Extract from IPRO’s article “Data Culling: 6 Best Practices to Avoid Overcollection and Reduce eDiscovery Expenses”
It’s no secret that eDiscovery is expensive—and that’s a problem for organizations that are looking for ways to cut costs in an inflationary economy. But at the same time, the amount of data that organizations generate and manage is increasing, further driving up the cost of eDiscovery. What’s a cost-conscious organization to do?
Ideally, you’d have less data to manage. One way to accomplish that is to stop overcollecting data during the early stages of eDiscovery. Collecting more than necessary means you’ll spend more money storing and transferring data and more hours processing, reviewing, and analyzing it—wasting precious time and resources that proportional collection could have saved.
Luckily, your organization can avoid overcollection of data by improving its data culling practices, keeping costs down and saving you time. What are some of the best practices for data culling and avoiding overcollection of evidence? Keep reading to find out.
What is data culling?
Data culling is the act of paring down information to eliminate everything irrelevant, redundant, and otherwise unhelpful. You can separate this information from relevant data by file type, search term or keyword, email sender or recipient, and date range. You can also identify and eliminate duplicate data. Data that is culled isn’t subject to the document review and analysis process; it’s cut from the corpus of relevant information, so it no longer needs to be managed through eDiscovery.
