Analytics is a hot topic today in business and the news and many people are likely familiar with use of analytics to predict consumer preferences. The legal community may also be familiar with analytics applications to predict the outcome of current cases from historic data.
Analytics can also have an extremely valuable role in litigation-related technical investigations but use of analytics in technical investigations is different from its uses to predict case outcomes and consumer preferences. Analytics is also not the type of analysis of inspection observations and test results that is often performed in technical investigation; analytics goes well beyond this.
Analytics is used in technical investigations in a way that is broader and more integrated with fundamental scientific principles. Similar to predicting case outcomes and consumer preferences, data mining in technical investigations, using statistics and artificial intelligence, identifies empirical trends in data from a failure or accident that provides insight as to what happened, but beyond this, technical investigations also use data modeling based on fundamental scientific principles and data generated by academia and industry to (1) fill gaps in the limited data available from a failure or accident, (2) validate test methods and results, and (3) uniquely provide insight as to what scientific principles were violated and why they were violated.
The rules of evidence typically require use of the scientific method as the standard for technical investigations and analytics is a key aspect of the practice of science today as demonstrated by Virginia Tech’s College of Science having made analytics an overarching theme of its graduate degree programs. A recent message from the Dean of Virginia Tech’s College of Science stated “At the Virginia Tech College of Science, we have reimagined scientific research…. We are focused not on data itself, but amplifying the relevance of that data with analysis, modeling, and interpretation.” (Va. Tech Science, Fall 2019).
This approach to technical investigations powered by science and analytics has been practiced by Dr. Fildes throughout his career, and he now also collaborates with a team with decades of experience leading scientific and licensed engineering firms with hundreds of scientists and engineers who have conducted thousands of litigation-related technical investigations to advance this approach to the use of analytics, which we call Litigation Analytics, for the legal community and for businesses.
Read about the key elements of Litigation Technical Investigations Powdered by Science & Analytics below on this page, or use the following links to access a variety of resources to help you understand Litigation Analytics, when to use it, how it works, and what benefits it provides.
Litigation today involves increasingly complex and multidisciplinary issues where the key issues underlying the accident or failure are not at all obvious, yet litigators who need a technical expert still tend to immediately call a domain expert. This focuses the investigation on a narrow technical scope that may not address the underlying key technical issues. Industry faces the same problem litigators do in that industry has to evaluate numerous sources of technical insights, innovations, and developments. Industry takes a broader approach that combines science and analytics. Using this approach solves many important challenges that litigators face:
Accidents and failures happen because the laws of science are violated. Violations of codes and standards does not necessarily cause an accident or failure. Analytics can be practiced by engineers, scientists, and other technical people, but in any case, they will have solid training and experience in the sciences that underlie the incident under investigation. In many cases experts may have training and experience in chemistry and physics because these two disciplines underlie many of the engineering disciplines.
In addition to taking a broad view of a failure or accident while being sufficiently knowledgeable and skilled to apply the requisite domain knowledge, analytics requires experts to be far more experienced with searching for and identifying relevant scientific and engineering studies that exist in the vast amount of data available today. This is a skill in itself and one in which many experts lack sufficient depth. The challenge is partly in finding sources of data, but also in being able to recognize that data is relevant because most available data was not developed for establishing the cause and origin of failures and accidents, nor was this data developed for the situation under investigation. This is why data modeling based on relevant, well-established fundamental scientific principles is another defining feature of how a litigation investigation powered by science and analytics differs from a conventional investigation. Data modeling based on fundamental scientific principles adapts the available data to the situation being investigated and supplies additional data to fill gaps that will always exist in the available data.
Industry publishes much technical data for marketing and regulatory purposes and academia publishes much applied research, and some of this data can often be used to estimate properties that define a box, usually a very small box, that establishes the range of possibilities for the cause and origin of a failure or accident. This data and its analysis also establish estimates of what results testing should produce and significantly limits unsupported creative interpretation of testing and the events surrounding the failure or accident. This phase of an investigation should occur as early as possible, certainly before testing so that the insight developed can be used to resolve cases early and to properly guide detailed inspections and testing if the case proceeds through discovery. In reality, this phase of the investigation and the extent to which it should be conducted never occurs in too many cases.
This is why it is so important that experts have solid training and experience in the chemistry and physics that underlies many of the engineering disciplines. Experts using analytics work from a much larger base of information, much of it initially appearing to be irrelevant of unusable, and they have skills and experience to use advanced statistics and artificial intelligence to analyze (or mine) this data to extract the insight it holds for the specific situation under investigation, and to combine this data with well-established, fundamental scientific and engineering principles to adapt this data to the situation under investigation and to use this data in applicable models to fill in gaps in the data that exists.
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