AI Accelerates Data Work, Does Not Replace Analysts

Florian Schimpe, Director of Data & Analytics at TWT Group, stresses that data-driven decision-making often hinges on organizational and cultural factors rather than just technology. Speaking with Scavenger AI, he highlighted common pitfalls companies encounter when implementing a data-centric approach.

Schimpe identifies the disconnect between technical and business sides as a frequent cause of data project failures. "Data work is always a change management issue," he stated, noting that confronting established practices with factual data requires organized facilitation.

He advises starting with small, clearly defined projects, delivering quick proofs of concept, and visibly communicating successes. Common entry points include A/B testing, visualizing customer journeys, and automating reporting processes.

Clear data ownership is presented as a critical factor. It establishes the meaning of metrics, assigns responsibility for data quality, and determines who makes decisions based on data. Schimpe confirms that without it, companies face endless KPI debates and compromised data quality.

AI is transforming data work by offering acceleration. "Today, I don't need to be an analyst to answer initial questions," Schimpe explained. While AI can automate analyses, optimize campaigns, and generate reports, its effectiveness is dependent on the quality and structure of existing data.