Regulatory Compliance Starts With A Data Management Strategy

Data Management

The banking industry faces mounting pressure to comply with looming industry and government regulations. Financial institutions must demonstrate that they have sufficient stability and capital reserves to withstand a period of economic stress as well as to pay all depositors in the event of default.

While the details of many of these regulations have yet to be determined, one thing issure: having a solid data management strategy is an essential prerequisite to meeting the regulatory mandate.

As a result of both M&A and de-centralised IT leadership, a majority of banks have information housed in separate data silos, back-office systems and packages, making it difficult to grasp the full-picture of liquidity and risk. Furthermore, as new customer channels like mobile computing are introduced, the information challenge is exacerbated.

Regulatory compliance requires the right architecture for data integration and cleansing to turn this regulatory burden into a revenue-generating opportunity. This overriding architecture includes data quality, data integration, master data management, data governance andbusiness intelligence. Let’s look at these in turn.

Data Integration solutions support the identification, linking, and synchronisation of information across different systems and data, both internal and external. They are important not only for tying various systems and data together, and resolving divergent data definitions, but also for synchronising the timing of when transaction data is available for time-critical activities. Some data needs to be accessed in real time on a fine-grained basis. Other data may be examined in bulk a day, week, or month later.

Data Quality solutions evaluate, monitor, and manage data to eliminate inconsistencies among information systems, as well as to prevent incorrect data from entering these systems in the first place. For example, using pre-defined business rulesand criteria, data cleansing routines eliminate mistakes within databases.

During the cleansing process, missing entries can be amended, and completed fields standardised to a specific format. Merging and matching promotes consistency by automatically uncovering related entries within the same system – or across multiple systems – then linking, matching, or merging records as needed.

Data Governance is all about control – control of the people, processes, and technology required to properly handle an organisation’s data across the enterprise. Its main objective is to ensure that banks can access the full value of their informational assets while protecting their data, and their institution, from risk. It goes hand in hand with Master Data Management, which involves consolidating information into a “single source of truth” to ensure consistency among information systems.

While nobody knows fully what the new banking regulations will entail, it’s clear that compliance will be more difficult in the absence of a solid data management strategy. Creating a framework to ensure the confidentiality, quality, and integrity of data is essential to meet both internal and external requirements, such as financial reporting, regulatory compliance, and privacy policies. Getting your data in order is the first step for any compliance reporting initiative, a key to mitigating risk, and the start of a new era in customer intelligence, profitability, loyalty and customer experience.

Jon M. Deutsch is Vice President and Global Managing Principal for Banking, Financial Services & Insurance at Information Builders, a New York-based software company that brings smarter decision-making and streamlined processes to leading organisations in business, government, and education worldwide.