 From theCUBE Studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is a CUBE Conversation. Hey, welcome back, everybody. Jeff Frick here with theCUBE. We're in our Palo Alto Studios today for a CUBE Conversation. We're talking about data. We're always talking about data. And it's really interesting. You know, we like to go out and get you the first person insight from the people that start the companies, run the companies, the practitioners, and get the insight directly from them. We also like to go out and get original research and hear from original research. And this is a great opportunity to hear from both. So we're excited to have, and welcome back into the studio. He's Aaron Cobb. He's the co-founder of Elation, many-time CUBE alumni. Aaron, great to see you. Yeah, the things you're having with me. It's great to be here. Yeah, it's very cool. But today it's a special thing. We've never done this before with you. You guys are releasing a brand new report called the Elation State of Data Culture Report. So really interesting report. A lot of great information that we're going to dig in here for the next few minutes. But before we do, tell us kind of the history of this report. This is kind of the inaugural release. What was kind of behind it? Why did you guys do this? And give us a little background before we get into the details. Absolutely. So yes, that's exactly right. It's debuting today that we plan to kind of update this research quarterly and kind of see the trends over time. And this emerged because, part of my job I talked to chief data officers and chief analytics officers across our customer base and prospects. And I kept hearing anecdotally over and over that establishing a data culture is often the number one priority for these data leaders and for these organizations. And so we wanted to really say, can we quantify that? Can we agree upon a definition of data culture? And can we create sort of a simple yardstick to more objectively measure where organizations are on this sort of data maturity curve to get a data culture? Right. I love it. So you created this data index, right? The data culture index. And I think it's important to look at methodologies. I think people a lot of times go right to the results on reports before talking about the methodologies. And let's talk about the methodologies because we're supposed to be talking about data, right? So you talked to 300 some odd executives, correct? And I think it's really interesting and you broke it down into three kind of buckets of data literacy, if you will. Data search and discovery. Number one, data to kind of literacy in terms of their ability to work with the data. And then the third bucket is really data governance. And then in good form, ABCD, you gave them a four point score and basically are they doing it well? Are they doing it in the majority of the time? Are they doing it in about half? They got one or they got a zero and you get this four point scale and you end up with a 12 point scale which we're all familiar with from school, from an A to an A minus and B, et cetera. Just dig it a little bit on those three categories and how you chose those. So the first one again is kind of their data search and discovery, can they find it? And then their competency, if you will, and then governance and compliance. Kind of dig into each of those three buckets a little bit. For sure. So the end goal in data culture is to have an organization in which data is valued and decisions are made based on data and evidence, right? Versus a culture in which we go with the highest paid person's opinion or what we did last quarter or any of these other ways things get done. And so the idea is to make that possible. As you said, you have to be able to find the data when you need it. That's the data search and discovery. You have to be able to interpret that data correctly and draw valid conclusions from it. And that's data literacy, excuse me. And both of those are contingent upon having data governance in place so that data is well-defined and has high data quality and all these other aspects so that it's possible to find it and understand it properly. Right. And one of the things too that I think is really important that we call that and again we're going to dive into the details is your perceived execution versus the reported execution by the people that are actually providing data. And I think you've found and you've highlighted on specific slides that there's not necessarily a match there and sometimes what you perceive is happening isn't necessarily what's happening when you go down and query the people in the field. So really important to come up with a number. And I think you said this is going to be an ongoing thing over a period of time so you kind of start to see longitudinal changes in these organizations. Absolutely. And we're very excited to see those trends over time but even at the outset this very striking effect emerges which is as you said if we ask one of these 300 data leaders all around the world actually if we ask how is the data culture at your company overall in this very broad general top-down way and have them graded on the A to S scale we get results where there's a large gap between kind of that level of maturity and what emerges in a bottom up methodology excuse me in which you ask about governance and literacy and search kind of by department in a more bottom up way. And so we do see that it can be helpful even for data people to have a more granular metric and framework for quantifying their progress. Right. So let's jump into some of the results. It's fascinating. They're kind of all over the map but there's some definite trends. One of the trends you talked about is that there's a lot of questions on the quality of the data that that's a real inhibitor to people whether that suspicion is because it's not good data and I don't know this question