Interview with Carlos Rivero, Chief Data Officer for the Commonwealth of Virginia


Hello and welcome to the AI Today podcast. I'm your host Kathleen Mulch. And I'm your host Ronald schmelzer Our Guest today is Carlos Rivera. Who is the chief data officer for the Commonwealth of Virginia off Carlos. Thank you so much for joining us on AI today Hey Ron. Thanks for having me. Yeah, welcome Carlos and thanks so much for joining us. We'd like to start by having you introduce yourself to our listeners. Tell them a little bit about your background. Check your current role as Chief data officer. Fantastic Kathleen. So yes in my current role on the chief data officer for the Commonwealth of Virginia before that. I've been in that role since August of 2018. And before that I was a chief data officer and chief Enterprise architect or the Federal Transit Administration at the US Department of Transportation that was there for a little over two years as well. And then prior to that I was physical scientist with Genoa Fisheries down at the southeast Fisheries Science Center for about fifteen years. So I've been in public service right now going over nineteen years in both federal and state experience. Well, that's great. I think that provides a lot of real Nice diverse set of experience, you know from Fisheries to the federal government to state government. And I think that's part of reason why we love to have your participation that we had your participation at the data for a-week confirmation that ran from September 14th thru 18th 2020 was of course a virtual conferences everything as these days and we were focusing on the data side of AI and for our listeners who may be interested that content is actually still available so you can come and you can hear the panel that Carlos was on when we were focusing on some of the state and the local challenges for AI and data management. If you go to data, that's spelled like data package i c o n f. It's free so you can go on there and you can check all that content will be made available for many months. So you definitely should check it out and Carlos was on a panel really sharing some of the unique insights of applying a machine learning and also some of the interesting challenges of wrangling data at the state level. So maybe Carlos you for those who weren't Intense or maybe even to motivate folks to listen to the family. What are some of them? Sites that you have seen in terms of just the challenge of managing data and getting it to do some magical things like machine learning at the state level. Well, I mean really one of the most basic things is getting people involved in the process. And in fact has plays a key role in that obviously more, you know, as we kind of evolved in once a leveraged data as of CJ Cassat within the Commonwealth, we realize that the participation of individuals not just horizontal across the organization, but also a vertically through different levels of state government is critical for our ability to integrate those data assets in a meaningful way and when I talk about the vertical, how are the patients I'm talking about, you know data storage data custodians data owners executive sponsors being able to participate in the overall governance discussion because everyone has a role to play in our ability to leverage data as a CJ asset to be able to incorporate that into our data analytics to write better intelligence and within that, you know, a comes in machine learning and artificial intelligence briefing. Jane as much value and insight from the data assets than we currently have. Yes indeed. Go ahead Kelly. Yeah, definitely and kind of to follow up with that on this podcast. We talked a lot about Ai and data at the national level, but maybe you can dig a little bit deeper into what are some of the unique challenges around data at this point level because I know that you know in general there's a general data challenges, but then we can also talk about you know, there's differences between State versus local versus Federal. So the fun thing about state is that you get to play with all the businesses at one time, you know in the federal space like when I was no Fisheries, we're very focused on fisheries and Fisheries applications. Mind you, you know as a physical scientist. I really worked with a lot of different data sets. As I was really more in a fraction of those individual populations and their environments right and anthropogenic impact on those environments and how does that change the behavior of individuals within a species right? And so you have to look at the bigger picture and yep. Integrate data from a variety of different sources other Noah Services resolved as live in North Fisheries, but we also have satellite service. We have the ocean service. We have the weather service. So being able to bring in data assets from a variety of different Services different lines of business. If you will to give you a better picture of what's happening in an environment that's very unique like more often than not individuals within that particular industry. We only focus on the data that they collect they work with on a regular basis and not really look at the bigger picture of what other data assets they can bring in same thing for in Federal Transit right in Federal Transit. It was very limited in their you know, what their perspective was with regards to you know, what data asked us what we going to bring in to really understand what's happening out in the world. They're really focused on providing, you know grants of Transit agencies and authorities to make sure that people are able to get to use public transportation in the most effective way. So it's very very silent. But then when you talk about a state government, can you talk about you know being able to leverage data as an asset at that level you really talking about across all of the different page? Business whether it's education Transportation criminal justice, you know environment what-have-you Health, you know, all of those lines of business now come under your purview and you really have to start to understand. What are they unique perspectives and how can you engage those individuals within each of those lines of businesses to be able to see the value in integrating their data assets and making better data-driven decisions home from that integration. So from a state perspective you really start to get a better handle on the overall picture of what's happening out in the real world versus a very I don't want to use this term negative in my topic view of you know, what your assembly looks like and only that which Falls but then you're suddenly are you paying attention to but at the same time, I've also realize that you know data governance and use of data as an ass is really a fractal type of problem where it doesn't matter. What kind of scale you look at it. It's going to have the same patterns associated with some of the same issues that we dealt with at the federal level we deal with birth. Level we deal with at the local level because it's not a matter of our these issues different. It's just a scale at which we operate in that just kind of gives you a little bit of a difference in wage issue is but the reality is that it's very poor the majority of the issues we do with with regards to data governance and data sharing and leveraging data and analytics a machine learning really comes back to the process and the people aspect of the peace process technology interaction.

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