right our field staff smarter and our dealers smarter we're not looking to shut down dealers we just want to educate them on how to do better well and to your point about a single version of the truth if you will the the lines of business kind of owning their own data that's critical because you're not spending all your time you know pointing at fingers trying to understand the data if the if the users own it then they own it I and so how does self-service fit in were you able to achieve you know some level of self-service how far could you and you go there we were we did use some other tools I'll be quite honest aside from just the IBM tools today that's enabled some of that self-service analytics si PSAC was one of them Alteryx is another big one that we like to that our analyst team likes to use today to wrangle and bring that data together but that really allowed for our analysts spread in our reporting teams to start to build their own derivations their transformations for reporting themselves because they're more user interface space versus going in the backend systems and having to write straight pull right sequel queries things of that nature it usually takes time then requires a deeper level of knowledge then what we'd like to allow for our analysts right to have today I can say the same thing with the data scientist scheme you know they use a lot of the R and Python coding today what we've tried to do is make sure that the tools are available so that they can do everything they need to do without us really having to touch anything and I will be quite honest we have not had to touch much of anything we have a very skilled data scientist team so I will tell you that the tools that we put in place today Watson explore some of the other tools as well they haven't that has enabled the data scientists to really quickly move do what they need to do for reporting and even in cases where maybe Watson or Explorer may not be the optimal technology right for them to use we've also allowed for them to use some of our other resources are open source resources to build some of the models that they're that they were looking to build well I'm glad you brought that up Victoria because IBM makes a big deal out of you know being open and so you're kind of confirming that you can use third-party tools and and if you like you know tool vendor ABC you can use them as part of this framework yeah it's really about TCO right so take a look at what you have today if it's giving you at least 80% of what you need for the business or for your data scientists or reporting analysts right to do what they need to do it's to me it's good enough right it's giving you what you need it's pretty hard to find anything that's exactly 100 percent it's about being open though to when you're scientists or your analysts find another reporting tool right that requires minimal maintenance or let's just say did a scientist flow that requires minimal maintenance it's free right because it's open source IBM can integrate with that and we can enable that to be a quicker way for them to do what they need to do versus telling them no right you can't use the other technologies or the other open source information out there for you today you've got to use just these spools that's pretty tough to do and I think that would shut most IT shops down pretty quick within larger enterprises because it would really act as a roadblock to allow most of our teams right to do what they need to do reporting well last question so a big part of this the data ops you know borrowing from DevOps is this continuous integration continuous improvement you know kind of ongoing MOOC raising the bar if you will what do you see going from here oh I definitely see I see a world I see a world of where we're allowing for that rapid prototyping like I was talking about earlier I see a very big change in the data industry you said it yourself right we are in the brink of big data and it's only gonna get bigger there are organizations right right now that have literally understood how much of an asset their data really is today but they're starting to sell their data ah to other of their similar people are smaller industries right similar vendors within the industry similar spaces right so they can make money off of it because data truly is an asset now the key to it that was obviously making sure that it's curated that it's cleanse that it's rusted so that when you are selling that back you can't really make money off of it but we've seen though and what I really see on the horizon is the ability to vet that data right is in the past what have you been doing the past decade or just buying big data sets we're trusting that it's you know good information we're not doing a lot of profiling at most organizations arts you're gonna pay this big top dollar you're gonna receive this third-party data set and you're not gonna be able to use it the way you need to what I see on the horizon is us being able to do that you know we're building data Lake houses if you will right we're building um really those Hadoop link environments those data lakes right where we can land information we can quickly access it we can quickly profile it with tools that it would take hours for an ALICE write a bunch of queries do to understand what the profile of that data look like we did that recently at harley-davidson we bought and some third-party data evaluated it quickly through our agile scrum team right within a week we determined that the data was not as good as it as the vendor selling it right pretty much sold it to be and so we told the vendor we want our money back the data is not what we thought it would be please take the data sets back now that's just one use case right but to me that was golden it's a way to save money and start betting the data that we're buying otherwise what I would see in the past or what I've seen in the past is many organizations are just buying up big third-party data sets and just saying okay now it's good enough we think that you know just because it comes from the motorcycle and council right for motorcycles and operation Council then it's good enough it may not be it's up to us to start vetting that and that's where technology is going to change data is going to change analytics is going to change is a great example you're really in the cutting edge of this whole data op trend really appreciate you coming on the cube and sharing your insights and there's more in the crowd chatter crowd chatter off the Thank You Victoria for coming on the cube well thank you Dave nice to meet you it was a pleasure speaking with you yeah really a pleasure was all ours and thank you for watching everybody as I say crowd chatting at flash data op or more detail more Q&A this is Dave Volante for the cube keep it right there but right back right after this short break [Music]

>> DataOps is a term derived from DevOps. The idea is to apply agile methods to your data pipeline as a way to improve data quality and reduce the elapsed time that it takes to get from a corpus of raw data to actual insights. Now, leading organizations are taking advantage of this approach really to operationalize data and evolve their data-driven culture. Many organizations struggle to deliver sustainable and meaningful business value through data. DataOps addresses this problem and is really designed to help you transform your data operation throughout the entire life cycle. Hi everybody. This is Dave Vellante. I'm with the CUBE and on May 27th we're gathering our community in a crowd chat made possible with support from IBM. Now what we're doing is we're going to hear from four data experts and these practitioners have successfully deployed DataOps within their companies. In this event, you're going to learn how they did it, what skills and processes they required and which technologies they deployed, and very importantly, how it impacted their business, so go to crowdchat.net/dataops and add the chat to your calendar. Hope to see you there. (mellow music) (upbeat music)

from the cube studios in Palo Alto in Boston connecting with thought leaders all around the world this is a cube conversation hi everybody this is David on tape with the cube and welcome to the special digital presentation we're really digging into how IBM is operationalizing and automating the AI and data pipeline not only for its clients but also for itself and with me is Julie Lochner who looks after offering management and IBM's data and AI portfolio Julie great to see you again okay great to be here thank you talk a little bit about the role you have here at IBM sure so my responsibility in offering management in the data and AI organization is really twofold one is I lead a team that implements all of the back-end processes really the operations behind anytime we deliver a product from the data AI team to the market so think about all of the release cycle management pricing product management discipline etc the other roles that I play is really making sure that um we are working with our customers and making sure they have the best customer experience and a big part of that is developing the data ops methodology it's something that I needed internally from my own line of business execution but it's now something that our customers are looking for to implement in their shops as well well good I really want to get into that and so let's let's start with data ops I mean I think you know a lot of people are familiar with DevOps not maybe not everybody's familiar with the data Ops what do we need to know about data well I mean you bring up the point that everyone knows DevOps and and then in fact I think you know what data Ops really does is bring a lot of the benefits that DevOps did for application development to the data management organizations so when we look at what is data ops it's a data management it's a it's a data management set of principles that helps organizations bring business ready data to their consumers quickly it takes it borrows from DevOps similarly where you have a data pipeline that associates a business value requirement I have this business initiative it's gonna drive this much revenue or this much cost savings this is the data that I need to be able to deliver it how do I develop that pipeline and map to the data sources know what data it is know that I can trust it so ensuring that it has the right quality that I'm actually using the data that it was meant for and then put it to use so in in history most dated management practices deployed a waterfall like methodology or implementation methodology and what that meant is all the data pipeline projects were implemented serially and it was dawn based on potentially a first-in first-out program management office with a DevOps mental model and the idea of being able to slice through all of the different silos that's required to collect the data to organize it to integrate it to validate its quality to create those data integration pipelines and then present it to the dashboard like if it's a Cognos dashboard for a operational process or even a data science team that whole end-to-end process gets streamlined through what we're calling data ops methodology so I mean as you well know we've been following this market since the early days of a dupe and people struggle with their data pipelines it's complicated for them there's a raft of tools and and and they spend most of their time wrangling data preparing data improving data quality different roles within the organization so it sounds like you know to borrow from from DevOps data OPS's is all about REME lining that data pipeline helping people really understand and communicate across end to end as you're saying but but what's the ultimate business outcome that you're trying to drive so when you think about projects that require data to again cut cost to automate a business process or drive new revenue initiatives how long does it take to get from having access to the data to making it available that duration for every time delay that is spent wasted trying to connect to data sources trying to find subject matter experts that understand what the data means and can verify its quality like all of those steps along those different teams and different disciplines introduces delay in delivering high quality data fast so the business value of data Ops is always associated with something that the business is trying to achieve but with a time element so if it's for every day we don't have this data to make a decision we're either making money or losing money that's the value proposition of data ops so it's about taking things that people are already doing today and figuring out the quickest way to do it through automation through workflows and just cutting through all of the political barriers that often happens when these data's cross different organizational boundaries yeah so speed time to insights is critical but to in and then you know with DevOps you're really bringing together the skill sets into sort of you know one super dev or one super ops it sounds with data ops it's really more about everybody understanding their role and having communication and line-of-sight across the entire organization it's not trying to make everybody a superhuman data person it's the whole it's the group it's the team effort really it's really a team game here isn't it well that's a big part of it so just like any type of practice there's people aspects process aspects and technology right so people process technology and while you're you're describing it like having that super team that knows everything about the data the only way that's possible is if you have a common foundation of metadata so we've seen a surgeons in the data catalog market and last you know six seven years and what what the what that the innovation in the data catalog market has actually enabled us to be able to drive more data ops pipelines meaning as you identify data assets you've captured the metadata you capture its meaning you capture information that can be shared whether they're stakeholders it really then becomes more of a essential repository for people to really quickly know what data they have really quickly understand what it means in its quality and very quickly with the right proper authority like privacy rules included put it to use for models you know dashboards operational processes okay and and we're gonna talk about some examples and one of them of course is ibm's own internal example but but help us understand where you advise clients to start I want to get into it where do I get started yeah I mean so traditionally what we've seen with these large data management data governance programs is that sometimes our customers feel like this is a big pill to swallow and what we've said is look there's an opera there's an opportunity here to quickly define a small project align it to a high-value business initiative target something that you can quickly gain access to the data map out these pipelines and create a squad of skills so it includes a person with DevOps type programming skills to automate an instrument a lot of the technology a subject matter expert who understands the data sources and its meaning a line of business executive who can translate bringing that information to the business project and associating with business value so when we say how do you get started we've developed a I would call it a pretty basic maturity model to help organizations figure out where are they in terms of the technology where are they in terms of organizationally knowing who the right people should be involved in these projects and then from a process perspective we've developed some pretty prescriptive project plans that help you nail down what are the data elements that are critical for this business business initiative and then we have for each role what their jobs are to consolidate the datasets map them together and present them to the consumer we find that six-week projects typically three sprints are perfect times to be able to in a timeline to create one of these very short quick win projects take that as an opportunity to figure out where your bottlenecks are in your own organization where your skill shortages are and then use the outcome of that six-week sprint to then focus on filling in gaps kick off the next project and iterate celebrate the success and promote the success because it's typically tied to a business value to help them create momentum for the next one all right that's awesome I want to now get into some examples I mean or you're we're both massachusetts-based normally you'd be in our studio and we'd be sitting here face-to-face obviously with kovat 19 in this crisis we're all sheltering in place you're up in somewhere in New England I happen to be in my studio believe it but I'm the only one here so relate this to kovat how would data ops or maybe you have a concrete example in in terms of how it's helped inform or actually anticipate and keep up-to-date with what's happening with building yeah well I mean we're all experiencing it I don't think there's a person on the planet who hasn't been impacted by what's been going on with this coded pandemic crisis so we started we started down this data obscurity a year ago I mean this isn't something that we just decided to implement a few weeks ago we've been working on developing the methodology getting our own organization in place so that we could respond the next time we needed to be able to you know act upon a data-driven decision so part of step one of our journey has really been working with our global chief data officer Interpol who I believe you have had an opportunity to meet with an interview so part of this year journey has been working with with our corporate organization I'm in the line of business organization where we've established the roles and responsibilities we've established the technology stack based on our cloud pack for data and Watson knowledge catalog so I use that as the context for now we're faced with a pandemic crisis and I'm being asked in my business unit to respond very quickly how can we prioritize the offerings that are gonna help those in critical need so that we can get those products out to market we can offer a you know 90-day free use for governments and Hospital agencies so in order for me to do that as a operations lead for our team I needed to be able to have access to our financial data I needed to have access to our product portfolio information I needed to understand our cloud capacity so in order for me to be able to respond with the offers that we recently announced you know you can take a look at some of the examples with our Watson citizen assistant program where I was able to provide the financial information required for us to make those products available for governments hospitals state agencies etc that's a that's a perfect example now to to set the stage back to the corporate global chief data office organization they implemented some technology that allowed us to ingest data automatically classify it automatically assign metadata automatically associate data quality so that when my team started using that data we knew what the status of that information was when we started to build our own predictive models and so that's a great example of how we've partnered with a corporate central organization and took advantage of the automated set of capabilities without having to invest in any additional resources or headcount and be able to release products within a matter of a couple of weeks and in that automation is a function of machine intelligence is that right and obviously some experience but but you couldn't you and I when we were consultants doing this by hand we couldn't have done this we could have done it at scale anyways it is it machine intelligence an AI that allows us to do this that's exactly right and as you know our organization is data and AI so we happen to have the a research and innovation teams that are building a lot of this technology so we have somewhat of an advantage there but you're right the alternative to what I've described is manual spreadsheets it's querying databases it's sending emails to subject matter experts asking them what this data means if they're out sick or on vacation you have to wait for them to come back and all of this was a manual process and in the last five years we've seen this data catalog market really become this augmented data catalog and that augmentation means it's automation through AI so with years of experience and natural language understanding we can comb through a lot of the metadata that's available electronically we can comb through unstructured data we can categorize it and if you have a set of business terms that have industry standard definitions through machine learning we can automate what you and I did as a consultant manually in a matter of seconds that's the impact the AI is had in our organization and now we're bringing this to the market and it's a it's a big part of where I'm investing my time both internally and externally is bringing these types of concepts and ideas to the market so I'm hearing first of all one of the things that strikes me is you've got multiple data sources and data lives everywhere you might have your supply chain data and your ERP maybe that sits on Prem you might have some sales data that's sitting in the SAS store in a cloud somewhere you might have you know a weather data that you want to bring in in theory anyway the more data that you have the better insights that you can gather assuming you've got the right data quality but so let me start with like where the data is right so so it sits anywhere you don't know where it's gonna be but you know you need it so that that's part of this right is being able to read it quickly yeah it's funny you bring it up that way I actually look a little differently it's when you start these projects the data was in one place and then by the time you get through the end of a project you find out that it's a cloud so the data location actually changes while we're in the middle of projects we have many or coming even during this this pandemic crisis we have many organizations that are using this as an opportunity to move to SAS so what was on Prem is now cloud but that shouldn't change the definition of the data it shouldn't change its meaning it might change how you connect to it um it might also change your security policies or privacy laws now all of a sudden you have to worry about where is that data physically located and am I allowed to share it across national boundaries right before we knew physically where it was so when you think about data ops data ops is a process that sits on top of where the data physically resides and because we're mapping metadata and we're looking at these data pipelines and automated workflows part of the design principles are to set it up so that it's independent of where it resides however you have to have placeholders in your metadata and in your tool chain where we oughta mating these workflows so that you can accommodate when the data decides to move because of corporate policy change from on-prem to cloud then that's a big part of what data Ops offers it's the same thing by the way for DevOps they've had to accommodate you know building in you know platforms as a service versus on from the development environments it's the same for data ops and you know the other part that strikes me and listening to you is scale and it's not just about you know scale with the cloud operating model it's also about what you're talking about is you know the auto classification the automated metadata you can't do that manually you've got to be able to do that in order to scale with automation that's another key part of data Ops is it not it's well it's a big part of the value proposition and a lot of a part of the business base right then you and I started in this business you know and Big Data became the thing people just move all sorts of data sets to these Hadoop clusters without capturing the metadata and so as a result you know in the last 10 years this information is out there but nobody knows what it means anymore so you can't go back with the army of people and have them query these data sets because a lot of the contact was lost but you can use automated technology you can use automated machine learning with natural under Snatcher Alang guaa Jing to do a lot of the heavy lifting for you and a big part of data ops workflows and building these pipelines is to do what we call management-by-exception so if your algorithms say you know 80% confident that this is a phone number and your organization has a you know low risk tolerance that probably will go to an exception but if you have a you know a match algorithm that comes back and says it's 99 percent sure this is an email address right and you I have a threshold that's 98% it will automate much of the work that we used to have to do manually so that's an example of how you can automate eliminate manual work and have some human interaction based on your risk threshold now that's awesome I mean you're right the no schema on right said I throw it into a data leg the data link becomes the data swap we all know that joke okay I want to understand a little bit and maybe you have some other examples of some of the use cases here but there's some of the maturity of where customers are I mean it seems like you got to start by just understanding what data you have cataloging it you're getting your metadata act in order but then you've got a you've got a data quality component before you can actually implement and get yet to insight so you know where our customers on the on the maturity model do you have any other examples that you can share yeah so when we look at our data ops maturity model we tried to simplify it I mentioned this earlier that we try to simplify it so that really anybody can get started they don't have to have a full governance framework implemented to take advantage of the benefits data ops delivers so what we did we said if you can categorize your data ops programs into really three things one is how well do you know your data do you even know what data you have the second one is and you trust it like can you trust its quality can you trust its meeting and the third one is can you put it to use so if you really think about it when you begin with what data do you know right the first step is you know how are you determining what data you know the first step is if you are using spreadsheets replace it with a data catalog if you have a department line of business catalog and you need to start sharing information with the departments then start expanding to an enterprise level data catalog now you mentioned data quality so the first step is do you even have a data quality program right have you even established what your criteria are for high quality data have you considered what your data quality score is comprised of have you mapped out what your critical data elements are to run your business most companies have done that for they're they're governed processes but for these new initiatives and when you identify I'm in my example with the Kovach crisis what products are we gonna help bring to market quickly I need to be able to find out what the critical data elements are and can I trust it have I even done a quality scan and have teams commented on its trustworthiness to be used in this case if you haven't done anything like that in your organization that might be the first place to start pick the critical data elements for this initiative assess its quality and then start to implement the workflows to remediate and then when you get to putting it to use there's several methods for making data available you know one is simply making a data Mart available to a small set of users that's what most people do well first they make a spreadsheet of the data available but then if they need to have multiple people access it that's when like a data Mart might make sense technology like data virtualization eliminates the need for you to move data as you're in this prototyping phase and that's a great way to get started it doesn't cost a lot of money to get a virtual query set up to see if this is the right join or the right combination of fields that are required for this use case eventually you'll get to the need to use a high performance ETL tool for data integration but Nirvana is when you really get to that self-service data prep where users can query a catalog and say these are the data sets I need it presents you a list of data assets that are available I can point and click at these columns I want as part of my you know data pipeline and I hit go and it automatically generates that output for data science use cases for a Cognos dashboard right that's the most mature model and being able to iterate on that so quickly that as soon as you get feedback that that data elements are wrong or you need to add something you can do it push button and that's where data observation to bring organizations to well Julie I think there's no question that this kovat crisis is accentuated the importance of digital you know we talk about digital transformation a lot and it's it's certainly real although I would say a lot of people that we talk to will say well you know not on my watch or I'll be retired before that all happens will this crisis is accelerating that transformation and data is at the heart of it you know digital means data and if you don't have your data you know story together and your act together then you're gonna you're not going to be able to compete and data ops really is a key aspect of that so you know give us a parting word all right I think this is a great opportunity for us to really assess how well we're leveraging data to make strategic decisions and if there hasn't been a more pressing time to do it it's when our entire engagement becomes virtual like this interview is virtual write everything now creates a digital footprint that we can leverage to understand where our customers are having problems where they're having successes you know let's use the data that's available and use data ops to make sure that we can iterate access that data know it trust it put it to use so that we can respond to those in need when they need it Julie Locker your incredible practitioner really hands-on really appreciate you coming on the Kuban and sharing your knowledge with us thank you okay thank you very much it was a pleasure to be here all right and thank you for watching everybody this is Dave Volante for the cube and we will see you next time [Music]

