Data Science: The Hard Parts

Chapter 1. So What? Creating Value with Data Science

Data science (DS) has seen impressive growth in the past two decades, going from a relatively niche field that only the top tech companies in Silicon Valley could afford to have, to being present in many organizations across many sectors and countries. Nonetheless, many teams still struggle with generating measurable value for their companies.

So what is the value of DS to an organization? I’ve found that data scientists of all seniorities struggle with this question, so it’s no wonder the organizations themselves do so. My aim in this first chapter is to delineate some basic principles of value creation with DS. I believe that understanding and internalizing these principles can help you become a better data scientist.

What Is Value?

Companies exist to create value to shareholders, customers, and employees (and hopefully society as a whole). Naturally, shareholders expect to gain a return on their investment, relative to other alternatives. Customers derive value from the consumption of the product, and expect this to be at least as large as the price they paid.

In principle, all teams and functions ought to contribute in some measurable way to the process of value creation, but in many cases quantifying this is far from obvious. DS is not foreign to this lack of measurability.

In my book Analytical Skills for AI and Data Science (O’Reilly), I presented this general approach to value creation with data (Figure 1-1). The idea ...