The Forrester Wave™: Business Intelligence Platforms, Q2 2025

Business Intelligence Platforms Vendor Product Comparisons 2025

Key features and pros and cons of top Business Intelligence Platforms products:

Microsoft - Leaders

Pros:

Robust strategy with market dominance and consistent investment in innovation
Strong partner ecosystem supporting broad platform adoption
Excels at ambient (embedded) BI capabilities with seamless Microsoft Teams integration
Comprehensive augmented BI and numerous genAI (Copilot) features
Integration with Microsoft Fabric addressing previous gaps (enterprise-grade semantic layer, direct query capabilities)

Cons:

Strategy and roadmap focus primarily on Azure, Fabric, and M365 ecosystem with limited support outside Microsoft platform
Only available for deployment on Azure (on-premises version meant for limited deployments)
Needs more out-of-the-box integrations with popular enterprise business applications
Platform needs to open to more widely used LLMs beyond Microsoft's
Clients should validate new Fabric-based Power BI capabilities
Shortage of DAX knowledgeable talent
Some Power BI limitations (e.g., number of data points in a visualization)

Tableau - Leaders

Pros:

Gold standard for data visualization that other vendors aspire to reach
Impressive platform BI capabilities with advanced data visualizations
Excels in ambient BI and genAI functionality
Strong integration with Salesforce and inherits advanced analytics/AI from Agentforce
Only option for OOTB analytics seamlessly embedded in Salesforce Data Cloud
High availability of talent - most data professionals already know Tableau
Customer Favorite designation in this evaluation

Cons:

Vision, investments, and roadmap somewhat bifurcated between data-agnostic platform and Salesforce affiliation
Upcoming platform enhancements will deepen Tableau's dependence on Salesforce
Needs increased investment in integration with other popular business applications beyond Salesforce
Limited SaaS deployment options beyond Amazon Web Services (AWS)
Continuing development needed for Tableau Next (integrated version of Tableau and Tableau Pulse)
Customers wish for more flexibility in pricing, especially free 'view only' license

Amazon Web Services - Strong Performers

Pros:

First among competitors to bring innovative capabilities, especially genAI, to QuickSight
Excels in augmented BI with multiple single-click ML algorithms for citizen data scientists
Native AutoML functionality for data science professionals
Rich NLQ and NLG capabilities including differentiated long-form data stories
Autoscalable and serverless architecture with pay-per-use pricing
Seamless integration with AWS cloud infrastructure and Bedrock
Broad partner ecosystem covering technology and professional services providers

Cons:

Little in current strategy to broaden customer adoption outside AWS ecosystem
Business and data leaders often unaware of QuickSight as AWS primarily known for appeal to developers and data scientists
Must invest in more integration with popular enterprise business applications
Limited customization options
Need for richer GUI and data visualizations (addressing via bundled Highcharts in public preview)
Only available for deployment on AWS

Domo - Strong Performers

Pros:

Vision for full data/analytics/AI stack allowing organizations to focus on deploying solutions vs. pursuing integration
Impressive investments in innovation despite being relatively small vendor with limited R&D budgets
Strengths in augmented BI, genAI architecture and NLQ, data lineage/explainability, usage analytics
Role-based personalization capabilities
Natively read/write (translytical) platform enabling use as low-code app dev platform
Cloud-native full-stack platform
Capacity-based pricing making procurement easier
Innovation, ease of use, and excellent customer support
Pre-integrated platform enables rapid implementation

Cons:

Relatively small vendor with limited R&D budgets competing against much larger players
Comparatively fewer partnerships with leading technology and professional services providers
Only offers cloud SaaS deployment options, making it less attractive to clients with critical mass of on-premises data
Some users express concerns about platform complexity
Recent shift to usage-based pricing model found less predictable by some customers

Google Cloud - Strong Performers

Pros:

LookML semantic layer appeals to app dev pros and forms strong foundation for genAI-based solutions
API-first design ideal for building data-as-a-service-based applications
Strong foundation for enterprise BI fabric
Significant investment in integrating Looker with key products (BigQuery, Vertex, Gemini, Workspace)
Unlike other hyperscalers, continues to support Looker deployments on-premises and on other popular cloud platforms
Two integrated products: Looker (for customized enterprise BI) and Looker Studio (for traditional BI users)
Customers praise LookML as 'gold mine, a game changer'

Cons:

