The Forrester Wave™: Data Management For Analytics, Q1 2023

Data Management For Analytics Vendor Product Comparisons 2023

Key features and pros and cons of top Data Management For Analytics products:

Databricks - Leaders

Pros:

Real-time analytics
Scalability
Broad use cases

Cons:

Lags in data modeling
Vendor becoming less responsive according to some customers
Query performance issues reported
Lack of cost visibility

Google - Leaders

Pros:

Data persistence
Streaming data
Visualization
Real-time analytics
Data security
Performance
DMA tools

Cons:

Lags in deployment in hybrid cloud
Concerns about maturity of offering to support regulatory compliance
Platform-as-a-service capabilities need improvement

IBM - Leaders

Pros:

Data modeling
Data integration
Data transformation
Data access
Data security
Data governance
In-platform analytics
Streaming data
High availability and disaster recovery capabilities

Cons:

Needs to improve visualization
DMA-integrated management and tools need improvement
Multicloud deployment options need enhancement
Scale and high-end performance need improvement
Time-consuming platform upgrades
Lack of marketing effort related to DMA platform

Informatica - Leaders

Pros:

Data integration
Data security

Cons:

Lags in full DMA platform integration
Data persistence needs improvement
Visualization tools need improvement
Declined to participate in full Forrester Wave evaluation process

Oracle - Leaders

Pros:

Real-time analytics
Data integration
Data access
Data security
Tools
In-platform analytics
Platform evolution
Partner and vendor support

Cons:

Can improve support for data modeling
Data transformation needs improvement
Data governance needs improvement
Visualization needs improvement
Commercial model lags the competition
Pricing not easily calculated without engaging vendor's sales team

SAP - Leaders

Pros:

In-platform analytics
Data modeling
Data transformation
Data access
Data governance
Visualization
Partner ecosystem
Business value
Overall customer experience

Cons:

Lags in scalability, especially in high-end scenarios
Performance on large data sets
Broad connectivity to data sources needs improvement

Teradata - Leaders

Pros:

Data persistence
Data transformation
Data access
Broad set of analytics use cases
Reliability
Query optimization
Platform availability
In-database analytics

Cons:

Can improve support for streaming data
Data modeling needs improvement
Data security needs improvement
Data governance needs improvement
Visualization needs improvement
Concerns around technical support
Interoperability issues
Cost concerns

Databricks - Strong Performers

Pros:

Real-time analytics
Scalability
Broad use cases

Cons:

Lags in data modeling
Vendor becoming less responsive according to some customers
Query performance issues reported
Lack of cost visibility

InterSystems - Strong Performers

Pros:

Deployment options
Data integration
Performance
Commitment to customer success
Supporting and respecting startups

Cons:

Lags in partner ecosystem
Can improve on streaming data
Data transformation needs improvement
Data security needs improvement
Data persistence needs improvement
Back-end challenges with data aggregation
Cost to scale

Microsoft - Strong Performers

Pros:

Real-time analytics
In-platform analytics
Data governance
Visualization
Support for broad set of DMA use cases

Cons:

Lags in offering an integrated DMA platform
Needs to improve on streaming data
Data modeling needs improvement
Data integration needs improvement
Performance needs improvement
Scale needs improvement
Tools need improvement
Did not provide any reference customers for evaluation

Snowflake - Strong Performers

Pros:

Data persistence
Tools
Broad use cases
Extremely easy-to-use
Highly performant data management platform
Works as designed
Incident- and issue-free operation

Cons:

Lags in data modeling
Visualization needs improvement
Concerns around cost control
Growing ecosystem complexity
Governance and manageability concerns

HPE - Contenders

Pros:

Scalability

Cons:

Lags in data modeling
Governance needs improvement
Visualization needs improvement
Weak hybrid cloud support
Data governance concerns
Quality of support staff
Lack of roadmap clarity
Changing priorities

OpenText - Contenders

Pros:

Deployment options
In-platform analytics
Data access
Customer service

Cons:

Lags in streaming data
Data modeling needs improvement
Visualization needs improvement
Documentation concerns
Storage access latency monitoring and alerting
Integrated DMA platform needs improvement

SAS - Contenders

Pros:

In-platform analytics
Data transformation
Data access
Visualization

Cons:

Lags in an integrated DMA platform
Deployment options need improvement
Real-time analytics need improvement
Performance needs improvement
Scalability needs improvement
Declined to participate in full Forrester Wave evaluation process

Incorta - Challengers

Pros:

On par for deployment options
Real-time analytics
Data access

Cons:

Lacks foundational DMA capabilities
Behind competition in offering integrated DMA platform
Data persistence
Data modeling
Data security
Data governance
Visualization
Performance
Tools
Does not have strong strategy or roadmap for DMA
Declined to participate in full Forrester Wave evaluation process

FAQs

Q: Which vendors are Leaders in 2023?

A: Databricks, Google, IBM, Informatica, Oracle, SAP, Teradata

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

A: Leaders in the Forrester Wave represent vendors that have the most comprehensive and strong current offerings combined with strong strategy. They demonstrate superior capabilities across key evaluation criteria including architecture, streaming and real-time data, data modeling/integration/transformation, security and governance, performance/scale/availability, visualization and tools, and use cases. Leaders also show strong strategic vision, execution roadmap, performance roadmap, planned enhancements, commercial model, and partner ecosystem. These vendors are positioned to best serve organizations needing robust, scalable DMA solutions for complex workloads including real-time analytics and modern business use cases.

Q: How do I interpret a Forrester Wave?

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

Report:

Top Vendor Products for Data Management For Analytics in 2023

Data Management For Analytics 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?

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