In a year of nonstop buzz about AI and data, it’s no surprise that law firms report a record use of analytics. Indeed, in a survey by Lex Machina, 68 percent of legal professionals said they were using legal analytics, up from 61 percent in 2021.
Specifically, when it comes to the business side of the legal profession, firms say they use data for pitches and demonstrating expertise (69 percent); competitive intelligence and new business sourcing (50 percent); and pricing (29 percent).
The application and use of data to identify and evaluate legal talent hasn’t made the top three yet, but it’s happening at law firms around the world, and is likely to spread, as staffing remains a primary stressor throughout the profession. Many law firms are eager to try a new approach to talent acquisition, and one that’s based on data and insights rather than the more conventional “who knows who” school of hiring.
Data-driven decision-making is critical to the success of a firm, to be sure—and one that is central to our purpose at Decipher Investigative Intelligence. The problem: applying data to people can be fraught with pitfalls, especially for firms that start out by “winging it.”
As we wrote for the Legal Value Network, the application of data to lateral hiring is both an art and a science, and one that must be done strategically and comprehensively. What worked for pricing or proposals won’t necessarily translate to talent: Human subjects carry myriad variables that do not conveniently map to a single spreadsheet.
To ensure results and an acceptable ROI, a well-constructed talent data framework must avoid these seven major missteps:
One: Data without strategy.
Before taking any action, address two questions: What are the firm’s actual objectives for its lateral hiring initiatives, and what data points are required to assess a candidate’s ability to accomplish these objectives? What are the positive outcomes that the firm is actually trying to achieve?
While specific strategies may vary, your talent strategy must address key growth-related questions, among them:
- Geography: What markets are your focus areas?
- Services: What practice areas will you add or expand?
- Client Focus: Which clients’ needs matter most?
- Demographics: Where does the firm need succession planning, bench strength, new skill sets or diversity?
Commit this plan to writing; this will not only prevent you from being distracted by whims and trends, but it will save time and effort on the front end. Candidates that will not help the firm meet its goals can be disqualified fast, and those who meet objective criteria can advance for further study.
Two: Poor process.
To be meaningful, data should be integrated throughout the entire hiring process, from the talent identification stage, through the initial lateral partner questionnaire (LPQ), to due diligence and finally, to onboarding.
Consider the LPQ. The first line of defense in lateral hiring, the LPQ can be the first opportunity to meaningfully assess your pipeline data; it is exchanged before the firm becomes overly invested in candidates, and it also requires candidates to self-report, making it a solid litmus test of candor.
Unfortunately, too many law firms leave themselves vulnerable by starting with an incomplete LPQ or failing to insist on thorough completion. In a review of more than 1,000 LPQs by Decipher Investigative Intelligence, a full one-third neglected to answer the basic question, “What is your billing rate?”
Other areas of concern:
- 21 percent of candidates failed to adequately complete the client section of the LPQ;
- 25 percent of LPQs did not ask or require recent billable hours;
- 55 percent of LPQs did not ask or require billing history beyond the current year; and
- 60 percent of candidates did not disclose references when requested.
To successfully incorporate data into hiring, you need….data. Incomplete LPQs cripple the process from the very beginning. Firms must insist on their prompt and thorough completion. (Wondering what to ask for? You can download Decipher’s sample LPQ here.)
Three: Small sample size.
Small sample sizes are extremely dangerous and can lead to incorrect conclusions, and most firms simply do not have a large enough pipeline to meaningfully identify patterns and predictors. For a meaningful comparison, you need consistent and accurate data across practice groups, location, experience levels, and more. Working with data professionals can often act as a force multiplier to rectify these challenges.
Four: Reverse engineering.
Here is a dangerous but common scenario: To start its data analytics program, a law firm chooses 10 lawyers deemed to be “successful” lateral hires and looks for common threads.
This is problematic for many reasons. Many success metrics are not objective, and it’s difficult to gauge intangibles like drive, chemistry, and plain luck. Many success metrics are not static and are dependent on other factors, such as location and practice area. Many suggested success metrics—especially in a small sample size—are misleading: If all 10 successful hires do not have Twitter, does that mean you would reject someone who does?
Simply trying to replicate what seems to have worked before—without a coherent strategy, an adequate sample population, or a true understanding of causation and correlation—will inevitably lead to ill-informed decisions.
Five: Violating laws and regulations.
A meaningful pre-hire due diligence and data program will collect information that can be subject to various rules and regulations, and even well-meaning violations of these laws can carry consequences. For instance, failure to comply with the Fair Credit Reporting Act can subject your firm to statutory damages of up to $1,000 per violation (as well as some major embarrassment). The ever-growing and ever-evolving web of privacy laws further complicate this issue, as what might be allowed in Connecticut will not be permissible in California.
This becomes even more complex when screening laterals in multiple countries. It is imperative to structure a screening program to be above board, and it is easy to unknowingly misstep.
Six: Data dabbling.
Many well-meaning law firms will start data experiments with talented but untrained business professionals. This often takes place with firms that have had some success in pricing, or firms that have strong Knowledge Management staff; they assume that these research and analytical skills will automatically transfer over into talent.
Your law firm professionals certainly have the curiosity, organization and brainpower—but more likely than not, they lack the breadth of resources, from comparison data sets to compensation trends, and knowledge of critical nuance (see the laws and regulations above). Specific training and tools are necessary to ensure an effective, compliant data set.
Seven: Data without action.
Data is meant to inform the decision-making process. Your talent data should inspire action: If you’ve built a shortlist, what happens next? What do you need to know next? (Hint: Interviews, well-structured and done right, are qualitative data collection.)
In the rapidly changing landscape of post-pandemic lateral hiring, talent data gets obsolete fast. It’s imperative to build each step of the data process anticipating the next decision point—and the next set of data to collect. Analysis that sits in an email inbox for months is a waste of effort.
Good intentions aside, the biggest problems occur when these transgressions build on one another; when DIY data results in work product that is nonstrategic, incomplete, unreliable and/or noncompliant.
A flawed approach to talent data can burn valuable firm resources, and worse, it can lead to decisions made on faulty evidence. It can drive false conclusions that actually work against the point of talent data in the first place—to make better decisions that protect the firm from bad hires and capitalize on new opportunities.
Law firms are to be applauded for starting to take a data-driven approach to hiring; they simply need a purposeful and well-thought-out approach.