Predicting Discovery? Initial Results of 120-Second Provider Predictive Coding Survey - ComplexDiscovery Archived Content

Feb 17, 2013

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120 Seconds of Predictive Coding – Survey Results

Provided below for your consideration and use are the in-progress results of the Predictive Coding and Providers: A 120-Second Survey launched by ComplexDiscovery on 2/10/13. The goal of the survey is to provide a quick, non-comprehensive overview of the use of the technology-assisted review feature of predictive coding among leading eDiscovery providers as represented by those providers. The areas covered in the short survey include:

Technology Development (Developed Internally, Licensed Externally or Hybrid Development)
Technology Partners (Content Analyst, Equivio, OrcaTec, Recommind or Other)
Offering Integration (Architectural Integration, Process Integration or Other)
Machine Learning Approach (Supervised Learning, Active Learning or Other)
Sampling Approach (Random, Judgmental or Other)

The in-progress results consist of survey answers harvested directly from the online survey form as completed by provider representatives. Additional survey responses from the eDiscovery provider community will be added to this listing as they are completed.

Additional responders are welcome and encouraged. Click here to go to survey.

Updated 7/23/2013

Current Responders

@Legal
AccessData
AlphaLit
Altep
BIA
Catalyst Repository Systems
Content Analyst
D4
Daegis
Deloitte
Driven
DTI
Equivio
Exterro
Hudson Legal
Huron Legal
kCura
Kroll Ontrack
NightOwl Discovery
Nuix
Orange Legal Technologies
OrcaTec
Prolorem
Rational Retention
Recommind
RenewData
Servient
Symantec/Clearwell
TCDI
UBIC
UHY Advisors
Valora Technologies
Xerox Litigation Services
ZyLAB

Current Responses (By Provider)

@Legal (CasePoint TAR) @AtLegal

Developed Internally: Proprietary development of predictive coding technology.
Architecturally Integrated: Predictive coding technology is integrated into the software architecture (application integration) of core eDiscovery platform.
Supervised Learning: Human chooses a set of documents (training set) and feeds the documents into the system. The system learns the difference between responsive and non-responsive documents and classifies remaining documents accordingly.
Random Sampling: A statistical sampling approach that gives each document an equal chance of being chosen for inclusion within a sample.
Judgmental Sampling: A sampling approach that draws in part from subjective factors when determining inclusion within a sample.

AccessData (Summation – Predictive Coding Feature) @AccessData Group

Developed Internally: Proprietary development of predictive coding technology.
Architecturally Integrated: Predictive coding technology is integrated into the software architecture (application integration) of core eDiscovery platform.
Supervised Learning: Human chooses a set of documents (training set) and feeds the documents into the system. The system learns the difference between responsive and non-responsive documents and classifies remaining documents accordingly.
Judgmental Sampling: A sampling approach that draws in part from subjective factors when determining inclusion within a sample.

AlphaLit (E-Predict | Backstop – Content Analyst)

Hybrid Development: Combination of proprietary and licensed predictive coding technology.
Technology Partners: Backstop, Content Analyst
Architecturally Integrated: Predictive coding technology is integrated into the software architecture (application integration) of core eDiscovery platform.
Supervised Learning: Human chooses a set of documents (training set) and feeds the documents into the system. The system learns the difference between responsive and non-responsive documents and classifies remaining documents accordingly.
Random Sampling: A statistical sampling approach that gives each document an equal chance of being chosen for inclusion within a sample.

Altep (Tag | Content Analyst) @Altep_Inc

Developed Internally: Proprietary development of predictive coding technology.
Technology Partners: Content Analyst
Architecturally Integrated: Predictive coding technology is integrated into the software architecture (application integration) of core eDiscovery platform. (Available as a standalone application and integrated into the Inspicio offering.)
Supervised Learning: Human chooses a set of documents (training set) and feeds the documents into the system. The system learns the difference between responsive and non-responsive documents and classifies remaining documents accordingly.
Active Learning: System chooses a set of documents and feeds the documents to humans. Humans make a decision about the documents and then the system applies the learning provided by the humans against the rest of the documents in the collection.
Random Sampling: A statistical sampling approach that gives each document an equal chance of being chosen for inclusion within a sample.

BIA (BIA Predictive Coding Engine | Coda™) @BIA_eDiscovery

Developed Internally: Proprietary development of predictive coding technology.
Architecturally Integrated: Predictive coding technology is integrated into the software architecture (application integration) of core eDiscovery platform.
Process Integrated: Predictive coding technology is available as an eDiscovery offering, but not integrated into the software architecture of core eDiscovery platform (process integration).
Supervised Learning: Human chooses a set of documents (training set) and feeds the documents into the system. The system learns the difference between responsive and non-responsive documents and classifies remaining documents accordingly.
Active Learning: System chooses a set of documents and feeds the documents to humans. Humans make a decision about the documents and then the system applies the learning provided by the humans against the rest of the documents in the collection.
Random Sampling: A statistical sampling approach that gives each document an equal chance of being chosen for inclusion within a sample.
Judgmental Sampling: A sampling approach that draws in part from subjective factors when determining inclusion within a sample.

Catalyst Repository Systems (Insight Predict) @CatalystSecure

Developed Internally: Proprietary development of predictive coding technology.
Architecturally Integrated: Predictive coding technology is integrated into the software architecture (application integration) of core eDiscovery platform.
Globally Adaptive K-Nearest Neighbor machine learning approach.
Random Sampling: A statistical sampling approach that gives each document an equal chance of being chosen for inclusion within a sample.
Judgmental Sampling: A sampling approach that draws in part from subjective factors when determining inclusion within a sample.

Content Analyst (Conceptual Categorization) @Content_Analyst

Developed Internally: Proprietary development of predictive coding technology. (Content Analyst provides analytics technology, not end user solutions).
Architecturally Integrated: Predictive coding technology is integrated into the software architecture (application integration) of core eDiscovery platform.
Supervised Learning: Human chooses a set of documents (training set) and feeds the documents into the system. The system learns the difference between responsive and non-responsive documents and classifies remaining documents accordingly (based on licensee approach).
Active Learning: System chooses a set of documents and feeds the documents to humans. Humans make a decision about the documents and then the system applies the learning provided by the humans against the rest of the documents in the collection (based on licensee approach).
Random Sampling: A statistical sampling approach that gives each document an equal chance of being chosen for inclusion within a sample (based on licensee approach).
Judgmental Sampling: A sampling approach that draws in part from subjective factors when determining inclusion within a sample (based on licensee approach).

D4 Discovery (Equivio Relevance – Relativity Assisted Review) @D4Discovery

Licensed Externally: Predictive coding technology completely licensed from software developer.
Technology Partners: Equivio, Content Analyst
Architecturally Integrated: Predictive coding technology is integrated into the software architecture (application integration) of core eDiscovery platform.
Active Learning: System chooses a set of documents and feeds the documents to humans. Humans make a decision about the documents and then the system applies the learning provided by the humans against the rest of the documents in the collection.
Random Sampling: A statistical sampling approach that gives each document an equal chance of being chosen for inclusion within a sample.
Judgmental Sampling: A sampling approach that draws in part from subjective factors when determining inclusion within a sample.

Daegis (Acumen) @Daegis

Developed Internally: Proprietary development of predictive coding technology.
Architecturally Integrated: Predictive coding technology is integrated into the software architecture (application integration) of core eDiscovery platform.
Active Learning: System chooses a set of documents and feeds the documents to humans. Humans make a decision about the documents and then the system applies the learning provided by the humans against the rest of the documents in the collection.
Random Sampling: A statistical sampling approach that gives each document an equal chance of being chosen for inclusion within a sample.

Deloitte (Dynamic Review) @DeloitteFinSvcs

Developed Internally: Proprietary development of predictive coding technology.
Process Integrated: Predictive coding technology is available as an eDiscovery offering, but not integrated into the software architecture of core eDiscovery platform (process integration).
Supervised Learning: Human chooses a set of documents (training set) and feeds the documents into the system. The system learns the difference between responsive and non-responsive documents and classifies remaining documents accordingly.
Random Sampling: A statistical sampling approach that gives each document an equal chance of being chosen for inclusion within a sample.
Judgmental Sampling: A sampling approach that draws in part from subjective factors when determining inclusion within a sample.

Driven (START (Simplified Technology Assisted Review Tool)) (Content Analyst)

Developed Internally: Proprietary development of predictive coding technology.
Technology Partners: Content Analyst
Architecturally Integrated: Predictive coding technology is integrated into the software architecture (application integration) of core eDiscovery platform.
Supervised Learning: Human chooses a set of documents (training set) and feeds the documents into the system. The system learns the difference between responsive and non-responsive documents and classifies remaining documents accordingly.
Random Sampling: A statistical sampling approach that gives each document an equal chance of being chosen for inclusion within a sample.

DTI (Technology-Assisted Review | Equivio)

Licensed Externally: Predictive coding technology completely licensed from software developer.
Technology Partner: Equivio
Process Integrated: Predictive coding technology is available as an eDiscovery offering, but not integrated into the software architecture of core eDiscovery platform (process integration).
Active Learning: System chooses a set of documents and feeds the documents to humans. Humans make a decision about the documents and then the system applies the learning provided by the humans against the rest of the documents in the collection.
Random Sampling: A statistical sampling approach that gives each document an equal chance of being chosen for inclusion within a sample.

Equivio (Relevance) @Equivio

Developed Internally: Proprietary development of predictive coding technology.
Architecturally Integrated: Predictive coding technology is integrated into the software architecture (application integration) of core eDiscovery platform.
Active Learning: System chooses a set of documents and feeds the documents to humans. Humans make a decision about the documents and then the system applies the learning provided by the humans against the rest of the documents in the collection.
Random Sampling: A statistical sampling approach that gives each document an equal chance of being chosen for inclusion within a sample.

Exterro (Fusion Predictive Intelligence™) @Exterro

Developed Internally: Proprietary development of predictive coding technology.
Architecturally Integrated: Predictive coding technology is integrated into the software architecture (application integration) of core eDiscovery platform.
Supervised Learning: Human chooses a set of documents (training set) and feeds the documents into the system. The system learns the difference between responsive and non-responsive documents and classifies remaining documents accordingly.
Active Learning: System chooses a set of documents and feeds the documents to humans. Humans make a decision about the documents and then the system applies the learning provided by the humans against the rest of the documents in the collection.
Random Sampling: A statistical sampling approach that gives each document an equal chance of being chosen for inclusion within a sample.
Judgmental Sampling: A sampling approach that draws in part from subjective factors when determining inclusion within a sample.

Hudson Legal (Predictive Coding | Recommind – Relativity – Kroll Ontrack) @HudsonLegal

Licensed Externally: Predictive coding technology completely licensed from software developer.
Technology Partner: Recommind (Offer Relativity and Kroll Ontrack capabilities also.)
Process Integrated: Predictive coding technology is available as an eDiscovery offering, but not integrated into the software architecture of core eDiscovery platform (process integration).
Active Learning: System chooses a set of documents and feeds the documents to humans. Humans make a decision about the documents and then the system applies the learning provided by the humans against the rest of the documents in the collection.
Random Sampling: A sampling approach that gives each document an equal chance of being chosen for inclusion within a sample.

Huron Legal (Integrated Analytics | PureDiscovery) @HuronLegal

Licensed Externally: Predictive coding technology completely licensed from software developer.
Technology Partner: PureDiscovery
Process Integrated: Predictive coding technology is available as an eDiscovery offering, but not integrated into the software architecture of core eDiscovery platform (process integration).
Supervised Learning: Human chooses a set of documents (training set) and feeds the documents into the system. The system learns the difference between responsive and non-responsive documents and classifies remaining documents accordingly.
Random Sampling: A statistical sampling approach that gives each document an equal chance of being chosen for inclusion within a sample.
Judgmental Sampling: A sampling approach that draws in part from subjective factors when determining inclusion within a sample.

kCura (Relativity Assisted Review | Content Analyst) @kCura

Hybrid Development: Combination of proprietary and licensed predictive coding technology.
Technology Partner: Content Analyst
Architecturally Integrated: Predictive coding technology is integrated into the software architecture (application integration) of core eDiscovery platform.
Supervised Learning: Human chooses a set of documents (training set) and feeds the documents into the system. The system learns the difference between responsive and non-responsive documents and classifies remaining documents accordingly.
Active Learning: System chooses a set of documents and feeds the documents to humans. Humans make a decision about the documents and then the system applies the learning provided by the humans against the rest of the documents in the collection.
Random Sampling: A statistical sampling approach that gives each document an equal chance of being chosen for inclusion within a sample.
Judgmental Sampling: A sampling approach that draws in part from subjective factors when determining inclusion within a sample.

Kroll Ontrack (Intelligent Review Technology (IRT) | Ontrack® Inview™) @KrollOntrack

Developed Internally: Proprietary development of predictive coding technology.
Architecturally Integrated: Predictive coding technology is integrated into the software architecture (application integration) of core eDiscovery platform.
Supervised Learning: Human chooses a set of documents (training set) and feeds the documents into the system. The system learns the difference between responsive and non-responsive documents and classifies remaining documents accordingly.
Active Learning: System chooses a set of documents and feeds the documents to humans. Humans make a decision about the documents and then the system applies the learning provided by the humans against the rest of the documents in the collection.
Random Sampling: A statistical sampling approach that gives each document an equal chance of being chosen for inclusion within a sample.
Judgmental Sampling: A sampling approach that draws in part from subjective factors when determining inclusion within a sample.

NightOwl Discovery (Predictive Coding | Equivio – Relativity) @NightOwlDMS

Licensed Externally: Predictive coding technology completely licensed from software developer.
Technology Partners: Equivio, Relativity
Architecturally Integrated: Predictive coding technology is integrated into the software architecture (application integration) of core eDiscovery platform.
Supervised Learning: Human chooses a set of documents (training set) and feeds the documents into the system. The system learns the difference between responsive and non-responsive documents and classifies remaining documents accordingly.
Active Learning: System chooses a set of documents and feeds the documents to humans. Humans make a decision about the documents and then the system applies the learning provided by the humans against the rest of the documents in the collection.
Random Sampling: A statistical sampling approach that gives each document an equal chance of being chosen for inclusion within a sample.
Judgmental Sampling: A sampling approach that draws in part from subjective factors when determining inclusion within a sample.

Nuix (Automatic Classification) @Nuix

Developed Internally: Proprietary development of predictive coding technology.
Architecturally Integrated: Predictive coding technology is integrated into the software architecture (application integration) of core eDiscovery platform.
Supervised Learning: Human chooses a set of documents (training set) and feeds the documents into the system. The system learns the difference between responsive and non-responsive documents and classifies remaining documents accordingly.
Judgmental Sampling: A sampling approach that draws in part from subjective factors when determining inclusion within a sample.

