When it comes to legal analytics, two maxims hold true: trust, but verify, and don't limit yourself. While there are a host of innovative legal analytics products available on the market, law firms should look beyond only using out of the box solutions and build their own analytics discipline to develop customized insights layered with additional data sets and to avoid bad data assumptions that can skew crucial reporting.

As a follow up to an earlier piece we wrote discussing how law firms can build successful infrastructures for data analysis, this article will focus on why building an analytics discipline is necessary for law firms to get analytics tailored to their specific use cases and needs, and why it's key for controlling underlying data assumptions to retain confidence in their reporting.

|

The Limits of Out of the Box Analytics

Most of the commercially available legal analytics products have analytics engines that make assumptions about their data, ranging from basic, low-level assumptions to complex, AI-driven assumptions to normalize data and build entity relationships. But when out of the box analytics products only provide analytics outputs without providing the underlying data, it requires an unspoken reliance on the algorithms and AI behind those analytics, throwing trust, but verify out the window.