for you is do they think it's not relevant to the decision that's being made? Is it an incomplete data set or the wrong data set? There seems to be that keeps coming up over and over about decision makers not necessarily having confidence in the data. What can you share a little bit more color around that? Yeah, it's quite interesting actually. So what we find is that 90% so nine in 10 people, 10 executives are thought to question the data sometimes often are always but the part that's maybe disappointing or concerning is the two thirds of executives are believed to ignore the data and make a decision kind of pushing the data aside which is really quite striking when you think about it why have all this data if more often than not you're sort of disregarding it to make your final answer and so you're absolutely correct that when we dug into why what are the reasons behind pushing data aside? Data quality was number one and I think it is a question of oh is the data inaccurate? Is it out of date? These are sort of concerns sort of we hear from customers and prospects but as we dig in deeper in the survey results, excuse me we see some other reasons behind that one is a lack of collaboration between the data and analytics folks and the business folks and so there's a question of I don't know exactly where this data came from or to your point kind of how it was produced what was the methodology? How was it sourced? And maybe because of that disconnect there's a lack of trust so trust really is the ultimate I think barrier to having data culture really take root Right and it's trust in this trust as you said not only in the data per se the source of the data, the quality of the data the relevance of the data but also the people who are providing you with the data and obviously you get some data sets sometimes you didn't get other data sets so that's really a little bit disconcerting the other thing I thought was kind of interesting is it seems to be consistent that the primary reason that people are using big data projects is around operations and operation efficiency a little bit about compliance but you know it's interesting we had you on at the MIT CDOIQ Chief Data Information Officer Pauli Symposium and you talked about the goodness of people moving from kind of a defensive posture to an offensive posture, you know using data in terms of product development and innovation and what comes across in this survey is that's kind of down the list behind you know kind of operational efficiency we're seeing a little bit of governance and regulation but the quest for data as a tool for innovation didn't really shine through in this report well you know it's very interesting it depends whether you look at the aggregate level or you break things down a little bit more so one thing we did after we got that 0 to 12 scale on the data culture index or DCI is it actually we were able to break it down into thirds and among the sort of bottom third that has the least well-established data culture by this yardstick we found that governance and regulatory compliance was the number one application of data but among the top third of respondents we actually found the opposite where things like providing a great customer experience doing product innovation those sort of things actually came to the fore and governance fell behind so I think there is this curve where it's table stakes to get the sort of defense side of data figured out and then you can move on to offense and using data to make your organization meet its other goals right, right and then I wonder too to get your take on kind of the democratization of data right this is a trend that's been going on and really I think you've said before you know your guys whole mission is to empower curious and rational world to give people the ability to ask the right questions have the right data and get the right answer so you know we've seen democratization in terms of the access to the data the access to the tools the ability to do something with the data and the tool and then the actual authority to execute a business decision based on that the results on that seem a little bit split here because a lot of the problems seem to be focused on leadership not necessarily taking a database decision move but on the good hand a lot of people trying to break down data silos and make data more accessible for a larger group of people so that more people in the organization are making database decisions seems kind of like this little bit of a bifurcation between the C-suite and everybody else trying to get their job done. Absolutely there's always this question of you know sort of the organizational wide initiative and then what's happening on the ground one thing we saw that was very heartening and aligns with where our customers have had success is a real emphasis being placed on having data governance and data context and data literacy factors sort of be embedded at the point of use so not expecting people to just like take a course and look things up and kind of interrupt their workflow to be able to use data quickly and accurately and interpret it in valid ways so that was really exciting to see as an initiative it sort of bridges that gap along with initiatives to have more collaboration and integration between the data people and the business people because really they exist to serve one another but in terms of the disconnect between the C-suite and other parts of the org there was a really interesting inverse correlation well or maybe it's not inverse depending on how you look at it but basically when we talk to C-level executives and ask does the C-suite ignore data do they question data, et cetera those numbers came in lower than when we talk to a senior director about the C-suite so the farther you get there's a difference there from my perspective I almost wonder whether that distance is actually a more