 Welcome, everyone, to this CUBE Conversation on Elations, the State of Data Culture Maturity. It was a research report that they recently finished up. I'm your analyst and host, Rob Streche. I'm joined today by Julie Smith, Director of Data and Analytics with Elation. Welcome, Julie. Hi, Rob. Good to be here. Glad to have you on board, because I think this is super exciting information. The CUBE research that I'm an analyst part of, we really look at this and look at how people are building data products, how they're really bringing AI to bear in their organizations, and a lot of that has to do with being able to have the right data in that product, in that AI, so that you get the right results. And I think today I'm excited that we're going to unpack some of the research Elation has done on this front, because I think that really that is one of the big keys. So let's kind of jump into it, and why don't you kind of give an overview of who Elation is, and tell us a little bit more the next level down on the State of Data Culture Maturity research report that you just finished up. Well, absolutely. So Elation's business is healthy organizations find, understand, and trust data. And we do that using our data catalog, our data intelligence platform. And believe it or not, I was a customer of Elation before I came to work for Elation to perform their data analytics function internally for them. So I actually represent inside Elation what our customers go through, what our customers are seeking. And I think Elation, something that I tried to meet with Elation as a company, as a proposition, was the fact they recognized some time ago the pivotal role that people and data culture play in an organization's success, in particular to organizations that want to become data-driven and use data in other ways, with AI being the most recent evolution of that. We've seen our own customers who range from Fortune 100 customers down to small to mean rent prizes face similar challenges in slightly different guises. And we wanted to understand a little bit more about that. So since 2020, Elation had been doing research into data culture and the trends inside there and the perception of it by industry professionals. So this year, we went out to almost 300 industry professionals. And these are not all Elation customers at all. And they're very widespread so that we're not getting any sort of leanings in the data towards people who were already owning a data catalog. And we've got the results to see now. And speaking as someone who is a data professional, I find it really interesting to see these things out there in the wider world. It could be pretty isolating if we didn't get to talk to other professionals in networking and also reading these reports and seeing people like me and their perception of what's happening. It gives you a good sense about what's out there that you're not the only one fighting challenges and similar and yeah, you know, how we can progress this and see what challenges are out there and how they're going to get to solve. I think that is key because I think that it is so data catalogs aren't necessarily new, but I think how they're being utilized and the importance of them has just grown immensely over the last year, you know, since we just went by chat GPT's birthday and all of that and how people build data products to be able to be used. And I think those new personas, and I think we're still in early days from as you were talking about, organizations really having a data culture. So, you know, let's dive into the findings. The report found that building data culture is fundamental and necessary for organizations aiming to thrive and drive business success in a digital age, which is just key as people are going through their digital transformations and they're trying to build data products sometimes off existing data, sometimes off net new data, data across different silos. And I think this was some of the key elements of what a mature data culture and how can organizations measure their progress in that journey was really, some of the things that you uncovered was here's some of the kind of the KPIs that people can utilize to measure themselves. And not to say, well, as data people, we always like to measure ourselves against things. I think the thing about data culture, although it's an often used term now is it can be hard to sort of get a handle on it and to articulate exactly what it involves and therefore to measure yourself against that. And so actually on building on our research into the state of data culture over recent years, and we've now developed our own sort of data maturity framework which looks at four different pillars. So we look at leadership, which we look at the drive from the top to promote the use of data, be that through your CDOs, CDAOs, whatever guys that is, but is there a strong leader in the organization to help permeate that and who else in the leadership roles in the organization are helping to drive that. Then there's governance, data governance, which is evolving a lot and much more rapidly these days than it has for some time in to enable us to use data. It's about compliance, yes, but it's also about facilitating the use of data and giving so much more value to it. And there's data, as you see, understanding the data, what it can do, what it is telling you, how to respect it in the entire life cycle at MyHealth. And finally, the one that I think probably was the thing that elation sprung from in the first place, which was the data searching discovery. Because you might have great data out there, but if people don't know where it is, the context of it, how to access it, then they're not gonna be able to be data driven because they have no data to be driven by. Yeah, no, I think you hit on two really key, I think really key and important pieces of this and when we start to talk to data professionals ourselves and from data engineering, data developers, you had app developers, now we see this role of data developer coming about where it's somewhat of a hybrid of data engineering, you have platform engineering, you have data platforms, it's very complex. And I think not only that, it's not like all of this is net new data. So what are the challenges companies are facing when they're trying to integrate data governance, which is not the easiest thing in the world, into existing systems and how are they looking or how can they overcome these challenges of bringing that data governance to already existing data? I think that the technical challenges behind data governance do exist and having to try and drive those changes into them and getting alignment on definitions and the compliance side of it and access and security and so on and so forth. But when we're interacting with people and those data governance professionals around what are their biggest challenges, you have in implementing this. And the main answer, the top answer