Corporate Governance – An Operating Perspective and Data Analytics Tools

Corporate governance is a broad term that is often used when referring to the formal legal structure of a company and the duties of directors and officers. While this represents an important component of corporate governance, it can provide a narrow perspective with which to assess corporate conduct in litigation and investigations. To systematically address corporate governance allegations, a broader view of the company’s operating system may be required. This broader context includes internal and external communications, information and data systems, internal controls, policies and procedures, and reporting structures that underpin well-functioning information flows and decision making.

Understanding how data is generated and communicated in operational, technical, economic, and accounting terms can contribute towards developing a fuller picture of how the organisation utilised information to make its decisions. Mapping this flow of information can serve as a tool with which the alleged conduct can be carefully evaluated. This approach is highly versatile across jurisdictions, industries, and allegations.

Operating Perspective in Litigation

Corporate governance, when viewed through an operational lens, focuses largely on a company’s information flows used to inform decisions. The operating perspective considers how a company generates data and uses information to support its business decisions. In addition, it includes understanding how that information progresses through the corporate hierarchy (and in what format and frequency) and how that information reaches individuals within the organisation who have the expertise and experience to act upon that information.

The operating perspective of corporate governance can be applied to a wide range of matters that cast doubt on the nature of a corporate decision. It can be used to address allegations related to bribery, consumer protection, cyber security, disclosures, fiduciary duties, insolvent trading, internal controls, and other Board decisions and allegations of misconduct.

It is not uncommon in corporate governance matters for plaintiffs to point to isolated pieces of internal information as evidence of wrongdoing. For example, plaintiffs might use a piece of information, such as an e-mail, as the basis to argue that the company’s managers produced misleading financial statements, or had that information been disclosed at that time, the company’s share price would have fallen earlier. In insolvent trading matters, plaintiffs may identify an accounts payable report as evidence that the directors “knew” that the company was insolvent at that time. Alternatively, in a consumer protection matter, plaintiffs may focus on internal correspondence by a junior engineer that alerted management to a potential defect months before a product recall.

“Smoking guns” can be addressed by showing that appropriate decision makers had the information and addressed it. Thus, understanding the system of information flows allows isolated pieces of information to be considered in their proper context. For instance, adopting an operating system view might show that the information was considered in a timely manner at an appropriate committee meeting in which careful validating assessments were made by qualified individuals.

Corporate Governance as an Operating System

Data analytics tools such as chronologies, network diagrams, and Natural Language Processing (NLP), a sub-field of Artificial Intelligence (AI), can help generate hypotheses and arguments early in a case.

Chronologies

A chronology is an effective tool for organising and understanding flows of information. For example, in a case involving a product defect, the chronology may integrate information on key individuals and their communication flows over time including the nature and quality of the technical and financial information surrounding the defect. The chronology may include data on initial product out of tolerance tests, batch testing data, and financial impact modelling. This rich context can help address what was “known” about the defect at specific points of time, the quality of the information generated, whether the information flowed to individuals who possessed the required technical expertise, and what subsequent actions were taken. Automating the preparation of a chronology can provide early case insights with respect to forming arguments, identifying areas for analytical deep dives, and recognising potential topics for expert opinion.

Network Diagrams

Another useful tool for analysing communication flows are network diagrams. These are visual aids that reveal linkages between individuals within an organisation. By graphically representing information flows, roles, and communication patterns among individuals, these diagrams offer a set of different lenses through which to investigate how organisations operate or how events transpired. In the case of a product defect, a network diagram could map the paths through which internal correspondence, financial reports, or technical data related to a defect circulated between key individuals. Key nodes (personnel) in the network where critical information was amplified or stifled may be highlighted. Network specific data analytics can also be applied to automatically extract information such as well connected (or disconnected) groups.

Natural Language Processing (NLP): Text, Content, & Sentiment Analysis

NLP methods, can analyse large quantities of communications and identify critical information about the organisation and allegations. NLP tools are becoming increasingly important in litigation due to the expanding volume and availability of electronic communications. Text analysis, content analysis, and sentiment analysis are NLP tools that can increase the efficiency of document processing in corporate governance litigation.

Text analysis refers to the process of automatically extracting and classifying information from text using AI and is particularly useful when dealing with large text corpora. In addition, text analysis can utilise AI to identify relevant documents even when those documents are of inferior quality.

Content analysis refers to the systematic and objective assessment of message characteristics. It is a replicable form of analysis that relies on rules for describing the message characteristics. For example, on a product defect matter, media coverage can be analysed to objectively assess the prominence of the news story and in what contexts the story was told. Content analysis is a versatile tool that can be applied to a range of corporate misconduct matters including false advertising, product misrepresentation, and securities cases.

Sentiment analysis refers to the analysis of a message’s tone using text analysis techniques. This analysis can add valuable insight when applied to corporate governance litigation particularly in the case of large data sets. For example, on a product defect matter, sentiment analysis can be used to help lawyers identify instances where an employee may have expressed concern about a manufacturing process or product quality. Sentiment analysis can also be used to identify instances of management pressure or intent by employees to take a particular action.

The analysis of internal communications including through private messaging apps has become an important source of evidentiary inquiry. For example, the US Securities and Exchange Commission has recently investigated the conduct of broker dealers in the US for potential breaches in record keeping rules. This investigation included a review of the communications of thousands of employees made via apps on their personal devices.

Conclusion

The operational view of corporate governance combined with data analytics tools provides a versatile approach for analysing corporate conduct. Data analytic tools such as automated chronologies, network analysis, and NLP can help lawyers efficiently navigate large volumes of information captured by organisations. These tools can be particularly helpful early in a case when developing hypotheses and case strategy.

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