2022 Gartner Magic Quadrant for Analytics and Business Intelligence Platforms

 

Today’s analytics and BI platforms are augmented throughout and enable users to compose low/no-code workflows and applications. Cloud ecosystems and alignment with digital workplace tools are key selection factors. This research helps data and analytics leaders plan for and select these platforms.

Analytics and business intelligence (ABI) platforms enable less technical users, including businesspeople, to model, analyze, explore, share and manage data, and collaborate and share findings, enabled by IT and augmented by artificial intelligence (AI). ABI platforms may optionally include the ability to create, modify or enrich a semantic model including business rules.

Today’s ABI platforms have an emphasis on visual self-service for end users, augmented by AI to deliver automated insights. Increasingly, the focus of augmentation is shifting from the analyst persona to the consumer or decision maker. To achieve this, automated insights must not only be statistically relevant, but they must also be relevant in context of the user’s goals, their workflow and the actions they need to take based on the data. ABI platforms are beginning to capture more information about user behavior and interests in order to deliver a more impactful experience to the consumer. This trend will continue to increase as ABI tools are integrated further into personal productivity tools where additional user behaviors can be tracked. Complementing this with interaction methods, such as natural language query (NLQ) or conversational analytics interfaces, and natural language generation (NLG) descriptions delivered as a response, democratizes access to data to make decisions.

Many platforms are adding capabilities for users to easily compose low-code or no-code automation workflows and applications. This blend of capabilities is helping to expand the vision for analytics beyond simply delivering datasets and presenting dashboards to delivering enriched contextualized insights, refocusing attention on decision-making processes, and ultimately taking actions that will deliver business value.

The ABI, data science and machine learning (DSML), and cloud data and analytics (D&A) markets continue to converge, often in the form of intelligent composable applications for customers. Vendors are pushed to improve their analytics capabilities while simultaneously helping their customers maintain a balance between control and agility as their platforms scale across multipersona users, advanced analytics capabilities, diverse data and emerging use cases.

Vendors in the ABI market are diverse and include startups backed by venture capital funds, large enterprise application companies, independent analytics companies and all the large cloud hyperscalers. The vast majority of new spending by customers in this market is on cloud deployments, as they look to address scalability and performance needs in the face of the increasing complexity of analytics use cases and the types and volumes of data. In many cases, ABI platforms are entry points for a wider set of cloud data and analytics capabilities offered by cloud vendors and their ecosystems.

ABI platform functionality includes the following 12 critical capabilities, which have been updated to reflect areas of change and differentiation by vendors, particularly in capabilities more closely associated with augmented analytics:

• Security: Capabilities that enable platform security, administering of users, auditing of platform access and authentication.
• Governance (formerly called “manageability”): Capabilities that track usage and manage how information is created and shared from prototype to production.
• Cloud-enabled analytics: The ability to build, deploy and manage 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 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).
• Catalog: The ability to display content to make it easy to find and consume. The catalog is searchable and makes recommendations to users.
• Automated insights: A core attribute of augmented analytics, this is the ability to apply machine learning (ML) techniques to automatically generate insights for end users (for example, by identifying the most important attributes in a dataset).
• Data visualization: Support for highly interactive dashboards and the exploration of data through the manipulation of chart images. This includes 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 generate news style data stories — combining headlines, narrative text, data visualizations and audiovisual content based on the ongoing monitoring of findings.
• 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.
• Reporting: This capability provides pixel-perfect, parameterized and paginated reports that can be scheduled and burst to a large user community.

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