>>from the Cube Studios in >>Palo Alto and Boston connecting with thought leaders all around the world. This is a cube conversation. Everybody welcome this special digital presentation where we're covering the topic of data ops and specifically how IBM is really operationalize ing and automating the data pipeline with data office. And with me is Interpol Bhandari, who is the global chief data officer at IBM and Paul. It's always great to see you. Thanks for coming on. >>My pleasure. >>So, you know the standard throwaway question from guys like me And you know what keeps the chief data officer up at night? Well, I know what's keeping you up that night. It's coverted 19. How you >>doing? It's keeping keeping all of us. >>Yeah, for sure. Um, So how are you guys making out as a leader I'm interested in, You know, how you have responded would whether it's communications. Obviously you're doing much more stuff you remotely You're not on airplanes. Certainly like you used to be. But But what was your first move when you actually realized this was going to require a shift? >>Well, I think one of the first things that I did wants to test the ability of my organization, You work remotely. This was well before the the recommendations came in from the government just so that we wanted to be sure that this is something that we could pull off if there were extreme circumstances where even everybody was. And so that was one of the first things we did along with that. I think another major activity that's being boxed off is even that we have created this Central Data and AI platform for idea using our hybrid, multi cloud approach. How could that the adaptive very, very quickly help them look over the city? But those were the two big items that my team and my embarked on and again, like I said, this is before there was any recommendations from the government or even internally, within IBM. Have any recommendations be? We decided that we wanted to run ahead and make sure that we were ready to ready to operate in that fashion. And I believe a lot of my colleagues did the same. Yeah, >>there's a there's a conversation going on right now just around productivity hits that people may be taking because they really weren't prepared it sounds like you're pretty comfortable with the productivity impact that you're achieving. >>Oh, I'm totally comfortable with the politics. I mean, in fact, I will tell you that while we've gone down this spot, we've realized that in some cases the productivity is actually going to be better when people are working from home and they're able to focus a lot more on the work, you know, And this could. This one's the gamut from the nature of the jaw, where you know somebody who basically needs to be in the front of the computer and is remotely taking care of operations. You know, if they don't have to come in, their productivity is going to go up Somebody like myself who had a long drive into work, you know, which I would use a phone calls, but that that entire time it can be used a lot more productivity, locked in a lot more productive manner. So there is. We realized that there's going to be some aspect of productivity that will actually be helped by the situation. Why did you are able to deliver the services that you deliver with the same level of quality and satisfaction that you want Now there were certain other aspect where you know the whole activity is going to be effective. So you know my team. There's a lot off white boarding that gets done there lots off informal conversations that spot creativity. But those things are much harder to replicate in a remote and large. So we've got a sense off. You know where we have to do some work? Well, things together. This is where we're actually going to be mobile. But all in all, they're very comfortable that we can pull this off. >>That's great. I want to stay on Cove it for a moment and in the context of just data and data ops, and you know why Now, obviously, with a crisis like this, it increases the imperative to really have your data act together. But I want to ask you both specifically as it relates to covert, why Data office is so important. And then just generally, why at this this point in time, >>So, I mean, you know, the journey we've been on. Thank you. You know, when I joined our data strategy centered around cloud data and ai, mainly because IBM business strategy was around that, and because there wasn't the notion off AI and Enterprise, right, there was everybody understood what AI means for the consumer. But for the enterprise, people don't really understand. Well, what a man. So our data strategy became one off, actually making IBM itself into an AI and and then using that as a showcase for our clients and customers who look a lot like us, you make them into AI. And in a nutshell, what that translated to was that one had two in few ai into the workflow off the key business processes off enterprise. So if you think about that workflow is very demanding, right, you have to be able to deliver. They did not insights on time just when it's needed. Otherwise, you can essentially slow down the whole workflow off a major process within an end. But to be able to pull all that off you need to have your own data works very, very streamlined so that a lot of it is automated and you're able to deliver those insights as the people who are involved in the work floor needed. So we've spent a lot of time while we were making IBM into any I enterprise and infusing AI into our key business processes into essentially a data ops pipeline that was very, very streamlined, which then allowed us to do very quickly adapt do the over 19 situation and I'll give you one specific example that will go to you know how one would someone would essentially leverage that capability that I just talked about to do this. So one of the key business processes that we have taken a map, it was our supply chain. You know, if you're a global company and our supply chain is critical, you have lots of suppliers, and they are all over the globe. And we have different types of products so that, you know, has a multiplication factors for each of those, you have additional suppliers and you have events. You have other events, you have calamities, you have political events. So we have to be able to very quickly understand the risks associated with any of those events with regard to our supply chain and make appropriate adjustments on the fly. So that was one off the key applications that we built on our central data. And as Paul about data ops pipeline. That meant we ingest the ingestion off those several 100 sources of data not to be blazingly fast and also refresh very, very quickly. Also, we have to then aggregate data from the outside from external sources that had to do with weather related events that had to do with political events. Social media feeds a separate I'm overly that on top off our map of interest with regard to our supply chain sites and also where they were supposed to deliver. We also leave them our capabilities here, track of those shipments as they flowed and have that data flow back as well so that we would know exactly where where things were. This is only possible because we had a streamline data ops capability and we have built this Central Data and AI platform for IBM. Now you flip over to the Coleman 19 situation when Corbyn 19 merged and we began to realize that this was going to be a significant significant pandemic. What we were able to do very quickly wants to overlay the over 19 incidents on top of our sites of interest, as well as pick up what was being reported about those sites of interests and provide that over to our business continuity. So this became an immediate exercise that we embark. But it wouldn't have been possible if you didn't have the foundation off the data office pipeline as well as that Central Data and AI platform even plays to help you do that very, very quickly and adapt. >>So what I really like about this story and something that I want to drill into is it Essentially, a lot of organizations have a really tough time operational izing ai, infusing it to use your word and the fact that you're doing it, um is really a good proof point that I want to explore a little bit. So you're essentially there was a number of aspects of what you just described. There was the data quality piece with your data quality in theory, anyway, is going to go up with more data if you can handle it and the other was speed time to insight, so you can respond more quickly if it's talk about this Covic situation. If you're days behind for weeks behind, which is not uncommon, sometimes even worse, you just can't respond. I mean, the things change daily? Um, sometimes, Certainly within the day. Um, so is that right? That's kind of the the business outcome. An objective that you guys were after. >>Yes, you know, So Rama Common infuse ai into your business processes right over our chain. Um, don't come metric. That one focuses on is end to end cycle time. So you take that process the end to end process and you're trying to reduce the end to end cycle time by several factors, several orders of magnitude. And you know, there are some examples off things that we did. For instance, in my organ organization that has to do with the generation of metadata is data about data. And that's usually a very time consuming process. And we've reduced that by over 95%. By using AI, you actually help in the metadata generation itself. And that's applied now across the board for many different business processes that, you know IBM has. That's the same kind of principle that was you. You'll be able to do that so that foundation essentially enables you to go after that cycle time reduction right off the bat. So when you get to a situation like over 19 situation which demands urgent action. Your foundation is already geared to deliver on that. >>So I think actually, we might have a graphic. And then the second graphic, guys, if you bring up a 2nd 1 I think this is Interpol. What you're talking about here, that sort of 95% reduction. Ah, guys, if you could bring that up, would take a look at it. So, um, this is maybe not a cove. It use case? Yeah. Here it is. So that 95% reduction in the cycle time improvement in data quality. What we talked about this actually some productivity metrics, right? This is what you're talking about here in this metadata example. Correct? >>Yeah. Yes, the metadata. Right. It's so central to everything that one does with. I mean, it's basically data about data, and this is really the business metadata that you're talking about, which is once you have data in your data lake. If you don't have business metadata describing what that data is, then it's very hard for people who are trying to do things to determine whether they can, even whether they even have access to the right data. And typically this process is being done manually because somebody looks at the data that looks at the fields and describe it. And it could easily take months. And what we did was we essentially use a deep learning and natural language processing of road. Look at all the data that we've had historically over an idea, and we've automated metadata generation. So whether it was, you know, you were talking about the data relevant for 19 or for supply chain or far receivable process any one of our business processes. This is one of those fundamental steps that one must go through. You'll be able to get your data ready for action. And if you were able to take that cycle time for that step and reduce it by 95% you can imagine the acceleration. >>Yeah, and I like you were saying before you talk about the end to end concept, you're applying system thinking here, which is very, very important because, you know, a lot of a lot of clients that I talk to, they're so focused on one metric maybe optimizing one component of that end to end, but it's really the overall outcome that you're trying to achieve. You may sometimes, you know, be optimizing one piece, but not the whole. So that systems thinking is very, very important, isn't it? >>The systems thinking is extremely important overall, no matter you know where you're involved in the process off designing the system. But if you're the data guy, it's incredibly important because not only does that give you an insight into the cycle time reduction, but it also give clues U N into what standardization is necessary in the data so that you're able to support an eventual out. You know, a lot of people will go down the part of data governance and the creation of data standards, and you can easily boil the ocean trying to do that. But if you actually start with an end to end, view off your key processes and that by extension the outcomes associated with those processes as well as the user experience at the end of those processes and kind of then work backwards as one of the standards that you need for the data that's going to feed into all that, that's how you arrive at, you know, a viable practical data standards effort that you can essentially push forward so that there are multiple aspect when you take that end to end system view that helps the chief legal. >>One of the other tenants of data ops is really the ability across the organization for everybody to have visibility. Communications is very key. We've got another graphic that I want to show around the organizational, you know, in the right regime, and it's a complicated situation for a lot of people. But it's imperative, guys, if you bring up the first graphic, it's a heritage that organizations, you know, find bringing the right stakeholders and actually identify those individuals that are going to participate so that this full visibility everybody understands what their roles are. They're not in silos. So, guys, if you could show us that first graphic, that would be great. But talk about the organization and the right regime there. Interpol? >>Yes, yes, I believe you're going to know what you're going to show up is actually my organization, but I think it's yes, it's very, very illustrative what one has to set up. You'll be able to pull off the kind of impact that I thought So let's say we talked about that Central Data and AI platform that's driving the entire enterprise, and you're infusing AI into key business processes like the supply chain. Then create applications like the operational risk in size that we talked about that extended over. Do a fast emerging and changing situation like the over 19. You need an organization that obviously reflects the technical aspects of the right, so you have to have the data engineering on and AI on. You know, in my case, there's a lot of emphasis around deep learning because that's one of those skill set areas that's really quite rare, and it also very, very powerful. So uh huh you know, the major technology arms off that. There's also the governance on that I talked about. You have to produce the set off standards and implement them and enforce them so that you're able to make this into an impact. But then there's also there's a there's an adoption there. There's a There's a group that reports into me very, very, you know, Empowered Group, which essentially has to convince the rest of the organization to adopt. Yeah, yeah, but the key to their success has been in power in the sense that they're on power. You find like minded individuals in our key business processes. We're also empowered. And if they agree that just move forward and go and do it because you know, we've already provided the central capabilities by Central. I don't mean they're all in one location. You're completely global and you know it's it's It's a hybrid multi cloud set up, but it's a central in the sense that it's one source to come for for trusted data as well as the the expertise that you need from an AI standpoint to be able to move forward and deliver the business out. So when these business teams come together, be an option, that's where the magic happens. So that's another another aspect of the organization that's critical. And then we've also got, ah, Data Officer Council that I chair, and that has to do with no people who are the chief data officers off the individual business units that we have. And they're kind of my extended teams into the rest of the organization, and we levers that bolt from a adoption off the platform standpoint. But also in terms of defining and enforcing standards. It helps them stupid. >>I want to come back over and talk a little bit about business resiliency people. I think it probably seen the news that IBM providing supercomputer resource is that the government to fight Corona virus. You've also just announced that that some some RTP folks, um, are helping first responders and non profits and providing capabilities for no charge, which is awesome. I mean, it's the kind of thing. Look, I'm sensitive companies like IBM. You know, you don't want to appear to be ambulance chasing in these times. However, IBM and other big tech companies you're in a position to help, and that's what you're doing here. So maybe you could talk a little bit about what you're doing in this regard. Um, and then we'll tie it up with just business resiliency and importance of data. >>Right? Right. So, you know, I explained that the operational risk insights application that we had, which we were using internally, we call that 19 even we're using. We're using it primarily to assess the risks to our supply chain from various events and then essentially react very, very quickly. Do those doodles events so you could manage the situation. Well, we realize that this is something that you know, several non government NGOs that they could essentially use. There's a stability because they have to manage many of these situations like natural disaster. And so we've given that same capability, do the NGOs to you and, uh, to help that, to help them streamline their planning. And there's thinking, by the same token, But you talked about over 19 that same capability with the moment 19 data over layed on double, essentially becomes a business continuity, planning and resilience. Because let's say I'm a supply chain offers right now. I can look at incidents off over night, and I can I know what my suppliers are and I can see the incidents and I can say, Oh, yes, no, this supplier and I can see that the incidences going up this is likely to be affected. Let me move ahead and stop making plans backup plans, just in case it reaches a crisis level. On the other hand, if you're somebody in revenue planning, you know, on the finance side and you know where you keep clients and customers are located again by having that information over laid that those sites, you can make your own judgments and you can make your own assessment to do that. So that's how it translates over into business continuity and resolute resilience planning. True, we are internally. No doing that now to every department. You know, that's something that we're actually providing them this capability because we build rapidly on what we have already done to be able to do that as we get inside into what each of those departments do with that data. Because, you know, once they see that data, once they overlay it with their sights of interest. And this is, you know, anybody and everybody in IBM, because no matter what department they're in, there are going to decide the interests that are going to be affected. And they haven't understanding what those sites of interest mean in the context off the planning that they're doing and so they'll be able to make judgments. But as we get a better understanding of that, we will automate those capabilities more and more for each of those specific areas. And now you're talking about the comprehensive approach and AI approach to business continuity and resilience planning in the context of a large IT organization like IBM, which obviously will be of great interest to our enterprise, clients and customers. >>Right? One of the things that we're researching now is trying to understand. You know, what about this? Prices is going to be permanent. Some things won't be, but we think many things will be. There's a lot of learnings. Do you think that organizations will rethink business resiliency in this context that they might sub optimize profitability, for example, to be more prepared crises like this with better business resiliency? And what role would data play in that? >>So, you know, it's a very good question and timely fashion, Dave. So I mean, clearly, people have understood that with regard to that's such a pandemic. Um, the first line of defense, right is is not going to be so much on the medicine side because the vaccine is not even available and will be available for a period of time. It has to go through. So the first line of defense is actually think part of being like approach, like we've seen play out across the world and then that in effect results in an impact on the business, right in the economic climate and on the business is there's an impact. I think people have realized this now they will honestly factor this in and do that in to how they do become. One of those things from this is that I'm talking about how this becomes a permanent. I think it's going to become one of those things that if you go responsible enterprise, you are going to be landing forward. You're going to know how to implement this, the on the second go round. So obviously you put those frameworks and structures in place and there will be a certain costs associated with them, and one could argue that that would eat into the profitability. On the other hand, what I would say is because these two points really that these are fast emerging fluid situations. You have to respond very, very quickly. You will end up laying out a foundation pretty much like we did, which enables you to really accelerate your pipeline, right? So the data ops pipelines we talked about, there's a lot of automation so that you can react very quickly, you know, data injection very, very rapidly that you're able to do that kind of thing, that meta data generation. That's the entire pipeline that you're talking about, that you're able to respond very quickly, bring in new data and then aggregated at the right levels, infuse it into the work flows on the delivery, do the right people at the right time. Well, you know that will become a must. But once you do that, you could argue that there's a cost associated with doing that. But we know that the cycle time reductions on things like that they can run, you know? I mean, I gave you the example of 95% 0 you know, on average, we see, like a 70% end to end cycle time where we've implemented the approach, and that's been pretty pervasive within IBM across the business. So that, in essence, then actually becomes a driver for profitability. So yes, it might. You know this might back people into doing that, but I would argue that that's probably something that's going to be very good long term for the enterprises and world, and they'll be able to leverage that in their in their business and I think that just the competitive director off having to do that will force everybody down that path. But I think it'll be eventually ago >>that end and cycle time. Compression is huge, and I like what you're saying because it's it's not just a reduction in the expected loss during of prices. There's other residual benefits to the organization. Interpol. Thanks so much for coming on the Cube and sharing this really interesting and deep case study. I know there's a lot more information out there, so really appreciate your done. >>My pleasure. >>Alright, take everybody. Thanks for watching. And this is Dave Volante for the Cube. And we will see you next time. Yeah, yeah, yeah.

