 Oh, and welcome to DataVersity Talks, a podcast where we discuss with industry leaders and experts how they had built their careers around data. I'm your host, Shannon Kemp, and today we're talking to a friend, Rodriguez, the data manager at the U.S. Department of Defense. With a robust catalog of courses offered on demand and industry-leading live online sessions throughout the year, the DataVersity Training Center is your launchpad for career success. Browse the complete catalog at training.dataversity.net and use code dbtalks for 20% off your purchase. Hello and welcome. My name is Shannon Kemp, and I'm the Chief Digital Officer at DataVersity, and this is my career in data at DataVersity Talks podcast dedicated to learning from those who have careers in data management to understand how they got there and to be talking with people who help make those careers a little bit easier. To keep up to date in the latest in data management education, go to DataVersity.net forward slash subscribe. And today we are joined by Efrain Rodriguez, the data manager at the U.S. Department of Defense. And normally this is where a podcast host would read a short bio of the guest, but in this podcast, your bio is what we're here to talk about. Efrain, hello and welcome. Hey, thank you very much for the invite. Everything is all good. So you're the data manager at the U.S. Department of Defense. So what does that mean? What is it you do? Interesting question actually. Combat support agencies and the DOD has what they call work roles and what they call job titles. So basically what they call a job role, a work role is related to the discipline that you're supporting. And what they assign is different job titles in terms of this is in what capacity you're supporting that particular role. So my work role is data manager, specifically collection data manager. In terms of my title, I am a data steward. And no different data steward across the private sector doing exactly the same role. But in my capacity as a data steward, I'm involved also into the entire life cycle of data from capturing the need of customers within DOD, even intelligence community at times, for a particular set of information, collecting, acquiring, procuring certain data, doing all the data flow portion and supporting the data flow governance to bring the data from the source to our data lake and ensuring that we answer the customer needs. My capacity as a data steward, for the most part was focusing into ensuring that the data that it was acquired, supported and addressed the needs of the warfighter, as well as intelligence community, but mostly making sure that we created a policy and law compliance framework to ensure that the data was properly used. It was freed up so people can use it in a compliant manner for most of all ensuring that it is the proper data at the proper moment and at the proper time. So that's pretty much the encompassing of the bigger scheme on the role. So as a data steward, my responsibility is lying at that particular capacity. Never at all moment I'll tell you that it's a really encompassing role. And for all data stewards out there, I hear you, I feel you. Don't worry, these two shall pass. Just press on because it's really worth it. Oh, but I love it. I love the great advisor too. So that is a fascinating role. I'm sure that keeps you that keeps you very busy. So you know, tell me, is this what you wanted to be when you grew up? When you were when you were very young, you're thinking to yourself, I'm going to be a data manager, I'm going to be a data steward. No, not my long stretch. But when I was younger, particularly middle school, that's when I was kind of discover what I really wanted to do. I wanted to be an astronaut. So that was my goal. So when I was looking, I remember when I was in it was a freshman in high school, we went to Kennedy Space Center of all places in Florida, that NASA, and I started looking at the Visitor Center, we're all the buyers of all the astronauts are looking at the common theme. All of them are either very well versed in exporting to STEM fields. And or they were pilots for military. So knowing that I was on a pilot, the only chance that I had to do that was if I went deep into a career in STEM. So I went to school for electrical engineer. That's where my background is. I did apply for for the astronaut program back in Jesus Christ. I'm going to do myself. It will be 1997. So I decided to give it a try. And unfortunately, I was not selected. They were looking for it was mission specialist at a time. But that didn't define my goals of I like what I was doing. STEM was a really great field and a lot of growth, a lot of great opportunities to design to do things differently. So I started as a hardware engineer or many things. So I was working for a defense company that is based in it was based at a time in the Northeast. And they did my my work there was just designing systems that was designing receivers, radar systems, all components on computers and weapons systems as well. And I was pretty much creating the artifacts that were creating data. So that's how we think started in terms of my carrying that I was creating the system that were collected out in the first place, particularly receivers. It was then that I switched my gears and moved to word directly with the Department of Defense. So now I wasn't the provider of the equipment I was doing. I was the customer in this case in terms of now in the capacity, I started doing just analysis. So I was just a consumer and the user of the data. So I started looking at all the things that the data provided creating the reports disseminating those reports and showing the people understood what they were looking on in terms of what kind of value the data was providing. It was then that within the DOD I started moving up in the ranks and as an analyst and I