 Hi, I'm Peter Burris, and welcome to another CUBE Conversation from theCUBE Studios in beautiful Palo Alto, California. Got a great conversation today. We're going to be talking about some of the new advances that are associated with big data analytics and improving the rate at which human beings, people who actually work with data, can get more out of their data, be more certain about their data and improve the social system that actually is dependent upon data. And to do that, we've got Aaron Kalb of Elation here with us. Aaron is the co-founder and is VP of Design and Strategic Initiatives. Aaron, welcome back to theCUBE. Thanks so much for having me, Peter. So Aaron, let's start this off. The concern that a lot of folks have when they think about analytics, big data, and the promise of some of these new advanced technologies is they see how they could be generating significant business value, but they observe that it often falls short. And it falls short for technological reasons. Setting up the infrastructure is very, very difficult, but we've started solving that by moving a lot of these workloads to the cloud. They also are discovering that the tool change could be very complex, but they're starting to solve that by working with companies, with vision like Elation, about how you can bring these things together more easily. So there are some good things happening within the analytics space, but one of the biggest challenges is that even if you set up your pipelines and your analytics systems and applications right, you still encounter resistance inside the business because human beings don't necessarily have a natural affinity for data. Data is not something that's easy to consume. It's not something that's easy to recognize. People just haven't been trained in it. So we need more that makes it easy to identify data quality, data issues, et cetera. Tell us a little bit about what Elation's doing to solve that human side, the adoption side of the challenge. It's a great point and a great question, Peter. So fundamentally what we see is it used to be a problem of quantity. There wasn't enough ability to generate data assets and to distribute them and to get to them. And now there's just an overwhelming amount of places to gather data. And so the problem becomes finding development data for your need, understanding it and putting it into context and most fundamentally trusting that it's actually telling you a true story about the world. And what we find now is there's been more and more self-service analytics. There's more and more dashboards and queries and content being generated. And often an executive will look at two different answers to the same question that are trending in totally different directions and they'll say I can't trust any of this. On paper I want to be data driven, but in actuality I'm just going to go back to my gut because the data is not always trustworthy and it's hard to tell what's trustworthy and what's not. So this is even after they found the data and enough people have been working on it to say, to put it in context, to say yes, this data is being used in marketing or this data is being used in operations production. So there's another layer of branding or whatnot that we can put on data that says this data is appropriate for use in this way. Is that kind of what we're talking about here? Absolutely right. So to help with finding and understanding data, you can sort of group it and make it browsable by topic. You can enable keyword search over it in natural language and stuff that Elation has done in the past. And what we're excited to unveil now is this idea of trust check, which is all about saying wherever you're at and sort of that data value chain of taking raw data and schematizing it and eventually producing pretty dashboards and visualizations that at every step we can ensure that only the most trustworthy data sets are being used because any problem upstream flows downstream. So trust check. Trust check. So trust check, it's something that comes out of Elation. Is it also being used with other visualization tools or other sources or other applications? That's, it's a great question. So it's all over the above. So trust check starts with saying if I'm an analyst who wants to create a dashboard or a visualization, I might have to write some SQL query to do that. And what we've done in that context with Elation Compose is our sort of homegrown SQL tool. It's provided a tool and trust check kind of gets its name from spell check, right? It used to be there was a dictionary and you could look it up by hand and you could look it up online, but that's a lot of work for every single word to check it. And then, and then, you know, Microsoft I think was the first innovative saying, oh, let's put a little red squiggle that you can't miss right in your workflow as your writing. So you didn't have to go to it. It comes to you. And we do the exact same thing. I'm about to query a table that is, you know, deprecated or has a data quality issue. I immediately see bright red on my screen, can't miss it, and I can fix my behavior. That's as I'm creating a data asset. We also, through our partnerships with Salesforce and with Tableau, each of whom have, you know, very popular visualization tools to say if people are consuming a dashboard, not a SQL query, but looking at a Tableau dashboard or visualization in Salesforce, Einstein Analytics, what would it mean to badge right there in them but a stamp of approval on the most trustworthy sources and a warning or caveat on things that might have an upstream data quality problem? So when you say warning or caveat, you're saying literally that there are exceptions or there are other concerns associated with data and revealing that as part of the analytic process. That's exactly right. So much like, again, spell check, you know, underlines or looking at, if you think about, you know, if I'm driving in my car with ways and it says, oh, traffic up ahead, you know, view out this way, what does it mean to get in the user interface, where people live, whether they're a business user in Salesforce or Tableau or a data analyst in a query tool, right there in their flow having on-screen indications of everything happening kind of below that tip of the iceberg that affects their work and the trustworthiness of the data sets they're using. So that's what it is. So I'll tell you a quick story about spell check. Many years ago, I'm old enough that I was one of the first users of some of these tools. And when you typed in IBM, Microsoft Word often changed it to D-U-M, which was kind of interesting, given the things that were going on between them. But it lead you to ask questions. So how does this work? I mean, how does spell check work? Well, how does trust check work? Because that's going to have an enormous implication. People have to trust how trust test works. Tell us a little bit about how trust check works. Absolutely. So how do you trust trust check? So the little red or yellow or kind of bright salient indicators we've designed are just to get your attention. Then as a user, you can sort of click into those indicators and see why is this appearing. And the biggest reason that an indicator will appear in a trust check context is that a person, a data curator or data steward, has put a warning or a deprecation on the data set. So it's not, oh, IBM doesn't like Microsoft or vice versa. You can kind of see the sourcing. It isn't just, oh, because maybe Webster says so. It sort of emerges from the logic of your own organization. But now Elation has this entire catalog backing trust check where it gives a bunch of signals that can help those curators and stewards to decide what indicators to put on what objects. So