(upbeat music) >> Hey guys, welcome back to the show floor of KubeCon + CloudNativeCon '22 North America from Detroit, Michigan. Lisa Martin here with John Furrier. This is day one, John at theCUBE's coverage. >> CUBE's coverage. >> theCUBE's coverage of KubeCon. Try saying that five times fast. Day one, we have three wall-to-wall days. We've been talking about Kubernetes, containers, adoption, cloud adoption, app modernization all morning. We can't talk about those things without addressing security. >> Yeah, this segment we're going to hear container and Kubernetes security for modern application 'cause the enterprise are moving there. And this segment with Red Hat's going to be important because they are the leader in the enterprise when it comes to open source in Linux. So this is going to be a very fun segment. >> Very fun segment. Two guests from Red Hat join us. Please welcome Doron Caspin, Senior Principal Product Manager at Red Hat. Michael Foster joins us as well, Principal Product Marketing Manager and StackRox Community Lead at Red Hat. Guys, great to have you on the program. >> Thanks for having us. >> Thank you for having us. >> It's awesome. So Michael StackRox acquisition's been about a year. You got some news? >> Yeah, 18 months. >> Unpack that for us. >> It's been 18 months, yeah. So StackRox in 2017, originally we shifted to be the Kubernetes-native security platform. That was our goal, that was our vision. Red Hat obviously saw a lot of powerful, let's say, mission statement in that, and they bought us in 2021. Pre-acquisition we were looking to create a cloud service. Originally we ran on Kubernetes platforms, we had an operator and things like that. Now we are looking to basically bring customers in into our service preview for ACS as a cloud service. That's very exciting. Security conversation is top notch right now. It's an all time high. You can't go with anywhere without talking about security. And specifically in the code, we were talking before we came on camera, the software supply chain is real. It's not just about verification. Where do you guys see the challenges right now? Containers having, even scanning them is not good enough. First of all, you got to scan them and that may not be good enough. Where's the security challenges and where's the opportunity? >> I think a little bit of it is a new way of thinking. The speed of security is actually does make you secure. We want to keep our images up and fresh and updated and we also want to make sure that we're keeping the open source and the different images that we're bringing in secure. Doron, I know you have some things to say about that too. He's been working tirelessly on the cloud service. >> Yeah, I think that one thing, you need to trust your sources. Even if in the open source world, you don't want to copy paste libraries from the web. And most of our customers using third party vendors and getting images from different location, we need to trust our sources and we have a really good, even if you have really good scanning solution, you not always can trust it. You need to have a good solution for that. >> And you guys are having news, you're announcing the Red Hat Advanced Cluster Security Cloud Service. >> Yes. >> What is that? >> So we took StackRox and we took the opportunity to make it as a cloud services so customer can consume the product as a cloud services as a start offering and customer can buy it through for Amazon Marketplace and in the future Azure Marketplace. So customer can use it for the AKS and EKS and AKS and also of course OpenShift. So we are not specifically for OpenShift. We're not just OpenShift. We also provide support for EKS and AKS. So we provided the capability to secure the whole cloud posture. We know customer are not only OpenShift or not only EKS. We have both. We have free cloud or full cloud. So we have open. >> So it's not just OpenShift, it's Kubernetes, environments, all together. >> Doron: All together, yeah. >> Lisa: Meeting customers where they are. >> Yeah, exactly. And we focus on, we are not trying to boil the ocean or solve the whole cloud security posture. We try to solve the Kubernetes security cluster. It's very unique and very need unique solution for that. It's not just added value in our cloud security solution. We think it's something special for Kubernetes and this is what Red that is aiming to. To solve this issue. >> And the ACS platform really doesn't change at all. It's just how they're consuming it. It's a lot quicker in the cloud. Time to value is right there. As soon as you start up a Kubernetes cluster, you can get started with ACS cloud service and get going really quickly. >> I'm going to ask you guys a very simple question, but I heard it in the bar in the lobby last night. Practitioners talking and they were excited about the Red Hat opportunity. They actually asked a question, where do I go and get some free Red Hat to test some Kubernetes out and run helm or whatever. They want to play around. And do you guys have a program for someone to get start for free? >> Yeah, so the cloud service specifically, we're going to service preview. So if people sign up, they'll be able to test it out and give us feedback. That's what we're looking for. >> John: Is that a Sandbox or is that going to be in the cloud? >> They can run it in their own environment. So they can sign up. >> John: Free. >> Doron: Yeah, free. >> For the service preview. All we're asking for is for customer feedback. And I know it's actually getting busy there. It's starting December. So the quicker people are, the better. >> So my friend at the lobby I was talking to, I told you it was free. I gave you the sandbox, but check out your cloud too. >> And we also have the open source version so you can download it and use it. >> Yeah, people want to know how to get involved. I'm getting a lot more folks coming to Red Hat from the open source side that want to get their feet wet. That's been a lot of people rarely interested. That's a real testament to the product leadership. Congratulations. >> Yeah, thank you. >> So what are the key challenges that you have on your roadmap right now? You got the products out there, what's the current stake? Can you scope the adoption? Can you share where we're at? What people are doing specifically and the real challenges? >> I think one of the biggest challenges is talking with customers with a slightly, I don't want to say outdated, but an older approach to security. You hear things like malware pop up and it's like, well, really what we should be doing is keeping things into low and medium vulnerabilities, looking at the configuration, managing risk accordingly. Having disparate security tools or different teams doing various things, it's really hard to get a security picture of what's going on in the cluster. That's some of the biggest challenges that we talk with customers about. >> And in terms of resolving those challenges, you mentioned malware, we talk about ransomware. It's a household word these days. It's no longer, are we going to get hit? It's when? It's what's the severity? It's how often? How are you guys helping customers to dial down some of the risk that's inherent and only growing these days? >> Yeah, risk, it's a tough word to generalize, but our whole goal is to give you as much security information in a way that's consumable so that you can evaluate your risk, set policies, and then enforce them early on in the cluster or early on in the development pipeline so that your developers get the security information they need, hopefully asynchronously. That's the best way to do it. It's nice and quick, but yeah. I don't know if Doron you want to add to that? >> Yeah, so I think, yeah, we know that ransomware, again, it's a big world for everyone and we understand the area of the boundaries where we want to, what we want to protect. And we think it's about policies and where we enforce it. So, and if you can enforce it on, we know that as we discussed before that you can scan the image, but we never know what is in it until you really run it. So one of the thing that we we provide is runtime scanning. So you can scan and you can have policy in runtime. So enforce things in runtime. But even if one image got in a way and get to your cluster and run on somewhere, we can stop it in runtime. >> Yeah. And even with the runtime enforcement, the biggest thing we have to educate customers on is that's the last-ditch effort. We want to get these security controls as early as possible. That's where the value's going to be. So we don't want to be blocking things from getting to staging six weeks after developers have been working on a project. >> I want to get you guys thoughts on developer productivity. Had Docker CEO on earlier and since then I had a couple people messaging me. Love the vision of Docker, but Docker Hub has some legacy and it might not, has does something kind of adoption that some people think it does. Are people moving 'cause there times they want to have these their own places? No one place or maybe there is, or how do you guys see the movement of say Docker Hub to just using containers? I don't need to be Docker Hub. What's the vis-a-vis competition? >> I mean working with open source with Red Hat, you have to meet the developers where they are. If your tool isn't cutting it for developers, they're going to find a new tool and really they're the engine, the growth engine of a lot of these technologies. So again, if Docker, I don't want to speak about Docker or what they're doing specifically, but I know that they pretty much kicked off the container revolution and got this whole thing started. >> A lot of people are using your environment too. We're hearing a lot of uptake on the Red Hat side too. So, this is open source help, it all sorts stuff out in the end, like you said, but you guys are getting a lot of traction there. Can you share what's happening there? >> I think one of the biggest things from a developer experience that I've seen is the universal base image that people are using. I can speak from a security standpoint, it's awesome that you have a base image where you can make one change or one issue and it can impact a lot of different applications. That's one of the big benefits that I see in adoption. >> What are some of the business, I'm curious what some of the business outcomes are. You talked about faster time to value obviously being able to get security shifted left and from a control perspective. but what are some of the, if I'm a business, if I'm a telco or a healthcare organization or a financial organization, what are some of the top line benefits that this can bubble up to impact? >> I mean for me, with those two providers, compliance is a massive one. And just having an overall look at what's going on in your clusters, in your environments so that when audit time comes, you're prepared. You can get through that extremely quickly. And then as well, when something inevitably does happen, you can get a good image of all of like, let's say a Log4Shell happens, you know exactly what clusters are affected. The triage time is a lot quicker. Developers can get back to developing and then yeah, you can get through it. >> One thing that we see that customers compliance is huge. >> Yes. And we don't want to, the old way was that, okay, I will provision a cluster and I will do scans and find things, but I need to do for PCI DSS for example. Today the customer want to provision in advance a PCI DSS cluster. So you need to do the compliance before you provision the cluster and make all the configuration already baked for PCI DSS or HIPAA compliance or FedRAMP. And this is where we try to use our compliance, we have tools for compliance today on OpenShift and other clusters and other distribution, but you can do this in advance before you even provision the cluster. And we also have tools to enforce it after that, after your provision, but you have to do it again before and after to make it more feasible. >> Advanced cluster management and the compliance operator really help with that. That's why OpenShift Platform Plus as a bundle is so popular. Just being able to know that when a cluster gets provision, it's going to be in compliance with whatever the healthcare provider is using. And then you can automatically have ACS as well pop up so you know exactly what applications are running, you know it's in compliance. I mean that's the speed. >> You mentioned the word operator, I get triggering word now for me because operator role is changing significantly on this next wave coming because of the automation. They're operating, but they're also devs too. They're developing and composing. It's almost like a dashboard, Lego blocks. The operator's not just manually racking and stacking like the old days, I'm oversimplifying it, but the new operators running stuff, they got observability, they got coding, their servicing policy. There's a lot going on. There's a lot of knobs. Is it going to get simpler? How do you guys see the org structures changing to fill the gap on what should be a very simple, turn some knobs, operate at scale? >> Well, when StackRox originally got acquired, one of the first things we did was put ACS into an operator and it actually made the application life cycle so much easier. It was very easy in the console to go and say, Hey yeah, I want ACS my cluster, click it. It would get provisioned. New clusters would get provisioned automatically. So underneath it might get more complicated. But in terms of the application lifecycle, operators make things so much easier. >> And of course I saw, I was lucky enough with Lisa to see Project Wisdom in AnsibleFest. You going to say, Hey, Red Hat, spin up the clusters and just magically will be voice activated. Starting to see AI come in. So again, operations operator is got to dev vibe and an SRE vibe, but it's not that direct. Something's happening there. We're trying to put our finger on. What do you guys think is happening? What's the real? What's the action? What's transforming? >> That's a good question. I think in general, things just move to the developers all the time. I mean, we talk about shift left security, everything's always going that way. Developers how they're handing everything. I'm not sure exactly. Doron, do you have any thoughts on that. >> Doron, what's your reaction? You can just, it's okay, say what you want. >> So I spoke with one of our customers yesterday and they say that in the last years, we developed tons of code just to operate their infrastructure. That if developers, so five or six years ago when a developer wanted VM, it will take him a week to get a VM because they need all their approval and someone need to actually provision this VM on VMware. And today they automate all the way end-to-end and it take two minutes to get a VM for developer. So operators are becoming developers as you said, and they develop code and they make the infrastructure as code and infrastructure as operator to make it more easy for the business to run. >> And then also if you add in DataOps, AIOps, DataOps, Security Ops, that's the new IT. It seems to be the new IT is the stuff that's scaling, a lot of data's coming in, you got security. So all that's got to be brought in. How do you guys view that into the equation? >> Oh, I mean you become big generalists. I think there's a reason why those cloud security or cloud professional certificates are becoming so popular. You have to know a lot about all the different applications, be able to code it, automate it, like you said, hopefully everything as code. And then it also makes it easy for security tools to come in and look and examine where the vulnerabilities are when those things are as code. So because you're going and developing all this automation, you do become, let's say a generalist. >> We've been hearing on theCUBE here and we've been hearing the industry, burnout, associated with security professionals and some DataOps because the tsunami of data, tsunami of breaches, a lot of engineers getting called in the middle of the night. So that's not automated. So this got to get solved quickly, scaled up quickly. >> Yes. There's two part question there. I think in terms of the burnout aspect, you better send some love to your security team because they only get called when things get broken and when they're doing a great job you never hear about them. So I think that's one of the things, it's a thankless profession. From the second part, if you have the right tools in place so that when something does hit the fan and does break, then you can make an automated or a specific decision upstream to change that, then things become easy. It's when the tools aren't in place and you have desperate environments so that when a Log4Shell or something like that comes in, you're scrambling trying to figure out what clusters are where and where you're impacted. >> Point of attack, remediate fast. That seems to be the new move. >> Yeah. And you do need to know exactly what's going on in your clusters and how to remediate it quickly, how to get the most impact with one change. >> And that makes sense. The service area is expanding. More things are being pushed. So things will, whether it's a zero day vulnerability or just attack. >> Just mix, yeah. Customer automate their all of things, but it's good and bad. Some customer told us they, I think Spotify lost the whole a full zone because of one mistake of a customer because they automate everything and you make one mistake. >> It scale the failure really. >> Exactly. Scaled the failure really fast. >> That was actually few contact I think four years ago. They talked about it. It was a great learning experience. >> It worked double edge sword there. >> Yeah. So definitely we need to, again, scale automation, test automation way too, you need to hold the drills around data. >> Yeah, you have to know the impact. There's a lot of talk in the security space about what you can and can't automate. And by default when you install ACS, everything is non-enforced. You have to have an admission control. >> How are you guys seeing your customers? Obviously Red Hat's got a great customer base. How are they adopting to the managed service wave that's coming? People are liking the managed services now because they maybe have skills gap issues. So managed service is becoming a big part of the portfolio. What's your guys' take on the managed services piece? >> It's just time to value. You're developing a new application, you need to get it out there quick. If somebody, your competitor gets out there a month before you do, that's a huge market advantage. >> So you care how you got there. >> Exactly. And so we've had so much Kubernetes expertise over the last 10 or so, 10 plus year or well, Kubernetes for seven plus years at Red Hat, that why wouldn't you leverage that knowledge internally so you can get your application. >> Why change your toolchain and your workflows go faster and take advantage of the managed service because it's just about getting from point A to point B. >> Exactly. >> Well, in time to value is, you mentioned that it's not a trivial term, it's not a marketing term. There's a lot of impact that can be made. Organizations that can move faster, that can iterate faster, develop what their customers are looking for so that they have that competitive advantage. It's definitely not something that's trivial. >> Yeah. And working in marketing, whenever you get that new feature out and I can go and chat about it online, it's always awesome. You always get customers interests. >> Pushing new code, being secure. What's next for you guys? What's on the agenda? What's around the corner? We'll see a lot of Red Hat at re:Invent. Obviously your relationship with AWS as strong as a company. Multi-cloud is here. Supercloud as we've been saying. Supercloud is a thing. What's next for you guys? >> So we launch the cloud services and the idea that we will get feedback from customers. We are not going GA. We're not going to sell it for now. We want to get customers, we want to get feedback to make the product as best what we can sell and best we can give for our customers and get feedback. And when we go GA and we start selling this product, we will get the best product in the market. So this is our goal. We want to get the customer in the loop and get as much as feedback as we can. And also we working very closely with our customers, our existing customers to announce the product to add more and more features what the customer needs. It's all about supply chain. I don't like it, but we have to say, it's all about making things more automated and make things more easy for our customer to use to have security in the Kubernetes environment. >> So where can your customers go? Clearly, you've made a big impact on our viewers with your conversation today. Where are they going to be able to go to get their hands on the release? >> So you can find it on online. We have a website to sign up for this program. It's on my blog. We have a blog out there for ACS cloud services. You can just go there, sign up, and we will contact the customer. >> Yeah. And there's another way, if you ever want to get your hands on it and you can do it for free, Open Source StackRox. The product is open source completely. And I would love feedback in Slack channel. It's one of the, we also get a ton of feedback from people who aren't actually paying customers and they contribute upstream. So that's an awesome way to get started. But like you said, you go to, if you search ACS cloud service and service preview. Don't have to be a Red Hat customer. Just if you're running a CNCF compliant Kubernetes version. we'd love to hear from you. >> All open source, all out in the open. >> Yep. >> Getting it available to the customers, the non-customers, they hopefully pending customers. Guys, thank you so much for joining John and me talking about the new release, the evolution of StackRox in the last season of 18 months. Lot of good stuff here. I think you've done a great job of getting the audience excited about what you're releasing. Thank you for your time. >> Thank you. >> Thank you. >> For our guest and for John Furrier, Lisa Martin here in Detroit, KubeCon + CloudNativeCon North America. Coming to you live, we'll be back with our next guest in just a minute. (gentle music)

