The Forrester Wave™: Augmented Business Intelligence Platforms, Q2 2023

Augmented Business Intelligence Platforms Vendor Product Comparisons 2023

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

Microsoft - Leaders

Pros:

Core enterprise BI capabilities
Natural language query (NLQ)
BI fabric and governance
Collaboration via seamless integration with Microsoft Teams
Globalization
Security and compliance
XOps (DevOps, DataOps, MLOps)
Broad adoption
Overall BI strategy including vision, innovation, roadmap, partner ecosystem, adoption, and pricing flexibility and transparency

Cons:

Weak integration with Oracle and SAP business applications
Deployment options limited to Azure or on-premises
Challenges in hybrid environments with data movement, synchronizing security, and provisioning data/users on-premises and on multiple clouds
Reference customers wished for better tech support

Oracle - Leaders

Pros:

Core enterprise BI
Advanced analytics
Natural language query (NLQ)
Data architecture and middleware
XOps (DevOps, DataOps, ModelOps)
Translytical low-code app development
Seamless integration with Oracle business apps
Impressive partner ecosystem
Pricing flexibility and transparency
Abundant set of industry vertical and business domain specific Fusion Analytics applications

Cons:

Integration with SAP business apps is lacking
Reference customers want improvements in core enterprise BI functionality, NLG, and extensibility
Platform lacks appeal for a broader set of business users
Main strategy focused on Oracle ecosystem may limit broader market appeal

Salesforce - Leaders

Pros:

Core enterprise BI capabilities
Natural language generation (NLG), especially notable since 2021 Salesforce's acquisition of Narrative Science
BI fabric and governance
Collaboration
Deployment on Amazon Web Services (AWS)
Integration with Salesforce business apps
Great vision
Existing ecosystem
Cross-selling to Salesforce customers
Combined strength of Salesforce/Tableau customer and partner ecosystem

Cons:

Translytical low-code app development currently mostly limited to Salesforce use cases
Integration with Oracle and SAP business apps needs improvement
Reference customers want improvements in NLQ and extensibility
Uncertainty about how much Salesforce will continue to invest in Tableau as an independent enterprise BI platform

TIBCO Software - Leaders

Pros:

Advanced analytics
Data architecture and middleware
Deployment on AWS
Extensibility
Translytical low-code app development
No-code integration with business apps
Innovation and strong product roadmap
Rich set of data connectors including read and writeback
Process workflow orchestration
Integration with TIBCO's data virtualization and streaming platforms

Cons:

Should boost BI fabric, governance, and collaboration features
Reference customers wished for improvements in NLQ and NLG functionality
Some advanced analytics require purchasing separate data science modules

Amazon Web Services - Strong Performers

Pros:

Natural language query (NLQ)
Natural language generation (NLG)
Globalization
Security and compliance
Extensibility
Cloud-native architecture
Innovation and product roadmap
Partner ecosystem
Pricing flexibility and transparency (pay-per-use)
Overall business value

Cons:

Needs improvement in BI fabric and governance
Data architecture and middleware needs enhancement
Collaboration capabilities need improvement
Hybrid deployments limited (AWS is the only deployment option)
Translytical low-code app development needs enhancement
Integration with business apps (Oracle, Salesforce, SAP) needs improvement
Reference customers want better support for data science pros and more administration options

Domo - Strong Performers

Pros:

Deployment options on AWS and Azure
Extensibility
Translytical low-code app development
No-code integration with business apps (more connectors than any other vendor in this evaluation)
Overall business value
Support for data science professionals
Architecture/scalability
Platform stability
Licensing model
Innovative database interaction via APIs

Cons:

NLQ capabilities need improvement (currently available only via a partner integration)
Collaboration needs enhancement
Integration with Oracle business apps needs improvement
Hybrid deployments limited (no on-premises deployment option)
Relatively small partner ecosystem

Google - Strong Performers

Pros:

Collaboration
Extensibility
Hybrid deployments
Deployment on GCP
LookML semantic layer as foundation for BI fabric or headless BI
Architected natively to be managed with modern SDLC procedures based on Git
Support for data pros
Significant partner ecosystem

Cons:

NLG needs investment
Integration with popular business apps (Oracle, Salesforce, SAP) needs improvement
Needs to level up appeal to business users
Dependency on GCP components, specifically BigQuery for certain ML and geospatial analytics capabilities
Non-GCP customers should be cautious

Qlik - Strong Performers

Pros:

Natural language query (NLQ)
Deployment on AWS
Globalization
Security and compliance capabilities
Extensibility
Overall business value (strongest reference customers in this evaluation)
Time to value
Support for data pros
Core enterprise BI functionality
Collaboration
Architecture and scalability
Administration
Tech support

Cons:

