I had many interesting  discussions in the last few days. One of them is, "What is the common  misunderstanding business have when it comes to data analytics/ data science?"

My answer is, "Hire a fresh "data scientist" and expect them to come up with use cases/applications."

To be fair, most bootcamps are focused on machine learning which is only a part of the toolkit that data scientists use. However,  scoping of uses cases and applications has an assumption that the data  scientist knows the following:

1) Able to seek out the requirements of the business stakeholders.
2) Putting together the dataset for the use case/applications
3)  Not all use cases need machine learning, some of them can be solved  simply with statistics, others are thru optimization methods.
4) Deciding where in the business process to slot in your machine learning models.

and many more.

This can be overwhelming for someone fresh from a program or boot camp.

For  companies who are interested to climb up the learning curve and be able  to use data well, to gain value from it. It is best to get someone's  experience at the start, to lay out the possible use cases first before  deciding on the next step in gaining value from data.

In other words, articulate the possible value first then decide on the approach, with cost consideration in mind. :)

Your thoughts?

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