Running Results: Predictive Coding One-Question Provider Implementation Survey - ComplexDiscovery Archived Content

Mar 5, 2013

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Predictive Coding One-Question Provider Implementation Survey – Initial Results

Provided below for your consideration and use are the in-progress results of the One-Question Provider Implementation Survey launched by ComplexDiscovery on 3/3/13.

The goal of the survey is to provide a specific and detailed look at the use of the technology-assisted review feature of predictive coding among leading eDiscovery providers as represented by those providers. The specific technologies highlighted in the one-question survey include:

Active Learning
Language Modeling
Latent Semantic Analysis
Linguistic Analysis
Naive Bayesian Classifier
Nearest Neighbor Classifier
Probabilistic Latent Semantic Analysis
Relevance Feedback
Support Vector Machine
Other (Provider Machine Learning Approach Not Listed)

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.

Note: The running results of a previously presented general survey on eDiscovery provider use of predictive coding are available for review(2) (click here for survey results). The initial 120-second survey(3) (click here for initial survey form) contained six high level questions related to technology development, offering integration, machine learning approach and sampling approach of providers in relation to predictive coding. The following one-question provider implementation survey was designed to build on the machine learning question from the initial general survey by providing additional and important layers of detail.

Updated 9/16/2013

Current Responders

@Legal
Altep
BIA
Catalyst Repository Systems
Content Analyst
D4
Daegis
Driven
Huron Legal
kCura
Kroll Ontrack
Liquid Litigation Management (LLM)
Nuix
Orange Legal Technologies
OrcaTec
Prolorem
Recommind
Servient
Symantec/Clearwell
TCDI
UBIC
Valora Technologies
Xerox Litigation Services

Current Responses (By Provider)

@Legal (CasePoint TAR) @AtLegal

Nearest Neighbor Classifier: A classification system that categorizes documents by finding an already classified example that is very similar (near) to the document being considered. It gives the new document the same category as the most similar trained example.
Support Vector Machine: A mathematical approach that seeks to find a line that separates responsive from non-responsive documents so that, ideally, all of the responsive documents are on one side of the line and all of the non-responsive ones are on the other side.

Altep (Tag | Content Analyst) @Altep_Inc

Active Learning: An iterative process that presents for reviewer judgment those documents that are most likely to be misclassified. In conjunction with Support Vector Machines, it presents those documents that are closest to the current position of the separating line. The line is moved if any of the presented documents has been misclassified.
Latent Semantic Analysis: A mathematical approach that seeks to summarize the meaning of words by looking at the documents that share those words. LSA builds up a mathematical model of how words are related to documents and lets users take advantage of these computed relations to categorize documents.

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

Active Learning: An iterative process that presents for reviewer judgment those documents that are most likely to be misclassified. In conjunction with Support Vector Machines, it presents those documents that are closest to the current position of the separating line. The line is moved if any of the presented documents has been misclassified.
Language Modeling: A mathematical approach that seeks to summarize the meaning of words by looking at how they are used in the set of documents. Language modeling in predictive coding builds a model for word occurrence in the responsive and in the non-responsive documents and classifies documents according to the model that best accounts for the words in a document being considered.
Latent Semantic Analysis: A mathematical approach that seeks to summarize the meaning of words by looking at the documents that share those words. LSA builds up a mathematical model of how words are related to documents and lets users take advantage of these computed relations to categorize documents.
Linguistic Analysis: Linguists examine responsive and non-responsive documents to derive classification rules that maximize the correct classification of documents.
Naïve Bayesian Classifier: A system that examines the probability that each word in a new document came from the word distribution derived from trained responsive document or from trained non-responsive documents. The system is naïve in the sense that it assumes that all words are independent of one another.
Nearest Neighbor Classifier: A classification system that categorizes documents by finding an already classified example that is very similar (near) to the document being considered. It gives the new document the same category as the most similar trained example.
Probabilistic Latent Semantic Analysis: A second mathematical approach that seeks to summarize the meaning of words by looking at the documents that share those words. PLSA builds up a mathematical model of how words are related to documents and lets users take advantage of these computed relations to categorize documents.
Relevance Feedback: A computational model that adjusts the criteria for implicitly identifying responsive documents following feedback by a knowledgeable user as to which documents are relevant and which are not.
Support Vector Machine: A mathematical approach that seeks to find a line that separates responsive from non-responsive documents so that, ideally, all of the responsive documents are on one side of the line and all of the non-responsive ones are on the other side.

Catalyst Repository Systems (Insight Predict) @CatalystSecure

Nearest Neighbor Classifier: A classification system that categorizes documents by finding an already classified example that is very similar (near) to the document being considered. It gives the new document the same category as the most similar trained example.

Content Analyst (Conceptual Categorization) @Content_Analyst

Active Learning: An iterative process that presents for reviewer judgment those documents that are most likely to be misclassified. In conjunction with Support Vector Machines, it presents those documents that are closest to the current position of the separating line. The line is moved if any of the presented documents has been misclassified.
Latent Semantic Analysis: A mathematical approach that seeks to summarize the meaning of words by looking at the documents that share those words. LSA builds up a mathematical model of how words are related to documents and lets users take advantage of these computed relations to categorize documents.

D4 Discovery (Equivio Relevance) @D4Discovery

Active Learning: An iterative process that presents for reviewer judgment those documents that are most likely to be misclassified. In conjunction with Support Vector Machines, it presents those documents that are closest to the current position of the separating line. The line is moved if any of the presented documents has been misclassified.
Support Vector Machine: A mathematical approach that seeks to find a line that separates responsive from non-responsive documents so that, ideally, all of the responsive documents are on one side of the line and all of the non-responsive ones are on the other side.

Daegis (Acumen) @Daegis

Active Learning: An iterative process that presents for reviewer judgment those documents that are most likely to be misclassified. In conjunction with Support Vector Machines, it presents those documents that are closest to the current position of the separating line. The line is moved if any of the presented documents has been misclassified.
Probabilistic Latent Semantic Analysis: A second mathematical approach that seeks to summarize the meaning of words by looking at the documents that share those words. PLSA builds up a mathematical model of how words are related to documents and lets users take advantage of these computed relations to categorize documents.

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

Active Learning: An iterative process that presents for reviewer judgment those documents that are most likely to be misclassified. In conjunction with Support Vector Machines, it presents those documents that are closest to the current position of the separating line. The line is moved if any of the presented documents has been misclassified.
Latent Semantic Analysis: A mathematical approach that seeks to summarize the meaning of words by looking at the documents that share those words. LSA builds up a mathematical model of how words are related to documents and lets users take advantage of these computed relations to categorize documents.
Nearest Neighbor Classifier: A classification system that categorizes documents by finding an already classified example that is very similar (near) to the document being considered. It gives the new document the same category as the most similar trained example.
Relevance Feedback: A computational model that adjusts the criteria for implicitly identifying responsive documents following feedback by a knowledgeable user as to which documents are relevant and which are not.

Huron Legal (Integrated Analytics | PureDiscovery) @HuronLegal

Nearest Neighbor Classifier: A classification system that categorizes documents by finding an already classified example that is very similar (near) to the document being considered. It gives the new document the same category as the most similar trained example.

kCura (Relativity Assisted Review | Content Analyst)

Latent Semantic Analysis: A mathematical approach that seeks to summarize the meaning of words by looking at the documents that share those words. LSA builds up a mathematical model of how words are related to documents and lets users take advantage of these computed relations to categorize documents.
Nearest Neighbor Classifier: A classification system that categorizes documents by finding an already classified example that is very similar (near) to the document being considered. It gives the new document the same category as the most similar trained example.
Relevance Feedback: A computational model that adjusts the criteria for implicitly identifying responsive documents following feedback by a knowledgeable user as to which documents are relevant and which are not.

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

Active Learning: An iterative process that presents for reviewer judgment those documents that are most likely to be misclassified. In conjunction with Support Vector Machines, it presents those documents that are closest to the current position of the separating line. The line is moved if any of the presented documents has been misclassified.
Other: Logistical Regression is well-accepted by computer science and information retrieval communities as a sound statistical modeling approach for data analysis and predictive modeling. Logistic regression is a form of supervised learning, in that a logistic regression model is produced by “training” on a set of documents that have been manually categorized. Once trained, the logistic regression model can be used to estimate the probability that a new document belongs to each of the possible categories. The model can use both content features such as words and phrases, and metadata features such as custodian, date, file type, and contextual information. They can be applied to data sets with millions of documents and billions of content features, and are one of the most effective approaches in a wide range of text and data mining tasks.

Liquid Litigation Management (Technology-Assisted Review) @LLMinc

Active Learning: An iterative process that presents for reviewer judgment those documents that are most likely to be misclassified. In conjunction with Support Vector Machines, it presents those documents that are closest to the current position of the separating line. The line is moved if any of the presented documents has been misclassified.
Latent Semantic Analysis: A mathematical approach that seeks to summarize the meaning of words by looking at the documents that share those words. LSA builds up a mathematical model of how words are related to documents and lets users take advantage of these computed relations to categorize documents.

Nuix (Automatic Classification) @Nuix

Naïve Bayesian Classifier: A system that examines the probability that each word in a new document came from the word distribution derived from trained responsive document or from trained non-responsive documents. The system is naïve in the sense that it assumes that all words are independent of one another.

Orange Legal Technologies (Predictive Review | OrcaTec) @OrangeLT

Language Modeling: A mathematical approach that seeks to summarize the meaning of words by looking at how they are used in the set of documents. Language modeling in predictive coding builds a model for word occurrence in the responsive and in the non-responsive documents and classifies documents according to the model that best accounts for the words in a document being considered.

