A data team’s product is decisions

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This post was originally published by TJ Palanca at Towards Data Science

Strategic decisions are luxury, bespoke, and artisanal decisions; decisions that are made infrequently but have a large impact. There is a large amount of uncertainty and it’s unlikely there is going to be a frequent feedback loop. For augmenting these decisions one would benefit from a business mindset, communication skills, and an ability to extract information from limited data (heavily custom linear models, or Bayesian techniques).

Operational decisions are mass-produced, repeatable decisions that are made hundreds or thousands of times a day. They may not be that impactful individually, but the sheer volume makes optimizing these kinds of decisions important as well. The decision systems behind operational decisions are usually algorithmic in order to be cost-beneficial and would need machine learning, data science, and strong data engineering to productionize a system.

When designing a data team’s roadmap, I’ve found that it’s important to be conscious of the difference between the two types of “products”, and to ensure that the mix is appropriate for the business.

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This post was originally published by TJ Palanca at Towards Data Science

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