Orange Legal Technologies (Predictive Review | OrcaTec) @OrangeLT

Hybrid Development: Combination of proprietary and licensed predictive coding technology.
Technology Partner: OrcaTec
Architecturally Integrated: Predictive coding technology is integrated into the software architecture (application integration) of core eDiscovery platform.
Active Learning: System chooses a set of documents and feeds the documents to humans. Humans make a decision about the documents and then the system applies the learning provided by the humans against the rest of the documents in the collection.
Random Sampling: A statistical sampling approach that gives each document an equal chance of being chosen for inclusion within a sample.

OrcaTec (OrcaPredict) @OrcaTec

Developed Internally: Proprietary development of predictive coding technology.
Architecturally Integrated: Predictive coding technology is integrated into the software architecture (application integration) of core eDiscovery platform.
Active Learning: System chooses a set of documents and feeds the documents to humans. Humans make a decision about the documents and then the system applies the learning provided by the humans against the rest of the documents in the collection.
Random Sampling: A statistical sampling approach that gives each document an equal chance of being chosen for inclusion within a sample.

Prolorem (Prolorem eDi)

Hybrid Development: Combination of proprietary and licensed predictive coding technology.
Technology Partners: Open Source Licensed APIs and Tools
Architecturally Integrated: Predictive coding technology is integrated into the software architecture (application integration) of core eDiscovery platform.
Supervised Learning: Human chooses a set of documents (training set) and feeds the documents into the system. The system learns the difference between responsive and non-responsive documents and classifies remaining documents accordingly.
Random Sampling: A statistical sampling approach that gives each document an equal chance of being chosen for inclusion within a sample.

Rational Retention (Rational Intelligence)

Developed Internally: Proprietary development of predictive coding technology.
Process Integrated: Predictive coding technology is available as an eDiscovery offering, but not integrated into the software architecture of core eDiscovery platform (process integration).
Supervised Learning: Human chooses a set of documents (training set) and feeds the documents into the system. The system learns the difference between responsive and non-responsive documents and classifies remaining documents accordingly.
Random Sampling: A statistical sampling approach that gives each document an equal chance of being chosen for inclusion within a sample.
Judgmental Sampling: A sampling approach that draws in part from subjective factors when determining inclusion within a sample.

Recommind (Predictive Coding | Axelerate) @Recommind

Developed Internally: Proprietary development of predictive coding technology.
Architecturally Integrated: Predictive coding technology is integrated into the software architecture (application integration) of core eDiscovery platform.
Supervised Learning: Human chooses a set of documents (training set) and feeds the documents into the system. The system learns the difference between responsive and non-responsive documents and classifies remaining documents accordingly.
Random Sampling: A statistical sampling approach that gives each document an equal chance of being chosen for inclusion within a sample.

RenewData (Computer-Assisted Coding | Equivio – Content Analyst) @RenewData

Licensed Externally: Predictive coding technology completely licensed from software developer.
Technology Partners: Equivio, Content Analyst
Process Integrated: Predictive coding technology is available as an eDiscovery offering, but not integrated into the software architecture of core eDiscovery platform (process integration).
Supervised Learning: Human chooses a set of documents (training set) and feeds the documents into the system. The system learns the difference between responsive and non-responsive documents and classifies remaining documents accordingly.
Random Sampling: A statistical sampling approach that gives each document an equal chance of being chosen for inclusion within a sample.

Servient (Servient Predictive Review) @Servient

Developed Internally: Proprietary development of predictive coding technology.
Architecturally Integrated: Predictive coding technology is integrated into the software architecture (application integration) of core eDiscovery platform.
Supervised Learning: Human chooses a set of documents (training set) and feeds the documents into the system. The system learns the difference between responsive and non-responsive documents and classifies remaining documents accordingly.
Active Learning: System chooses a set of documents and feeds the documents to humans. Humans make a decision about the documents and then the system applies the learning provided by the humans against the rest of the documents in the collection.
Random Sampling: A statistical sampling approach that gives each document an equal chance of being chosen for inclusion within a sample.
Judgmental Sampling: A sampling approach that draws in part from subjective factors when determining inclusion within a sample.

Symantec (Clearwell Systems) (Transparent Predictive Coding) @SYMCeDiscovery

Developed Internally: Proprietary development of predictive coding technology.
Architecturally Integrated: Predictive coding technology is integrated into the software architecture (application integration) of core eDiscovery platform.
Supervised Learning: Human chooses a set of documents (training set) and feeds the documents into the system. The system learns the difference between responsive and non-responsive documents and classifies remaining documents accordingly.
Active Learning: System chooses a set of documents and feeds the documents to humans. Humans make a decision about the documents and then the system applies the learning provided by the humans against the rest of the documents in the collection.
Random Sampling: A statistical sampling approach that gives each document an equal chance of being chosen for inclusion within a sample.
Judgmental Sampling: A sampling approach that draws in part from subjective factors when determining inclusion within a sample.

TCDI (Suggestive Coding | Content Analyst)

Hybrid Development: Combination of proprietary and licensed predictive coding technology.
Technology Partner: Content Analyst
Architecturally Integrated: Predictive coding technology is integrated into the software architecture (application integration) of core eDiscovery platform.
Supervised Learning: Human chooses a set of documents (training set) and feeds the documents into the system. The system learns the difference between responsive and non-responsive documents and classifies remaining documents accordingly.
Active Learning: System chooses a set of documents and feeds the documents to humans. Humans make a decision about the documents and then the system applies the learning provided by the humans against the rest of the documents in the collection.
Random Sampling: A statistical sampling approach that gives each document an equal chance of being chosen for inclusion within a sample.
Judgmental Sampling: A sampling approach that draws in part from subjective factors when determining inclusion within a sample.

UBIC (CJK TAR)

Developed Internally: Proprietary development of predictive coding technology.
Architecturally Integrated: Predictive coding technology is integrated into the software architecture (application integration) of core eDiscovery platform.
Supervised Learning: Human chooses a set of documents (training set) and feeds the documents into the system. The system learns the difference between responsive and non-responsive documents and classifies remaining documents accordingly.
Active Learning: System chooses a set of documents and feeds the documents to humans. Humans make a decision about the documents and then the system applies the learning provided by the humans against the rest of the documents in the collection.
Random Sampling: A statistical sampling approach that gives each document an equal chance of being chosen for inclusion within a sample.
Judgmental Sampling: A sampling approach that draws in part from subjective factors when determining inclusion within a sample.

UHY Advisors (Assisted Review | Relativity)

Licensed Externally: Predictive coding technology completely licensed from software developer.
Technology Partners: Relativity
Architecturally Integrated: Predictive coding technology is integrated into the software architecture (application integration) of core eDiscovery platform.
Supervised Learning: Human chooses a set of documents (training set) and feeds the documents into the system. The system learns the difference between responsive and non-responsive documents and classifies remaining documents accordingly.
Active Learning: System chooses a set of documents and feeds the documents to humans. Humans make a decision about the documents and then the system applies the learning provided by the humans against the rest of the documents in the collection.
Random Sampling: A statistical sampling approach that gives each document an equal chance of being chosen for inclusion within a sample.
Judgmental Sampling: A sampling approach that draws in part from subjective factors when determining inclusion within a sample.

Valora Technologies (Valora Auto Review Services) @ValoraTech

Developed Internally: Proprietary development of predictive coding technology.
Architecturally Integrated: Predictive coding technology is integrated into the software architecture (application integration) of core eDiscovery platform.
Probabilistic Hierarchical Context-Free Grammars approach to machine learning.
Stratified Sampling: A statistical sampling approach in which populations are stratified by attributes (which can be automatically obtained or obtained from human input), then selected for sampling so that the sample strata match the population strata.

Xerox Litigation Services (CategoriX) @Xerox_XLS

Developed Internally: Proprietary development of predictive coding technology.
Architecturally Integrated: Predictive coding technology is integrated into the software architecture (application integration) of core eDiscovery platform.
Supervised Learning: Human chooses a set of documents (training set) and feeds the documents into the system. The system learns the difference between responsive and non-responsive documents and classifies remaining documents accordingly.
Random Sampling: A statistical sampling approach that gives each document an equal chance of being chosen for inclusion within a sample.
Judgmental Sampling: A sampling approach that draws in part from subjective factors when determining inclusion within a sample.

Xerox Litigation Services – Lateral Data (View Point Assisted Review) @Xerox_XLS

Developed Internally: Proprietary development of predictive coding technology.
Architecturally Integrated: Predictive coding technology is integrated into the software architecture (application integration) of core eDiscovery platform.
Active Learning: System chooses a set of documents and feeds the documents to humans. Humans make a decision about the documents and then the system applies the learning provided by the humans against the rest of the documents in the collection.
Random Sampling: A statistical sampling approach that gives each document an equal chance of being chosen for inclusion within a sample.
Judgmental Sampling: A sampling approach that draws in part from subjective factors when determining inclusion within a sample.

ZyLAB (ZyLAB Technology-Assisted Review) @ZyLAB

Developed Internally: Proprietary development of predictive coding technology.
Architecturally Integrated: Predictive coding technology is integrated into the software architecture (application integration) of core eDiscovery platform.
Supervised Learning: Human chooses a set of documents (training set) and feeds the documents into the system. The system learns the difference between responsive and non-responsive documents and classifies remaining documents accordingly.
Random Sampling: A statistical sampling approach that gives each document an equal chance of being chosen for inclusion within a sample.

Current Responses (By Answer)

Technology Development

Developed Internally: Proprietary development of predictive coding technology.

@Legal
AccessData
Altep
BIA
Catalyst Repository Systems
Content Analyst (Content Analyst provides analytics technology, not end user solutions).
Daegis
Deloitte
Driven
Equivio
Exterro
Nuix
OrcaTec
Kroll Ontrack
Rational Retention
Recommind
Servient
Symantec/Clearwell
UBIC
Valora Technologies
Xerox Litigation Services (Viewpoint Assisted Review and CategoriX)
ZyLAB

Licensed Externally: Predictive coding technology completely licensed from software developer.

D4 (Equivio, Content Analyst)
DTI (Equivio)
Hudson Legal
Huron Legal (PureDiscovery)
NightOwl Discovery
RenewData (Equivio, Content Analyst)
UHY Advisors

Hybrid Development: Combination of proprietary and licensed predictive coding technology.

AlphaLit (Backstop, Content Analyst)
kCura (Content Analyst)
Orange Legal Technologies (OrcaTec)
Prolorem (Open Source Licensed APIs and Tools)
TCDI (Content Analyst)

Offering Integration

Architecturally Integrated: Predictive coding technology is integrated into the software architecture (application integration) of core eDiscovery platform.

@Legal
AccessData
AlphaLit
Altep (Available as a standalone offering or as part of the Inspicio offering.)
Catalyst Repository Systems
Content Analyst (Content Analyst provides analytics technology, not end user solutions.)
D4
Daegis
Driven
Equivio
Exterro
kCura
Kroll Ontrack
NightOwl Discovery
Nuix
OrcaTec
Prolorem
Recommind
Servient
Symantec/Clearwell
TCDI
UBIC
UHY Advisors
Valora Technologies
Xerox Litigation Services (Viewpoint Assisted Review and CategoriX)
ZyLAB

Process Integrated: Predictive coding technology is available as an eDiscovery offering, but not integrated into the software architecture of core eDiscovery platform (process integration).

BIA
DTI
Deloitte
Hudson Legal
Huron Legal
Orange Legal Technologies
Rational Retention
RenewData

Machine Learning Approach

Supervised Learning: Human chooses a set of documents (training set) and feeds the documents into the system. The system learns the difference between responsive and non-responsive documents and classifies remaining documents accordingly.

@Legal
AccessData
AlphaLit
Altep
BIA
Content Analyst (Based on licensee machine learning approach as Content Analyst provides analytics technology, not end user solutions).
Deloitte
Driven
Exterro
Huron Legal
kCura
Kroll Ontrack
NightOwl Discovery
Nuix
Orange Legal Technologies
OrcaTec
Prolorem
Rational Retention
Recommind
RenewData
Servient
Symantec/Clearwell
TCDI
UBIC
UHY Advisors
Valora Technologies
Xerox Litigation Services (CategoriX)
ZyLAB

Active Learning: System chooses a set of documents and feeds the documents to humans. Humans make a decision about the documents and then the system applies the learning provided by the humans against the rest of the documents in the collection.

Altep
BIA
Content Analyst (Based on licensee machine learning approach as Content Analyst provides analytics technology, not end user solutions).
Exterro
D4
Daegis
DTI
Equivio
Exterro
Hudson Legal
kCura
Kroll Ontrack
NightOwl Discovery
Orange Legal Technologies
OrcaTec
Servient
Symantec/Clearwell
TCDI
UBIC
UHY Advisors
Xerox Litigation Services (Viewpoint Assisted Review)

Other Machine Learning Approaches:

Catalyst Repository Systems: Globally Adaptive K-Nearest Neighbor machine learning approach.
Valora Technologies: Probabilistic Hierarchical Context-Free Grammars approach to machine learning.

Sampling Approach

Random Sampling: A statistical sampling approach that gives each document an equal chance of being chosen for inclusion within a sample.

@Legal
AlphaLit
Altep
BIA
Catalyst Repository Systems
Content Analyst (Based on licensee sampling approach as Content Analyst provides analytics technology, not end user solutions).
Exterro
D4
Daegis
Deloitte
Driven
DTI
Equivio
Exterro
Hudson Legal
Huron Legal
kCura
Kroll Ontrack
NightOwl Discovery
Orange Legal Technologies
OrcaTec
Prolorem
Rational Retention (Preferred Method)
Recommind
RenewData
Servient
Symantec/Clearwell
TCDI
UBIC
UHY Advisors
Xerox Litigation Services (Viewpoint Assisted Review and CategoriX)
ZyLAB

Judgmental Sampling: A sampling approach that draws in part from subjective factors when determining inclusion within a sample.

@Legal
AccessData
BIA
Catalyst Repository Systems
Content Analyst (Based on licensee machine sampling approach as Content Analyst provides analytics technology, not end user solutions).
Exterro
D4
Deloitte
Exterro
Huron Legal
kCura
Kroll Ontrack
NightOwl Discovery
Nuix
Rational Retention
Servient
Symantec/Clearwell
TCDI
UBIC
UHY Advisors
Xerox Litigation Services (Viewpoint Assisted Review and CategoriX)

Other Sampling Approach

Valora Technologies: Stratified Sampling: A statistical sampling approach in which populations are stratified by attributes (which can be automatically obtained or obtained from human input), then selected for sampling so that the sample strata match the population strata.

