>> Announcer: From around the globe. It's theCUBE covering Data Citizens '21 brought to you by Collibra. >> Everybody, John Walls here on theCUBE, continuing our coverage of Data Citizens '21 with Michael Kuzma, who is a Senior Data Engineer at Lockheed Martin but he has just not any Senior Data Engineer. He is the Collibra Ranger of the Year, an outstanding award that certainly honors Michael's dedication to training and evaluation, and development. He is the top dog. And so it is our real pleasure to welcome Michael in this morning. Michael, first off congratulations on the recognition. I know it is well deserved, but, I'm certainly it's been a long time in the making for you. So congratulations on that. >> Thanks, John, thanks so much. >> Yeah, let's talk about the award a little bit here because you're the top Collibra Ranger. The fact that you've undergone this intensive training and evaluation process, what has that or what is that doing for you in terms of your professional development and what you're able to provide Lockheed Martin? >> Well, I think the ranger program definitely has helped with my understanding of the tool. First of all, we're standing up Collibra as sort of the key pillar of data governance within Lockheed Martin. So it's important to have people who are subject-matter experts on the tool that can help the different business areas to be able to stand up and just extract as much value as they can from it. >> Yeah, why did this matter to you? I mean, a lot of work, I mean, a lot of work that went into this and to reach the pinnacle required I know sacrifice and commitment on your part and on your team's part for that matter. But why was this of paramount importance to you? >> Well, I think it was partially because I was early on in my Collibra journey when I took the ranger certification and went through it. So it definitely helped to solidify my understanding of the tool and get more into it. That way I can just provide that value to the customers. We also wanted to see what would it look like for other people at Lockheed Martin to become rangers and get proficient in the tool. So I was kind of the Guinea pig for Lockheed and we were evaluating just how it would help us with standing it up. >> Yeah, I mean, talk about the process, if you will a little bit and share with us just what you went through in terms of how many hours this required, what kind of work you had to do, what kind of training and the evaluation process. So kind of take us through there from A to Z if you will, on your journey. >> Yeah, well, it started off, we had to get a virtual environment stood up just so that we could do some of the exercises that the ranger certification requires. So that was an intensive process of just making sure we had all the infrastructure in place to run the sandbox environment. And then once we got that up, it was mainly doing the exercises of, you're provided with the data landscape. How are you going to represent it in the tool? That way your users both business and technical users could go in and see the data that's in there and be able to get value, be able to get insights from it. And I think it was challenging for sure, to just figure out what all is required for standing up the Collibra environment 'cause that was a piece of the ranger not only how to work the tool, but how to stand it up, how to administrate it and in an effective way and get the metamodel set up in an effective way that way you have that longterm sustainability. So it was good seeing all of those different pieces come together. And then after you put it all together, I had the interviews with the Collibra team where you go over everything you did. So it definitely helps when you have to explain it to somebody they're asking questions. It sort of provides you with that dry run for when people in your business area and your company are going to be trying to use the tool and they might not understand about it or what value it can provide. So having that interview almost like a dry run that you can then help customers when they have questions and come to you. >> Yeah, how helpful was that? I mean, you raised a point, interesting point and really thought about that. You're basically going before the board, if you will, and answering a lot of how's and why's about your process, your thinking process, and what you put into place and how you implemented the tools, what have you. What did you find interesting about that? Or what did you find out about yourself perhaps in your knowledge base through that process? >> I definitely think it stretched my knowledge base for it. It was definitely nerve wracking having to go in and explain your rationale to people but it turned out well. And I feel like if you can explain something, like if you do your prep work and you're able to explain it to somebody else, it sort of proves that you have the true understanding on your side of it. So it was definitely a lot of prep work to just anticipate all the different questions, figure it out on my side first and then be able to answer it effectively. >> Yeah, we all like softballs, but what about curve balls? Were there any curve balls that perhaps that came up in that evaluation process? They're like, "Oh, no, I hadn't thought of that. Or I didn't anticipate that." You know sometimes it's those curve balls that really keep us on our toes. >> Yeah, I can't remember any specific questions. I do remember getting thrown some of those curve balls where you give the answer you think it's sufficient and then there's the build on follow on questions to that where you're like, "Okay, well, I didn't of that." And so you're trying to think through it on the spot. So I definitely got some of those I don't remember the exact questions but it definitely helps to be prepared. >> Yeah, it keeps you on your toes for sure. You mentioned that the value of this, perhaps within Lockheed Martin and being