Summary Data Analytics
Q3. When Desai’s team first reached out to business units in search of questions where data science might be useful, he did not require them to utilize “big data” (i.e., look for patterns to explore in the data). Why not? Do you agree?
There were unclear connections between data, metrics, and the impact they had on the business.
There were unclear connections between data, metrics, and the impact they had on the business. There were times when the team was not sure whether the impact of a question and answer on the business was thoroughly analyzed. In other cases, it was not clear whether the managers’ questions were matched to answers that would lead to practical actions for the business. Moreover, it was difficult to tell whether decisions for a business unit would have been appropriate for Target’s success.
The interactions between BI analysts and managers advanced the questions they were asking. The analysts started questions that would require tools to answer. Also, there were times that managers required the BI analysts to verify decisions instead of answering questions. Sometimes the analysts experienced challenges inaccurate data to answer questions since the retail environment kept changing, and so did the question. I agree with Desai’s idea (Datar & Bowler, 2015). The interaction managers had with the BI analysts gave the team the ability to understand data science to succeed in Target’s success.
Datar, S., & Bowler, C. N. (2015). Data science at target.
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