Magic Quadrant for Analytics and Business Intelligence Platforms 2023

Earlier this month Gartner released a new version of the Magic Quadrant for Analytics and Business Intelligence Platforms (ABI). As always it is interesting to see what is happening in the market.

Market Definition and Overview

The market definition and overview in the Gartner report on “Magic Quadrant for Analytics and Business Intelligence Platforms” provides an overview of the analytics and business intelligence market, its growth potential, and the factors driving its growth.

The report defines analytics and business intelligence platforms as software that enables users to access and analyze data, create reports and visualizations, and share insights with others. The market for these platforms is expected to grow rapidly as organizations increasingly recognize the value of data-driven decision-making and seek to leverage the vast amounts of data they generate.

The report also notes that the market is evolving rapidly, with vendors investing heavily in innovation and expanding their offerings to meet changing customer needs. This has led to a crowded and competitive market, with a wide range of vendors offering varying levels of functionality, ease of use, and pricing models.

Overall, the report highlights the importance of analytics and business intelligence platforms in helping organizations to gain insights and make better decisions, and it provides an overview of the factors driving growth in the market.

12 Critical Platform Capabilities

  1. Automated insights – This capability utilizes machine learning techniques to automatically generate insights for end-users.
  2. Analytics catalog – The platform has a searchable catalog that displays analytic content to make it easy for users to find and consume. It also makes recommendations to users.
  3. Data preparation – This capability allows users to combine data from different sources using a drag-and-drop interface. It also enables the creation of analytic models, such as user-defined measures, sets, groups, and hierarchies.
  4. Data source connectivity – The platform allows users to connect to and ingest structured data from various storage platforms, both on-premises and in the cloud.
  5. Data storytelling – The platform can combine interactive data visualization with narrative techniques to package and deliver insights in a compelling, easily understood form for presentation to decision-makers.
  6. Data visualization – This capability supports highly interactive dashboards and exploration of data through the manipulation of chart images. It also offers an array of visualization options, such as heat and tree maps, geographic maps, scatter plots, and other special-purpose visuals.
  7. Governance – This capability tracks usage and manages how information is shared and promoted.
  8. Natural language query – Users can ask questions of the data using terms that are either typed into a search box or spoken.
  9. Reporting – This capability provides pixel-perfect, paginated reports that can be scheduled and bursted to a large user community.
  10. Data Science integration – This capability enables citizen data scientists and data scientists to develop and prototype composable data science and machine learning models with integration into the broader data science and machine learning ecosystem.
  11. Metrics store – The platform provides a virtualized layer that allows users to create and define metrics as code, govern those metrics from data warehouses, and service all downstream analytics, data science, and business applications. This also includes capabilities such as goal management.
  12. Collaboration – This capability enables a broad spectrum of users to simultaneously co-produce an analytics project in a collaborative environment.

Evaluation Criteria

The evaluation criteria used in the Gartner Magic Quadrant report are as follows:

  1. Ability to Execute: This criterion evaluates vendors on their ability to meet customer needs and expectations, deliver a high-quality product, and demonstrate a clear understanding of the market.
  2. Completeness of Vision: This criterion assesses vendors on their ability to anticipate customer needs and market trends, and to articulate a clear and differentiated vision for their product.
  3. Market Understanding: This criterion evaluates vendors on their ability to understand the market they operate in, including market trends, customer needs, and the competitive landscape.
  4. Marketing Strategy: This criterion assesses vendors on their ability to effectively promote their product to potential customers, and to differentiate themselves from competitors.
  5. Sales Strategy: This criterion evaluates vendors on their ability to effectively sell their product, including their sales approach, channel strategy, and pricing model.
  6. Offering (Product) Strategy: This criterion assesses vendors on their product strategy, including their ability to innovate, differentiate, and address customer needs.
  7. Business Model: This criterion evaluates vendors on their ability to build and sustain a profitable business, including their pricing model, revenue streams, and cost structure.
  8. Vertical/Industry Strategy: This criterion assesses vendors on their ability to address the specific needs of different verticals or industries, and to develop solutions that meet those needs.
  9. Innovation: This criterion evaluates vendors on their ability to innovate and introduce new and differentiated features or capabilities to their product.
  10. Geographic Strategy: This criterion assesses vendors on their ability to effectively target and address different geographic markets, including their go-to-market strategy and localization capabilities.