move on to do engineering jump again, but a little bit different. In this case, it was in the data flow portion of the data life cycle. So I was focusing on how do I engineer, transform and load all the ETL in terms of how do I bring the data from the source to destination. And I started looking at gaps and as I realized that there's a lot of things that we needed to do in order to make that a more available and free to others to use. Then I switched out to a more operational and strategic approach in terms of how do I make this happen. Engineering was not the challenge. I recognized the most of the challenge that we had was because of compliance and mostly because of how do we govern the data in the first place. So that's where my feet went into my data store role that I'm sitting today. And even at this stage of the game, lot of things that I still learning, things that I recognize that are still long ways to go. The fact that we have a robust infrastructure toward the data becomes something that we just need to deal with. But at the same time, how do we ensure that we evolve this to support in the future? Big data is always growing. And I think that this is the same for everybody. In the Department of Defense, that's not an exception. Now we're talking about supplying the needs of over one million people, including civilians and military personnel all across the globe. How do we ensure that the data on the timeline matter at the same time, the proper data that they really need? So that's how I evolve all the way from an astronaut away to data steward in 30 seconds or less. But for the most part, I still consider that data management generally is a very encompassed discipline. It is a one discipline that incorporates not just only the engineering and the technology side, but also encompass the legal policy, human factories, including to that as well. And it's a very multi-discipline field. So there's a lot of growth potential opportunities there. And that's what I said. You know what? It's a place that I wanted to stay. I'm providing good value. I'm learning a lot. And no use only that. I'm seeing the fruits of the produce because of the sacrifices, the hard work that we've been doing over the last five, 10 years. We have a long ways to go. Absolutely. But I definitely take those as a challenge. And if I look forward to continue growing in this capacity and learning how data management and data governance can help DOD manage that as an asset and how it can even provide support to other fields, particularly when looking into data science methodologies, artificial intelligence, machine learning, which is one of the fields that I'm exploring right now. A really, really interesting field in which data management provides great value proposition. Yeah, I find it fascinating. You're actually the third person I've interviewed who started out wanting to be an astronaut and then went into STEM. I think it's so interesting. I definitely feel that STEM is a really unappreciated field. I think that that's something that we should continue fostering in the young generation because there's a lot of potential for growth. Not just only because you're building something, but you're learning as you move along. And you're part of an evolution. You're serving society in a way that STEM can only provide that stepping stone to get us to the next level. And I definitely recommend others to continue to follow that field. It's a very diverse and it will take you places. I admit that. You'll be surprised. Yeah. Let me ask you this too. You mentioned policies and privacy and governance. I imagine there's a lot of regulations that you have to follow. But is that all data governance? Is for you or are there other aspects that you use it for in your work with data? So I'll definitely would like to expand further into that portion just to clarify. And this is same everywhere. This is not only exclusive to the US Federal Government or Department of Defense. Policies, procedures, laws provide the boundaries of how far can we go to support a particular activity in a specific, excuse me, how do we use data to support a particular activity? So it is not a hindrance to the country. It is a way to establish a scope of the best way to use the data. Coming from an engineering background, when the world, when you have a hammer, the world is a nail for you. So I was approaching everything for engineering STEM, but when reality, it is a multi-discipline approach in order to properly support users of the data from the governance perspective, from the management perspective. Those are the things that I have to learn as I was continuing evolving into my career. And that is something that I definitely recommend people that want to get into this field is that get acquainted and at least cognizant on how does policies, how does these boundaries help shape how the use of data is more effectively done, but for the most part, because we are a government entity, we are held to a higher standard. We have to protect not just only privacy of citizens, but also abide by the Constitution of the United States. So for us, it's the utmost importance to, yes, preposition the data to be used to establish all the infrastructure to make it happen in a proper manner, but within the boundaries of the law. And that is very important. It's the most almost priority that we always do. So we are accountable for that. We have audits on that. We be bored on that. And we ensure that we would, no matter what, we'd never step out of those boundaries. And although sometimes you can be seen as hindrance, you'd be surprised how well those boundaries establish and shape. How can you evolve tradecraft moving into the future when you're starting to sell new technologies? Now we're talking about creating new analytics, artificial intelligence, machine learning, how these boundaries and policy