objective viewpoint and when you're in that role it's hard to even see your cognitive biases and your tendency to ignore data when it doesn't suit you. Right, right so there's some other interesting things here so one of them is kind of predictors one of the whole reason to do studies and collect data so that we can have some predictive ability and it comes out here that the reporting structure is a strong predictor of a company's data tier structure so there's the whole rise of the chief data officer and the chief analytics officer and the chief data analytics officer and lots of conversations about those rules and what exactly are those rules and who do they report to your study finds a pretty compelling leading indicator that if that role is reporting to either the CEO or the executive board which is often one of the same person that that's actually a terrific indicator of success in moving to a more data centric culture. That's absolutely correct so we found that that top third of organizations on the data culture index were much more likely to have a chief data executive a CDO, CAO or CDAO in fact they're more likely to have folks with the analytics in their title because in some organizations data is thought to mean sort of raw data infrastructural defense and analytics is sort of where it gets infused into business processes and value but certainly that top third is much more likely to have that the chief data executive reporting into the executive board or CEO when the highest ranking data executive is under the CIO or some other part of the organization those orgs tend to score far lower on the DCI. Right, right, so it's interesting you know you're a really interesting guy you've been doing this for a while and you were at Siri before you were at Elation so you have a really good feel for kind of what data can do and can't do and natural human or natural language processing and human voice interaction with these devices are really interesting case study and they can do a really good job within a small defined dataset and instruction set but they don't do necessarily so well once you kind of get outside how they're trained and you've talked a lot about how metaphors shape the way that we think and I know you and Dave and talked about data oil and data lakes and I don't want to necessarily go down that whole path but I do think it's important and what came out of the study and the way people think about data you know there's a lot of conversation how do you value data? Is data used to just be an expense that we had to buy servers to store the stuff we weren't sure what we ever did with it so I wonder if there's any kind of top level metaphorish level kind of thought or process or framing in the companies that you studied that came out maybe not necessarily in the top line data but maybe in some of the notes that help define why some people are being successful at making this transition and putting kind of data out front of their decision processing versus data either behind as a supporting thing or maybe data I just don't have time with it or I don't trust it or God knows where you got that and it's not the data that I wanted was there any kind of tangential or anecdotal stuff that came out of the study that's more reflective of the softer parts of a data culture versus the harder parts in terms of titles and roles and job responsibilities? Yeah, it's a really interesting place to explore. I do think there's a, I don't want to make this overly simplistic or binary but at the end of the day it's like anything else within an organization you can view data as a liability to say, okay, we have for example, customers names and phone numbers and passwords and we just need to prevent an adverse event in which there's a leak or some sort of infosec problem that could cause bad press and fines and other negative consequences. And I think the issue there is if data is a liability the best case is that it's worth zero as opposed to some huge negative on your company's balance sheet. And I think intuitively if you really want to prevent data misuse and data problems, one fails safe but I think ultimately in this only risky way to do that was just not collect any data and not store it. So I think that the transition is to say, look data must be protected and taken care of that's set to zero but it's really just the beginning and data is this asset that can be used to inform the huge company level strategic decisions that are made in annual planning at the board level down to the millions of little decisions every day in the work of people in customer support and in sales and in product management and in various roles across industries. And I think once you have that shift the upside is potentially unbounded and it just changes the way you think. And suddenly instead of saying, oh data needs to be kind of hidden away it's more like, oh people need to be trained on data use and empowered with data and it's all about not if it's used and if it's misused but really how it's used and why it's used what is being used for to make a real impact. Right. It's funny, I just remember being back in business school one of the great things that helps teach you is to think in terms of data, right? And you always have the infamous center consulting interview question how many manhole covers are there in Manhattan? Right, so to start to think about that problem from a data centric point of view really gives you a leg up and even where to start and how to attack those types of problems. And I thought it was interesting talking about challenges for people to have a more data centric point of view. It's interesting. The report said basically everybody said there's all kinds of challenges around data quality and compliance and data democratization but the bottom companies, the bottom companies said that the biggest challenge was lack of buy-in from company leadership. So