A Ticking Time Clock Tells The Tale Of Hours Worked

Analysis Of Time Entries And User Activity Used To Determine Totality Of Workday

Wage and hour class actions require specificity of aligning claims to the class. Much of this data usually resides in a multitude of platforms ranging from off-the-shelf software to proprietary timekeeping systems that often prove challenging to align and fully understand.

A telemarketing firm was faced with a claim of several years of unpaid time was owed to employees because their timekeeping software used to register employees’ time was not properly recording the start and end of their workday. The claim alleged that the system did not synchronize time entries properly with the actual time each employee started their workday, taking anywhere from 15 to 30 minutes to register their initial punch in time. There were also claims by the class that their workday often extended beyond the time they clocked out. Overall, the claim alleged that employees had been underpaid on average of thirty minutes each day spanning a period of approximately three years and sought reparations for these unpaid hours. 

ESI Analyst was utilized to combine both the timecard entries with each employees’ email activity over the course of the period of the claim. The digital trail revealed that periods of work activity were in sync with the timekeeping system.

Data available included exports from the timekeeping system, which reflected each employees’ time in and out for each day worked, as well as internal ticketing system that leveraged email notifications and responses, reflecting when each user was actively resolving open requests. ESI Analyst was utilized to combine both the timecard entries with each employees’ email activity over the course of the period of the claim. The digital trail revealed that periods of work activity were in sync with the timekeeping system.

At its core, ESI Analyst is designed to combine a multitude of disparate data types in a simple and intuitive fashion, allowing case teams to quickly identify differing activities and filter by the correlating individual or group. Sets of data can then be easily identified and time-lined to demonstrate clear patterns of work activity across a variety of differing data types. 

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