In business, strategy is king. It is the bridge between a vision and reality, creating a roadmap to achieve desired outcome. Legal teams need strategy too, to align to the wider business’s objectives and for their own internal growth. Crucial to the formation of a viable strategy is business insight and metrics which allow for analysis of what needs to change and what the future can hold.
Legal teams are not known for their collection and analysis of data, they have typically been reactionary in their approach. Asking for an analysis of how the team is performing, with quantifying data, is often met with a scramble for spreadsheets. Reframing legal’s relationship with data and metrics allows them to move from reactionary to pro-active and to strategize effectively.
There are further reasons that an in-house legal strategy is pertinent, to do with workloads and, of course, Covid-19. In-house legal teams have been under immense pressure during the pandemic, not only have they frequently been asked to take on additional work as companies moved from outsourcing to insourcing to manage legal spend, but they’ve faced the cutting eye of CFOs seeking to reduce company costs.
In fact, workloads for in-house legal teams are expected to increase by approximately 25 percent over the next three years, in a time when legal teams are already being asked to do more with less. When faced with this impending workload and the scrutiny over the cost of the legal team, metrics can allow teams to be informed and operate from a position of knowledge. Data allows for a greater understanding of a legal team's position and reduces risk in future decision-making.
It’s time we introduced our analytics maturity model. This is a tool we use to assess where an in-house team is in their metrics journey. They could be operating in four stages: hindsight (reactionary), insight (presently), foresight (pro-actively), or in amplified intelligence & automation (intelligently). The goal of legal technology is to take each in-house team along this journey to a point where they can harness machine learning and cognitive intelligence to make informed decisions for their team.
The first step in this journey is locating the legal data. People are often daunted by this task, thinking that without data engineers they cannot capture, track and analyze data. In fact, the legal data is all there, it just needs to be captured in a system of record. Legal technology has the capacity to capture the data as an underlying activity in a way that also improves productivity whilst structuring, surfacing, and using the data.
We call this the data engine. By working on one central platform and recording every matter, contract, file, task, and person involved, this information can be analyzed by machine learning and churned out as a data set. These data sets inform us as analytics and can be used to establish universal best practices such as KPIs from metrics, organizational playbooks, templates, benchmarking, and assumed value stories.
For those who are wondering, what do we mean when we talk about “data” and “metrics” and how are the terms distinct to “measurements”?
Data is raw information. According to PWC, a measurement is a data point in a single point in time. It doesn’t need to be 100 percent accurate or eliminate all uncertainty; it simply reduces uncertainty based on one or more observations.
A metric is defined as a data point in context. Metrics convey information at one point in time relative to another point in time, or data about one organization relative to another organization. Metrics are more useful than measurements because they provide context to support decisions.
This is where it’s clear to see the benefits of having structured data collected in one place where it can be surfaced and used for metrics, bringing value to the numbers.
We like to think of metrics as a weapon. Legal can be likened to the shield of an organization, but with metrics on their side, they can become a sword. Metrics allows the legal team to serve the business, harnessing knowledge to become a more effective function and one that can look to the future with relative certainty.