One of the primary benefits of enterprise legal management (ELM) systems is the ability to aggregate information from previously disparate sources including e-billing, matter management and accounts payable. Once the data is assemble, corporate legal departments have an incredibly powerful source of existing information about their legal matters, to analyze:
- How long a specific matter should take to resolve?
- How much will this matter cost to resolve?
- What are the signals that a specific matter is exceeding predictable expenses?
For example, consider the following hypothetical anecdote:
Imagine a matter that has been active for 16 months – perhaps a complex employment litigation case. Because this data has been aggregated over time, the general counsel (GC) can see from looking at the information of other employment litigation cases that this type of case typically last 27 months.
The GC compares this matter to 75 other employment litigation cases over the last four years and sees that in its sixth month, the costs for this particular matter spiked well above the average cost for this type of work – and costs have continued to be above average. Armed with this historical information, the GC wants to know what drove up those costs.
With a simple click the GC can view a summary showing the number of depositions for this matter was more than twice that of other similar matters. In addition, this matter has had 25% more partner involvement than other typical matters.
This type of knowledge would be powerful in at least four ways:
1) Forecast costs for the remainder of the year.
The forecast for the rest of the year is now informed by the fact that this matter is an outlier. With six months left in the year and 16 months already into this matter, the data suggests that by month 22 of a typical matter like this, the total expense would be around $30,000. However, given that this matter has been running at about twice the cost of similar matters, without deliberate management intervention a more reasonable forecast would be between $50 and $60k.
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2) Analyze how to better manage the matter.
In this matter, inside counsel data and analytics can be put to practical use: the data provides information with which to better manage how the matter is handled going forward. The GC may want to investigate why this matter has 25 percent more partner work than in similar matters. Can the corporate lawyer who has responsibility for this matter go back to the law firm and ask them to better leverage lower costing resources? If so, perhaps the matter’s lifetime cost can be managed back to a normal $30,000.
3) Data opens a dialogue.
Access to these analytics allows corporate legal departments to measure what matters about matters and provides a basis for a transparent dialogue with the law firm handling the matter. Imagine how powerful it would be for the in-house attorney who owns this matter to bring the “similar matter” averages to the law firm and ask them to explain why so many more depositions were really needed and why a partner was doing so much more work. Using facts to underpin this conversation changes the dynamic entirely.
4) Create a baseline for early case assessment.
The aggregation of data for similar matters provides a baseline to use for early case assessment when new legal matters arise. An analytics tool enables corporate attorneys assessing new matters to narrow down to matters of like complexity, jurisdiction and exposure, among other factors. This assists with pricing decisions, including making lawyers more comfortable with what fixed fee amounts might be appropriate and empowering lawyers to push outside counsel on establishing good budgets.
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As most GCs know, outside counsel are prone to suggesting that every matter is unique, and they can’t tell exactly what something should cost. Analytics that inform early case assessment would become pretty powerful tools for turning information into action and communicating that you’ve had experience with matters like this in the past, and the range of costs is consistently between X and Y.
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