Magic Quadrant for Analytics and Business Intelligence Platforms

Munish Goswami
Sep 23
2 min read

Oracle recognized as a Leader in the 2025 Gartner® Magic Quadrant™ for Analytics and Business Intelligence Platforms

I am delighted to see Oracle in the leader board on Gartner's magic quadrant for Analytics and Business Intelligence (ABI). The Oracle Analytics platform was recognised for the following key attributes

Business application integration
AI enhancements
Industry-leading data preparation

Gartner Magic Quadrant for Analytics and Business Intelligence

The following mandatory and common features were assessed in this years Gartner assessment to position the various analytics vendors and their products in relevant quadrant.

Mandatory Features

Visualization: Supports custom visualizations, allows users to visualize and analyse graphs and integrates with charting libraries, supports geographic mapping, and recommends which visualization type to use for different data types.
Content management: This capability allows administrators and D&A stewards to manage the security, certification and life cycle of analytics content.
Reporting: This capability provides pixel-perfect, paginated reports that can be scheduled and shared with a large user community.
Data preparation: Supports drag-and-drop, user-driven combination of data from different sources and the creation of analytic models, such as user-defined measures, data pipelines, sets, groups and hierarchies.
Automated insights: The ability to apply machine learning (ML) techniques to automatically generate insights for end users; for example, identifying the most important attributes in a dataset, conducting time series forecasting and identifying clusters within datasets. These features also enable the incorporation of usage and feedback data from users to display content that is most relevant for their role and use case.
Natural language query (NLQ): This capability enables users to ask questions of the data using terms that are either typed into a search box or spoken aloud.
Data source connectivity: Capabilities that enable users to connect to and ingest data contained in various types of storage platforms, both on-premises and in the cloud.
Analytics catalog: Portal-like curation and collaboration of ABI content, enabling users to share, find, search, comment and certify dashboards, reports and datasets from a diverse range of platforms in one place.

The 8 analytics capabilities included for assessment — Analytics capabilities for assessment

Common Features

Metrics layer: A virtualized layer that allows users to create and define business metrics as code, govern those metrics from data warehouses, and serve the downstream analytics, data science and business applications.
Embedded analytics: These capabilities integrate analytical capabilities directly into business applications, websites and portals, enabling collaboration and communication of insights. They also support interactive and customizable reports and dashboards, offer robust API and SDK integration, allow data write-back to various sources, and automate data-driven workflows to trigger business actions.
Platform administration: These capabilities allow users to track platform usage statistics, manage costs by monitoring capacity or credits, and intervene by pausing or prioritizing jobs. They also measure query performance and provide feedback to content authors, offering recommendations to optimize datasets and reports to minimize inefficient or slow queries.
Natural language generation (NLG): The ability to automatically produce written or spoken responses, allowing users to specify the level of detail, verbosity and tone. NLG also supports complex analyses, such as variance analysis and outlier detection, and it contextualizes narratives based on user personas by leveraging active metadata, background and preferences.