As promised here is an article on the Gartner® analytics and Business Intelligence tools comparator based on the capabilities and use cases of the tools. There are interesting differences on the final scores compared to the Magic Quadrant which uses other criteria and a non-transparent weighting on the criteria.

Just clic on an editor on the header line to choose it and compare it to all others ==>

Product/Service Class Definition

An analytics and business intelligence platform supports IT-enabled analytic content development. It is defined by a self-contained architecture that enables nontechnical users to autonomously execute full-spectrum analytic workflows from data access, ingestion and preparation, to interactive analysis and the collaborative sharing of insights.

Critical Capabilities Definition

Analytics and business intelligence platform functionality is composed of the 15 critical capability areas outlined below. These were substantially updated for this research to reflect the refocus on enterprise reporting and the increased importance of augmentation.


Capabilities that enable platform security, administering of users, auditing of platform access and authentication.


Capabilities that track usage, manage how information is shared (and by whom), perform impact analysis and work with third-party applications.


The ability to support building, deploying and managing analytics and analytic applications in the cloud, based on data both in the cloud and on-premises, and across multicloud deployments.

Data Source Connectivity

Capabilities that enable users to connect to, and ingest, structured and unstructured data contained in various types of storage platforms, both on-premises and in the cloud.

Data Preparation

Support for drag-and-drop, user-driven combination of data from different sources, and the creation of analytic models (such as user-defined measures, sets, groups and hierarchies).

Model Complexity

Support for complex data models, including the ability to handle multiple fact tables, interoperate with other analytic platforms and support knowledge graph deployments.


The ability to automatically generate and curate a searchable catalog of the artefacts created and used by the platform and their dependencies.

Automated Insights

A core attribute of augmented analytics, this is the ability to apply machine learning techniques to automatically generate insights for end users (for example, by identifying the most important attributes in a dataset).

Advanced Analytics

Advanced analytical capabilities that are easily accessed by users, being either contained within the platform itself, or usable through the import and integration of externally developed models.

Data Visualization

Support for highly interactive dashboards and the exploration of data through the manipulation of chart images.
Included are an array of visualization options that go beyond those of pie, bar and line charts, such as heat and tree maps, geographic maps, scatter plots and other special-purpose visuals.

Natural Language Query

This enables users to query data using terms that are either typed into a search box or spoken.

Data Storytelling

The ability to combine interactive data visualization with narrative techniques in order to package and deliver insights in a compelling, easily understood form for presentation to decision makers.


Capabilities include an SDK with APIs and support for open standards in order to embed analytic content into a business process, an application or a portal.

Natural Language Generation

The automatic creation of linguistically rich descriptions of insights found in data. Within the analytics context, as the user interacts with data, the narrative changes dynamically to explain key findings or the meaning of charts or dashboards.


The ability to create and distribute (or “burst”) grid-layout, multipage, pixel-perfect reports to users on a scheduled basis.

Use Cases

Visual Self-Service Analytics

This use case is for business analysts that want to directly connect to and combine a variety of data sources, and build visualizations of the blended datasets.
The highest-weighted capabilities in this use case include:
  • Data visualization
  • Data preparation
  • Data source connectivity

Enterprise Analytics

This use case emphasizes centralized manageability and governance. It enables an enterprise to distribute analytic content to a large community of analytic consumers.
The highest-weighted capabilities in this use case include:
  • Manageability
  • Catalogs
  • Reporting
  • Security

General Analytics

This use case equally values centralized control and decentralized empowerment.
In addition, it equally values traditional capabilities such as data visualization and reporting as well as the emerging capabilities of natural language query and automated insights.
All 15 critical capabilities were weighted equally in this use case.

Embedded Analytics

This use case is for application developers that rely on the SDK and APIs of the analytic and BI platform to build analytic content to be embedded in a business application.
The highest-weighted capabilities in this use case include:
  • Embedded
  • Data visualization
  • Reporting

Augmented Analytics

This use case is for organizations that want to automate many of the data integration, analysis, and visualization tasks that are currently slow, manual processes.
The highest-weighted capabilities in this use case include:
  • Automated insights
  • Natural language query
  • Data storytelling
  • Natural language generation

Cloud Analytics

This use case is for buyers that are particularly interested in a vendor’s ability to support hybrid and multicloud deployment methods.
The highest-weighted capabilities in this use case include:
  • Cloud
  • Data source connectivity
  • Security

Advanced Analytics

This use case is for buyers that are interested in the more sophisticated capabilities including both predictive modeling and complex data models such as knowledge graphs.
The highest-weighted capabilities in this use case include:
  • Advanced analytics
  • Model complexity
  • Data preparation