OrcaTec (OrcaPredict) @OrcaTec

Language Modeling: A mathematical approach that seeks to summarize the meaning of words by looking at how they are used in the set of documents. Language modeling in predictive coding builds a model for word occurrence in the responsive and in the non-responsive documents and classifies documents according to the model that best accounts for the words in a document being considered.

Prolorem (Prolorem eDi)

Support Vector Machine: A mathematical approach that seeks to find a line that separates responsive from non-responsive documents so that, ideally, all of the responsive documents are on one side of the line and all of the non-responsive ones are on the other side.

Recommind (Predictive Coding | Axelerate) @Recommind

Probabilistic Latent Semantic Analysis: A second mathematical approach that seeks to summarize the meaning of words by looking at the documents that share those words. PLSA builds up a mathematical model of how words are related to documents and lets users take advantage of these computed relations to categorize documents.
Support Vector Machine: A mathematical approach that seeks to find a line that separates responsive from non-responsive documents so that, ideally, all of the responsive documents are on one side of the line and all of the non-responsive ones are on the other side.

Servient (Servient Predictive Review) @Servient

Active Learning: An iterative process that presents for reviewer judgment those documents that are most likely to be misclassified. In conjunction with Support Vector Machines, it presents those documents that are closest to the current position of the separating line. The line is moved if any of the presented documents has been misclassified.
Language Modeling: A mathematical approach that seeks to summarize the meaning of words by looking at how they are used in the set of documents. Language modeling in predictive coding builds a model for word occurrence in the responsive and in the non-responsive documents and classifies documents according to the model that best accounts for the words in a document being considered.
Latent Semantic Analysis: A mathematical approach that seeks to summarize the meaning of words by looking at the documents that share those words. LSA builds up a mathematical model of how words are related to documents and lets users take advantage of these computed relations to categorize documents.
Linguistic Analysis: Linguists examine responsive and non-responsive documents to derive classification rules that maximize the correct classification of documents.
Naïve Bayesian Classifier: A system that examines the probability that each word in a new document came from the word distribution derived from trained responsive document or from trained non-responsive documents. The system is naïve in the sense that it assumes that all words are independent of one another.
Nearest Neighbor Classifier: A classification system that categorizes documents by finding an already classified example that is very similar (near) to the document being considered. It gives the new document the same category as the most similar trained example.
Relevance Feedback: A computational model that adjusts the criteria for implicitly identifying responsive documents following feedback by a knowledgeable user as to which documents are relevant and which are not.
Support Vector Machine: A mathematical approach that seeks to find a line that separates responsive from non-responsive documents so that, ideally, all of the responsive documents are on one side of the line and all of the non-responsive ones are on the other side.

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

Active Learning: An iterative process that presents for reviewer judgment those documents that are most likely to be misclassified. In conjunction with Support Vector Machines, it presents those documents that are closest to the current position of the separating line. The line is moved if any of the presented documents has been misclassified.
Relevance Feedback: A computational model that adjusts the criteria for implicitly identifying responsive documents following feedback by a knowledgeable user as to which documents are relevant and which are not.
Support Vector Machine: A mathematical approach that seeks to find a line that separates responsive from non-responsive documents so that, ideally, all of the responsive documents are on one side of the line and all of the non-responsive ones are on the other side.

TCDI (Suggestive Coding | Content Analyst)

Latent Semantic Analysis: A mathematical approach that seeks to summarize the meaning of words by looking at the documents that share those words. LSA builds up a mathematical model of how words are related to documents and lets users take advantage of these computed relations to categorize documents.

UBIC (CJK TAR)

Active Learning: An iterative process that presents for reviewer judgment those documents that are most likely to be misclassified. In conjunction with Support Vector Machines, it presents those documents that are closest to the current position of the separating line. The line is moved if any of the presented documents has been misclassified.
Language Modeling: A mathematical approach that seeks to summarize the meaning of words by looking at how they are used in the set of documents. Language modeling in predictive coding builds a model for word occurrence in the responsive and in the non-responsive documents and classifies documents according to the model that best accounts for the words in a document being considered.
Latent Semantic Analysis: A mathematical approach that seeks to summarize the meaning of words by looking at the documents that share those words. LSA builds up a mathematical model of how words are related to documents and lets users take advantage of these computed relations to categorize documents.
Nearest Neighbor Classifier: A classification system that categorizes documents by finding an already classified example that is very similar (near) to the document being considered. It gives the new document the same category as the most similar trained example.
Probabilistic Latent Semantic Analysis: A second mathematical approach that seeks to summarize the meaning of words by looking at the documents that share those words. PLSA builds up a mathematical model of how words are related to documents and lets users take advantage of these computed relations to categorize documents.
Relevance Feedback: A computational model that adjusts the criteria for implicitly identifying responsive documents following feedback by a knowledgeable user as to which documents are relevant and which are not.
Support Vector Machine: A mathematical approach that seeks to find a line that separates responsive from non-responsive documents so that, ideally, all of the responsive documents are on one side of the line and all of the non-responsive ones are on the other side.

Valora Technologies (Valora Auto Review Services) @ValoraTech

Other: Probabilistic Hierarchical Context-Free Grammars approach to machine learning.

Xerox Litigation Services (CategoriX) @Xerox_XLS

Active Learning: An iterative process that presents for reviewer judgment those documents that are most likely to be misclassified. In conjunction with Support Vector Machines, it presents those documents that are closest to the current position of the separating line. The line is moved if any of the presented documents has been misclassified.
Language Modeling: A mathematical approach that seeks to summarize the meaning of words by looking at how they are used in the set of documents. Language modeling in predictive coding builds a model for word occurrence in the responsive and in the non-responsive documents and classifies documents according to the model that best accounts for the words in a document being considered.
Linguistic Analysis: Linguists examine responsive and non-responsive documents to derive classification rules that maximize the correct classification of documents.
Probabilistic Latent Semantic Analysis: A second mathematical approach that seeks to summarize the meaning of words by looking at the documents that share those words. PLSA builds up a mathematical model of how words are related to documents and lets users take advantage of these computed relations to categorize documents.
Relevance Feedback: A computational model that adjusts the criteria for implicitly identifying responsive documents following feedback by a knowledgeable user as to which documents are relevant and which are not.
Support Vector Machine: A mathematical approach that seeks to find a line that separates responsive from non-responsive documents so that, ideally, all of the responsive documents are on one side of the line and all of the non-responsive ones are on the other side.

Current Responses (By Answer)

Active Learning

Active Learning: An iterative process that presents for reviewer judgment those documents that are most likely to be misclassified. In conjunction with Support Vector Machines, it presents those documents that are closest to the current position of the separating line. The line is moved if any of the presented documents has been misclassified.

Altep
BIA
Content Analyst
D4
Daegis
Driven
Kroll Ontrack
Liquid Litigation Management (LLM)
Servient
Symantec/Clearwell
UBIC
Xerox Litigation Services

Language Modeling

Language Modeling: A mathematical approach that seeks to summarize the meaning of words by looking at how they are used in the set of documents. Language modeling in predictive coding builds a model for word occurrence in the responsive and in the non-responsive documents and classifies documents according to the model that best accounts for the words in a document being considered.

BIA
Orange Legel Technologies
OrcaTec
Servient
UBIC
Xerox Litigation Services

Latent Semantic Analysis

Latent Semantic Analysis: A mathematical approach that seeks to summarize the meaning of words by looking at the documents that share those words. LSA builds up a mathematical model of how words are related to documents and lets users take advantage of these computed relations to categorize documents.

Altep
BIA
Content Analyst
Driven
kCura
Liquid Litigation Management (LLM)
Servient
TCDI
UBIC

Linguistic Analysis

Linguistic Analysis: Linguists examine responsive and non-responsive documents to derive classification rules that maximize the correct classification of documents.

BIA
Servient
Xerox Litigation Services

Naive Bayesian Classifier

Naïve Bayesian Classifier: A system that examines the probability that each word in a new document came from the word distribution derived from trained responsive document or from trained non-responsive documents. The system is naïve in the sense that it assumes that all words are independent of one another.

BIA
Nuix
Servient

Nearest Neighbor Classifier

Nearest Neighbor Classifier: A classification system that categorizes documents by finding an already classified example that is very similar (near) to the document being considered. It gives the new document the same category as the most similar trained example.

@Legal
BIA
Catalyst Repository Systems
Driven
Huron Legal
kCura
Servient
UBIC

Probabilistic Latent Semantic Analysis

Probabilistic Latent Semantic Analysis: A second mathematical approach that seeks to summarize the meaning of words by looking at the documents that share those words. PLSA builds up a mathematical model of how words are related to documents and lets users take advantage of these computed relations to categorize documents.

BIA
Daegis
Recommind
UBIC
Xerox Litigation Services

Relevance Feedback

Relevance Feedback: A computational model that adjusts the criteria for implicitly identifying responsive documents following feedback by a knowledgeable user as to which documents are relevant and which are not.

BIA
Driven
kCura
Servient
Symantec/Clearwell
UBIC
Xerox Litigation Services

Support Vector Machine

Support Vector Machine: A mathematical approach that seeks to find a line that separates responsive from non-responsive documents so that, ideally, all of the responsive documents are on one side of the line and all of the non-responsive ones are on the other side.