End of Survey Results

Updated to remove “Statistical” from Sampling Approach “header” and from Judgmental Sampling definition.

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Editor's Note: Within this report lies a nuanced forecast of the eDiscovery market, suggesting its course from 2023 to 2028. While the detailed analysis meticulously outlines the growth in aggregate and across distinct segments, an underlying theme resonates throughout the advent of generative artificial intelligence (GenAI) and its anticipated influence on the sector.

Although not explicitly detailed in each section, the implications of GenAI's transformative power are woven into the fabric of the report's projections. It is an invisible yet potent force expected to usher in a new era of efficiency and innovation within eDiscovery. The potential for GenAI to redefine the contours of the market is significant, with its capacity to automate complex tasks and unlock new levels of analytical depth. Readers should infer GenAI's impact as a constant undercurrent that promises to propel the market forward, reshaping strategies and operational efficiencies.

Source Note: Taken from a combination of public market sizing estimations* as shared in leading electronic discovery publications, posts, data points, and discussions, and developed by ComplexDiscovery OÜ market models beginning in 2012, the following eDiscovery Market Size Mashup** shares general market sizing estimates for the software and services area of the electronic discovery market for the years between 2023 and 2028.

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Industry Research

The Next Five Years in eDiscovery: Market Size Forecast for 2023-2028

ComplexDiscovery Staff In an era where digital data proliferates at an unprecedented rate, the eDiscovery market has become a critical component in the legal, corporate, and government sectors. This comprehensive report delves into the eDiscovery market, providing a detailed analysis of its size and growth from 2023 to 2028. It examines the market in aggregate and segmented into software and services, providing insights into the Compound Annual Growth Rates (CAGR) and shifts in market shares. The narratives presented herein reflect the current state of the market and project the trends expected to shape the future of eDiscovery. As organizations navigate through vast quantities of data for legal and regulatory purposes, understanding these trends is paramount for strategic decision-making and maintaining a competitive edge.

eDiscovery Market Size and Trends: Aggregate, Software, and Services (Figures 1, 2, and 3) Aggregate Market Growth The eDiscovery market is set to experience steady growth from 2023 through 2028. The aggregate market size is anticipated to grow from $15.09 billion to $22.65 billion, with a Compound Annual Growth Rate (CAGR) of 8.46%. This solid growth trajectory highlights the increasing significance of eDiscovery across various legal, corporate, and government activities, driven by the burgeoning volume of data and the escalating need for efficient data management and analysis. Software (SW) Segment Growth The software segment of the eDiscovery market is forecasted to grow at an even more impressive CAGR of 11.40%, reflecting the rapid technological advancements in the field. Starting at $5.28 billion in 2023, the market size for eDiscovery software is projected to reach $9.06 billion by 2028. This significant growth can be attributed to the rising demand for sophisticated eDiscovery platforms that offer enhanced analytics, artificial intelligence, and machine learning capabilities to streamline the review and analysis of large datasets. Services Segment Growth While the services segment grows at a slower rate than software, with a CAGR of 6.73%, it maintains a larger share of the overall market. The total market size for eDiscovery services is expected to increase from $9.81 billion in 2023 to $13.59 billion in 2028. The services segment includes consulting, managed services, and support, which remain in high demand as legal and regulatory complexities increase and organizations seek specialized expertise to navigate eDiscovery challenges. Percentage Share of Software and Services In terms of market composition, the share of eDiscovery software is projected to increase from 35% in 2023 to 40% in 2028, while services are expected to decrease from 65% to 60% of the market share. This shift underscores a market that is becoming more software-driven as end-users increasingly adopt technology solutions to automate and enhance various aspects of the eDiscovery process. The overall market expansion and the shift towards a greater reliance on software solutions reflect the evolving nature of eDiscovery. The market is responding to the need for greater efficiency and advanced analytical capabilities to handle the growing complexity and volume of data. These trends suggest a vibrant eDiscovery industry that is adapting to meet the changing demands of the digital age, with significant opportunities for both established players and new entrants in the software and services domains.

On and Off-Premise Software Market (Figure 4) The landscape of eDiscovery software solutions shows a clear trend towards off-premise or cloud-based offerings over traditional on-premise installations. This shift is reflected in the changing percentages of market share between the two types of software deployment. In 2023, on-premise software accounts for 32% of the market. However, this is expected to steadily decrease to 27% by 2028. In contrast, off-premise software is set to grow from holding 68% of the market in 2023 to a dominant 73% by 2028. This growth indicates a growing preference for cloud-based eDiscovery solutions, which may be attributed to their scalability, cost-effectiveness, and the increasing comfort level of organizations with cloud security. In terms of spending, the total market size for on-premise eDiscovery software is expected to rise from $1.69 billion in 2023 to $2.45 billion in 2028. Although this represents a growth in dollar value, it is modest compared to the surge in off-premise software spending, which is forecasted to jump from $3.59 billion in 2023 to $6.61 billion in 2028. The off-premise software's growth in spending by almost $3 billion over five years significantly outpaces that of on-premise solutions, further emphasizing the market's shift towards cloud-based services. The increase in off-premise software spending suggests that the eDiscovery industry is embracing the flexibility and accessibility that cloud solutions provide. This increase could be driven by the growing volumes of data managed in eDiscovery, the distributed nature of modern workforces requiring remote access, and the ongoing digital transformation trends across the legal sector. The trajectory for eDiscovery software is straightforward: off-premise, cloud-based solutions are on the rise, reshaping how legal professionals approach the discovery process. Service providers in this space will likely continue to innovate and invest in their off-premise offerings to meet their clients' rising demand and evolving expectations.

Cloud Software (Off-Premise) Market (Figure 5) The Cloud Software segment within the eDiscovery market, comprising software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS), is showing interesting trends in its composition and growth. Software as a Service (SaaS) is the leading contributor to the cloud software segment's revenue. However, its percentage of total off-premise spending is expected to slightly decline from 68% in 2023 to 65% by 2028. Despite this slight decrease in market share percentage, the total spending on SaaS is projected to increase significantly from $2.44 billion to $4.30 billion in the forecast period. This robust increase underscores the continued adoption and reliance on SaaS offerings within the eDiscovery realm, likely due to the convenience, scalability, and cost-efficiency they offer to law firms and corporate legal departments. Platform as a Service (PaaS) shows a gradual increase in its market share percentage, from 14% in 2023 to an anticipated 17% by 2028. In line with this, the spending on PaaS is forecasted to double, growing from $0.50 billion in 2023 to $1.12 billion in 2028. This growth trajectory suggests an increasing preference for platforms that offer developers the tools and components to build and manage custom applications, which are becoming more critical as eDiscovery processes become more data-intensive and complex. Infrastructure as a Service (IaaS) maintains a steady 18% share of the cloud software market throughout the forecast period, with total spending expected to rise from $0.65 billion in 2023 to $1.19 billion in 2028. The consistent share and steady growth in spending on IaaS reflect the foundational role that infrastructure services play in supporting the entire eDiscovery process, from data storage to processing and analysis. The overall growth in cloud software spending indicates the broader trend towards cloud-based eDiscovery solutions. This move is fueled by the legal industry's growing comfort with cloud security, the need for scalable resources to handle fluctuating data volumes, and the desire for more cost-effective and flexible software deployment options. As cloud computing continues to mature and become more ubiquitous, we can expect eDiscovery professionals to increasingly leverage cloud-based services to manage the complexities of the discovery process more efficiently. The growth in PaaS and IaaS also points to a market that is becoming more sophisticated, as users not only consume software but also build and manage their applications and infrastructure in the cloud.

Geographical Market Size (Figure 6) The eDiscovery market is seeing a dynamic shift in its geographical composition. The United States, traditionally the largest market for eDiscovery services, is forecasted to decrease its relative market share gradually from 2023 to 2028. In 2023, the US market share stands at 70%, expected to reduce to 65% by 2028. This 5% shift over the five years suggests a maturation of the US market and possibly a saturation of eDiscovery service offerings. Conversely, the Rest of the World (ROW) market is anticipated to increase its share from 30% to 35% over the same period. This uptick reflects the globalizing nature of legal services and the expanding adoption of eDiscovery technologies and practices in international markets. Emerging markets, increased global data regulations, and international litigation activities may drive this growth. Despite the decreasing percentage of market share, the total market size in the US shows a robust upward trajectory, with the market expected to grow from $10.57 billion in 2023 to $14.72 billion in 2028. This growth translates to an approximate 39% increase over the forecasted period, indicating a healthy expansion in absolute terms. This growth suggests that while the US's share of the global market may be shrinking, the overall demand for eDiscovery within the US is growing significantly, likely driven by the increasing complexity of legal cases, the growth of data, and the adoption of new technologies such as AI and machine learning in the legal sector. The combined picture is one of a global eDiscovery market that is growing in size and becoming more distributed across international boundaries. This trend could signal an opportunity for eDiscovery service providers to expand their global footprint and for legal professionals to increase their cross-border competencies and capabilities.

Government and Non-Government Market Size (Figure 7) The eDiscovery market is bifurcated into government/regulatory and non-government segments, each demonstrating distinct trends in terms of market share and total spending. Government/Regulatory Segment The government/regulatory segment's proportion of the market is on a gradual decline, moving from 45% in 2023 to an anticipated 40% by 2028. Despite this relative decrease, absolute government/regulatory sector spending is projected to grow from $6.79 billion in 2023 to $9.06 billion in 2028. This uptrend indicates that while the government's proportionate market size is shrinking relative to the non-government sector, the actual investment in eDiscovery tools and services is increasing. This increase could be due to the rising volume of data involved in regulatory actions and litigations, increased emphasis on compliance and governance, and the modernization of government IT infrastructure. Non-Government Segment Conversely, the non-government segment's market share is expected to increase from 55% in 2023 to 60% by 2028. The spending in this segment is forecasted to see a more pronounced rise from $8.30 billion to $13.59 billion over the same period. This robust growth can be attributed to the large-scale adoption of eDiscovery solutions in the private sector, driven by corporate litigations, internal investigations, and compliance requirements. As businesses continue to digitize their operations and face complex legal challenges, their reliance on eDiscovery solutions intensifies. The diverging trends between the two segments highlight the dynamic nature of the eDiscovery market. While both sectors are growing in terms of absolute market size, the non-government sector is expanding more rapidly, likely due to the scalability and agility offered by eDiscovery solutions that are becoming increasingly critical in the face of fast-paced business environments and complex data landscapes. The steady growth within the government sector underscores the ongoing need for robust eDiscovery practices amidst an environment of heightened regulatory scrutiny. At the same time, the accelerated growth in the non-government sector suggests that enterprises are becoming the main drivers of innovation and revenue within the eDiscovery market. As the volume and variety of data sources continue to expand, the importance of eDiscovery tools and services is set to become even more pronounced across both segments.

Estimated Spending by eDiscovery Task (Figure 8) The eDiscovery market's spending patterns reveal telling insights when segmented by task—Review, Processing, and Collection. Each task is a critical component of the eDiscovery process, but their shares of total spending are shifting, reflecting evolving priorities and technological advancements in the field. Review The Review task commands the largest share of eDiscovery spending, although its percentage is forecasted to decrease from 65% in 2023 to 60% by 2028. In terms of dollar value, however, the spending on review is expected to see a significant rise from $9.81 billion to $13.59 billion. This indicates that while technology and efficiencies may be reducing the relative cost of review as part of the total eDiscovery spend, the absolute cost is growing due to the increasing volume and complexity of data needing review. This trend underscores the ongoing challenge of sifting through vast amounts of data to find relevant information and the importance of investing in sophisticated review tools and technologies. Processing Spending on processing maintains a consistent 20% of the total eDiscovery task spending across the forecast period. The total spending on processing is projected to increase from $3.02 billion in 2023 to $4.53 billion by 2028. This steady growth reflects the critical nature of processing in preparing data for review and analysis and suggests a stable demand for services that can handle increasingly complex data types and formats. Collection The Collection task is expected to grow its market share from 15% in 2023 to 20% by 2028. This is reflected in the total spending, which is projected to double from $2.26 billion to $4.53 billion. The increase in both percentage and spending points to the growing recognition of the importance of effective data collection practices, particularly as data sources become more diverse and dispersed. With the advent of new data types and the expansion of data creation points, the need for sophisticated collection tools and services that can navigate complex digital environments is becoming more pronounced. These shifting spending patterns reflect the evolving nature of the eDiscovery market. While the review continues to take up the majority of resources, there is a notable shift towards investing in the collection, which could be indicative of the market's response to the ever-expanding universe of data that organizations must manage. This also suggests that eDiscovery practitioners are investing more in the early stages of the eDiscovery process, recognizing that efficient collection can significantly streamline subsequent phases, such as review and processing.

Estimated Direct Delivery by Provider (Figure 9) The distribution of eDiscovery tasks by different providers indicates how the landscape of eDiscovery is managed across corporations, law firms, and service providers. The data reflects a shift in where the work is completed, with corporations and governments currently handling the majority of eDiscovery tasks directly. Delivery by Corporations and Governments Corporations and government entities are the most significant contributors to eDiscovery delivery, although their relative share is forecasted to decline from 74% in 2023 to 69% by 2028. Despite this decrease in percentage, the total spending by corporations and governments is expected to grow from $11.17 billion to $15.63 billion. This growth suggests an increasing volume of work being conducted in-house due to factors such as the rise in regulatory scrutiny, a greater emphasis on privacy, and the need for quicker access to data. Delivery by Law Firms Law firms consistently represent a smaller portion of eDiscovery delivery, with their share expected to increase slightly from 14% to 16% over the forecast period. The spending by law firms on eDiscovery tasks is anticipated to grow from $2.11 billion to $3.62 billion. This increase reflects the growing complexity of legal work, the specialization of law firms in managing intricate cases, and the expansion of legal services to include more comprehensive eDiscovery capabilities. Direct Delivery by Service Providers Service providers are projected to see a gradual increase in their market share from 12% to 15% by 2028, with total spending increasing from $1.81 billion to $3.40 billion. This trend indicates a growing reliance on specialized eDiscovery service providers, who offer expertise in managing and analyzing large datasets, as well as advanced technology solutions that may not be available in-house. Total Providers Delivery The total spending on delivery by all providers is expected to rise from $15.09 billion to $22.65 billion, reflecting the overall growth of the eDiscovery market. This substantial increase suggests that despite the relative shifts in delivery by different providers, the demand for eDiscovery services is on a solid upward trajectory. The trends in delivery indicate a nuanced eDiscovery ecosystem where in-house capabilities are being bolstered in corporations and governments, law firms are deepening their eDiscovery practices, and service providers are carving out a larger role due to their specialized offerings. This dynamic points to a future where the collaboration between various types of eDiscovery providers becomes increasingly important to handle the scale and complexity of discovery in the digital age.