that comes back is people, is the data culture. And technically, they might say, we know what to do. It's getting that understanding of what's involved and how that governance will work. Yeah, that totally makes sense. I think we hear the same thing. It's not necessarily the technology. It's the people in the process is that you wrap around the technology or are enabled by the technology. But I think you had some really good, I read through the report, got an early look at it, which was, I really appreciate, because I think there's some really key data points in the report. The report found a link between data leadership and revenue returns with 89% of respondents with strong data leadership, say their organization met or exceeded revenue goals in the past year. Kind of walk us through some of the data points that you found in this. Yeah, so, I mean, that's quite an overwhelming figure, isn't it? That figure, that overwhelming number, especially in the current climate, gained that so much more benefit. And I think it just underlines the fact that using good data to inform decision making results in good decisions and that strong leadership will influence the progress on all those pillars of data culture to try and achieve those results. This is the stuff when people talk about being data driven, it's those kind of results coming out of this that vindicate the whole effort being put in there. And some of the results that actually came out of our survey was about how the transition now is that data governance is so much more about business value and showing that and it's less about compliance than otherwise. And so people are now listening to the data leadership that's coming in and it's showing that business value, it's showing the benefits to the business of what it can do for all of them. No, that's key. And I think that, you know, again, what you highlighted earlier on, having that top down buy-in for governance and all of this and data leadership in particular and building that culture from the top down, not just from the bottoms up, building of applications and data products is really key. I also think, you know, again, taking the next step down into some of the data, you know, modern organizations also have a heightened focus on data governance with 70% of the respondents sharing that their organizations are focused on improving governance in the coming year. What emerging trends in data governance and culture should organizations be prepared to be able to be and stay competitive in this market? Yeah, I mean, as you recognize there, 70% of the respondents were recognizing that they wanted to build on their data governance. But the other part of the statistics that came out there was only a quarter of them felt they'd actually achieved widespread data governance. So there's a huge scope for people to carry on improving in this area. And I think it's this evolution of data governance now, you know, the perception of data governance while the word governance, it gives this sense of it's the ministry of data. They're here to put the shackles on us. It's all about compliance. It's all about, you know, auditing, et cetera, et cetera. And now people are realizing that actually it is about the business value that can be unlocked and that if you have well-run data, you know, it can provide you with so much more advantage than data that isn't looked after and isn't of good quality and similar. And we're seeing what we classified as defensive data governance where it's there to protect the organization from fines. Too offensive, which is we're using this to gain our competitive advantage. We're using it to get our data in good shape so that we really can exploit it for the asset it really is. I think something else we're seeing as well is that, you know, you've got centralized models for data governance. We're seeing more of that become federated as people within the business organization to realize that they can possibly get more adaptive and flexible about how things are performed in those different areas. And I think that, you know, the more people understand around it, the more that model will become common as people embrace it rather than defensive around it and don't want it to come into their areas. You mentioned AI and, you know, this is one of those hype cycles, you know, it was another trend that's coming in. But I love the fact that it suddenly focuses as attention on our data and what AI can possibly do for people. And I think that it is translate that to what you're showing people the possibilities they can do with their data that helps the data governance cause, if you like, and that has so many more wider benefits. And so the need for AI, I'm hoping, we'll see will drive data governance improvement by that same token. Better data governance can drive better AI because for trusted AI, you need trusted data. And it's that foundation that we still need for all of these things. So I see that data governance is gonna be much more prominent because people realize we need this for this AI. And whichever next technology comes in, it gets all excited. Let's kind of jump into the modern organization is really changing and also having to have an, I would say a heightened focus on data governance. I think one of the stats that really jumped out at me was the 70% of respondents shared that their organizations are focused on improving governance in the coming year. What emerging trends in data governance and culture should organizations be prepared for to stay competitive in this market? I think it's huge as people look at where they're going in the future and how they build these data products and AI. What should they really be focused on? Yeah, the study you came up with there, which is the 70% of the organizations are looking to strengthen their data governance, which we've highlighted in our report. There was also another stat in there that showed only a quarter of organizations felt they had widespread governance and how long this governance been about for that to be the case. And I think there's a new change in the perception of data governance and that has led to this 70% now actually concentrating and making it one of their priorities. And people are realizing that your governance is no longer just compliance. It is about business value. It is about the advantages that having well-run data can provide to you. We used to have what was now regarded as defensive data governance where you are fending off fines from various organizations when actually now we're looking at offensive data governance. I don't mean it's insulting. I mean, it's actually trying to get better to do an advantage via the use of data to achieve your business priorities and objectives. What we're also seeing is as the business becomes more aware of the advantages it can give, you're not looking at a monolithic data governance organization very much centralized when they still exist, but there'll