What AI Actually Changes About Being a Data Analyst
A grounded early-career perspective on how AI is changing data work — and why context, communication, judgment, and trust still matter.
AI is already changing the day-to-day work of data analysts. Queries, reports, documentation, and even first-pass insights can now be drafted or accelerated faster than ever. That creates a practical question for students and early-career professionals: if AI can handle more of the technical work, what still makes a data analyst valuable?
This talk offers a grounded perspective from early-career business data work, not from theory or hype. Using simplified composite scenarios, it will look at where AI can genuinely help, where it can create new risks, and why technical output alone is not enough to build trust.
The session will focus on four skills that become more important as tools improve: understanding business context, validating assumptions, communicating clearly with stakeholders, and taking ownership of the outcome. Attendees will leave with practical habits they can use immediately, including what to ask before building a report, how to treat AI-generated work as a first draft rather than the final answer, and how to build credibility when speed is no longer the only advantage.
AI may accelerate the work, but it does not replace judgment, ownership, or trust.