 Hey, everyone. I'm Lisa Martin covering Data Citizens 22, brought to you by Calibra. This next conversation is going to focus on the importance of data culture. One of our CUBE alumni is back. Stan Christians is Calibra's co-founder and it's Chief Data Citizen. Stan, it's great to have you back on the CUBE. Hey, Lisa. Nice to be here. We're going to be talking about the importance of data culture, data intelligence, maturity, all those great things. When we think about the data revolution that every business is going through, you know, it's so much more than technology innovation. It also really requires cultural transformation, community transformation. Those are challenging for customers to undertake. Talk to us about what you mean by data citizenship and the role that creating a data culture plays in that journey. Right. So, as you know, our event is called Data Citizens because we believe that, in the end, a data citizen is anyone who uses data to do their job. And we believe that today's organization, you have a lot of people, most of the employees in an organization are somehow going to be a data citizen. Right. So, you need to make sure that these people are aware of it. You need to make sure that these people have the skills and competencies to do with data what is necessary. And that's on all levels. Right. So, what does it mean to have a good data culture? It means that if you're building a beautiful dashboard to try and convince your boss we need to make this decision, that your boss is also open to and able to interpret, you know, the data presented in that dashboard to actually make that decision and take that action. Right. And once you have that, why through the organization, that's when you have a good data culture. Now, that's a continuous effort for most organizations because they're always moving somehow, they're hiring new people. And it has to be a continuous effort because we've seen that on the one hand, organizations continue to be challenged with controlling their data sources and where all the data is flowing, right? Which in itself creates a lot of risk. But also on the other hand of the equation, you have the benefit. You know, you might look at the regulatory drivers, like we have to do this, right? But it's much better right now to consider the competitive drivers, for example. And we did an IDC study earlier this year. Quite interesting. I can recommend anyone to read it. And one of the conclusions they found as they surveyed over a thousand people across organizations worldwide is that the ones who are higher in maturity, so the organizations that really look at data as an asset, look at data as a product and actively try to be better at it, tend to have three times as good a business outcome as the ones who are lower on the maturity scale, right? So you can say, okay, I'm doing this, you know, data culture for everyone, waking them up as data citizens. I'm doing this for competitive reasons. I'm doing this for regulatory reasons. You're trying to bring both of those together. And the ones that get data intelligence right are just going to be more successful and more competitive. That's our view. And that's what we're seeing out there in the market. Absolutely. We know that just generally, stand right, the organizations that are really creating a data culture and enabling everybody within the organization to become data citizens are, we know that in theory they're more competitive, they're more successful. But the IDC study that you just mentioned demonstrates they're three times more successful and competitive than their peers. Talk about how Calibra advised its customers to create that community, that culture of data when it might be challenging for an organization to adapt culturally. Of course, of course it's difficult for an organization to adapt. But it's also necessary, as you just said, imagine that, you know, you're a modern day organizer, phones, laptops, what have you. You're not using those ID assets, right? You're delivering them throughout the organization but not enabling your colleagues to actually do something with that asset. Same thing is true with data today, right? If you're not properly using the data asset and your competitors are, they're going to get more advantage. So as to how you get this done or how you establish this culture, there's a few angles to look at, I would say, Lisa. So one angle is obviously the leadership angle whereby whoever is the boss of data and the organization, you typically have multiple bosses there, like a chief data officer, sometimes there's multiple, but they may have a different title, right? So I'm just going to summarize it as the data leader for a second. So whoever that is, they need to make sure that there's a clear vision, a clear strategy for data and that strategy needs to include the monetization aspect. How are you going to get value from data? Yes. Now, that's one part because then you can clearly see the example of your leadership in the organization and also the business value. And that's important because those people, their job in essence really is to make everyone in the organization think about data as an asset. And I think that's the second part of the equation of getting that culture, right? It's not enough to just have that leadership out there, but you also have to get the hearts and minds of the data champions across the organization. You really have to win them over. And if you have those two combined and obviously a good technology to connect those people and have them execute on their responsibilities, such as a data intelligence platform like Calibra's, then you have the pieces in place to really start upgrading that culture inch by inch. Yes, I like that. The recipe for success. So you are the co-founder of