To this uh part of the uh webinar. I'm uh uh Laurie Wade. I'm excited to be here uh virtually with you for the uh Women in Tech Global Conference. I am the Intelligence Community Chief Data officer, and the assistant DN I for data and partnership interoperability.And I'll tell you in a minute what that means. But what I really wanted to do uh to this session here and I'll try to keep it tight so that we can have some time if there are questions is really to give you an understanding of what we're doing in the Federal government and intelligence community and how we're evolving our thinking and, and evolving how we are looking at data from uh the point of collection all the way through to exploitation and to dissemination.

And that's important because I think we spend a lot of time focused on uh the technology part of it. We don't think through how the actual data is gonna be collected and how we're gonna discover the data and how we're gonna use the data. And that's what I'm really focused on and that's why I refer to it as unlocking uh the mission value of our data so that we can use the data for decision making and to help us as we shape the future of how we're going to go forward and do intelligence. So with that, I'll just get into. So my role that I have is really taking all of the intelligence community. Uh Chief data Officers, I chair a council. What we're really looking at, what is the intelligence community strategy for? How do we do data processing, visualization exploitation, the fusion and dissemination of all data, uh our intelligence community strategy uh which uh is currently in the process of coming out is basically looked at the end to end data management. How do we deliver data interoperability at speed and scale? How do we make our data A I ready and consumable by both humans and machines? And then how do we advanced partnerships? How do we work with the private sector and the academic or organizations in a way for data research and data innovation involve our thinking around how we are processing and visualizing our data and how we're fusing that data together.

And then how do we develop an IC workforce that is data driven? And that means both how do we raise the data? A and also how do we raise the um data trade craft if you will? So let me just get into some of the things I wanna share. You know, we find ourselves at a pivotal time. We're experiencing the rapid adoption and use of technologies that have been decades in the, in the making. Of course, as you hear all over the news, there's a lot of talk about uh chat GP T generative A I immersive technologies in the next decade, the internet will evolve. Uh The immersive technologies will start to see how even more convergence amongst the social and human interaction and, and machines. And what do we need to do to prepare ourselves for that? If we are looking into the future of how data is evolving, we need to understand what does that mean in real terms and very specific, not vague, but what are the de defined steps that we need to be taking as an intelligence community as the national security landscape and attack surface shifts and changes to meet where the technologies are going?

We've made a lot of progress. But here we are at this uh pivotal point where just thinking of how we process and use data today is no longer efficient or sufficient. We need to be thinking about what is data privacy look like in the future? How does how where is social interaction gonna be happening? What does that mean for the volumes of data and how do we use the technologies and the platforms that are coming? Taught us to help us do that at speed and scale? Where right now we have manual data transfer. How do we take that? And and make that into automated. How do we basically take capabilities and through the data and through the assistance of A I and immersive technologies. This is something that we're taking a step back as we go to the future to see how do we work with the private sector and the and the academic organizations to see how do we organize for future data and analytic issues? What are the kinds of questions we need to be asking ourselves? So how should organizations prepare to manage the volume size, velocity and diverse types of data generated by these capabilities? These immersive capabilities A I, how are we supporting those capabilities through the work that we're doing?

The skill horizon is shifting and changing. It's gonna take broader disciplines than just beyond the the current data professionals. How do we bring our targets, our analysts and others along in their data tradecraft to see, you know, where the data will be coming from and how do we process that data? How do we gain insights and draw um new, you know, development of data through the fusion of data? All of these are questions that we want to explore as we go forward and build our strategies around that. What are the types of opportunities uh for the interoperability between all of the intelligence screen organizations but also more broadly than that. How are we integrating um data and technology with human analysis? We're gonna have to look to see how do we reduce the barriers that are preventing us from having that interoperability today through our systems? We had a very, we've had a very system centric view. How do we shift that to a data centric view? Those are gonna be an important discussions as we go forward and how we evolve our thinking. So how do we look at how the IC can start to do analytics at speed?

We focused a lot on the collection of data but we haven't really thought through the since making part of it. So how do we take and and basically get that maturing of our existing data services and add new services? How do we bring A I and automation at the point of collection? Meaning if we're bringing in images, how do we apply more A I to that so that we can speed the actual analytic part at the front end versus waiting for it to come at the back end. That is what we're committed to doing and we're trying to figure out what's the best plan of action as we go forward. Our strategy focuses currently on the 2023 to 25 time frames. We have a lot of foundational elements that we have to put in place. But we need to be looking at the future. I recently stood up a data future group to be able to help us shape and form. What is uh what is the intelligence community? What does intelligence look like? How do we process and analyze data in the future to help us make decisions that go to the policymaker?

But also how do we support the work that's being done amongst our analysts as we look and see what is national security look like in, in this evolving tech and data heavy world that we're in all of these things that we're putting into place now are going to try to get us to the future.

Um the ability to harness these capabilities and overcome the security challenges and the threats that we have. That's one aspect. But what I'm more interested in is how do we take the technology and bring the opportunities forward into these large volumes of data. We look at the private sector and how they're able to take Zetta bytes of data a day and do synth making out of that. And they've brought automation and A I to that we have a lot of uh places across the intelligence community where it's still very manual, there's a lot of manual data transfer and, and I have a lot of conversations with individuals about how do we take that? How do you scale that? Uh That's something that we need to be focused on. We have a lot of legacy data and a lot of legacy systems. How do we boldly modernize on top of those systems as legacy systems while we're also trying to advance and evolve? Um How we do data management. Uh From that end to end aspect, these are all the kinds of things that we're starting to have conversations with the private sector and with our uh you know, basically across the academic organizations, there's a lot of research that's going on around um you know, the future of data and how do we take what we're doing today and put it into a place where we have the machines, help us to do this.

