Glossary

An AI data platform provides the data infrastructure needed to build, run, and scale AI and machine learning applications — combining data integration, storage, governance, and AI tooling in a unified environment.

Agentic AI refers to AI systems that can autonomously plan, reason, and take multi-step actions to complete a goal — going beyond answering questions to actually executing tasks on behalf of users.

An analytics platform combines data acquisition, storage, preparation, and analysis in a single solution — giving business users self-service access to insights without relying on IT.

A cloud analytics platform delivers BI and data analytics as a managed cloud service — eliminating on-premises infrastructure costs while scaling to meet growing enterprise data demands.

Financial data analytics helps organizations turn raw financial data into actionable insights — improving profitability, cash flow, risk management, and compliance across the enterprise.

A data analytics platform is a unified solution covering the full analytics lifecycle — from data ingestion and preparation to analysis and visualization — without the complexity of traditional stacks.

Data connectors automatically extract and transfer data between systems — enabling faster decisions, real-time analytics, and improved organizational agility. Here's how they work.

Data mapping is the process of identifying and linking data fields from source systems to a destination database — the foundation of any analytics, BI, or data integration pipeline.

Decision intelligence is the discipline of turning data into better business decisions — combining analytics, AI, and contextual business logic to help organizations act faster and more confidently.

ETL (Extract, Transform, Load) is the process of pulling data from source systems, transforming it into an analysis-ready format, and loading it into a data warehouse or analytics platform.

Financial analytics is the analysis of financial data to assess business viability, profitability, and performance — enabling faster forecasting, better planning, and smarter decision-making.

Financial planning and analysis encompasses several key office of finance activities that collectively help an organization manage its financial strength and business strategy over time.

Manufacturing analytics can help organizations improve and streamline their manufacturing operations.

Operational analytics is the study of business operations data to identify performance gaps, reduce costs, and improve efficiency — across manufacturing, maintenance, sales, and more.

Oracle Analytics Cloud (OAC) is Oracle's fully managed, cloud-native BI platform - and the official successor to OBIEE. Learn what it does, its key limitations, and how Incorta compares.

OBIEE (Oracle Business Intelligence Enterprise Edition) is a business intelligence tool from Oracle Corporation used to gather, store and visualize enterprise data.

A procurement analytics dashboard visualizes spend, supplier performance, and purchasing KPIs in one place — helping finance and procurement teams identify cost-saving opportunities and improve cash flow.

Self-service analytics lets business users query data and generate reports independently — without waiting on IT or data engineering teams — enabling faster, more confident decisions.

A semantic layer translates complex database structures into plain business terms — giving analysts and business users a consistent, governed way to query data without needing to know SQL or table schemas.

Supply chain analytics combines data from procurement, inventory, warehousing, and fulfillment into a single view — helping organizations reduce costs, avoid stockouts, and optimize operations.

Unified data analytics is the practice of bringing together data from all of an organization's enterprise systems into a single, analysis-ready environment — eliminating silos and giving teams a complete, consistent picture of the business.