help shape a proper governance for all these new methods by minimizing variability in data. And that is a data management, data quality issue, data quality feature. So you can see how these policies help shape the usage of data. And I think these are very important. And that's something that we take to heart. I love that perspective on data governance that is really something that I think people struggle to find and they do find it a hindrance. So I think that is such great advice and a great way to look at it. And it's something that is something of value in terms of now you understand the rationale, why you're doing where you're doing because now you have a different vantage point to see exactly what is happening. You know, just only the technology side, but definitely a multi-discipline approach. And I definitely can be inside. There's a lot of growth and improvement on that area for everybody. And although you might see new policies coming in, you got GDPR, HIPAA for health records here in the United States. These are just only things that, although my sunlight restriction, these are the ones that will make your life simpler because you know what you're getting into and they're clear expectations of how you're going to be going to data moving forward. Makes sense. So with all this work with data, especially for so many years as you've been involved in it, you know, what is your definition of data? Data is considered, I consider data as an asset in terms of it provides the raw materials to build up bigger and better things. Data is the building block of information because you add context to that. Data blocks, context equals information. And that's what we, that's our better matter. That's what we use for a living. That's how we provide to our customers. So we had to treat data as an asset, something that we use as a building block for bigger things. So what we want to make sure is that when we gather this information, we acquire these data. We treat it as such that we establish the initial boundary, the scope and how properly use it. And most importantly, how can we widely be leveraged by others? So it doesn't become siloed into a particular category or section, something that can be used by everybody else. So from my perspective, I always see data as an asset, no different than when you have steel to build a car, data becomes my steel to be my products that the DOD uses worldwide. Perfect. So, and you kind of answered this a little bit already, but let me ask you specifically, you know, do you see the importance of data management and the number of jobs working with data increasing or decreasing over the next 10 years? And why? So actually, that's an interesting question. I will see that as we continue evolving in the use of data moving forward and we create new tradecraft from using the data, I consider that the data management and data governance feel it becomes a major relevance. Not just only because it is the one who provides and preposition all the artifacts required to use the data, treat the data as an asset and also to govern it, but mostly because it is instrumental to evolve technologies moving forward. In terms of growth, I started really seeing it. I mean, one of the things that you start seeing, for example, on the new paradigm in terms of using AI and machine learning, the fact that they focus so much on the usage of the data, they realize, oh wait, there are certain things that I have to understand on the data that I need to do on the data before even thinking about creating algorithms in the first place. And this is what data management provides. This is how the need of data management and governance establishes those, the established frame of references for all these technologies to flourish, but also the fact that now we are evolving data management into different fields that I see definitely growth into that. One of the things that I would like to start, I think I'm going to start seeing is that how data management is being included in curriculums like college, for example, and even the potential of certain skills or transfer of skills that data management provides at lower levels of abstraction. And you start seeing these right now, for example, when you go to K to 12, the first thing that you see to issue is the scientific method. Look at how the scientific method in terms of the hypotheses, the evidence, all the workflow, all the structure, you still follow the fundamentals of data management. You had to do it as such, you curate it, you make it available, you speculate on that, you draw your conclusions. So I think that there's a growth in education. There's a lot of emphasis now in data governance. I already seen people already stating they're making grad studies in data governance, which is something unheard of when I started this career over many decades ago. But in terms of the use as itself, now we want to incorporate, grow what we have today in data management. And I think that that's going to be the big, the big ticket moving forward, particularly data quality. I think that data quality is something that we're going to start seeing a lot of uptick just because there's a lot required to get the data right before you use it. You heard the story of 80% of my effort is just to clean up and ensure that the quality of my data is enough for me to spend the rest of 20% of my time using it. Now we're going to be trying to evolve these to make it a more streamlined way to do it. And that is the part that I think that there's going to be a great growth coming forward in the next decade. I definitely consider that we'll start seeing this a little more incorporated earlier in the career. You start seeing probably on the grads starting notions of these kind of things, just because it'll be incorporated as part of their curriculums on all their coursework within data science, for example. And