Transparency and Clarity as Incentive

With apologies to Shakespeare and Hamlet, “To be (transparent) or not to be (transparent), that is the question.”

Arguably, in an era of Big Data where The American Lawyer provides sophisticated analysis on BigLaw’s financial results via Tableau, clients are building repositories of data regarding work performed by their outside counsel, and electronic billing vendors are packaging and reselling law firms’ billing data back to the law firms themselves (!), the question of financial transparency and the typical BigLaw partner’s access to financial data may no longer be the relevant one. Given that data are all around us, the discussion within law firms should be less about access to data by their internal and external constituencies’ (i.e., their lawyers and clients, respectively), but instead should be focused on how best to present the multitude of available financial data in a manner that is contextual, useful and meaningful. This article is focused on the presentation of meaningful information in the context of partner performance, but the same principles can be applied wherever data are used to make key business decisions.

Data <> Information

Let us first establish the difference between data and information. In the best-case scenario, data without appropriate context can be noisy; in a worst-case scenario, data can be misunderstood; in a worst, worst-case scenario, data can be purposefully misrepresented. Information, however, is data that have been filtered, sliced and diced to tell a story that lessens the risk of misunderstanding/misrepresentation/misuse.

The massive amounts of data available within a law firm are both a blessing and a curse. While I am a proponent of transparency, I do recognize that unfettered access to unfiltered data can be a dangerous prospect. Inevitably, it seems, those who are most interested in access to data “without supervision” are those most likely to misuse it (although not necessarily intentionally). Accordingly, it is the responsibility of the custodians of a firm’s data to ensure that information is presented in a way that provides a narrative consistent with the organization’s goals and objectives, even if that narrative is not necessarily a shiny and happy one—which can, of course, be the case in evaluating partner performance.

Information and Incentives

By its nature, the compensation-setting process implies a certain amount of discretion (understandably so) as well as a need for data about performance. Too often, though, the data are presented without context or focus—picture a “partner data sheet” that is six pages long and/or unreadable without a magnifying glass. It is highly unlikely that any such document tells a coherent story, or is even comprehensible by members of a compensation committee who probably see such data only once per year (during the compensation process) or, even worse, are new to the committee and have never been briefed on what the various data points represent or why the are important. That may sound like an extreme scenario, but it is a real life example and one that does not exist in isolation.

Imagine, instead, a process that synthesizes the underlying data in a manner that helps an evaluator understand how the various metrics relate to one another for the purpose of determining an overall rating or ranking. Imagine, instead, a process that clearly demonstrates the relative importance of these metrics in a way that is consistent with the organization’s strategic plan and objectives. Imagine, instead, a process where these metrics are presented on a consistent basis all throughout the year. Imagine, instead, a process where incentives are directly matched to the information provided to the decision makers.

Context Matters: An Example

What follows is a stripped down example based on annual hours worked. Assume that the expectation for this firm is 1,600 hours per year, and that the evaluators are provided with five years of data per partner. This may seem simplistic, but remember that the hours worked are only one data point of many that may be provided to the compensation committee—and that one complete set of data per partner must be evaluated. The process of wading through that much data can be a mind-numbing exercise on even a good day. It would be easy to miss key takeaways or, at least, it may be more difficult to draw simple conclusions.

Year Hours worked (annualized) Average hours worked (annualized) – practice group Average hours worked (annualized) – firm
2015 1,635 1,602 1,614
2014 1,535 1,590 1,601
2013 1,450 1,610 1,605
2012 1,575 1,598 1,610
2011 1,610 1,605 1,609


At first glance, these figures seem to indicate production that is fairly consistent within a reasonable range on an annual basis setting aside a dip in 2013 and a nice bump in the most recent year. The compensation committee may conclude a solid performance in 2015 on this particular data point with some presumed explanation for the low point in 2013 (and which is likely an anomaly that was considered by 2013’s compensation committee). In addition, this partner appears to perform better than average as compared to other partners in the firm.

In contrast to the data as presented in the table above, consider these metrics:

Metric – Hours worked Result
2015 hours worked 1,635
Year-over-year change 5%
Comparison to three-year average 6%
Comparison to five-year average 7%
2015 quartile – cf. practice group partners First
2015 average – practice group partners 1,602
2015 quartile – cf. all partners Second
2015 average – all partners 1,614


From these metrics, the following takeaways are relatively easy to discern:

  • 2015 was a good year vis-à-vis work production within a five-year range
  • This partner ranks very well compared to others in her practice group (first quartile)
  • This partner performs better than average for the firm overall, but not in the top tier (second quartile)
  • The practice group is somewhat weaker than the most productive parts of the firm (due to the nature of the practice, current economic conditions, structural issues in that particular practice?)
  • In excluding the individual data points, the anomalous outcome in 2013 does not distract from the current year result being evaluated.

Some of these conclusions could also be drawn from the data in table 1, although only by navigating 12 individual data points. The information in table 2, meanwhile, tells a clearer story via reference to a single data point and seven metrics linked to that data point. In other words, this example effectively illustrates the difference between data without context, and information that tells a story. In this case, the story is one of improvement in productivity in the measured period; above average individual productivity; and solid, if not spectacular, productivity compared to other parts of the firm.

Now, imagine a single page of such metrics for other data points (realization, book of business, leverage on responsible matters, pro bono work, number of strategic clients served, contribution to firm management or knowledge, etc.) instead of multiple pages with table upon table of individual figures. (Again, data does not equal information!) Further, imagine that these data are available to the individual partners throughout the year in a manner consistent with how the compensation committee will evaluate them. Such consistency can only better serve the acceptance of compensation decisions while regularly reinforcing understanding of the economic results that the firm deems important. Of course, this assumes that the design of any such metrics is consistent with those outcomes—a point that should not necessarily be taken for granted.

Data Visualization

While I do not pretend to be well versed in the field of data visualization, transparency and clarity could be taken one step further by using tools such as Tableau in presenting data in a visual format. This is a much more sophisticated approach, but one which is likely to expand as firms and vendors seek more effective ways to present the massive amounts of data at their disposal. Examples where visualization is currently being used to present data in a meaningful12way across a range of applications include Bryan Cave’s Octagon, The American Lawyer’s presentation of AmLaw results and TyMetrix 360°’s LegalVIEW Analytics.


Whatever the underlying purpose—data for compensation committees, monthly financial and management reporting, dashboard applications, client-facing reporting—the days of presenting row upon row of data are numbered (and rightfully so). Considering that the audience for these data is one whose choice of profession (i.e., the law) may have been directly influenced by its lack of involvement with numbers, presenting facts and figures in a manner that is meaningful, easy to digest and contextual can only improve the experience for a financial manager’s internal customers. In the context of compensation decisions, transparency, clarity, and effective presentation of the data being provided connects objectives and incentives and enhances understanding of the decisions that are ultimately made.

About the Author

Michael Byrd is the principal of SOAR Legal Consulting, which focuses on finance, operations, technology and business development solutions for the legal sector. He can be reached at


Send this to a friend