be a federated model with more people doing governance in areas of the business. And that, I think, will help it permeate more readily through the business than it might have done previously. I think that we've mentioned AI. I don't think any webinar at the moment doesn't mention AI. There's a huge focus on it. And I love the focus because I love not purely for AI and the possibilities that it's gonna garner people working with data and businesses, et cetera. But it's the attention it's focusing on the need for good data because people are realizing that you need trusted data for trusted AI. And again, this is gonna help the data governance cause and the overall data cause to feed the fact that, well, for good AI, we need better governance. So let's put input into our governance so we can then trust our AI. So the two are kind of pulling each other along cause the need for AI in the current hype cycle that we see in there and then what it's dragging us for or with data governance. But also data governance, the more forward thinking we are around that, the more it will pull on what we're able to do with AI. So the two are going hand in hand. And I think that the focus that it's having and the imagination that it's capturing out there in people who perhaps aren't particularly data people, but how to go with chat GPT and so on. And they're really starting to get the picture. And yeah, I'm excited by what it's gonna yield. Yeah, I think there's two very important things you hit on. One is kind of almost what we've talked about with security, which is there was a shift left and it becomes a federated security model where the developers have to take on more of that responsibility. I think you're dead on with the data governance. It's gotta be that way. It can't be one monolithic. There can be, I think standards put out centrally, but they have to actually be that federated governance. And I think it's gonna take a good amount of tooling to actually make that happen in a really efficient manner. And I think the second part you hit on with AI is that it definitely has raised and we're hearing it from the organizations. We talked to, I'm sure you're hearing it from every organization is how do I understand what data is going into my AI? Where is it from? What is this lineage? How do I understand? And I understand the quality because garbage in, garbage out. And depending on what you're using that AI for, that use case becomes critical. Same thing with building data products. If you can always build a data product one way and depending if you're using AI or even analytics or what have you, you may get a separate answer that you was unexpected, which really is not what is intended. So, but I think let's kind of jump back to the data culture because I think really, this is a place where I think jumped out at me in this report was organizations seem to not yet have embraced a data culture. They're building data products but maybe not in a data culture way. You know, what advice would you give to organizations that are starting their journey towards a data-driven culture? I would say do not underestimate it because of this situation, which is technology, that's pretty straightforward to deal with. People on the other hand, which make up the culture, they're always going to be hard because not one size fits all. You need to adapt approaches. You need to empathize with them and understand where they're going to. You can have the most amazing data strategy laid out for where you want to go, how you want your data to be used, where you're gonna source your data from, how you're gonna assess its quality and other things around that. But if you haven't thought about the change management around that and the involvement of the people so they're gonna feel ownership around it and what they're gonna understand around it. One of my favorite piece of Drucker's saying of culturally strategy for breakfast, he comes to question. So, you know, tackle it head on. Make sure you're raising the people elements as part of your strategy and your drive forward. And measure where you are now, though, as well. I mean, you know, we discussed earlier about that we create this framework with the sort of four pillars which you can sort of gauge yourself against having that ability to look at it and show progress as well because culture can sometimes be such an intangible thing. But having a way of measuring it, have a way of understanding it and showing that progress is a great benefit because if you can show progress, it encourages more progress and the benefits that you're yielding from it. So it's not gonna happen overnight, you know? But if you can get that strong leadership across the org, if you can put appropriate provisions in place for data literacy, if you can modernize your data governance program to make it appealing and not sound like you're putting the shackles on everybody. And, you know, that will newly empower your workforce to go out and discover that data and trust that data to the right level. And it will serve down so much the better. And, you know, it can become self-fulfilling as people see the success that areas are having. They all themselves want that success. Yeah, no, it makes sense. And I think, again, this is one of those things where I think the rise of AI actually helps in this because people are very interested in what's going on with data, with their data and things of that nature. And I think organizations really are super interested in how they bring their organizations to think differently about data. So, you know, that has to be one of the keys that you see within your customers and within the study. Yeah, indeed. I mean, you use the term there, the rise of AI, which sparks in my head, the terminator, the rise of the machine. And, you know, I mean, data literacy can help people understand a bit more about what we're talking about when we talk about AI. And, you know, the balance with it there isn't that because you can have people who will blindly follow it and not question what is being generated versus those will be incredibly skeptical about it because they don't fully understand what's going on. And so good education, good understanding of what's happening and what's being fed into it and the context in which to take the results. And we'll definitely yield a pair out from for anybody using it. Totally agree. Well, Julie, I really thank you for coming on this CUBE conversation today. You know, again, the insights you're bringing from the data and from having been the customer are key. Thank you very much, Rob, and it's been a pleasure. And thank you all for tuning in to this CUBE conversation. For more insights on data, data organization, data products, data platforms, data here, data there, data everywhere in this fast evolving area, stay tuned. I'm Rob Streche, your analyst and host. You're watching the CUBE, your leader in enterprise tech, news and analysis.