for example, we might observe, this table used to be refreshed frequently. It hasn't in a while. Does that mean it's right for getting a bit of a warning on it? Or people aren't really using this data set. Is there a reason for that? Something upstream was just flag having to get a quality issue. That data quality issue might flow downstream like pollution in a creek. And that can be an indication of another reason why you might want to label data as not trustworthy. So in Elation context with Salesforce and Tableau partners and perhaps some others, this trust check ends up being a social moniker for what constitutes good data that is branded as a consequence of both technological as well as social activities around that data captured violation. Have I got that right? That's exactly right. We're taking technical signals and social signals because what happens in our customers today before we launch trust check, what they would do is if you had the time you would phone a friend and you'd say, hey, you seem to be data savvy. Does this number look weird to you? Do you know what's going on? Is something wrong with the table that it sourced from? And the problem is, that happens on vacation and you're out of luck, right? And this is saying, let's push everything we know across that entire chain from the rawest data to the most polished asset and all that information pushed up to where you live in the moment you're making a decision. Should I trust this data? How should I use it? So in the whole, going back to this whole world, the big data analytics, we're moving more of the workloads to the cloud to get rid of the infrastructure problems. We're utilizing more integrated tool chains to get rid of the complexity associated with a lot of the analytic pipelines. How does trust check then applied go back to this notion of human beings not being willing to accept somebody else's data? Give us that kind of use case of how someone's going to sit down in a board room or in a strategic meeting or whatever else it is and see trust check and go, I get it. Absolutely, it's a fantastic question. So there's two reasons why even though all organizations are 80% according to Gartner, claim they're committed to being data driven, you still have these moments where people say, yeah, I see the numbers, but I'm going to ignore them or discount them or be very skeptical of them. One issue is just how much of the data that gets to you in the board room or the exact team meeting is wrong. We had an incredibly successful data driven customer who did an internal audit and found that a third of the numbers that appeared in the PowerPoint presentations on which major business decisions were being made, a full third of them were off by an extraordinary amount so big that it would, the decision would have cut the other way had the number been accurate. So the sheer volume of bad data coming in to undermine trust and the second is even if only 5% of the data were untrustworthy, if you don't know which is which, the 95% of the trustworthy and the 5% it's not, you still might not be able to use it with confidence. And we believe that having trust check be at every stage in this data value chain will solve actually both problems by having that sort of spell check experience in the query tool which is where most analytics projects start. We can reduce the amount of garbage going into the meeting rooms where business choices are being made and by putting that badge saying this is certified or takes with grain of salt or this is totally wrong by putting that badge on the visualizations that business leaders are looking at in Salesforce and Tableau and over time and ideally every tool that anybody would use in an enterprise. We can also help distinguish the wheat from the chaff in that context as well. So we think we're attacking both parts of this problem and that will really drive a data-driven culture truly being adoptable in order to do so. I want to tie a couple of things as you said here. You mentioned the word design a couple of times and you're the VP of design at Elation but it also sounds like when you're talking about design you're not just talking about design of the interface or the software you're talking about design of how people are going to use the software. What is the extent to which design or what's the scope of design as you see it in this context of advanced analytics and is trust check just a first step that you're taking? Tell us a little bit about that. Yeah, it's a great set of questions Peter. So design for us means really looking at humans and starting by listening and watching. You know, a lot of people in the cataloging space and the governance space, they list a lot of should statements. People should adopt this process because otherwise mistakes will be made. As Gardner said, 80% of you dev. Right, exactly. And we think the shoulds only get you so far. We want to really understand the human psychology. How do people actually behave when they're under pressured to move quickly in a rapidly changing environment? When they're afraid of being caught having made a mistake. There's all these precious people are under. And so it's not realistic to say, again you could imagine saying, oh, every time before you go out the door go to MapQuest or some sort of traffic website and look up the route and print it out so you make sure you plot correctly. No one has time for that. Just like no one has time to look up every single word in their essay or their memo or their email and look it up in the dictionary to see if it's right. But when you have an intervention that comes into somebody's flow and is impossible to miss and is an angel on your shoulder keeping you from making a mistake or in car navigation that tells you in real time here's how you should route. Those sort of things fit into somebody's lifestyle and actually move impact. So our idea is let's meet people where they are, acknowledge the challenges that humans face and make technology that really helps them and comes to them instead of scolding them and saying, oh, you should change your flow in this uncomfortable way and come to us and it's the only way you'll achieve the outcome. So invest the tool into the process and then the activity as opposed to force people to alter the activity around the limitations and capabilities of the tool. Exactly right. And so while design is optimizing the exact color and size and UI, UX both in our own tools and working with our partners to optimize that it's sort of an even bigger level of saying how do we design the entire workflow so humans can do what they do best and the computer just gives them what they need in real time. And if something is important and kind of takes a full circle if something is important and potentially strategic as advanced analytics having that holistic view is really going to determine success or failure in a lot of businesses. That is absolutely right, Peter. And you asked earlier, is this just the beginning? And that's absolutely true. Our goal is to say whatever part of the analytics process you are in that you get these real time interventions to help you get the information that's relevant to you understand what it means in the context you're in, right? And make sure that it's trustworthy and reliable so people can be truly data driven. Yeah, well there's a lot of invention going on but what we're really seeking here is changes in social behavior that lead to consequential improvements in business. Aaron Kalb, VP of Design and Strategic Initiatives at Elation, thanks very much for talking about this important advance and how we think about analytics. Thanks much for having me, Peter. And this is again, Peter Burris. This has been a CUBE conversation. Until next time.