When it comes to handling ESI during a discovery process, too much redundancy can severely impact your speed, budget and security.
If you’re using one platform for processing, another for data analysis and yet another for review, each platform is likely to contain both original data that was preserved for the matter and the ingested data that was normalized, deconstructed and stored. Each time data is exported from one platform and imported into another, new copies of the data are being injected into each disparate platform.
The result? You could be duplicating a significant amount of data with each export/import.
The problem? Processing, managing and storing data isn’t free and unknowingly contributing to increased data sprawl comes with a high cost.
We have identified four significant areas in which you can impact your overall budget by reducing the amount of redundant data in your eDiscovery ecosystem:
The bigger your data set, the more time it takes to process, analyze, review and produce. Large datasets understandably require additional human and technological resources to make it to the finish line.
Unnecessarily redundant data consumes server, network and storage resources that could be used elsewhere (or not purchased at all).
Most eDiscovery platforms are priced based on volume of data. The more data you’re running through the system, the more each of those platforms will cost.
Every time data is exported, imported or duplicated, the risk of something going wrong increases. Additionally, different service providers all have different audit capabilities and security protocols, making consistent oversight a challenge.
How to Reduce Redundancy
The best way to reduce redundancy is to reduce the number of software solutions used for eDiscovery. Minimize the number of times you need to export and import your data into various platforms and you also minimize the risk of duplicating unnecessary material.
Additionally, make sure your service provider or software solution of choice offers single-instance storage so duplicate source data isn’t just eliminated from the workflow, but pulled from the data set entirely. For example, while 25 email users might generate 100 emails per day, you can significantly reduce that number if you looked at only unique content generated by those users.
By storing unique content only in a single end-to-end software solution, eDiscovery operations can drastically reduce the amount of data being stored, the amount of time required to manage the data, and the costs associated with each project.
Is redundant data sneaking into your eDiscovery process? Find out.