(modern music) >> Welcome back, everyone to theCUBE's live coverage here in San Francisco for VMware Explore, formerly VMworld. theCUBE's been here 12 years today, we've been watching the evolution of the user conference. It's been quite a journey to see and, you know, virtualization just explode. We got two great guests here, we're going to break it all down. Ash McCarty, director of Multicloud Product Management Dell Technologies, no stranger to the VMworld, now VMware Explore, and Josh Prewitt, Chief Product Officer at Rackspace Technology. Great to see you guys, thanks for coming on. >> Absolutely. >> Yeah, thanks so much, thanks for having us. >> So, you know, the theme this year is multicloud, but it's really all about vSphere 8's out, you got VxRail, you got containers, you got the magic going on around cloud native, which it really points to the future state of where this is going, which is agile enterprises, infrastructure as code, high performance under the hood, I mean, all the things that you guys have been doing for many, many years and decades and business, but now with VMware putting it all together, it feels like, this year, it's like you got visibility into the value proposition, people have clear line of sight into where the performances are from the hardware software and now Cloud, it's kind of coming together, feels like it's coming together. Let's talk about that and the relationship between you guys, Rackspace and Dell and VMware. >> Perfect. That sounds great. Well, thanks so much for having us. You know, I'll sort of kick that off. We've got a huge lifelong partnership and relationship with Dell and VMware and the technologies that these guys create that we're able to put in front of our customers are really what allows us to go drive those business outcomes. So, yeah, happy to dive into it. >> Yeah, and I think to add to that, we understand that customers have a tremendously complex challenge ahead of them on managing their infrastructure. That's why with VxRail, we have intelligent infrastructure. We want it to simplify the outcomes for customers no matter if they're managing VMware or if they're managing the actual hardware infrastructure underneath it. >> Yeah, one of the things that we always talk about, you know, you read about it on the blogs and the news and the startup world, is "Oh, product-market fit," and, well, it kind of applies here, if you think about what's going on on the product side with the Edge emerging, hybrid cloud on pace with private cloud, and obviously, cloud native is great too if you have native applications in there, but now, putting it all together, you're hearing things like the telco cloud, I hear buzzwords like that, I hear supercloud, which we promoting, which you see in companies becoming cloud themselves, with the CapEx being handled by either public cloud or optimized on premise or hosted hardware. I mean, this is now, this is not all about everything's going to the cloud, this is now cloud operations on premise and in hosting hardware, so I'd love to get your perspective on that because you guys are huge hosting, you've got huge experience there, modernizing all the time. What does the modern era look like for the customer? >> Yeah, yeah, so, I mean, I think it's very clear to everybody that it's a multicloud world, right? I think the main question is, are you multicloud as a strategy, or are you multicloud as a situation? Because everybody's multicloud. That ship has sailed, right? >> Yeah, exactly. >> And so, when I look at the capabilities that we have with the partnership with Dell and the VxRail technologies, you know, life-cycle management that you have to go and perform across your fleet can be extremely difficult, and whenever you take something like the VxRail and you add, you know, you have the hardware and you have the software all fully integrated there, it makes it much easier to do life-cycle management, so for a company like Rackspace, where we have tens of thousands of nodes that we're managing for customers across 29 global data centers, and we're all over the place, the ability to have that strength with Dell's hardware, the VMware platform improve life-cycle management makes it so much easier for us to manage our fleet and be able to deliver those outcomes even faster for customers. >> So assuming that VxRail isn't a virtual railroad that delivers data to Rackspace data centers, if it's not that, what is it, Ash? Give us a little premier on what VxRail is. >> Well, VxRail is the first and only jointly engineered HCI system with VMware, so everything we do with VMware is better. >> So hyperconverged infrastructure. >> Hyperconverged infrastructure. >> What we used to call a server because all the bits are in the box, right? >> All the storage is computed in there. >> Everything's in there. Right. >> Simplifies management. And we built in with the VxRail HCI system software, which is really our secret sauce, we built in to actually add those automation capabilities with VMware, so it allows you to scale out very quickly, scale up very quickly. And one of our big capabilities is our life-cycle management, which is full stack, meaning it life-cycles the entire vSphere stack as well as the hardware infrastructure underneath as one continuously validated state, meaning that customers can focus more on their business outcomes and driving their business forward versus spending time managing their infrastructure. >> And when you talk about customers, it's also the value proposition that's flowing through Rackspace because Rackspace, when you install these systems, how long does it take to spin up to have a VM available for use when you install one of these systems? >> Oh, so you can have the system up and running very quickly. So we automate all the day one deployment, so you can have the system up and running in your labs, in your data centers in 45 minutes, and you can have VMs up in provision very shortly after that. >> So what do you do with that kind of agility? >> Oh my gosh, so we've actually taken that, and we've taken the VxRail platform and we've created what we call Rackspace Services for VMware Cloud, and this is our platform that is based on VxRail, it's based on vCloud Director from VMware, and by having the VxRail is already RackStacked, ready to go for our customers, we're able to sign a customer up today, and then, within a matter of minutes, give them access to a vCloud Director portal where they can go in and spin up a new VM anytime they want, but then, it also integrates into all of those cloud management platforms and tools, right? It integrates into your Terraform, so you've got, you know, your full CI/CD pipeline, and so you have that full end-to-end capability. If you want to go click around on a portal, you can using vCloud Director and using vSphere and all that great stuff. If you want to automate it, you can do that too. And we do it all in the backs of that VxRail hyperconverged infrastructure. >> Talk about the DPU dynamic. We're hearing a lot about DPUs. VxRail, you guys have some HCI-like vibe there with DPUs. How is that impacting performance, can you guys see? 'Cause we're hearing a lot of buzz around the VxRail and the VMware DPUs really making things much faster. >> I mean, it's the thing we talk about most with customers now is their challenges with scaling their infrastructure, and VxRail is going to be the first and only jointly engineered system that will have vSphere 8 with DPUs functionality and will have the full life-cycle management, and what this really empowers customers to do is, as they're growing their environments that they're scaling out their workloads in the data center, they need a way to scale to that next generation of networking and network security, and that's what DPUs allow you to do. They give you that offload and that high performance capability. >> Talk about the... I'd love to get your guys' perspective, while we're just riffing on this real quick sidebar for a second, if VxRail has these capabilities which you guys are promoting it does and some of the things go on in the modern era, the next gen apps are going to look a lot different. We're kind of calling it supercloud, if you will, for lack of a better description. Yeah, multicloud is a state, I agree. It's a situation and a state, but supercloud is really the functionality of what cloud does. So what do you guys see as, maybe it's tea leaves reading now or dots connecting, what are some of those next gen apps? I mean the Edge is there with, "Oh, the Edge is going to explode," and I can see the Edge having new kinds of apps that we've never seen before, whether it's on premise building lights and however they work or IoT changing. What do you guys see as the next gen app/apps coming out that's not looking the same as now, or how are apps today changing for next gen? 'Cause you get more performance at the Edge, you get more action, you get more co-locations in GEOS, so it's clear multicloud multi-presence is happening too, right? So what are you guys seeing? What's this... >> Yeah, I would say two areas that resonate most with customers is customers transitioning to their cloud native journey, so beginning it and using things like Tanzu for Kubernetes Operations, which we fully support and have a white paper out there list for customers, another area is really in the AIML space, so we've been partnering with both VMware and Nvidia to simplify how customers deploy new AIML infrastructure. I mean, it's challenging, complex, a lot of customers are wanting to dive in because it really enables them to better operate and operate on insights and analytics they get from running their business. >> Josh? >> And, you know, I think it really comes down to, whether you want to call it Edge or IoT or, you know, smart things, whatever, right? It all comes down to how we are expected, now, to capture all of the data to create a better user experience, and that's what we're seeing the modern applications being built around, right, is how do you leverage all of the data that's now at your fingertips, whether it's from wearables, machine vision, whatever it may be, and drive that improved user experience. And so that's the apps that we're seeing now, right? You know, of course, you still have all your business apps, all your ERP capabilities that need to exist and all of that great stuff, but at the same time, I also expect that, whenever, you know, now, whenever I'm walking into a store and their machine vision picks me up and they're pinging my phone and pushing me push notifications, I expect to have a better user experience. >> And do a database search on you too, by the way. >> Yeah, exactly, right? >> No search warrants out for 'em, you know, you're good. >> That's exactly it, so, you know, you kind of expect that better user experience and that's where I'm seeing a lot of the new app development. >> Yeah, it's fun, as these cases are intoxicating to think about all the weird coolness around it. The thing that I want to get your thoughts on is, we were just talking on the analyst session earlier in theCUBE, if DevOps is here and won, which we believe it has and infrastructure as code is happening, the cloud native discussion, shifting left CI/CD pipeline, that's DevOps in my mind, that's like cloud native developers, that's like traditional IT in my mind, so that's all part of the coding. DataOps and Security Ops seem to be the most robust areas of conversations where that's the new Ops, right? So, I mean, I made the term up, but new Ops, in terms of the focus, what are you making more efficient? What are you optimizing for? What's your guys reaction to that? Because all the conversations that we talk about is data, security, and then the rest seems to be cool, all good on the developer's side. Yeah, shift left events happening up there, Kubernetes containers, but all the action on the Ops side seems to be data and security. >> Yeah. >> What's your reaction to that? Is that right? >> So personally, I do think that it's right. I think that, you know with great power comes great responsibility, right? And so the clouds have brought that to us, all of your infrastructure as code has brought that to us. We have that great power now, right? But then you start to see, kind of, the pipeline attacks that are starting to become more and more popular. And so how you secure something that is as complex as, you know, a cloud native development pipeline is really hard, it's really challenging, so I do think that it warrants the attention. Then on the data side, I think that that matters because when I talked about those examples of a better user experience, I don't want my better user experience tomorrow, I don't want it 20 minutes from now. I want that real time capability, and so with that comes massive requirements from a compute and hardware perspective, massive requirements from a software perspective, and from, you know, what folks are now calling DataOps perspective >> Data addressability, having the data available to be delivered in real time. >> You know, there there's been a lot of talk, here at the conference, about the disaggregation of, you know, the brainularism, if we're going to make up words, you know, the horsepower that's involved, CPU, DPU, GPU. I'll make up another word. We're familiar with the thermometers used during COVID to measure temperature. Pretend that I've invented a device called a Care-o-meter and I'm pointing at various people's foreheads, who needs to care about DPUs and GPUs and CPUs? You know, John was referencing the idea of security at the Edge, data. Well, wow, we've got GPUs that can do things. Who needs to care about that? Obviously, we care about it. You care about it. You care about it. You're building this stuff, you're deploying this stuff, but at what level in the customer stack do they need to care about it? Are you going in, is RackSpace engaging customers and saying, "Look, here's the value proposition: we understand your mission to be this. We believe we can achieve your mission." How far down in the organization do you go before you get to someone where you have to have the DPU conversation? 'Cause we didn't even define DPU yet here, which is always offensive to me. >> I think I defined it actually. >> Did you define DPU? Good. Thank you John. >> Yeah, yeah. >> But so who should care? Who should really care about that? >> Oh, that's such a complex question, right? Because everybody, Rackspace included >> But a good one. But a good question. >> Oh, it's a great question. >> Thank you. >> Great question. (laughing) >> Everybody, Rackspace included, is talking about selling business outcomes, right? And ultimately, that is what matters. It is what matters, is selling those business outcomes to the customer. And so of course we're dealing with our business buyers who are just looking for, "Hey, improve my KPIs, make this run faster, better, stronger, all of that great stuff," but ultimately you get down to an IT staff, and to the IT staff, these things matter because the IT staff, they all have budgets that they have to hit. The realities start to hit them and they can't just go and spend whatever they want, you know, trying to hit the KPIs of the marketing department or the finance department, right? And so you have your business buyers that do care significantly about buying their outcomes, and so we're having, you know, the business outcomes conversations with them and then, oftentimes, they will come back to us and say, "Okay, but now we need you to talk to this person over in our IT organization. We need you to talk with our CIO, with our VP of infrastructure," whatever that may be, where we really get down to the nuts and bolts and we talk about how, you know, we can stretch the hardware coming from Dell, we can stretch the software coming from VMware, and we can deliver a higher caliber experience, a lower TCO, by taking advantage of some of the new technologies coming out. >> Yeah, so there's a reason why I ask that awesome question, and it's because I can imagine a scenario where, and this speaks to RackSpace's position in the market today and moving forward and what your history has been, people want to know, "Well, why should I work with Rackspace instead of some mega-hyper-monster-cloud?" If part of the answer is: well, it's because, for very specific application environments, like healthcare we talked about earlier, that might be a conversation where you're actually bringing in Dell to have a conversation about how you are specifically optimizing hardware and software to achieve things that otherwise can't be achieved with t-shirt sizes of servers in a hyperscale cloud. I mean, is that part of the Rackspace value proposition moving forward, that you can do things like that with partners like Dell that the other folks aren't going to focus on? >> Absolutely, it is, right? And a lot of the power of Rackspace is that, you know, we're the best-in-class pure play cloud solutions provider, and we can talk to you about your AWS, your Azure, your GCP, all of that great stuff, but we can also talk to you about private cloud solutions that are built on the backs of Dell Technologies, and in this multicloud world, you don't have that one size fits all for every single application. There are some things that run great in a hyperscale provider, and we can help you get there, but just exactly like you said, there are these verticals where you have applications that don't necessarily run all that well or they're not modernized, they haven't been refactored to be able to take advantage of cloud native services. And if all you're going to do is run that on bare metal in VMs, a hosted private cloud is, by far, the best way to do that, right? And Rackspace provides that hosted private cloud on the backs of Dell technology, on the backs of VMware technology, and we can go deliver those custom bespoke solutions to customers. >> So the infrastructure and the hardware still matters, Ash, yes? >> Absolutely, and I think he just highlighted, while what he does with his customers and what's important to his internal organization is being to deliver faster outcomes, better outcomes, give those customers, to meet those KPIs of those customers consuming their infrastructure at Rackspace, so I think, really, what the DPU and the underlying infrastructure enables is all that full stack integration to allow them to quickly scale to the demands of those customers and what they need in their infrastructure. >> Guys, while we got you here, what do you think about this year's VMware Explore, a lot of anticipation around how many people are going to show up and, you know, all kinds of things around the new name and Broadcom. Big attendance here, I mean, I was very surprised about the size of the attendance and the show floor, the ecosystem, this train is not stopping. I mean, this is VMware's third act, no matter what the contextual situation is. What's your observation of the show? Do you agree, or is there anything that you could want to share about for folks who didn't make it, what they missed? >> Yeah, I mean it really highlights, I mean, you've seen the breadth of the show, I know people that aren't here that aren't able to see it are really missing the excitement. So there's a lot of great announcements around multicloud, around all the announcements, around the vSphere 8 with the DPUs, the vSAN Express Storage architecture, ton of new exciting technologies that are really empowering how customers, you know, the future of how customers are going to consume their workloads in their data centers. >> Josh, they're not short on products and stuff. A lot of moving parts. vSphere 8, a bunch of new stuff. And the cloud native stuff's looking pretty good too, off the tee. >> You know, it does feel like a focus on the core, though, in a way. So I don't think there's been a lot of peripheral noise at the show. Sometimes it's, you know, "And we got this, and this, and this, and this." It's vSphere 8, vSAN 8, cloud software, you know, really hammering it home and refining it. >> But you don't think of it as a little bit of a circus act. I mean the general keynote was theatrical, I thought, I mean, I thought they did a good job on that. I think vSphere 8 was buried a little bit, I thought they could have... They checked the box at the beginning. >> That's true, that's true. >> I mean, they mentioned it, but we didn't see the demos. You know? Demos are usually great. But that's my only criticism. >> Well, that's why we supplemented it with the VxRail announcements, right? With our big announcements around vSphere 8 and with the DPUs as well as the vSAN Express Storage architecture being integrated into VxRail, so I think, you know, it's always that ongoing partnership and, you know, doing what's best for our customers, showing them the next generation and how they consume that technology. >> Yeah, you guys got good props on VxRail. We had a great chat about it yesterday. Rackspace, you guys doing good? Quick update on what's happening with you guys. Give a quick plug. What's going on at Rackspace? What's hot? What's going on? Give a quick plug for what the services are and the products you got going on there. >> Yeah, absolutely. So we are that end-to-end cloud provider, right? And so we've got really exciting offers in market, helping customers take advantage of all the hyperscale providers, and then giving them that private cloud experience. We've got everything from single-tenant running in our data centers on the backs of vSphere, vCloud Director, and VxRails, all the way through to, like, multi-tenant burstable capability that runs within our own data centers as well. It's a really exciting time for technology, a really exciting time for Rackspace. >> Congratulations, we've been following your journey for a long time. Dell, you guys do continue to do a great job and end-to-end phenomenal work. The telco thing's a huge opportunity, we didn't even go there. But Ash, thanks. Josh, thanks for coming on. Appreciate it. >> Yeah, thanks so much. Thanks for having us. >> Thank you very much. >> Okay, thanks for watching theCUBE. We're live, day two of three days of wall-to-wall coverage. Two sets here in Moscone West on the ground level, in the lobby, checking out all the action. Stay with us for more coverage after this short break. (modern music)

(upbeat music) >> Hello, welcome to this CUBE conversation I'm John Furrier your host here in theCUBE in Palo Alto, California, featuring Cribl a hot startup taking over the enterprise when it comes to data pipelining, and we have a CUBE alumni who's the co-founder and CEO, Clint Sharp. Clint, great to see you again, you've been on theCUBE, you were on in 2013, great to see you, congratulations on the company that you co-founded, and leading as the chief executive officer over $200 million in funding, doing this really strong in the enterprise, congratulations thanks for joining us. >> Hey, thanks John it's really great to be back. >> You know, remember our first conversation the big data wave coming in, Hadoop World 2010, now the cloud comes in, and really the cloud native really takes data to a whole nother level. You've seeing the old data architectures being replaced with cloud scale. So the data landscape is interesting. You know, Data as Code you're hearing that term, data engineering teams are out there, data is everywhere, it's now part of how developers and companies are getting value whether it's real time, or coming out of data lakes, data is more pervasive than ever. Observability is a hot area, there's a zillion companies doing it, what are you guys doing? Where do you fit in the data landscape? >> Yeah, so what I say is that Cribl and our products and we solve the problem for our customers of the fundamental tension between data growth and budget. And so if you look at IDCs data data's growing at a 25%, CAGR, you're going to have two and a half times the amount of data in five years that you have today, and I talk to a lot of CIOs, I talk to a lot of CISOs, and the thing that I hear repeatedly is my budget is not growing at a 25% CAGR so fundamentally, how do I resolve this tension? We sell very specifically into the observability in security markets, we sell to technology professionals who are operating, you know, observability in security platforms like Splunk, or Elasticsearch, or Datadog, Exabeam, like these types of platforms they're moving, protocols like syslog, they're moving, they have lots of agents deployed on every endpoint and they're trying to figure out how to get the right data to the right place, and fundamentally you know, control cost. And we do that through our product called Stream which is what we call an observability pipeline. It allows you to take all this data, manipulate it in the stream and get it to the right place and fundamentally be able to connect all those things that maybe weren't originally intended to be connected. >> So I want to get into that new architecture if you don't mind, but let me first ask you on the problem space that you're in. So cloud native obviously instrumentating, instrumenting everything is a key thing. You mentioned data got all these tools, is the problem that there's been a sprawl of things being instrumented and they have to bring it together, or it's too costly to run all these point solutions and get it to work? What's the problem space that you're in? >> So I think customers have always been forced to make trade offs John. So the, hey I have volumes and volumes and volumes of data that's relevant to securing my enterprise, that's relevant to observing and understanding the behavior of my applications but there's never been an approach that allows me to really onboard all of that data. And so where we're coming at is giving them the tools to be able to, you know, filter out noise and waste, to be able to, you know, aggregate this high fidelity telemetry data. There's a lot of growing changes, you talk about cloud native, but digital transformation, you know, the pandemic itself and remote work all these are driving significantly greater data volumes, and vendors unsurprisingly haven't really been all that aligned to giving customers the tools in order to reshape that data, to filter out noise and waste because, you know, for many of them they're incentivized to get as much data into their platform as possible, whether that's aligned to the customer's interests or not. And so we saw an opportunity to come out and fundamentally as a customers-first company give them the tools that they need, in order to take back control of their data. >> I remember those conversations even going back six years ago the whole cloud scale, horizontally scalable applications, you're starting to see data now being stuck in the silos now to have high, good data you have to be observable, which means you got to be addressable. So you now have to have a horizontal data plane if you will. But then you get to the question of, okay, what data do I need at the right time? So is the Data as Code, data engineering discipline changing what new architectures are needed? What changes in the mind of the customer once they realize that they need this new way to pipe data and route data around, or make it available for certain applications? What are the key new changes? >> Yeah, so I think one of the things that we've been seeing in addition to the advent of the observability pipeline that allows you to connect all the things, is also the advent of an observability lake as well. Which is allowing people to store massively greater quantities of data, and