The Forrester Wave™: Document Mining And Analytics Platforms, Q2 2024

Document Mining And Analytics Platforms Vendor Product Comparisons 2024

Key features and pros and cons of top Document Mining And Analytics Platforms products:

Expert.ai - Leaders

Pros:

Knowledge-based/symbolic AI capabilities clearly stand out
Hybrid AI platform combining linguistic rules with domain-specific ontologies
Strong vision with significant investments in ML-based AI
Leveraging genAI in platform, including validating outputs of genAI-based processes
Labeling documents for supervised ML learning
Cross-document mining using unsupervised ML to classify and categorize documents
Document-level mining such as topic and entity extraction
Support for multiple text mining and analytics use cases
Out-of-the-box accuracy and language model explainability

Cons:

Must continue to expand partner ecosystem to achieve broader market penetration
Needs to continue improving capabilities to mine information from complex semi-structured documents
Must add to current security and regulatory compliance capabilities
Expertise required to build or modify language models

Google - Leaders

Pros:

Plethora of ML models, LLMs, and multimodal models like Gemini 1.5 Pro available on Vertex AI
Strong innovation and roadmap based on vision for future of AI
Excellent partner ecosystem
Pricing flexibility and transparency
ML-based AI capabilities
Leveraging genAI in document mining and analytics processes including prompt engineering, RAG, and validating LLM-generated outputs
Mining data from complex documents and forms
Strong DevOps capabilities
Excellent globalization capabilities
Broad security and regulatory compliance capabilities
Business value and time to value praised by reference customers

Cons:

Lags on knowledge-based/symbolic AI
Limited deployment options - Google Cloud only
Lags on workflow orchestration capabilities
Not suitable for customers with data gravity on other public cloud platforms or on-premises requirements

Hyperscience - Leaders

Pros:

Challenges low-code trend with code-based approach using Python
Strong strategic vision and roadmap
High customer adoption
Strong partner ecosystem
Out-of-the-box with several hundred code blocks for common functions
HITL accuracy verification and improvement processes
ML-based AI capabilities
Labeling documents for supervised ML
Multiple deployment options
Mining information from complex forms and documents
Strong DevOps capabilities
Human-readable code that can be easily extended, customized, and embedded

Cons:

Needs to improve platform pricing flexibility and transparency
Lags in knowledge-based/symbolic AI capabilities
Lags in analytics (business metrics and dashboards)
Lags in document-level text mining for topic extraction
Some reference customers expressed concern about vendor help desk and upgrade experience

IBM - Leaders

Pros:

Extensive and sophisticated document mining and analytics capabilities
Strong knowledge-based/symbolic AI
Using genAI in document mining processes
Document labeling for supervised ML
Document-level mining for topic extraction
Global capabilities
Broad security and regulatory compliance capabilities
Support for most relevant use cases including highly complex contract intelligence
Extensive investments in NLP R&D and patents
Broad partner ecosystem including partnerships with academia

Cons:

Does not actively market and sell document mining and analytics suite outside existing IBM ecosystem
Intelligent Document Processing portfolio consists of multiple products that need integration (Automation Document Processing, Watson Discovery, watsonx Orchestrate, Datacap)

OpenText - Leaders

Pros:

IDOL platform equipped to handle any and all document mining and analytics tasks
Close to impossible to find gaps in IDOL's capabilities
Strategic innovation and strong roadmap
Standout capabilities across majority of criteria evaluated
Overall business value
HITL accuracy verification and improvement capabilities
Cross-document mining using unsupervised ML to classify and categorize documents
Strong architecture (stability and scalability)
Data security capabilities
Business user UX
Well-established community of customers through OpenText

Cons:

Must work on improving pricing flexibility and transparency
Platform has so many capabilities it can be a challenge to navigate
UX may not be consistent across multiple IDOL products
IDOL platform is new to OpenText as part of Micro Focus acquisition in 2023

UiPath - Leaders

Pros:

Formidable process automation and document mining platform
Balanced and innovative investments in both automation and document mining
Acquisition of Re:infer in 2022 added NLP capabilities
Broad customer adoption confirms strong strategy and vision
HITL accuracy verification and improvement processes
ML-based AI capabilities
Document labeling for supervised ML
Using genAI in document mining processes including pre-processing (prompt engineering, RAG) and post-processing (validation)
Classifying and categorizing documents
Multiple deployment options
Workflow orchestration
Mining information from complex forms and documents
Broad security and regulatory compliance capabilities
Integration of document processing and process automation as differentiator

Cons:

Lags in knowledge-based/symbolic AI capabilities

Amazon Web Services - Strong Performers

Pros:

Stellar set of broad document mining and analytics cloud services
High customer adoption
Pricing flexibility and transparency
Broad partner ecosystem
Services often preferred choice for embedding in custom-built applications or third-party solutions
Outstanding DevOps capabilities
Excellent globalization capabilities
Broad security and regulatory compliance capabilities

Cons:

Needs to continue to invest in improving document mining analytics
Limited deployment options - AWS only
Not a good fit for customers with data gravity on other public cloud platforms or on-premises requirements
Declined to participate in full Forrester Wave evaluation process

Automation Anywhere - Strong Performers

Pros:

Innovative intelligent process automation vendor
Excellent automation capabilities (low-code, document mining, AI, RPA) across many business domains
Formidable global partner ecosystem
Document mining workflow orchestration capabilities
One of the better HITL interfaces for accuracy validation and improvement
Variety of deployment options
Top automation platform for intelligent process automation use cases

