Like most industries, modern technology has changed the way litigation works. The American Bar posits that younger attorneys often adapt quickly to these changes. More senior attorneys might need to make a concerted effort to familiarize themselves with the changes technology has prompted. They also need to plan for how these changes have translated into new expectations from their clients. Getting to know these main eDiscovery concepts can simplify expanding this skill set.
- The use of technology, specifically electronic discovery (eDiscovery) software, cloud computing and storage, has changed the way litigation works.
- Senior attorneys may need to make an effort to adapt to these changes and understand new expectations from clients.
- eDiscovery software, such as Microsoft Purview, rely on cloud computing to handle electronic discovery requests and reduce costs.
- Software solutions typically offer options for filtering and sorting document collections, virtual tools for reviewing and highlighting evidence, and data transfer.
- Attorneys should familiarize themselves with concepts such as identifying duplicate files, identifying system files, converting file formats, and extracting metadata from files.
1. Cloud Computing and Cloud Storage for eDiscovery
Attorneys typically use software, such as Microsoft Purview, to handle electronic discovery requests. These software options tend to rely on cloud computing to execute tasks. Organizations will also likely use cloud storage to reduce costs and secure data.
The physical storage of cloud data spans multiple servers owned and secured by the hosting company. These companies provide authorized users with rapid, remote access to vast amounts of data. Because this discovery solution doesn't require firms to purchase or manage their own hardware, it can save on discovery costs.
There are two types of cloud storage: public and private. Public cloud storage services are open to anyone who wants to use them, while private cloud storage services are only accessible to authorized users. eDiscovery solutions often utilize a combination of both public and private cloud storage.
2. Parameters for Document Searches
Electronic discovery software provides a range of options for filtering and sorting document collections. Legal teams can use these options to narrow search results and ensure they only review relevant data. Parameter data is also useful for conducting manual reviews to ensure software did not miss key files. Here are examples of parameters attorneys can set for electronic discovery searches:
- File type
- Date ranges
3. Virtual Tools for Reviewing and Highlighting Evidence
Many eDiscovery solutions offer virtual tools for reviewing and highlighting evidence. Legal teams can use these tools to organize and comment on documents and collaborate with other attorneys on a case:
- Annotations: Highlight text and add notes to documents
- Review: Organize documents into groups so that legal teams can review them more efficiently
- Redaction: Black out sensitive information in documents
- Collaboration: Share documents and collaborate with other attorneys on a case
- Comments: Add comments to the documents they have handled so far
- Tagging: Tag documents with keywords or labels to make them easier to find
4. Data Transfer Via Drag-and-Drop Uploads
Gone are the days of eDiscovery requests that required firms to print, scan, and upload millions of pages of documents. With modern electronic discovery solutions, attorneys can simply drag and drop document collections into the software interface.
This is a crucial time-saving feature because it eliminates the need to convert data into a compatible format before uploading it into electronic discovery software. Once the documents are in the software, they can be indexed and searched using various criteria.
Here are related concepts attorneys should familiarize themselves with:
- Identifying duplicate files: In eDiscovery, near duplicates are files that share a great deal of content but have slight variations, such as different file formats or versions of the same document. These files can be identified using discovery software and de-duplicated, so they only need to be reviewed once.
- Identifying system files: System files are generated by an operating system and don't typically contain user-created content. These files can be identified using software and excluded from the review process.
- Converting file formats: Software can convert files into various formats to be reviewed in the native application or viewed in a web browser.
- Extracting metadata from files: Put simply, metadata is data about data. It can include information such as the document's author, the date when the author created it and the file type. Legal teams can extract metadata from documents by using eDiscovery software, which then narrows search results.
- Text recognition: Software can read images and PDFs to generate text-based content that can be searched and analyzed using keyword searches. This is critical for older law firms that digitized documents by scanning.
- Extracting embedded files: Software can extract files embedded in other documents. This is important for capturing and reviewing email attachments and files that teams previously converted to PDFs.
5. Technology-Assisted Review for Large eDiscovery Data Volumes
Technology-assisted review is a process of using discovery software to prioritize documents for review. This is done by training the software to identify relevant documents based on a set of seed documents manually reviewed and coded by attorneys.
Legal teams can use TAR for document collections of any size, but it is especially convenient for large data sets. TAR can significantly reduce the time and cost of discovery by reducing the number of documents that teams need to review manually.
These are standard TAR methodologies:
- Predictive coding: This methodology uses algorithms to learn from seed documents and predict which other documents are relevant.
- Continuous active learning: This methodology relies on feedback from attorneys to continuously train and update the software. Each update allows it to better predict document relevance.
- Clustering: This methodology organizes documents into group clusters based on their content. Documents in the same cluster are likely to be relevant to each other.
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