our Studios in the heart of Silicon Valley Palo Alto California this is a cute conversation hi welcome to the cube studio for another cube conversation where we go in-depth with thought leaders creating new business outcomes with technology I'm your host Peter Burris one of the biggest challenges that enterprises face as they try to get more value out of their data is how to establish as the practices how to establish the processes and the tooling necessary to both discover data liberate data and communicate data and its value to the organization to have that conversation we've got a great guest Aaron kal who's a co-founder and chief data officer revelation Aaron welcome to the conversation Peter thank you for having me so give us a quick update what's going on with relation things relations are very very exciting so you know from the very beginning we had one main goal to create technology that empowers people to be more curious make better choices to help them find relevant data understand the trust that uses it and we use it that the organization and we're just very happy to keep getting more and more customers and make a bother and broader impact through them now showing how unbelievably attentive I am I noticed that I that you are now chief data officer so your titles changed what does that entail what's going on yeah it's a pretty recent change in a moment were very excited about I think from the very beginning you know we've been preaching you know data-driven organization but we haven't been able to practice what we've preached as much since we've been comparatively so much smaller than our customers what's exciting now is that we've collected enough data and a wide enough network of customers there's an opportunity to really be more data-driven internally and also to kind of have all of these chief data officer x' and other data people they donors like me in our network to synthesize the learnings across all of them about how to build a data culture and kind of take that in and share it back out so we can all go through this journey together so I'm gonna tell you I have been a chief data officer skeptic but I'll explain why but if I could just summarize what you just said there's gonna be an operational part of your job generally but also an advocacy part of your job externally to help catalyze some of your conversations but let me tell you why I've been something of a skeptic and you tell me how things are gonna change you know what what vector were on so to speak see do as a job often was something that people went for because of digital change or a new media change and new types of marketing it's been a job that's been all over the map it's had different definitions different roles different sets of responsibilities when I think of any chief I say you give the chief title to someone who's going to generate you know superior returns on the assets entrusted to them so what that means to me is that the chief data officer should be someone who's going to create competitive or superior returns on the data assets that have been entrusted to them is that kind of how you see it - that's exactly right and this is a term in a title that we're borrowing from our customers who've been very very successful with it and and the goal is exactly that first of all to protect the data and ensure that it's being used appropriately and is well governed that's the defense but then going on offense and ensuring that all that data is actually driving business value and business impact that's the fundamental role of the position the only thing I would I would maybe amend what you said is as chief my management style is really it's just about empowering everybody in the organization within the division and across the company 2 billion drivers impact well it's a leadership job exactly yeah so chiefest you know you're supposed to you're supposed to use the resources at your disposal to generate returns out of those resources and it's obviously it's a leadership job but let's let's walk through that a little bit not so much focusing on how elations CDO is going to operate but let's talk about your customers because one of the observations I'd make is that elation now has a large enough footprint and presence in the industry where you now have significant numbers of customers and I'm sure you're seeing the variety of insights and practices that customers are using to get value out of data so I got to believe that partly this is discovering those new practices those new procedures turning that into a pedagogy something that folks can actually use to improve the way they do things and then helping alation build or participate in the tool change necessary to actually establish those disciplines how far off am i you are spot on so so as you said we have over a hundred production customers now well over that and they all are different in different ways depending on on their geography and their vertical but there are many commonalities we see and our goal is to basically learn from all of them and synthesize those learnings and then push them back out to our network and also apply them internally and sometimes applying that means making changes to our software and sometimes it means just sharing best practices and thought leadership within our network and and beyond so to give a very particular example you know one thing that we'd thought about a little bit but we really learned from our customers was the power that kind of competition and and and and kind of game theory can can play in helping people be successful in their data initiative so gamification gamification exactly yes so we saw for example some of our customers there what they called data duals or metadata duals where a different departments would compete to document their data more thoroughly for accurate outcomes and and they would get cakes they had you know metadata on them it's gotta fund on time we'd seen the word metadata printed on a cake probably in the history of baking an email a different customer in a different region different vertical came out with a doc you Jam which is taking the idea of a hackathon and that's a little bit less competitive and what more collaborative people kind of shoulder-to-shoulder doing data documentation it's a very similar thing of using kind of human psychology to better drive forward data projects we saw in two different places and we thought okay how can we have strapped out a principle from this and we're looking both at integrating some of these principles directly into our product and also sharing other ways the different customers could benefit from the basic concept all right so where are we you've got 100-plus hundred-plus customers now you're an acknowledged leader in the in the catalog world we generally believe the catalogs are going to be an important feature of virtually