Calibra. You've worn many different hats along this journey. Now you're building Calibra's own data office. I like how before we went live, we were talking about Calibra is drinking its own champagne. I always loved to hear stories about that. You're speaking at Data Citizens 2022. Talk to us about how you are building a data culture within Calibra. And what maybe some of the specific projects are that Calibra's data office is working on? Yes, and it is indeed data citizens. There are a ton of speakers here, I'm very excited. We have Barb from MIT speaking about data monetization. We have DJ Patil at the last minute on the agenda. So really exciting agenda. Can't wait to get back out there. But essentially, you're right. So over the years at Calibra, we've been doing this now since 2008. So a good 15 years. And I think we have another decade of work ahead in the market just to be very clear. Data is here to stick around as are we. And myself, when you start a company, we were four people in a garage, if you will. So everybody's wearing all sorts of hats at that time. But over the years, I've run pre-sales at Calibra, I've run both sales partnerships, product, et cetera. And as our company got a little bit bigger, we're now 1,200 something like that people in the company, I believe. Systems and processes become a lot more important. So we said, Calibra isn't the size of our customers yet, but we're getting there in terms of organization, structure, process systems, et cetera. So we said, it's really time for us to put our money where our mouth is and to set up our own data office, which is what we were seeing that all of our customers are doing and which is what we're seeing that organizations worldwide are doing. And Carter was predicting us as well. They said, okay, organizations have an HR unit, they have a finance unit. And over time, they'll all have a department, if you will, that is responsible somehow for the data. So we said, okay, let's try to set an example with Calibra, let's set up our own data office in such a way that other people can take away with it, can take away from it. So we set up a data strategy, we started building data products to care of the data infrastructure, that sort of good stuff. And in doing all of that, we said exactly, as you said, we said, okay, we need to also use our own product and our own practices, right? And from that use, learn how we can make the product better, learn how we can make the practice better and share that learning with all of the markets, of course. And on the Monday mornings, we sometimes refer to that as eating our own dog food on Friday evenings, we refer to that as drinking our own champagne. So we had the driver to do this, there's a clear business reason. So we involved, we included that in the data strategy and that's a little bit of our origin. Now, how do we organize this? We have three pillars. And by no means is this a template that everyone should follow. This is just the organization that works at our company, but it can serve as an inspiration. So we have a pillar, which is data science, the data product builders, if you will, or the people who help the business build data products. We have the data engineers who help keep the lights on for that data platform to make sure that the products, the data products can run, the data can flow, and the quality can be checked. And then we have a data intelligence or data governance builder where we have those data governance, data intelligence stakeholders who help the business as a sort of data partner to the business stakeholders. So that's how we've organized it. And then we started following the Culebra approach, which is, well, what are the challenges that our business stakeholders have in HR, finance, sales, marketing, all over? And how can data help overcome those challenges? And from those use cases, we then just started to build a roadmap and started execution on use case after use case. And a few important ones there are very simple. We see them with all our customers as well. People love talking about the catalog, right? The catalog for the data scientists to know what's in their data lake, for example, and for the people and legal and privacy, so they have their process registry and they can see how the data flows. So that's a popular starting place and that turns into a marketplace, so that if new analysts and data citizens join Culebra, they immediately have a place to go to, to look and say, okay, what data is out there for me as an analyst or a data scientist or whatever to do my job, right? So they can immediately get access to the data. And another one that we did is around trusted business reporting. We've seen that since 2008, self-service BI allowed everyone to make beautiful dashboards, pie charts, I always, my pet peeve is the pie chart because I love pie and you shouldn't always be using pie charts, but essentially there's become a proliferation of those reports and now executives don't really know, okay, should I trust this report or that report? They're reporting on the same thing, but the numbers seem different, right? So that's why we have trusted business reporting. So we know if a report, a dashboard, a data product essentially is built, we know that all the right steps are being followed and that whoever is consuming that can be quite confident in the results, either silver or brown. Exactly, yes. Absolutely. Talk a little bit about some of the key performance indicators that you're using to measure the success of the data office. What are some of those KPIs? The KPIs and measuring is a big topic in the data chief data officer profession, I would say. And again, it always varies with respect to your organization, but there's a few that we use that might be of interest to use. So remember, you