Oracle Analytics Cloud

Oracle is a global provider of enterprise cloud computing, offering software, platform, infrastructure and even data as a service. With over 164,000 employees, Oracle is one of the giants in the market.

The Oracle Analytics offering rests on three major pillars: Oracle Analytics Cloud (OAC), Oracle Analytics Server (OAS) as well as predefined content and embedded analytics created using OAC, now combined into the Fusion Data Intelligence offering. OAC is Oracle’s flagship product for BI & analytics. The suite runs on Oracle Cloud Infrastructure (OCI) and can leverage OCI services such as Autonomous Data Warehouse. Oracle Analytics Server, the on-premises version of OAC, brings the capabilities of the cloud platform annually to organizations requiring on-premises deployments or wanting to run it in other clouds. For Oracle’s business application customers, Fusion Data Intelligence (FDI) combines predefined content developed with Oracle technologies such as Oracle Analytics Cloud and OCI Data Lakehouse and equips customers with preconfigured connectivity to Oracle Cloud Applications (Oracle Fusion Applications, Oracle NetSuite, Oracle Health etc.) as well as options to connect to third-party data. Furthermore, the product serves customers with prebuilt AI models configured to support a specific operational task such as predicting sales order delivery. In addition, FDI will be the basis for a range of intelligent applications that Oracle says will go beyond dashboards and reports with prebuilt insights and AI/ML-driven recommendations to facilitate decisions and actions by role, task and industry.

Oracle Analytics Cloud is a platform for dashboards, formatted reports, ad hoc reports, analysis, data preparation and machine learning. It offers different clients for different user types. Business users typically work with the modern web-based environment, which offers augmented data preparation and custom data transformations via Data Flows as well as visual analysis features. More mature clients, such as Answers (ad hoc reporting) and Publisher (for developer-oriented pixel-perfect reporting), still exist and have been equipped with the same visualization engine. Mobile clients are also available.

All the clients can connect to Oracle’s enterprise semantic layer, which offers strong calculation and caching features and a common and governed business logic. The suite typically connects directly to underlying data using native connectors, OEM software, REST APIs or standards such as OData and JDBC/ODBC without replicating data. To modernize this layer, the vendor has introduced a new semantic modeler, a web-based environment for creating SMML-based models. SMML is a JSON-based Semantic Modeler Markup Language that describes the semantic model’s objects. The model has also been integrated with Git to support software development and facilitate deployment, maintenance and version control. Moreover, Oracle has opened its semantic layer to be consumed by third-party applications such as Power BI. In terms of openness, Oracle now offers REST APIs for Oracle Analytics to provide developers with access to functionality in OAC. And most recently, the vendor has developed a story exchange format, which allows customers to integrate with third-party video GenAI services to provide interactive and spoken stories using AI.

Business users can use the semantic layer, which is typically created by technically savvy users, or create their own data models (data sets). Oracle offers a self-optimized in-memory engine, which is used to ingest and cache data instead of connecting live to sources, making it a compelling option to boost performance and productivity without leaving the governance delivered by the platform.

With Automated Insights and ‘Explain’, users can leverage automated insights generation powered by ML and NLG. Relevant drivers, patterns and clusters are identified and visualized. Beyond that, forecasts leveraging weighting algorithms can be created based on the drivers identified. Natural language queries (‘Ask’) can be used to analyze data without deep technical expertise or having to search across all data sets cataloged in many supported languages. Moreover, a GenAI assistant has been introduced and is now in limited availability in certain geographic regions.

Complementing the portfolio, machine learning algorithms are embedded out of the box with Oracle Analytics Cloud. R and Python scripts can be embedded to leverage the results of advanced analytics models. Additionally, ML algorithms embedded in Oracle databases can be leveraged without shifting data, providing enhanced speed and flexibility for analytics.