@Legal
BIA
D4
Prolorem
Recommind
Servient
Symantec/Clearwell
UBIC
Xerox Litigation Services

Other: Logistical Regression

Kroll Ontrack

Other: Probabilistic Hierarchical Context-Free Grammars

Valora Technologies

End of Survey Results

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Business Confidence

Editor’s Note: The 36th edition of the eDiscovery Business Confidence Survey provides a compelling narrative of an industry at the crossroads of optimism and innovation. This Fall 2024 report not only captures the sentiment of 61 seasoned professionals across diverse sectors but also offers critical insights into the ongoing challenges and technological shifts shaping the eDiscovery landscape. The findings highlight the industry's resilience, the increasing adoption of AI technologies, and the persistent complexities of data management and budget constraints. As the eDiscovery ecosystem continues to evolve, this survey serves as an invaluable guide for professionals navigating both opportunities and uncertainties in a rapidly transforming environment.

[taq_review]

Background Note: The eDiscovery Business Confidence Survey is a quarterly research initiative designed to measure and analyze business sentiment within the eDiscovery ecosystem. Since its launch in 2016, the survey has become a trusted resource for understanding the industry's evolving dynamics. Over its 36 editions, the survey has collected insights from approximately 3,451 participants in total, averaging 96 respondents per iteration. This longitudinal approach enables the survey to capture trends in revenue, profitability, and emerging challenges, offering a consistent pulse on the industry's health.

The Fall 2024 edition of the survey was administered from November 12 to December 6, 2024, leveraging a robust promotional strategy that included direct outreach by ComplexDiscovery and its long-standing partner, the Electronic Discovery Reference Model (EDRM). Additional support from industry organizations and publications, such as Emerging Europe and eDiscovery Today broadened the survey's reach, ensuring the inclusion of diverse professional perspectives.

A total of 61 respondents* participated in the Fall 2024 survey, reflecting strong engagement from professionals across sectors like software and services, law firms, corporations, and consultancies. Their responses offer valuable insights into the current state of the eDiscovery industry, including sentiment around business conditions, financial projections, technological integration, and ongoing operational challenges.

The continued high-quality participation and thoughtful feedback in this edition affirm the survey's role as a critical tool for benchmarking and guiding decision-making within the eDiscovery ecosystem. As the industry navigates rapid technological advancements and complex challenges, the survey provides actionable intelligence to support professionals in adapting to an ever-evolving landscape.

Industry Survey

Optimism and Innovation in eDiscovery: Fall 2024 Business Confidence Survey Insights

ComplexDiscovery Staff

The Fall 2024 eDiscovery Business Confidence Survey, the 36th edition of this cornerstone industry report, provides a detailed snapshot of the eDiscovery landscape. With 61 respondents from various sectors, the survey explores trends in business sentiment, financial projections, technological adoption, and emerging challenges. The findings reveal a resilient industry marked by optimism, innovation, and ongoing adaptation to complex demands.

Industry Representation and Demographics

The Fall 2024 survey captures a diverse range of perspectives, reflecting the varied ecosystem of eDiscovery. Software and services providers accounted for 50.82% of respondents, maintaining their position as the largest segment. Law firms followed with 21.31%, while corporations made up 9.84% of participants. Other key sectors include consultancies (8.20%), media and research organizations (4.92%), and governmental entities (1.64%).

Geographically, the industry remains centered in North America, with 91.80% of respondents conducting business primarily in the United States. Limited representation from Europe (6.56%) and Canada (1.64%) underscores the continued dominance of the U.S. market in shaping global eDiscovery trends.

The survey also highlights the roles of respondents within their organizations. Legal and litigation support professionals comprise the majority at 63.93%, with business support roles contributing 29.51%. Nearly 40% of respondents identify as executive leaders, demonstrating the strategic focus of the survey participants and the emphasis on leadership in navigating industry complexities.

This diverse representation reinforces the interconnectedness of various roles in the eDiscovery ecosystem. For professionals, it highlights the importance of collaboration across legal, technical, and business functions. The strong presence of executive leaders among respondents also signals that decision-making in eDiscovery is increasingly strategic, emphasizing the need for professionals to align their initiatives with broader organizational goals and market demands.

Business Confidence and Financial Outlook

The Fall 2024 survey reveals sustained optimism about current and future business conditions in the eDiscovery sector. Over 54% of respondents describe the current business climate as "Good," while 39.34% view conditions as "Normal." Only a small percentage (6.56%) report unfavorable conditions, reflecting limited negative pressures.

Looking to the future, 52.46% of respondents anticipate improved business conditions over the next six months, compared to 44.26% who expect stability and just 3.28% who foresee a downturn. These findings highlight the confidence of industry professionals in the sector's growth potential.

Revenue projections further underscore this optimism. Over 57% of respondents expect higher revenues in the next six months, while 39.34% anticipate stable revenues. Similarly, profit expectations are positive, with 39.34% forecasting increases and 54.10% predicting no significant change. Only a small fraction (6.56%) expect lower profits, reinforcing the overall stability of financial outlooks within the industry.

The positive business sentiment and financial outlook provide a strong foundation for eDiscovery professionals to pursue innovation and growth. For individuals and teams, this means a window of opportunity to invest in professional development, adopt new tools, and explore emerging markets. The sustained optimism also encourages firms to strategically position themselves for future opportunities while mitigating risks in areas of uncertainty.

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Key Challenges Facing the Industry

While the eDiscovery sector demonstrates resilience, challenges persist. The increasing complexity of data remains a top concern, with 24.59% of respondents citing the proliferation of data types as the most pressing issue and 21.31% highlighting data volume. Budgetary constraints are another major hurdle, reflecting the ongoing pressure to deliver value within tight financial parameters.

Workforce and technological challenges also feature prominently. A 14.75% share of respondents cite personnel shortages, while 11.48% are concerned with data security issues. Inadequate technology, mentioned by 8.20% of participants, underscores the need for continual innovation to address evolving industry demands.

These challenges highlight the critical need for eDiscovery professionals to stay agile and proactive. Mastering data governance strategies, leveraging automation, and developing expertise in emerging technologies can help professionals tackle data complexity. Meanwhile, addressing workforce shortages by upskilling and cross-training teams will ensure organizations remain competitive despite resource constraints.

AI and Technological Innovation

The Fall 2024 survey highlights the growing adoption of generative artificial intelligence (GAI) and large language models (LLMs) as transformative tools in eDiscovery. Nearly 40% of respondents report active integration and deployment of these technologies, while 27.87% are evaluating their potential and 26.23% are testing or piloting AI-driven solutions.

The benefits of AI adoption are clear. Improved service and product delivery remains the most cited advantage, identified by 44.26% of respondents. Competitive differentiation (26.23%) and cost savings (14.75%) also rank highly, reflecting the strategic importance of AI in enhancing efficiency and gaining market advantage.

However, challenges remain significant. 29.51% of respondents point to concerns about results accuracy, while high costs and regulatory compliance, each cited by 19.67%, pose additional barriers to broader adoption. Despite these obstacles, the survey findings suggest that AI integration is increasingly viewed as essential to staying competitive in the eDiscovery landscape.

For professionals, the growing integration of AI technologies offers both opportunities and challenges. Those with expertise in AI-enabled tools and workflows are well-positioned to drive efficiency and innovation within their organizations. However, ensuring ethical and accurate use of AI will be critical. Professionals must balance the potential of these technologies with a strong commitment to compliance, transparency, and reliability.

Operational Metrics and Financial Health

The Fall 2024 survey provides valuable insights into key operational metrics that reflect the financial health of the industry. Monthly recurring revenue (MRR) is on the rise for 54.24% of respondents, indicating strong client retention and reliable revenue streams. Meanwhile, 38.98% report unfluctuating Days Sales Outstanding (DSO), though 18.64% have seen increases, which may signal challenges in managing payment cycles and maintaining cash flow.

Revenue distribution across customer bases also presents a mixed picture. 42.37% of respondents report growth, while 35.59% indicate stability. However, 6.78% note declines in revenue distribution, reflecting competitive pressures and challenges in client retention for some organizations.

These operational metrics suggest a need for vigilance in managing cash flow and client relationships. For professionals, this means closely monitoring payment cycles, strengthening client retention strategies, and identifying new opportunities for recurring revenue. Ensuring financial stability will provide the foundation necessary to support innovation and growth efforts.

A Thriving and Evolving Industry

The findings from the Fall 2024 eDiscovery Business Confidence Survey reflect an industry that is both thriving and adapting to evolving challenges. Business confidence remains high, supported by stable revenue and profit projections. The adoption of advanced technologies, particularly generative AI and LLMs, continues to drive innovation, though challenges such as data complexity, budgetary constraints, and AI implementation hurdles persist.

For eDiscovery professionals, the survey's findings underscore the importance of resilience, adaptability, and strategic planning. Those who embrace technological advancements, refine operational efficiencies, and address key challenges will be well-positioned for long-term success in this dynamic and competitive industry. As the eDiscovery landscape evolves, the insights from this survey will remain invaluable for navigating the path forward.

Fall 2024 Survey Results

n = 61 Respondents

Business Confidence Questions

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

Part of the eDiscovery ecosystem where your organization resides

Software and/or Services Provider: 50.82%
Law Firm: 21.31%
Corporation: 9.84%
Consultancy: 8.20%
Media/Research Organization: 4.92%
Governmental Entity: 1.64%
Other: 3.28%

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

2. How would you rate the current general business conditions for eDiscovery in your segment?

Subjective feeling of business performance when compared with business expectations

Good: 54.10%
Normal: 39.34%
Bad: 6.56%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2024/12/Current-Business-Climate-Overview-Fall-2024.pdf" title="Current Business Climate Overview - Fall 2024"]

3. How do you think the business conditions will be in your segment six months from now?

Subjective feeling of business performance when compared with business expectations

Better: 52.46%
Same: 44.26%
Worse: 3.28%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2024/12/Business-Climate-Overview-Six-Months-Fall-2024.pdf" title="Business Climate Overview + Six Months - Fall 2024"]

4. How would you guess revenue in your segment of the eDiscovery ecosystem will be six months from now?

Revenue is income generated from eDiscovery-related business activities

Higher: 57.38%
Same: 39.34%
Lower: 3.28%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2024/12/Revenue-Overview-Six-Months-Fall-2024.pdf" title="Revenue Overview + Six Months - Fall 2024"]

5. How would you guess profits in your segment of the eDiscovery ecosystem will be six months from now?

Profit is the amount of income remaining after accounting for all expenses, debts, additional revenue streams, and operating costs

Higher: 39.34%
Same: 54.10%
Lower: 6.56%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2024/12/Profits-Overview-Six-Months-Fall-2024.pdf" title="Profits Overview + Six Months - Fall 2024"]

6. Of the six items presented below, what is the issue that you feel will most impact the business of eDiscovery over the next six months?

