Data Analytics and the Law: Analysis

For data-based evidence, the analysis is the heart of the content: the output of the data compiled for a case. In most instances, the analytics do not need to be complex. Indeed, powerful results can be derived by simply calculating summary statistics (mean, median, standard deviation). More complicated techniques, like regressions, time-series models, and pattern analyses, do require a background in statistics and coding languages. But even the most robust results are ineffective if an opposing witness successfully argues they are immaterial to the case. Whether simple or complex, litigants and expert witnesses should ensure an analysis is both relevant and robust against criticism.

 

What type of result would provide evidence of a party’s assertion? The admissibility and validity of statistical evidence varies by jurisdiction. In general, data-based evidence should be as straightforward as possible; more complex models should only be used when necessary. Superfluous analytics are distractions, leading to expert witnesses “boiling the ocean” in search of additional evidence. Additionally, courts still approach statistical techniques with some skepticism, despite their acceptance in other fields.

 

If more complex techniques are necessary, like regressions, litigants must be confident in their methods. For example, what kind of regression will be used? Which variables are “relevant” as inputs? What is the output, and how does it relate to a party’s assertion of fact? Parties need to link outputs, big or small, to a “therefore” moment: “the analysis gave us a result, therefore it is proof of our assertion in the following ways.” Importantly, this refocuses the judge or jury’s attention to the relevance of the output, rather than its complex derivation.

 

Does the analysis match the scope of the complaint or a fact in dispute? Is the certified class all employees, or just a subset of in a company? Is the location a state, or a county within a state? If the defendant is accused of committing fraud, for how many years? Generalizing from a smaller or tangential analysis is inherently risky, and an easy target for opposing witnesses. If given a choice, avoid conjecture. Do not assume that an analysis in one area, for one class, or for one time automatically applies to another.

 

A key component of analytical and statistical work is replicability. In fields such as finance, insurance, or large scale employment cases, the analysis of both parties should be replicable. Outside parties should be able to analyze the same data and obtain the same results. In addition, replicability can expose error, slights of hand, or outright manipulation.

 

Data-based evidence requires focus, clarity, and appropriate analytical techniques, otherwise an output is just another number.

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.