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05. March 2021

3 Keys to Improving eDiscovery Efficiency with Better Data Management

Here are several ways you can improve your eDiscovery efficiency and save yourself time, effort, and money in the long run.

The purpose of eDiscovery is to review and discover those electronic documents that may be relevant to pending litigation. Prior to reviewing the documents to determine whether or not they are relevant, the documents have to be collected from the appropriate parties or data custodians. Unfortunately, the collection process can be a landmine to navigate if your data is pooling from multiple sources.

Usually the collection process takes time, while the responding party gathers data from a particular date range and set of custodians specified in the production requests. Even so, there are often missing pieces that are found later that need to be added to the data set. In addition, once the documents are collected, the document review team must delete or “cull” any duplicate or trivial data during a first pass review and tag or redact information that may be subject to confidentiality or “privilege.”

Next, the data can finally be reviewed for relevance to the case at hand and tagged accordingly as “relevant” or “non-relevant,” before being prepared and sent off in response to the opposing counsel’s production requests.

Redundant or trivial data greatly increases the amount of electronic data that must be processed, uploaded and reviewed in a case, and it can cause a significant slow-down on the insights you can glean from the collected data. However, there are several ways you can improve your eDiscovery efficiency and save yourself time, effort, and money in the long run.

3 Keys to Improving eDiscovery Efficiency

1. Reduce Your Data Set Before Review

Reducing your data set before sending the data to outside counsel or your document review team ensures that you are sending the exact data that needs to be sent and not any redundant or trivial documents that inflate the amount of data that needs to ultimately be reviewed. By reducing the data set, you can save both time and a significant amount of money on the hosting, processing and review of documents. In addition, you might be able to proactively exclude confidential or sensitive information (which sometimes gets produced by accident).

The data set you collect during discovery eventually becomes the evidence that makes up the heart of the case, and it could be the thing standing between a successful and an unsuccessful outcome. Reducing the data set you send out for review may also reduce the time your outside counsel spends reviewing the data, which can set the case strategy early. If your counsel finds a “smoking gun” early enough, you might save millions in review or litigation costs by settling the case before trial and risking damage to your company’s reputation.

Aparavi allows you to index and access digital files across multiple storage locations and custodians so when you compile data, you can quickly find the relevant data to which outside counsel needs access (and exclude irrelevant information), saving time for your in-house and outside counsel.

![Classification policies](
Using intelligent classification of files and legal documents also reduces the possibility of human error and makes it easier for smaller in-house teams to address discovery requests. Aparavi’s intelligent classification platform makes it easy to categorize data by user, time frame, and more than 140 pre-defined classification policies. By classifying your data with this intelligent method, you can speed up how quickly you can find and process data, as well as how quickly outside counsel can gain insights from that data.

2. Avoid Data Silos

When multiple data sources are involved, the in-house counsel or person in charge of compiling the data might not have access to all of the information that is potentially relevant to the case, which means having to ask others for help or access. Often, your legal department does not have internal resources to dedicate to tracking down the relevant documents, so you end up over-producing and sending more data than necessary to be reviewed outside your company.

With Aparavi, you can simplify the collection process by breaking down data silos and using an automated data mapping and classification platform that compiles all of the data from different owners/users and sources, such as cloud storage systems, servers, or endpoints in one single point of access. With Aparavi, you can search by context (like keywords) and/or metadata (like custodians, date ranges or file type) to create laser-focused searches with high confidence levels, reducing the risk of false negatives and false positives.

Simplifying the collection process can make a significant difference in how quickly you supply outside counsel or the document review team with data to review, which is increasingly important when you are up against tight production deadlines.

By only using one unified platform – The Aparavi Platform – to organize, copy, and manage data, you can simplify the data collection process, and, in doing so, greatly reduce the money and resources you are spending to manage data on multiple platforms.

3. Pay Attention to Data Patterns in Early Case Assessment

![Data gaps](
A large part of the discovery process of litigation is having access to information from everyone involved in the case. If it seems like the data is a little light in some places, that could be an important clue that information is missing (either intentionally or unintentionally), which might be important to the eventual outcome of the case. Not being able to figure out which data you are missing could present major problems and lead to accusations of “spoliation” or intentional destruction/withholding of information.

Aparavi helps you notice patterns in data so that you can pinpoint where and when data is missing, such as data from a particular person or from a particular date. Missing data patterns can indicate that documents are being withheld or that data has been misplaced. Aparavi’s unique platform reveals hidden data by searching for data from every major user, allowing you to account for name variations and fringe users who are working off remote storage.

Improving the efficiency of your eDiscovery collection process means you can quickly and easily access the relevant data you need for legal proceedings without running into redundant or unrelated documents. The best way to do that is to reduce your data set, simplify your collection process by avoiding data silos, and exploit data patterns for early case assessment. For more information about how you can improve eDiscovery efficiency, please contact Aparavi.