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You're reading from  The Economics of Data, Analytics, and Digital Transformation

Product typeBook
Published inNov 2020
PublisherPackt
ISBN-139781800561410
Edition1st Edition
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Bill Schmarzo
Bill Schmarzo
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Bill Schmarzo

Bill Schmarzo, The Dean of Big Data is a University of San Francisco School of Management Executive Fellow and an Honorary Professor at the School of Business and Economics at the National University of Ireland-Galway where he teaches and mentors students in his courses “Big Data MBA” and “Thinking Like a Data Scientist". He is the author of Big Data: Understanding How Data Powers Big Business, Big Data MBA: Driving Business Strategies with Data Science, and The Art of Thinking Like a Data Scientist. He has written countless whitepapers, articles and blogs, and given keynote presentations and university lectures on the topics of data science, artificial intelligence/machine learning, data economics, design thinking and team empowerment.
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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.

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The Economics of Data, Analytics, and Digital Transformation
Published in: Nov 2020Publisher: PacktISBN-13: 9781800561410

Author (1)

author image
Bill Schmarzo

Bill Schmarzo, The Dean of Big Data is a University of San Francisco School of Management Executive Fellow and an Honorary Professor at the School of Business and Economics at the National University of Ireland-Galway where he teaches and mentors students in his courses “Big Data MBA” and “Thinking Like a Data Scientist". He is the author of Big Data: Understanding How Data Powers Big Business, Big Data MBA: Driving Business Strategies with Data Science, and The Art of Thinking Like a Data Scientist. He has written countless whitepapers, articles and blogs, and given keynote presentations and university lectures on the topics of data science, artificial intelligence/machine learning, data economics, design thinking and team empowerment.
Read more about Bill Schmarzo