Challenges that may directly impact the business performance of your organization

Increasing Types of Data: 24.59%
Increasing Volumes of Data: 21.31%
Budgetary Constraints: 19.67%
Lack of Personnel: 14.75%
Data Security: 11.48%
Inadequate Technology: 8.20%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2024/12/Issues-Impacting-eDiscovery-Business-Performance-Fall-2024.pdf" title="Issues Impacting eDiscovery Business Performance - Fall 2024"]

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

The location from which you are basing your business assessments

North America – United States: 91.80%
Europe – United Kingdom: 4.92%
North America – Canada: 1.64%
Europe – Non-UK: 1.64%

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

8. What area best describes your primary function in the conduct of your organization's eDiscovery-related business?

Legal/Litigation Support: 63.93%
Business/Business Support (All Other Business Functions): 29.51%
IT/Product Development: 6.56%

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

9. What area best describes your level of support in the conduct of your organization's eDiscovery-related business?

Executive Leadership: 37.70%
Operational Management: 36.07%
Tactical Execution: 26.23%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2024/12/Survey-Respondents-by-Level-of-Support-Fall-2024.pdf" title="Survey Respondents by Level of Support - Fall 2024"]

10. How would you characterize the use of LLMs and GAI in your organization's operations or offerings?

Integrating and Deploying: 39.34%
Considering and Evaluating: 27.87%
Testing and Piloting: 26.23%
No Plans: 6.56%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2024/12/Use-of-LLMs-and-GAI-in-Organizations-Operations-or-Offerings-Fall-2024.pdf" title="Use of LLMs and GAI in Organization's Operations or Offerings - Fall 2024"]

11. Of the options provided, which do you perceive as the primary benefit of integrating LLMs and GAI into your organization's operations or offerings?

Improved Service/Product Delivery: 44.26%
Competitive Advantage: 26.23%
Cost Savings: 14.75%
Enhanced Decision Making: 9.84%
No Perceived Benefit: 4.92%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2024/12/Primary-Benefit-of-Integrating-LLMs-and-GAI-into-Organizations-Operations-or-Offerings-Fall-2024.pdf" title="Primary Benefit of Integrating LLMs and GAI into Organization's Operations or Offerings - Fall 2024"]

12. Of the options provided, which do you perceive as the primary challenge of integrating LLMs and GAI into your organization's operations or offerings?

Results Accuracy: 29.51%
High Costs: 19.67%
Regulatory and Privacy Compliance: 19.67%
Unclear ROI: 16.39%
Skill Gap: 8.20%
Ethical Concerns: 6.56%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2024/12/Primary-Challenge-of-Integrating-LLMs-and-GAI-into-Organizations-Operations-or-Offerings-Fall-2024.pdf" title="Primary Challenge of Integrating LLMs and GAI into Organization's Operations or Offerings - Fall 2024"]

13. How would you characterize the trajectory of your organization's Days Sales Outstanding (DSO) during the last quarter?

n=59 Respondents

Increasing: 18.64%
Unfluctuating: 38.98%
Decreasing: 8.47%
Do Not Know: 33.90%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2024/12/eDiscovery-Business-Metric-Trajectory_-Days-Sales-Outstanding-Fall-2024.pdf" title="eDiscovery Business Metric Trajectory_ Days Sales Outstanding - Fall 2024"]

14. How would you characterize the trajectory of your organization's Monthly Recurring Revenue (MRR) during the last quarter?

n=59 Respondents

Increasing: 54.24%
Unfluctuating: 35.59%
Decreasing: 10.17%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2024/12/eDiscovery-Business-Metric-Trajectory_-Monthly-Recurring-Revenue-Fall-2024.pdf" title="eDiscovery Business Metric Trajectory_ Monthly Recurring Revenue - Fall 2024"]

15. Which of the following statements best describes the distribution of your organization's revenue across your customer base during the last quarter?

n=59 Respondents

Increasing: 42.37%
Unfluctuating: 35.59%
Decreasing: 6.78%
Do Not Know: 15.25%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2024/12/eDiscovery-Business-Metric-Trajectory_-Distribution-of-Revenue-Across-Customer-Base-Fall-2024.pdf" title="eDiscovery Business Metric Trajectory_ Distribution of Revenue Across Customer Base - Fall 2024"]

Onward and Upward

The Fall 2024 Survey numbers demonstrate an industry marked by sustained optimism, growth in AI adoption, and continued challenges in managing data complexity and budgets. This quarter's results reflect stability in key areas, offering actionable insights for professionals navigating a dynamic eDiscovery ecosystem.

*Survey methodology focuses on the achievement of at least 50 responses with the least number of emails sent to the ComplexDiscovery industry professional database. This approach seeks to minimize the number of requests for participation in surveys while ensuring a solid number of responses from which to generally assess market sentiment in survey areas of interest.

Respondent Backgrounder

Aggregate Surveys (Winter 2016 - Fall 2024)

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

Spring Surveys (Fall 2016 - Fall 2024)

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

Complete Presentation: Market Kinetics - eDiscovery Business Confidence - Fall 2024 (PDF) - Mouseover to Scroll

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2024/12/0-Business-Confidence-121224.pdf" title="0 - Business Confidence - 121224"]

Assisted by GAI and LLM Technologies

News Source

Robinson, R., & Robinson, H. (2024). Optimism and innovation in eDiscovery: Fall 2024 business confidence survey insights. ComplexDiscovery OÜ.

Additional Reading

Source: ComplexDiscovery OÜ

[taq_review]

Industry Survey

Optimism and Innovation in eDiscovery: Fall 2024 Business Confidence Survey Insights

ComplexDiscovery Staff

Industry Representation and Demographics

Business Confidence and Financial Outlook

[the_ad id="45753"]

Key Challenges Facing the Industry

AI and Technological Innovation

Operational Metrics and Financial Health

A Thriving and Evolving Industry

Fall 2024 Survey Results

n = 61 Respondents

Business Confidence Questions

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

Part of the eDiscovery ecosystem where your organization resides

Software and/or Services Provider: 50.82%
Law Firm: 21.31%
Corporation: 9.84%
Consultancy: 8.20%
Media/Research Organization: 4.92%
Governmental Entity: 1.64%
Other: 3.28%

2. How would you rate the current general business conditions for eDiscovery in your segment?

Subjective feeling of business performance when compared with business expectations

Good: 54.10%
Normal: 39.34%
Bad: 6.56%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2024/12/Current-Business-Climate-Overview-Fall-2024.pdf" title="Current Business Climate Overview - Fall 2024"]

3. How do you think the business conditions will be in your segment six months from now?

Subjective feeling of business performance when compared with business expectations

Better: 52.46%
Same: 44.26%
Worse: 3.28%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2024/12/Business-Climate-Overview-Six-Months-Fall-2024.pdf" title="Business Climate Overview + Six Months - Fall 2024"]

4. How would you guess revenue in your segment of the eDiscovery ecosystem will be six months from now?

Revenue is income generated from eDiscovery-related business activities

Higher: 57.38%
Same: 39.34%
Lower: 3.28%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2024/12/Revenue-Overview-Six-Months-Fall-2024.pdf" title="Revenue Overview + Six Months - Fall 2024"]

5. How would you guess profits in your segment of the eDiscovery ecosystem will be six months from now?

Profit is the amount of income remaining after accounting for all expenses, debts, additional revenue streams, and operating costs

Higher: 39.34%
Same: 54.10%
Lower: 6.56%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2024/12/Profits-Overview-Six-Months-Fall-2024.pdf" title="Profits Overview + Six Months - Fall 2024"]

6. Of the six items presented below, what is the issue that you feel will most impact the business of eDiscovery over the next six months?

Challenges that may directly impact the business performance of your organization

Increasing Types of Data: 24.59%
Increasing Volumes of Data: 21.31%
Budgetary Constraints: 19.67%
Lack of Personnel: 14.75%
Data Security: 11.48%
Inadequate Technology: 8.20%

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

The location from which you are basing your business assessments

North America – United States: 91.80%
Europe – United Kingdom: 4.92%
North America – Canada: 1.64%
Europe – Non-UK: 1.64%

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

8. What area best describes your primary function in the conduct of your organization's eDiscovery-related business?

Legal/Litigation Support: 63.93%
Business/Business Support (All Other Business Functions): 29.51%
IT/Product Development: 6.56%

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

9. What area best describes your level of support in the conduct of your organization's eDiscovery-related business?

Executive Leadership: 37.70%
Operational Management: 36.07%
Tactical Execution: 26.23%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2024/12/Survey-Respondents-by-Level-of-Support-Fall-2024.pdf" title="Survey Respondents by Level of Support - Fall 2024"]

10. How would you characterize the use of LLMs and GAI in your organization's operations or offerings?

Integrating and Deploying: 39.34%
Considering and Evaluating: 27.87%
Testing and Piloting: 26.23%
No Plans: 6.56%

11. Of the options provided, which do you perceive as the primary benefit of integrating LLMs and GAI into your organization's operations or offerings?

