 The next step in discussing data science and communicating is to talk about actionable insights or information that can be used productively to accomplish something. Now to give sort of a bizarre segue here, you look at a game controller, it may be a pretty thing, it may be a nice object. But remember, game controllers exist to do something, they exist to help you play the game, and to do it as effectively as possible. They have a function, they have a purpose. Same way, data is for doing. Now that's a paraphrase for one of my favorite historical figures, and this is William James, the father of American psychology and pragmatism and philosophy. And he has this wonderful quote. He said, my thinking is first and last and always, for the sake of my doing. And the idea applies to analysis, your analysis and your data is for the sake of your doing. And so you're trying to get some sort of specific insight in how you should proceed. What you want to avoid is the opposite of this from one of my other favorite cultural heroes, the famous Yankees catcher, Yogi Berra, who said we're lost, but we're making good time. And so the idea here is that frantic activity does not make up for a lack of direction. You need to understand what you're doing so you can reach the particular goal. And your analysis is supposed to do that. So when you're giving your analysis, you're going to try to point the way. Remember, why was the project conducted? The goal is usually to direct some kind of action reach some kind of goal for your client. And that the analysis should be able to guide that action in an informed way. One thing you want to do is you want to be able to give the next steps to your client, give the next steps, tell them what they need to do now. You want to be able to justify each of those recommendations with the data and your analysis, as much as possible, be specific, tell them exactly what they need to do. Make sure it's doable by the client that it's within their range of capability, and that each step should build on the previous step. Now, that being said, there is one really fundamental sort of philosophical problem here. And that's the difference between correlation and causation. Basically, it goes this way, your data gives you correlation, you know that this is associated with that. But your client doesn't simply want to know what's associated, they want to know what causes something. Because if they're going to do something, that's an intervention is designed to produce a particular result. So really, how do you get from the correlation, which is what you have in the data to the causation, which is what your client wants. Well, there's a few ways to do that. One is experimental studies. These are randomized controlled trials. Now, that's theoretically the simplest path to causality, but it can be really tricky in the real world. There are quasi experiments. And these are methods, a whole collection of methods that use non randomized data, usually observational data, adjusted in particular ways to get an estimate of causal inference. Or there's the theory and experience. And this is research based theory and domain specific experience. And this is where you actually get to rely on your clients information, they can help you interpret the information, especially if they have greater domain expertise than you do. Another thing to think about are the social factors that affect your data. Now, you remember the data science Venn diagram, we've looked at it lots of times, it's got these three elements, some people have proposed adding a fourth circle to this Venn diagram, and we'll kind of put that in there, and say that social understanding is also important, critical really, to valid data science. Now, I love that idea. And I do think that it's important to understand how things are going to play out. There's a few kinds of social understanding, you want to be aware of your client's mission, you want to make sure that your recommendations are consistent with your client's mission. Also, that your recommendations are consistent with your client's identity, not just this is what we do, but this is really who we are. You need to be aware of the business context, sort of the competitive environment and the regulatory environment that they're working in, as well as the social context, and that can be outside of the organization, but even more often within the organization, your recommendations will affect relationships within the client's organization. And you're going to try to be aware of those as much as you can to make it so that your recommendations can be realized the way they need to be. So in some data science is a goal focused. And when you're focusing on that goal for your client, you need to give specific next steps that are based on your analysis and justifiable from the data. And in doing so, be aware of the social, political and economic context that gives you the best opportunity of getting something really useful out of your analysis.