Cons:

Lags competition in customer adoption of document-centric process automation
Needs to beef up knowledge-based/symbolic AI capabilities
Must broaden security and regulatory compliance capabilities
Reference customers declined to respond or didn't respond in time to outreach

Microsoft - Strong Performers

Pros:

Solid set of document mining and analytics services on Azure
Pricing flexibility and transparency
Broad partner ecosystem
Investment and partnership with OpenAI
Strong analytics capabilities via Power BI
Document-level mining for topic extraction
Strong globalization support
Strong DevOps capabilities
Broad security and regulatory compliance capabilities

Cons:

Must continue to improve overall document mining and analytics vision and roadmap
Needs to continue to invest in HITL-based accuracy verification and improvement processes
Limited deployment options - Azure only
Needs improvement in extracting information from highly complex forms and documents
Not a good fit for customers with data gravity on other public cloud platforms or on-premises requirements
Declined to participate in full Forrester Wave evaluation process

Rossum - Strong Performers

Pros:

Specialized focus on transactional documents (invoices, bills of lading, purchase orders)
Created custom transactional large language model (T-LLM)
Differentiated approach using discriminative decoder technique
Forces T-LLM to reference every answer directly in document, reducing hallucinations
Calibrated confidence scores reduce need to verify LLM-generated results
Using specialized fine-tuned model to validate results and confidence scores
Overall business value praised by reference customers
Strong relationship with vendor
Good business user and data/analytics professional UX

Cons:

Must invest in broader global partner ecosystem
Needs to work on building out knowledge-based/symbolic AI
Limited to transactional documents - not suitable for all document types

WorkFusion - Strong Performers

Pros:

Virtual 'digital workers' for financial crime use cases
Preconfigured solutions for sanctions and adverse media screening, customer and transaction screening, KYC processes
Open intelligent automation platform configurable for any use case
Variety of deployment options
Mining information from complex documents and forms
Business value praised by reference customers
Overall relationship with vendor
HITL accuracy verification and improvement capabilities
Business users' self-service capabilities
Workflow automation and orchestration
Strong architecture and data security functionality

Cons:

Must continue to improve knowledge-based/symbolic AI capabilities
Lags in cross-document level mining using unsupervised ML
Needs to improve global capabilities (supported languages and regional deployment options)
Must broaden support for document mining and analytics use cases beyond financial crime

EdgeVerve - Contenders

Pros:

Formidable breadth and depth of industry- and business-domain-specific prebuilt software robots
Good track record of solid executions and customer support
Based on Infosys parent company's core strengths
Can leverage huge Infosys customer base

Cons:

Lacks differentiated document mining and analytics market vision
Doesn't have partner ecosystem necessary for broader market penetration
Must continue to build out knowledge-based/symbolic AI capabilities
Needs improvement in topic extractions from documents
Must improve globalization capabilities
Needs to broaden security and regulatory compliance capabilities
Reference customers did not respond to Forrester's outreach

SS&C Blue Prism - Contenders

Pros:

SS&C's acquisition of Blue Prism brings document mining to automation portfolio
Elevated from RPA pure play to full-fledged process automation platform
Strong automation capabilities

Cons:

Must continue to invest in comprehensive and advanced document mining and analytics capabilities
Needs more investment in knowledge-based/symbolic and ML-based AI
Lags in leveraging genAI in document mining processes
Needs improvement in document labeling for supervised ML
Lags in analytics (business metrics and dashboards)
Needs improvement extracting information from complex forms
Must improve DevOps capabilities
Needs better globalization (support for more languages and regional deployment options)
Must broaden support for all relevant use cases
Relied on partners for mining highly complex documents, especially with free-form text
Customers should perform due diligence around specific document mining requirements
Declined to participate in full Forrester Wave evaluation process

SAS - Contenders

Pros:

Continues to invest in modernizing text mining and analytics platform
SAS Visual Text Analytics based on modern, highly scalable Viya architecture
SAS Text Miner offers advanced text mining within SAS Enterprise Miner
Seamlessly integrated data visualization and analytics platform (SAS Visual Analytics with VTA)
Embeds text mining into industry- and business-domain specific solutions

Cons:

Hasn't had success penetrating market beyond existing customer ecosystem
Lags in innovation
Needs to add more product features to roadmap
Needs to improve pricing flexibility and transparency
Lags on HITL-based accuracy verification and improvement processes
Weak knowledge-based/symbolic AI
Lags in genAI-based text mining processes
Needs improvement in automation capabilities
Lags in extracting information from complex forms
Weak DevOps support
Must broaden support for security and regulatory compliance standards
Declined to participate in full Forrester Wave evaluation process

FAQs

Q: Which vendors are Leaders in 2024?

A: Expert.ai, Google, Hyperscience, IBM, OpenText, UiPath

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

A: Leaders in the Forrester Wave are vendors that offer the strongest current product offerings and demonstrate the most compelling strategies for the future in the document mining and analytics platforms market. Leaders excel across most evaluation criteria, have strong customer adoption, and demonstrate clear vision and innovation for the market.

Q: How do I interpret a Forrester Wave?

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

Report:

Top Vendor Products for Document Mining And Analytics Platforms in 2024

Document Mining And Analytics Platforms Vendor Product Comparisons 2024

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FAQs

Q: Which vendors are Leaders in 2024?

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

Q: How do I interpret a Forrester Wave?

Reference