A diagnosis for data governance — Informed preparation is the best medicine

Published: December 18, 2014 by Guest Contributor

This is the second post in a three-part series.

Imagine the circumstances of a traveler coming to a never before visited culture. The opportunity is the new sights, cuisine and cultural experiences. Among the risks is the not before experienced pathogens and the strength of the overall health services infrastructure. In a similar vein, all too frequently we see the following conflict within our client institutions.

The internal demands of an ever-increasing competitive landscape drive businesses to seek more data; improved ease of accessibility and manipulation of data; and acceleration in creating new attributes supporting more complex analytic solutions. At the same time, requirements for good governance and heightened regulatory oversight are driving for improved documentation and controlled access, all with improved monitoring and documented and tested controls. As always, the traveler/businessman must respond to the environment, and the best medicine is to be well-informed of both the perils and the opportunities.

The good news is that we have seen many institutions invest significantly in their audit and compliance functions over the past several years. This has provided the lender with both better insights into its current risk ecosystem and the improved skill set to continue to refine those insights. The opportunity is for the lender to leverage this new strength. For many lenders, this investment largely has been in response to broadening regulatory oversight to ensure there are proper protocols in place to confirm adherence to relevant rules and regulations and to identify issues of disparate impact.

A list of the more high-profile regulations would include:

  • Equal Credit Opportunity Act (ECOA) — to facilitate enforcement of fair lending laws and enable communities, governmental entities and creditors to identify business and community development needs and opportunities of women-owned, minority-owned and small businesses.
  • Home Mortgage Disclosure Act (HMDA) — to require mortgage lenders to collect and report additional data fields.
  • Truth in Lending Act (TLA) — to prohibit abusive or unfair lending practices that promote disparities among consumers of equal creditworthiness but of different race, ethnicity, gender or age.
  • Consumer Financial Protection Bureau (CFPB) — evolving rules and regulations with a focus on perceptions of fairness and value through transparency and consumer education.
  • Gramm-Leach-Bliley Act (GLBA) — requires companies to give consumers privacy notices that explain the institutions’ information-sharing practices. In turn, consumers have the right to limit some, but not all, sharing of their information.
  • Fair Debt Collections Practices Act (FDCPA) — provides guidelines for collection agencies that are seeking to collect legitimate debts while providing protections and remedies for debtors.

Recently, most lenders have focused their audit/compliance activities on the analytics, models and policies used to treat consumer/client accounts/relationships. This focus is understandable since it is these analytics and models that are central to the portfolio performance forecasts and Comprehensive Capital Analysis and Review (CCAR)–mandated stress-test exercises that have been of greater emphasis in responding to recent regulatory demands.

Thus far at many lenders, this same rigor has not yet been applied to the data itself, which is the core component of these policies and frequently complex analytics. The strength of both the individual consumer–level treatments and the portfolio-level forecasts is negatively impacted if the data underlying these treatments is compromised.

This data/attribute usage ecosystem demands clarity and consistency in attribute definition; extraction; and new attribute design, implementation to models and treatments, validation and audit.

When a lender determines there is a need to enhance its data governance infrastructure, Experian® is a resource to be considered. Experian has this data governance discipline within our corporate DNA — and for good reason. Experian receives large and small files on a daily basis from tens of thousands of data providers. In order to be sure the data is of high quality so as not to contaminate the legacy data, rigorous audits of each file received are conducted and detailed reports are generated on issues of quality and exceptions. This information is shared with the data provider for a cycle of continuous improvement. To further enhance the predictive insights of the data, Experian then develops new attributes and complex analytics leveraging the base and developed attributes for analytic tools. This data and the analytic tools then are utilized by thousands of authorized users/lenders, who manage broad-ranging relationships with millions of individuals and small businesses. These individuals and businesses then have rights to reproach Experian for error(s) both perceived and actual. This demanding cycle underscores the value of the data and the value of our rigorous data governance infrastructure. This very same process occurs at many lenders sites. Certainly, a similar level of data integrity born from a comprehensive data governance process also is warranted.

In the next and final blog in this series, we will explore how a disciplined business review of an institution’s data governance process is conducted.

Discover how a proven partner with rich experience in data governance, such as Experian, can provide the support your company needs to ensure a rigorous data governance ecosystem. Do more than comply. Succeed with an effective data governance program.