In the ever-evolving landscape of enterprise data, getting value from ERP, HCM, and SCM systems can feel like a massive lift. Traditionally, creating a data warehouse that integrates all these sources required a team of DBAs, data engineers, and countless hours of pipeline development. But our team’s recent experience with Oracle Fusion Data Intelligence (FDI) and the Oracle Autonomous Database (ADB) has completely changed that story.

At the heart of FDI is the Oracle Autonomous Data Warehouse (ADW), a fully managed, cloud-native data warehouse. While ADW itself is powerful, pairing it with the built-in Model Context Protocol (MCP) server) unlocks capabilities that were previously out of reach for most organizations. MCP provides structured modeling, semantic context, and AI-assisted insights directly in the database. Imagine having a full 3NF data warehouse with all your business-critical data, ERP, HCM, and SCM, ready for AI-powered action without the need to manually orchestrate every process.

Even more exciting, the team can now interact conversationally with ERP data. During the exploratory data analysis (EDA) phase, MCP helps the team surface insights, generate initial queries, and explore relationships in the data. These AI-assisted queries are then reviewed and refined by our engineers, ensuring that people remain in the driver’s seat while the AI accelerates exploration. This approach allows iterative exploration of the data, testing hypotheses, and refining models efficiently, without relinquishing human judgment.

This approach is already driving tangible results. Recently, a client the team is working with has been using this data warehouse for their Procurement function, specifically Spend Analysis. All the data that flows into their ERP system is immediately available in the DW. Using ADW and MCP, the team has been able to analyze Procure-to-Pay processes, uncover insights, and improve decision-making. The results have been outstanding, with faster analysis cycles, more accurate spend categorization, and better operational performance.

What makes this approach unusual is that, while FDI offers a lot of value through the Oracle Analytics Cloud with its rich subject areas, this client is highly database-focused. As a result, the team has concentrated almost exclusively on the ADW portion of FDI. This allows direct work with the full 3NF data warehouse, taking advantage of the built-in MCP server and advanced modeling capabilities, rather than relying primarily on pre-packaged analytics layers or dashboards.

Working directly with ADW is even easier using tools like the VS Code SQL Developer extension. This extension integrates seamlessly with the database, letting developers and analysts browse objects, run queries, and refine models without leaving the VS Code environment, making database-centric workflows far more efficient and productive.

Looking ahead, Oracle’s native support for Apache Iceberg adds a whole new dimension. With a built-in Lakehouse, the team is exploring how to combine warehouse and lakehouse capabilities to further enhance analytics, expand AI-driven insights, and handle semi-structured or high-volume data with the same level of governance and performance.

The combination of FDI, ADW, and MCP has allowed the team to:

• Centralize business data, with ERP, HCM, and SCM information living in one place with a fully normalized schema.
• Simplify management, as online console management replaces the need for large teams maintaining pipelines and servers.
• Unlock AI potential, with advanced predictive models, semantic refinement, and context-aware analytics running directly in the database.
• Accelerate insights, so business decisions are informed by reliable, timely, and actionable intelligence.
• Interact conversationally, developing queries with MCP during EDA, and refining them with engineers to keep people in the driver’s seat.

This experience demonstrates how modern cloud data platforms are transforming enterprise analytics. With the right combination of Oracle technologies, developer-friendly tools like the VS Code SQL Developer extension, and emerging Lakehouse capabilities, teams can break free from traditional bottlenecks and harness operational data to drive business outcomes.