How to Clean Dirty Legal Data

Although there is no shortage of data running through legal departments, much of it is dirty data, that is, data that isn’t accurate, readily available, or well-understood. There are several reasons why legal departments lack clean, usable data, according to a recent Deloitte article in the Wall Street Journal.

In most cases, the legal department doesn’t collect its own data. Instead, it relies on business systems and data that support other organizational functions. Even when legal collects its data, it may be pulled from different systems and isn’t uniform.

For clean data, you will need to have a consolidated view of the clean-up process, a way to identify status and data at each stage, and the ability to track any obligations that may create risk.

Here are four data-cleaning takeaways:

Define data standards. Different legal teams may have different application requirements, such as searchable contracts, e-signatures, metadata, or AI-based contract creation. Using common data management standards will ensure these requirements are deployed to the legal function.

Identify tools. Determine the tools and processes needed to clean your existing data. This might include using defined standards, correcting metadata, removing duplications, discarding stale data, and managing data variances.

Develop a repository strategy. If there are multiple systems in your organization, operationalize the data flow from one system to another. This will reduce the time and cost of manual intervention.

Create a data curation strategy. This could result in simplifying data entry by minimizing input points, reducing the cost of data quality, and applying data cleanup practices, which might include AI advanced analytics as curation tools.

Appoint a professional to lead the legal data cleanup efforts, and draw on collaborative inter-departmental relationships, given that much of your data originates in other parts of the organization.