July 2016 JD Supra
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Is Big Data a Misunderstood Moving Target?     was published by JD Supra on 7/27/16.

The Oxford English Dictionary defines Big Data (BD) as data sets that are complex, hard to understand and difficult to process utilizing conventional methods. Historically, typical Information Technology (IT) systems have been incapable of capturing, storing, managing and analyzing BD, which is constantly evolving and accumulating in the world’s data vaults; making Big Data A MOVING TARGET.

Its use is based on the idea that more information equals more accurate analysis. Its attraction is the availability of
additional information contained in a single large set of “related data” points permitting correlations not otherwise easily
available. It is all about relational data analytics.
The fascination with BD rests with its “perceived” ability for shedding light and providing insight into processes, consumer
trends, sports predictions, voting elections predictions, purchasing recommendations, traffic flows and even epidemic
detection/prevention. The four main characteristics (and definitions) of Big Data that have been studied are:
 The size of the data pool – Volume
 The speed at which data is processed – Velocity
 The structure of the data – Variety
 The (trusted) source of the data – Validity
Much has been written about erroneous conclusions drawn from inadequate analyses of BD. Big Data by its nature could
create an environment where dishonesty is more difficult to detect and control and security issues are masked or
Our courts are increasingly in need of forensic accounting “experts” to analyze BD data sets efficiently, securely and
accurately. In high-end litigation, accurate and objective forensic accounting is often critical to the development and
support of evidence. Objectivity and independent analysis are required for avoiding the appearance of advocacy during
expert testimony.
Because efficiently and effectively working with BD is an evolving skill set, retained “experts” are undertaking a learning
curve involved with understanding meta-data analytics, calculating related damages, detecting fraud and security
Don’t be a victim of your own making. When the data volume in a matter is overwhelming, you are facing BD. Be sure
that your forensic accounting expert is well trained and experienced in BD relational data analytics.