have those three pillars, right? And we have metrics across those pillars. So for example, a pillar on the data engineering side is going to be more related to that uptime, right? Is the data platform up and running? Are the data products up and running? Is the quality in them good enough? Is it going up? Is it going down? What's the usage? But also, and especially if you're in the cloud and if consumption is a big thing, you have metrics around cost, for example, right? So that's one set of examples. Another one is around the data science and the products, are people using them? Are they getting value from it? Can we calculate that value in a monetary perspective, right? And so that we can, to the rest of the business, continue to say, we're tracking all those numbers and those numbers indicate that value is generated and how much value is estimated in that region. And then you have some data intelligence, data governance metrics, which is, for example, you have a number of domains in the data mesh spirit. People talk about being the owner of a data domain, for example, like product or customer. So how many of those domains do you have covered? How many of them are already part of the program? How many of them have owners assigned? How well are these owners organized, executing on their responsibilities? How many tickets are open, closed? How many data products are built according to process and so on and so forth? So these are a set of examples of KPIs. There's a lot more, but hopefully those can already inspire the audience. Absolutely. So we've talked about the rise in cheap data offices. It's only accelerating. You mentioned this is like a 10-year journey. So if you were to look into a crystal ball, what do you see in terms of the maturation of data offices over the next decade? So we've seen, indeed, the role sort of grow up. I think in 2010, there may have been like 10 cheap data offices or something. Gartner has exact numbers on them. But then they grew, you know, 400. They were like mostly in financial services, but they expanded then to all industries and the number is estimated to be about 20,000 right now. And they evolved in a sort of stack of competencies, defensive data strategy, because the first cheap data offices were more regulatory driven. Offensive data strategy, support for the digital program, and now all about data products, right? So as a data leader, you'd now need all of those competencies and need to include them in your strategy. How is that going to evolve for the next couple of years? I wish I had one of those crystal balls, right? But essentially, I think for the next couple of years, there's going to be a lot of people, you know, still moving along with those four levels of the stack. A lot of people I see are still in version one and version two of the cheap data office review. So you'll see over the years, that's going to evolve more digital and more data products. So for the next three, five years, my prediction is it's all going to be about data products because it's an immediate link between the data and the dollar essentially, right? So that's going to be important. And quite likely, some new things will be added on which nobody can predict yet, but we'll see those pop up in a few years. I think there's going to be a continued challenge for the Chief Data Officer role to become a real executive role, as opposed to, you know, somebody who claims that they're executive, but then they're not, right? So the real reporting level into the board, into the CEO, for example, will continue to be a challenging point. But the ones who do get that done will be the ones that are successful. And the ones who get that done will be the ones that do it on the basis of data monetization, right? Connecting value to the data and making that very clear to all the data citizens in the organization. Right. And in that sense, they'll need to have both, you know, technical audiences and non-technical audiences aligned, of course, and they'll need to focus on adoption. Again, it's not enough to just have your data office be involved in this. It's really important that you're waking up data citizens across the organization, and you make everyone in the organization think about data as an asset. Absolutely, because there's so much value that can be extracted if organizations really strategically build that data office and democratize access across all those data citizens. Stan, this is an exciting arena. We're definitely gonna keep our eyes on this. Sounds like a lot of evolution and maturation coming from the data office perspective, from the data citizen perspective. And as the data show that you mentioned in that IADC study mentioned Gartner as well, organizations have so much more likelihood of being successful and being competitive. So we're gonna watch this space. Stan, thank you so much for joining me on theCUBE at Data Citizens 22. We appreciate it. Thanks for having me over. From Data Citizens 22, I'm Lisa Martin. You're watching theCUBE, the leader in live tech coverage. Okay, this concludes our coverage of Data Citizens 2022 brought to you by Calubra. Remember, all these videos are available on demand at thecube.net. And don't forget to check out siliconangle.com for all the news and wikibod.com for our weekly breaking analysis series where we cover many data topics and share survey research from our partner, ETR, Enterprise Technology Research. If you want more information on the products announced at Data Citizens, go to calibra.com. There are tons of resources there. You'll find analyst reports, product demos. It's really worthwhile to check those out. Thanks for watching our program and digging into Data Citizens 2022 on theCUBE, your leader in enterprise and emerging tech coverage. We'll see you soon.