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These are tough challenges. They're complex. But the exciting thing is they're surmountable. It's a lot of work. A lot of things have to happen. But we can get there. And we're at the cusp of I would say, a revolution that's really going to be spurred by better and more access to data. We call this data enabled missions. Data enabled mission outcome, where we are in this journey is we are pretty much, we pretty much know how to develop and deploy a software. We pretty much know how to develop and deploy a product. Data is also a product. So from that journey prospective where we see ourselves that we are open, for empowering our business users. AI immediately go to the existential threat that it poses. Bending the cost curve in health care, something we've tried for many, many years to do, and I think relatively unsuccessfully. And so when we think about something like that, the ability to look at patterns in data and see how we can address these types of health challenges, look at some of the models that can identify things like cancer, emergence of patterns that lead to or predict cancer. Extract frames the faces from three dimensional landmarks, the intonation of their voice, what they're saying and fuse of all of these, what we call modalities together to be able to help and assist the oncologist or the research nurse to figure out exactly what level of pain is this cancer patient in. It's very difficult and quite challenging, but as you can see, the long term results is really enabling a data mission for health. We've lived through the hype cycles in the past. This is fundamentally different. We're using data as an accelerator across the department to build seamless veteran interactions. Master data’s important to that. Ensuring that a veteran only has to update their address information once with the department is huge. Boasts accuracy. It's respectful of the veteran’s time, and that means you have to have high reliability in the data that we curate, which means you spend a lot of time in the nitty gritty with some of those transactional systems ensuring that they represent those business processes for the department. We are a mission focused agency. I mean, our first thing is to protect the land, facilitate lawful trade. But, the data can be not only our strong point, but also our weak points. If we're not managing it correctly, if we're not using it correctly, if we're not securing it correctly, that's why we need these guardrails. Data modernization starts with your IT. We're looking to cloud. Most prominently not gov cloud. We're actually going to go commercial cloud from the standpoint that we have open data. We want to share our data. You know, you think about NOAH and weather data that might be coming from, you know, satellites in space, and so the way you collect that and then have to transmit to a ground station, the size and the speed and performance of that data is going to be inherently really different from, for example, CBP, where it might be, you know, maybe law enforcement related data. DOE is a great example, of like providing data as a platform for industry and for society to be able to innovate on top of- That was another great example where you're doing some of that and then providing data sets. And so, then when you think about AI in that context, and, who is using it and for what purpose, I think comes into play as well. So we actually created a discovery zone that is the DOE environment where we have Google, we've added Microsoft. It's interesting that you're mentioning commercial because we've done commercial Microsoft. And now that they have federal working, we started adding that because again, our data is in all of the platforms and we know that we need to be able to share across. Is the journey ever complete? Of course not. It’s always going to evolve.