also different types of data. So data that might not traditionally fit into a data warehouse, or might not traditionally fit into a data lake architecture, things like deployment artifacts, or things like packet captures. These are binary types of data that, you know, it's not designed to work in a database but yet they want to be able to ask questions like, hey, during the Log4Shell vulnerability, one of all my deployment artifacts actually had Log4j in it in an affected version. These are hard questions to answer in today's enterprise. Or they might need to go back to full fidelity packet capture data to try to understand that, you know, a malicious actor's movement throughout the enterprise. And we're not seeing, you know, we're seeing vendors who have great log indexing engines, and great time series databases, but really what people are looking for is the ability to store massive quantities of data, five times, 10 times more data than they're storing today, and they're doing that in places like AWSS3, or in Azure Blob Storage, and we're just now starting to see the advent of technologies we can help them query that data, and technologies that are generally more specifically focused at the type of persona that we sell to which is a security professional, or an IT professional who's trying to understand the behaviors of their applications, and we also find that, you know, general-purpose data processing technologies are great for the enterprise, but they're not working for the people who are running the enterprise, and that's why you're starting to see the concepts like observability pipelines and observability lakes emerge, because they're targeted at these people who have a very unique set of problems that are not being solved by the general-purpose data processing engines. >> It's interesting as you see the evolution of more data volume, more data gravity, then you have these specialty things that need to be engineered for the business. So sounds like observability lake and pipelining of the data, the data pipelining, or stream you call it, these are new things that they bolt into the architecture, right? Because they have business reasons to do it. What's driving that? Sounds like security is one of them. Are there others that are driving this behavior? >> Yeah, I mean it's the need to be able to observe applications and observe end-user behavior at a fine-grain detail. So, I mean I often use examples of like bank teller applications, or perhaps, you know, the app that you're using to, you know, I'm going to be flying in a couple of days. I'll be using their app to understand whether my flight's on time. Am I getting a good experience in that particular application? Answering the question of is Clint getting a good experience requires massive quantities of data, and your application and your service, you know, I'm going to sit there and look at, you know, American Airlines which I'm flying on Thursday, I'm going to be judging them based on off of my experience. I don't care what the average user's experience is I care what my experience is. And if I call them up and I say, hey, and especially for the enterprise usually this is much more for, you know, in-house applications and things like that. They call up their IT department and say, hey, this application is not working well, I don't know what's going on with it, and they can't answer the question of what was my individual experience, they're living with, you know, data that they can afford to store today. And so I think that's why you're starting to see the advent of these new architectures is because digital is so absolutely critical to every company's customer experience, that they're needing to be able to answer questions about an individual user's experience which requires significantly greater volumes of data, and because of significantly greater volumes of data, that requires entirely new approaches to aggregating that data, bringing the data in, and storing that data. >> Talk to me about enabling customer choice when it comes around controlling their data. You mentioned that before we came on camera that you guys are known for choice. How do you enable customer choice and control over their data? >> So I think one of the biggest problems I've seen in the industry over the last couple of decades is that vendors come to customers with hugely valuable products that make their lives better but it also requires them to maintain a relationship with that vendor in order to be able to continue to ask questions of that data. And so customers don't get a lot of optionality in these relationships. They sign multi-year agreements, they look to try to start another, they want to go try out another vendor, they want to add new technologies into their stack, and in order to do that they're often left with a choice of well, do I roll out like get another agent, do I go touch 10,000 computers, or a 100,000 computers in order to onboard this data? And what we have been able to offer them is the ability to reuse their existing deployed footprints of agents and their existing data collection technologies, to be able to use multiple tools and use the right tool for the right job, and really give them that choice, and not only give them the choice once, but with the concepts of things like the observability lake and replay, they can go back in time and say, you know what? I wanted to rehydrate all this data into a new tool, I'm no longer locked in to the way one vendor stores this, I can store this data in open formats and that's one of the coolest things about the observability late concept is that customers are no longer locked in to any particular vendor, the data is stored in open formats and so that gives them the choice to be able to go back later and choose any vendor, because they may want to do some AI or ML on that type of data and do some model training. They may want to be able to forward that data to a new cloud data warehouse, or try a different vendor for log search or a different vendor for time series data. And we're really giving them the choice and the tools to do that in a way in which was simply not possible before. >> You know you are bring up a point that's a big part of the upcoming AWS startup series Data as Code, the data engineering role has become so important and the word engineering is a key word in that, but there's not a lot of them, right? So like how many data engineers are there on the planet, and hopefully more will come in, come from these great programs in computer science but you got to engineer something but you're talking about developing on data, you're talking about doing replays and rehydrating, this is developing. So Data as Code is now a reality, how do you see Data as Code evolving from your perspective? Because it implies DevOps, Infrastructure as Code was DevOps, if Data as Code then you got DataOps, AIOps has been around for a while, what is Data as Code? And what does that mean to you Clint? >> I think for our customers, one, it means a number of I think sort of after-effects that maybe they have not yet been considering. One you mentioned which is it's hard to acquire that talent. I think it is also increasingly more critical that people who were working in jobs that used to be purely operational, are now being forced to learn, you know, developer centric tooling, things like GET, things like CI/CD pipelines. And that means that there's a lot of education that's going to have to happen because the vast majority of the people who have been doing things in the old way from the last 10 to 20 years, you know, they're going to have to get retrained and retooled. And I think that one is that's a huge opportunity for people who have that skillset, and I think that they will find that their compensation will be directly correlated to their ability to have those types of skills, but it also represents a massive opportunity for people who can catch this wave and find themselves in a place where they're going to have a significantly better career and more options available to them. >> Yeah and I've been thinking about what you just said about your customer environment having all these different things like Datadog and other agents. Those people that rolled those out can still work there, they don't have to rip and replace and then get new training on the new multiyear enterprise service agreement that some other vendor will sell them. You come in and it sounds like you're saying, hey, stay as you are, use Cribl, we'll have some data engineering capabilities for you, is that right? Is that? >> Yup, you got it. And I think one of the things that's a little bit different about our product and our market John, from kind of general-purpose data processing is for our users they often, they're often responsible for many tools and data engineering is not their full-time job, it's actually something they just need to do now, and so we've really built tool that's designed for your average security professional, your average IT professional, yes, we can utilize the same kind of DataOps techniques that you've been talking about, CI/CD pipelines, GITOps, that sort of stuff, but you don't have to, and if you're really just already familiar with administering a Datadog or a Splunk, you can get started with our product really easily, and it is designed to be able to be approachable to anybody with that type of skillset. >> It's interesting you, when you're talking you've remind me of the big wave that was coming, it's still here, shift left meant security from the beginning. What do you do with data shift up, right, down? Like what do you, what does that mean? Because what you're getting at here is that if you're a developer, you have to deal with data but you don't have to be a data engineer but you can be, right? So we're getting in this new world. Security had that same problem. Had to wait for that group to do things, creating tension on the CI/CD pipelining, so the developers who are building apps had to wait. Now you got shift left, what is data, what's the equivalent of the data version of shift left? >> Yeah so we're actually doing this right now. We just announced a new product a week ago called Cribl Edge. And this is enabling us to move processing of this data rather than doing it centrally in the stream to actually push this processing out to the edge, and to utilize a lot of unused capacity that you're already paying AWS, or paying Azure for, or maybe in your own data center, and utilize that capacity to do the processing rather than having to centralize and aggregate all of this data. So I think we're going to see a really interesting, and left from our side is towards the origination point rather than anything else, and that allows us to really unlock a lot of unused capacity and continue to drive the kind of cost down to make more data addressable back to the original thing we talked about the tension between data growth, if we want to offer more capacity to people, if we want to be able to answer more questions, we need to be able to cost-effectively query a lot more data. >> You guys had great success in the enterprise with what you got going on. Obviously the funding is just the scoreboard for that. You got good growth, what are the use cases, or what's the customer look like that's working for you where you're winning, or maybe said differently what pain points are out there the customer might be feeling right now that Cribl could fit in and solve? How would you describe that ideal persona, or environment, or problem, that the customer may have that they say, man, Cribl's a perfect fit? >> Yeah, this is a person who's working on tooling. So they administer a Splunk, or an Elastic, or a Datadog, they may be in a network operations center, a security operation center, they are struggling to get data into their tools, they're always at capacity, their tools always at the redline, they really wish they could do more for the business. They're kind of tired of being this department of no where everybody comes to them and says, "hey, can I get this data in?" And they're like, "I wish, but you know, we're all out of capacity, and you know, we have, we wish we could help you but we frankly can't right now." We help them by routing that data to multiple locations, we help them control costs by eliminating noise and waste, and we've been very successful at that in, you know, logos, like, you know, like a Shutterfly, or a, blanking on names, but we've been very successful in the enterprise, that's not great, and we continue to be successful with major logos inside of government, inside of banking, telco, et cetera. >> So basically it used to be the old hyperscalers, the ones with the data full problem, now everyone's got the, they're full of data and they got to really expand capacity and have more agility and more engineering around contributions of the business sounds like that's what you guys are solving. >> Yup and hopefully we help them do a little bit more with less. And I think that's a key problem for our enterprises, is that there's always a limit on the number of human resources that they have available at their disposal, which is why we try to make the software as easy to use as possible, and make it as widely applicable to those IT and security professionals who are, you know, kind of your run-of-the-mill tools administrator, our product is very approachable for them. >> Clint great to see you on theCUBE here, thanks for coming on. Quick plug for the company, you guys looking for hiring, what's going on? Give a quick update, take 30 seconds to give a plug. >> Yeah, absolutely. We are absolutely hiring cribl.io/jobs, we need people in every function from sales, to marketing, to engineering, to back office, GNA, HR, et cetera. So please check out our job site. If you are interested it in learning more you can go to cribl.io. We've got some great online sandboxes there which will help you educate yourself on the product, our documentation is freely available, you can sign up for up to a terabyte a day on our cloud, go to cribl.cloud and sign up free today. The product's easily accessible, and if you'd like to speak with us we'd love to have you in our community, and you can join the community from cribl.io as well. >> All right, Clint Sharp co-founder and CEO of Cribl, thanks for coming to theCUBE. Great to see you, I'm John Furrier your host thanks for watching. (upbeat music)

(cheerful music) >> Hello everyone, welcome to today's session of theCUBE's presentation of AWS Startup Showcase, New Breakthroughs in DevOps, Data Analytics, Cloud Management Tools, featuring Okera from the cloud management migration track. I'm John Furrier, your host. We've got two great special guests today, Nong Li, founder and CTO of Okera, and Sanjeev Mohan, principal @SanjMo, and former research vice president of big data and advanced analytics at Gartner. He's a legend, been around the industry for a long time, seen the big data trends from the past, present, and knows the future. Got a great lineup here. Gentlemen, thank you for this, so, life in the trenches, lessons learned across compliance, cloud migration, analytics, and use cases for Fortune 1000s. Thanks for joining us. >> Thanks for having us. >> So Sanjeev, great to see you, I know you've seen this movie, I was saying that in the open, you've at Gartner seen all the visionaries, the leaders, you know everything about this space. It's changing extremely fast, and one of the big topics right out of the gate is not just innovation, we'll get to that, that's the fun part, but it's the regulatory compliance and audit piece of it. It's keeping people up at night, and frankly if not done right, slows things down. This is a big part of the showcase here, is to solve these problems. Share us your thoughts, what's your take on this wide-ranging issue? >> So, thank you, John, for bringing this up, and I'm so happy you mentioned the fact that, there's this notion that it can slow things down. Well I have to say that the old way of doing governance slowed things down, because it was very much about control and command. But the new approach to data governance is actually in my opinion, it's liberating data. If you want to democratize or monetize, whatever you want to call it, you cannot do it 'til you know you can trust said data and it's governed in some ways, so data governance has actually become very interesting, and today if you want to talk about three different areas within compliance regulatory, for example, we all know about the EU GDPR, we know California has CCPA, and in fact California is now getting even a more stringent version called CPRA in a couple of years, which is more aligned to GDPR. That is a first area we know we need to comply to that, we don't have any way out. But then, there are other areas, there is insider trading, there is how you secure the data that comes from third parties, you know, vendors, partners, suppliers, so Nong, I'd love to hand it over to you, and see if you can maybe throw some light into how our customers are handling these use cases. >> Yeah, absolutely, and I love what you said about balancing agility and liberating, in the face of what may be seen as things that slow you down. So we work with customers across verticals with old and new regulations, so you know, you brought up GDPR. One of our clients is using this to great effect to power their ecosystem. They are a very large retail company that has operations and customers across the world, obviously the importance of GDPR, and the regulations that imposes on them are very top of mind, and at the same time, being able to do effective targeting analytics on customer information is equally critical, right? So they're exactly at that spot where they need this customer insight for powering their business, and then the regulatory concerns are extremely prevalent for them. So in the context of GDPR, you'll hear about things like consent management and right to be forgotten, right? I, as a customer of that retailer should say "I don't want my information used for this purpose," right? "Use it for this, but not this." And you can imagine at a very, very large scale, when you have a billion customers, managing that, all the data you've collected over time through all of your devices, all of your telemetry, really, really challenging. And they're leveraging Okera embedded into their analytics platform so they can do both, right? Their data scientists and analysts who need to do everything they're doing to power the business, not have to think about these kind of very granular customer filtering requirements that need to happen, and then they leverage us to do that. So that's kind of new, right, GDPR, relatively new stuff at this point, but we obviously also work with customers that have regulations from a long long time ago, right? So I think you also mentioned insider trading and that supply chain, so we'll talk to customers, and they want really data-driven decisions on their supply chain, everything about their production pipeline, right? They want to understand all of that, and of course that makes sense, whether you're the CFO, if you're going to make business decisions, you need that information readily available, and supply chains as we know get more and more and more complex, we have more and more integrated into manufacturing and other verticals. So that's your, you're a little bit stuck, right? You want to be data-driven on those supply chain analytics, but at the same time, knowing the details of all the supply chain across all of your dependencies exposes your internal team to very high blackout periods or insider trading concerns, right? For example, if you knew Apple was buying a bunch of something, that's maybe information that only a select few people can have, and the way that manifests into data policies, 'cause you need the ability to have very, very scalable, per employee kind of scalable data restriction policies, so they can do their job easier, right? If we talk about speeding things up, instead of a very complex process for them to get approved, and approved on SEC regulations, all that kind of stuff, you can now go give them access to the part of the supply chain that they need, and no more, and limit their exposure and the company's exposure and all of that kind of stuff. So one of our customers able to do this, getting two orders of magnitude, a 100x reduction in the policies to manage the system like that. >> When I hear you talking like that, I think the old days of "Oh yeah, regulatory, it kind of slows down innovation, got to go faster," pretty basic variables, not a lot of combination of things to check. Now with cloud, there seems to be combinations, Sanjeev, because how complicated has the regulatory compliance and audit environment gotten in the past few years, because I hear security in a supply chain, I hear insider threats, I mean these are security channels, not just compliance department G&A kind of functions. You're talking about large-scale, potentially combinations of access, distribution, I mean it seems complicated. How much more complicated is it now, just than it was a few years ago? >> So, you know the way I look at it is, I'm just mentioning these companies just as an example, when PayPal or Ebay, all these companies started, they started in California. Anybody who ever did business on Ebay or PayPal, guess where that data was? In the US in some data center. Today you cannot do it. Today, data residency laws are really tough, and so now these organizations have to really understand what data needs to remain where. On top of that, we now have so many regulations. You know, earlier on if you were healthcare, you needed to be HIPAA compliant, or banking PCI DSS, but today, in the cloud, you really need to know, what data I have, what sensitive data I have, how do I discover it? So that data discovery becomes really important. What roles I have, so for example, let's say I work for a bank in the US, and I decide to move to Germany. Now, the old school is that a new rule will be created for me, because of German... >> John: New email address, all these new things happen, right? >> Right, exactly. So you end up with this really, a mass of rules and... And these are all static. >> Rules and tools, oh my god. >> Yeah. So Okera actually makes a lot of this dynamic, which reduces your cloud migration overhead, and Nong used some great examples, in fact, sorry if I take just a second, without mentioning any names, there's one of the largest banks in the world is going global in the digital space for the first time, and they're taking Okera with them. So... >> But what's the point? This is my next topic in cloud migration, I want to bring this up because, complexity, when you're in that old school kind of data center, waterfall, these old rules and tools, you have to roll this out, and it's a pain in the butt for everybody, it's a hassle, huge hassle. Cloud gives the agility, we know that, and cloud's becoming more secure, and I think now people see the on-premise, certainly things that'd be on-premises for secure things, I get that, but when you start getting into agility, and you now have cloud regions, you can start being more programmatic, so I want to get you guys' thoughts on the cloud migration, how companies who are now lifting and shifting, replatforming, what's the refactoring beyond that, because you can replatform in the cloud, and still some are kind of holding back on that. Then when you're in the cloud, the ones that are winning, the companies that are winning are the ones that are refactoring in the cloud. Doing things different with new services. Sanjeev, you start. >> Yeah, so you know, in fact lot of people tell me, "You know, we are just going to lift and shift into the cloud." But you're literally using cloud as a data center. You still have all the, if I may say, junk you had on-prem, you just moved it into the cloud, and now you're paying for it. In cloud, nothing is free. Every storage, every processing, you're going to pay for it. The most successful companies are the ones that are replatforming, they are taking advantage of the platform as a service or software as a service, so that includes things like, you pay as you go, you pay for exactly the amount you use, so you scale up and scale down or scale out and scale in, pretty quickly, you know? So you're handling that demand, so without replatforming, you are not really utilizing your- >> John: It's just hosting. >> Yeah, you're just hosting. >> It's basically hosting if you're not doing anything right there. >> Right. The reason why people sometimes resist to replatform, is because there's a hidden cost that we don't really talk about, PaaS adds 3x to IaaS cost. So, some organizations that are very mature, and they have a few thousand people in the IT department, for them, they're like "No, we just want to run it in the cloud, we have the expertise, and it's cheaper for us." But in the long run, to get the most