LO
Data analyst — tasks, one by one
The unit of analysis is the task, not the job title. Each one below carries its direction, whether the judgement rests on evidence or on platform inference, the reasoning, and what it does not establish.
Every task on this page#
Writing queries and building dashboards
Automating✓ Evidence-backedTranslating 'how many users did X last month' into SQL, and wiring the result into a chart someone can refresh.
Natural-language-to-SQL and chart generation are among the most mature applications of language models, and the BI vendors have built them into the products analysts already use. The mechanical translation step that filled much of an analyst's week is now a prompt, when the data is clean and the question is clear.
'When the data is clean and the question is clear' is a large condition. In most companies neither holds, and the tools produce confident wrong answers on ambiguous schemas.
Working out what is really being asked
Still human-led≈ Platform inferenceTurning a manager's vague worry into a measurable question, and knowing which definition of 'active user' they mean.
The hard part of analysis is upstream of the query: knowing the organisation's definitions, its politics, and what decision the number will feed. A tool answers the question as asked; the analyst's value was always in noticing the question was wrong.
Self-service tools let managers ask their own questions badly rather than ask an analyst. Framing stays valuable, but the analyst has to be in the room to supply it, and fewer are.
Knowing when the data is lying
Still human-led✓ Evidence-backedSpotting the broken pipeline, the duplicated events, the timezone bug, the definition that changed in March.
This requires history with the specific data — remembering what happened to it and why — which is exactly what a model without organisational memory lacks. Faster query generation makes this more important, not less, because wrong answers now arrive faster and look more polished.
Institutional memory of the data is built by working with it daily. If the daily querying moves to self-service, the memory that makes this task possible stops accumulating.
Making a number change a decision
Being augmented≈ Platform inferencePresenting a finding so that the person who has to act on it understands it, believes it, and does.
Drafting the narrative and the slides is faster. Persuading a sceptical director in a meeting, and knowing which caveat to lead with for this particular audience, remains a human task because it depends on reading the room and on the analyst's own credibility.
Credibility is personal and accrues slowly, which protects established analysts and no one else. A first analyst hire has none of it and now has fewer routine tasks to earn it with.
Owning the definitions the tools rely on
New task≈ Platform inferenceMaintaining the metric definitions, the documentation and the semantic layer that make natural-language querying produce correct answers.
Natural-language analytics only works on top of well-defined metrics, so the value of the analyst shifts from answering questions to curating the layer that lets everyone answer their own. This is new work, closer to data governance than to reporting, and it is where analyst headcount is being redirected.
Curating definitions is a smaller job than answering questions was, and it is one per organisation. Headcount being 'redirected' into it is a euphemism for a smaller team doing different work.