NLG functionality could use a boost
Reference clients cite need for improvements in NLQ, NLG, BI fabric, and governance capabilities
Strategy of moving into data fabric makes sense but doesn't clearly differentiate from competition

SAP - Strong Performers

Pros:

Core enterprise BI
Data architecture and middleware
Collaboration
Translytical low-code app development
Globalization
Security and compliance
SAP business apps integration
Rich SAP partner ecosystem
Pricing flexibility and transparency
Based on cloud HANA platform

Cons:

XOps (DevOps, DataOps, ModelOps) needs enhancement
No on-premises deployment option (SaaS only)
Reference customers complain about lack of easy integration with Oracle or Salesforce business apps
Reference customers want better support for data science pros and improved product extensibility
Little to suggest SAP can extend its strength outside its latest cloud and legacy applications

SAS - Strong Performers

Pros:

Core enterprise BI
Advanced analytics
Deployment on Azure
Translytical low-code app development
Support for data science pros
Architecture/scalability
Globalization
Risk and compliance features
Vendor relationship
Pre-integrated components supporting entire data-to-insights cycle via SAS Viya

Cons:

NLQ capabilities need significant improvement
BI fabric and governance need enhancement
XOps (DevOps, DataOps, ModelOps) need improvement
Integration with popular enterprise business applications (Oracle, Salesforce, SAP) needs improvement
Reference customers want features to improve platform enterprise adoption, NLQ and NLG capabilities, extensibility, and licensing options
Limited roadmap and strategy to create differentiated products beyond SAS ecosystem

Sisense - Strong Performers

Pros:

Advanced analytics
Collaboration capabilities
Deployment on AWS
Extensibility
Overall business value
Support for data science pros
Platform stability
Vendor relationship
API-first architecture

Cons:

NLQ capabilities need boost
BI fabric and governance need improvement
Data architecture and middleware need enhancement
Integration with popular business apps (Oracle, SAP) needs improvement
Reference customers want improved NLQ and NLG capabilities
Strategic decision to focus on embedded BI market may limit broader appeal
Needs to invest in boosting partner ecosystem

IBM - Contenders

Pros:

Natural language generation (NLG) capabilities
Hybrid deployments
Integration with Watson Studio for ML and AutoML features
Watson Knowledge Catalog for data governance and lineage

Cons:

Advanced analytics relies somewhat on other Watson products
Translytical low-code app development needs improvement
Integration with popular enterprise business applications (Oracle, SAP) needs improvement
Reference customers want improvements in overall business value, support for business users, core enterprise BI, NLQ, NLG, architecture/scalability, vendor relationship, and tech support
Innovation and roadmap items not enough to differentiate in crowded and mature market

MicroStrategy - Contenders

Pros:

BI fabric and governance
Data architecture and middleware
Globalization
Security and compliance
Hybrid deployment options
Large enterprise grade semantic layer addressing most middleware requirements

Cons:

Augmented BI, specifically NLQ and NLG, needs improvement
Collaboration needs enhancement
XOps (DevOps, DataOps, ModelOps) needs improvement
Extensibility needs enhancement
Integration with popular enterprise business apps (Oracle, SAP) needs improvement
Vision, innovations, and roadmap have not been enough to stay current in race against others evaluated
Did not provide reference customers for this evaluation

ThoughtSpot - Contenders

Pros:

Natural language query (NLQ)
Natural language generation (NLG)
Deployment on GCP
Pioneered conversational UX for BI

Cons:

Core enterprise BI needs improvement
BI fabric and governance need enhancement
Data architecture and middleware need improvement
Collaboration needs enhancement
Globalization, security, compliance need improvement
XOps (DevOps, DataOps, MLOps) need enhancement
Deployment options on other popular cloud platforms needed
Translytical low-code app development needs improvement
Integration with popular enterprise business apps (Oracle, Salesforce, SAP) needs improvement
Positioning as complement rather than replacement for enterprise BI platform no longer works
Declined to participate in full Forrester Wave evaluation process

FAQs

Q: Which vendors are Leaders in 2023?

A: Microsoft, Oracle, Salesforce, TIBCO Software

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

A: Leaders in the Forrester Wave evaluation demonstrate strong current offerings and strategy. These vendors have products that meet or exceed current market needs with a clear vision and roadmap for future innovation. Leaders typically show strength across most evaluation criteria including core capabilities, augmented BI features, enterprise functionality, deployment options, partner ecosystem, and market adoption. They are positioned to help enterprises succeed with their BI initiatives both now and in the future.

Q: How do I interpret a Forrester Wave?

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

Report:

Top Vendor Products for Augmented Business Intelligence Platforms in 2023

Augmented Business Intelligence Platforms Vendor Product Comparisons 2023

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FAQs

Q: Which vendors are Leaders in 2023?

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

Q: How do I interpret a Forrester Wave?

Reference