Oracle Analytics Cloud - 2026 Pricing, Features, Reviews & Alternatives

Oracle Analytics is a cloud native service designed to help organizations unlock new insights from their data. It combines powerful data preparation, data enrichment, and visualization capabilities with embedded machine learning that enable organizations to gain new, unbiased, and otherwise-never-considered insights quickly and easily from their data. It is designed to help all people in any role quickly and easily explore, transform, and visualize their data without the need for people with additional specialized data science, data engineering, coding, or analytics skills. Oracle Analytics provides a single platform for all of an organization’s analytics needs without resorting to multiple expensive tools that are loosely integrated and result in complicated architectures. Start on-premises or in the cloud—Oracle Analytics supports a hybrid deployment strategy, providing flexible paths to the cloud.

Data visualization: Visually explore data to create and share compelling stories using Oracle Analytics. Discover the signals in data that can turn complex relationships into engaging, meaningful, and easy-to-understand communications. Accelerate the data analytics process and make decisions with actionable information. A code-free, drag-and-drop interface enables anyone in the organization to build interactive data visualizations without specialized skills.

Machine learning: With the volume, variety, and sources of data constantly growing, machine learning (ML) helps users discover unseen patterns or insights from data. ML built into Oracle Analytics removes human bias and enables users to easily interpret possible outcomes and opportunities. Integrate OCI AI Services for use directly in analytics projects. Everyone—from clickers to coders—can use embedded ML to build custom, business-specific models for better decision-making. Business users do not need special technical or programming skills to use ML. In addition, data scientists, engineers, and developers can accelerate model building, training, and publishing by using the Oracle Autonomous Database environment as a high performance computing platform with your choice of language, including Python, R, and SQL.

Data preparation and enrichment: Use self-service data preparation to ingest, profile, repair, and extend datasets, local or remote, greatly saving time and reducing errors. Data quality insights provides a quick view of data to identify anomalies and help with corrections. The custom reference knowledge capability enables Oracle Analytics to identify more business-specific information and make relevant data enrichment recommendations. Build visual dataflows to transform, merge, and enrich data and save results in Oracle Analytics storage, a connected relational database (e.g. Snowflake or MySQL), or Oracle Essbase.

Data sources: Unify data across the organization and from multiple data sources for a complete and consistent view. Oracle Analytics offers more than 35 out-of-the-box native data connection choices, including JDBC (Java Database Connectivity). Securely create, manage, and share data connections with individuals, teams, or the entire organization. Access data wherever it is located: public cloud, private cloud, on-premises, data lakes, databases, or personal datasets, such as spreadsheets or text-based extracts.

Data modeling: Gain trusted and consistent information across the enterprise with a business representation of data using a shared semantic model, without compromising governance. Users access data through nontechnical business terms, predefined hierarchies, consistent calculations, and metrics. Create seamless views across data sources visually explore them using native queries that deliver high performance. Easily configure the balance of live and cached connections to ensure high-performance data access. Support multiple data visualization tools, such as Microsoft Power BI, and retain a consistent and trusted view of enterprise metrics.