par, I think a great example for others within your organization. What about just kind of in the data community at large or your colleagues at other enterprises. What would you say to them in terms of the value in pursuing this kind of honor, this kind of recognition and how it could be put into good use in their work on the day-to-day side of operations? >> Well, I think for people who are early on and trying to stand it up, the video curriculum definitely helped me out for sure. Learning about both the administrative side, as well as how to use the tool as an end user. If you can put your mind yourself in the mindset of an end user, that's where you can really figure out where the most value is going to be coming from. And it was also good just getting that hands-on experience in a sandbox environment, that you could build it out and not have to worry about it breaking anything for your organization, but also figuring out how are you going to set up the metamodel and get it working before people populate the tool? 'Cause it's a lot harder to make updates when people are using it. It's good to try to get that as well established upfront as possible. So it's definitely good to get that hands-on experience with standing that up. And I think it helps you sort of think through all the different intricacies and nuances for standing up your own environment and getting the most value for your company. >> You know, let's talk about Lockheed Martin a little bit and obviously I'm going to take, everybody's pretty well familiar obviously with your work. I mean 110,000 employees worldwide footprint and obviously security and data security is a critical importance. What does Collibra do for you in that respect in terms of whatever peace of mind you might get in terms of data privacy and data security and reliability all these things that really factor, I would assume in the Lockheed Martin's operations. >> Yeah, it does and we're still thinking through all of the things especially with classified information, but it being metadata helps a lot. People are a lot less apprehensive knowing that it's just metadata in the tool. You're not actually keeping the data itself in the tool. So that way we can still have our security pieces on the underlying data. It's more for that discovery piece for us and we're able to see what shared reports are out there to be able to get lineage for different systems and help people's just business understanding of the things that are out there and the technical users as well, getting value from the lineage and system setups. So I think being able to lock down the view permissions that helps too, you know, puts people's minds at ease if you're able to say, "Okay, well, we can make sure only certain people are able to see this." You know, we have some of those built-in as well. >> Yeah, I mean, that's something I know you've done a lot at Lockheed in terms of working on the tech side and the non-tech side. And trying to explain policies, governance, and determining accessibility and putting the right governance controls in place. From a data perspective, again, sharing your insights what you have learned in that regard at Lockheed Martin what would you say to your fellow data colleagues if you will, again, at other enterprises in terms of getting that kind of collaboration and feedback and input from just not the, just the tech side but also the non-tech side of your house? >> Yeah, it's definitely important to get that business side as well because the technical users that while they work with it so much they might not understand that business users are not going to know what all of these things mean and that they're going to need some sort of human readable version of it. So we have people from the different business areas both business representatives and technical representatives who we work with on a consistent basis to get that continual feedback. And that way we're getting what are the priorities from both sides and seeing sort of where the synergies are across the different business areas as well. That way we're not duplicating effort, but we're trying to make it a comprehensive tool that everybody can use. >> Now I know that your relationship at Lockheed Martin with Clipper goes back some four years now. So you have a maturing relationship for sure. And the value there seems to be pretty well-documented. What would you say to others in your space, again not only about, just about Collibra, but about the data, evolution of data in general in terms of giving advice to somebody who's looking at this as a career, or maybe somebody who is just now getting into a more sophisticated look at their data footprint? >> Yeah it's definitely a large field. There's always new things to learn. It's always evolving too. So I think that that first step for an individual is to be willing to to learn those new things, to learn those new systems, processes, ways of thinking and take on tasks that sort of stretch you in your career. Things that you might not have said yes to before but saying yes could give you more of a comprehensive view of the business or give you a better data view as well. And from the company, it's just trying to figure out where the most value lies. Trying to get everybody sort of on the same page when it's the wild west it becomes a lot harder to extract value and move towards value. So trying to get everybody standardized but also give them the flexibility for their individual program or business needs but try to keep people to where there's a common understanding of the data. >> Now, spoken by someone who's been there and is doing that, Michael, we appreciate the insights. And once again, congratulations on the honor. It is a well-deserved. >> Thank you. Thank you. >> You bet Michael Kuzma joining us from Lockheed Martin as the Collibra Ranger of the Year. We continue our discussion here, Data Citizens '21 on theCUBE. (upbeat music)