Quadrant Descriptions

The four quadrants that can be differentiated in the Gartner Magic Quadrant are:

  1. Leaders: These vendors are those who have a strong ability to execute their product vision and have a proven track record of delivering high-quality products. They are also generally recognized as market leaders.
  2. Challengers: These vendors have a strong ability to execute their product vision but may lack the innovation and market understanding of the Leaders.
  3. Visionaries: These vendors have a clear vision of where the market is headed and are often driving innovation. However, they may have less ability to execute their product vision.
  4. Niche Players: These vendors are typically focused on specific market segments or have limited ability to execute their product vision. They may lack the resources to compete with the larger vendors in the market.

Vendor’s Strength & Cautions

Magic Quadrant for Analytics and Business Intelligence Platforms 2023

There are a few vendors I have been looking at because of my work as General Manager at Pong. When it comes to creating Data Products we focus on Microsoft Data & AI and Snowflake ecosystem. That’s where my interest in the following vendors (in random order) comes from:

  • Microsoft Power BI (Microsoft Data & AI)
  • Thoughtspot (Snowflake ecosystem)
  • Tellius (Snowflake ecosystem)
  • Pyramid Analytics (Snowflake ecosystem)
  • Google Cloud’s Looker (Snowflake ecosystem)

Microsoft Power BI

Microsoft is a Leader in ABI platforms with its primary offering, Power BI, which has significant market reach and momentum due to its integration with Microsoft 365, Azure, and Teams. Microsoft’s Power BI has many strengths, including its alignment with Microsoft 365 and Azure Synapse, a strong price-value combination, and an ambitious product roadmap with a clear vision for cross-utilization of Power BI, Power Apps, and Power Automate.

However, there are some cautions to be aware of. Customers have expressed concerns over governance of the content creation and publication process in Power BI, with multiple ways to accomplish most tasks making it difficult to enforce standard governance practices. Power BI also has limited headless, open architecture, which some customers would like to see more interoperability with competitive BI platforms. Finally, Azure is the only deployment option for Power BI, which limits customers’ flexibility to choose a cloud IaaS offering.


ThoughtSpot is a Visionary in this Magic Quadrant due to its consumer-friendly user experience, ability to handle complex analysis, and scalability for analyzing large datasets. ThoughtSpot’s search experience indexes and ranks data models, metadata, and data values, which enables the software to guide users to more granular insights. SpotIQ, ThoughtSpot’s automated insight capability, continuously monitors data to produce more personalized insights. Notably, ThoughtSpot runs its search and automated insight workloads via direct queries of databases to minimize data movement.

ThoughtSpot’s strengths include market recognition for its augmented analytics platform, its augmented consumer-centric product offering, and its embedded product offering strategy that enables direct-query Live Analytics at scale on a governed, modern data stack. ThoughtSpot also offers gamification features and data storytelling capabilities, as well as a code-free integration with its partner dbt Labs, integration with Sync (formerly SeekWell), and a free web plug-in for Google Sheets.

However, ThoughtSpot also has some cautions, such as narrowing functional differentiation as other vendors invest in AI and natural language capabilities, a lack of a full D&A stack solution, and a smaller customer installed base compared to vendors in the Leaders quadrant.


Tellius is considered a Visionary in this Magic Quadrant due to several strengths that it possesses. Firstly, it provides a range of capabilities for different types of users, including consumers, analysts, and citizen data scientists. It offers insights through its “What?,” “Why?,” and “How?” interfaces, each designed to address specific needs. Secondly, Tellius is an NLQ-first platform, providing robust Q&A capabilities, including learning from user behavior and providing contextualized insights. It is evaluated highly for its automated insights capabilities such as key driver analysis, outlier detection, and clustering. Thirdly, Tellius supports multipersona, automating key aspects of the analytics workflow and supporting citizen data scientists and data scientists through guided AutoML capabilities.

