Does the application, industry, data, interpretation, and quality of talent matter when implementing a data-driven strategy?


Chapter 1: "Models versus Experts"



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Data Analytics Review 2 2024
Chapter 1: "Models versus Experts"
In this chapter, the "Experts" initially had harsh words for the "Models" until they had shown acceptable results over time.


What are your thoughts about the initial examples provided in this chapter regarding the Models and the responses from the Experts?
I doubted that the models could do a better job than the experts.

  • Come-on now. How can a linear regression method that predicts an outcome variable, or dependent variable, and using a set of independent variables?

  • I was in the same region as Robert Parker, the world’s most influential wine expert who said “Ashenfelter is an absolute total sham” like a movie critic.1

  • Using CART model is more acceptable for the SCOTUS by using one tree to predict liberal decision, and another tree to conservative decision.2

  • Healthcare Quality Assessment can be done by using logistics response Function:

    • Good quality care educates patients and controls costs

    • Need to assess quality for proper medical interventions

    • Nosinglesetofguidelinesfordefiningqualityof healthcare

    • Health professionals are experts in quality-of-care assessment.

Were the responses from the experts warranted?
Yes. While humans can accurately analyze small amounts of information, models allow larger scalability

Should experts be more open to change for the better? Why or why not?
Experts should be open to using Models as a teammate to make their analysis better since experts can be limited in the amount of information that they can use.

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