Five Great Reads on Data Discovery and Legal Discovery for January 2019

Five Great Reads on Data Discovery and Legal Discovery is a subscription newsletter regularly highlighting at least five articles to inform and update legal and information technology professionals on the art and science of data discovery¹ and legal discovery.²

Individuals and Organizations Highlighted in the January Edition include:

James Murphy
Myles McLeod
Brian Sommer
Dean Takahashi
Kimberly Whitler
Neil Morgan
Heli Tiirmaa-Klaar
ComplexDiscovery

January Edition (PDF) Mouseover and Scroll

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Click Here for Original Version

¹Data Discovery is the exploration of patterns and trends within unstructured data with the objective of uncovering insight and driving action.
²Electronic Discovery (Legal Discovery) is the process of identifying, preserving, collecting, processing, searching, reviewing and producing electronically stored information that may be relevant to a civil, criminal, or regulatory matter.

Additional Reading

Source: ComplexDiscovery

Considering eDiscovery Providers? Competencies, Attributes, and Distribution Frameworks

Understanding that a provider’s organizational character is made up of more than capabilities, it seems reasonable that a common set of descriptive elements for competency, attributes, and distribution frameworks might be beneficial for the provider comparison process.

Objectifying the Subjective: Evaluating eDiscovery Vendor Viability

This updated article highlights one approach to evaluating the selection of eDiscovery vendors that may help decision makers by providing an objective framework useful for the competitive comparison and contrasting of subjective evaluation elements. While the components and ratings used in the approach may be adjusted and weighted based on the experiences and preference of evaluators, the framework is beneficial for ensuring a holistic evaluation process for determining vendor viability.

Drunks, DNA and Data Transfer Risk in eDiscovery

Just as there are many tasks in electronic discovery, many times there are multiple technologies and platforms involved in the complete electronic discovery process. When there are multiple technologies and platforms involved, data must be transferred from disparate technologies and platforms to other disparate technologies and platforms. This data transfer can be considered a risk factor that impacts the overall electronic discovery process.

Considering eDiscovery Review? Task, Spend, and Cost Data Points

Based on the complexity of data and legal discovery, it is a continual challenge to understand the relationships, opportunities, and costs of specific eDiscovery tasks. With this challenge in mind and to provide information and context to discussions around the eDiscovery task of review, the following review-centric task, spend, and cost data points may be beneficial to legal, business, and IT professionals considering the economics of eDiscovery.

Considering eDiscovery Processing? Task, Spend, and Cost Data Points

Based on the complexity of data and legal discovery, it is a continual challenge to understand the relationships, opportunities, and costs of specific eDiscovery tasks. With this challenge in mind and to provide information and context to discussions around the eDiscovery task of processing, the following processing-centric task, spend, and cost data points may be beneficial to legal, business, and IT professionals considering the economics of eDiscovery.

Considering eDiscovery Collection? Task, Spend, and Cost Data Points

Based on the complexity of data and legal discovery, it is a continual challenge to understand the relationships, opportunities, and costs of specific eDiscovery tasks. With this challenge in mind and to provide information and context to discussions around the eDiscovery task of collection, the following collection-centric task, spend, and cost data points may be beneficial to legal, business, and IT professionals considering the economics of eDiscovery.

TAR vs. Keyword Search Challenge, Round 6 (Instant Feedback)

Even with a large pool of participants, ample time, and the ability to hone search queries based on instant feedback, nobody was able to generate a better production than Technology-Assisted Review (TAR) when the same amount of review effort was expended. It seems fair to say that keyword search often requires twice as much document review to achieve a production that is as good as what you would get TAR.

Actively Learning? Predictive Coding Technologies and Protocols Survey – Spring 2019 Results

The Predictive Coding Technologies and Protocols Survey is a non-scientific survey designed to help provide a general understanding of the use of predictive coding technologies, protocols, and workflows by data discovery and legal discovery professionals within the eDiscovery ecosystem.