it's a good start. But again, you grow in this career, you learn from this career. And as a evolving field, I consider that the growth also comes in terms of the personal self-growth, taking your courses, getting your certifications, networking with others, noticing and acknowledging that the fact that you hire people to do these and you look at the job openings everywhere and you start seeing an uptick in data hires in terms of these are the things that are going to support the future. One big example that I saw there today was even within the government, agencies are going through digital transformation these days. And on the utmost foundation for digital transformation is how do we position data management culture that would allow this transformation to be supported. And as you continue going into that direction, definitely love to grow with great opportunities for people to move laterally or sometimes vertically, there's a lot of growth. It's just a matter of now, why is your flavor, what's your taste? More and more companies are considering investing in data literacy education, but still have questions about its value, purpose and how to get the ball rolling. Introducing the newest monthly webinar series from Dataversity, Elevating Enterprise Data Literacy, where we discuss the landscape of data literacy and answer your burning questions. Learn more about this new series and register for free at Dataversity.net. That makes a lot of sense and you've given a little bit of advice already in a couple of different points. So any additional advice you'd give to people to looking to get into career in data management, maybe specifically data stewardship or to address data quality? So in general, and I'm going to be from the data management perspective, I'll try to be a little more inclusive. I always single out data quality because I think that there is a portion of data management that drives a lot of growth, a lot of usage of the data and preposition the initial baseline for the data to be usable. But for the most part, if someone wants to start a career in data management, the first thing that I recommend is get a mentor. Identify someone who you can identify that follow a path of things that you like most because they have to be in a particular discipline. But at the same time, you learn the life cycle of data all the way from establishing the need, procuring the data, processing the data, storing the data, even transporting the data itself. And there's a lot of things in between that data management does provide support. So getting a mentor in any of these at least helps you notionally what portions of the life cycle you're more interested in the best. And once you start seeing the themes that you like the most, then that's the part that you get a little further educated. There is a notion that you want to be deep, knowledgeable in one field, but broad enough on others so you have better context. And this is the part that is the only way to better, at least from a DOD perspective, help me out shape my career moving forward, being cognizant about the entire life cycle itself. If you look at the Dhamma book, for example, you start seeing the Dhamma wheel itself, all the different portions of it, the expectations do not become everything, but choose the field that is more appealing to you, that allows you to grow, but at the same time challenge you as well. And that goes to my second piece of advice. Oftentimes, the only things that people want to avoid, but at the same time, there's a one that become more rewarding. Data quality is hard. It's not for us. Everybody can tell exactly the same thing, but you start getting into a group of things in terms of, okay, I might not be able to solve all the problems, but I would like to contribute to maturing this straight graph moving forward for the next that comes along, gets a little bit better than what I had left something a little bit better the way I found it before. I only learned data quality. That's something that I see that a great area of growth, just because that is the foundation, foundational of the usage of that itself. So you have your mentors, you have your area of expertise, network. Oh, geez, that is very important network. You'll be surprised, you're not on island, you'll be surprised that it takes a village to get that a match to work right. And getting a support network of people that think just like you, from other industries, from government, from pharmaceutical healthcare, you'll be surprised how they tackle the same problem from different perspective, or how much you learn from their experiences. So even though I might not relate to much for the person who does data management for pharmaceutical company, I do recognize that they have an approach that even my scale, if I tried a little bit different in my particular government specific needs. So this is the part of network is important. You start learning from knowledge, you start in getting your name out there and say, look, I'm willing to share my war stories. Let's share our so we can learn from one another. These are things that probably want to be written in books, but you start building your own book of knowledge. And that's the part that you add an extra tool to your toolbox to be more successful. So now they have your network, this is the part of C's opportunities. Sometimes it's just to be in the right place in the right time and getting your name out there means a lot. When they start seeing the acumen that you have provided, the artifacts that you share with others. And again, it sounds a little bit greedy at times, but at the same time, it helps evolve. This is the part that now you start seeing other activities that you say, you know what, I might already done my portion here. How can I go to the next level? And because of all the diverse interdisciplinary activities within data