How Corporate Counsel Can Get Rid of Dead Weight Data



by Rene Laurens on June 18, 2015

Information Governance , Product Spotlight

Digging through digital information to answer questions about the strategy, risks, and compliance efforts behind your business is a growing function of corporate counsel. Given today’s data volumes, though, finding the right information quickly is often a harder task than it might seem.

This is a difficult process because it largely involves finding, evaluating, and setting aside the wrong data—in other words, the dead weight data stored on your company’s resources even though it has nothing to do with core business operations. Fortunately, as legal technology advances, it’s becoming easier to eliminate that unnecessary information at many points in the legal process, particularly during information governance and e-discovery.

Often, the key is to shave off a little bit of information at each stage. It’s never too late—or too early—to trim down your data.

Step 1: Dive in with data remediation.

Using strong information governance practices, you can exclude useless data right away by proactively organizing your enterprise information. Proper information governance means less to collect, process, and review during e-discovery.

If you’re ready to start organizing your existing data—a process called data remediation, which is a great first step toward broader IG policies—ask yourself these three questions:

  1. What’s the content of the data?
  2. What’s the context of the data?
  3. Who are the data’s stakeholders?

Once you have a holistic understanding of your data, it becomes clearer what you can defensibly get rid of. You can perform much of this process with your existing e-discovery software, as the workflows are quite similar: identify what data needs attention, collect it into a single system, and review and analyze it for relevance to your business. A successful data remediation project will yield useful lessons for building a custom IG policy that suits your needs, enabling you to eliminate irrelevant data with more regularity in the future.

Step 2: Chip away at data while collecting and processing.

After e-discovery has begun, two additional opportunities to get rid of that dead weight data come up during collection and processing. By using filtering options during these steps, you can sort data into buckets that are easy to define, include, and exclude.

Collection data visualization tools, for example, allow you to view and assess data according to file types, folder structure, date filters, and more. Exclude the date range you don’t need, isolate sources and folders that aren’t relevant to your project, and collect the remaining data, leaving all that dead weight data out of your collection. Tools like Relativity Collection’s scout feature make this process easy.

Similarly, during processing, you can use basic NIST, file type, and email domain standards to filter data before it gets to review. Essentially, during this stage of the project, you can take your big blob of data, sort out the junk, and pump the rest into your database in a structured way. Clean data is easier to group, sort, and search during the rest of the project.

Step 3: Rely on analytics early and often.

Even after review has begun, several text analytics workflows can help you reduce the amount of attention your team expends on any remaining dead weight data in the project.

Several tools can be unleashed immediately on your data set. For example, clustering will automatically sort documents by similar concepts and give you a bird’s eye view of topics in your data so you can quickly deprioritize the irrelevant ones. Email threading will better organize conversations in your data, so you can easily see which messages can be found in their entirety within other, more inclusive threads.

Workflows such as categorization will help you amplify your efforts across the data set, teaching the system your definitions of relevance and irrelevance. Throughout the project, you can also use keyword expansion and similar document detection to identify irrelevant data. In the end, eliminating dead weight data with analytics means you won’t waste time and resources.

Where to Go Next

Getting creative with your workflows to drop dead weight data can help you move through projects must faster. It’s never too late or too early to get rid of what’s slowing you down.

 

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