Data Analysis in Class Action Lawsuits and Multi-District Litigation  

Data Analysis in Class Action Lawsuits and Multi-District Litigation  

From basic discovery with Plaintiff Fact Sheets to post-settlement Proof of Claim Forms, data analysis provides fact based evidential support, ensures complete information for each plaintiff, enables the integration of interactive forecast models, allows for the identification of sub-classes, provides multi-dimensional observations of data, supports quality control, and is the backbone of quality assurance.   HR data analysis    Any given class action, multi-district litigation, or mass tort can collect anywhere from hundreds to millions of Plaintiff Fact Sheets (PFS) (aka Plaintiff Profile Forms (PPF), Client Information Sheets (CIS), and/or Proof of Claim Forms (POC)) containing from the most basic information to some of the most detailed and personal aspects of a plaintiff’s life. With Fact Sheets reaching into the tens of pages per plaintiff and potentially hundreds of pages more in attachments per plaintiff, it can be easy to become overwhelmed by the informational overload, though this information can make or break your case!

The role of data analysis in class action lawsuits has surfaced to reveal the true potential of information collected to benefit both the defense and plaintiff sides of any class. Since both sides are out to prove a point, the use of intense data analysis in association with customized data management solutions allow both basic and complex trends to be found out and graphically represented to the court. It is one thing to say there are “X” amount of each symptom type, it is another thing entirely too visually display the symptoms by subclass, location, or severity for the court to see a clear image of the size or magnitude of the situation at hand.

The benefits to class attorneys who utilized data analysis services range by type of class action, but common benefits can be seen throughout all case types. Such benefits include the basics of tracking plaintiffs, plaintiff complaints, documents related to each plaintiff, and the general quantities, averages, and general locations of the plaintiffs; however, more advanced benefits can be realized by utilizing a professional data analyst. Such advanced benefits could include multi-dimensional observations; interactive forecast models based on specific location or type; data preparation and incorporation into court presentations; and graphical representations of plaintiff groups by type, location, attorney, and so on as the captured information allows.

Comprehensive Information

The first and most important concept in data analysis is the necessity of comprehensive information. When information is lacking, a thorough analysis is not possible or certainly far more difficult, time consuming, and less accurate, which is why your class action data management system should contain a strong integrated deficiency curing process. The deficiency curing process (when properly implemented) will allow your agents to quickly eliminate holes in plaintiff information enabling analysis to ensue.

Data Quality

The quality of the data needs to be addressed both before the Plaintiff Fact Sheet (or other form) is created and throughout the data collection process. Without high quality data (standardized data formats), the more complex and often more beneficial analysis cannot occur. Non-standardized datasets complicate and often negate many of the benefits sought through class actions, mass torts, and multi-district litigations forcing plaintiff information to be reviewed individually instead of on the whole.

Data quality can be quickly reviewed from an analytical standpoint using advanced and often custom data analysis techniques. These techniques can vary greatly depending upon the type of information being collected, the quantity of files being reviewed, and the extent of the data management system’s built in quality control features. Strong techniques will increase the efficiency of data analysis, decrease the associated costs of analysis, increase the accuracy of the analysis, thus increasing the speed of linear fact based evidential support, the identification of sub-classes, exploring multi-dimensional observations, and the integration of interactive forecast models.

Linear Facts

On a base level, the information collected during the early stages of a class action can be used in a straight forward manner such as the quantity of claims with “X” symptom or percent of claims with “X” symptom. This base level of information is referred to as linear fact based evidential support and is useful in directing the focus, tempo, and further analysis for the case. The usefulness of linear facts is largely dependent upon the complexity of the case and the inclusion of other limiting factors surrounding the case. As cases become more complex, diverse, or multi-dimensional, a more advanced approach to data analysis is required.

Identification of Sub-Classes

The identification of sub-classes is often used when plaintiff health or location are factors in the case. For instance, a sub-class can be identified based on the quantity or severity of health issues related to a case and affects the final settlement amounts paid by each subclass as in the following example. Manufacturer “A” was found to have less related health issues and less severe health issues for its “Defective Product” than manufacturer “B”, thus the monetary compensation for settlement from manufacturer “A” might include a smaller value placed on health compensation though retain a similar value for replacement costs as that of manufacturer “B”. An example of a location based subclass might relate to the deterioration or safety of a product based on the region the product was used or installed whereas climate and weather conditions could intensify or slow down the product’s failure rate.

Multi-Dimensional Observations

Multi-dimensional observations can be used to intensify scrutiny of a sub-class to either relieve the subclass from liability or to increase the perceived liability of the group. Expanding upon the example above, let’s say that Manufacturer “A” with failed products in locations “1”, “2”, and “3” had less severe health issues than in location “4” indicating that location may have played a significant role in the severity of health issues, thus excluding or limiting the impact of said health conditions in the suit and implying an externality exists in location “4” to cause the more severe health issues. To uncover the most beneficial multi-dimensional observations, such as those above, you will require the services of a professional data analyst with the experience and technical capabilities to make a logical hypothesis which can be tested and proven with the data collected.

Interactive Forecast Models

 

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