The term data work covers the various fields of data science, business intelligence, digital analytics, etc. If your profession touches data in any way, it’s considered data work.
Data worker covers any professional that works in data work. Whether you’re a data scientist who optimizes machine learning algorithms, a PowerBI developer building dashboards, or a marketing analyst who adds tracking to websites – you’re a data worker.
Why do we need the terms “data work” and “data worker?”
One of the annoying things about data science, analytics, and business intelligence is that all the terms used to describe our industry mean different things to different people.
When I interviewed people for my book, one of my favorite questions was to ask people “What does the title Data Scientist mean to you?”
I got a different answer almost every time.
This becomes a problem for people who write about data work-related topics, like myself. When I wrote my book, Data Work: A Jargon-Free Guide to Managing Successful Data Teams. I mostly had reporting teams in mind while writing it, but the advice is applicable to most other teams in the data profession.
Whenever I referred to someone that worked in data, I would call them business intelligence developer on one page, analyst on the next, and data professional later.
Same thing with the actual industry name. Business intelligence implies reporting. Data science implies machine learning. Analytics implies… well, whatever the person you’re talking to thinks it implies.
Adopting the terms “data work” and “data worker” solves these problems for those who write about data.