As technology evolves, organizations maintain increasingly complex and substantial volumes of structured and unstructured data. In addition to core data sources, it is common for organizations to have complexities such as migrated data sources, user-created copies or extracts, legacy data sources, obsolete and irrelevant repositories, and gaps or known issues with specific data sources. To accommodate these data challenges, ad hoc tools and processes often develop to identify, manage, and analyze this information. Over time, it becomes increasingly difficult to identify golden sources of information, untangle technical workflows that developed on top of ad hoc tools, unwind data processes that evolved from short-term fixes, and recall decisions that led to the current state of the data. Adding to these complexities, there is typically a lack of current data maps and workflow documentation, and system owners with historical knowledge about the data are rare.
Secretariat’s analytics and data strategy professionals are experts at resolving complex data challenges in response to a dispute, investigation, or litigation and proactively improving data visibility and defensibility before an event occurs.
Our analytics and data strategy professionals are experts at identifying, aggregating, standardizing, and analyzing large volumes of disparate structured and unstructured data. We work with organizations and counsel to identify relevant data sources, garner actionable insights, and mitigate data risks in a dispute, investigation, or litigation by strategically leveraging our experience in data, analytics, and technology. Our expertise adds significant value when large volumes of disparate sources and data types are at issue in a matter, data files are available on an iterative or rolling basis, or data conversion processes are required to read or analyze the information. Secretariat’s analytics offerings are developed to be repeatable, defensible, and auditable with rigorous attention to quality and efficiency.
As experts in data and data-driven processes, we also work with clients to proactively identify and remediate potential data risks before an event occurs, improve the defensibility of data within an organization, and modernize internal processes through automation and artificial intelligence. Our offerings are designed to enhance an organization’s data-driven processes to gain early, actionable insights and develop repeatable, defensible solutions while minimizing business interruption.
Our analytics & data strategy team works with each client to deeply understand their data challenges to develop a solution that addresses the specific need, rather than forcing a pre-designed product. We provide insights into known data risks, areas of potential exposure, and concerns raised by plaintiffs or regulators. Whether we are engaged to perform a “follow the funds” analysis, automate a data-driven workflow, calculate exposure amounts or damages, or design a data quality framework, we develop a tiered approach with the client to deliver incremental successes. Each engagement is led by one or more senior team members who deliver with a leveraged team. Our delivery model is built upon consistency of team, transparency of process and communication, and the highest quality of work product.
Global Financial and Economic Crime Outlook 2025
Our Global Financial and Economic Crime Risk Outlook 2025 provides an in-depth, data-driven analysis of these emerging threats, helping businesses navigate the complexities of financial crime with confidence.
Impact of Large Language Models (LLMs) on Dispute Processes in Law
Large language models (LLMs) have ushered in a new era of legal practice, significantly disrupting traditional dispute resolution methodologies. Beyond their application in enhancing legal research and e-discovery, they are poised to revolutionize alternative dispute resolution (ADR) mechanisms.
Challenges to Quantifying Economic Harm from Data Breaches
Quantifying economic harm resulting from data breaches is challenging. It requires sound economic analysis to address a range of issues, including issues related to personal data exposure and the valuation of privacy, the use of dark-web data for benchmarking, and the difficulties in assessing mitigation costs for class-action lawsuits.