management, there's great potential for growth, great potential opportunities to learn something new. And most of all, evolve the field moving forward. I definitely consider that as an extra piece of advice, if possible, is how can you contribute to the craft? I've seen a lot of great consultants out there, probably during the diversity conferences, that they show their experiences and they tell the stories and they provide that insight that might allow others to use and flourish and evolve the field in a different way in a better way. How do we interact with different disciplines with your organization? Data management is not just only business, it's IT, it's people, it's culture. This is the part that you want to be well-rounded into that. So don't focus only on technology, focus on culture, focus on people. People always come first, listen to their needs to ensure that you understand what they're coming from. But most of all, how do you get those experiences out so you can tell your processes, your frameworks, your methodologies to better provide a better product and a service as a whole. So I think that the last thing that I would say in terms of advice to you moving career in this data management field is that same with any other field related to science, just always an evolution. So you might not see now the fruits of your sacrifice or hard work, but down the road, that foundational work that you've done will help others to build upon that and do a better and involve the trachea for data management forward. Be good instead of data governance. This is the part that sometimes it will help address some of the needs that you have. So you don't have to be involved in data governance to be a data governor. I think everybody's empowered to do that on their own way. And I definitely tell, own it, commit to your role, and make sure that people understand that, look, I might not have all the answers, but I have my ways to get the answer, to get it done and provide a top quality product move forward. It is rewarding. It is really exciting. And I definitely think that it's a long journey of learning. So if you're looking into something like that, you're on the right track. Data management is for you. What amazing advice. I just really appreciate that. It's so good. And so many different points within there. It has been my experience, one of my favorite parts of being so involved with the data community is that everybody is so willing to network and is so willing to help each other out. And really just the camaraderie and the support of each other has just, it always blows me away. And it never stops surprising me and just, it's just so much joy in that. And I've seen you at our conferences and of course networking and sharing your knowledge as well. And being here, I appreciate you sharing your knowledge. This is really, it's really, really nice. And I heard in there too, to follow your passion and then push yourself to learn more. Following your passion doesn't mean it's easy. I would never say that. But one of the things that I recognize, and I actually noticed that as I was participating on some of these activities, most people are bright new into these. You start seeing when they show hands at the conferences, like more than half people are bright new into data mesh and data governance. So there's something appealing to that. There's value for us. You start seeing that this is not a niche market. You start seeing that it's something that is widely adopted by many companies, many disciplines. And again, the government in which I provide support as well. But at the same time, this new generation of data governors, data managers, that they come along, they're looking up to us, who will be doing this for some time to, to share those stories. So that's the reason why this little community, which is not that little anymore, you're surprised how many people start showing up and doing it at different capacities, or they don't know that we're doing it, which is surprising, like I didn't know that that was called data quality. Yeah, that is called data quality, but you didn't know that. But at the same time, that's again, the relevancy of how these feel is growing. As you start seeing many people from many industries entertain and government, you name it. I think that now it's up to everyone to, to make sure that we continue growing this discipline and definitely maturing it to, to make sure that it become as it is that data becomes what is supposed to be a great asset to deal with and a great asset to treat All right, well, I'm going to ask you one more question here. Clarify a little point there. You know, I'm hearing it a lot that the people aspect is the most important and, and because you do need to interact with so many people being in data. How do you build that skill? So many people in data are introverts, right? And it struggle with that a bit. So is there a way to build that skill? And is it listening? You mentioned listening. You'd be surprised. Yeah, I think that, yeah, I hear you. I think that this is not about introvert or extrovert mostly that it is. What is the bottom line? When you're in a position that you consider that your transfer of skill used to be soft skills going today might not be of value, might not be adequate to support a particular activity, understand that you're not alone doing this, that you surround yourself with people that have empowered you to learn these things and you continue doing it, but not seen as a hindrance. I consider that even though you could consider as an introvert or they consider themselves not as outspoken, they consider like great value in support of customer needs. I would say if you have to learn how to deal with people, how to talk to that, you'd be surprised how having that at least that technical common frame of reference helps a lot to communicate because one thing