I guess the good news, bad news is is that there's a real opportunity to make a significant change and get your company from the bottom third to a middle third or a top third simply by taking a change in attitude about putting data in a much more central role in your decision-making process. Cause all the other stuff's kind of operational execution challenges that we all have and all the people, blah, blah, blah. But in terms of attitude of leadership and prioritization that's something that's very easy to change if you so choose and really seems to be the key to unlock this real journey as opposed to the minutiae of a lot of the little details that are a challenge for everybody. Absolutely and your changing attitudes might be the easiest thing or the hardest thing depending on who the person is, but I think you're absolutely right. The first step, which could maybe should be easy is admitting that you have a problem or maybe to put it more positively realizing you have an opportunity. I love that. And then just again, looking at the top tier companies, the other thing that I thought was pretty interesting in the study is I'm looking at it here is getting champions in each of the operational segments. So rather than, I mean, a cheap data officer is important and somebody kind of at the high level to shepherd it in this executive suite as we just discussed, but within each of the individual tasks and functions and roles, whether that's operations or customer service or product development or operational efficiency, you need some type of champion, some type of person, you know, banging the gavel, collecting the data, smoothing out the complexities, helping people get their thing together. And again, another way to really elevate your position on the score. Absolutely. And I think this idea of again, bridging between, you know, if data is centralized, you have a chance to try to really get excellent practices within the data org, but then it becomes even more essential to have those ambassadors, people who are in the business and understand all the business context who can sort of make the data relevant, identify the key areas where data can really help, maybe demystify data and pick the right metaphors and the right examples to make it real for the people in their function. Right, right. So Aaron, there's a lot of great stuff. People can go to the website at elation.com. I'm sure you'll have a link to this very prominently displayed, but, and they should, and they should check it out and really think about it and think about how it applies to their own situation in their own department company, et cetera. I just wanted to give you the last word before we sign off, you know, kind of what was the most, you know, kind of positive affirmation or not the most, one or two of the most kind of affirming outcomes of this exercise? And what were one or two of the things that were a little concerning or, you know, kind of surprises on the downside that came out of this research? Yeah, so I think one thing that was maybe surprising or concerning the biggest one is sort of where we started with that disconnect between, you know, what people would say as an off the cuff overall assessment and the disconnect between that and what emerges when we go department by department and process to be pillars of data culture from a search and discovery to data literacy to data governance. The thing that disconnect, you know, should give one pause. I think certainly it should make one think, hmm, maybe I shouldn't look from 10,000 feet, but actually be a little more systematic and considering the framework I use to assess data culture if that is the most important thing to my organization. I think though, there's this quote that you move what you measure just having this hopefully simple but not simplistic yardstick to measure data culture and the data culture index should help people be a little bit more realistic in that quantification and the track their progress, you know, quarter over quarter. So I think that's very promising. I think another thing is that, you know, sometimes we ask how long have you had this initiative? How much progress have you made? And it can sometimes seem like pushing a boulder uphill. Obviously the COVID pandemic and the economic impacts of that has been really tragic and really hard. You know, a tiny silver lining in that is the survey results showed that organizations have really observed a shift in how much they're using data because sometimes things are changing but it's like a frog in boiling water. You don't realize it. And so you just assume that the future is going to look like the recent past and you don't look at the data or you ignore the data or you miss parts of the data. And a lot of organizations said, you know, COVID was this really troubling wake-up call but it could even after this crisis is over produce an enduring change in which people are consulting data more and making decisions in a more data-driven way. Yeah, certainly an accelerant. That is for sure. Whether you wanted it, didn't want it. Thought you had it at the time, didn't have time. You know, COVID is definitely the digital transformation accelerant and data is certainly the thing that powers that. Well, again, it's the elation state of data culture report available. Go check it out elation.com. Aaron, always great to catch up. And again, thank you for doing the work and supporting this research. And I think it's really important stuff and it's going to be interesting to see how it changes over time. Cause that's really when these types of reports really start to add value. Thanks for having me, Jeff. And I really look forward to discussing some of those trends as the research is completed. All right, thanks a lot, Aaron. Take care. All right. He's Aaron, I'm Jeff. You're watching theCUBE. Follow out though. Thanks for watching. We'll see you next time.