Top 6 Mistakes to Avoid When Running a Data Science POC

Scaling AI Julien Moreschetti

A proof of concept (POC) is a popular way for businesses to evaluate the viability of a system, product, or service to ensure it meets specific needs or sets of predefined requirements. But what does running a POC mean in practice specifically for data science?

man looking through magnifying glass

Ultimately, when it comes to the evaluation of data science solutions, POCs should prove not just that a solution solves one particular, specific problem, but that the solution in question will provide widespread value to the company: that it’s capable of bringing a data-driven perspective to a range of the business’s strategic objectives.

After working side by side with lots of businesses of all sizes and from all kinds of industries on data science POCs, we've seen it all. That's why we've released a white paper, The 7 Steps to Driving a Successful Data Science POC, which we hope will help companies find more success and be more efficient no matter what they're evaluating.

But we've also compiled a list of the top 6 mistakes to avoid during data science POCs — a crash course, if you will,  to ensure companies get off on the right foot (you can see a larger version here):

DAT_POC-Infographic 2020@3x

 

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