We're, we're not at a place where we're doing that across the board. So we need to grow and expand um our thinking around that space. One of the things we're doing is how do we expand our partnerships? How do we rethink how we work with the private sector in a, in a much different way? I've just started a new series called The Future Now series where we're bringing leaders from the private sector together with leaders in the intelligence community to have discussions around what is intelligence look like in the metaverse? How do we bring the technologies and those platforms and bring it together in a way that we haven't in the past at the problem identification stage? So we don't wait till the very end uh to be thinking about how we might be able to use those, those opportunities. There's a lot of um instances now where we have a large volumes of data that we've collected and we haven't thought through how best to um process that data in a way that confuse it, but also send it across the multiple agencies at one time. And we need to be looking at what are the technologies to be able to do that. Uh There's a lot of work that's going on on uh integrated data layers.

But I think it's beyond just that, it's, it's really thinking through how the dynamics of data is shifting and changing and how do we need to have our analysts and the non data professionals work in the broadest way possible. And it's gonna take a broad uh you know, capabilities and the skill horizon that it's gonna take to do. That is something that I'm concerned about. And I think globally, uh there's a lot of uh focus on on these technologies like the chat GP TA I um metaverse and all these other technologies. But I don't know that through our education programs, through the, the the training, through our skills horizons that we have mapped the skills that it's gonna take to be able to do that work on the kind of scale that we're talking about. And so that's something that we're very interested in and we're very focused on in the intelligence community as well. All of those factors beyond the technology is what it's gonna take for us to be able to unlock our data, the the mission value of that data. And by mission value, I mean, really the use uh there's a lot of focus on getting data and there's a lot of interest in the processing, but we tend to focus less on the discoverability and the usability of that data.

And so one of the things that we've been focused on is the data centric experience. How do we take from the point of collection all the way through to the dissemination and who, who are the partners that we need to work with that? What is the cybersecurity piece of that? We hear a lot about zero trust but how does zero trust help us with unlocking that data? Um We've learned a lot from the private sector but how do we bring that into a government setting? I've been working with our um IC cio uh and our, our IC cybersecurity on a di a digital C suite concept. How do we work together and soft for those intersections? And how are we doing this at speed and scale are gonna be important pieces of this data centric experience as we go forward. We're, we're putting together a partnership uh framework by which we bring um a private uh public talent exchange program into place. It's called PPTE.

I'm actually here currently um at the um Utah State University talking to them and their data program about how do we bring some of the data research and some of the work that they're doing into the, the, the intelligence community? And how do we take intelligence community officers and bring them here so they can learn some of the things that they're doing around data modeling and simulation. All of these factors are gonna be critical as we go forward. We are looking to see how do we um basically put together a program on opening up the and reducing the barriers to entry for the private sector to work with the federal government and the intelligence community. We have a, a front door, an intelligence community front door where we're trying to bring in companies that are traditionally ones that we haven't worked with and see how do we bring that into what we're doing. Again? I know that this is a tech women in tech, but there's a lot of organizational uh factors. We have to, to, to worry about there's compliance, there's privacy and civil liberties we have to be concerned with. There's a lot of organizational construct, things that we have in place today that we need to change and to be able to evolve so that we can prepare ourselves to work in a very different way in the future.

We understand there's a lot of work um and a lot of um the evolution of, of thinking of, of data and analytics that we are just starting to focus on in a way that is more practical and direct. There's a lot of vague um work that goes and talk that goes on around a lot of these technology uh, buzzwords. Uh A lot of people talk about how, you know, a I as a silver bullet for everything, but we really don't get down to the, what are the deliberate and direct steps that we need to be taking and how do we, how do we define those in a way that shows us what are the steps we need to be taking to optimize, to get to the future?

And that's what I'm really focused on. Um There's a lot that's going into this, a lot that I can't cover in this short amount of time that we're talking about here. But I just wanted to kind of give the broad brush of the kinds of things and questions, more questions storming that we're having as we look at as, as we take this step forward into the future and as we sit at this pivotal point, um and in, in the world really, uh as we go into these virtual societies immersive technologies, when we look at where social interaction and where data um data will be occurring, data growth, uh data use and discovery and what are all the factors that we have to, to think about beyond the technology.

And at the end of the day, how do we take that and apply it to the national security context? And so that's really quick. Um I know, I know I'm getting the, the warning here and I wanna see if there are any questions but I just wanted to give you instead of doing some, you know, deep dive into each topic. But the broad brush of, of where we're focused on and when really it's the end to end data management, it's delivering data interoperability at speed and scale making our data all data uh consumable by human and machines. The partnering piece is so critical to us. How do we partner in a different way and how do we evolve our thinking around the the where data and analytics are going? And then how do we bring that into um those opportunities into the intelligence community? And then also how are we evolving the skill horizon? Uh our data acumen and our data tradecraft and that's really the focus that we have in the next two years around our um data strategy. Um And so with that, I will just stop because I know we have only a few minutes left if, if there are any questions or any thoughts that anyone has.