Improved Service/Product Delivery: 44.26%
Competitive Advantage: 26.23%
Cost Savings: 14.75%
Enhanced Decision Making: 9.84%
No Perceived Benefit: 4.92%

12. Of the options provided, which do you perceive as the primary challenge of integrating LLMs and GAI into your organization's operations or offerings?

Results Accuracy: 29.51%
High Costs: 19.67%
Regulatory and Privacy Compliance: 19.67%
Unclear ROI: 16.39%
Skill Gap: 8.20%
Ethical Concerns: 6.56%

13. How would you characterize the trajectory of your organization's Days Sales Outstanding (DSO) during the last quarter?

n=59 Respondents

Increasing: 18.64%
Unfluctuating: 38.98%
Decreasing: 8.47%
Do Not Know: 33.90%

14. How would you characterize the trajectory of your organization's Monthly Recurring Revenue (MRR) during the last quarter?

n=59 Respondents

Increasing: 54.24%
Unfluctuating: 35.59%
Decreasing: 10.17%

15. Which of the following statements best describes the distribution of your organization's revenue across your customer base during the last quarter?

n=59 Respondents

Increasing: 42.37%
Unfluctuating: 35.59%
Decreasing: 6.78%
Do Not Know: 15.25%

Onward and Upward

Respondent Backgrounder

Aggregate Surveys (Winter 2016 - Fall 2024)

Spring Surveys (Fall 2016 - Fall 2024)

Complete Presentation: Market Kinetics - eDiscovery Business Confidence - Fall 2024 (PDF) - Mouseover to Scroll

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2024/12/0-Business-Confidence-121224.pdf" title="0 - Business Confidence - 121224"]

Assisted by GAI and LLM Technologies

News Source

Robinson, R., & Robinson, H. (2024). Optimism and innovation in eDiscovery: Fall 2024 business confidence survey insights. ComplexDiscovery OÜ.

Additional Reading

Source: ComplexDiscovery OÜ

Optimism and Innovation in eDiscovery: Fall 2024 Business Confidence Survey Insights

Market Sizing

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.

[taq_review]

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.

ComplexDiscovery. 2023 eDiscovery Business Confidence Surveys. (Running Listing). November 2023.
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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.
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ComplexDiscovery. 2022 eDiscovery Business Confidence Surveys. (Running Listing). November 2022.
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U.S. Bureau of Economic Analysis (Department of Commerce), 2022. Gross Domestic Product (Advance Estimate), Third Quarter 2022. October 27, 2022.
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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
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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.
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Securities and Exchange Commission. 2021. Form 10-K Statement for KLDiscovery, Inc. March 18, 2021.
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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.
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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.
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Selected Industry eDiscovery Providers (Discussion), 2020. eDiscovery Market Forecasting. May 1, 2020.
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Relativity Fest Panel. “State of the eDiscovery Union.” Panelist Ryan O’Leary (IDC). October 22, 2019.
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KL Discovery. “Investor Presentation.” Pivotal, KL Discovery. May 2019.
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IDC. “Worldwide eDiscovery Software Forecast, 2018-2022.” Ryan O’Leary, Sean Pike. June 2018.
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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.
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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)

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[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

Pricing

Editor’s Note: The Summer 2024 eDiscovery Pricing Survey by ComplexDiscovery OÜ, conducted in partnership with the Electronic Discovery Reference Model (EDRM), now in its twelfth 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 898 individuals, offering a robust and diverse perspective on pricing trends. Its focus spans 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 Summer 2024 survey with 61 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.

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Industry Research

What’s New in eDiscovery Pricing? Summer 2024 Market Report*

ComplexDiscovery Staff

The Summer 2024 eDiscovery Pricing Survey, conducted by ComplexDiscovery OÜ in partnership with the Electronic Discovery Reference Model (EDRM), draws on insights from 61 industry professionals to provide a detailed analysis of current pricing trends in the eDiscovery industry. Since its inception in 2019, the ongoing survey series has amassed data from 898 individual responses over 12 surveys. This report offers an in-depth look at the pricing dynamics for key eDiscovery tasks, including collection, processing, and review, highlighting the unique trends and challenges in each segment.

The following sections from the Summer 2024 survey examine specific pricing areas, providing valuable insights that can assist professionals in cybersecurity, information governance, and legal discovery.

Detailed Collection Pricing Analysis (Figures 1-3)

Onsite Collection: The per hour cost for onsite collection by a forensic examiner predominantly falls between $250 and $350, as reported by 59.0% of respondents. This reflects the need for specialized skills and equipment.
Remote Collection: Remote collection shows a broader distribution of costs, with 45.9% indicating per hour costs between $250 and $350, and 31.1% reporting less than $250. This variation highlights the efficiency and cost benefits associated with remote operations.
Device-Specific Collections: Costs for desktop/laptop and mobile device collections remain high. For desktop/laptop collections, 45.9% of respondents report costs greater than $350 per device, while 47.5% cite similar costs for mobile device collections, underscoring the technical complexities involved.
Investigative Services and Expert Testimony: For investigation, analysis, and report generation, 45.9% of respondents charge between $350 and $550 per hour. Expert witness testimony commands higher rates, with 49.2% charging between $350 and $550 per hour and 19.7% exceeding $550 per hour.

Collection Pricing: Trends and Implications

The comparison between the Winter 2024 and Summer 2024 surveys reveals several shifts in collection pricing. Onsite collection costs have seen a slight diversification, with more respondents indicating both lower and higher cost brackets. Specifically, the proportion of respondents reporting per hour costs of less than $250 increased from 5.3% to 11.5%, while those indicating costs greater than $350 rose from 9.2% to 13.1%. This suggests that while specialized and urgent onsite collections remain premium services, there is also an emerging trend of cost-competitive offerings, possibly driven by increased market competition or the availability of more efficient technologies.

Remote collection has experienced a significant decrease in costs, with 31.1% of respondents in the Summer 2024 survey reporting per hour costs of less than $250, compared to 15.8% in Winter 2024. This trend indicates growing adoption and confidence in remote collection methods, driven by advancements in technology that make remote operations more efficient and cost-effective.

Device-specific collection costs for desktops, laptops, and mobile devices have also seen a slight reduction in higher cost reports. For instance, the cost for desktop/laptop collections greater than $350 per device decreased from 51.3% to 45.9%. Similarly, mobile device collection costs over $350 per device decreased from 50.0% to 47.5%. These changes may reflect improved tools and methodologies for handling diverse data sources, leading to cost savings.

In terms of investigative services and expert testimony, the market has seen a slight shift towards lower and alternative pricing models. The proportion of respondents charging between $350 and $550 per hour for investigation, analysis, and report generation decreased from 53.9% to 45.9%, indicating a potential push towards more competitive pricing or alternative fee structures that better align with client needs.

Comprehensive Processing Pricing Analysis (Figures 4-9)

Cost Efficiency in Large Volume Processing: For data volumes over 251 GB, 49.2% of respondents report processing costs at the ingestion stage as less than $25 per GB. This cost efficiency continues at the completion of processing, where 63.9% charge less than $100 per GB, demonstrating economies of scale in handling large data sets.
Project Management Pricing: Basic project management support costs between $100 and $200 per hour for 70.5% of respondents, while advanced project management tasks cost more than $200 per hour for 62.3% of respondents, indicating the significant investment required for effective project oversight.

Processing Pricing: Trends and Implications

The processing segment has shown notable cost reductions, especially for larger data volumes. For data volumes over 251 GB, the proportion of respondents reporting ingestion costs of less than $25 per GB increased from 32.9% in Winter 2024 to 49.2% in Summer 2024. This significant cost efficiency suggests that service providers are achieving greater economies of scale and are passing these savings onto their clients.

Similar trends are observed at the completion of processing. For data volumes over 251 GB, respondents indicating costs less than $100 per GB increased from 46.1% to 63.9%. These trends underscore the enhanced cost-effectiveness of handling large data sets, likely due to advancements in processing technologies and methodologies that streamline the workflow.

Hosting costs have also seen a downward trend. The proportion of respondents reporting hosting costs without analytics for 250 GB or less at less than $10 per GB per month increased from 28.9% to 41.0%. For hosting with analytics, the proportion reporting costs less than $15 per GB per month increased from 35.5% to 50.8%. These reductions highlight increasing market competition and efficiency improvements, making hosting more accessible and affordable.

Project management support costs for eDiscovery have shown some upward movement in mid-range costs. The proportion of respondents charging between $100 and $200 per hour for basic support increased from 56.6% to 70.5%, reflecting the sustained importance of project management in successful eDiscovery projects. For advanced support, the costs remain consistently high, with a slight decrease in respondents charging more than $200 per hour (64.5% to 62.3%), indicating that while complex project management remains a premium service, there might be efforts to offer more competitive rates.

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

Predictive Coding in TAR: For predictive coding as part of TAR, costs for datasets under 250 GB are less than $75 per GB for 36.1% of respondents and 42.6% for datasets over 251 GB, highlighting cost variations based on dataset size. Alternative pricing models for predictive coding are becoming more prevalent, with 31.1% for datasets under 250 GB and 29.5% for datasets over 251 GB, reflecting a shift towards customized pricing strategies.
Document Review Costs: Onsite document review costs are higher, with 45.9% exceeding $40 per hour. Remote review is more economical, with 49.2% of respondents reporting costs between $25 and $40 per hour, indicating the growing acceptance and cost benefits of remote review models.
Per Document Review Costs: Onsite reviews cost more than $1.00 per document for 27.9% of respondents, reflecting the meticulous and labor-intensive nature of the task. Remote reviews show a more balanced cost distribution, with 44.3% reporting costs between $0.50 and $1.00 per document.