benefit, people should think of using cloud as a service. >> Nong what's your take, because you see examples of companies, I'll just call one out, Snowflake for instance, they're essentially a data warehouse in the cloud, they refactored and they replatformed, they have a competitive advantage with the scale, so they have things that others don't have, that just hosting. Or even on-premise. The new model developing where there's real advantages, and how should companies think about this when they have to manage these data lakes, and they have to manage all these new access methods, but they want to maintain that operational stability and control and growth? >> Yeah, so. No? Yeah. >> There's a few topics that are all (indistinct) this topic. (indistinct) enterprises moving to the cloud, they do this maybe for some cost savings, but a ton of it is agility, right? The motor that the business can run at is just so much faster. So we'll work with companies in the context of cloud migration for data, where they might have a data warehouse they've been using for 20 years, and building policies over that time, right? And it's taking a long time to go proof of access and those kind of things, made more sense, right? If it took you months to procure a physical infrastructure, get machines shipped to your data center, then this data access taking so long feels okay, right? That's kind of the same rate that everything is moving. In the cloud, you can spin up new infrastructure instantly, so you don't want approvals for getting policies, creating rules, all that stuff that Sanjeev was talking about, that being slow is a huge, huge problem. So this is a very common environment that we see where they're trying to do that kind of thing. And then, for replatforming, again, they've been building these roles and processes and policies for 20 years. What they don't want to do is take 20 years to go migrate all that stuff into the cloud, right? That's probably an experience nobody wants to repeat, and frankly for many of them, people who did it originally may or may not be involved in this kind of effort. So we work with a lot of companies like that, they have their, they want stability, they got to have the business running as normal, they got to get moving into the new infrastructure, doing it in a new way that, you know, with all the kind of lessons learned, so, as Sanjeev said, one of these big banks that we work with, that classical story of on-premise data warehousing, maybe a little bit of Hadoop, moved onto AWS, S3, Snowflake, that kind of setup, extremely intricate policies, but let's go reimagine how we can do this faster, right? What we like to talk about is, you're an organization, you need a design that, if you onboarded 1000 more data users, that's got to be way, way easier than the first 10 you onboarded, right? You got to get it to be easier over time, in a really, really significant way. >> Talk about the data authorization safety factor, because I can almost imagine all the intricacies of these different tools creates specialism amongst people who operate them. And each one might have their own little authorization nuance. Trend is not to have that siloed mentality. What's your take on clients that want to just "Hey, you know what? I want to have the maximum agility, but I don't want to get caught in the weeds on some of these tripwires around access and authorization." >> Yeah, absolutely, I think it's real important to get the balance of it, right? Because if you are an enterprise, or if you have diversive teams, you want them to have the ability to use tools as best of breed for their purpose, right? But you don't want to have it be so that every tool has its own access and provisioning and whatever, that's definitely going to be a security, or at least, a lot of friction for you to get things going. So we think about that really hard, I think we've seen great success with things like SSO and Okta, right? Unifying authentication. We think there's a very, very similar thing about to happen with authorization. You want that single control plane that can integrate with all the tools, and still get the best of what you need, but it's much, much easier (indistinct). >> Okta's a great example, if people don't want to build their own thing and just go with that, same with what you guys are doing. That seems to be the dots that are connecting you, Sanjeev. The ease of use, but yet the stability factor. >> Right. Yeah, because John, today I may want to bring up a SQL editor to go into Snowflake, just as an example. Tomorrow, I may want to use the Azure Bot, you know? I may not even want to go to Snowflake, I may want to go to an underlying piece of data, or I may use Power BI, you know, for some reason, and come from Azure side, so the point is that, unless we are able to control, in some sort of a centralized manner, we will not get that consistency. And security you know is all or nothing. You cannot say "Well, I secured my Snowflake, but if you come through HTFS, Hadoop, or some, you know, that is outside of my realm, or my scope," what's the point? So that is why it is really important to have a watertight way, in fact I'm using just a few examples, maybe tomorrow I decide to use a data catalog, or I use Denodo as my data virtualization and I run a query. I'm the same identity, but I'm using different tools. I may use it from home, over VPN, or I may use it from the office, so you want this kind of flexibility, all encompassed in a policy, rather than a separate rule if you do this and this, if you do that, because then you end up with literally thousands of rules. >> And it's never going to stop, either, it's like fashion, the next tool's going to come out, it's going to be cool, and people are going to want to use it, again, you don't want to have to then move the train from the compliance side this way or that way, it's a lot of hassle, right? So we have that one capability, you can bring on new things pretty quickly. Nong, am I getting it right, this is kind of like the trend, that you're going to see more and more tools and/or things that are relevant or, certain use cases that might justify it, but yet, AppSec review, compliance review, I mean, good luck with that, right? >> Yeah, absolutely, I mean we certainly expect tools to continue to get more and more diverse, and better, right? Most innovation in the data space, and I think we... This is a great time for that, a lot of things that need to happen, and so on and so forth. So I think one of the early goals of the company, when we were just brainstorming, is we don't want data teams to not be able to use the tools because it doesn't have the right security (indistinct), right? Often those tools may not be focused on that particular area. They're great at what they do, but we want to make sure they're enabled, they do some enterprise investments, they see broader adoption much easier. A lot of those things. >> And I can hear the sirens in the background, that's someone who's not using your platform, they need some help there. But that's the case, I mean if you don't get this right, there are some consequences, and I think one of the things I would like to bring up on next track is, to talk through with you guys is, the persona pigeonhole role, "Oh yeah, a data person, the developer, the DevOps, the SRE," you start to see now, developers and with cloud developers, and data folks, people, however they get pigeonholed, kind of blending in, okay? You got data services, you got analytics, you got data scientists, you got more democratization, all these things are being kicked around, but the notion of a developer now is a data developer, because cloud is about DevOps, data is now a big part of it, it's not just some department, it's actually blending in. Just a cultural shift, can you guys share your thoughts on this trend of data people versus developers now becoming kind of one, do you guys see this happening, and if so, how? >> So when, John, I started my career, I was a DBA, and then a data architect. Today, I think you cannot have a DBA who's not a developer. That's just my opinion. Because there is so much of CICD, DevOps, that happens today, and you know, you write your code in Python, you put it in version control, you deploy using Jenkins, you roll back if there's a problem. And then, you are interacting, you're building your data to be consumed as a service. People in the past, you would have a thick client that would connect to the database over TCP/IP. Today, people don't want to connect over TCP/IP necessarily, they want to go by HTTP. And they want an API gateway in the middle. So, if you're a data architect or DBA, now you have to worry about, "I have a REST API call that's coming in, how am I going to secure that, and make sure that people are allowed to see that?" And that was just yesterday. >> Exactly. Got to build an abstraction layer. You got to build an abstraction layer. The old days, you have to worry about schema, and do all that, it was hard work back then, but now, it's much different. You got serverless, functions are going to show way... It's happening. >> Correct, GraphQL, and semantic layer, that just blows me away because, it used to be, it was all in database, then we took it out of database and we put it in a BI tool. So we said, like BusinessObjects started this whole trend. So we're like "Let's put the semantic layer there," well okay, great, but that was when everything was surrounding BusinessObjects and Oracle Database, or some other database, but today what if somebody brings Power BI or Tableau or Qlik, you know? Now you don't have a semantic layer access. So you cannot have it in the BI layer, so you move it down to its own layer. So now you've got a semantic layer, then where do you store your metrics? Same story repeats, you have a metrics layer, then the data centers want to do feature engineering, where do you store your features? You have a feature store. And before you know, this stack has disaggregated over and over and over, and then you've got layers and layers of specialization that are happening, there's query accelerators like Dremio or Trino, so you've got your data here, which Nong is trying really hard to protect, and then you've got layers and layers and layers of abstraction, and networks are fast, so the end user gets great service, but it's a nightmare for architects to bring all these things together. >> How do you tame the complexity? What's the bottom line? >> Nong? >> Yeah, so, I think... So there's a few things you need to do, right? So, we need to re-think how we express security permanence, right? I think you guys have just maybe in passing (indistinct) talked about creating all these rules and all that kind of stuff, that's been the way we've done things forever. We get to think about policies and mechanisms that are much more dynamic, right? You need to really think about not having to do any additional work, for the new things you add to the system. That's really, really core to solving the complexity problem, right? 'Cause that gets you those orders of magnitude reduction, system's got to be more expressive and map to those policies. That's one. And then second, it's got to be implemented at the right layer, right, to Sanjeev's point, close to the data, and it can service all of those applications and use cases at the same time, and have that uniformity and breadth of support. So those two things have to happen. >> Love this universal data authorization vision that you guys have. Super impressive, we had a CUBE Conversation earlier with Nick Halsey, who's a veteran in the industry, and he likes it. That's a good sign, 'cause he's seen a lot of stuff, too, Sanjeev, like yourself. This is a new thing, you're seeing compliance being addressed, and with programmatic, I'm imagining there's going to be bots someday, very quickly with AI that's going to scale that up, so they kind of don't get in the innovation way, they can still get what they need, and enable innovation. You've got cloud migration, which is only going faster and faster. Nong, you mentioned speed, that's what CloudOps is all about, developers want speed, not things in days or hours, they want it in minutes and seconds. And then finally, ultimately, how's it scale up, how does it scale up for the people operating and/or programming? These are three major pieces. What happens next? Where do we go from here, what's, the customer's sitting there saying "I need help, I need trust, I need scale, I need security." >> So, I just wrote a blog, if I may diverge a bit, on data observability. And you know, so there are a lot of these little topics that are critical, DataOps is one of them, so to me data observability is really having a transparent view of, what is the state of your data in the pipeline, anywhere in the pipeline? So you know, when we talk to these large banks, these banks have like 1000, over 1000 data pipelines working every night, because they've got that hundred, 200 data sources from which they're bringing data in. Then they're doing all kinds of data integration, they have, you know, we talked about Python or Informatica, or whatever data integration, data transformation product you're using, so you're combining this data, writing it into an analytical data store, something's going to break. So, to me, data observability becomes a very critical thing, because it shows me something broke, walk me down the pipeline, so I know where it broke. Maybe the data drifted. And I know Okera does a lot of work in data drift, you know? So this is... Nong, jump in any time, because I know we have use cases for that. >> Nong, before you get in there, I just want to highlight a quick point. I think you're onto something there, Sanjeev, because we've been reporting, and we believe, that data workflows is intellectual property. And has to be protected. Nong, go ahead, your thoughts, go ahead. >> Yeah, I mean, the observability thing is critically important. I would say when you want to think about what's next, I think it's really effectively bridging tools and processes and systems and teams that are focused on data production, with the data analysts, data scientists, that are focused on data consumption, right? I think bridging those two, which cover a lot of the topics we talked about, that's kind of where security almost meets, that's kind of where you got to draw it. I think for observability and pipelines and data movement, understanding that is essential. And I think broadly, on all of these topics, where all of us can be better, is if we're able to close the loop, get the feedback loop of success. So data drift is an example of the loop rarely being closed. It drifts upstream, and downstream users can take forever to figure out what's going on. And we'll have similar examples related to buy-ins, or data quality, all those kind of things, so I think that's really a problem that a lot of us should think about. How do we make sure that loop is closed as quickly as possible? >> Great insight. Quick aside, as the founder CTO, how's life going for you, you feel good? I mean, you started a company, doing great, it's not drifting, it's right in the stream, mainstream, right in the wheelhouse of where the trends are, you guys have a really crosshairs on the real issues, how you feeling, tell us a little bit about how you see the vision. >> Yeah, I obviously feel really good, I mean we started the company a little over five years ago, there are kind of a few things that we bet would happen, and I think those things were out of our control, I don't think we would've predicted GDPR security and those kind of things being as prominent as they are. Those things have really matured, probably as best as we could've hoped, so that feels awesome. Yeah, (indistinct) really expanded in these years, and it feels good. Feels like we're in the right spot. >> Yeah, it's great, data's competitive advantage, and certainly has a lot of issues. It could be a blocker if not done properly, and you're doing great work. Congratulations on your company. Sanjeev, thanks for kind of being my cohost in this segment, great to have you on, been following your work, and you continue to unpack it at your new place that you started. SanjMo, good to see your Twitter handle taking on the name of your new firm, congratulations. Thanks for coming on. >> Thank you so much, such a pleasure. >> Appreciate it. Okay, I'm John Furrier with theCUBE, you're watching today's session presentation of AWS Startup Showcase, featuring Okera, a hot startup, check 'em out, great solution, with a really great concept. Thanks for watching. (calm music)

>> From around the globe, it's theCUBE, covering Data Citizens '21. Brought to you by Collibra. >> For the past decade organizations have been effecting very deliberate data strategies and investing quite heavily in people, processes and technology, specifically designed to gain insights from data, better serve customers, drive new revenue streams we've heard this before. The results quite frankly have been mixed. As much of the effort is focused on analytics and technology designed to create a single version of the truth, which in many cases continues to be elusive. Moreover, the world of data is changing. Data is increasingly distributed making collaboration and governance more challenging, especially where operational use cases are a priority. Hello, everyone. My name is Dave Vellante and you're watching theCUBE coverage of Data Citizens '21. And we're pleased to welcome Michele Goetz who's the vice president and principal analyst at Forrester Research. Hello, Michele. Welcome to theCUBE. >> Hi, Dave. Thanks for having me today. >> It's our pleasure. So I want to start, you serve have a wide range of roles including enterprise architects, CDOs, chief data officers that is, analyst, the analyst, et cetera, and many data-related functions. And my first question is what are they thinking about today? What's on their minds, these data experts? >> So there's actually two things happening. One is what is the demand that's placed on data for our new intelligent digital systems. So we're seeing a lot of investment and interest in things like edge computing. And then how does that intersect with artificial intelligence to really run your business intelligently and drive new value propositions to be both adaptive to the market as well as resilient to changes that are unforeseen. The second thing is then you create this massive complexity to managing the data, governing the data, orchestrating the data because it's not just a centralized data warehouse environment anymore. You have a highly diverse and distributed landscape that you both control internally, as well as taking advantage of third party information. So really what the struggle then becomes is how do you trust the data? How do you govern it, and secure, and protect that data? And then how do you ensure that it's hyper contextualized to the types of value propositions that our intelligence systems are going to serve? >> Well, I think you're hitting on the key issues here. I mean, you're right. The data and I sort of refer to this as well is sort of out there, it's distributed at the edge. But generally our data organizations are actually quite centralized and as well you talk about the need to trust the data obviously that's crucial. But are you seeing the organization change? I know you're talking about this to clients, your discussion about collaboration. How are you seeing that change? >> Yeah, so as you have to bring data into context of the insights that you're trying to get or the intelligence that's automating and scaling out the value streams and outcomes within your business, we're actually seeing a federated model emerge in organizations. So while there's still a centralized data management and data services organization led typical enterprise architects for data, a data engineering team that's managing warehouses as in data lakes. They're creating this great platform to access and orchestrate information, but we're also seeing data, and analytics, and governance teams come together under chief data officers or chief data and analytics officers. And this is really where the insights are being generated from either BI and analytics or from data science itself and having dedicated data engineers and stewards that are helping to access and prepare data for analytic efforts. And then lastly, this is the really interesting part is when you push data into the edge the goal is that you're actually driving an experience and an application. And so in that case we are seeing data engineering teams starting to be incorporated into the solutions teams that are aligned to lines of business or divisions themselves. And so really what's happening is if there is a solution consultant who is also overseeing value-based portfolio management when you need to instrument the data to these new use cases and keep up with the pace of the business it's this engineering team that is part of the DevOps work bench to execute on that. So really the balances we need the core, we need to get to the insights and build our models for AI. And then the next piece is how do you activate all that? And there's a team over there to help. So it's really spreading the wealth and expertise where it needs to go. >> Yeah, I love that. You took a couple of things that really resonated with me. You talked about context a couple of times and this notion of a federated model, because historically the sort of big data architecture, the team, they didn't have the context, the business context, and my inference is that's changing and I think that's critical. Your talk at Data Citizens is called how obsessive collaboration fuels scalable DataOps. You talk about the data, the DevOps team. What's the premise you put forth to the audience? >> So the point about obsessive collaboration is sort of taking the hubris out of your expertise on the data. Certainly there's a recognition by data professionals that the business understands and owns their data. They know the semantics, they know the context of it and just receiving the requirements on that was assumed to be okay. And then you could provide a data foundation, whether it's just a lake or whether you have a warehouse environment where you're pulling for your analytics. The reality is that as we move into more of AI machine learning type of model, one, more context is necessary. And you're kind of balancing between what are the things that you can ascribe to the data globally which is what data engineers can support. And then there's what is unique about the data and the context of the data that is related to the business value and outcome as well as the feature engineering that is being done on the machine learning models. So there has to be a really tight link and collaboration between the data engineers, the data scientists, and analysts, and the business stakeholders themselves. You see a lot of pods starting up that way to build the intelligence within the system. And then lastly, what do you do with that model? What do you do with that data? What do you do with that insight? You now have to shift your collaboration over to the work bench that is going to pull all these components together to create the experiences and the automation that you're looking for. And that requires a different collaboration model around software development. And still incorporating the business expertise from those stakeholders, so that you're satisfying, not only the quality of the code to run the solution, but the quality towards the outcome that meets the expectation and the time to value that your stakeholders have. So data teams aren't just sitting in the basement or in another part of the organization and digitally disconnected anymore. You're finding that they're having to work much more closely and side by side with their colleagues and stakeholders. >> I think it's clear that you understand this space really well. Hubris out context in, I mean, that's kind of what's been lacking. And I'm glad you said you used the word anymore because I think it's a recognition that that's kind of what it was. They were down in the basement or out in some kind of silo. And I think, and I want to ask you this. I come back to organization because I think a lot of organizations look the most cost effective way for us to serve the business is to have a single data team with hyper specialized roles. That'll be the cheapest way, the most efficient way that we can serve them. And meanwhile, the business, which as you pointed out has the context is frustrated. They can't get to data. So there's this notion of a federated governance model is actually quite interesting. Are you seeing actual common use cases where this is being operationalized? >> Absolutely, I think the first place that you were seeing it was within the operational technology use cases. There the use cases where a lot of the manufacturing industrial device. Any sort of IOT