Why Build Your Own Analytics Discipline? – Jeff Cox Writes in Law.com
Developing your own analytics discipline has wide ranging benefits for law firm knowledge management, business development, and competitive intelligence. With Legal Data as a Service (LDaaS) firms can use normalized legal data to build their own analytics and power their applications, dashboards, and data lakes with real-time access to court data, attorney data, law firm data, party data, and judge data.
We’re excited to share UniCourt’s latest article in Law.com written by our Director of Content, Jeff Cox. Jeff’s article, “Why Build Your Own Analytics Discipline?” is a companion piece to our earlier article, “How Law Firms Can Build Successful Infrastructures for Data Analysis,” and expands upon why analytics developed in-house can help firms gain deeper insights and retain much needed confidence in analytics reporting used for core functions.
Here below is an excerpt from our article in Law.com:
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.
Why does this matter? Because leaning on analytics assumptions without the opportunity to verify the underpinning information can lead to potentially damaging decisions. Take, for example, a firm using a litigation analytics tool to vet the litigation experience of a new lateral candidate or to research an opposing counsel practice specific experience. If one simple, but critical, name variant of either the lateral candidate or opposing counsel is not captured by an analytics engine it could hide a series of bad cases with questionable litigation decisions by the lateral and/or obscure opposing counsel’s on point experience in niche practice areas and in front of particular judges.
Similarly, if a law firm is using out of the box litigation analytics to inform their business development and legal marketing, it’s crucial that they are able to see the data behind those analytics to verify the veracity of the assumptions made related to parties involved in lawsuits, who could be potential leads and future clients. Much like our scenarios with recruiting a lateral and seeking competitive intelligence on opposing counsel, if an analytics engine misses key variations in an entity’s name, or does not properly associate an entity’s subsidiaries and affiliates, or incorrectly ascribes unrelated cases to that entity, it could cause business development professionals to waste precious time and resources marketing to that potential lead when more fruitful opportunities are available.
The tl;dr version: unverified data can lead to bad business decisions, and law firms can, and should, invest in building their own analytics disciplines to avoid making uninformed decisions.
You can read the full article here on Law.com.