By William Davis, Senior Director, Trifacta
The financial services sector is constantly adapting to shifting regulatory requirements. As markets face increasing scrutiny in the wake of a tumultuous macro-economic context, so too is regulation growing ever more granular and nuanced. Research from Thomson Reuters Regulatory Intelligence service estimates that financial institutions track and address an average of 200 new international regulatory revisions every day.
To field compliance queries, financial institutions are managing a vast volume and variety of transaction data. Ensuring that this data doesn’t have any quality issues is imperative, otherwise it can severely skew downstream reporting and put millions of dollars at stake. As such, financial institutions must meticulously prepare data, which can account for up to 80 percent of the time required to meet compliance demands.
Traditionally, financial institutions have relied on common spreadsheets like Excel – a technology first introduced nearly 40 years ago – to complete regulatory reporting, which is dependable, yet reaching its limits as the speed of regulatory reporting accelerates and demands faster and more robust data preparation. Below, we review some of the core challenges of using Excel for regulatory reporting, which has led to the increasing adoption of intelligent and automated data preparation platforms among financial institutions.
Excel has data lineage limitations
When performing regulatory reporting, data lineage is of the utmost importance. Both internal and external stakeholders need visibility into understanding exactly how data has been transformed prior to being submitted to a regulator. Excel has data lineage limitations, which makes it difficult for data analysts to demonstrate the work they’ve done. Often, they will have to go back through their work and manually demonstrate each step they took with a variety of stakeholders in order to ensure accuracy. Instead, financial institutions need to look for data preparation solutions that record all of the data transformation steps taken, allowing analysts to easily demonstrate how they’ve arrived at any particular answer and sign off on work.
Excel is manual
Every transformation in Excel must be done manually—there is no embedded intelligence that guides the data preparation process, nor many shortcuts that allow users to skip certain segments. Instead, nearly every transformation must be built from scratch and remembered for each new dataset. Not only does this leave analysts more prone to errors, but it doesn’t allow for easy collaboration among team members. Completing regulatory reporting, as is the case with most initiatives, is best executed as a team, where different members can contribute their unique knowledge about the data sets at hand and how they should be transformed. By operating in a siloed fashion with Excel, there is more opportunity for mistakes to go unnoticed or be repeated.
Excel doesn’t offer visual guidance
Completing regulatory reporting often requires leveraging a number of different data sets, both from within the organisation, as well as externally. Remodifying quality issues is the top priority, which abound from misspellings and inconsistencies to anomalies. Certainly, analysts can find and address these data quality issues with Excel, but without visual guidance, it can require lots time spent scrolling and searching. Instead, financial institutions should look for a tool that automatically and visually surfaces quality issues so that they are easy to identify and correct early on in the preparation process—instead of after the reporting has been completed. This saves untold hours in redoing previous work and accelerates time spent sifting through datasets to find these data quality issues.
Excel can’t handle large data volume or data complexity
As regulatory reporting grows increasingly nuanced—demanding more and more types of data at higher volumes—Excel is hitting its limits. Excel was (and continues to be) a great tool for preparing small amounts of structured data, but slows down considerably with big data, and often can’t handle the complexity of modern semi-structured data sources. It’s important for analysts to be able to work with all of their data within one technology (instead of breaking it into different segments for different analysts to prepare) to remain consistent and get the full view of their data.
The bottom line
While Excel is still a reliable and useful tool to prepare data at a small scale, entrusting it with your regulatory reporting—the accuracy of which can be worth millions of dollars—isn’t ideal. You need a powerful, agile data preparation platform to enhance the power of your regulatory reporting department. This platform must provide intelligent suggestions, an intuitive interface, and collaboration capabilities that will help accurately streamline data preparation processes across the department.
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