Review Pricing: Trends and Implications

The review segment, particularly predictive coding in technology-assisted review (TAR), has witnessed significant cost reductions. For datasets under 250 GB, the proportion of respondents reporting costs less than $75 per GB increased from 21.1% in Winter 2024 to 36.1% in Summer 2024. For larger datasets over 251 GB, this proportion increased from 23.7% to 42.6%. These changes indicate improved affordability and efficiency of predictive coding technologies, making advanced review techniques more accessible to a broader range of cases and clients.

Document review costs have also shown notable trends. Onsite review costs exceeding $40 per hour slightly decreased from 48.7% to 45.9%, while remote review costs in the $25 to $40 per hour range increased from 46.1% to 49.2%. This shift underscores the growing feasibility and acceptance of remote review models, which offer significant cost benefits and access to a broader talent pool.

The cost per document for review has also become more balanced, with a higher proportion of respondents reporting costs between $0.50 and $1.00 per document for remote reviews (27.6% to 44.3%). This trend highlights the increasing cost-effectiveness and competitive pricing in the document review market, driven by technological advancements and process efficiencies.

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

The Summer 2024 survey predominantly represents professionals from the United States, accounting for 93.4% of respondents, emphasizing the influence of North American practices on the eDiscovery industry. Other regions such as the United Kingdom and Asia/Asia Pacific are also represented, though to a lesser extent.

Professionals from software and services providers constitute the largest segment at 41.0%, followed by law firms at 27.9%. This highlights the critical role of technology and legal practices in the eDiscovery ecosystem. Consultancies and corporations also contribute significantly, representing 13.1% and 9.8%, respectively.

A majority of respondents (67.2%) are involved in legal or litigation support roles, while 29.5% are in business and business support functions, underscoring the interdisciplinary nature of eDiscovery operations. The remaining 3.3% in IT and product development roles reflect the importance of technological innovation in eDiscovery solutions.

Adapting to Market Implications in eDiscovery

The observed trends in the Summer 2024 survey indicate a market that is becoming increasingly cost-efficient and competitive. The notable cost reductions in processing and review tasks reflect advancements in technology and methodologies, allowing service providers to achieve greater efficiencies and pass these savings onto clients. The growing prevalence of remote operations, both in collection and review, underscores a broader industry shift towards leveraging technology to optimize costs and improve accessibility.

These trends suggest that eDiscovery service providers are continuously innovating to offer more competitive and flexible pricing models, aligning their services with the evolving needs of their clients. For eDiscovery professionals, these insights provide valuable benchmarks for strategic planning and informed decision-making, helping them navigate the complex and dynamic landscape of eDiscovery services.

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Summer 2024 eDiscovery Pricing Survey Responses

n=61 Respondents

Collection Pricing

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

Less than $250 per hour. 11.5%
Between $250 and $350 per hour. 59.0%
Greater than $350 per hour. 13.1%
Do not know. 14.8%
Alternative pricing model for per hour costs. 1.6%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2024/08/Collection-Pricing_-Per-Hour-Cost-for-an-Onsite-Collection-by-a-Forensic-Examiner-Summer-2024.pdf" title="Collection Pricing_ Per Hour Cost for an Onsite Collection by a Forensic Examiner - Summer 2024"]

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

Less than $250 per hour. 31.1%
Between $250 and $350 per hour. 45.9%
Greater than $350 per hour. 4.9%
Do not know. 14.8%
Alternative pricing model for per hour costs. 3.3%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2024/08/Collection-Pricing_-Per-Hour-Cost-for-a-Remote-Collection-by-a-Forensic-Examiner-Summer-2024.pdf" title="Collection Pricing_ Per Hour Cost for a Remote Collection by a Forensic Examiner - Summer 2024"]

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

Less than $250 per device. 13.1%
Between $250 and $350 per device. 18.0%
Greater than $350 per device. 45.9%
Do not know. 13.1%
Alternative pricing model for per device costs. 9.8%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2024/08/Collection-Pricing_-Per-Device-Cost-for-a-Desktop_Laptop-Computer-Collection-by-a-Forensic-Examiner-Summer-2024.pdf" title="Collection Pricing_ Per Device Cost for a Desktop_Laptop Computer Collection by a Forensic Examiner - Summer 2024"]

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

Less than $250 per device. 11.5%
Between $250 and $350 per device. 19.7%
Greater than $350 per device. 47.5%
Do not know. 13.1%
Alternative pricing model for per device costs. 8.2%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2024/08/Collection-Pricing_-Per-Device-Cost-for-a-Mobile-Device-Collection-by-a-Forensic-Examiner-Summer-2024.pdf" title="Collection Pricing_ Per Device Cost for a Mobile Device Collection by a Forensic Examiner - Summer 2024"]

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

Less than $350 per hour. 32.8%
Between $350 and $550 per hour. 45.9%
Greater than $550 per hour. 3.3%
Do not know. 14.8%
Alternative pricing model for per hour costs. 3.3%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2024/08/Collection-Pricing_-Per-Hour-Cost-for-Investigation-Analysis-and-Report-Generation-by-an-FE-Summer-2024.pdf" title="Collection Pricing_ Per Hour Cost for Investigation, Analysis and Report Generation by an FE - Summer 2024"]

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

Less than $350 per hour. 11.5%
Between $350 and $550 per hour. 49.2%
Greater than $550 per hour. 19.7%
Do not know. 16.4%
Alternative pricing model for per hour costs. 3.3%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2024/08/Collection-Pricing_-Per-Hour-Cost-for-Expert-Witness-Testimony-In-Person-and-Written-by-an-FE-Summer-2024.pdf" title="Collection Pricing_ Per Hour Cost for Expert Witness Testimony (In-Person and Written) by an FE - Summer 2024"]

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. 42.6%
Between $25 and $75 per GB. 34.4%
Greater than $75 per GB. 8.2%
Do not know. 9.8%
Alternative pricing model for per GB costs. 4.9%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2024/08/Processing-Pricing_-Per-GB-Cost-to-Process-ESI-Based-on-Volume-at-Ingestion-for-250-GB-or-Less-Summer-2024.pdf" title="Processing Pricing_ Per GB Cost to Process ESI Based on Volume at Ingestion for 250 GB or Less - Summer 2024"]

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. 49.2%
Between $25 and $75 per GB. 32.8%
Greater than $75 per GB. 3.3%
Do not know. 11.5%
Alternative pricing model for per GB costs. 3.3%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2024/08/Processing-Pricing_-Per-GB-Cost-to-Process-ESI-Based-on-Volume-at-Ingestion-for-251-GB-or-More-Summer-2024.pdf" title="Processing Pricing_ Per GB Cost to Process ESI Based on Volume at Ingestion for 251 GB or More - Summer 2024"]

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. 57.4%
Between $100 and $150 per GB. 18.0%
Greater than $150 per GB. 3.3%
Do not know. 13.1%
Alternative pricing model for per GB costs. 8.2%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2024/08/Processing-Pricing_-Per-GB-Cost-to-Process-ESI-Based-on-Volume-at-Completion-for-250-GB-or-Less-Summer-2024.pdf" title="Processing Pricing_ Per GB Cost to Process ESI Based on Volume at Completion for 250 GB or Less - Summer 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. 63.9%
Between $100 and $150 per GB. 13.1%
Greater than $150 per GB. 1.6%
Do not know. 13.1%
Alternative pricing model for per GB costs. 8.2%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2024/08/Processing-Pricing_-Per-GB-Cost-to-Process-ESI-Based-on-Volume-at-Completion-for-251-GB-or-More-Summer-2024.pdf" title="Processing Pricing_ Per GB Cost to Process ESI Based on Volume at Completion for 251 GB or More - Summer 2024"]

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. 41.0%
Between $10 and $20 per GB per month. 37.7%
Greater than $20 per GB per month. 6.6%
Do not know. 13.1%
Alternative pricing model for per GB costs. 1.6%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2024/08/Processing-Pricing_-Per-GB-Cost-Per-Month-to-Host-ESI-without-Analytics-for-250-GB-or-Less-Summer-2024.pdf" title="Processing Pricing_ Per GB Cost Per Month to Host ESI without Analytics for 250 GB or Less - Summer 2024"]

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. 57.4%
Between $10 and $20 per GB per month. 24.6%
Greater than $20 per GB per month. 3.3%
Do not know. 13.1%
Alternative pricing model for per GB costs. 1.6%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2024/08/Processing-Pricing_-Per-GB-Cost-Per-Month-to-Host-ESI-without-Analytics-for-251-GB-or-More-Summer-2024.pdf" title="Processing Pricing_ Per GB Cost Per Month to Host ESI without Analytics for 251 GB or More - Summer 2024"]

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. 50.8%
Between $15 and $25 per GB per month. 27.9%
Greater than $25 per GB per month. 6.6%
Do not know. 13.1%
Alternative pricing model for per GB costs. 1.6%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2024/08/Processing-Pricing_-Per-GB-Cost-Per-Month-to-Host-ESI-with-Analytics-for-250-GB-or-Less-Summer-2024.pdf" title="Processing Pricing_ Per GB Cost Per Month to Host ESI with Analytics for 250 GB or Less - Summer 2024"]

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. 62.3%
Between $15 and $25 per GB per month. 19.7%
Greater than $25 per GB per month. 4.9%
Do not know. 11.5%
Alternative pricing model for per GB costs. 1.6%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2024/08/Processing-Pricing_-Per-GB-Cost-Per-Month-to-Host-ESI-with-Analytics-for-251-GB-or-More-Summer-2024-1.pdf" title="Processing Pricing_ Per GB Cost Per Month to Host ESI with Analytics for 251 GB or More - Summer 2024"]

