Share this article
Legal teams often run into unanticipated problems during eDiscovery after they go through the initial data collection stage. At this point, it’s necessary to sort through the information, understand what you’re working with, and move the data forward for use in a legal setting. Unfortunately, this can be an overwhelming and difficult experience that slows down your entire operation.
If you’re struggling to understand eDiscovery data processing, you’ve come to the right place. Read on to learn what data processing entails during eDiscovery, why it’s important, and some best practices to make it easier and more efficient.
What Is eDiscovery Data Processing?
At a high level, eDiscovery data processing involves analyzing, reviewing, reducing, and preparing data for use in a legal setting such as a court case or audit.
Data processing is a critical part of eDiscovery that you can’t skip over. It is necessary regardless of whether you’re working with large or small data sets. Before you make information available for review, you must first understand where the data came from and what it contains. In addition, you may need to reduce the data in the collection.
Data processing serves a few different purposes. First and foremost, it prevents overloading legal teams with too much data. This expedites the legal process and reduces eDiscovery costs. At the same time, it protects your client by ensuring that only necessary and relevant information becomes discoverable.
How Does eDiscovery Data Processing Work?
It’s important to realize there is no single industry standard to follow for eDiscovery processing. However, many legal teams choose to follow the Electronic Discovery Reference Model (EDRM), which is a trusted, multi-step eDiscovery framework.
Here’s a breakdown of how eDiscovery data processing works according to the EDRM:
Ingestion and File Extraction
The first step in the process is to ingest data. Your processing system should be able to ingest multiple types of electronically stored information (ESI) including office documents, text messages, emails, social media, and audio and video files, among other types.
During the ingestion process, you receive and extract data, identify content and file types. The EDRM also recommends “hashing,” or creating individual fingerprints for individual files. It’s a good idea to create an exceptions list for files that fail during processing in order to retrieve them at a later stage, if necessary.
The next step is to filter data. Your processing software should be able to identify system and program files, remove duplicate files, and filter them by date range and file types. This enables you to pinpoint the data you need.
Text, Metadata, and Image Extraction
After you extract and filter files, you then need to extract content. The EDRM recommends extracting text files and then moving them forward for indexing and searching.
In addition, the software should extract fielded information, or metadata, and offer the option to include tracked changes in documents as well as hidden activity and content.
The EDRM also advises using software that tracks files you receive from a data collection from the onset, as well as the actions that you take on those files. All data should be available for searching, sorting, and filtering.
To learn more, check out the EDRM guidelines for processing output.
Best Practices for eDiscovery Data Processing
While the EDRM provides a solid roadmap for data processing and other eDiscovery components, it still leaves plenty of room for interpretation. As such, your team should ultimately form its own eDiscovery policies and procedures.
Here are some additional best practices to keep in mind for eDiscovery data processing.
Be Selective About What You Process
At the beginning of an eDiscovery project, you could encounter very large datasets from multiple sources. However, you may only need a small sample.
Before you dive in and start processing data, consider the scope of the project at hand. Prioritize the data that you need and where it likely resides in the collection, and target that information first. In the event that you require further information, you can go in and extract it as needed.
Of course, larger audits and investigations may call for full data processing and analysis. So, you should treat each collection on a case-by-case basis.
Use Data Governance and Identity Management
Suffice it to say that managing eDiscovery and processing data is a major liability. Failure to properly protect data can result in court losses and lead to lawsuits and penalties. So, it’s critical to take active measures to protect client data when it is in your custody.
Data governance restricts who can access information, as well as where they can access it from. For example, you can set policies restricting remote access to a database or the time of day or night that someone can access it.
It’s also a good idea to use identity management so that you have full visibility and control over everyone who can access systems and files. This prevents unauthorized individuals from accessing or manipulating sensitive records.
Keep Information Accessible for Future Discovery
After you collect and process data, it’s critical to keep the information you’re not using and make it accessible for future use. It may be necessary to return to the data at a later point during eDiscovery, so it’s a good idea to keep the information active.
Automate Data Collection and Processing
Collecting, processing, and reviewing data is time-consuming and laborious. Most legal teams lack the resources to manually extract and analyze information — especially busy firms that manage multiple clients.
One of the best ways to streamline the process is to automate data collection and processing. In other words, you can use artificial intelligence (AI) to rapidly sort through large data volumes, discover relevant data, and present the information for review. AI drastically reduces time and effort during data collection and processing and frees legal teams to focus on higher-level responsibilities. It’s an easy way to do a whole lot more with less.
How Can Software Streamline eDiscovery Data Processing?
As you can see, eDiscovery data processing is complex and difficult. But it’s critical for the success of any eDiscovery project, and requires precision and skill.
Recent advancements in eDiscovery management software make it infinitely easier to manage large datasets. Now, it’s possible to consolidate the process and run all data through a centralized platform instead of using multiple tools and services.
By using eDiscovery management software with embedded AI, it’s possible to view all data over a secure, user-friendly portal. eDiscovery software also makes it safer to export and share information with other legal teams and reduces the chances of costly data breaches and security incidents.
Venio Systems offers a flexible, purpose-built eDiscovery platform with AI that is highly accurate and easy to use. With the help of Venio, your team can process data with greater accuracy and ease. In fact, Venio reduces data processing by a power of 10 and eliminates data volumes by up to 90 percent. The platform saves time, drastically lowers project costs, and can help every eDiscovery team improve their outcomes.
Venio’s flexible platform is ideal for corporations, government agencies, law firms, and eDiscovery providers. If your organization needs to streamline eDiscovery, Venio is the ideal platform to enhance performance and productivity.
To experience Venio in action, schedule a demo today.
About the Author
Justin Reynolds, Akshita Singhal & Lianna Vaughan
This post was written by Justin Reynolds. Justin is a freelance writer who enjoys telling stories about how technology, science, and creativity can help workers be more productive. In his spare time, he likes seeing or playing live music, hiking, and traveling. This post was reviewed and published by Akshita and Lianna, the in-house team of Venio.