Analytics and business intelligence (ABI) platforms prepare, model, analyze and visualize data to support decision making. They deliver insights through AI-powered conversational experiences, interactive dashboards and classic reporting. They support collaboration between business and technical users for defining the dimensions, measures and business rules used to create and maintain semantic models. The platforms provide functionality for agentic analytics, where AI agents coordinate tasks across the data-to-insight workflow to automate insight delivery under governance and audit controls. Analytics and business intelligence platforms integrate data from multiple sources, such as databases, spreadsheets, cloud services and external data feeds, to provide a unified view of data, breaking down silos and transforming raw data into meaningful insights. They also allow users to clean, transform and prepare data for analysis, in addition to creating data models that define relationships between different data entities.
Data virtualization technology is based on the execution of distributed data management processing, primarily for queries, against multiple heterogeneous data sources, and federation of query results into virtual views. This is followed by the consumption of these virtual views by applications, query/reporting tools, message-oriented middleware or other data management infrastructure components. Data virtualization can be used to create virtualized and integrated views of data in-memory, rather than executing data movement and physically storing integrated views in a target data structure. It provides a layer of abstraction above the physical implementation of data, to simplify querying logic. (Retired as of Apr-22-2026).