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. 14.8%
Between $50 and $100 per user per month. 55.7%
Greater than $100 per user per month. 6.6%
Do not know. 11.5%
Alternative pricing model for user license costs. 11.5%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2024/08/Processing-Pricing_-User-License-Fee-Per-Month-for-Access-to-Hosted-Data-for-250-Users-or-Less-Summer-2024.pdf" title="Processing Pricing_ User License Fee Per Month for Access to Hosted Data for 250 Users or Less - Summer 2024"]

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. 18.0%
Between $50 and $100 per user per month. 54.1%
Greater than $100 per user per month. 4.9%
Do not know. 11.5%
Alternative pricing model for user license costs. 11.5%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2024/08/Processing-Pricing_-User-License-Fee-Per-Month-for-Access-to-Hosted-Data-for-251-Users-or-More-Summer-2024.pdf" title="Processing Pricing_ User License Fee Per Month for Access to Hosted Data for 251 Users or More - Summer 2024"]

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

Less than $100 per hour. 3.3%
Between $100 and $200 per hour. 70.5%
Greater than $200 per hour. 16.4%
Do not know. 9.8%
Alternative pricing model for per hour costs. 0.0%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2024/08/Processing-Pricing_-Per-Hour-Cost-of-Basic-Project-Management-Support-for-eDiscovery-Summer-2024.pdf" title="Processing Pricing_ Per Hour Cost of Basic Project Management Support for eDiscovery - Summer 2024"]

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

Less than $100 per hour. 0.0%
Between $100 and $200 per hour. 27.9%
Greater than $200 per hour. 62.3%
Do not know. 9.8%
Alternative pricing model for per hour costs. 0.0%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2024/08/Processing-Pricing_-Per-Hour-Cost-of-Advanced-Project-Management-Support-for-eDiscovery-Summer-2024.pdf" title="Processing Pricing_ Per Hour Cost of Advanced Project Management Support for eDiscovery - Summer 2024"]

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. 36.1%
Between $75 and $150 per GB. 14.8%
Greater than $150 per GB. 3.3%
Do not know. 14.8%
Alternative pricing model for per GB costs. 31.1%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2024/08/Review-Pricing_-Per-GB-Cost-for-Predictive-Coding-in-a-Technology-Assisted-Review-for-250-GB-or-Less-Summer-2024.pdf" title="Review Pricing_ Per GB Cost for Predictive Coding in a Technology-Assisted Review for 250 GB or Less - Summer 2024"]

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. 42.6%
Between $75 and $150 per GB. 8.2%
Greater than $150 per GB. 3.3%
Do not know. 16.4%
Alternative pricing model for per GB costs. 29.5%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2024/08/Review-Pricing_-Per-GB-Cost-for-Predictive-Coding-in-a-Technology-Assisted-Review-for-251-GB-or-More-Summer-2024.pdf" title="Review Pricing_ Per GB Cost for Predictive Coding in a Technology-Assisted Review for 251 GB or More - Summer 2024"]

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

Less than $25 per hour. 6.6%
Between $25 and $40 per hour. 24.6%
Greater than $40 per hour. 45.9%
Do not know. 19.7%
Alternative pricing model for per hour costs. 3.3%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2024/08/Review-Pricing_-Per-Hour-Cost-for-Document-Review-Attorneys-to-Review-Documents-Onsite-Summer-2024.pdf" title="Review Pricing_ Per Hour Cost for Document Review Attorneys to Review Documents Onsite - Summer 2024"]

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

Less than $25 per hour. 8.2%
Between $25 and $40 per hour. 49.2%
Greater than $40 per hour. 24.6%
Do not know. 14.8%
Alternative pricing model for per hour costs. 3.3%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2024/08/Review-Pricing_-Per-Hour-Cost-for-Document-Review-Attorneys-to-Review-Documents-Remote-Summer-2024.pdf" title="Review Pricing_ Per Hour Cost for Document Review Attorneys to Review Documents Remote - Summer 2024"]

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. 6.6%
Between $0.50 and $1.00 per document. 31.1%
Greater than $1.00 per document. 27.9%
Do not know. 24.6%
Alternative pricing model for per document costs. 9.8%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2024/08/Review-Pricing_-Per-Document-Cost-for-Document-Review-Attorneys-to-Review-Documents-Onsite-Summer-2024.pdf" title="Review Pricing_ Per Document Cost for Document Review Attorneys to Review Documents Onsite - Summer 2024"]

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. 11.5%
Between $0.50 and $1.00 per document. 44.3%
Greater than $1.00 per document. 13.1%
Do not know. 21.3%
Alternative pricing model for per document costs. 9.8%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2024/08/Review-Pricing_-Per-Document-Cost-for-Document-Review-Attorneys-to-Review-Documents-Remote-Summer-2024.pdf" title="Review Pricing_ Per Document Cost for Document Review Attorneys to Review Documents Remote - Summer 2024"]

Background Information

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

North America – United States. 93.4%
Europe – United Kingdom. 3.3%
Asia/Asia Pacific. 3.3%
Europe – Non-UK. 0.0%
North America – Canada. 0.0%
Middle East/Africa. 0.0%
Central/South America. 0.0%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2024/08/Survey-Respondents-by-Geographic-Region-Summer-2024.pdf" title="Survey Respondents by Geographic Region - Summer 2024"]

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

Software and/or Services Provider. 41.0%
Law Firm. 27.9%
Consultancy. 13.1%
Corporation. 9.8%
Media/Research Organization/Educational Association. 8.2%
Government Entity. 0.0%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2024/08/Survey-Respondents-by-Organizational-Segment-Summer-2024.pdf" title="Survey Respondents by Organizational Segment - Summer 2024"]

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

Legal/Litigation Support. 67.2%
Business/Business Support (All Other Business Functions). 29.5%
IT/Product Development. 3.3%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2024/08/Survey-Respondents-by-Primary-Function-Summer-2024.pdf" title="Survey Respondents by Primary Function - Summer 2024"]

Background: eDiscovery Pricing Survey Respondents by Survey

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2024/08/Survey-Respondents-Individual-and-Aggregate-Overview-Summer-2024.pdf" title="Survey Respondents (Individual and Aggregate Overview) - Summer 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

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

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2024/08/0-Market-Kinetics-eDiscovery-Pricing-Summer-2024-080524.pdf" title="0 - Market Kinetics - eDiscovery Pricing - Summer 2024 - 080524"]

Source: ComplexDiscovery OÜ

[exclude_from_rss]

[taq_review]

[/exclude_from_rss]

Industry Research

What’s New in eDiscovery Pricing? Summer 2024 Market Report*

ComplexDiscovery Staff

Detailed Collection Pricing Analysis (Figures 1-3)

Onsite Collection: The per hour cost for onsite collection by a forensic examiner predominantly falls between $250 and $350, as reported by 59.0% of respondents. This reflects the need for specialized skills and equipment.
Remote Collection: Remote collection shows a broader distribution of costs, with 45.9% indicating per hour costs between $250 and $350, and 31.1% reporting less than $250. This variation highlights the efficiency and cost benefits associated with remote operations.
Device-Specific Collections: Costs for desktop/laptop and mobile device collections remain high. For desktop/laptop collections, 45.9% of respondents report costs greater than $350 per device, while 47.5% cite similar costs for mobile device collections, underscoring the technical complexities involved.
Investigative Services and Expert Testimony: For investigation, analysis, and report generation, 45.9% of respondents charge between $350 and $550 per hour. Expert witness testimony commands higher rates, with 49.2% charging between $350 and $550 per hour and 19.7% exceeding $550 per hour.

Collection Pricing: Trends and Implications

Comprehensive Processing Pricing Analysis (Figures 4-9)

Cost Efficiency in Large Volume Processing: For data volumes over 251 GB, 49.2% of respondents report processing costs at the ingestion stage as less than $25 per GB. This cost efficiency continues at the completion of processing, where 63.9% charge less than $100 per GB, demonstrating economies of scale in handling large data sets.
Project Management Pricing: Basic project management support costs between $100 and $200 per hour for 70.5% of respondents, while advanced project management tasks cost more than $200 per hour for 62.3% of respondents, indicating the significant investment required for effective project oversight.

Processing Pricing: Trends and Implications

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

Predictive Coding in TAR: For predictive coding as part of TAR, costs for datasets under 250 GB are less than $75 per GB for 36.1% of respondents and 42.6% for datasets over 251 GB, highlighting cost variations based on dataset size. Alternative pricing models for predictive coding are becoming more prevalent, with 31.1% for datasets under 250 GB and 29.5% for datasets over 251 GB, reflecting a shift towards customized pricing strategies.
Document Review Costs: Onsite document review costs are higher, with 45.9% exceeding $40 per hour. Remote review is more economical, with 49.2% of respondents reporting costs between $25 and $40 per hour, indicating the growing acceptance and cost benefits of remote review models.
Per Document Review Costs: Onsite reviews cost more than $1.00 per document for 27.9% of respondents, reflecting the meticulous and labor-intensive nature of the task. Remote reviews show a more balanced cost distribution, with 44.3% reporting costs between $0.50 and $1.00 per document.

