Value Engineering: The Secret Sauce for Data Science Success

If we believe that the Big Data Business Model Maturity Index described in Chapter 1, The CEO Mandate: Become Value-driven, Not Data-driven, is what organizations could do to become more effective at leveraging data and analytics to power their business models, then your next question is "How can I achieve that?"

Let me introduce you to the Data Science Value Engineering Framework (see Figure 2.1).

Figure 2.1: Data Science Value Engineering Framework

The Data Science Value Engineering Framework (process) provides a simple yet effective methodology for exploiting the economic value of your data and analytic assets; a methodology to drive the collaboration between the business subject matter experts (stakeholders) and your data science team to apply data and analytics to improve the operational and business effectiveness of all industries including healthcare, public safety, manufacturing, transportation, energy, education, the environment, sports, entertainment, financial services, retail, and more.

Let's drill into each of the steps of the Data Science Value Engineering Framework—the "How to do it" framework.