based use case really recognized that without applying data and intelligence to whatever process was going to be executed. It was really going to be challenging to know that you're creating the right foundation, meeting the SLA requirements, and then ultimately bringing the right quality and integrity to the data, let alone any sort of data protection and regulatory compliance that has to be necessary. So you already started seeing the solution teams coming together with the data engineers, the solution developers, the analysts, and data scientists, and the business stakeholders to drive that. But that is starting to come back down into more of the IT mindset as well. And so DataOps starts to emerge from that paradigm into more of the corporate types of use cases and sort of parrot that because there are customer experience use cases that have an IOT or edge component to though. We live on our smart phones, we live on our smart watches, we've got our laptops. All of us have been put into virtual collaboration. And so we really need to take into account not just the insight of analytics but how do you feed that forward. And so this is really where you're seeing sort of the evolution of DataOps as a competency not only to engineer the data and collaborate but ensure that there sort of an activation and alignment where the value is going to come out, and still being trusted and governed. >> I got kind of a weird question, but I'm going. I was talking to somebody in Israel the other day and they told me masks are off, the economy's booming. And he noted that Israel said, hey, we're going to pay up for the price of a vaccine. The cost per dose out, 28 bucks or whatever it was. And he pointed out that the EU haggled big time and they don't want to pay $19. And as a result they're not as far along. Israel understood that the real value was opening up the economy. And so there's an analogy here which I want to come back to my organization and it relates to the DataOps. Is if the real metric is, hey, I have an idea for a data product. How long does it take to go from idea to monetization? That seems to me to be a better KPI than how much storage I have, or how much geometry petabytes I'm managing. So my question is, and it relates to DataOps. Can that DataOps, should that DataOps individual maybe live, and then maybe even the data engineer live inside of the business and is that even feasible technically with this notion of federated governance? Are you seeing that and maybe talk a little bit more about this DataOps role. Is it. >> Yeah. >> Fungible. >> Yeah, it's definitely fungible. And in fact, when I talked about sort of those three units of there's your core enterprise data services, there's your BI and data, and then there's your line of business. All of those, the engineering and the ops is the DataOps which is living in all of those environments and being as close as possible to where the value proposition is being defined and designed. So absolutely being able to federate that. And I think the other piece on DataOps that is really important is recognizing how the practices around continuous integration and continuous deployment using agile methodologies is really reshaping. A lot of the waterfall approaches that were done before where data was lagging 12 to 18 months behind any sort of insights, but a lot of the platforms today assume that you're moving into a standard mature software development life cycle. And you can start seeing returns on investment within a quarter, really, so that you can iterate and then speed that up so that you're delivering new value every two weeks. But it does change the mindset this DataOps team aligned to solution development, aligned to a broader portfolio management of business capabilities and outcomes needs to understand how to appropriately scope the data products that they're delivering to incremental value-based milestones. So the business feels that they're getting improvements over time and not just waiting. So there's an MVP, you move forward on that and optimize, optimize, extend scale. So again, that CICD mindset is helping to not bottleneck and wait for the complete field of dreams to come from your data and your insights. >> Thank you for that, Michelle. I want to come back to this idea of collaboration because over the last decade we've seen attempts, I've seen software come out to try to help the various roles collaborate and some of it's been okay, but you have these hyper specialized roles. You've got data scientists, data engineers, quality engineers, analysts, et cetera. And they tend to be in their own little worlds. But at the end of the day we rely on them all to get answers. So how can these data scientists, all these stewards, how can they collaborate better? What are you seeing there? >> You need to get them onto the same process. That's really what it comes down to. If you're working from different points of view, that's one thing. But if you're working from different processes collaborating is really challenging. And I think the one thing that's really come out of this move to machine learning and AI is recognizing that you need processes that reinforce collaboration. So that's number one. So you see agile development in CICD not just for DataOps, not just for DevOps, but also encouraging and propelling these projects and iterations for the data science teams as well or even if there's machine learning engineers incorporated. And then certainly the business stakeholders are inserted within there as appropriate to accept what it is that is going to be developed. So processes is number one. And number two is what is the platform that's going to reinforce those processes and collaboration. And it's really about what's being shared. How do you share? So certainly what we're seeing within the platforms themselves is everybody contributing into some sort of a library where their components and products are being ascribed to and then that's able to help different teams grab those components and build out what those solutions are going to be. And in fact, what gets really cool about that is you don't always need hardcore data scientists anymore as you have this social platform for data product and analytic product development. This is where a lot of the auto ML begins because those who are less data science-oriented but can build an insight pipeline, can grab all the different components from the pipelines to the transformations, to capture mechanisms, to bolting into the model itself and allowing that to be delivered to the application. So really kind of balancing out between process and platforms that enable and encourage, and almost force you to collaborate and manage through sharing. >> Thank you for that. I want to ask you about the role data governance. You've mentioned trust and that's data quality, and you've got teams that are focused on and specialists focused on data quality. There's the data catalog. Here's my question. You mentioned edge a couple of times and I can see a lot of that. I mean, today, most AI is are a lot of value, I would say most is modeling. And in the future, you mentioned edge it's going to be a lot of influencing in real time. And people maybe not going to have the time or be involved in that decision. So what are you seeing in terms of data governance, federate. We talked about federated governance, this notion of a data catalog and maybe automating data quality without necessarily having it be so labor intensive. What are you seeing the trends there? >> Yeah, so I think our new environment, our new normal is that you have to be composable, interoperable, and portable. Portability is really the key here. So from a cataloging perspective and governance we would bring everything together into our catalogs and business glossaries. And it would be a reference point, it was like a massive Wiki. Well, that's wonderful, but why just how's it in a museum. You really want to activate that. And I think what's interesting about the technologies today for governance is that you can turn those rules, and business logic, and policies into services that are composable components and bring those into the solutions that you're defining. And in that way what happens is that creates portability. You can drive them wherever they need to go. But from the composability and the interoperability portion of that you can put those services in the right place at the right time for what you need for an outcome so that you start to become behaviorally driven on executing on governance rather than trying to write all of the governance down into transformations and controls to where the data lives. You can have quality and observability of that quality and performance right at the edge and context of behavior and use of that solution. You can run those services and in governance on gateways that are managing and routing information at those edge solutions and we synchronization between the edge and the cloud comes up. And if it's appropriate during synchronization of the data back into the data lake you can run those services there. So there's a lot more flexibility and elasticity for today's modern approaches to cataloging, and glossaries, and governance of data than we had before. And that goes back into what we talked about earlier of like, this is the new wave of DataOps. This is how you bring data products to fruition now. Everything is about activation. >> So how do you see the future of DataOps? I mean, I kind of been pushing you to a more decentralized model where the business has more control 'cause the business has the context. I mean, I feel as though, hey, we've done a great job of contextualizing our operational systems. The sales team they know when the data is crap within my CRM, but our data systems are context agnostic generally. And you obviously understand that problem well. But so how do you see the future of DataOps? >> So I think what's kind of interesting about that is we're going to go to governance on greed versus governance on right more so. What do I mean by that? That means that from a business perspective there's two sides of it. There's ensuring that where governance is run is as we talked about before executing at the appropriate place at the appropriate time. It's semantically domain-centric driven not logical and systems centric. So that's number one. Number two is also recognizing that business owners or business operations actually plays a role in this, because as you're working within your CRM systems, like a Salesforce, for example you're using an iPaaS MuleSoft to connect to other applications, connect to other data sources, connect to other analytics sources. And what's happening there is that the data is being modeled and personalized to whatever view insight our task has to happen within those processes. So even CRM environments where we think of as sort of traditional technologies that we're used to are getting a lift, both in terms of intelligence from the data but also your flexibility and how you execute governance and quality services within that environment. And that actually opens up the data foundations a lot more and avoids you from having to do a lot of moving, copying centralizing data and creating an over-weighted business application and an over, both in terms of the data foundation but also in terms of the types of business services, and status updates, and processes that happen in the application itself. You're drawing those tasks back down to where they should be and where performance can be managed rather than trying to over customize your application environment. And that gives you a lot more flexibility later too for any sort of upgrades or migrations that you want to make because all of the logic is contained back down in a service layer instead. >> Great perspectives, Michelle, you obviously know your stuff and it's been a pleasure having you on. My last question is when you look out there anything that really excites you or any specific research that you're working on that you want to share, that you're super pumped about? >> I think there's two things. One is it's truly incredible the amount of insight and growth that is coming through data profiling and observation. Really understanding and contextualizing data anomalies so that you understand is data helping or hurting the business value and tying it very specifically to processes and metrics, which is fantastic as well as models themselves like really understanding how data inputs and outputs are making a difference whether the model performs or not. And then I think the second thing is really the emergence of more active data, active insights. And as what we talked about before your ability to package up services for governance and quality in particular that allow you to scale your data out towards the edge or where it's needed. And doing so not just so that you can run analytics but that you're also driving overall processes and value. So the research around the operationalization and activation of data is really exciting. And looking at the networks and service mesh to bring those things together is kind of where I'm focusing right now because what's the point of having data in a database if it's not providing any value. >> Michele Goetz, Forrester Research, thanks so much for coming on theCUBE. Really awesome perspectives. You're in an exciting space, so appreciate your time. >> Absolutely, thank you. >> And thank you for watching Data Citizens '21 on theCUBE. My name is Dave Vellante. (upbeat music)

>> From around the globe, it's theCube covering Data Citizens '21, brought to you by Collibra. >> For the past decade, organizations have been effecting very deliberate data strategies investing quite heavily in people, processes, and technology specifically designed to gain insights from data, better serve customers, drive new revenue streams, we've heard this before. The results quite frankly have been mixed. As much of the effort is focused on analytics and technology designed to create a single version of the truth, which in many cases continues to be elusive. Moreover, the world of data is changing, data is increasingly distributed making collaboration in governance more challenging especially where operational use cases are a priority. Hello, everyone, my name is Dave Vellante and you're watching theCube's coverage of Data Citizens '21. And we're pleased to welcome Michele Goetz, who's the Vice President and Principal Analyst at Forrester Research. Hello, Michele, welcome to theCube. >> Hi, Dave thanks for having me today. >> It's our pleasure. So I want to start, you serve have a wide range of roles including enterprise architects, CDOs, chief data officers that is, the analyst et cetera, and many data related functions. And my first question is what are they thinking about today? What's on their minds? These data experts. >> So there's actually two things happening. One is what is the demand that's placed on data for our new intelligent digital systems. So we're seeing a lot of investment and interest in things like edge computing. And then how does that intersect with artificial intelligence to really run your business intelligently and drive new value propositions, to be both adaptive to the market as well as resilient to changes that are unforeseen. The second thing is then you create this massive complexity to managing the data, governing the data, orchestrating the data, because it's not just a centralized data warehouse environment anymore. You have a highly diverse and distributed landscape that you both control internally, as well as taking advantage of third party information. So really what the struggle then becomes is how do you trust the data? How do you govern it and secure or protect that data? And then how do you ensure that it's hyper-contextualized to the types of value propositions that our intelligence systems are going to serve? >> Well, I think you're hitting on the key issues here. I mean, you're right, the data and I sort of refer to this as well as sort of out there it's distributed as at the edge, but generally our data organizations are actually quite centralized. And as well, you talk about the need to trust the data, obviously that's crucial. But are you seeing the organization change? I know you're talking about this to clients, your discussion about collaboration. How are you seeing that change? >> Yeah, so as you have to bring data into context of the insights that you're trying to get or the intelligence that's automating and scaling out the value streams and outcomes within your business. We're actually seeing a federated model emerge in organizations. So while there's still a centralized data management and data services organization led typically by enterprise architects for data, a data engineering team that's managing warehouses and data lakes. They're creating this great platform to access and orchestrate information, but we're also seeing data and analytics and governance teams come together under chief data officers or chief data and analytics officers. And this is really where the insights are being generated from either BI and analytics or from data science itself and having dedicated data engineers and stewards that are helping to access and prepare data for analytic efforts. And then lastly, this is the really interesting part is when you push data into the edge, the goal is that you're actually driving an experience and an application. And so in that case, we are seeing data engineering teams starting to be incorporated into the solutions teams that are aligned to lines of business or divisions themselves. And so really what's happening is if there is a solution consultant who is also overseeing value-based portfolio management when you need to instrument the data to these new use cases and keep up with the pace of the business, it's this engineering team that is part of the DevOps work bench to execute on that. So really the balances we need the core, we need to get to the insights and build our models for AI. And then the next piece is how do you activate all that and there's a team over there to help? So it's really spreading the wealth and expertise where it needs to go. >> Yeah, I love that you to, a couple of things that really resonated with me. You talked about context a couple of times and this notion of a federated model, because historically the sort of big data architecture, the team, they didn't have the context, the business context, and you're the, my inference is that's changing. And I think that's critical. Your talk at Data Citizens is called how obsessive collaboration fuels scalable DataOps. You talk about the data, the DevOps team. What's the premise you put forth to the audience? >> So the point about obsessive collaboration is sort of taking the hubris out of your expertise on the data. Certainly, there's a recognition by data professionals that the business understands and owns their data. They know the semantics, they know the context of it and just receiving the requirements on that was assumed to be okay. And then you could provide a data foundation whether it's just a lake or whether you have a warehouse environment where you're pulling for your analytics. The reality is that as we move into more of AI machine learning type of model, one, more context is necessary and you're kind of balancing between what are the things that you can ascribe to the data globally which is what data engineers can support. And then there's what is unique about the data and the context of about the data that is related to the business value and outcome as well as the feature engineering that is being done on the machine learning models. So there has to be a really tight link and collaboration between the data engineers, the data scientists, and analysts, and the business stakeholders themselves. You see a lot of pods starting up that way to build the intelligence within the system. And then lastly, what do you do with that model? What do you do with that data? What do you do with that insight? You now have to shift your collaboration over to the work bench that is going to pull all these components together to create the experiences and the automation that you're looking for. And that requires a different collaboration model around software development and still incorporating the business expertise from those stakeholders so that you're satisfying, not only the quality of the code to run the solution, but the quality towards the outcome that meets the expectation and the time to value that your stakeholders have. So data teams aren't just sitting in the basement or in another part of the organization and digitally, disconnected anymore. You're finding that they're having to work much more closely and side by side with their colleagues and stakeholders. >> I think it's clear that you understand this space really well, hubris out, context in, I mean, that's kind of what's been lacking. And I'm glad you said, you used the word anymore because I think it's a recognition that that's kind of what it was. They were down in the basement or out in some kind of silo. And I think, and I want to ask you this, I'll come back to organization because I think a lot of organizations, look the most cost effective way for us to serve the businesses to have a single data team with hyper-specialized roles, that'll be the cheapest way, the most efficient way that we can serve them. And meanwhile, the business which as you pointed out has the context is frustrated. They can't get to data. So this notion of a federated governance model is actually quite interesting. Are you seeing actual common use cases where this is being operationalized? >> Absolutely, I think the first place that you were seeing it was within the operational technology use cases. The use cases where a lot of the manufacturing, industrial device, any sort of IoT-based use case really recognized that without applying data and intelligence to whatever process was going to be executed, it was really going to be challenging to know that you're creating the right foundation, meeting the SLA requirements, and then ultimately bringing the right quality and integrity to the data, let alone any sort of data protection and regulatory compliance that has to be necessary. So you already started seeing the solution teams coming together with the data engineers, the solution developers, the analysts, and data scientists, and the business stakeholders to drive that. But that is starting to come back down into more of the IT mindset as well. And so DataOps starts to emerge from that paradigm into more of the corporate types of use cases and sort of parrot that because there are customer experience use cases that have an IoT or edge component to them. We live on our smart phones, we live on our smart watches, we've got our laptops, all of us have been put into virtual collaboration. And so we really need to take into account not just the insight of analytics, but how do you feed that, you know, feed that forward. And so this is really where you're seeing sort of the evolution of DataOps as a competency not only to engineer the data and collaborate, but ensure that there sort of an activation and alignment where the value is going to come out and still being trusted and governed. >> I've got kind of a weird question, but I'm going to (indistinct). I was talking to somebody in Israel the other day and they told me masks are off, the economy's booming. And he noted that Israel said, "Hey, we're going to pay up for the price of a vaccine, the cost per dose around 28 bucks," or whatever it was. And he pointed out that the EU haggled big time and they go, "We're going to pay $19." And as a result, they're not, you know, as far along Israel understood that the real value was opening up the economy. And so there's an analogy here, which I want to come back to my organization and it relates to the DataOps. If the real metric is, "Hey, I have an idea for a data product." How long does it take to go from idea to monetization? That seems to me to be a better KPI than, you know, how much storage I have or how much petabytes I'm managing. So my question is, and it relates to DataOps, can that DataOps, should that DataOps individual maybe live and then maybe even the data engineer live inside of the business and is that even feasible technically with this notion of federated governance? Are you seeing that? And maybe talk a little bit more about this DataOps role. Is it-- >> Yeah. >> Fungible? >> Yeah, it's definitely fungible. And in fact, when I talked about sort of those three units of there's your core enterprise data services, there's your BI and data and then there's your line of business. All of those, the engineering and the ops is the DataOps which is living in all of those environments and being as close as possible to where the value proposition is being defined and designed. So absolutely being able to federate that. And I think the other piece on DataOps that is really