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top data management challenges

The Top Data Management Challenges Faced by Financial Firms

Shifting data management challenges

The top data management challenges faced by financial firms are not new. These issues have persisted for decades. But what will it take to get financial firms to stop federating data, or worse yet, keeping it separated in silos or handling it manually? An enormous compliance penalty? Excessive costs for managing too many data repositories or fixing discrepancies between error-prone data sets? Or just a desire to simplify and improve their data management environment?

Federated and siloed data

Any one of these reasons could be the incentive, but some are more extreme than others. To correct the top data management challenges, firms first must acknowledge the issue. More than a decade ago, the concept of federating data arose as a response to siloed data. Each business line, subsidiary or country was allowed to manage their own data in the way that was best for them. The challenge of aggregating it at the enterprise or group level was deemed a separate and secondary issue.

Still, managing financial data in this manner ended up raising data operations costs and the number of redundancies among data sets in the silos. If one federated source doesn’t move at the speed needed by another user, that user might redundantly duplicate something from another federated source. Large financial firms have ended up with exactly this issue – overlaps of data among the various federated silos. Worse still, where similar data, such as customer data, is held in different formats in multiple locations, the failure to standardize means that insights into customer behavior are difficult to convert in additional business revenue. Similarly with multiple stores of securities reference data, quality becomes inconsistent and diminishes the confidence that decision-makers and governance stakeholders have in reports

The human API

An even more primitive method than federation was the “human API,” which basically meant cutting and pasting or copying and pasting data from one location to another. This method has often been the stop-gap solution, an alternative where automated workflows fail to move data to and from silos or within and between federated systems.

Solving the top data management challenges

The prevailing recent trend among financial firms to remove the top data management challenges has been to reduce the complexity of their data environments. Data and analytics systems architecture is the key consideration in these endeavors. Currently, aside from silos, many firms are working with multiple security masters, entity masters (often called customer or party masters), and they manage fund or product data in spreadsheets. They seek a streamlined technology architecture to handle them all in a way that can leverage the value of analytics when disparate data types are linked and cross-referenced. Firms acquiring other firms that run different data operations systems have also often lacked enough time to properly integrate them all into a single architecture. So they have been left with a tough-to-manage complex data environment that additionally had data quality issues baked in as a result. These factors, along with cost and governance concerns, have driven the need to simplify data management environments.

Centralized data

Centralized, automated data management solves many of the top data management challenges. It can link entity, customer, product and security depositories. This practice can also ensure that instrument-level data gets converted to include ISIN or SEDOL identifiers. For instance, a large investment bank with an isolated repository of customers may have issues linking that customer data with security masters, thus making it difficult to cross-reference between entity, customer and security data.

For investment firms, reporting for regulations such as the EU’s SFDR can prove very difficult, however, without centralized and linked data. Satisfying the regulation and complying with client mandates requires linking issuer data or separate entity masters, with your firm’s internal representation of customer data in the form of customer masters. Also, a solid cross-reference of entity, issuer and security master data goes a long way toward the credibility of ESG investment products.

Aside from avoiding the risk of non-compliance with rules and regulations, firms should not wait until they face a potential financial loss or higher operational costs due to the top data management challenges that affect trading or investment management. Those that want their data to shine can take solace in using centralized data management as a way forward.

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