every successful data-driven digital business because it's going to be one of the places where you actually or data and other assets derived from that data models and whatnot so where are we as as this new CDO where are we in the adoption of what you today would regard as the best practices how is that happening in the industry we have a skills gap are we starting to see that be closed a bit as as more companies start to gain the experience they need to be successful in this yeah you know it's funny there's sort of a learning curve with any new technology or any principal and we see customers and prospects all along that curve and we start kind of mapping out the shape just to give a sense of different extremes you know a few years ago what everybody was talking about people would say I'm a data person and there's people in my company who just don't get it who see the data and instead of being appropriately skeptical and saying I'm not sure how this was sourced they'll just say add the other shmurda here's how I used to do it at my old job and we're just gonna do it that way because it's how we've always done it and you know there was there was that sort of a defensive Nisour resistance to data now we're seeing some customers who have jumped way past that I was talking to a data scientist at one of our customers who said basically they have a recommendation engine in their enterprise and people who years ago might have been completely ignoring it are now just blindly doing whatever it said and and she was saying his own set of implications it does and she said look you know as a data scientist I know how the sausage is made for this engine I wouldn't want to eat that sausage it worries me the people are just putting it on their mouths so to speak and this elaborate metaphor and so I think I think you know the pendulum can swing back and forth what we're trying to work on with our customers is how do you teach individuals to engage in that data culture to be skeptical in the right ways not defensive but to ask where did this come from how is it computed you know the questions that can actually help you interpret it correctly and put it to use and I'd go to the other extreme of you know basically a deferring to the algorithm entirely and taking out all human judgment well and I think that's the important thing is that all these any systems accommodation machines doing things and human beings doing things will take out those animal driven systems for many years ago machines doing things and people doing things and you when you use machines to do things the tech industry has been really good at diffusing knowledge very very quickly so it's over time it's difficult to have your machinery be the source of differentiation so over time humans will consistently be the source of differentiation in your business and how they render judgment and what they determine the priorities and the commitments that they make and sustaining keeping those commitments so catalog to me seems to be an especially important feature of any digital transformation or data-driven process going forward because it touches people and because people use it and it will will also touch other systems and other elements but people remain essential to catalog design and the notion of catalog experience are you seeing that as well and is that helping you to stay close to these CDOs and you know really driving the people oriented process or knowledge about people or any processes you know absolutely throughout our time adulation over the last you know seven years we've always seen people as extremely central and I think one of our key differentiators philosophically was where a lot of data management was sort of thinking about what's good for the computer oh we can save a couple bits by using some lookup code that doesn't mean that's you know comprehensive all we said well what is the human consumer of data what do they need and a lot of our technology has actually been again bringing the human back into the fold of what's been to kind of computer and machine dominated and then the other thing you mentioned it's really critical is we're in an age where automation is very exciting there are a lot of wins there one thing that I hear from C do after C do as I talk to is a three-phased process for bringing data into the organization phase one is is descriptive analytics what happened last quarter then there's predictive analytics what's gonna happen next quarter and the final goal is prescriptive analytics for your computer what holds you what to do well yeah and and where the computer can act you know before any humans even looked at it or been in the loop and I think it's an interesting aspiration especially for certain things that are really really urgent but these are all garbage in garbage out processes and the good news is that if you're looking at a place where the humans in the loop they can say you know what that doesn't look right in that graph and maybe it's a problem with the ETL job or with thesaurus data and they can set them for something Bad's happened so I think as you progress down this evolution there are great rewards but also greater risks and our hope is that with a catalog you can make sure that whatever process you're feeding instead of garbage in garbage out it's the best data that's up-to-date that's trustworthy its contextualized for the business process ok one last question you've you're now in a new role operational external what's the first two things that you want to accomplish in this new role especially on the as you as it pertains to working with your customers what are you really focused on right now yeah so one of our core values at halation is that we listen as though we could be wrong because we know that that's part where data company is you know how do we learn from from numeric and other kinds of signals that come in to always be growing and improving and so step one unambiguously is to listen as much as I can to the incredibly smart innovative thoughtful customers that we have and try to synthesize the best learnings across all of them I think the next step is to then is to then do that synthesis and say oh what do we see this happening in retail that could pertain to finance or vice versa and figure out kind of what is that that curve and how can we kind of either push everybody up the steep parts of the curve so we can all be more data-driven and more curious and I'm more rational together or even have you know the software kind of lower that curve and right before your two great points so it's faster up or use the tool to flatten a curve exactly it's very wise man interview well Aaron this has been a great conversation once again I want to thank you for joining us on another cube conversation my name is Peter Burris see you next time thank you Peter you [Music]