Book Image

The Economics of Data, Analytics, and Digital Transformation

By : Bill Schmarzo
5 (2)
Book Image

The Economics of Data, Analytics, and Digital Transformation

5 (2)
By: Bill Schmarzo

Overview of this book

In today’s digital era, every organization has data, but just possessing enormous amounts of data is not a sufficient market discriminator. The Economics of Data, Analytics, and Digital Transformation aims to provide actionable insights into the real market discriminators, including an organization’s data-fueled analytics products that inspire innovation, deliver insights, help make practical decisions, generate value, and produce mission success for the enterprise. The book begins by first building your mindset to be value-driven and introducing the Big Data Business Model Maturity Index, its maturity index phases, and how to navigate the index. You will explore value engineering, where you will learn how to identify key business initiatives, stakeholders, advanced analytics, data sources, and instrumentation strategies that are essential to data science success. The book will help you accelerate and optimize your company’s operations through AI and machine learning. By the end of the book, you will have the tools and techniques to drive your organization’s digital transformation. Here are a few words from Dr. Kirk Borne, Data Scientist and Executive Advisor at Booz Allen Hamilton, about the book: "Data analytics should first and foremost be about action and value. Consequently, the great value of this book is that it seeks to be actionable. It offers a dynamic progression of purpose-driven ignition points that you can act upon."
Table of Contents (14 chapters)
10
Other Books You May Enjoy
11
Index
Appendix A: My Most Popular Economics of Data, Analytics, and Digital Transformation Infographics

Step 4: Identify Supporting Analytics

Now that we know our top priority use case, we want to identify the predictive and prescriptive analytics that supports the targeted use case. Sometimes it is easier to identify the supporting analytics by asking the stakeholders what Questions they need to answer with respect to the targeted use case.

Then we can walk the stakeholders through the "Thinking Like a Data Scientist" process to convert those questions into predictions and prescriptive actions (see Figure 2.5).

Figure 2.5: Transitioning Questions into Predictions

Figure 2.5 shows some questions and the resulting predictions and the prescriptive actions using an agricultural company example. We start with the question and then convert the question into a predictive statement, such as:

  • "What were revenues and profits last year?" (the question) converts into "What will revenues and profits likely be next year?" (the prediction).
  • "How much fertilizer did I use last planting season?" (the question) converts into "How much fertilizer will I likely need next planting season?" (the prediction).

Next, we ask the stakeholders if we had those predictions, how would you use those predictions to make operational decisions (which then becomes the focus of the prescriptive actions)?

It's a simple process that builds upon the questions that the stakeholders are already asking today and then guides the stakeholders to the necessary predictive and prescriptive analytics…the key to thinking like a Data Scientist.