Enterprise Data Governance

An Introduction to an Important Topic in Capital Market Firms Today

In recent years, new and revised capital markets regulations-including Dodd-Frank, Basel III, and KYC-AML-are forcing financial institutions to adopt sound enterprise data governance policies for compliance purposes.  At the same time, a tectonic shift has occurred in many of the historically lucrative sales and trading businesses on Wall Street.  One such example is OTC derivatives trading, where the standardization of much of the OTC order flow via swap execution facilities and clearing to central counterparties has resulted in significant margin compression.  In response to this new competitive environment, Monticello Consulting is partnering with financial firms across the industry to develop and implement sound enterprise data governance programs to compete more efficiently, reduce operational risk, and preserve capital.

Financial institutions rely on accurate and up-to-date information regarding their new and historical trading activities—both client-facing and internal.  Trade data is recorded and stored throughout the enterprise in various systems; some are interconnected, and some are standalone.  It is an often-cited best practice for managing data that storing the information centrally can save time and reduce costs by eliminating reconciliation efforts to ensure the integrity of duplicate data.  Sound enterprise data governance, however, is not simply achieved by building a single storage container or data warehouse.  Enterprise data governance is an overall framework of quality and control that traces where data is sourced, where data is distributed, and what data is permissioned to each user group.  This traceability is referred to as data lineage.

Implementing sound enterprise data governance includes standards for data management, processes for improving data quality, and policies that ensure accuracy and completeness of data flows throughout the organization.  The cases presented in this newsletter highlight how our consultants are at work in the financial services industry today, helping implement sound enterprise data governance for our clients to meet their regulatory obligations as well as compete more effectively to win and retain their customers' business.

Terms to Remember

Data Governance - The formal management processes established to ensure that data is accurate and can be trusted within an organization.

Data Quality - The reliability of one’s data.  Not only does data quality refer to the physical data, but it also refers to data storage and data retrieval.  Characteristics of data quality include: accuracy, reliability, consistency, and timeliness.

Data Stewards - Individual(s) responsible for creating firm policies that govern data definitions, usage, distribution, security, and enterprise data governance structures.  Generally, the data stewards are representatives from distinct business groups that can speak to the data needs of each business area that consumes data.

Business Intelligence - The use of software technology to analyze data in order to make informed decisions, recognize trends, and identify areas of competitive strength and weakness.  BI solutions are being deployed by Monticello to help our clients identify trends in accounts payable processes and to collate root causes of breaks in the control and reconciliation processes.

Data Privacy - A measure employed to define how information is shared with third parties.  

Data Security Principles - Considerations for providers and consumers of data.  These principles should be strictly adhered to when sharing sensitive information.  Principles to consider include: confidentiality, integrity, availability, privacy, authentication, and auditability.