Closing Summary: The Next Five Years in eDiscovery The eDiscovery market is on the cusp of significant transformation. The period from 2023 to 2028 is expected to witness substantial growth, driven by the increasing complexity of data management and the need for sophisticated analysis tools. This report has traversed the eDiscovery market landscape, highlighting the aggregate growth within software and services. It has shed light on the rising prominence of software solutions and the steady demand for specialized eDiscovery services. As we look towards the future, it is clear that the industry will continue to evolve, influenced by technological innovation and the growing demands of an increasingly digital legal environment. Stakeholders in the eDiscovery market should consider these insights to strategize for the years ahead, ensuring they are well-equipped to meet the challenges and opportunities that lie on the horizon. Potential Impact of Generative AI on the eDiscovery Market As we enter the age of generative artificial intelligence (GenAI), its potential to disrupt the eDiscovery market warrants a focused discussion. GenAI stands to revolutionize data analysis and management by introducing unprecedented efficiencies across various market segments. The integration of GenAI into eDiscovery tools and services can dramatically streamline processes, reduce manual labor, and expedite the delivery of results. Impact on Aggregate Market The adoption of GenAI across the eDiscovery market is expected to enhance the aggregate market's productivity. By automating complex tasks such as document classification, pattern recognition, and predictive coding, GenAI can significantly reduce the time and resources required for data processing. This efficiency can lead to cost reductions and faster turnaround times, potentially increasing the volume of eDiscovery work that can be handled within the same time frames and budgets. Impact on Software (SW) Segment Within the software segment, GenAI is poised to offer sophisticated analytical capabilities that could greatly refine the process of sifting through large data sets. GenAI-powered software can learn from data to improve its performance over time, leading to increasingly accurate identification of relevant documents. This could shift the software segment's CAGR upward as firms and organizations increasingly seek out these advanced capabilities to gain a competitive edge. Impact on Services Segment For the services segment, GenAI can act as a force multiplier. Service providers equipped with GenAI technologies can offer enhanced services, such as predictive analytics and intelligent case assessments, which could result in the segment capturing a larger market share than currently projected. By reducing the human effort required for repetitive tasks, GenAI allows legal professionals to focus on more strategic activities, thereby increasing the value and appeal of eDiscovery services. The integration of GenAI into the eDiscovery market is not a matter of if but when. Its impact is anticipated to be transformative across all segments, driving efficiencies and creating opportunities for innovation. As we look to the future, market players should consider GenAI as a critical element in their strategic planning, preparing for a landscape where speed, accuracy, and efficiency are greatly enhanced by these advanced technologies. Those who embrace GenAI early may find themselves at the forefront of a new era in eDiscovery, leading the charge toward a more efficient and effective digital legal process.

Lagniappe: Narrative on the Growth of the eDiscovery Software and Services Market 2012-2028 (Figure 10) From 2012 to 2028, the eDiscovery market, encompassing both software and services, exhibited significant growth. This period marks a transformative era, reflecting the increasing reliance on digital data in legal and regulatory frameworks. Software Segment Growth The software segment of the eDiscovery market has shown a remarkable Compound Annual Growth Rate (CAGR) of approximately 12.28%. Starting from a spend of $1.42 billion in 2012, it has steadily risen to a projected $9.06 billion by 2028. This substantial growth underscores the evolving technological landscape in eDiscovery, with software solutions becoming increasingly sophisticated. The integration of advanced analytics, artificial intelligence, and machine learning has driven this segment, reflecting a shift towards more efficient, automated, and accurate data processing and analysis. Services Segment Growth The services segment, while growing at a slightly slower pace, has still demonstrated a significant CAGR of approximately 9.23%. From a spend of $3.31 billion in 2012, it is expected to reach $13.59 billion by 2028. This growth can be attributed to the expanding complexity of legal cases, the proliferation of data sources, and the increasing need for specialized expertise in data management and analysis. The services sector has continued to play a crucial role, providing the necessary support, consultation, and managed services integral to the eDiscovery process. Total Market Growth and CAGR Over the span from 2012 to 2028, the total market spend in eDiscovery has reached approximately $211.67 billion, with a combined CAGR of around 10.28%. This reflects the overall expansion of the eDiscovery market, driven by both technological advancements and the growing demand for comprehensive legal and regulatory data management. The Future Through the Lens of the Past The evolution of the eDiscovery market from 2012 to 2028 paints a picture of an industry adapting to and growing with the increasing digitalization of information. The substantial growth in both software and services segments highlights the escalating importance and complexity of eDiscovery in the modern age. With the market continuing to expand, stakeholders in the legal, corporate, and government sectors must stay abreast of these trends, leveraging the advancements in technology and services to meet their evolving needs.

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The mashup charts (Figures 1-10) represent one interpretation of aggregated and modeled available information that focuses on eDiscovery market trends, size, growth, and segmentation. Figure 1 - eDiscovery Software and Services Market [pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2023/11/eDiscovery-Software-and-Services-Market-2023-2028-Updated.pdf" title="eDiscovery Software and Services Market (2023-2028) – Updated"]

Figure 2 - eDiscovery Software Market [pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2023/11/eDiscovery-Software-Market-All-2023-2028.pdf" title="eDiscovery Software Market – All (2023-2028)"]

Figure 3 - eDiscovery Services Market [pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2023/11/eDiscovery-Services-Market-2023-2028.pdf" title="eDiscovery Services Market (2023-2028)"]

Figure 4 - eDiscovery Software by Off-Premise and On-Premise Market [pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2023/11/eDiscovery-Software-Market-2023-2028.pdf" title="eDiscovery Software Market (2023-2028)"]

Figure 5 - Cloud Software for Off-Premise Market [pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2023/11/eDiscovery-Cloud-Software-Market-2023-2028.pdf" title="eDiscovery Cloud Software Market (2023-2028)"]

Figure 6 - eDiscovery Market Overview by Geography [pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2023/11/eDiscovery-Market-Geographical-Overview-2023-2028.pdf" title="eDiscovery Market Geographical Overview (2023-2028)"]

Figure 7 - eDiscovery Market Overview by Government/Non-Government Markets [pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2023/11/eDiscovery-Government-and-Non-Government-Market-Overview-2023-2028.pdf" title="eDiscovery Government and Non-Government Market Overview (2023-2028)"]

Figure 8 - eDiscovery Market Overview by Task [pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2023/11/eDiscovery-Market-By-Task-2023-2028.pdf" title="eDiscovery Market By Task (2023-2028)"]

Figure 9 - Estimated Direct Delivery by Provider [pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2023/11/eDiscovery-Market-By-Direct-Delivery-Approach-2023-2028.pdf" title="eDiscovery Market By Direct Delivery Approach (2023-2028)"]

Figure 10 - [Lagniappe] eDiscovery Market Sizing: Past and Projected [pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2023/11/eDiscovery-Market-Sizing-Past-and-Projected-2024.pdf" title="eDiscovery Market Sizing – Past and Projected – 2024"]

*Market Size Sources Sources for eDiscovery market sizing estimations and recent adjustments have been aggregated since the initial eDiscovery Market Size Mashup published in 2012 and are included in a model that is used to develop annual eDiscovery market size mashups. These sources include but are not limited to publicly available content (including abstracts, excerpts, quotes, references, and data points) from the following sources.

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Facts and Factors. 2021. Global Market Size and Share will Reach USD $24.12 Billion by 2026. February 10, 2021.
Market and Markets. 2021. eDiscovery Market worth $12.9B by 2025. January 2021.
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American Medical Association. 2020. The Year Ahead—Exclusive Interview With Anthony Fauci, MD, NIAID Director. November 7, 2020.
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ComplexDiscovery. “Summer 2019 eDiscovery Pricing Survey Results.” June 2019.
KL Discovery. “Investor Presentation.” Pivotal, KL Discovery. May 2019.
Gartner, Inc. “Defining Your E-Discovery Process Will Lower Costs and Reduce Risks.” Julian Tirsu. March 29, 2019.
Georgetown Law Center for the Study of the Legal Profession and the Thomson Reuters Legal Executive Institute. “2019 Report on the State of the Legal Market.” January 2019.
ComplexDiscovery. “Winter 2019 eDiscovery Pricing Survey Results.” December 2018.
ComplexDiscovery. “2018 eDiscovery Business Confidence Surveys. (Running Listing).” November 2018.
FRONTEO. “Form 20-F (073118).” UBIC Annual Report for Foreign Investors. July 2018.
IDC. “Worldwide eDiscovery Software Forecast, 2018-2022.” Ryan O’Leary, Sean Pike. June 2018.
Markets and Markets. “eDiscovery Market by Component, Deployment Type, Organizational Size, Vertical, and Region – Global Forecast to 2023.” June 2018.
Georgetown Law Center for the Study of the Legal Profession and the Thomson Reuters Legal Executive Institute. “2018 Report on the State of the Legal Market.” January 2018.
Greentarget. “2018 Legal Industry Outlook.” January 2018.
Catalyst Investors. “Legal Tech Market Overview.” Kirk Mahoney. November 29, 2017.
Gartner, Inc. “Market Guide for E-Discovery Solutions.” Julian Tirsu, Garth Landers, Shane Harris. October 24, 2017.
P&S Market Research. “eDiscovery Market for Software and Services.” October 18, 2017.
ComplexDiscovery. eDiscovery Business Confidence Surveys – Running Listing. ComplexDiscovery. October 18, 2017.
Forbes. “Global Legal Tech Is Transforming Service Delivery.” Mark A. Cohen. August 29, 2017.
Aberdeen. “Key Strategies To Improve The Performance Of E-Discovery Teams.” Michael Caton. August 2017
IDC. “Worldwide eDiscovery Services Market Shares, 2016: Global Leaders Emerge Through Industry Consolidation.” Angela Gelnaw. June 2017.
ACG Partner. “Legal Tech: Seeded and Ready to Launch.”, Trevor Martin, Ben Howe, Jon Guido, Fred Joseph. April 2017.
IDC. “Marketscape: Worldwide eDiscovery Services 2017 Vendor Assessment.” Angela Gelnaw. March 2017.
ComplexDiscovery. Annual eDiscovery Market Size Mashups – 2012 – 2017, ComplexDiscovery, March 4, 2017.
Zion Market Research. “eDiscovery Market for Government, Regulatory Agencies, Enterprises, and Law Firms.” November 23, 2016.
Markets and Markets. “E-Discovery Market by Solution, Service, Deployment Type, and Vertical – Global Forecast to 2021.” November 2016.
Future Market Insights (FMI). “eDiscovery Market Analysis – Global Industry Analysis and Opportunity Assessment.” July 5, 2016.
IDC. “Worldwide eDiscovery Software Market Forecast, 2016-2020: Back to Basics.” Angela Gelnaw. June 30, 2016.
Gartner, Inc. “Market Guide for E-Discovery Solutions.” Jie Zhang. June 30, 2016.
U.S. Department of Commerce, International Trade Administration. “2016 Top Markets Report – Cloud Computing.” April 14, 2016.
IDC. “Worldwide eDiscovery Services Forecast 2014-2019.” Sean Pike, Angela Gelnaw. December 2015.
Gartner, Inc. “Critical Capabilities for E-Discovery Software.” Jie Zhang, Garth Landers. October 6, 2015.
Transparency Market Research. “eDiscovery Market – Global Industry Analysis, Size, Share, Growth, Trends and Forecast 2014-2022.” July 6, 2015
Markets and Markets. “E-Discovery Market By Solution, Deployment, Industry, & Region – Global Forecast to 2020.” July 2015.
Global Industry Analysts, Inc. “eDiscovery (Software and Services) Global Strategic Business Report.” May 28, 2015.
Gartner, Inc. “Magic Quadrant for E-Discovery Software.” Jie Zhang, Garth Landers. May 18, 2015.
The Radicati Group. “eDiscovery Market, 2014-2018.” Sara Radicati. December 3, 2014.
Transparency Market Research. “eDiscovery Market – Global Industry Analysis, Size, Share, Growth, Trends, and Forecast, 2014-2020).” June 2014.
Gartner, Inc. “Magic Quadrant for E-Discovery Software.” Jie Zhang, Debra Logan, Garth Landers. June 19, 2014.
IDC. “Worldwide eDiscovery Software 2014-2018 Forecast.” Sean Pike. May 2014.
The Radicati Group. “eDiscovery Market, 2013-2017.” Sara Radicati. August 2013.
Gartner, Inc. “Magic Quadrant for E-Discovery Software.” Debra Logan, Alan Dayley, Sheila Childs. June 10, 2013.
The Radicati Group. “eDiscovery Market, 2012-2016.” Sara Radicati, Todd Yamasaki. October 2012.
Transparency Market Research. “World e-Discovery Software & Service Market Study.” August 2012.
Rand Institute For Civil Justice. “Where the Money Goes: Understanding Litigant Expenditures for Producing Electronic Discovery.” Nicolas Pace and Laura Zakaras. April 2012.
IDC. “MarketScape: Worldwide Standalone Early Case Assessment Applications Vendor Analysis.” Vivian Tero. September 19, 2011.
Industry Observer Estimations (Multiple Observers)

Additional Reading

**What is a Mashup? A mashup is a combination or mixing of content from different sources to create a new way of looking at data. The main characteristic of a mashup includes combinations, visualizations, and aggregation. Mashups can help make existing data more useful, moreover for personal and professional use. (Wikipedia)

Purchase Access to the Complete Presentation of Charts: Market Kinetics – eDiscovery Market Size Mashup 2023-28 (PDF)

Complete Presentation: Market Kinetics – eDiscovery Market Size Mashup 2023-28 (PDF) – Click to Download and Mouseover to Scroll [pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2023/11/0-eDiscovery-Market-Size-Mashup-112623.pdf" title="0 – eDiscovery Market Size Mashup – 112623"]

