How to Deliver Business Impact with Data

Tips for Developing Data Products

Post was originally published in January 2020 and has been updated in May, June, and October 2020, April, November and December 2021, January + November 2022 for relevance.

After developing data products for 10 years, I learned to ask all of these questions during the kick-off(s) the hard way. Data Analytics/Business Intelligence/Data Science/Analytics/Machine Learning should not be done in a vacuum or for the sake of doing BI/ML/AI. Instead, it should aim to be part of a data product deliverable that helps bring value back to the customers and the business. 

What is a data product?

What does a data product deliverable look like?

Ahead of any development, it helps to align with your stakeholder by understanding about the business and the stakeholder/customer ask, to help you understand the impact of the work, to scope out and discuss what it will take to (iteratively) deliver a data product.

Here are the questions I ask in an intake meeting (and follow-up) meetings to help me and my potential collaborators get there.

Context, e.g. "Why?"

We want to see <behavior change> so that <impact> [1], to help you answer why the company is interested in the analyses now.

End Goal

By answering these questions, you'll be able to form a "user story": 


Technical Requirements

Next Steps

Parting Advice

Do you need an expert to guide you on your next data product, to improve your product market fit and have happier customers? Please reach out.

Keywords: Data products, business impact and value

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