 Is it culturally OK to start early here? No? I'll be in trouble from the get-go if I start early. I packed a lot in here, so I'm going to go ahead and get started. I think if we have Vice President of Product Development for IHS, we have an office here in Bangalore. We're a global company. Data and analytics is our area of expertise, particularly a strong position in oil and gas, and then a lot of areas in the supply chain very close to oil and gas automotive, technology, chemicals, defense, and aerospace. We are probably a company that you have certainly heard from, but probably have no idea who we are. We're the ones that are behind the scenes. We're reporting on forecasts of oil and gas prices, a lot of the economic risk. If there's something in dealing with analytics and forecasting, most likely IHS has been involved. Today we're going to talk about myth-busting software estimation. How many people are familiar with myth-busters? It's the American TV program. The idea of these guys, a couple of scientists, they get together, and they design really cool experiments, usually involving explosives and blowing things up in order to take a look at myths and figure out whether those myths are real or not. I wanted to do that. I asked Noresh, can I get a really big budget and blow things up? He said, sorry, it's not going to work. Instead of blowing things up, what I'm going to do is go and look at real data. As much as possible, go with real scientific data. There's a lot of postulation of different ideas out there. That's what creates many of these myths. I'm going to try to take a look at some real data, some data of my own data, other data from other researchers, to actually take a look at the various myths that we have. We tend to have a lot of them in software estimation. So as a good Agilis, what we're going to start with is test first. So with the myth busters, when the myth busters get together, they look and they have three potential outcomes, either it's busted, the myth is absolutely not true, it confirmed the myth, or they go in between and say it might be plausible. So I'm going to go through it. We'll start with 10 myths, and then we'll go through them each one. So the first one, estimation challenges are well understood by general management, project management, and teams. And it's normal to be able to estimate projects within 25% accuracy. How many people think that's absolutely true? Come on. How come when I ask my team, they say, absolutely, it's going to be done? No problem, sir. So now, how many people think it's going to be busted? Mostly. How many people think it's plausible? Maybe. All right, keep moving on. I'm in trouble with it.