Using Analytics To Make Ediscovery More Efficient
Data analytics is employed across virtually every industry in the working world. With the rise of AI-based analytics tools, even legal corporate firms are substituting the previously linear eDiscovery process with one-sided analytics.
Using eDiscovery analytics increases the efficiency, accuracy, and cost-effectiveness of the entire litigation process. However, while firms have established that using these tools is the more efficient method to carry out the eDiscovery procedure, they’re still trying to figure out the best path to incorporate modern technology into their methodology.
Here are the best ways you can use analytics to streamline your process.
1. Use AI-Based eDiscovery Analytics
Even the best data analysts in their field cannot stack up to the meticulous nature of a machine-based analytics system. Using artificial intelligence to modernize and streamline the eDiscovery process can improve your firm’s performance while enabling your team to cut costs.
The combined efforts of advanced analytics with artificial intelligence also increase the efficiency and precision of eDiscovery. The operation becomes more effective when the firm uses a competent AI-based analytics tool.
2. Expand the Keywords
Often, the eDiscovery review process requires more precision and scrutiny because the system can miss crucial keywords. With keyword expansion, you can identify and bookmark all the relevant terms you might have missed.
The system will then review these added terms and associated documents. Keyword expansion enhances a firm’s reviewing capacity and allows them to identify and add the extra documents that might require a closer look.
3. Employ Email Threading
Keeping track of extensive email threads can take a lot of time and effort.
With email threading, the entire process becomes more efficient and consistent. The email thread consists of the original email, all the responses, forwards, and attachments.
4. Use Language Identification
Sometimes, several documents and files exist in languages other than English. You can parse and identify all such records with a comprehensive and reliable language identification system.
The system initiates a thorough check of non-English documents, extracts the data from them, and analyzes each line of the text. Language identification is a necessary component of eDiscovery analytics, especially if you want to analyze the data quickly to allocate appropriate resources.
5. Identify Clusters Of Concepts
Concept clustering is another process that makes large data sets more manageable. The system efficiently uses a defined algorithm to group similar unlabeled documents together. After grouping diverse data and documents, the system generates a simple description of each concept.
This makes it easier for the user to determine whether or not the documents are relevant. Concept clustering allows the user to minimize the review population and focus only on documents relevant to the case.
6. Incorporate Active Learning
Active Learning uses machine learning technology to learn from your inputs to predict and rank unreviewed documents. Active Learning assists with review prioritization, review automation, and dynamic batching.
Then, it automatically groups documents based on their ranking. This way, you can review the documents most likely to be relevant to the case before anything else.
Wrapping Up
AI-based solutions are present in every corner of every industry. As a result, complex processes are becoming simple, and simple processes are becoming even simpler.
Incorporating AI-based analytics in eDiscovery enhances the operation’s efficiency, productivity, and cost-effectiveness.
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