Blog  |  February 16, 2021

Migrating Your Data From Point A to Point B? The Ease of Doing So Will Depend on the Type of Data Being Migrated

eDiscovery is about workflows and the best illustration of this fact is the EDRM model.  Aside from the Information Governance node, which is depicted as a circle because it’s perpetual, the phases from Identification through Presentation are tied to a triggering event, such as a litigation case being filed, an investigation being launched, or an audit being scheduled.  But people often miss the bottom shading of the model, which reflects the flow of data throughout the life cycle where the volume of data diminishes as non-relevant data is filtered out while the percentage of remaining relevant data rises.  Migration of data happens at various stages throughout the life cycle, but how easy it is to migrate that data can vary widely, depending on the type of data being migrated.  Here are a couple of examples of just how varied the workflows for data migration can be.

Migration of Legacy Data

When an organization has a need to migrate data from a platform that it determines no longer meets their needs – or the requirements of a case dictate that it collects data from a legacy platform to meet its discovery obligations, the types of data to be migrated from these legacy platforms can vary widely.  These projects are the Forrest Gump “life is like a box of chocolates” type projects because you never know what you are going to get when you’re working with legacy data.  As a result, there may be limitations to automating legacy data migrations because they are often one-time migrations with specific goals.  To help ensure a successful result, here are five considerations to keep in mind when migrating legacy data:

  1. Treat it Like a Separate Project: There are more steps in a typical legacy data migration than you might expect, so it’s important to plan for legacy data migration as you would any other project. You need to develop a project plan with a budget, expected timelines and identified roles and team members.
  2. Create a “Pre-Mortem” Before You Start: Instead of learning through the mistakes you make, set aside some time at the start of the project to identify what could go wrong and what steps to take to mitigate those risks before they happen.
  3. Set Reasonable Timelines: There is always a push to get things done “yesterday”, but there are often a lot of unexpected variables with legacy data that can cause you to miss deadlines. Build in some flexibility and enough time for ample testing of the process and the results.
  4. Document Chain of Custody: As always, you need to document the chain of custody and QC checks you perform to ensure that all data being migrated is being accounted for in terms of successfully migrated data and exceptions for which migration could not be completed.
  5. Rely on Experience: Legacy data migration projects can be difficult to manage so it’s advisable to work with someone who has done a lot of them and knows the types of issues to avoid. The best mistakes are the ones you know how to avoid in the first place.

Recurring Migrations

However, when there is a need to migrate data on a recurring basis, automating those processes can make much more sense.  In many cases, there are already mechanisms in place for you so that you don’t have to reinvent the wheel.  When taking a “best of breed” approach to working with eDiscovery technology or needing to share data on a regular basis with other team members, it’s great to take advantage of processes and mechanisms that have been already developed out there in the marketplace to move data where it’s needed most during the EDRM lifecycle.  Need to take your ECA data out of Nuix and perform matter analytics in H5?  Absolutely.  Need to migrate your Relativity workspace into Relativity One?  No problem.  Why “reinvent the wheel” when you can work with someone who has already addressed the need with a repeatable process or mechanism.

Don’t Underestimate Data Migration

It’s easy to underestimate what it takes to migrate data to support eDiscovery use cases.  Many people think it’s simply moving data from point A to point B, but there is so much more to it than that.  You want to rely on experts who can help you manage difficult migrations, as well as implement repeatable processes with mechanisms already available. As Forrest Gump would say to people who are trying to migrate data without expertise, “Stupid is as stupid does.”

For more information regarding Cimplifi’s CI Migrate automation tool, click here.