Using governance to achieve data quality objectives
Every bank, asset manager, insurance company and other financial services institutions have been persuaded to change their operations through a variety of recent regulations. All these organizations have also witnessed exponential increases in the volume of data they collect from a variety of sources.
The regulatory landscape that permeates among banks and financial institutions has created a dichotomy that these organizations need to reconcile. On the one hand, these institutions have to manage their data practices in a manner compliant with the regulatory stipulations. On the other hand, the same institutions have to leverage data to glean insights to deliver new products and services to the market, understand customers better, reduce operational costs, improve risk management, achieve topline growth, etc.
In short, the tradeoffs these institutions are compelled to make between data insights and data governance affect major business areas.
Financial institutions and regulators have long realized that data quality is central to achieving complete transparency into markets and banking operations. Dodd-Frank regulations established rules to closely monitor and improve data quality of derivative asset classes. Likewise, the Basel Committee for Banking Supervision articulated the importance of data quality and sound data governance in its BCBS 239 regulation.
Within the scope of the current regulations, several banks are implementing data governance to manage data with the ultimate objective of producing regulatory reports in a compliant and timely manner. The next challenge facing these institutions and regulators is to leverage data as a strategic asset by deriving insights through analytics at the macro (understand markets and manage groups of banks and their subsidiaries) and micro level (understand of a specific product).
At a high level, strong data governance processes with sound validations at the time of ingestion, a well-designed enterprise data model and metadata management are important to produce good quality data. Historically, data transparency and quality have been instrumental in organizational success. Data quality not only directly impacts the success of major digital transformation initiatives launched by financial institutions, but it also plays a crucial role in their business agility and productivity.
Furthermore, poor data quality attracts penalties, fines, loss of reputation and trust. The basic requirement for any of these initiatives to succeed is high quality and trustworthy data, so the market trends and risks can be quickly identified and properly managed to exploit opportunities and to mitigate or circumvent risks.
Maintaining good data quality is an on-going activity that requires diligent application of processes and best practices by both business and IT teams. To maximize the investments made in data quality management, the first step involves gathering an enhanced understanding of the consequences of poor data quality and how data quality impacts the achievement of business axioms.
Gaps in data quality should be identified and resolved to harvest the intended benefits. Data quality management approaches should also attempt to quantify, in monetary terms, the direct and indirect costs of poor data quality. These measures will also help determine approaches to resolve any data inconsistencies and seek the executive support for data quality initiatives.
Before embarking on large scale data quality programs, an assessment that identifies the business areas most impacted by data quality should be performed. Following this evaluation, a high-level plan should be developed and the effectiveness of a data quality remediation program assessed.
Data quality does not operate in vacuum; for a lasting impact, a ‘Quality Mindset’ is mandatory.
The speed of cultural change in achieving this mindset depends on executive support to a large extent.