important is recognizing how the practices around continuous integration and continuous deployment using agile methodologies is really reshaping a lot of the waterfall approaches that were done before where data was lagging 12 to 18 months behind any sort of insights, but a lot of the platforms today assume that you're moving into a standard mature software development life cycle. And you can start seeing returns on investment within a quarter really, so that you can iterate and then speed that up so that you're delivering new value every two weeks. But it does change the mindset, this DataOps team align to solution development, align to a broader portfolio management of business capabilities and outcomes needs to understand how to appropriately stop the data products that they're delivering to incremental value based milestones. So the business feels that they're getting improvements over time and not just waiting. So there's an MVP, you move forward on that and optimize, optimize, extend scale. So again, that CICD mindset is helping to not bottleneck and wait for the complete field of dreams to come from your data and your insights. >> Thank you for that, Michele. I want to come back to this idea of collaboration 'cause over the last decade, we've seen attempts. I've seen software come out to try to help the various roles, collaborate and some of it's been okay, but you have these hyper-specialized roles. You've got data scientists, data engineers, quality engineers, analysts, et cetera. And they tend to be in their own little worlds. But at the end of the day, we rely on them all to get answers. So how can these data scientists, all these stewards, how can they collaborate better? What are you seeing there? >> You need to get them onto the same process, that's really what it comes down to. If you're working from different points of view, that's one thing. But if you're working from different processes, collaborating is really challenging. And I think the one thing that's really come out of this move to machine learning and AI is recognizing that you need processes that reinforce collaboration. So that's number one. So you see agile development in CICD not just for DataOps, not just for DevOps, but also encouraging and propelling these projects and iterations before the data science teams as well or even if there's machine learning engineers incorporated. And then, certainly the business stakeholders are inserted within there as appropriate to accept what it is that is going to be developed. So process is number one. Number two is what is the platform that's going to reinforce those processes and collaboration. And it's really about what's being shared. How do you share? So certainly what we're seeing within the platforms themselves is everybody contributing into some sort of a library where their components and products are being ascribed to and then that's able to help different teams grab those components and build out what those solutions are going to be. And in fact, what gets really cool about that is you don't always need hardcore data scientists anymore as you have this social platform for data product and analytic product development. This is where a lot of the auto ML begins because those who are less data science oriented but can build an insight pipeline, can grab all the different components from the pipelines to the transformations, to capture mechanisms, to bolting into the model itself and allowing that to be delivered to the application. So really kind of balancing out between process and platforms that enable and encourage and almost force you to collaborate and manage through sharing. >> Thank you for that I want to ask you about the role of data governance. You've mentioned trust and that's data quality and you've got teams that are focused on and specialists focused on data quality. There's the data catalog and here's my question. You mentioned edge a couple of times and I can see a lot of that. I mean, today, most AI is a lot of the AI, I would say most is modeling. And in the future, you mentioned edge. It's going to be a lot of inferencing in real-time. And you know people maybe not going to have the time or be involved in that decision. So what are you seeing in terms of data governance, federate, we talked about federated governance, this notion of a data catalog and maybe automating data quality without necessarily having it be so labor-intensive. What are you seeing trends there? >> Yeah, so I think our new environment, our new normal is that you have to be composable, interoperable, and portable. Portability is really the key here. So from a cataloging perspective, in governance we would bring everything together into our catalogs and business glossaries. And it would be a reference point. It was like a massive Wiki. Well, that's wonderful, but why just how's it in a museum you really want to activate that. And I think what's interesting about the technologies today for governance is that you can turn those rules and business logic and policies into services that are composable components and bring those into the solutions that you're defining. And in that way, what happens is that creates portability. You can drive them wherever they need to go. But from the composability and the interoperability portion of that, you can put those services in the right place at the right time for what you need for an outcome so that you start to become behaviorally-driven on executing on governance, rather than trying to write all of the governance down into transformations and controls to where the data lives. You can have quality and observability of that quality and performance right at the edge in context of behavior and use of that solution. You can run those services and in governance on gateways that are managing and routing information at those edge solutions and where synchronization between the edge and the cloud comes up. And if it's appropriate during synchronization of the data back into the data lake, you can run those services there. So there's a lot more flexibility and elasticity for today's modern approaches to cataloging and glossaries and governance of data than we had before. And that goes back into what we talked about earlier of like this is the new wave of DataOps. This is how you bring data products to fruition now everything is about activation. >> So how do you see the future of DataOps? I mean, I kind of been pushing you to a more decentralized model where the business has more control 'cause the business has the context. I mean, I feel as though, hey, we've done a great job of contextualizing our operational systems. The sales team, they know when the data is crap within my CRM, but our data systems are context agnostic, which you know, generally and you obviously understand that problem well but so how do you see the future of DataOps? >> So I think what's kind of interesting about that is we're going to go to governance on greed versus governance on right, more so. What do I mean by that? That means that from a business perspective there's two sides of it. There's ensuring that where governance is run as we talked about before executing at the appropriate place at the appropriate time. It's semantically domain centric driven not logical and systems centric. So that's number one. Number two is also recognizing that business owners or business operations actually plays a role in this because as you're working within your CRM systems like a Salesforce, for example, you're using an I-PASS environment MuleSoft to connect to other applications, connect to other data sources, connect to other analytics sources, and what's happening there is that the data is being modeled and personalized to whatever view, insight, or task has to happen within those processes. So even CRM environments where we think of as sort of traditional technologies that we're used to are getting a lift to both in terms of intelligence from the data but also your flexibility and how you execute governance and quality services within that environment. And that actually opens up the data foundations a lot more and avoids you from having to do a lot of moving, copying, centralizing data, and creating an over-weighted business application and an over, you know, both in terms of the data foundation but also in terms of the types of business services and status updates and processes that happen in the application itself. You're drawing those tasks back down to where they should be and where performance can be managed rather than trying to over customize your application environment. And that gives you a lot more flexibility later too for any sort of upgrades or migrations that you want to make because all of the logic is contained back down in a service layer instead. >> Great perspectives, Michele, you obviously know your stuff and it's been a pleasure having you on. My last question is when you look out there anything that really excites you or any specific research that you're working on that you want to share that you're super-pumped about. >> I think there's two things. One is it's truly incredible the amount of insight and growth that is coming through data profiling and observation, really understanding and contextualizing data anomalies so that you understand is data helping or hurting the business value. And, you know tying it very specifically to processes and metrics which is fantastic as well as models themselves like really understanding how data inputs and outputs are making a difference whether the model performs or not. And then I think the second thing is really the emergence of more active data, active insights, as what we talked about before your ability to package up services for governance and quality in particular that allow you to scale your data out towards the edge or where it's needed and doing so, you know not just so that you can run analytics but that you're also driving overall processes and value. So the research around the operationalization and activation of data is really exciting. And looking at the networks and service mesh to bring those things is kind of where I'm focusing right now because what's the point of having data in a database if it's not providing any value. >> Michele Goetz, Forrester Research, thanks so much for coming on theCube really awesome perspectives. You're in an exciting space. So appreciate your time. >> Absolutely, thank you. >> And thank you for watching Data Citizens '21 on theCube. My name is Dave Vellante. (upbeat music)

>> Narrator: From around the globe, It's theCUBE with digital coverage of IBM Think 2021 brought to you by IBM. >> Hi, welcome back to theCUBE's coverage of IBM Think 2021 virtual. We're not yet in real life. We're doing another remote interviews with two great guests Cube Alumni. Of course, I'm John Furrier your host of theCUBE. We've got Jason McGee, IBM fellow VP and CTO of IBM's cloud platform and Octavian the Nazis senior vice president Hybrid Cloud engineering at NetApp. Both Cube alumni, is great to see you both. Thanks for coming on theCUBE >> Yeah, great to be here. >> Thanks for having us. >> So we were just talking before we came on camera that, it feels like we've had this conversation, a long time ago we have, Hybrid Cloud has been on a trajectory for both of you guys in many times on theCUBE. So now it's mainstream, it's here in the real world. Everyone gets it. There's no real debate. Now multicloud that's people are debating that which means that's right around the corner. So Hybrid Cloud is here and now Jason this is really the focus. And this is also brings together the NetApp in your partnership and talk about the relationship first with Hybrid Cloud. >> Yeah, I mean, you know look we've talked a number of times together. I think in the industry, maybe a few years ago people were debating whether Hybrid Cloud was a real thing. We don't have that conversation anymore. I think, you know, enterprises today, especially maybe in the face of COVID and kind of how we work differently now realize that their cloud journey is going to be a mix of on-prem and off-prem systems probably going to be a mix of multiple public cloud providers. And what they're looking for now is how do I do that? And how do I manage that hybrid environment? How do I have a consistent platform across the different environments I want to operate in? And then how do I get more and more of my workload into those environments? And it's been interesting. I think the first waves of cloud were infrastructure centric and externally application focused they were easier things. And now we're moving into more mission critical more stateful, more data oriented workloads. And that brings with a new challenges on where applications run and how we leverage the club. >> Octavian, you guys had a great relationship with IBM over the years, data centric company NetApp has always been great engineering team. You're on the cloud, Hybrid Cloud engineering. What's the current status of the relationship. Give us an update on how it's vectoring into the Hybrid Cloud since you're a senior vice president of Hybrid Cloud engineering. >> Well, so first of all, I want to recognize 20 years of a successful partnership with IBM. I think NetApp and IBM have been companies that have embraced digital transformation and technology trends to enable that digital transformation for our customers. And we've been very successful. I think there is a very strong joint Hybrid Cloud value proposition for customers on NetApps storage and data services compliment what IBM does in terms of products and solutions both for on-premise deployments in the cloud. I think together we can build more complete solutions, solutions that span data mobility, data governance for the new workrooms that Jason has talked about. >> And how has some of the customer challenges that you're seeing obviously software defined networking software defined storage, DevOps is now turned into DevSecOps. So you have now that programmability requirement with for dynamic applications, applications driven, infrastructure, all these buzz words point to one thing, the infrastructure has to be resilient and respond to the applications. >> Yeah, I would say infrastructure will continue to be top of mind for everybody, whether they're building a private cloud or whether they we're trying to leverage, something like IBM cloud. I think, you know, people want to consume, infrastructure is an API. I think they want to simplicity, security. I think they want to manage their costs very well. I think we're very proud to be partnering with IBM cloud to build such capabilities. >> Jason how are you guys helping some of these customers as they look at new things and sometimes retrofitting and refactoring previous stuff during transforming, but also innovating at the same time. There's a lot of that going on. What are you guys doing to help with the Hybrid challenges? >> Yeah, I mean, you know, there's a lot of dimensions to that problem but the one that I think has been kind of most interesting over the last year has been how kind of the consumption model of public cloud, API driven, self service, capabilities operated for you how that consumption model is starting to spread because I think one of the challenges with hybrid and one of the challenges as customers are looking at these, more mission critical data centric kind of workloads was well, I can't always move that application to the public cloud data center or I need that application to live out on the network, closer to my end users. So, you know, out where data is being generated, maybe in an IOT context and when you had those requirements you had to kind of switch operating models. You, you had to kind of move away from a public cloud service consumption model to a software deployment model. And we have a common platform and things like open shift that can run everywhere but the missing piece was how do I consume everything as a service everywhere? And so recently we launched this thing called IBM been satellite, which we've been working with Octavian and his team on, on how we can actually extend the public cloud experience back into the data center, out to the edge and allow people to kind of mix both location flexibility with public cloud consumption. And when you do that, you of course running a much more diverse infrastructure environment. You have to integrate with different storage environments and you wind up with like multi-tiered applications you know, some stuff on the edge and some stuff in the core. And so data replication and data management start to become really interesting because you're kind of distributing your workloads across this complex environment. >> Here we've seen that relationship between compute and storage change a lot over the past decade as the evolution goes. Octavian, I got to ask you this is critical path for companies. They want the storage ready infrastructure. You guys have been doing that for many decades pardon me with IBM, for sure. But now they're all getting Hybrid Cloud big time and it's not, it's attributed computing. It's what it is. It's the operating model. When someone asks you guys what your capabilities are how do you answer that in today's world? Because you have storage as well known. You got a great product people know that. But what is NetApp's capabilities? When I say I'm going all in and Hybrid Cloud complete changeover. >> So what we have been doing is basically rewriting a lot of our software with a few design points in mind. The software-defined has been definitely one of the key design points. The second is the Hybrid Cloud in the containerization of our operating systems. So they can run both in traditional environments as well as in the cloud. I think the last thing that we wanted to do it's enabled the speed of scale. And that has been by building, you know intrinsically in the product, both support or in also using Kubernetes as an infrastructure to achieve that agility, that scale. >> So how about this data fabric vision? Because to me, this is comes up all the time in my conversations with practitioners the number one problem at their, and problem that they're to solve in the conversation tends to I hear what was that control plane Kubernetes, horizontally scalable this all points to data being available., So how do you create that availability? What is data fabric mean? What does all this mean in a hybrid context? >> Well if you think about it, data fabric it's a Hybrid Cloud concept, right? This is about enabling data governance, data, mobility data security in an environment where some of the applications were run on premises or at the edge or the smart edge and many of the, perhaps data links and analytics, and services, rich services will be in a central locations or many or perhaps some large know data centers. So you need to have, the type of capabilities data services to enable that mobility that governance, that security across this continuum that spans the edge, the core and the cloud. >> Jason, you mentioned satellite before Cloud Satellite. Could you go into more detail on that? I know it's kind of a new product, what is that about and tell me what's the benefits and why is it exist and what problems does it solve? >> Yeah, so in the most simple terms, Cloud Satellite is the capability to extend IBM's, public cloud into on-prem infrastructure at the edge or in a multicloud context to other public cloud infrastructures. And so you can consume all the services in the public cloud that you need to to build your application, OpenShift as a service database, as DevTools, AI capabilities instead of being limited to only being able to consume those services in IBM's re you know, cloud regions you can now add your private data center or add your Metro provider, or add your AWS or Azure accounts and now consume those services consistently across all those environments. And that really allows you to kind of combine the benefits of public cloud with kind of location independence you see in hybrid and lets us solve new problems. Like, you know, it's really interesting. We're seeing like AI and data being a primary driver. You know I need my application to live in a certain country or to live next to my mainframe or to live like, in a Metro because all of my, I'm doing like video analytics on a bunch of cameras and I'm not going to stream all that data back to halfway across the country to some cloud region. And so it lets you extend out in that way. And when you do that, of course, you now move the cloud into a more diverse infrastructure environment. And so like we've been working with NetApp on how do we then expose NetApp storage into this environment when I'm running in the data center or I'm running at the edge and I need to store that data replicate the data, secure it. Well, how do I kind of plug those two things together? I think John, at the beginning you kind of alluded to this idea of, things are becoming more application centric, right? And we're trying to run a IT architecture that's more centered around the application. Well, by combining clouds knowledge of kind of where everything's running with that common platform like OpenShift with Kubernetes aware of data fabric and storage layer, you really can achieve that. You can have an application centric kind of management that spans those environments. >> Yeah, I'm want to come back to that whole impact on IT because this has come up as a major theme here. Think at the it transformation is going to be more about cloud scale, but I want to get to Octavian on the satellite on NetApp's role and how you compliment that. How do you guys fit in? He just mentioned that you guys are playing with Cloud Satellite. Obviously this looks like an operating model. How does NetApp fit in. >> Simply put we extend and enable the capabilities that IBM satellite platform provides. I think Jason referred to the storage aspects and you know what we are doing it's enabling not only storage but rich data services around new theory based on temperature, or replicated snapshots or capabilities around, you know, caching, high availability, encryption and so forth. So we believe that our technology integrate very well with red hat openShift and the Kubernetes aspect enable the application mobility in that translation of really distributed computing at scale, from the traditional data center to the edge and to the massive Ops that IBM is building. >> You know, I got to say, but watching you guys work together for many decades now and covering you with theCUBE for the past 10 years or 11 years now been a great partnership. I got to say one thing, that's obvious to me and our team and mainly the world is now you've got a new CEO over at IBM you have a Cloud Focus that's on unwavering. Arvind loves the cloud we all know that. Ecosystems are changing without, you already had a big ecosystem and partnerships. Now it seems to be moving to a level where you got to have that ecosystem really thrive in the cloud. So I guess we'll use the last couple of minutes. If you guys don't mind explaining how the IBM NetApp relationship in the new context of this new partnership new ecosystem or a new kind of world helps customers and how you guys are working together. >> Yeah, I mean, I could start, I mean I think you're right, that cloud is all about platforms and about kind of the overall environment people operate in and the ecosystem is really critical. And I think things like satellite have given us new ways to work together. I mean, IBM and NetApp, as we said I've been working together for a long time. We rely on them all in our public cloud, for example, in our storage tiers. But with the kind of idea of distributed cloud in the boundaries of public cloud spreading to all of these new environments, those were just new places where we can build really interesting valuable integrations for our clients so that they can deal with data, deal with these more complex apps, in all the places that they exist. So I think it's been actually really exciting to kind of leverage that opportunity to find, new ways to work together and deliver solutions for our clients. >> Octavian. >> I will say that data is the ecosystem and we all know that there's more data right now being created outside of the traditional data center be it in the cloud or at the edge. So our mission is to enable that, Hybrid Cloud or data mobility and enabled persistence rich data, storage services, whatever data is being created. I think IBM's new satellite platform, comes in and broadens the aperture of people being able to consume IBM's services at the edge and or a remote office and I think that's very exciting. >> You guys are both experts and solely season executives to DevOps, DevSecOps, DataOps, whatever you want to call data's here, ecosystems. Guys thanks for coming on theCUBE Really appreciate the insight. >> Thank you. >> Thank you. >> Okay, IBM Think CUBE coverage. I'm John Furrier, your host. Thanks for watching. (upbeat music) (tranquil music)