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\u201cData analytics is the science of analyzing raw data in order to make conclusions about that information.\u201d[4]<\/a>\u00a0 Data analytics can be used in conjunction with factual evidence and the common law to formulate legal arguments.\u00a0 In short, stick to data the opposing counsel is not contesting \u2013 preferably data provided and conceded by opposing counsel.\u00a0 Using opposing counsel\u2019s own data against them will result in a data analytics conclusion and legal argument that will be difficult for opposing counsel to rebut.<\/p>\n <\/p>\n In 1968, the California Supreme Court stated in People v. Collins<\/em> that \u201c[m]athematics . . . while assisting the trier of fact in the search for truth, must not cast a spell over [them].\u201d[5]<\/a>\u00a0 In Collins<\/em>, using data analytics, the prosecution sought to establish that there was an overwhelming probability that the defendants were guilty because they matched certain descriptions of the suspects provided by the victim and a witness.[6]<\/a><\/p>\n <\/p>\n In dispute, however, were the very descriptions of the suspects; the victim\u2019s description did not include a ponytail, whereas the witness\u2019s description included a ponytail.[7]<\/a>\u00a0 Despite the ponytail description difference, the prosecution\u2019s data analytics included that the female defendant had a ponytail.[8]<\/a><\/p>\n <\/p>\n In addition, the prosecution\u2019s data analytics included various factors such as the probability that a man has a beard and the probability that a man has a moustache; however, the prosecution did not take into account overlapping categories: the probability that a man has both a beard and a moustache.[9]<\/a>\u00a0 These discrepancies skewed the raw data; thus, the prosecution\u2019s argument fell flat on its face because the data analytics conclusion was nothing more than a fallacious blunder.[10]<\/a><\/p>\n <\/p>\n So, what is the solution?\u00a0 The solution is to use data the opposing counsel is not contesting \u2013 preferably data provided and conceded by opposing counsel.\u00a0 In a simple example, suppose in a False Claims Act case, Corporation ABC initially reported only $4,000 worth of government contracts; whereas, the government\u2019s audit report found that the reported amount was supposed to be $20,000.[11]<\/a>\u00a0 In response, opposing counsel for Corporation ABC states that the reported amount was supposed to be $10,000.\u00a0 With this, Corporation ABC motions for summary judgment.<\/p>\n <\/p>\n In this hypothetical situation, it is important to note what has occurred.\u00a0 Although, the amount that was supposed to be reported is in dispute (government says $20,000; Corporation ABC says $10,000), opposing counsel and Corporation ABC has conceded that at least $10,000 was supposed to be reported.\u00a0 Therefore, because both the initial reported amount of $4,000 and opposing counsel\u2019s conceded amount of $10,000 are undisputed, these two amounts can be used in a data analysis.<\/p>\n <\/p>\n In our False Claims Act example, using data analytics, we can conclude that Corporation ABC had failed to report at least $6,000 ($10,000 – $4,000) of what was supposed to be reported.\u00a0 Or, that Corporation ABC failed to report at least 60% ($6,000 \/ $10,000) of what was supposed to be reported.\u00a0 Or, that Corporation ABC only reported at the most 40% ($4,000 \/ $10,000) of what was supposed to be reported.\u00a0 Remember, these data analytics conclusions are based off of undisputed or opposing counsel provided and conceded data; thus, these data analytics conclusions will be difficult for opposing counsel to rebut.<\/p>\n <\/p>\n The next step is to apply our data analytics conclusions to caselaw.\u00a0 In United States (<\/em>ex rel. Liotine) v. CDW Government, Inc.<\/em>, the court denied the defendant\u2019s motion for summary judgment.[12]<\/a>\u00a0 The court rationalized that genuine issues of material fact existed because a deposed witness stated that approximately 10% of the time at least one item on the invoice or order was not reported to the government.[13]<\/a><\/p>\n <\/p>\n In the case at hand, using opposing counsel\u2019s provided and conceded data, we have concluded that Corporation ABC failed to report at least 60% of what was supposed to be reported.\u00a0 Comparing Corporation ABC\u2019s 60% with Liotine\u2019s<\/em> 10% is monstrous.[14]<\/a>\u00a0 Again, opposing counsel will have a difficult time rebutting the data analytics conclusion and legal argument because opposing counsel provided and conceded the data. \u00a0Therefore, a court is most likely to deny the defendant\u2019s motion for summary judgment.<\/p>\n <\/p>\n Overall, although, this is a simple example, data analytics can be used to formulate legal arguments.\u00a0 Do not make the mistake in Collins<\/em>.[15]<\/a>\u00a0 Just remember to stick to data the opposing counsel is not contesting \u2013 preferably data provided and conceded by opposing counsel.<\/p>\n <\/p>\n [1]<\/a> How Lawyers Use AI to Win Before It Begins<\/em>, JD Supra (June 26, 2019), https:\/\/www.jdsupra.com\/legalnews\/how-lawyers-use-ai-to-win-before-it-90005.<\/p>\n [2]<\/a> Id.<\/em><\/p>\n [3]<\/a> Id.<\/em><\/p>\n [4]<\/a> Jake Frankenfield, Data Analytics<\/em>, Investopedia (July 1, 2020), https:\/\/www.investopedia.com\/terms\/d\/data-analytics.asp.<\/p>\n [5]<\/a> People v. Collins, 68 Cal. 2d 319, 320 (1968).<\/p>\n [6]<\/a> Id.<\/em> at 325.<\/p>\n [7]<\/a> Id.<\/em> at 321.<\/p>\n [8]<\/a> Id.<\/em> at 325.<\/p>\n [9]<\/a> Id.<\/em> at 328-29.<\/p>\n [10]<\/a> See<\/em> id.<\/em> at 332.<\/p>\n [11]<\/a> 31 U.S.C. \u00a7 3729 (2020).<\/p>\n [12]<\/a> United States (ex rel. Liotine) v. CDW Government, Inc., No. 05-33-DRH, 2012 WL 2807040, at *12 (S.D. Ill. July 10, 2012) (considering summary judgment for overcharges on freight and unpaid Industrial Funding Fee).<\/p>\n [13]<\/a> Id.<\/em>\u00a0 Important to note is that the witness stated that approximately 10% of the time at least one item on the invoice or order<\/em> was not reported to the government.\u00a0 Translated, this means that 10% of the time, the invoices or orders contained an error; whereas, if we were to consider each item irrespective of invoice or order, the percentage of error would be much less than 10%.\u00a0 Thus, the genuine issue of material fact threshold is much lower than that of 10%.<\/p>\n
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