Needs to invest in more OOTB integrations with popular enterprise business applications
Customers express concerns about seamless governance of multiple Google Cloud products
Need for improved Looker Studio integration
Despite investment in non-Google Cloud deployment options, some key capabilities closely tied to BigQuery, Gemini, and Vertex AI
Challenging for non-Google Cloud clients to completely disconnect from the cloud platform

MicroStrategy - Strong Performers

Pros:

Rich, enterprise-grade semantic layer remains top differentiator
Semantic layer forms solid foundation for genAI-based solutions and enterprise BI fabric
Strong BI vision maintained for 30 years
Excels in ambient BI and hybrid deployment options
Hyperscaler-independent, cross-functional enterprise BI platform
Especially strong with growth of genAI applications where semantics are key to success
Customers praise scalability, data security model, and semantic layer

Cons:

Distractions from ventures into non-BI markets (mobile, security, cryptocurrency) taking their toll
Falls somewhat behind competition with pace of introducing innovations
Comparatively fewer partnerships with leading technology and professional services providers
Must continue to invest in augmented BI and keep improving genAI-based features
Needs to provide OOTB connectivity to popular business applications
Customers note complexity in managing installations and high total cost of ownership

Qlik - Strong Performers

Pros:

Vision to diversify into broad data/analytics/AI provider has been paying off
Analytics engine differentiator excels at uncovering unknown unknowns beyond pre-programmed questions
Innovations and roadmap go beyond core BI to include data and analytics workflow automation
New functionality to mine and analyze unstructured data
Pre-integrated data/analytics/AI platform through strategic acquisitions
Customers praise excellent customer service and quick issue resolution
Platform flexibility

Cons:

Must invest in more cloud deployment options to appeal to broader customer segment
Customers wish for more advanced analytics capabilities for business users
Need to continue differentiating as other vendors catch up on core BI functionality

IBM - Contenders

Pros:

Rich semantic layer forms strong foundation for genAI-based solutions
Analytics Content Hub differentiates as BI fabric platform
Solid vision for core enterprise BI (reporting, data visualization, dashboards)
Full integration with IBM Planning Analytics and watsonx products
Part of IBM Cloud Pak for Data ecosystem
Customers like scalability and versatility

Cons:

Vision mostly targets existing IBM ecosystem clients
Roadmap does not address overlapping products (Cognos Assistant vs. watsonx BI)
Product overlaps with Framework Manager and Data Modules for semantic layer
SaaS version only runs on IBM Cloud
Does not offer OOTB integration to popular non-IBM business applications
Non-IBM clients struggle to justify continued investments in the product
IBM-centricity bleeds through platform decisions
Did not provide reference customers for this evaluation

ThoughtSpot - Contenders

Pros:

Conversing with data in natural language is where ThoughtSpot shines
Spotter is latest iteration of genAI-based conversational UI
Very good at helping business users query data
Full stack data/analytics/AI platform allowing focus on deploying business solutions
One of the broadest cloud SaaS deployment options
Customers praise platform's NLQ and NLG capabilities

Cons:

Original 'search-based BI' differentiator eroded as all leading vendors introduced conversational UI
Conversational capability expected to become table stakes for BI vendors
Customers increasingly struggle to justify side-by-side deployments with other BI platforms
Needs to invest in integration with popular enterprise business applications
Must beef up semantic layer capabilities
Vendor risk as relatively small BI pure-play vendor in highly mature, commoditized market
Did not provide reference customers for this evaluation

FAQs

Q: Which vendors are Leaders in 2025?

A: Microsoft, Tableau

Q: What does "Leader" mean in Forrester Wave terms?

A: Leaders demonstrate strong current offerings and robust strategies. Microsoft Power BI and Tableau are positioned as Leaders due to their comprehensive BI capabilities, market dominance, strong customer feedback, and continued innovation. Leaders excel across multiple evaluation criteria including current offering capabilities and strategic vision, with the highest combined scores in these areas.

Q: How do I interpret a Forrester Wave?

A: https://www.forrester.com/policies/forrester-wave-methodology/

Report:

Top Vendor Products for Business Intelligence Platforms in 2025

Business Intelligence Platforms Vendor Product Comparisons 2025

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FAQs

Q: Which vendors are Leaders in 2025?

Q: What does "Leader" mean in Forrester Wave terms?

Q: How do I interpret a Forrester Wave?

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