Source: ComplexDiscovery

[post_title] => The Next Five Years in eDiscovery: Market Size Forecast for 2023-2028 [post_excerpt] => In an era where digital data proliferates at an unprecedented rate, the eDiscovery market has become a critical component in the legal, corporate, and government sectors. This comprehensive report delves into the eDiscovery market, providing a detailed analysis of its size and growth from 2023 to 2028. It examines the market in aggregate and segmented into software and services, providing insights into the Compound Annual Growth Rates (CAGR) and shifts in market shares. The narratives presented herein reflect the current state of the market and project the trends expected to shape the future of eDiscovery. As organizations navigate through vast quantities of data for legal and regulatory purposes, understanding these trends is paramount for strategic decision-making and maintaining a competitive edge. [post_status] => publish [comment_status] => closed [ping_status] => closed [post_password] => [post_name] => the-next-five-years-in-ediscovery-market-size-forecast-for-2023-2028 [to_ping] => [pinged] => [post_modified] => 2024-01-31 06:40:09 [post_modified_gmt] => 2024-01-31 12:40:09 [post_content_filtered] => [post_parent] => 0 [guid] => https://complexdiscovery.com/?p=53083 [menu_order] => 0 [post_type] => post [post_mime_type] => [comment_count] => 0 [filter] => raw [show_in_menu] => 1 [link_link] => 1 [no_follow_link] => 0 [alt_link_text] => [custom_link_class] => [redirect_url] => [target_blank] => 0 [alt_title_attribute] => ) ) [post_count] => 1 [current_post] => -1 [before_loop] => 1 [in_the_loop] => [post] => WP_Post Object ( [ID] => 53083 [post_author] => 1 [post_date] => 2023-11-26 08:52:01 [post_date_gmt] => 2023-11-26 14:52:01 [post_content] =>

[taq_review]

Industry Research

The Next Five Years in eDiscovery: Market Size Forecast for 2023-2028

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ComplexDiscovery. 2023 eDiscovery Business Confidence Surveys. (Running Listing). November 2023.
Selected Industry eDiscovery Providers (Discussions), 2023. Research, Reports, and Interviews. November 2023.
U.S. Bureau of Economic Analysis (Department of Commerce), 2023. Gross Domestic Product (Advance Estimate), Third Quarter 2023. October 26, 2023.
Harvard Business Review. Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality (Working Paper). Dell'Acqua et. al. September 2023.
IDC. IDC Market Forecast: Worldwide eDiscovery and Forensics Applications Software Forecast, 2023-2027. Ryan O’Leary, June 2023.
IDC. IDC Media Release: Worldwide Spending on IT and Business Services. April 2023.
Industry Research. Global eDiscovery Industry Research Report 2023. January 2023.
Prescient Strategic Intelligence. eDiscovery Market Size and Analysis. November 2022.
ComplexDiscovery. 2022 eDiscovery Business Confidence Surveys. (Running Listing). November 2022.
Selected Industry eDiscovery Providers (Discussions), 2022. eDiscovery Market Trends and Trajectories. November 2022.
U.S. Bureau of Economic Analysis (Department of Commerce), 2022. Gross Domestic Product (Advance Estimate), Third Quarter 2022. October 27, 2022.
Research and Markets. eDiscovery Market Research Report by Deployment, Component, Verticals, Region, Global Forecast to 2027 - Cumulative Impact of COVID-19. 360iReseearch, October 2022.
Gartner. Market Guide for E-Discovery Solutions. Michael Hoeck, October 2022.
IDC. IDC MarketScape: Worldwide Review Software 2022 Vendor Assessment. Ryan O’Leary, October 2022.
Allied Market Research. Global eDiscovery Market: Opportunities and Forecast 2021 - 2031. G Akansha, G Ankit, K Vineet. September 2022.
IDC. IDC MarketScape: Worldwide eDiscovery Early Case Assessment Software 2022 Vendor Assessment. Ryan O’Leary, September 2022.
360 Market Updates. Global eDiscovery Industry Research Report - Competitive Landscape, Market Size, Regional Status, and Prospect. September 2022.
Houlihan Lokey. Legal Technology and Services Q3 Update. August 2022
Research and Markets. 2022. eDiscovery Market - Forecasts from 2021 to 2026. Knowledge Sourcing Intelligence LLP, December 2021.
ComplexDiscovery. Annual eDiscovery Market Size Mashups – 2012 – 2022. November 21, 2021.
Reports and Data. 2021. eDiscovery Market Size Expected to Reach USD 31.89 Billion at CAGR of 12.3%, By 2028. November 7, 2021.
ComplexDiscovery. 2021 eDiscovery Business Confidence Surveys. (Running Listing). November 2021.
Research and Markets. 2021. eDiscovery Market Research Report by Deployment, Component, Verticals, and Region - Global Forecast to 2026 - Cumulative Impact of COVID-19. October 2021.
U.S. Bureau of Economic Analysis (Department of Commerce), 2021. Gross Domestic Product (Advance Estimate), Corporate Profits, And GDP By Industry, Third Quarter 2021. October 28, 2021.
Nasdaq. 2021. LegalTechnology: Why the Legal Tech Boom is Just Getting Started. Casey Flaherty and Jae Um. October 11, 2021.
Research and Markets. 2021. eDiscovery Market Trends, Share, Size, Growth, Opportunity and Forecast 2021 - 2026. September 2021.
BMC. 2021. The State of SaaS in 2022: Growth Trends and Statistics. Laura Shriff and Chrissy Kidd. September 17, 2021.
EY. 2021. Top Five eDiscovery Trends in 2021. Harshavardhan Godugula. August 31, 2021.
Nuix. 2021. FY21 Financial Results Investor Presentation. August 31, 2021.
Securities and Exchange Commission. 2021. Form S-1 Statement for CS Disco, Inc. June 25, 2021.
ComplexDiscovery. 2021. Summer 2021 eDiscovery Pricing Survey Results. June 6, 2021.
eDiscovery Journal. 2021. A Different Perspective on eDiscovery Market Size. Greg Buckles. April 6, 2021.
Facts and Factors. 2021. Global Market Size and Share will Reach USD $24.12 Billion by 2026. February 10, 2021.
Market and Markets. 2021. eDiscovery Market worth $12.9B by 2025. January 2021.
Third-Party Market Studies (Independent Briefing). 2021. Industry Overview for eDiscovery Technology. May 2021.
Third-Party Market Studies (Independent Briefing). 2021. eDiscovery Sector Update. April 2021.
Securities and Exchange Commission. 2021. Form 10-K Statement for KLDiscovery, Inc. March 18, 2021.
ComplexDiscovery. Annual eDiscovery Market Size Mashups – 2012 – 2021. November 11, 2020.
American Medical Association. 2020. The Year Ahead—Exclusive Interview With Anthony Fauci, MD, NIAID Director. November 7, 2020.
U.S. Bureau of Economic Analysis (Department of Commerce), 2020. Gross Domestic Product (Advance Estimate), Corporate Profits, And GDP By Industry, Third Quarter 2020. October 29, 2020.
ComplexDiscovery. "Resetting the Baseline? eDiscovery Market Size Adjustments for 2020." October 26, 2020.
U.S. Bureau of Economic Analysis (Department of Commerce), 2020. Gross Domestic Product (Third Estimate), Corporate Profits (Revised), And GDP By Industry, Second Quarter 2020. September 30, 2020.
Selected Industry eDiscovery Analysts (Discussion), 2020. eDiscovery Market Forecasting. October 19, 2020.
ComplexDiscovery. “2020 eDiscovery Business Confidence Surveys. (Running Listing).” October 2020.
Industry eDiscovery Providers (Discussion), 2020. eDiscovery Market Forecasting. September 29, 2020.
IDC. “Worldwide eDiscovery Services Forecast, 2020-2024.” Ryan O’Leary. September 2020.
Selected Industry Investors (Discussion), 2020. eDiscovery Market Forecasting. September 15, 2020.
KL Discovery. "KL Discovery Inc. Announces Second Quarter Financial Results." KL Discovery. August 12, 2020.
IDC. “Worldwide eDiscovery Software Forecast, 2020-2024.” Ryan O’Leary. June 2020.
ComplexDiscovery. “Home or Away? New eDiscovery Market Sizing and Pricing Considerations.” June 15, 2020.
Mordor Intelligence. 2020. Global Digital Forensics Market, 2020–2025. May 2020.
ComplexDiscovery. “Summer 2020 eDiscovery Pricing Survey Results.” May 2020.
ComplexDiscovery. “Revisions and Decisions? New Considerations for eDiscovery Secure Remote Reviews.” May 4, 2020.
Selected Industry eDiscovery Providers (Discussion), 2020. eDiscovery Market Forecasting. May 1, 2020.
ComplexDiscovery. “2019 eDiscovery Business Confidence Surveys. (Running Listing).” November 2019.
Relativity Fest Panel. “State of the eDiscovery Union.” Panelist Ryan O’Leary (IDC). October 22, 2019.
IDC. “IDC Marketscape: Worldwide eDiscovery SaaS Review Software Assessment.” Ryan O’Leary. August 12, 2019.
Gartner, Inc. “Market Guide for E-Discovery Solutions.” Julian Tirsu, Michael Hoeck. June 27, 2019.
ComplexDiscovery. “Summer 2019 eDiscovery Pricing Survey Results.” June 2019.
KL Discovery. “Investor Presentation.” Pivotal, KL Discovery. May 2019.
Gartner, Inc. “Defining Your E-Discovery Process Will Lower Costs and Reduce Risks.” Julian Tirsu. March 29, 2019.
Georgetown Law Center for the Study of the Legal Profession and the Thomson Reuters Legal Executive Institute. “2019 Report on the State of the Legal Market.” January 2019.
ComplexDiscovery. “Winter 2019 eDiscovery Pricing Survey Results.” December 2018.
ComplexDiscovery. “2018 eDiscovery Business Confidence Surveys. (Running Listing).” November 2018.
FRONTEO. “Form 20-F (073118).” UBIC Annual Report for Foreign Investors. July 2018.
IDC. “Worldwide eDiscovery Software Forecast, 2018-2022.” Ryan O’Leary, Sean Pike. June 2018.
Markets and Markets. “eDiscovery Market by Component, Deployment Type, Organizational Size, Vertical, and Region – Global Forecast to 2023.” June 2018.
Georgetown Law Center for the Study of the Legal Profession and the Thomson Reuters Legal Executive Institute. “2018 Report on the State of the Legal Market.” January 2018.
Greentarget. “2018 Legal Industry Outlook.” January 2018.
Catalyst Investors. “Legal Tech Market Overview.” Kirk Mahoney. November 29, 2017.
Gartner, Inc. “Market Guide for E-Discovery Solutions.” Julian Tirsu, Garth Landers, Shane Harris. October 24, 2017.
P&S Market Research. “eDiscovery Market for Software and Services.” October 18, 2017.
ComplexDiscovery. eDiscovery Business Confidence Surveys – Running Listing. ComplexDiscovery. October 18, 2017.
Forbes. “Global Legal Tech Is Transforming Service Delivery.” Mark A. Cohen. August 29, 2017.
Aberdeen. “Key Strategies To Improve The Performance Of E-Discovery Teams.” Michael Caton. August 2017
IDC. “Worldwide eDiscovery Services Market Shares, 2016: Global Leaders Emerge Through Industry Consolidation.” Angela Gelnaw. June 2017.
ACG Partner. “Legal Tech: Seeded and Ready to Launch.”, Trevor Martin, Ben Howe, Jon Guido, Fred Joseph. April 2017.
IDC. “Marketscape: Worldwide eDiscovery Services 2017 Vendor Assessment.” Angela Gelnaw. March 2017.
ComplexDiscovery. Annual eDiscovery Market Size Mashups – 2012 – 2017, ComplexDiscovery, March 4, 2017.
Zion Market Research. “eDiscovery Market for Government, Regulatory Agencies, Enterprises, and Law Firms.” November 23, 2016.
Markets and Markets. “E-Discovery Market by Solution, Service, Deployment Type, and Vertical – Global Forecast to 2021.” November 2016.
Future Market Insights (FMI). “eDiscovery Market Analysis – Global Industry Analysis and Opportunity Assessment.” July 5, 2016.
IDC. “Worldwide eDiscovery Software Market Forecast, 2016-2020: Back to Basics.” Angela Gelnaw. June 30, 2016.
Gartner, Inc. “Market Guide for E-Discovery Solutions.” Jie Zhang. June 30, 2016.
U.S. Department of Commerce, International Trade Administration. “2016 Top Markets Report – Cloud Computing.” April 14, 2016.
IDC. “Worldwide eDiscovery Services Forecast 2014-2019.” Sean Pike, Angela Gelnaw. December 2015.
Gartner, Inc. “Critical Capabilities for E-Discovery Software.” Jie Zhang, Garth Landers. October 6, 2015.
Transparency Market Research. “eDiscovery Market – Global Industry Analysis, Size, Share, Growth, Trends and Forecast 2014-2022.” July 6, 2015
Markets and Markets. “E-Discovery Market By Solution, Deployment, Industry, & Region – Global Forecast to 2020.” July 2015.
Global Industry Analysts, Inc. “eDiscovery (Software and Services) Global Strategic Business Report.” May 28, 2015.
Gartner, Inc. “Magic Quadrant for E-Discovery Software.” Jie Zhang, Garth Landers. May 18, 2015.
The Radicati Group. “eDiscovery Market, 2014-2018.” Sara Radicati. December 3, 2014.
Transparency Market Research. “eDiscovery Market – Global Industry Analysis, Size, Share, Growth, Trends, and Forecast, 2014-2020).” June 2014.
Gartner, Inc. “Magic Quadrant for E-Discovery Software.” Jie Zhang, Debra Logan, Garth Landers. June 19, 2014.
IDC. “Worldwide eDiscovery Software 2014-2018 Forecast.” Sean Pike. May 2014.
The Radicati Group. “eDiscovery Market, 2013-2017.” Sara Radicati. August 2013.
Gartner, Inc. “Magic Quadrant for E-Discovery Software.” Debra Logan, Alan Dayley, Sheila Childs. June 10, 2013.
The Radicati Group. “eDiscovery Market, 2012-2016.” Sara Radicati, Todd Yamasaki. October 2012.
Transparency Market Research. “World e-Discovery Software & Service Market Study.” August 2012.
Rand Institute For Civil Justice. “Where the Money Goes: Understanding Litigant Expenditures for Producing Electronic Discovery.” Nicolas Pace and Laura Zakaras. April 2012.
IDC. “MarketScape: Worldwide Standalone Early Case Assessment Applications Vendor Analysis.” Vivian Tero. September 19, 2011.
Industry Observer Estimations (Multiple Observers)

Additional Reading

Purchase Access to the Complete Presentation of Charts: Market Kinetics – eDiscovery Market Size Mashup 2023-28 (PDF)

Source: ComplexDiscovery

The Next Five Years in eDiscovery: Market Size Forecast for 2023-2028

Editor’s Note: The Winter 2024 eDiscovery Pricing Survey by ComplexDiscovery OÜ, now in its eleventh edition, continues to provide an invaluable resource for understanding the pricing dynamics within the eDiscovery ecosystem. This non-scientific survey, initiated in the winter of 2019, has evolved to become a benchmark tool for professionals in the fields of cybersecurity, information governance, and legal discovery. The survey's methodology, emphasizing a significant response rate with minimal outreach, ensures that the findings are both comprehensive and relevant to current market conditions.

