A case for forensic data analytics | KPMG | CA
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Building the case

Building the case

In the increasingly complex world of modern litigation, there is little room for assumptions. Disputes are won and lost by one's ability to support a case with clear and credible data.

This is, of course, easier said than done; especially in disputes that deal with massive amounts of data collected from many disparate sources. Here is where advances in forensic data analytics (FDA) is giving organizations the tools and processes to improve the quality of their independent expert reports and, in so doing, build a stronger, more defensible position for clients.

The FDA advantage

With the right expertise, FDA can analyze complete data sets in multiple contexts at reasonable costs and within short timelines. Those qualities have proven invaluable for clients in disputes wherein deadlines are short and the volume of structured information to process is significant.

In short, using FDA helps to tell a more full and accurate story. With it, organizations can build more precise financial models; reduce the estimates and assumptions that may be challenged by the opposing party; provide a more efficient way of validating data; and prepare analysis quickly and methodically, therefore providing more weight and credibility to the expert report.

In a recent telecom dispute, KPMG in Canada's FDA team worked in collaboration with our forensic damage quantification professionals. The FDA team assessed the reliability of data used in the calculation by obtaining data from the client's warehouse and performing a technical review of the extraction method. By validating the data's accuracy, the dispute team was then able to provide a very reliable financial model, to support an expert report.

The judge determined that KPMG's model was both objective (the data we received was directly from the data warehouse) and complete (100 percent of the client's data was analyzed by KPMG's team).

FDA roadblocks

As with any technology, there are challenges in using FDA. Primarily, these challenges revolve around the quality and availability of data. They include:

  • Functional requirements: Organizations cannot predict the type or granularity of data they will need on a certain aspect of calculation. Many systems and databases are not designed with litigation support in mind. By maintaining their data systems and complying with best practices regarding data collection and retention, they can be better prepared to meet unforeseen requirements.
  • Accessibility: Depending on an organization's data systems and the parties involved in a dispute, it can be difficult to pinpoint where relevant case data lives. In other cases, extracting and collecting that data can be difficult, either due to system limitations or because the data resides on a third party's system.
  • An incomplete story: In litigation, there is certain data that cannot or should not be divulged to the other party. As a result, one side might not have the complete data in its possession and the resulting analysis might not reflect all of the circumstances in a given matter.
  • Duplicate records: There can sometimes be multiple versions of 'the truth' during a dispute process because the client is using multiple systems that are not synchronized. The multiplicity of systems can result from a business acquisition or system upgrade, thereby leading to questions and debates over which version of the data set is correct.

A solid start

Over the years, KPMG has used FDA to assist its clients and bolster their data capabilities moving forward. The ones that have benefitted most from these services, however, are those who took the followings steps to become FDA ready:

  • Training: Provide effective and regular training to employees. Most modern systems come with data-loading, reporting and querying tools that can be used to produce litigation-ready evidence, but users must know how to wield these new tools.
  • Standardization: Data entry, organization, and extraction methods should be standardized in order to help maintain quality and consistency. Data input standards and validations, maintaining a data dictionary, and designing a proper architecture that yields one version of the truth are critical objectives.
  • Internal controls: There should be controls in place to improve the quality of data. Along with input validation, adequate user access to ensure that data is not manipulated is critical.
  • Data retention and management: Data should only be retained as long as it is useful. If it is to be retained, processes are required to ensure it is up to date. Analytics performed on outdated information may not yield useful results.

Evolving litigation

Litigation is not immune to the changes brought about by evolving technology. The best litigators are increasingly those who make best use of the tools at their disposal – including understanding both the potential offered by data and how to fully leverage it.

Granted, not all disputes will require FDA. Yet, for counsels and clients that require thorough, accurate, and multi-contextual analysis of complex or large amounts of data at reasonable costs and within short timelines, the practice can go a long way towards producing an objective, credible, and thorough independent expert report. Indeed, those qualities represent great added value for clients in disputes wherein the volume or complexity of data to process is significant.