How AI changes the data analyst role

"Embrace AI as a force multiplier. Successful analysts will think of AI as a powerful collaborator rather than their replacement. Learn to effectively prompt AI systems, validate their outputs, and understand their limitations. This requires skills in prompt engineering, model evaluation, and AI governance-talents that complement rather than replace the traditional analyst role. Double down on domain expertise. While AI excels at pattern recognition, human analysts understand business context and business priorities leading them to know the right questions to ask."

"Become a trusted AI validator. AI-generated insights require human oversight to ensure accuracy, relevance, and business applicability. Develop frameworks for reviewing automated outputs, identifying potential biases or errors, and creating feedback loops that improve AI performance over time. This becomes especially critical as AI systems work with increasingly complex, multimodal data sets. Become a data storyteller. As AI handles more of the technical heavy lifting, analysts must excel at translating insights into compelling narratives."

Analysts must treat AI as a collaborative force multiplier and develop skills like prompt engineering, model evaluation, and AI governance. Deep domain expertise remains essential for asking the right business questions and interpreting AI outputs. Analysts should act as validators, creating frameworks to review outputs, detect biases, and establish feedback loops that improve models across complex, multimodal data. Data storytelling, visualization, executive communication, and change management become core responsibilities. Organizations must redefine analyst career paths, provide governance and training, and foster cross-functional collaboration to support the evolving role of analysts alongside AI.

Read at InfoWorld