One of the survey's strengths lies in its accumulated respondent base of 837 individuals, offering a robust and diverse perspective on pricing trends. Its focus spans across key eDiscovery tasks, such as collection, processing, and review, providing detailed insights into each segment's cost structure. The survey's approach, combining direct questions with options for alternative pricing models, reflects the evolving nature of the eDiscovery landscape, acknowledging the increasing complexity and specialization of services.

The results of the Winter 2024 survey with 76 respondents are particularly telling, highlighting a shift towards more flexible and technology-adaptive pricing models. This shift is indicative of the industry's response to emerging challenges and opportunities, such as the growing volume of digital data and the advancement of technologies like predictive coding in document review.

It's important to note that while the survey offers a comprehensive overview of pricing trends, its non-scientific nature means it should be used as a guide rather than a definitive pricing standard. The survey results provide valuable insights for eDiscovery professionals, assisting them in making informed decisions and strategically navigating the complex pricing landscape of eDiscovery services.

Industry Research

Shaping eDiscovery Strategies: Winter 2024 Pricing Report*

ComplexDiscovery Staff

The Winter 2024 eDiscovery Pricing Survey, executed by ComplexDiscovery OÜ and drawing on the perspectives of 76 industry professionals, offers a thorough and focused analysis of the prevailing pricing trends within the eDiscovery ecosystem. This survey, part of an ongoing series since 2019, has successfully built a comprehensive data set with contributions from 837 individual responses across eleven surveys. It meticulously explores the pricing intricacies associated with key eDiscovery tasks, including collection, processing, and review. Each task segment reveals its own unique trends and challenges, reflecting the diverse and complex nature of eDiscovery work.

The following sections from the Winter 2024 survey delve into these specific areas, shedding light on the intricate pricing mechanisms that underpin the eDiscovery process in its entirety, offering critical insights that may greatly benefit professionals engaged in cybersecurity, information governance, and legal discovery.

Detailed Collection Pricing Analysis (Figures 1-3)

Collection, a fundamental phase in eDiscovery, involves diverse tasks performed by forensic examiners. The survey provides an intricate look into the costs associated with these services.

Onsite Collection: The majority of respondents (68.4%) indicate the per hour cost for onsite collection by a forensic examiner falls between $250 and $350. This cost bracket reflects the need for specialized skills, equipment, and often, the urgency associated with onsite data collection.
Remote Collection: In contrast, remote collection, which has gained traction due to technological advancements and increased efficiency, shows a slight reduction in costs. About 63.2% of professionals cite costs between $250 and $350 per hour, while 15.8% report less than $250, illustrating the cost benefits of remote operations.
Device-Specific Collections: Desktop and laptop collections, as well as mobile device collections, are noted for their higher costs. A significant 51.3% quote more than $350 per device for desktop/laptop collections, while mobile device collection costs are similarly high, with 50.0% quoting above $350 per device. These figures highlight the technical complexities and expertise required in handling varied data sources.
Investigative Services and Expert Testimony: Investigative services, including analysis and report generation, command a high value, with 53.9% charging between $350 and $550 per hour. The cost for expert witness testimony is even higher, with 26.3% exceeding $550 per hour, reflecting the premium placed on specialized legal expertise and credibility in judicial proceedings.

Comprehensive Processing Pricing Analysis (Figures 4-9)

Processing ESI is a critical step in eDiscovery, involving the organization and preparation of data for review. The survey's findings in this segment shed light on the cost dynamics based on data volumes and processing stages.

Cost Efficiency in Large Volume Processing: Notably, for volumes over 251 GB, processing costs at ingestion stage are less, with 32.9% of the responses indicating less than $25 per GB. This trend towards lower costs for larger volumes persists at the completion of processing, where 46.1% charge less than $100 per GB. These figures underscore the economies of scale in eDiscovery, where handling larger data sets becomes relatively cost-effective.
Alternative Pricing Models: The survey also highlights a shift towards alternative pricing models in processing, with 19.7% of respondents favoring these for both ingestion and completion stages. This trend reflects a growing inclination towards pricing strategies that are tailored to the specific needs of each project, taking into account factors like data complexity and processing requirements.

Project management plays a pivotal role in the successful execution of eDiscovery projects, and its pricing is a key component of the overall cost structure.

Basic Project Management: The survey reveals that the majority (56.6%) of respondents charge between $100 and $200 per hour for basic project management support. This cost bracket suggests that even fundamental project management tasks in eDiscovery require a significant investment, underscoring the importance of efficient coordination and oversight in these projects.
Advanced Project Management: For more complex and demanding project management tasks, the costs escalate. The survey shows that a substantial proportion (64.5%) of professionals charge over $200 per hour for advanced project management support. This higher cost reflects the specialized skills and in-depth involvement required to manage intricate aspects of eDiscovery projects, such as handling complex data types, navigating legal intricacies, or coordinating extensive teams.

In-Depth Review Pricing Analysis (Figures 10-12)

Document review, a labor-intensive and critical phase in eDiscovery, has its own unique pricing trends, as revealed by the survey.

Predictive Coding in TAR: The use of predictive coding in technology-assisted review (TAR) is seeing a shift in pricing models. A substantial 39.5% for datasets under 250 GB and 40.8% for larger datasets suggest alternative pricing models, moving away from the traditional per GB pricing. This indicates an adaptation to the evolving technological landscape in document review, recognizing the specialized nature and varying complexities of predictive coding.
Cost Variations in Document Review: The survey reveals notable differences in the costs for onsite and remote document reviews. Remote reviews are more economical, with 46.1% of responses quoting between $25 and $40 per hour, compared to onsite reviews where 48.7% exceed $40 per hour. This disparity points towards the increasing feasibility and acceptance of remote review models, which offer cost advantages and access to a broader talent pool.
Per Document Review Costs: When it comes to per document costs for review, the survey shows a range of pricing, reflecting the meticulous nature of the task. For onsite reviews, 32.9% of respondents indicate costs greater than $1.00 per document, underlining the intensive labor and expertise required in document examination and analysis.

Demographic Breakdown: Geographical Reach, Business Segments, and Functional Roles of Respondents (Figures 13-15)

In the Winter 2024 eDiscovery Pricing Survey conducted by ComplexDiscovery OÜ, the demographics of the 76 respondents paint a vivid picture of the eDiscovery ecosystem and its diverse participants. The survey, with its broad reach and varied participant base, offers insights that are as much about the people behind the data as the data itself.

At the forefront of this demographic landscape is the overwhelming representation of professionals from the United States, who constitute 94.7% of the survey's respondents. This significant concentration of U.S.-based professionals emphasizes the strong influence of North American practices and standards on the eDiscovery industry. It also suggests that the survey's findings are particularly reflective of trends and sentiments within the U.S. market. While other regions like the United Kingdom, Asia/Asia Pacific, and non-UK Europe are also represented, their presence is markedly smaller, highlighting the survey's predominantly American perspective.

Diving deeper into the professional backgrounds of the respondents, the survey captures a wide array of roles and sectors within the eDiscovery sphere. Software and services providers emerge as the largest group, accounting for 40.8% of the responses. This segment's prominence underscores the crucial role that technology plays in eDiscovery, from software solutions to comprehensive service offerings. Law firms, making up 31.6% of the responses, stand as the second-largest group. Their substantial participation reflects the deep entwinement of legal practices with eDiscovery, given that law firms are often at the forefront of employing eDiscovery services in litigation and legal compliance.

Consultancies and corporations also contribute significantly to the survey's respondent pool, representing 10.5% and 9.2%, respectively. These professionals bring perspectives that range from strategic advisory roles to in-house management of eDiscovery processes. Though smaller in number, their input is vital in painting a complete picture of the eDiscovery landscape.

The survey also sheds light on the primary functions of the respondents within their organizations. A majority, 69.7%, are involved in legal or litigation support roles, indicating the predominance of legal considerations in eDiscovery operations. Business and business support functions account for 27.6% of the respondents, highlighting the cross-functional nature of eDiscovery as it intersects with various business operations. Those in IT and product development roles, though forming a smaller fraction at 2.6%, underscore the importance of technology and innovation in shaping eDiscovery solutions.

In essence, the demographic data from the Winter 2024 eDiscovery Pricing Survey provides a narrative of the eDiscovery industry's composition and its evolving dynamics. The strong U.S. presence, coupled with the diverse range of professional roles and sectors represented, reveals an industry that is both deeply rooted in legal practices and progressively influenced by technological advancements. This rich tapestry of respondents not only lends credibility to the survey's findings but also ensures that the insights gleaned are well-rounded and reflective of the multifaceted nature of eDiscovery.

Adapting to New Pricing Realities in eDiscovery

The Winter 2024 eDiscovery Pricing Survey offers a comprehensive and detailed view of the pricing landscape across various eDiscovery services. It highlights the industry's gradual shift towards more flexible, adaptive, and cost-efficient models in collection, processing, and review services. The insights from this survey are crucial for eDiscovery professionals, providing benchmarks and guiding principles for informed decision-making and strategic planning in this complex field.

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eDiscovery Pricing Survey Responses

n=76 Respondents

Collection Pricing

1a. What is the per hour cost for an onsite collection by a forensic examiner?

Less than $250 per hour. 5.3%
Between $250 and $350 per hour. 68.4%
Greater than $350 per hour. 9.2%
Do not know. 13.2%
Alternative pricing model for per hour costs. 3.9%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2023/12/Collection-Pricing-Per-Hour-Cost-for-an-Onsite-Collection-by-a-Forensic-Examiner-Winter-2024-Pricing.pdf" title="Collection Pricing – Per Hour Cost for an Onsite Collection by a Forensic Examiner – Winter 2024 Pricing"]

1b. What is the per hour cost for a remote collection by a forensic examiner?

Less than $250 per hour. 15.8%%
Between $250 and $350 per hour. 63.2%
Greater than $350 per hour. 2.6%
Do not know. 11.8%
Alternative pricing model for per hour costs. 6.6%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2023/12/Collection-Pricing-Per-Hour-Cost-for-a-Remote-Collection-by-a-Forensic-Examiner-Winter-2024-Pricing.pdf" title="Collection Pricing – Per Hour Cost for a Remote Collection by a Forensic Examiner – Winter 2024 Pricing"]

2a. What is the per device cost for a desktop or laptop computer collection by a forensic examiner?

Less than $250 per device. 3.9%
Between $250 and $350 per device. 15.8%
Greater than $350 per device. 51.3%
Do not know. 18.4%
Alternative pricing model for per device costs. 10.5%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2023/12/Collection-Pricing-Per-Device-Cost-for-a-Desktop-Laptop-Computer-Collection-by-a-Forensic-Examiner-Winter-2024-Pricing.pdf" title="Collection Pricing – Per Device Cost for a Desktop – Laptop Computer Collection by a Forensic Examiner – Winter 2024 Pricing"]

2b. What is the per device cost for a mobile device collection by a forensic examiner?

Less than $250 per device. 3.9%
Between $250 and $350 per device. 26.3%
Greater than $350 per device. 50.0%
Do not know. 14.5%
Alternative pricing model for per device costs. 5.3%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2023/12/Collection-Pricing-Per-Device-Cost-for-a-Mobile-Device-Collection-by-a-Forensic-Examiner-Winter-2024.pdf" title="Collection Pricing – Per Device Cost for a Mobile Device Collection by a Forensic Examiner – Winter 2024"]

3a. What is the per hour cost for investigation, analysis, and report generation by a forensic examiner?

Less than $350 per hour. 27.6%
Between $350 and $550 per hour. 53.9%
Greater than $550 per hour. 1.3%
Do not know. 13.2%
Alternative pricing model for per hour costs. 3.9%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2023/12/Collection-Pricing-Per-Hour-Cost-for-Investigation-Analysis-and-Report-Generation-by-an-FE-Winter-2024-Pricing.pdf" title="Collection Pricing – Per Hour Cost for Investigation, Analysis and Report Generation by an FE – Winter 2024 Pricing"]

3b. What is the per hour cost for expert witness testimony (in-person and written) by a forensic examiner?

Less than $350 per hour. 6.6%
Between $350 and $550 per hour. 51.3%
Greater than $550 per hour. 26.3%
Do not know. 11.8%
Alternative pricing model for per hour costs. 3.9%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2023/12/Collection-Pricing-Per-Hour-Cost-for-Expert-Witness-Testimony-In-Person-and-Written-by-an-FE-Winter-2024-Pricing.pdf" title="Collection Pricing – Per Hour Cost for Expert Witness Testimony (In-Person and Written) by an FE – Winter 2024 Pricing"]

Processing Pricing

4a. What is the per GB cost to process electronically stored information based on volume at ingestion for 250 GB or less?

Less than $25 per GB. 23.7%
Between $25 and $75 per GB. 38.2%
Greater than $75 per GB. 6.6%
Do not know. 11.8%
Alternative pricing model for per GB costs. 19.7%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2023/12/Processing-Pricing-Per-GB-Cost-to-Process-ESI-Based-on-Volume-at-Ingestion-for-250-GB-or-Less-Winter-2024-Pricing.pdf" title="Processing Pricing – Per GB Cost to Process ESI Based on Volume at Ingestion for 250 GB or Less – Winter 2024 Pricing"]

4b. What is the per GB cost to process electronically stored information based on volume at ingestion for 251 GB or more?

Less than $25 per GB. 32.9%
Between $25 and $75 per GB. 31.6%
Greater than $75 per GB. 2.6%
Do not know. 13.2%
Alternative pricing model for per GB costs. 19.7%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2023/12/Processing-Pricing-Per-GB-Cost-to-Process-ESI-Based-on-Volume-at-Ingestion-for-251-GB-or-More-Winter-2024-Pricing.pdf" title="Processing Pricing – Per GB Cost to Process ESI Based on Volume at Ingestion for 251 GB or More – Winter 2024 Pricing"]

5a. What is the per GB cost to process electronically stored information based on volume at completion of processing for 250 GB or less?

Less than $100 per GB. 42.1%
Between $100 and $150 per GB. 18.4%
Greater than $150 per GB. 3.9%
Do not know. 15.8%
Alternative pricing model for per GB costs. 19.7%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2023/12/Processing-Pricing-Per-GB-Cost-to-Process-ESI-Based-on-Volume-at-Completion-for-250-GB-or-Less-Winter-2024.pdf" title="Processing Pricing – Per GB Cost to Process ESI Based on Volume at Completion for 250 GB or Less – Winter 2024"]

5b. What is the per GB cost to process electronically stored information based on volume at completion of processing for 251 GB or more?

