What Is eDiscovery Analytics?

What Is eDiscovery Analytics

The term eDiscovery analytics is often associated with a stigma that is unfortunate and, in reality, unfounded. Regardless of the specific technology, eDiscovery analytics tools are highly sophisticated and continually evolving, so it is essential to maintain them and train employees to use them effectively. In fact, hiring experts in this area to analyze and manage your data can save significant time and money. That’s really the point of using eDiscovery analytics, isn’t it? Explore more about data analytics and eDiscovery at

Casepoint CaseAssist

With the release of its newest cloud-based analytics platform, Casepoint, the company is setting itself up to make the paradigm shift from eDiscovery to complex document analytics. Its proprietary technology is built on predictive models that anticipate user behavior and translate that data into actionable insights. This new tool also helps identify documents that may be subject to privilege, reducing the risk of costly litigation.

The Casepoint suite includes advanced analytics, batching and data ingestion tools, early case assessment tools, and technology-assisted review. These features allow users to save time and effort, as they don’t need to learn multiple software packages to use the platform. The integrated suite of tools helps users save time and prevents them from having to wait for other vendors to release new versions of their software. It also includes many features, making it a one-stop shop for eDiscovery administrators.

Conceptual Analytics

The use of Conceptual Analytics in eDiscovery can help organizations better organize and assess the semantic content of large, unstructured sets of documents. Conceptual analytics focuses on the underlying concepts within documents and reduces the time required for review. However, it’s important to remember that a high-quality index is essential for concept searching and predictive analytics to work effectively. This means that indexes should be free of disclaimers, footers, and basic legal text. If any of these features are missing, they may cause false weights and result in inaccurate results.

Early-stage data assessment workflows provide valuable insights into the corpus and defensible metrics to help attorneys make strategic decisions about review options. In addition, it can save time and money since analytics is used at an earlier stage in the review. Furthermore, early-stage workflows help identify issues and reduce the costs associated with eDiscovery.

Predictive Coding

While predictive coding has many potential applications for the eDiscovery process, it is still in its infancy. Although there are many companies and software programs that make use of it, there are fewer regulations or court rulings that apply it to eDiscovery. While it has many benefits, there are some concerns and a lack of judicial guidance on the use of such software.

One of the main concerns when choosing a predictive coding system is accuracy. Although a good algorithm can save time and money, the quality of the output may not be as high as a human reviewer. A faulty sample could compromise the accuracy of the algorithm. To avoid this problem, predictive coding algorithms should use a high-quality sample and a systematic approach to manual review. It should also have a cutoff point for each category so that it does not over- or under-categorize documents.

Prioritized Review

Most of us think of reviewed eDiscovery as the last step before the production of documents to the opposing party. However, prior to the review stage, a legal team gains more insight into the case and formulates a legal strategy based on the newly uncovered information. Advancements in eDiscovery analytics have given legal teams better factual insights before the review phase even begins.

A good predictive coding workflow stops reviewing documents at thresholds and identifies those most likely to be relevant to the case. It can handle cases with up to 1,000 documents and requires minimal training. Moreover, this method also reduces the total review time by identifying the documents with high predictive scores and likely responses. In addition to this, it includes monitoring tools to ensure that the model is working correctly. It can also prioritize documents for review based on their predictive scores.

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