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The Economics of Data, Analytics, and Digital Transformation

You're reading from   The Economics of Data, Analytics, and Digital Transformation The theorems, laws, and empowerments to guide your organization's digital transformation

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Product type Paperback
Published in Nov 2020
Publisher Packt
ISBN-13 9781800561410
Length 260 pages
Edition 1st Edition
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Author (1):
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Bill Schmarzo Bill Schmarzo
Author Profile Icon Bill Schmarzo
Bill Schmarzo
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Table of Contents (14) Chapters Close

Preface 1. The CEO Mandate: Become Value‑driven, Not Data-driven 2. Value Engineering: The Secret Sauce for Data Science Success FREE CHAPTER 3. A Review of Basic Economic Concepts 4. University of San Francisco Economic Value of Data Research Paper 5. The Economic Value of Data Theorems 6. The Economics of Artificial Intelligence 7. The Schmarzo Economic Digital Asset Valuation Theorem 8. The 8 Laws of Digital Transformation 9. Creating a Culture of Innovation Through Empowerment 10. Other Books You May Enjoy
11. Index
Appendix A: My Most Popular Economics of Data, Analytics, and Digital Transformation Infographics
1. Appendix B: The Economics of Data, Analytics, and Digital Transformation Cheat Sheet

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.

You have been reading a chapter from
The Economics of Data, Analytics, and Digital Transformation
Published in: Nov 2020
Publisher: Packt
ISBN-13: 9781800561410
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