Sure, some of my favorites actually come from my time at mathematical policy research when we were looking at the application of brain science and the development of our understanding around two generational approaches to poverty, and some of the work that was being done at the time at the Stanford poverty lab and at the Harvard Center on the Developing Child. And this recognition, again, that the policy world and the science world had evolved. We knew a lot more about what it took to make effective safety net programming and to update our models around what good case management within those systems looked like. The hardest part was proving, really, at that point, to thousands of case workers in the state that was held accountable to old federal regulations, that there was value in changing behavior, and so we spent a lot of time trying to drive data and evidence. For example, in the TANF program, we look at a metric that's very old and still today, is what states are held accountable for, called the work participation rate. And within that, there is this assumption that the thing we then care most about is getting people into work, when the reality is what you really care about is having resilient individuals who can recover from disruptions in their work experiences, right? Which means that you also want people then who have strong self regulation and strong executive function skills, and what we called, at that time, from the Harvard Center, adult core capabilities. And what that meant then is that you had to also let go of assumptions that had never been proven in the data, like, Oh, if they just work hard enough, they'll get there. Oh, you can just pull yourself up by your bootstraps. Those are things that we knew for a fact from the data were not true. And so as we were trying to then shift towards what were the things that we did understand from policy and science and human dynamics and brains and decision making, how did you get people then to apply those lessons learned in a model that was previously incentivized by a completely different structure, and in doing that right, we got very in the weeds around data and systems and those case management and programs, but also the recognition that humans don't live in silos. An individual, you know, in our case, on average, we're talking about a young mother with one and a half children who is trying to navigate a difficult situation and is experiencing some amount of scarcity, right? That's not the only thing going on in their life. And so you've got to be able to look across these systems and understand things about mental health and housing stability and physical health and transportation, right? All of those things are going to feed into not only someone's ability to effectively navigate what is otherwise a very complicated scenario, situation, policy landscape, but also meet their needs in these other ways, if what you want is success and stability and self sufficiency, ultimately, over time. And so we found all these interesting things in the data, but Lord it was really hard to get the data into a format where we could make those arguments, and that was when I really learned that all of that work we had done at Mathematica was not unique to any one of those programs or situations. It was ubiquitous. It's happening everywhere, all of the time, right? And so when you understand, you know, and you do, given your background, how technology has evolved, it's not that surprising sort of how we got here. What'sa little bit scary and surprising is that we're still here, right? The technology has now moved substantially and made things that were actually really difficult to do five or six years ago much easier. And so what was really helpful for me in thie State of California was the ability to demonstrate the value in investing in data and data science and data aggregation, to make the case, to undo the assumptions, to paint the picture, to drive better policy decisions and better resource allocation. Once you obviate it, you know there's no going back. Then people want more of that. And so it did lead to natural investments and shifts in the way that we thought about doing business. I mean, frankly, that Chief Data Officer role that I ended up taking didn't exist before I was the TANF director and demonstrated a lot of value driving around how we thought about policy and resource allocation, etc. And then we had a department and an agency who said, Oh my gosh, all this value that you're driving in TANF, you could be driving that in every one of our 13 major policy areas and programs, you know, from Child Welfare to housing to in home supportive services and beyond, right? And sothey actually created that role specifically so that I could step into it so that we could start to figure that out. And I wasn't alone. Fortunately, I was one of five chief data officers at the time named by the Newsom administration to go tackle these problems. So then I had, you know, some reinforcement. But you know, the hardest thing is not getting people excited about data, believe it or not, which I actually thought was going to be the biggest challenge. I thought for sure, after I gave my first few presentations, I made presentations. Hey, data is not scary. These are things you should want to know and be interested in knowing. I thought it'd be really hard to get people bought into that, and it wasn't. The hardest thing was actually getting the systems to do what we needed them to do, to actually allow people to have access and to federate access to data and to consolidate data. So that was a pleasant surprise, but a completely different challenge, you know, that led me to my current career during the I was the chief data officer, you know, during the pandemic. And talk about, you never working harder in your life. We were able to really unearth some very quick ways to solve for these problems in an environment where we had the authority and the permission to go fast and to break things and to do that, which was huge. I think the disappointing thing for many of us today is that we didn't take a lot of those lessons learned during the pandemic forward, and in some ways, you know, wasted that emergency in that we drove a lot of value by consolidating and accelerating a lot of data projects, but we didn't really learn from that. What was wrong with the systems before we kind of recreated them. We did the same thing we had done before, just in a faster, better way, and that was give away the keys to the kingdom, to, you know, other third parties. And you know, now you've got states kind of trying to pull it back. And so, yeah, I think I learned a lot in that time translating between policy and policy research and data and data science, and then data science and technology systems in those years in California, and I still have a lot of you know, friends that are still working for that administration, trying very hard to make these things real. And I will say, and you can probably speak to this as well. I imagine you see this across the higher ed and other landscapes as well. It is really hard to undo legacy infrastructure. There's a lot of fear and there's a lot of risk. And so to a question you'd asked a few minutes ago, being able to bring a really hyper rational risk reduction perspective to that and demonstrate not only the value but the safety in doing those things has been probably one of the most important things I figured out how to do in my career over the last three and a half years so that people will act. It's sometimes it's easy to convince them that they need to act, and then hard to get them to actually take the first step. Yeah. So, you know, there's really a thing that we have focused on is, how do you, how do you take people on that journey, holding their hand, putting one foot in front of the other? And a lot of times, you know, that's why I push my team and myself really hard to make sure that we're not experimenting with customers. We're not hoping it works. We are working very hard before we put things in front of public sector consumers to make sure that they work and that they work at scale. We don't sell black boxes. My team doesn't try to convince people that we know what we're doing. If we don't, you know, we're very honest, and I think that creates a space not only to be a trusted advisor, but of psychological safety oftentimes, you know, to a point I made before, we really need people to be willing to admit that they don't know what they don't know, or you can't make progress. And so somehow creating a space in which that is welcome and safe and encouraged and invited and applauded is really, really important.