Review Pricing: Trends and Implications

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

Adapting to Market Implications in eDiscovery

[the_ad id="45753"]

Summer 2024 eDiscovery Pricing Survey Responses

n=61 Respondents

Collection Pricing

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

Less than $250 per hour. 11.5%
Between $250 and $350 per hour. 59.0%
Greater than $350 per hour. 13.1%
Do not know. 14.8%
Alternative pricing model for per hour costs. 1.6%

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

Less than $250 per hour. 31.1%
Between $250 and $350 per hour. 45.9%
Greater than $350 per hour. 4.9%
Do not know. 14.8%
Alternative pricing model for per hour costs. 3.3%

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

Less than $250 per device. 13.1%
Between $250 and $350 per device. 18.0%
Greater than $350 per device. 45.9%
Do not know. 13.1%
Alternative pricing model for per device costs. 9.8%

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

Less than $250 per device. 11.5%
Between $250 and $350 per device. 19.7%
Greater than $350 per device. 47.5%
Do not know. 13.1%
Alternative pricing model for per device costs. 8.2%

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

Less than $350 per hour. 32.8%
Between $350 and $550 per hour. 45.9%
Greater than $550 per hour. 3.3%
Do not know. 14.8%
Alternative pricing model for per hour costs. 3.3%

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

Less than $350 per hour. 11.5%
Between $350 and $550 per hour. 49.2%
Greater than $550 per hour. 19.7%
Do not know. 16.4%
Alternative pricing model for per hour costs. 3.3%

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. 42.6%
Between $25 and $75 per GB. 34.4%
Greater than $75 per GB. 8.2%
Do not know. 9.8%
Alternative pricing model for per GB costs. 4.9%

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. 49.2%
Between $25 and $75 per GB. 32.8%
Greater than $75 per GB. 3.3%
Do not know. 11.5%
Alternative pricing model for per GB costs. 3.3%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2024/08/Processing-Pricing_-Per-GB-Cost-to-Process-ESI-Based-on-Volume-at-Ingestion-for-251-GB-or-More-Summer-2024.pdf" title="Processing Pricing_ Per GB Cost to Process ESI Based on Volume at Ingestion for 251 GB or More - Summer 2024"]

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. 57.4%
Between $100 and $150 per GB. 18.0%
Greater than $150 per GB. 3.3%
Do not know. 13.1%
Alternative pricing model for per GB costs. 8.2%

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. 63.9%
Between $100 and $150 per GB. 13.1%
Greater than $150 per GB. 1.6%
Do not know. 13.1%
Alternative pricing model for per GB costs. 8.2%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2024/08/Processing-Pricing_-Per-GB-Cost-to-Process-ESI-Based-on-Volume-at-Completion-for-251-GB-or-More-Summer-2024.pdf" title="Processing Pricing_ Per GB Cost to Process ESI Based on Volume at Completion for 251 GB or More - Summer 2024"]

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. 41.0%
Between $10 and $20 per GB per month. 37.7%
Greater than $20 per GB per month. 6.6%
Do not know. 13.1%
Alternative pricing model for per GB costs. 1.6%

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. 57.4%
Between $10 and $20 per GB per month. 24.6%
Greater than $20 per GB per month. 3.3%
Do not know. 13.1%
Alternative pricing model for per GB costs. 1.6%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2024/08/Processing-Pricing_-Per-GB-Cost-Per-Month-to-Host-ESI-without-Analytics-for-251-GB-or-More-Summer-2024.pdf" title="Processing Pricing_ Per GB Cost Per Month to Host ESI without Analytics for 251 GB or More - Summer 2024"]

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. 50.8%
Between $15 and $25 per GB per month. 27.9%
Greater than $25 per GB per month. 6.6%
Do not know. 13.1%
Alternative pricing model for per GB costs. 1.6%

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. 62.3%
Between $15 and $25 per GB per month. 19.7%
Greater than $25 per GB per month. 4.9%
Do not know. 11.5%
Alternative pricing model for per GB costs. 1.6%

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. 14.8%
Between $50 and $100 per user per month. 55.7%
Greater than $100 per user per month. 6.6%
Do not know. 11.5%
Alternative pricing model for user license costs. 11.5%

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. 18.0%
Between $50 and $100 per user per month. 54.1%
Greater than $100 per user per month. 4.9%
Do not know. 11.5%
Alternative pricing model for user license costs. 11.5%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2024/08/Processing-Pricing_-User-License-Fee-Per-Month-for-Access-to-Hosted-Data-for-251-Users-or-More-Summer-2024.pdf" title="Processing Pricing_ User License Fee Per Month for Access to Hosted Data for 251 Users or More - Summer 2024"]

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

Less than $100 per hour. 3.3%
Between $100 and $200 per hour. 70.5%
Greater than $200 per hour. 16.4%
Do not know. 9.8%
Alternative pricing model for per hour costs. 0.0%

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

Less than $100 per hour. 0.0%
Between $100 and $200 per hour. 27.9%
Greater than $200 per hour. 62.3%
Do not know. 9.8%
Alternative pricing model for per hour costs. 0.0%

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. 36.1%
Between $75 and $150 per GB. 14.8%
Greater than $150 per GB. 3.3%
Do not know. 14.8%
Alternative pricing model for per GB costs. 31.1%

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. 42.6%
Between $75 and $150 per GB. 8.2%
Greater than $150 per GB. 3.3%
Do not know. 16.4%
Alternative pricing model for per GB costs. 29.5%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2024/08/Review-Pricing_-Per-GB-Cost-for-Predictive-Coding-in-a-Technology-Assisted-Review-for-251-GB-or-More-Summer-2024.pdf" title="Review Pricing_ Per GB Cost for Predictive Coding in a Technology-Assisted Review for 251 GB or More - Summer 2024"]

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

Less than $25 per hour. 6.6%
Between $25 and $40 per hour. 24.6%
Greater than $40 per hour. 45.9%
Do not know. 19.7%
Alternative pricing model for per hour costs. 3.3%

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

Less than $25 per hour. 8.2%
Between $25 and $40 per hour. 49.2%
Greater than $40 per hour. 24.6%
Do not know. 14.8%
Alternative pricing model for per hour costs. 3.3%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2024/08/Review-Pricing_-Per-Hour-Cost-for-Document-Review-Attorneys-to-Review-Documents-Remote-Summer-2024.pdf" title="Review Pricing_ Per Hour Cost for Document Review Attorneys to Review Documents Remote - Summer 2024"]

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. 6.6%
Between $0.50 and $1.00 per document. 31.1%
Greater than $1.00 per document. 27.9%
Do not know. 24.6%
Alternative pricing model for per document costs. 9.8%

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. 11.5%
Between $0.50 and $1.00 per document. 44.3%
Greater than $1.00 per document. 13.1%
Do not know. 21.3%
Alternative pricing model for per document costs. 9.8%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2024/08/Review-Pricing_-Per-Document-Cost-for-Document-Review-Attorneys-to-Review-Documents-Remote-Summer-2024.pdf" title="Review Pricing_ Per Document Cost for Document Review Attorneys to Review Documents Remote - Summer 2024"]

Background Information

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

North America – United States. 93.4%
Europe – United Kingdom. 3.3%
Asia/Asia Pacific. 3.3%
Europe – Non-UK. 0.0%
North America – Canada. 0.0%
Middle East/Africa. 0.0%
Central/South America. 0.0%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2024/08/Survey-Respondents-by-Geographic-Region-Summer-2024.pdf" title="Survey Respondents by Geographic Region - Summer 2024"]

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

Software and/or Services Provider. 41.0%
Law Firm. 27.9%
Consultancy. 13.1%
Corporation. 9.8%
Media/Research Organization/Educational Association. 8.2%
Government Entity. 0.0%

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

Legal/Litigation Support. 67.2%
Business/Business Support (All Other Business Functions). 29.5%
IT/Product Development. 3.3%

[pdf-embedder url="https://complexdiscovery.com/wp-content/uploads/2024/08/Survey-Respondents-by-Primary-Function-Summer-2024.pdf" title="Survey Respondents by Primary Function - Summer 2024"]

Background: eDiscovery Pricing Survey Respondents by Survey

Assisted by GAI and LLM Technologies

Additional Reading

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

Source: ComplexDiscovery OÜ

What’s New in eDiscovery Pricing? Summer 2024 Market Report

M&A

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

Advertising + Metrics

Maximizing Market Influence: An Update on ComplexDiscovery OÜ’s 2024 Marketing Programs

Overview

Running Results: Predictive Coding One-Question Provider Implementation Survey - ComplexDiscovery Archived Content

ARCHIVED CONTENT

Predictive Coding One-Question Provider Implementation Survey – Initial Results

Current Responders

Current Responses (By Provider)

@Legal (CasePoint TAR) @AtLegal

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) @D4Discovery

Daegis (Acumen) @Daegis

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

Huron Legal (Integrated Analytics | PureDiscovery) @HuronLegal

kCura (Relativity Assisted Review | Content Analyst)

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

Liquid Litigation Management (Technology-Assisted Review) @LLMinc

Nuix (Automatic Classification) @Nuix

Orange Legal Technologies (Predictive Review | OrcaTec) @OrangeLT

OrcaTec (OrcaPredict) @OrcaTec

Prolorem (Prolorem eDi)

Recommind (Predictive Coding | Axelerate) @Recommind

Servient (Servient Predictive Review) @Servient

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

TCDI (Suggestive Coding | Content Analyst)

UBIC (CJK TAR)

Valora Technologies (Valora Auto Review Services) @ValoraTech

Xerox Litigation Services (CategoriX) @Xerox_XLS

Current Responses (By Answer)

Active Learning

Language Modeling

Latent Semantic Analysis

Linguistic Analysis

Naive Bayesian Classifier

Nearest Neighbor Classifier

Probabilistic Latent Semantic Analysis

Relevance Feedback

Support Vector Machine

Other: Logistical Regression

Other: Probabilistic Hierarchical Context-Free Grammars

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