Less than $100 per GB. 46.1%
Between $100 and $150 per GB. 14.5%
Greater than $150 per GB. 1.3%
Do not know. 14.5%
Alternative pricing model for per GB costs. 23.7%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2023/12/Processing-Pricing-Per-GB-Cost-to-Process-ESI-Based-on-Volume-at-Completion-for-251-GB-or-More-Winter-2024-Pricing.pdf" title="Processing Pricing – Per GB Cost to Process ESI Based on Volume at Completion for 251 GB or More – Winter 2024 Pricing"]

6a. What is the per GB per month cost to host electronically stored information without analytics for 250 GB or less?

Less than $10 per GB per month. 28.9%
Between $10 and $20 per GB per month. 42.1%
Greater than $20 per GB per month. 5.3%
Do not know. 10.5%
Alternative pricing model for per GB costs. 13.2%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2023/12/Processing-Pricing-Per-GB-Cost-Per-Month-to-Host-ESI-without-Analytics-for-250-GB-or-Less-Winter-2024-Pricing.pdf" title="Processing Pricing – Per GB Cost Per Month to Host ESI without Analytics for 250 GB or Less – Winter 2024 Pricing"]

6b. What is the per GB per month cost to host electronically stored information without analytics for 251 GB or more?

Less than $10 per GB per month. 34.2%
Between $10 and $20 per GB per month. 42.1%
Greater than $20 per GB per month. 2.6%
Do not know. 7.9%
Alternative pricing model for per GB costs. 13.2%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2023/12/Processing-Pricing-Per-GB-Cost-Per-Month-to-Host-ESI-without-Analytics-for-251-GB-or-More-Winter-2024-Pricing.pdf" title="Processing Pricing – Per GB Cost Per Month to Host ESI without Analytics for 251 GB or More – Winter 2024 Pricing"]

7a. What is the per GB per month cost to host electronically stored information with analytics for 250 GB or less?

Less than $15 per GB per month. 35.5%
Between $15 and $25 per GB per month. 27.6%
Greater than $25 per GB per month. 10.5%
Do not know. 10.5%
Alternative pricing model for per GB costs. 15.8%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2023/12/Processing-Pricing-Per-GB-Cost-Per-Month-to-Host-ESI-with-Analytics-for-250-GB-or-Less-Winter-2024-Pricing.pdf" title="Processing Pricing – Per GB Cost Per Month to Host ESI with Analytics for 250 GB or Less – Winter 2024 Pricing"]

7b. What is the per GB per month cost to host electronically stored information with analytics for 251 GB or more?

Less than $15 per GB per month. 42.1%
Between $15 and $25 per GB per month. 23.7%
Greater than $25 per GB per month. 6.6%
Do not know. 10.5%
Alternative pricing model for per GB costs. 17.1%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2023/12/Processing-Pricing-Per-GB-Cost-Per-Month-to-Host-ESI-with-Analytics-for-251-GB-or-More-Winter-2024-Pricing.pdf" title="Processing Pricing – Per GB Cost Per Month to Host ESI with Analytics for 251 GB or More – Winter 2024 Pricing"]

8a. What is the user license fee per month for access to hosted data for 250 users or less?

Less than $50 per user per month. 5.3%
Between $50 and $100 per user per month. 55.3%
Greater than $100 per user per month. 13.2%
Do not know. 10.5%
Alternative pricing model for user license costs. 15.8%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2023/12/Processing-Pricing-User-License-Fee-Per-Month-for-Access-to-Hosted-Data-for-250-Users-or-Less-Winter-2024-Pricing.pdf" title="Processing Pricing – User License Fee Per Month for Access to Hosted Data for 250 Users or Less – Winter 2024 Pricing"]

8b. What is the user license fee per month for access to hosted data for 251 users or more?

Less than $50 per user per month. 6.6%
Between $50 and $100 per user per month. 51.3%
Greater than $100 per user per month. 11.8%
Do not know. 14.5%
Alternative pricing model for user license costs. 15.8%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2023/12/Processing-Pricing-User-License-Fee-Per-Month-for-Access-to-Hosted-Data-for-251-Users-or-More-Winter-2024-Updated-1.pdf" title="Processing Pricing – User License Fee Per Month for Access to Hosted Data for 251 Users or More – Winter 2024 Updated"]

9a. What is the per hour cost of basic project management support for eDiscovery?

Less than $100 per hour. 2.6%
Between $100 and $200 per hour. 56.6%
Greater than $200 per hour. 27.6%
Do not know. 7.9%
Alternative pricing model for per hour costs. 5.3%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2023/12/Processing-Pricing-Per-Hour-Cost-of-Basic-Project-Management-Support-for-eDiscovery-Winter-2024-Pricing.pdf" title="Processing Pricing – Per Hour Cost of Basic Project Management Support for eDiscovery – Winter 2024 Pricing"]

9b. What is the per hour cost of advanced project management support for eDiscovery?

Less than $100 per hour. 1.3%
Between $100 and $200 per hour. 23.7%
Greater than $200 per hour. 64.5%
Do not know. 6.6%
Alternative pricing model for per hour costs. 3.9%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2023/12/Processing-Pricing-Per-Hour-Cost-of-Advanced-Project-Management-Support-for-eDiscovery-Winter-2024-Pricing.pdf" title="Processing Pricing – Per Hour Cost of Advanced Project Management Support for eDiscovery – Winter 2024 Pricing"]

Review Pricing

10a. What is the per GB cost to conduct predictive coding as part of a technology-assisted review for 250 GB or less?

Less than $75 per GB. 21.1%
Between $75 and $150 per GB. 9.2%
Greater than $150 per GB. 3.9%
Do not know. 26.3%
Alternative pricing model for per GB costs. 39.5%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2023/12/Review-Pricing-Per-GB-Cost-for-Predictive-Coding-in-a-Technology-Assisted-Review-for-250-GB-or-Less-Winter-2024-Pricing.pdf" title="Review Pricing – Per GB Cost for Predictive Coding in a Technology-Assisted Review for 250 GB or Less – Winter 2024 Pricing"]

10b. What is the per GB cost to conduct predictive coding as part of a technology-assisted review for 251 GB or more?

Less than $75 per GB. 23.7%
Between $75 and $150 per GB. 6.6%
Greater than $150 per GB. 2.6%
Do not know. 26.3%
Alternative pricing model for per GB costs. 40.8%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2023/12/Review-Pricing-Per-GB-Cost-for-Predictive-Coding-in-a-Technology-Assisted-Review-for-251-GB-or-More-Winter-2024-Pricing.pdf" title="Review Pricing – Per GB Cost for Predictive Coding in a Technology-Assisted Review for 251 GB or More – Winter 2024 Pricing"]

11a. What is the cost per hour for document review attorneys to review documents for an onsite managed review?

Less than $25 per hour. 1.3%
Between $25 and $40 per hour. 23.7%
Greater than $40 per hour. 48.7%
Do not know. 19.7%
Alternative pricing model for per hour costs. 6.6%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2023/12/Review-Pricing-Per-Hour-Cost-for-Document-Review-Attorneys-to-Review-Documents-Onsite-Winter-2024-Pricing.pdf" title="Review Pricing – Per Hour Cost for Document Review Attorneys to Review Documents Onsite – Winter 2024 Pricing"]

11b. What is the cost per hour for document review attorneys to review documents for a remote managed review?

Less than $25 per hour. 1.3%
Between $25 and $40 per hour. 46.1%
Greater than $40 per hour. 31.6%
Do not know. 14.5%
Alternative pricing model for per hour costs. 6.6%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2023/12/Review-Pricing-Per-Hour-Cost-for-Document-Review-Attorneys-to-Review-Documents-Remote-Winter-2024-Pricing.pdf" title="Review Pricing – Per Hour Cost for Document Review Attorneys to Review Documents Remote – Winter 2024 Pricing"]

12a. What is the cost per document for document review attorneys to review documents for an onsite managed review?

Less than $0.50 per document. 3.9%
Between $0.50 and $1.00 per document. 17.1%
Greater than $1.00 per document. 32.9%
Do not know. 27.6%
Alternative pricing model for per document costs. 18.4%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2023/12/Review-Pricing-Per-Document-Cost-for-Document-Review-Attorneys-to-Review-Documents-Onsite-Winter-2024-Pricing.pdf" title="Review Pricing – Per Document Cost for Document Review Attorneys to Review Documents Onsite – Winter 2024 Pricing"]

12b. What is the cost per document for document review attorneys to review documents for a remote managed review?

Less than $0.50 per document. 6.6%
Between $0.50 and $1.00 per document. 27.6%
Greater than $1.00 per document. 23.7%
Do not know. 25.0%
Alternative pricing model for per document costs. 17.1%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2023/12/Review-Pricing-Per-Document-Cost-for-Document-Review-Attorneys-to-Review-Documents-Remote-Winter-2024-Pricing.pdf" title="Review Pricing – Per Document Cost for Document Review Attorneys to Review Documents Remote – Winter 2024 Pricing"]

Background Information

13. In which geographical region do you primarily conduct eDiscovery-related business?

North America – United States. 94.7%
Europe – United Kingdom. 2.6%
Asia/Asia Pacific. 1.3%
Europe – Non-UK. 1.3%
North America – Canada. 0.0%
Middle East/Africa. 0.0%
Central/South America. 0.0%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2023/12/Survey-Respondents-by-Geographic-Region-Winter-2024-Pricing.pdf" title="Survey Respondents by Geographic Region – Winter 2024 Pricing"]

14. Which of the following segments best describes your business in eDiscovery?

Software and/or Services Provider. 40.8%
Law Firm. 31.6%
Consultancy. 10.5%
Corporation. 9.2%
Media/Research Organization/Educational Association. 7.9%
Government Entity. 0.0%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2023/12/Survey-Respondents-by-Organizational-Segment-Winter-2024-Pricing.pdf" title="Survey Respondents by Organizational Segment – Winter 2024 Pricing"]

15. What area best describes your primary function in the conduct of your organization’s eDiscovery business?

Legal/Litigation Support. 69.7%
Business/Business Support (All Other Business Functions). 27.6%
IT/Product Development. 2.6%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2023/12/Survey-Respondents-by-Primary-Function-Winter-2024-Pricing.pdf" title="Survey Respondents by Primary Function – Winter 2024 Pricing"]

Background: eDiscovery Pricing Survey Respondents by Survey

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2023/12/Survey-Respondents-Individual-and-Aggregate-Overview-Winter-2024.pdf" title="Survey Respondents (Individual and Aggregate Overview) – Winter 2024"]

*For the purpose of this ongoing industry survey, price and cost are used interchangeably and represent the amount a customer is willing to pay for the service. However, from a financial and accounting perspective, pricing is the amount a customer is willing to pay. The cost is typically the expense incurred for creating and delivering the service.

**Survey methodology focused on achieving at least 50 responses with the least number of emails sent to the ComplexDiscovery and EDRM industry professional databases. This approach sought to minimize the number of requests for survey participation while ensuring a solid number of responses from which to generally assess market sentiment in survey areas of interest.

Assisted by GAI and LLM Technologies

Additional Reading

Purchase Access to the Complete Presentation of Charts: Market Kinetics – eDiscovery Pricing Overview – Winter 2024 (PDF)

Complete Presentation: Market Kinetics – eDiscovery Pricing Overview – Winter 2024 (PDF) – Click to Download and Mouseover to Scroll

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2023/12/0-Market-Kinetics-eDiscovery-Pricing-Winter-2024-120523.pdf" title="0 – Market Kinetics – eDiscovery Pricing – Winter 2024 – 120523"]

Source: ComplexDiscovery

Industry Research

Shaping eDiscovery Strategies: Winter 2024 Pricing Report*

ComplexDiscovery Staff

Detailed Collection Pricing Analysis (Figures 1-3)

Collection, a fundamental phase in eDiscovery, involves diverse tasks performed by forensic examiners. The survey provides an intricate look into the costs associated with these services.

Onsite Collection: The majority of respondents (68.4%) indicate the per hour cost for onsite collection by a forensic examiner falls between $250 and $350. This cost bracket reflects the need for specialized skills, equipment, and often, the urgency associated with onsite data collection.
Remote Collection: In contrast, remote collection, which has gained traction due to technological advancements and increased efficiency, shows a slight reduction in costs. About 63.2% of professionals cite costs between $250 and $350 per hour, while 15.8% report less than $250, illustrating the cost benefits of remote operations.
Device-Specific Collections: Desktop and laptop collections, as well as mobile device collections, are noted for their higher costs. A significant 51.3% quote more than $350 per device for desktop/laptop collections, while mobile device collection costs are similarly high, with 50.0% quoting above $350 per device. These figures highlight the technical complexities and expertise required in handling varied data sources.
Investigative Services and Expert Testimony: Investigative services, including analysis and report generation, command a high value, with 53.9% charging between $350 and $550 per hour. The cost for expert witness testimony is even higher, with 26.3% exceeding $550 per hour, reflecting the premium placed on specialized legal expertise and credibility in judicial proceedings.

Comprehensive Processing Pricing Analysis (Figures 4-9)

Cost Efficiency in Large Volume Processing: Notably, for volumes over 251 GB, processing costs at ingestion stage are less, with 32.9% of the responses indicating less than $25 per GB. This trend towards lower costs for larger volumes persists at the completion of processing, where 46.1% charge less than $100 per GB. These figures underscore the economies of scale in eDiscovery, where handling larger data sets becomes relatively cost-effective.
Alternative Pricing Models: The survey also highlights a shift towards alternative pricing models in processing, with 19.7% of respondents favoring these for both ingestion and completion stages. This trend reflects a growing inclination towards pricing strategies that are tailored to the specific needs of each project, taking into account factors like data complexity and processing requirements.

Project management plays a pivotal role in the successful execution of eDiscovery projects, and its pricing is a key component of the overall cost structure.

Basic Project Management: The survey reveals that the majority (56.6%) of respondents charge between $100 and $200 per hour for basic project management support. This cost bracket suggests that even fundamental project management tasks in eDiscovery require a significant investment, underscoring the importance of efficient coordination and oversight in these projects.
Advanced Project Management: For more complex and demanding project management tasks, the costs escalate. The survey shows that a substantial proportion (64.5%) of professionals charge over $200 per hour for advanced project management support. This higher cost reflects the specialized skills and in-depth involvement required to manage intricate aspects of eDiscovery projects, such as handling complex data types, navigating legal intricacies, or coordinating extensive teams.

In-Depth Review Pricing Analysis (Figures 10-12)

Document review, a labor-intensive and critical phase in eDiscovery, has its own unique pricing trends, as revealed by the survey.

