Data Analytics & Data Recovery Expert Witnesses

In today’s era of “big data,” litigation teams must leverage tools for mining, organizing, interpreting, and presenting the underlying data in a manner comprehensible to the trier of fact.

Data Analytics, understood as the use of quantitative and computer-based tools and techniques, including databases, to analyze and characterize business problems, is a core DisputeSoft competency.

At DisputeSoft, we are data recovery expert witnesses who use data-driven analytics tools and techniques serve as a means for determining facts and for presenting those facts, whether in investigating trade secret misappropriation, inspecting defect records in a software project failure dispute, or reviewing source code repository modifications in a copyright suit.

Moreover, our data analytics experience and expertise can be leveraged in other disputes not involving IT failure or intellectual property, but for any case where large datasets need to be identified, collected, and analyzed for the purpose of proving or refuting claims at issue in the dispute.

In today’s era of “big data,” the traditional e-Discovery approach to database production is no longer adequate. Litigation teams must not only understand how to acquire and preserve data, including database systems, but must also leverage tools for mining, organizing, interpreting, and presenting the underlying data.

Data Acquisition, Preservation, and Analysis

DisputeSoft’s data experts are highly skilled in all aspects of data acquisition, preservation, and analysis. We routinely conduct high-level assessments of databases to understand their purpose, structure, and functionality. These assessments include audits of data tables, schemas, views, functions, and stored procedures to understand the “big picture” of a database environment. DisputeSoft staff are adept at comparing multiple database environments, both at the structural and substantive levels.

How We Can Help

Our Services

Our experts are experienced in using Structured Query Language (SQL) queries and predefined report templates to mine, analyze, and interpret structured and unstructured data. DisputeSoft routinely examines database revision histories to understand the timeframe and circumstances under which substantive data was added to, or deleted from a system. We also review database schema modifications to understand the history of structural changes to foundational data. Of course, in the era of cloud computing, litigants may prefer not to produce databases in their original format. In the interest of expediency, litigants often rely on public data interfaces or exported versions of data. Our experts are skilled in the examination of database export documents in a variety of file formats, including comma-separated value (CSV), Extensible Markup Language (XML), and JavaScript Object Notation (JSON) documents.

Given the size and complexity of datasets in the modern era, few litigants can afford to conduct an unstructured data review. DisputeSoft works closely with counsel to tailor our technical inspections through careful background research. By reviewing data dictionaries, data models, and data architecture diagrams, we focus our technical investigations on the most fruitful areas of research, saving our clients valuable time and money.

Featured Case

BearingPoint v. United States

DisputeSoft mined data from a customized SAP information system to assess percent complete and determine the causes and excusability of schedule delays.

SEE THE CASE

DisputeSoft leverages data analytics to interpret data on both a quantitative and qualitative level.

Our Experience

As our work in the following cases shows, we use a variety of proprietary and commercially available analytics tools and techniques to make the judgments required of an expert witness.

Kemper Corporate Services Inc. v. Computer Sciences Corporation et al.
- DisputeSoft utilized established tools such as SonarQube and JArchitect to characterize source code in connection with an analysis of compliance with Java code industry standards.
Hogan v. BP West Coast Products, LLC
- DisputeSoft reviewed and analyzed Help Desk trouble ticket reports using a relational database to determine whether BP met industry standards for downtime, which is the amount of time that software fails to perform as intended.
CAMS v. CINC
- DisputeSoft utilized a combination of proprietary internal software and manual review to examine source code and database schema elements for indicia of reverse-engineering or copying.
BearingPoint v. United States (Department of Interior)
- DisputeSoft developed SQL scripts and mined data from project management schedules to measure percent complete and apportion liability for delays.
Archonix Services, LLC v. Key Power International (KPI)
- DisputeSoft utilized data analysis techniques to complete an abstraction-filtration-comparison (AFC) test to determine whether there was evidence of non-literal copying of a competitor’s system.
Philips v. HTC
- DisputeSoft utilized proprietary tools to review HTC’s Java source code to determine the provenance of the code and how the applications handled key operations.

Need a Data Analytics Expert?

Does your firm need help mining and analyzing large amounts of structured or unstructured data? Among other tasks, DisputeSoft leverages data analytics to:

• Measure adherence to service-level agreements (SLAs)
• Review trouble ticket reports and databases to assess system defects
• Analyze deliverables and task completion to measure percent complete

• Determine source code provenance and assess changes over time
• Review timekeeping systems to conduct labor analyses

Our Deliverables

DisputeSoft leverages our data analysis work to compile written reports that interpret data on both a quantitative and qualitative level. As depicted below, we make extensive use of graphs, timelines, and other visual aids to make data comprehensible to educated laymen.

Interface Percent Completion Timeline

This chart illustrates an effective way to analyze and present task completion over time based on data mined from resource-loaded project schedules.

Defect Convergence Timeline

Defect convergence timelines visually depict the rates at which software defects are reported and resolved during software implementations. DisputeSoft routinely generates such graphics in software failure disputes to present our analysis of the quality of a vendor’s efforts to complete and stabilize a software system.

Defect Resolution Metrics

Average time for defect resolution can be highly probative of adherence to contractually agreed upon service level metrics (SLAs). DisputeSoft has deep experience mining defect closure data from databases, and then analyzing and presenting the data in a meaningful and persuasive way.

Project Task Completion

Similarly, percent completion analysis and earned value analysis (EVA) involves mining data at a task or activity level and comparing it to baseline plans and budgets to arrive at actual or projected project completion forecasts.