New Delhi, Sept. 25 -- Data and analytics have moved well beyond dashboards and periodic reporting. For many large enterprises, the problem today is not collecting more data. It is making sense of data spread across different systems, legacy infrastructure, cloud platforms and business functions.

Generative AI has made that problem harder to ignore. Enterprises experimenting with AI need reliable data pipelines, governance and enough context to move projects beyond pilots. Gartner has predicted that through 2026, organizations will abandon 60% of AI projects unsupported by AI-ready data.

That is also changing what enterprises expect from data and AI consulting firms. The work increasingly stretches from data engineering and cloud modern...