February 2019 – Economic Thinking In Action

Data Analytics and the Law: Acquiring Data

Evidence based on Data Analytics hinges on the relevance of its underlying sources. Determining what potential data sources can prove is as important as generating an analysis. The first question should be “What claims do I want to assert with data?” The type of case and nature of the complaint should inform litigants where they should start looking in discovery. For example, a dataset of billing information could determine whether or not a healthcare provider committed fraud. Structured data sources like Excel files, SQL servers, or third party databases (e.x. Oracle), are the primary source material for statistical analyses, particularly those using transactional data.

In discovery, it’s important that both parties be aware of these structured data sources. Often, these sources do not have a single designated custodian, rather they may be the purview of siloed departments or an IT group. For any particular analysis, rarely is all the necessary data all held in one place. Identifying valuable source material is more difficult as the complexity of interactions between different sources increases. To efficiently stitch together smaller databases and tables, a party should conduct detailed data mapping by identifying links between structured data sources. For example, how two tables relate to another, how a SQL table relates to an Excel file, or how a data cube relates to a cloud file. Data mapping identifies which structured data sources are directly linked to one another through their variables, and how they as a whole fit together in an analysis.

However when using data based evidence to answer a question, structured data is rarely clean and/or well organized. Variables defined in a table may be underutilized or unused. Legacy files imported into newer systems can become corrupted. The originators of macros or scripts for data pulls may no longer work for an organization and forgo detailed instructions. Sometimes the data simply do not exist: not from a party burying evidence, but by the very nature of electronically stored information (ESI).

Any defensible analysis is inherently limited by what data is available. With data analytics the maxim “evidence of absence, is not absence of evidence,” is apparent. It’s always more dangerous to exaggerate or generalize from the available data than to produce a narrow, but statistically sound result. Thus, given the data available, what questions can be asked? What questions can be answered? Finally, if there is no data, does it mean there is no problem?

Upcoming Changes to Census Data

The United States Census Bureau announced on September 2018 that their privacy policy regarding the 2020 Census Survey and other public use data projects will be undergoing changes, some of which could have an impact on many areas of data science.

According to a December 2018 report written by the Institute for Social Research and Data Innovation (ISRDI), University of Minnesota, the US Census Bureau’s new set of standards and methods for disclosure, known as differential privacy, may make it impossible to access usable microdata and severely limit access to other important public use data.

Data scientists, including those at EmployStats, have been regularly utilizing free reliable public Census Bureau data to analyze social and economic factors across America for over six decades. The US Census Bureau releases public microdata such as the Decennial Census and the American Community Survey, which collects information on demographics, employment, income, household characteristics, and other social and economic factors. EmployStats uses this data regularly to assist clients in Labor and Employment cases.

The ISRDI report can be found here.

To find out more about how EmployStats can assist you with your Labor and Employment case, please visit www.EmployStats.com and make sure to follow us on Twitter @employstatsnews

Case Update: Employee Misclassification

The EmployStats consulting team, lead by Matt Rigling, MA, recently worked on a case involving employee misclassification. EmployStats assisted attorneys by calculating potential damages for employees who were classified as exempt but potentially should have been classified as non-exempt and therefore owed FLSA overtime wages for hours worked over 40 in a workweek.

Matt Rigling and the EmployStats team also worked to use the case data and information provided to confirm whether the employees in question passed both the salary and duties tests for exemption purposes. According to the FLSA, an employee can be classified as exempt under the Administrative, Executive, or Professional exemption if they meet all of the requirements for salary and job duties.

In this case, EmployStats compared the employee information to the salary and job duty requirements of the Administrative and Executive exemptions. Under both exemptions, the employee must be paid a salary of at least $455, as well as meet the job duties specific to an Administrative or Executive employee. According to U.S. Department of Labor, Administrative employee’s primary job duty must be office work that is “directly related to the management or general business operations of the employer or employer’s customers” and must include “the exercise of discretion and independent judgement” for matters of importance. Similarly, Executive employee’s primary job duty has to be managing the company, or a department of the company. Additionally, they must also regularly direct at least two other full-time employees and have the authority to at least recommend the company fire, hire, or promote other employees.

To see how EmployStats can assist you with an employee misclassification case or another labor and employment matter, please visit www.EmployStats.com or give us a call at 512-476-3711. Also make sure to follow our blog and find us on social media! @employstatsnews

Data Analytics and the Law: The Big Picture

With businesses and government now firmly reliant on electronic data for their regular operations, litigants are increasingly presenting data-driven analyses to support their assertions of fact in court. This application of Data Analytics, the ability to draw insights from large data sources, is helping courts answer a variety of questions. For example, can a party establish a pattern of wrongdoing based on past transactions? Such evidence is particularly important in litigation involving large volumes of data: business disputes, class actions, fraud, and whistleblower cases. The use cases for data based evidence increasingly cuts across industries, whether its financial services, education, healthcare, or manufacturing.

Given the increasing importance of Big Data and Data Analytics, parties with a greater understanding of data-based evidence have an advantage. Statistical analyses of data can provide judges and juries with information that otherwise would not be known. Electronic data hosted by a party is discoverable, data is impartial (in the abstract), and large data sets can be readily analyzed with increasingly sophisticated techniques. Data based evidence, effectively paired with witness testimony, strengthens a party’s assertion of the facts. Realizing this, litigants engage expert witness to provide dueling tabulations or interpretations of data at trial. As a result, US case law on data based evidence is still evolving. Judges and juries are making important decisions based the validity and correctness of complex and at times contradictory analyses.

This series will discuss best practices in applying analytical techniques to complex legal cases, while focusing on important questions which must be answered along the way. Everything, from acquiring data, to preparing an analysis, to running statistical tests, to presenting results, carries huge consequences for the applicability of data based evidence. In cases where both parties employ expert witnesses to analyze thousands if not millions of records, a party’s assertions of fact are easily undermined if their analysis is deemed less relevant or inappropriate. Outcomes may turn on the statistical significance of a result, the relevance of a prior analysis to a certain class, the importance of excluded data, or the rigor of an anomaly detection algorithm. At worst, expert testimony can be dismissed.

Many errors in data based evidence, at their heart, are faulty assumptions on what the data can prove. Lawyers and clients may overestimate the relevance of their supporting analysis, or mold data (and assumptions) to fit certain facts. Litigating parties and witnesses must constantly ensure data-driven evidence is grounded on best practices, while addressing the matter at hand. Data analytics is a powerful tool, and is only as good as the user.

EmployStats is Expanding

The EmployStats team is thrilled to announce a new division of expertise that we can now provide to our clients.

Starting in January 2019, the new Wage and Hour Data consulting division began operation under the leadership of Consultant Matt Rigling. Matt Rigling obtained his Master’s of Arts in Economics from the University of Texas at Austin, and has been providing EmployStats’ clients with database and data analytics consulting for the past three years. Under this new division of EmployStats, the team will strive to provide our wage and hour clients with the expertise they need in the construction and tabulation of time and pay record databases, as well as providing wage and hour penalty calculations for our clients in states such as California and New York.

This type of consultation is perfect for both plaintiff and defense attorneys seeking to have the best support for their client in order to efficiently reach a settlement at mediation, as well as both private and government entities simply seeking to perform internal audits of their labor practices. EmployStats has the capability to swiftly handle large and cumbersome data sets that can sometimes bog down attorneys and paralegals attempting to handle the analysis in-house.

Follow this blog as we continue to post about tips for efficiently using data to bring your wage and hour cases to settlement, updates on upcoming events, and current events in the world of labor and employment law. For more information on Matt Rigling and the EmployStats team, please check us out on our website and social media accounts!

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