Is a Data Analyst Safe From AI?
The production side — writing queries, cleaning data, building dashboards — is automating faster than almost any analytical skill set. What isn't automating is knowing which question to ask, whether the data can actually answer it, and when a suspicious number means a broken pipeline rather than a real trend.
At Risk · Verdict: 9-15 months runway
AI Exposure Score
SQL copilots, auto-built dashboards, and one-click data cleaning have compressed what used to be a full analyst workday into minutes — and that's the majority of many analysts' hours. The defensible ground is upstream and downstream of the query: framing the question a stakeholder is actually asking, spotting when a clean-looking number is wrong because a pipeline silently broke, and saying 'this result shouldn't change your decision' when it shouldn't. Analysts who become the interpretation layer stay valuable; analysts who are the query layer are being priced out.
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