 I'm excited to be here. So, you're at the heart of that disruption in here, which is a good place to be. And it's like these traditional lines, even organizational lines, are starting to blur. They're changing within the customer base. What are you seeing there? Oh, data is a hot space, right? Everyone's talking about big data. It's a hot space. It's a hot space. It's a hot space. It's a hot space. It's a hot space. It's a hot space. It's a hot space. It's a hot space. It's a hot space. Everyone's talking about big data. What's interesting is, is, I actually gave a presentation earlier today around big data myths. You know, what are the myths? Well, it's only about volume. It's not about volume. We've talked about it. There are other Vs. There's a lot more complexity. We've talked about, oh, is big data doesn't include transactional data? That's not true. Most, actually, majority of big data implementations include integrating transactional data. Why? Because that's actually how most of the world operates in terms of business operations. And you want to integrate that with new data sources, new data types to actually make measurable impact at the point of contact. So it's interesting the conversations that are coming up and all that we're covering. Yeah, let's talk about that a little bit. I mean, the notion of bringing together analytic and transactional data. We've talked a little bit about in the past. What are you seeing some of your customers do in the future? Oh, sure. Probably a few use cases. I would say a couple of use cases. One is data warehouse augmentation is probably one of the dominant use cases where we've seen where clients have said, you know what, I want to either do one of two things. I either want to have a landing area to land a lot of data and use technologies like Hadoop as a pre-process kind of landing area before I target it to an operational data warehouse or data store or database. Or you have it in conjunction with the warehouse or database to do active archiving, right? Why active? Well, the nature of what we do and how we live, you don't actually know sometimes how to tier the data appropriately. And you don't know when it's going to be needed or when that context or the value of the data is going to be relevant to the particular queries that you're going to run at a given time. So we're seeing a lot more interaction around transactional data around data warehouse augmentation. In addition, we're seeing it, especially in real-time fraud, right? Why? Because what you're trying to do is run an analytic in the middle and interrupt, depending on the query you're doing, interrupt a transaction that may be happening. You swipe the card and before you fully process that transaction to hit someone's bill, you want to ensure that that really was the right person and the right geographic location and that transaction wasn't done 3,000 miles away in another location. You just take six months to figure that out. Yeah, now... I mean, I get a call from... Oh, I don't even get a letter. I actually get a text. Six months ago, six years ago, you get a letter. Remember? You get a letter. Oh, you might have been hacked. So what happens now? Oh, now I get text, I get an email, I get a call. I especially get calls when I'm traveling internationally. I love getting it via text because then I'm not interrupted as I'm doing it and I can text back and say, yep, I'm traveling internationally when I get a call. It's quite nice. Are you sure you want to make that extra shoe purchase? You know? Okay, so one of the talks you're giving here was big data how to get started. So a lot of clients say, you know, big data is big money, but is that a myth? Is that a reality? Is it a future promise? What are customers asking you in terms of the how to get started question? Is it how do I actually get value out of my data? Is it how do I actually deploy the technology? What are the big questions that you're getting? Probably the two questions I'm getting is one around where should I start meaning what use cases are kind of demonstrating real ROI and results. That gets to your point about value. And then we've determined that there's five dominant use cases that we've seen clients actually be quite successful in starting from. And then the second piece is do I have the right skills in house to get this stuff up and running? And the skills could be quite varied. The analytic skills, the applied math skills to write both the queries, data mining skills. It could be visualization techniques because once you get to larger pools of data to consume it can take quite a bit of time. Or do I have data integration and quality and governance and privacy skills to ensure that I'm actually putting the right governance around how that's being accessed and touched.