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Bridging the Gap: Integrating Data Science and Decision Science through Six Essential Questions
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Bridging the Gap: Integrating Data Science and Decision Science through Six Essential Questions

Last Updated on January 14, 2024 by Editorial Team

Author(s): Peyman Kor

Originally published on Towards AI.

Data Science is the discipline of making data useful — But How?

It has been now more than one decade since Thomas H. Davenport and DJ Patilthree wrote their famous Harvard Business Review article:

“Data Scientist: The Sexiest Job of the 21st Century”

The article made many discussions, and now, after a decade, we have thousands of job profiles titled “Data Scientist.”

Many organizations have embraced the idea of having an analytic team in their structure.

Yet, it is a little bit surprising to see that according to Gartner report estimated that:

“ 60 percent of big data projects will fail to go beyond piloting and experimentation, and will be abandoned.”

In this blog, I reflect on how the field of Data Science/analytics can meet expectations and fulfill the anticipated value.

I believe the main challenge facing the analytics field in the upcoming decade will involve reducing the gap between “Time Spent Analyzing Data” and the actual “Value Creation” with data within organizations.

Then, how can data be used for “value creation”? Well, the field of “Decision Analysis,” or a more refined one proposed by Cassie Kozyrkov's “Decision Intelligence” [1], has some answers on how to “Create Value.”

When we go to the literature on Descion Anlaysis [2] , we… Read the full blog for free on Medium.

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