Data science vs evaluation is a false dichotomy. Because data science and evaluation (or research) are not mutually exclusive.
Both data science and evaluation involve data. Depending on the purpose, occasionally there will be overlap. When data science is used to make judgements on the merit, worth or value of things it is evaluative.
Most evaluators are used to approaches where you start with questions, likely generated based on a program’s design or logic. You then find or collect data in order to answer those questions.
Data science is different. Instead of starting with questions, you start with a heap of data. Then you say, “what kind of insight might this data provide?” And then you go digging and searching. Data science comes from the idea that if we have a lot of data, we should be able to use that data to gain insight.
No, data science isn’t going to answer all the evaluative questions you need answering, but it’s not meant to. Likewise, most standard evaluation approaches are going to leave lots of data on the table. Because once you answer the questions you set out to answer, you tend to move on.
Evaluators can be touchy when they hear people touting the benefits of data science. It’s time to stop approaching this issue like it’s a debate and data science is on the other side. There’s some useful stuff here that we can use, and with data continuing to pile up in databases by the day, it’s time to start embracing some of these new approaches.
In this book, I’m going to take a broad view of data science. Here’s the definition I’ll work from:
Data science is the transformation of data using mathematics and statistics into valuable insights, decisions, and products.
This is a business-centric definition. It’s about a usable and valuable end product derived from data. Why? Because I’m not in this for research purposes or because I think data has aesthetic merit. I do data science to help my organization function better and create value; if you’re reading this, I suspect you’re after something similar.