EDRM Blog

What is Ediscovery Data Processing and Culling?

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[Editor’s Note: EDRM is proud to support our Trusted Partner, Zapproved’s, educational efforts.]

During the ediscovery process, potentially relevant electronically stored information (ESI) is identified and collected. After this process, in-house teams need to turn that ESI into a usable format for the review stage of a matter. In addition, whether you’re conducting review in-house or leveraging outside counsel for higher complexity or higher risk matters, the overall collected data volume needs to be culled down to a usable size. 

Narrowing down large data sets to the most relevant subset gives you more control over the scope of your review project, and therefore, your time and cost.

Zapproved

Narrowing down large data sets to the most relevant subset gives you more control over the scope of your review project, and therefore, your time and cost. Through the use of processing and culling, in-house legal teams can make informed decisions about whether to involve outside counsel/vendors or complete the review and production of the document set in-house.

Responding to your ediscovery obligations is all about effectively and efficiently culling your data to separate the wheat (non-duplicative and non-privileged documents that are responsive to a discovery request) from the chaff (everything else). Earlier culling — preserving in place, collecting only what is needed, and processing and culling internally — saves costs by limiting the amount of data for review. But you can continue your cost-saving measures throughout the review process as well by utilizing more efficient review protocols and document review software.

The Advantages of In-House Processing and Culling

Bringing ediscovery capabilities in-house is a common tactic for organizations that seek to gain control over their budgets and reduce their costs. Generally, the first place to invest is in managing the legal hold process. Investing in collection capabilities for common data types and storage repositories can also help reduce cost, especially when coupled with the right technologies and tools.

But for real cost reduction, consider performing initial culling in house, such as a simple cull by file type and date range, email domain, or duplicate status or something more substantial.

Narrowing down large data sets to the most relevant subset gives you more control over the scope of your review project, and therefore, your time and cost. Through the use of processing and culling, in-house legal teams can make informed decisions about whether to involve outside counsel/vendors or complete the review and production of the document set in-house.

Plus, processing data in house instead of sending your data to a costly outside vendor for processing, can help eliminate a line item from your invoices!

Improving the Efficiency of Review

Even in the face of growing data volumes, legal departments are receiving pressure to reduce legal spend. Document review is the most expensive stage of a matter, so legal teams need a realistic option for reducing these costs. Outsourcing everything is not sustainable precisely because of the growth of data. It’s simply too expensive, not to mention you are at the mercy of another company’s timelines. 

Modern, cloud-based ediscovery software enables corporate legal departments to bring routine document review in-house, requiring minimal training and staff resources while offering huge cost savings and security benefits.

In addition, knowing that review presents one of the largest opportunities for cost saving in the ediscovery process, it should be a given that efforts to reduce the volume of data are worthwhile. You can further lower your spending and improve your budget control by developing strong review protocols and playbooks, which can lessen the risk of needed rework, and ensure efficiencies such as avoiding the need for issue coding when simple responsiveness coding is sufficient.

Utilizing built-in analytics and automated culling features to eliminate redundancy in data sets, using tools such as de-duping, deNISTing and email threading to improve reviewer efficiency. From there, you can leverage technology to develop better search protocols and apply the results of statistical analyses to focus on critical data. Cost reduction isn’t all about technology, though: using experienced ediscovery reviewers, especially those familiar with your unique data, can also drive greater efficiency and lower costs.

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