Citizen Analyst Part 1

This post was originally featured in the 2/11/2026 newsletter found here: INBOX INSIGHTS: Why Generic AI Training Fails, Citizen Analyst Part 1

In this week’s Data Diaries, we’re going to revisit something from the archives that Katie wanted to dust off. Back in 2015 when I first started working with IBM (back then, it was IBM Analytics, now it’s Data and AI), the IBM Watson Analytics team popularized the term “citizen analyst” – regular folks who were data analysts in their spare time.

There was a campaign about the citizen analyst, promoting what was at the time known as IBM Watson Analytics, a web-based analysis tool similar in spirit to tools like Tableau. The vision of the campaign was that with tools like Watson Analytics, regular citizens could perform useful data analysis tasks and garner insights about causes they cared about. The tools would enable them to ask questions of their data and be data-driven.

For example, suppose you cared about pet adoption. Suppose there was lots of data available from local, state, and non-profit entities in spreadsheets scattered around the web, and you wanted to understand the big picture. Instead of trying to cobble together a pile of spreadsheets into a master spreadsheet, you’d load your data into Watson Analytics and have it do that, then give you a master data set you could create visualizations and analysis from. Maybe, as a volunteer, you’d find that a certain policy changed how fast pets were adopted, or how a pet’s age or breed influenced adoption rates.

The campaign was generally well-executed, though IBM sunset Watson Analytics a few years later in 2019; the tool struggled to find a place in the IBM family alongside Cognos and the broader Watson portfolio. (IBM would later launch watsonx in 2023 as its next-generation AI platform.)

But the concept itself was sound – empowering regular people who might want to do data analysis but lacked the skills for things like advanced data analysis in languages like Python or R. A web interface where you could load your data and ask questions of it with simplified tools was supposed to open up a golden age of analysis.

The funny thing is, generative AI is that age. IBM was ahead of the curve on the concept, but the technology took a decade to get to where it needs to be where regular folks can load up a spreadsheet into a generative AI tool like Gemini or ChatGPT or Claude and ask questions of their data. Instead of pre-baked tools, AI models now write their own analysis software on an ad hoc basis, generating Python scripts behind the scenes to do much of the data processing. (or explicitly, if you direct them to do so)

In the next couple of issues, we’ll deconstruct the citizen analyst and think through what it looks like today, plus give you some practical examples to try. But the citizen analyst’s time, in the age of AI, has finally come to bear.

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