Predictive Coding in TAR: The use of predictive coding in technology-assisted review (TAR) is seeing a shift in pricing models. A substantial 39.5% for datasets under 250 GB and 40.8% for larger datasets suggest alternative pricing models, moving away from the traditional per GB pricing. This indicates an adaptation to the evolving technological landscape in document review, recognizing the specialized nature and varying complexities of predictive coding.
Cost Variations in Document Review: The survey reveals notable differences in the costs for onsite and remote document reviews. Remote reviews are more economical, with 46.1% of responses quoting between $25 and $40 per hour, compared to onsite reviews where 48.7% exceed $40 per hour. This disparity points towards the increasing feasibility and acceptance of remote review models, which offer cost advantages and access to a broader talent pool.
Per Document Review Costs: When it comes to per document costs for review, the survey shows a range of pricing, reflecting the meticulous nature of the task. For onsite reviews, 32.9% of respondents indicate costs greater than $1.00 per document, underlining the intensive labor and expertise required in document examination and analysis.

Demographic Breakdown: Geographical Reach, Business Segments, and Functional Roles of Respondents (Figures 13-15)

Adapting to New Pricing Realities in eDiscovery

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eDiscovery Pricing Survey Responses

n=76 Respondents

Collection Pricing

1a. What is the per hour cost for an onsite collection by a forensic examiner?

Less than $250 per hour. 5.3%
Between $250 and $350 per hour. 68.4%
Greater than $350 per hour. 9.2%
Do not know. 13.2%
Alternative pricing model for per hour costs. 3.9%

1b. What is the per hour cost for a remote collection by a forensic examiner?

Less than $250 per hour. 15.8%%
Between $250 and $350 per hour. 63.2%
Greater than $350 per hour. 2.6%
Do not know. 11.8%
Alternative pricing model for per hour costs. 6.6%

2a. What is the per device cost for a desktop or laptop computer collection by a forensic examiner?

Less than $250 per device. 3.9%
Between $250 and $350 per device. 15.8%
Greater than $350 per device. 51.3%
Do not know. 18.4%
Alternative pricing model for per device costs. 10.5%

2b. What is the per device cost for a mobile device collection by a forensic examiner?

Less than $250 per device. 3.9%
Between $250 and $350 per device. 26.3%
Greater than $350 per device. 50.0%
Do not know. 14.5%
Alternative pricing model for per device costs. 5.3%

3a. What is the per hour cost for investigation, analysis, and report generation by a forensic examiner?

Less than $350 per hour. 27.6%
Between $350 and $550 per hour. 53.9%
Greater than $550 per hour. 1.3%
Do not know. 13.2%
Alternative pricing model for per hour costs. 3.9%

3b. What is the per hour cost for expert witness testimony (in-person and written) by a forensic examiner?

Less than $350 per hour. 6.6%
Between $350 and $550 per hour. 51.3%
Greater than $550 per hour. 26.3%
Do not know. 11.8%
Alternative pricing model for per hour costs. 3.9%

Processing Pricing

4a. What is the per GB cost to process electronically stored information based on volume at ingestion for 250 GB or less?

Less than $25 per GB. 23.7%
Between $25 and $75 per GB. 38.2%
Greater than $75 per GB. 6.6%
Do not know. 11.8%
Alternative pricing model for per GB costs. 19.7%

4b. What is the per GB cost to process electronically stored information based on volume at ingestion for 251 GB or more?

Less than $25 per GB. 32.9%
Between $25 and $75 per GB. 31.6%
Greater than $75 per GB. 2.6%
Do not know. 13.2%
Alternative pricing model for per GB costs. 19.7%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2023/12/Processing-Pricing-Per-GB-Cost-to-Process-ESI-Based-on-Volume-at-Ingestion-for-251-GB-or-More-Winter-2024-Pricing.pdf" title="Processing Pricing – Per GB Cost to Process ESI Based on Volume at Ingestion for 251 GB or More – Winter 2024 Pricing"]

5a. What is the per GB cost to process electronically stored information based on volume at completion of processing for 250 GB or less?

Less than $100 per GB. 42.1%
Between $100 and $150 per GB. 18.4%
Greater than $150 per GB. 3.9%
Do not know. 15.8%
Alternative pricing model for per GB costs. 19.7%

5b. What is the per GB cost to process electronically stored information based on volume at completion of processing for 251 GB or more?

Less than $100 per GB. 46.1%
Between $100 and $150 per GB. 14.5%
Greater than $150 per GB. 1.3%
Do not know. 14.5%
Alternative pricing model for per GB costs. 23.7%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2023/12/Processing-Pricing-Per-GB-Cost-to-Process-ESI-Based-on-Volume-at-Completion-for-251-GB-or-More-Winter-2024-Pricing.pdf" title="Processing Pricing – Per GB Cost to Process ESI Based on Volume at Completion for 251 GB or More – Winter 2024 Pricing"]

6a. What is the per GB per month cost to host electronically stored information without analytics for 250 GB or less?

Less than $10 per GB per month. 28.9%
Between $10 and $20 per GB per month. 42.1%
Greater than $20 per GB per month. 5.3%
Do not know. 10.5%
Alternative pricing model for per GB costs. 13.2%

6b. What is the per GB per month cost to host electronically stored information without analytics for 251 GB or more?

Less than $10 per GB per month. 34.2%
Between $10 and $20 per GB per month. 42.1%
Greater than $20 per GB per month. 2.6%
Do not know. 7.9%
Alternative pricing model for per GB costs. 13.2%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2023/12/Processing-Pricing-Per-GB-Cost-Per-Month-to-Host-ESI-without-Analytics-for-251-GB-or-More-Winter-2024-Pricing.pdf" title="Processing Pricing – Per GB Cost Per Month to Host ESI without Analytics for 251 GB or More – Winter 2024 Pricing"]

7a. What is the per GB per month cost to host electronically stored information with analytics for 250 GB or less?

Less than $15 per GB per month. 35.5%
Between $15 and $25 per GB per month. 27.6%
Greater than $25 per GB per month. 10.5%
Do not know. 10.5%
Alternative pricing model for per GB costs. 15.8%

7b. What is the per GB per month cost to host electronically stored information with analytics for 251 GB or more?

Less than $15 per GB per month. 42.1%
Between $15 and $25 per GB per month. 23.7%
Greater than $25 per GB per month. 6.6%
Do not know. 10.5%
Alternative pricing model for per GB costs. 17.1%

8a. What is the user license fee per month for access to hosted data for 250 users or less?

Less than $50 per user per month. 5.3%
Between $50 and $100 per user per month. 55.3%
Greater than $100 per user per month. 13.2%
Do not know. 10.5%
Alternative pricing model for user license costs. 15.8%

8b. What is the user license fee per month for access to hosted data for 251 users or more?

Less than $50 per user per month. 6.6%
Between $50 and $100 per user per month. 51.3%
Greater than $100 per user per month. 11.8%
Do not know. 14.5%
Alternative pricing model for user license costs. 15.8%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2023/12/Processing-Pricing-User-License-Fee-Per-Month-for-Access-to-Hosted-Data-for-251-Users-or-More-Winter-2024-Updated-1.pdf" title="Processing Pricing – User License Fee Per Month for Access to Hosted Data for 251 Users or More – Winter 2024 Updated"]

9a. What is the per hour cost of basic project management support for eDiscovery?

Less than $100 per hour. 2.6%
Between $100 and $200 per hour. 56.6%
Greater than $200 per hour. 27.6%
Do not know. 7.9%
Alternative pricing model for per hour costs. 5.3%

9b. What is the per hour cost of advanced project management support for eDiscovery?

Less than $100 per hour. 1.3%
Between $100 and $200 per hour. 23.7%
Greater than $200 per hour. 64.5%
Do not know. 6.6%
Alternative pricing model for per hour costs. 3.9%

Review Pricing

10a. What is the per GB cost to conduct predictive coding as part of a technology-assisted review for 250 GB or less?

Less than $75 per GB. 21.1%
Between $75 and $150 per GB. 9.2%
Greater than $150 per GB. 3.9%
Do not know. 26.3%
Alternative pricing model for per GB costs. 39.5%

10b. What is the per GB cost to conduct predictive coding as part of a technology-assisted review for 251 GB or more?

Less than $75 per GB. 23.7%
Between $75 and $150 per GB. 6.6%
Greater than $150 per GB. 2.6%
Do not know. 26.3%
Alternative pricing model for per GB costs. 40.8%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2023/12/Review-Pricing-Per-GB-Cost-for-Predictive-Coding-in-a-Technology-Assisted-Review-for-251-GB-or-More-Winter-2024-Pricing.pdf" title="Review Pricing – Per GB Cost for Predictive Coding in a Technology-Assisted Review for 251 GB or More – Winter 2024 Pricing"]

11a. What is the cost per hour for document review attorneys to review documents for an onsite managed review?

Less than $25 per hour. 1.3%
Between $25 and $40 per hour. 23.7%
Greater than $40 per hour. 48.7%
Do not know. 19.7%
Alternative pricing model for per hour costs. 6.6%

11b. What is the cost per hour for document review attorneys to review documents for a remote managed review?

Less than $25 per hour. 1.3%
Between $25 and $40 per hour. 46.1%
Greater than $40 per hour. 31.6%
Do not know. 14.5%
Alternative pricing model for per hour costs. 6.6%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2023/12/Review-Pricing-Per-Hour-Cost-for-Document-Review-Attorneys-to-Review-Documents-Remote-Winter-2024-Pricing.pdf" title="Review Pricing – Per Hour Cost for Document Review Attorneys to Review Documents Remote – Winter 2024 Pricing"]

12a. What is the cost per document for document review attorneys to review documents for an onsite managed review?

Less than $0.50 per document. 3.9%
Between $0.50 and $1.00 per document. 17.1%
Greater than $1.00 per document. 32.9%
Do not know. 27.6%
Alternative pricing model for per document costs. 18.4%

12b. What is the cost per document for document review attorneys to review documents for a remote managed review?

Less than $0.50 per document. 6.6%
Between $0.50 and $1.00 per document. 27.6%
Greater than $1.00 per document. 23.7%
Do not know. 25.0%
Alternative pricing model for per document costs. 17.1%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2023/12/Review-Pricing-Per-Document-Cost-for-Document-Review-Attorneys-to-Review-Documents-Remote-Winter-2024-Pricing.pdf" title="Review Pricing – Per Document Cost for Document Review Attorneys to Review Documents Remote – Winter 2024 Pricing"]

Background Information

13. In which geographical region do you primarily conduct eDiscovery-related business?

North America – United States. 94.7%
Europe – United Kingdom. 2.6%
Asia/Asia Pacific. 1.3%
Europe – Non-UK. 1.3%
North America – Canada. 0.0%
Middle East/Africa. 0.0%
Central/South America. 0.0%

14. Which of the following segments best describes your business in eDiscovery?

Software and/or Services Provider. 40.8%
Law Firm. 31.6%
Consultancy. 10.5%
Corporation. 9.2%
Media/Research Organization/Educational Association. 7.9%
Government Entity. 0.0%

15. What area best describes your primary function in the conduct of your organization’s eDiscovery business?

Legal/Litigation Support. 69.7%
Business/Business Support (All Other Business Functions). 27.6%
IT/Product Development. 2.6%

Background: eDiscovery Pricing Survey Respondents by Survey

Assisted by GAI and LLM Technologies

Additional Reading

Purchase Access to the Complete Presentation of Charts: Market Kinetics – eDiscovery Pricing Overview – Winter 2024 (PDF)

Complete Presentation: Market Kinetics – eDiscovery Pricing Overview – Winter 2024 (PDF) – Click to Download and Mouseover to Scroll

Source: ComplexDiscovery

Shaping eDiscovery Strategies: Winter 2024 Pricing Report

The Workstream of eDiscovery: Considering Processes and Tasks

An Abridged Look at the Business of eDiscovery: Mergers, Acquisitions, and Investments

Predicting Discovery? Initial Results of 120-Second Provider Predictive Coding Survey - ComplexDiscovery Archived Content

ARCHIVED CONTENT

120 Seconds of Predictive Coding – Survey Results

Current Responders

Current Responses (By Provider)

@Legal (CasePoint TAR) @AtLegal

AccessData (Summation – Predictive Coding Feature) @AccessData Group

AlphaLit (E-Predict | Backstop – Content Analyst)

Altep (Tag | Content Analyst) @Altep_Inc

BIA (BIA Predictive Coding Engine | Coda™) @BIA_eDiscovery

Catalyst Repository Systems (Insight Predict) @CatalystSecure

Content Analyst (Conceptual Categorization) @Content_Analyst

D4 Discovery (Equivio Relevance – Relativity Assisted Review) @D4Discovery

Daegis (Acumen) @Daegis

Deloitte (Dynamic Review) @DeloitteFinSvcs

Driven (START (Simplified Technology Assisted Review Tool)) (Content Analyst)

DTI (Technology-Assisted Review | Equivio)

Equivio (Relevance) @Equivio

Exterro (Fusion Predictive Intelligence™) @Exterro

Hudson Legal (Predictive Coding | Recommind – Relativity – Kroll Ontrack) @HudsonLegal

Huron Legal (Integrated Analytics | PureDiscovery) @HuronLegal

kCura (Relativity Assisted Review | Content Analyst) @kCura

Kroll Ontrack (Intelligent Review Technology (IRT) | Ontrack® Inview™) @KrollOntrack

NightOwl Discovery (Predictive Coding | Equivio – Relativity) @NightOwlDMS

Nuix (Automatic Classification) @Nuix

Orange Legal Technologies (Predictive Review | OrcaTec) @OrangeLT

OrcaTec (OrcaPredict) @OrcaTec

Prolorem (Prolorem eDi)

Rational Retention (Rational Intelligence)

Recommind (Predictive Coding | Axelerate) @Recommind

RenewData (Computer-Assisted Coding | Equivio – Content Analyst) @RenewData

Servient (Servient Predictive Review) @Servient

Symantec (Clearwell Systems) (Transparent Predictive Coding) @SYMCeDiscovery

TCDI (Suggestive Coding | Content Analyst)

UBIC (CJK TAR)

UHY Advisors (Assisted Review | Relativity)

Valora Technologies (Valora Auto Review Services) @ValoraTech

Xerox Litigation Services (CategoriX) @Xerox_XLS

Xerox Litigation Services – Lateral Data (View Point Assisted Review) @Xerox_XLS

ZyLAB (ZyLAB Technology-Assisted Review) @ZyLAB

Current Responses (By Answer)

Technology Development

Offering Integration

Machine Learning Approach

Sampling Approach

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