Legal and compliance teams critical to machine learning success

This Q&A with Jake Frazier is based on the first of a series of interviews I’m conducting with thought leaders who take a unified governance approach to increasing the value of information to their businesses while driving down costs.

Along with his role as senior managing director at FTI Consulting, Jake is a faculty member of CGOC, a founding member of the Electronic Discovery Reference Model (EDRM), a member of the Sedona Conference and an Advisory Cabinet Member of the Masters Conference. He has authored many articles and white papers on information governance issues and regularly addresses industry groups on the topic.

For this article, I asked Jake about the new and complex challenges around the adoption of machine learning (ML) technologies in enterprises. ML offers business users an unprecedented opportunity to take advantage of the massive amount of data they are collecting. However, ML is also increasingly important to legal and compliance teams.

First, these teams must ensure that all ML projects throughout the organization comply with evolving privacy and security regulations, while enabling proper preservation in the event of litigation. Second, ML technology can enable security and compliance teams to improve their own processes.

Information Management: In general, what are companies doing right when it comes to machine learning? What are they doing wrong?

Jake Frazier: Companies that have been successful with big data and machine learning initiatives typically start with a very narrowly defined use case that can be connected to a tangible business value or metric.

For example, a service provider wanted to leverage ML to improve customer support and reduce churn. It started by having staff review the recordings or transcripts of calls and tag phrases used by customers who then decided to switch. Next, the machine learning application was trained to look for similar indicators, so when it listened to calls in real-time, it could identify an at-risk customer and immediately escalate the call to a supervisor.

The success of this type of use case was easy to measure. The percentage of customers switching after using the indicators dropped significantly thanks to automatic identification and intervention.

Companies run into problems with ML in a couple of ways. First, and most dangerous, is the failure to involve legal and compliance teams in the formulation of ML projects. With the rapid evolution of privacy regulations, it’s essential for enterprises to ensure they remain compliant.

Another common issue is when companies focus on the technology first. Companies often invest millions of dollars and perhaps years developing a machine learning platform, convinced the organization will derive numerous benefits from different departments flocking to take advantage of it. Unsurprisingly, they don’t get the adoption they expect because they didn’t present a successful use case to their internal customers.

A third critical mistake organizations make is not understanding the human part of the equation, that is, failing to adequately train the machine learning engine. It’s essential to use an iterative approach to ensure the ML engine is accurate in its analysis or identification. Failure to do this will undoubtedly lead to a high error rate.