However, there are also some cautions that need to be taken into account. One of the main concerns is the lack of market momentum compared to other vendors in the market, as revealed by Gartner Peer Insights, gartner.com search, and client inquiry data. Gartner Peer Insights reviews also suggest that newer users may find it complex to grasp all the features in the platform. Secondly, Tellius does not focus on data storytelling capabilities, such as supporting infographics, connected slideshows, and allowing users to control verbosity, detail or tone of data stories. It also does not allow users to publish and share metrics or integrate with DSML platforms to access features in these sources. Lastly, although Tellius has customers distributed across the Americas, Europe, and Asia, prospects should carefully evaluate Tellius’s ability to provide global support given its emerging geographic strategy.

Pyramid Analytics

Pyramid Analytics is considered a Visionary in this Magic Quadrant due to several strengths. Firstly, it offers an integrated suite for modern ABI across the data life cycle, out of the box. Secondly, it provides accessibility for nontechnical users through a browser-based, no-code environment. Thirdly, it has augmented analytics at its core, with basic DSML capabilities and the ability to deliver prescriptive analytics. Additionally, Pyramid has invested in community building, which has resulted in growth in its user community.

However, there are also some cautions to be aware of. Pyramid lacks significant capabilities to integrate with third-party catalog tools, limiting catalog interoperability and user enrichment. The company also has less focus on developer-centric capabilities, which may pose challenges for organizations with existing code-focused development practices. Finally, while Pyramid has started to see market improvement and increases in media coverage, it continues to experience a lack of awareness among consumers looking for low-code/no-code ABI solutions, and Gartner rarely sees it shortlisted.

Google Cloud’s Looker

Google Cloud’s Looker is considered a Visionary in this Magic Quadrant. It is a cloud-architected analytics platform that provides self-service visualizations, dashboards, and a code-first semantic modeling layer based on LookML. Its strengths include a highly governed self-service semantic layer, decision support and workflow resources, and deep integration with open-source data technologies provided through the wider Google Cloud Platform. However, some cautions should be considered when using Looker, such as its developer and data engineer focus, incomplete analytical modeling loop, and limited native augmentation.

One of Looker’s strengths is its highly governed self-service semantic layer based on LookML, which offers native connectivity to Salesforce (Tableau) and Microsoft Power BI as a universal enterprise semantic layer. It also benefits from deep integration with open-source data technologies provided through the wider Google Cloud Platform. Furthermore, Looker includes “blocks,” prebuilt dashboards and data exploration capabilities for several industry verticals.

However, some cautions should be considered when using Looker. For example, Looker requires coding for developers and data modelers, which limits the ability for nontechnical analysts to contribute to the analytical data modeling. Also, there is no development path to incorporate the insights created in Looker Studio into the broader Looker semantic model, making it difficult for self-service users to promote its analytic content. Finally, automated insights remain weak compared to Looker’s competitors, and some capabilities in this category are only available when incorporating Google products outside Looker, such as BigQuery ML. Support for data storytelling, including narrative automation and infographic support, is notably missing.

Closing Statements

According to Gartner, the following conclusions can be drawn about the above mentioned BI tools:

  • Microsoft Power BI is in the Leaders quadrant. This tool have a strong ability to execute its vision, as well as a complete set of features to meet current market requirements.
  • ThoughtSpot, Google Cloud’s Looker, Pyramid Analytics, and Tellius are in the Visionaries quadrant. These tools have a strong vision for where the market is going and are executing on it, but may not yet have a complete set of features to meet all market requirements.

Overall, it’s good to take into account that each tool has its own strengths and weaknesses, and the best BI tool for a particular organization will depend on its unique needs and requirements.

It’s important to note that these evaluations are based on specific criteria and use cases, and may not necessarily reflect the best fit for every organization’s needs. Therefore, it’s recommended to conduct further research and evaluations before making a final decision on a BI and analytics tool.

Till next time.

Director Data & AI at Pong and Snowflake Data Superhero. Online better known as; DaAnalytics.

Daan Bakboord

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