Financial institutions, insurance companies as well as global asset management services companies have all significantly brought changes in their operations complying with the global regulations such as Basel III, Dodd-Frank, MiFID (Markets in Financial Instruments Directive), EU Solvency II and Volcker Rule in the regions of Europe and North America. Even regulatory reforms have also tightened up in Asia regions. At the same time, the financial sector over the last few years have witnessed phenomenal surge in their data volumes, which officials want to leverage at any cost in the quest to have a leaner organization, improve operational efficiencies, and enhance the revenues for their firms. Sound data quality remains a pivotal parameter in delivering unparalleled results for these billion dollar firms.
Generating volumes of data is one thing but making it to use and harness it to one’s competitive advantage is pretty complex and challenging for financial institutions. The concerns were even testified by a recent SAS sponsored study which revealed 35% of the banks had difficulties in aggregating customer data and managing the requirements that come with it. Supplementing to this, the US Postal Service estimates that 40% of its customer data repository is either incorrect or incomplete. This inaccurate information about customers is a major roadblock faced by the banks, which is jeopardizing their business objectives.
With the sheer volumes of data stored in the repository of the banks databases, not all data will be of intrinsic value to the banks. All the information stored in the bank will not make sense unless cleansed, standardized, validated, corrected, and enriched. Data quality tools thus play a crucial role where functions such as data profiling, data cleansing, and data de-duplication will be used abundantly to gain a wider control of the business and achieve competitive advantage in the market.