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What’s the difference between a data scientist and a decision scientis

By Justin Stoltzfus | Last updated: June 14, 2020

A decision scientist is a technology professional who is mainly focused on making technologies work for decision-making processes within an enterprise.

However, the term “decision scientist” is really meaningful when you contrast it with another similar job role referred to as a “data scientist” or “big data scientist.”

Here's the context — years ago, big data was the major way that people advanced technology. They poured enormous amounts of data into linear systems and created business insights. So the big data scientist was one of the most sought-after professionals in the tech world.

Advance the clock to today, and you have the emergence of artificial intelligence (AI) and machine learning (ML) systems. That has led to the emergence of the decision scientist.

Unlike the data scientist, the decision scientist is not mainly focused on working with huge amounts of data. Instead, that individual is looking at the output of the systems and what they do, and trying to make those systems work for various enterprise goals and objectives.

The amount of skill crossover and similarity between data scientists and decision scientists is enormous. They do so many of the same things that their job roles can be almost identical.

But that key difference is that for the data scientists, the focus is on the mathematics and algorithmic work of handling the data, while the decision scientist focuses more on the result and the context of use cases.

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Computer Science Artificial Intelligence Programming Languages Emerging Technology Identity & Access Governance Machine Learning Data Science

Written by Justin Stoltzfus | Contributor, Reviewer

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Justin Stoltzfus is a freelance writer for various Web and print publications. His work has appeared in online magazines including Preservation Online, a project of the National Historic Trust, and many other venues.

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