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As business leaders and data teams seek to navigate the complex vendor landscape for business intelligence (BI), Gartner’s Magic Quadrant for Analytics and Business Intelligence Platforms has earned a reputation as an invaluable resource. The annual report, which was released recently, helps to outline the trending players in the industry, based on the completeness of their vision and their ability to execute, but it’s not meant to represent the final word which platforms pack the greatest punch for the specifics of your situation.
For organizations to make a more informed decision on which big data platform to use, based on its existing capabilities, Gartner's Critical Capabilities for Analytics and Business Intelligence Platforms report is required reading. By focusing on the most important features offered by individual big data platforms, the report provides a more qualified view of what each platform offers in terms of its broader functionality, where it excels and where there’s room for improvement.
To understand the importance of these critical capabilities, it’s key to see how they support the four major use cases of BI platforms today.
Perhaps the most essential use case of BI today is metrics creation, which refers to the ability of platforms to connect to an organization’s data, prepare that data and analyze it and visualize it across standardized metrics that can easily be consumed by business decision makers.
The critical capabilities necessary to support metrics creation are headed by what Gartner defines as a “metrics layer,” which is a function through which users can create and define their business metrics as code and then determine the values of these metrics based on signals pulled from internal databases and data warehouses as well as third-party sources.
A second key capability here is data preparation, specifically user-friendly, drag-and-drop interfaces for combining data from multiple sources and tools that simplify the creation of analytical models. Increasingly, support for natural language querying is also viewed as a must-have function, as is data source connectivity, which simplifies the addition of new data sources, and content management features for securing and certifying analytics content.
With regard to these capabilities, Pyramid Analytics stands out in Gartner’s findings, for three reasons. Notably, it provides a comprehensive metrics layer with built-in AI processes for creating analytical models and generating descriptions of metrics, plus data preparation tools that enhance efficiency by inferring data relationships, types and hierarchies, with support for both advanced and novice users. Finally, its “GenBI” NLQ feature supports adaptive AI that refines its responses based on user feedback, ensuring continuous improvement in query handling, with real-time query suggestions.
Gartner’s report also highlights Strategy One as a key contender due to its powerful NLQ capability, which enables users to interact with their data in a natural, conversational way and adjust their preferences to fine-tune the responses it generates in real-time, adapting the narrative style to suit their purposes.
Elsewhere, Microsoft stands out for its powerful data prep features, notably its Power Query tool that enables data analysts to prepare and blend various datasets using an intuitive graphical user interface, while SAS Viya is identified as a top platform in terms of data source connectivity due to its direct query and import modes and native connectors to more than 80 common data sources and file formats.
Besides digging up essential metrics, BI platforms are widely used by enterprises for data storytelling, where interactive data visualizations are paired with narrative techniques to provide more digestible insights to non-technical users.
To enable this use case, BI platforms require flexible natural language generation tools, similar to what we see with ChatGPT, where users can specify the amount of detail provided and the tone it’s presented in. Platforms must also support natural language queries for users to ask questions of those narratives, together with automated insights that leverage machine learning to automatically identify the most important attributes in a dataset. Finally, advanced reporting capabilities are an essential element, enabling users to schedule and share the reports they generate.
Gartner marks ThoughtSpot as a clear leader in this specific application, thanks chiefly to its generative AI analyst Spotter, which generates conversational reports to users and has advanced reasoning capabilities that allow it to respond accurately to intricate user questions, regardless of the complexity of the underlying datasets.
It comes a close second to Pyramid Analytics, whose platform delivers powerful visual representations of data pipelines and an integrated chatbot experience that facilitates data queries while maintaining the history of earlier chats to support ongoing analysis.
Oracle also gets a mention due to the conversational capabilities of its AI assistant, which allows users to discover insights and create complex visualizations simply by asking it to do so. Its visualization capabilities are rated highly too, due to its advanced geographic mapping features and interactive layering, which enables them to be generated based on data insights rather than simple data types.
Composable analytics is all about combining multiple BI resources to support more varied and complex use cases, with insights that can be integrated directly within business workflows, where they can have an immediate impact on decision-making. To enable such a use case, Gartner has identified four key capabilities, with the most critical being support for embedded analytics.
What this means is that the platform must provide an easy way for its analytics capabilities to be integrated directly within business applications, websites and other portals, to enable teams to easily access, communicate and collaborate on the insights they deliver. Naturally, this means having robust API and SDK support and automated data-driven workflows to trigger business actions.
Gartner also underscores the need for a metrics layer feature, as outlined in the “Metrics Creation” use case, plus platform administration and analytics catalog capabilities. Platform administration refers to tools for tracking platform usage, managing costs and the ability for admins to intervene, while analytics catalog capabilities should enable dashboards, datasets and reports to be made searchable, discoverable and shareable.
Strategy One is hailed by Gartner as another highly integrated tool due to its strong support for customer-managed deployments on AWS, Microsoft Azure and Google Cloud, simplifying integration for customers with struct regulatory requirements. Gartner also points to its “excellent integration” with portals and apps that mimic the look and feel of hosting applications, providing a seamless user experience within any external tool.
The ever-present Pyramid Analytics ranks highly in the Composable Analytics category too. Alongside its powerful metrics layer, its content can easily be consumed via REST API, SDK and OData queries, enabling easy integration with other BI platforms and third-party apps.
Google’s Looker is hard to beat in terms of its embedded analytics capabilities, with its API-first approach and open architecture supporting its seamless integration with other BI tools and custom applications, Gartner notes. It also highlights its best-in-class integration with portals and applications, providing for “exceptional granularity” in terms of embedded components, and an “Action Hub” that enables workflow triggers in third-party systems.
Last but not least, it’s impossible to downplay the role of governance in BI systems, as they play an increasingly important role in enabling companies to scale their data and analytics operations in a way that’s compliant with regulations, privacy needs and cybersecurity requirements.
The key capability here is content management, which refers to administrative features that enable teams to manage security, certification and the life cycles of analytics content. Analytics cataloging is also critical for governance, making content shareable and discoverable, together with platform administration controls and a powerful metrics layer for organizing and defining key data metrics.
Looker is rated as one of the best options for governance, with its robust certification tools including watermarking for datasets and reports and access controls via manual and API-driven tagging. It also supports simple content management through its git-based version controls, and offers strong platform admin functionality, with admins able to dig up deep insights on database performance, throughput and health.
Another competitive platform in terms of governance is Strategy One, whose comprehensive metrics layer supports data governance at scale. It also provides multiple paths for connecting external analytics tools to semantic models, including connectors to rival BI tools, Python, JDBC and API library connections.
Once again, Pyramid is ranked highly for its powerful metrics layer governance features, followed by Qlik and Salesforce’s Tableau, which provide useful capabilities in terms of their comprehensive content management and platform administration tools.
When it comes to making a strategic choice regarding business intelligence platforms, organizations should first identify the most critical use cases before carefully analyzing the critical capabilities needed to support those applications.
Gartner’s report illustrates how various platforms stand out in specific areas, making them compelling options for organizations with narrow goals in mind. But for those companies looking for a BI platform that caters to multiple use cases, it’s important to consider the broader capabilities they provide, where a few clear winners stand out.
