In this post, I interview Jackson Lewis Principal Eric J. Felsberg about a team of statisticians and data scientists he leads. (See the firm’s 22 November 2016 press release for additional detail.)
Introduction
I think 2017 is more likely to be the year of legal data analytics and data science than it is to be the year of artificial intelligence (AI). AI makes headlines daily, both in legal and mainstream media. Yet outside of e-discovery document review and due diligence for corporate transactions, its impact remains limited. In contrast, data science and data analysis has value across both law practice and firm business operations today.
Data science does not garner headlines proclaiming legal robots or replacing associates. But it offers benefits now: it helps solves complex legal problems and allows law firms to help clients prevent legal problems. (I have written and Tweeted regularly about #DoLessLaw as the way to create real value for clients.)
Data scientists sometimes use AI tools, especially machine learning, but they also use a host of other tools, some old, some new. Data science and AI also have in common the need for big data sets to provide value.
Because I believe data science will become increasingly important in law practice, I wanted to learn more about, and share with readers, one of the few firms with a publicly-announced data science capability.
Q&A with Eric J. Felsberg
I was intrigued to read recently that Jackson Lewis has a data analytics team. When did it form, how big is it, and what are the profiles of the people on it?
Jackson Lewis launched its Data Analytics Group in November 2016. Our multi-disciplinary team of lawyers, statisticians, and data scientists use sophisticated analytics tools to advise clients across the Firm’s many practice groups. Hiring practices, attrition projections, headcount needs, compensation administration and liability exposure are just a few aspects of employers’ daily challenges that we can improve with our data science strategies – ultimately helping our clients make better, faster, more cost-effective workplace-related decisions.
How did you come to lead this? That is, what prompted you to build it and is there anything unusual in your background that led you to this path?
During my years with Jackson Lewis, I have primarily represented employers that conduct business with the federal government. These employers often are obligated to maintain affirmative action plans that require the tracking and analysis of workforce and personnel activity data. As a result, I have gained extensive experience analyzing and interpreting large data sets both within and outside the context of government audits. During my tenure, I also have helped employers with other analyses including those related to compensation administration, reductions in force, and performance evaluation systems, to name a few. With the launch of the Firm’s Data Analytics Group, Jackson Lewis expands our analytical offerings to help employers leverage the data, that they already likely possess, to manage their workplaces.
Which aspects of employment law can be best addresses with analytics? And how will it help?
At its core, the employment relationship involves people and a continuous series of decision-making. From the initial determination of an applicant’s qualifications, to how an employee moves through the organization, to how he or she ultimately leaves the organization, people are making decisions. But people are not perfect. Used properly and strategically, data analytics can remove potentially biased intuition and instead support decisions with reliable and neutral data science. When using workplace analytics, we recommend employers consider a hybrid approach. While effective workplace data analytics platforms provide guidance to companies faced with employment related decisions, the analytics alone should not drive the decision. Instead, the platforms should pave the way for employers to focus their efforts and resources in the most effective way possible.
The use of data analytics allows employers to make more informed data-driven decisions rather than relying on intuition alone – resulting in minimizing human error and ensuring decisions are made consistently and in compliance with applicable law. We can help employers digest and analyze large volumes of data in a time-efficient manner, thus better equipping the organization to move forward and succeed in its business goals.
How will clients benefit from the use of analytics?
Simply, the use of analytics can help employers manage their workplaces more efficiently and, in turn save on both short and long term costs. For example, by optimizing recruiting functions, talent acquisition teams will spend less time performing tedious and error-prone tasks when searching for candidates. This not only results in an immediate reduction in time and costs, but employers will realize the long-term cost savings of recruiting and hiring the best talent. Additionally, the use of analytics when setting pay also leads to reduction in time and costs, but also helps to retain talent. Using these data allows our attorneys to learn from our clients’ needs and help develop new and novel solutions to their problems.
Are the economics of the team the same as for the rest of the law firm? That is, is the fee structure similar, with a mix of hourly work and alternative fee arrangements?
The economics are very similar. Jackson Lewis prides itself on constructing alternative fee arrangements that allow clients to achieve their workplace goals and initiatives in an efficient and cost-certain manner. While each project brings various considerations, we work with our clients to identify the most appropriate fee structure for their business, whether it be hourly rates or alternative fee arrangements.
How easy or hard was it to persuade management to let you build it? Or, if everyone is a timekeeper, did that make it easy?
The use of data analytics is not new to the Firm. We understand the significance and speed in which technology is moving and changing businesses, therefore it just made sense to refine our service offerings to support our clients and their needs.
My experience is that many lawyers are not that comfortable with numbers. Yet the team focuses a lot on numbers. How do the practicing lawyers at the firm interact with the team and how do both sides bridge that potential gap?
Actually, Jackson Lewis attorneys are very comfortable with numbers; whether calculating overtime payments, analyzing compensation systems, or negotiating the financial terms of a collective bargaining agreement, we deal with numbers and use data analytics in many of our matters. However, our attorneys do rely on the Data Analytics Group for cutting edge statistical analyses, predictive modeling, and other sophisticated data work because it is in the best interest of our clients. As a result, the synergy between our Data Analytics Group and the attorneys across our many practice groups is strong.
What has been client reaction? Internal reaction?
For our clients the benefits to keeping data work in-house is two folds; (1) strategic, cost effective analyses of sensitive information that maximize the protections of the attorney-client privilege and (2) our experienced attorneys have direct control over data analyses, which allows them to be closely involved with determining strategic approaches to leveraging data to present the best possible defense and/or solution. Both are very strong selling points of our services, therefore our clients are very excited.
What are the tools the team uses to do its work?
We develop custom, proprietary solutions based on employers’ unique data sets specifically designed to provide efficient and cost effective solutions for complex statistical analysis projects.
Do you think other firms will have similar teams in the future? Will this become like IT or Marketing… meaning it would be hard envisioning NOT having such a team in, say, 10 years?
The technology transformation wave we are in is not going away. Firms that do not embrace technology and adjust their service offerings accordingly will be left behind.
