The New Role of Data Teams in the Agentic Analytics Era | The JetBrains Blog

In the evolving landscape of data analytics, the role of data teams is transforming with the rise of AI-driven analytics, emphasizing the need for a robust semantic layer to ensure reliable outcomes. The shift from traditional dashboard analytics to agentic analytics requires data analysts to focus on defining clear metrics, building semantic contracts, and establishing governance and versioning standards. This transition is supported by the development of a strong foundational framework, where metrics are standardized in code and managed through Git-based systems with strict guardrails. Emerging trends show a preference for small teams of specialized AI agents, each performing distinct roles such as discovery, semantic layer authoring, and auditing, with humans overseeing and approving critical decisions. The industry is also witnessing a division into three main approaches: end-to-end conversational analytics platforms, enterprise BI with AI add-ons, and headless semantic infrastructure, the latter offering greater control and scalability. The introduction of the Open Semantic Interchange (OSI) aims to create a vendor-neutral standard for semantic models, enhancing interoperability across tools. Databao, a new JetBrains product, supports this paradigm by helping data teams create a shared semantic context and develop data agents, fostering an AI-native analytics experience that business users can trust.