that management is that although it's a broad field, it's a well bounded field of knowledge. So when you predicate your needs, your requirements, we start speaking the same language. So it's a little bit easier when you speak the same language to communicate, to convey information, to listen better and to capture what this essential need is. If I feel that in terms of you being an introvert and how to deal with these customers, it comes from the bottom line of you're here to help them out. Listen, ensure that you understand and repeat back what they're telling you. So the clarification of the needs are well addressed, but this is not something that you're learning books. I believe that is how do you work around that particular uniqueness of your character helps you out to make more effective. And this is the part that it might be a great asset. You might be the best person to do this, it's just a matter of looking at the upside off, take these as a unique talent that nobody has, and how can I make this as a potential game changer for my particular corporation? I mean, we don't want to have the same kind of people doing calcics and things. The one of the things I appreciate in this community is that the diversity of the people that we see, getting introverts, extroverts, people that never done STEM in their lives. Yet, when we go into the management that are governance, we all speak the same language, we hear each other really well. And those things that you consider could be of hindrance. Look at as an asset, use that as your tool for success. And I think that as the moment you recognize that is not a limitation, that it is a potential to make things better, you start more, you feel more empowered to own and commit your role and your position, and you establish yourself as the accountable person, the responsible person to make success happen. And again, there are other skills, not just the introverts, the extroverts, things that, for example, are essentials. Even briefing skills, writing skills, because you have to communicate with different levels of management. In my case, different levels of responsibility. Sometimes you address a general, you address the chiefs that are officers, which have different levels of responsibilities. And mostly because they have a different bottom line, because of their position, you have to reconcile that somehow. And that is a skill that you still need to hone more than the introvert, the extroverts. How do you reconcile different bottom lines to ensure that you ensure mission success? When you go to the CDO, the CDO level, CIOs, how do you recognize business with IT? How do I recognize my customers? How do I differentiate my customers from my user base? Something as simple as that. These are the little things that when you start working with them, you start recognizing, I had to communicate a little bit different with that person because their bottom line is different. I had to recognize that I had to report and write a little bit different because their needs are different. And these are things that, yeah, you can learn in class, but also, like I said, going back to someone by advice, having a good mentor helps a lot because it will help you out of how do you properly convey the information? And most of all, how do you more effectively impact their mission and their bottom line? And one of the things that I learned over the years, like make their mission your mission. And that's how you integrate yourself. How you're making the showing that you care. Like, if it's important to you, it is important to me, Sarah, man, whatever. So bottom line is, I think that it's not a hindrance. I think that's something that you don't learn, but you also take as an asset moment forward. There are other things that you compliment on that. And that's package of different tools. That's why it makes you a better data manager, a better data governor. Such great advice. And I love that quote. If you make their mission your mission. Such a key phrase there in the whole thing. Oh, such a good device. I'm afraid you do not disappoint. I knew that I wanted to interview you because this has just been amazing. Anything else you want to add before we as we wrap it up? Sometimes if you feel that things are going to you're not going your way, understand that these two shall pass. So hanging there. Things are going to get better. Use your network. Continue learning. Be there for others. The most important will help grow this field bigger than it is today. And I definitely would like to appreciate what you guys do in that adversity to make this happen. And how do you provide venues and engagement activities for others to learn from others, but also to grow. And I think that that is very important. So I appreciate this opportunity. Thank you for the invite. I definitely look forward to continue engaging with the organization. I definitely continue seeing the big faces here and grow and evolve and continue to move forward with my career. And hopefully I can be a value of others. If I can be of a few of others that are on my line, just let me know how can I help. I'm here for you guys the same way that I was helping out on my skills. So thank you. Thank you very much. Oh, thank you. I mean, this has been so good. And thank you so much for taking the time to chat with us today. I think a lot of people will find everything very valuable. And for all of our listeners out there, if you'd like to keep up to date in the latest podcast and in the latest in data management education, you may go to dataversity.net slash subscribe until next time. Afrain, thank you. All right. Take it easy. Thank you for listening to Dataversity Talks brought to you by Dataversity. Subscribe to our newsletter for podcast updates and information about our free educational articles, blogs and webinars at dataversity.net forward slash subscribe.