[MUSIC] I hope I have convinced you that analytics is a leadership problem, and therefore a problem that applies to you personally. And what this means is that you will need to be involved in driving analytics at every step of the way. And the key question is, what makes you effective at managing in such an environment? I want to propose to you that what you need is a working knowledge of data science. Let me explain what motivates this. When I started my PhD at MIT, I took an econometrics class. Think of econometrics as statistics for economists. Now, for the first few weeks, I was completely lost. But despite the fact that I was lost, I never had the courage to raise my hand and ask the professor to stop and explain. The reason I didn't raise my hand was because I didn't know whether the professor's explanation was confusing or whether I was an admissions mistake. And there was a really good chance I was an admissions mistake. And so I didn't raise my hand because I didn't want to look incompetent to my peers. Now, one year later I took another class in a related topic. Did I know raise my hand and say, I don't understand what you're talking about? Absolutely, and the reason I did is because now I knew enough to know that what I was asking was not a dumb question. What leaders need is to be able to sit in a room with five PhD data scientists and be willing to raise their hand and say, I have no idea what you just talked about. But in order to say that, you actually need to know quite a bit of data science. Only then will you have the courage to raise your hand, and that is why you need a working knowledge of data science. This working knowledge of data science is going to give you three things. First, it will allow you to judge what good looks like when you are presented with analytics. If will allow you to determine whether you should believe the analytics and what questions to ask. Second, it will help you identify where analytics adds value. You see, currently most firms have two separate groups of people. You have the managers who own business problems and know the business. But most of them have little idea of what an analytics can realistically accomplish. On the other hand, you have data scientists who are very good at the technical stuff, but often don't get the business context. And so, this working knowledge bridges the gap between these two groups, and thereby helps you identify where analytics adds value. Finally, a working knowledge of data science allows you to lead with confidence. It allows you to ask the right questions and to say when needed, I don't know what you're talking about, can you please explain this another way. But what does it take to get that working knowledge of data science? Do you need to get a Ph.D.? I want to tell you something that may surprise you. The core skills that enable you to have great judgment about analytics. The core skills that make you comfortable with analytics, the core skills that allow you to easily talk with data scientists, they are not what I learned in my technical classes at MIT. What few people understand is that the most important skills in analytics are not technical skills at all, they are thinking skills. And the trick about these thinking skills is that you don't need to be great at math or statistics, or computer science to be good at them. Now, I'm not saying that they are very easy to acquire, but they are skills that are closely related to critical analytical thinking. They require a similar kind of muscle, but in a slightly different domain. And so my goal is to use the next module to give you a taste for what these skills are. Specifically, I'm going to focus on what you need to tell apart good analytics from bad analytics. [MUSIC]
