How Financial Services Firms Can Shift from Data Hoarding to Decision-Making

Today, many financial services institutions are hoarding significant amounts of operational data, including data about their people and finances. The volume of data organizations store will only increase as data continues to grow in importance. By 2025, IDC estimates that the data created and replicated in the world will reach 163 zettabytes, or 163 trillion gigabytes. That’s roughly 3.3 quadrillion four-drawer file cabinets. Annually.

Contrast this to a time in the not-too-distant past when companies weren’t storing data beyond what was legally required or essential to running their businesses. In fact, due to system constraints and physical storage limitations, storing large amounts of data simply wasn’t possible.

Yet here’s the rub—much of the internal data around finance and human resources (HR), which can be used to better run and grow the business, isn’t actionable because it can’t be combined with other available data or easily analyzed at all. While financial services firms have innovated their front office with new technologies to meet rising customer expectations, they may have overlooked the needs of their internal customers—their employees—who are operating on outdated systems that store data in siloes. Because of this, many organizations and their employees are at risk of overlooking critical information as they become increasingly overwhelmed with data without the means to handle it.

But with the right strategy and systems to make sense of information, you can turn valuable finance and HR data from a collection of facts into actionable insights.

Distinguish Value from Noise

As a financial services firm, where do you start in determining what data you need and how to use it? At the core of every business are its people, finance, and operations, so looking at data from these dimensions is a good beginning. This can be accomplished by identifying your business problem or goal, and then determining what data you have available that can be used to make informed decisions about those challenges.

For example, maybe your organization is a bank with a goal to improve customer satisfaction and increase customer loyalty. Your branch employees, who serve on the front lines of customer communications, are key to this. By comparing workforce data with operational data, organizations can better understand how employee performance correlates with branch performance and then recommend relevant training to get employees up-to-speed.

You also want to retain employees who excel at customer communications and loyalty. If you have the right HR and finance analytics capabilities, you’ll be able to identify high performers at risk of leaving based on historical data related to performance, tenure, and compensation.

Making sense of information requires a unified system that can not only disaggregate data to the line item level but can also pull in different data sources.

Improve the Back-End

The data deluge problem isn’t just about the amount of internal, operational data being stored, but also the level of granularity available. The finance and HR teams of many institutions still operate on outdated systems that are only able to store aggregate data with complex details summarized. While these systems may be sufficient for the purpose of financial reporting, they’re unable to keep up with the level of complexity needed to drive business decisions.

Making sense of information requires a unified system that can not only disaggregate data to the line item level but can also pull in different data sources. This way, financial services firms can get a comprehensive view into their organizations by comparing and analyzing performance across products, customer segments, regions, and other dimensions. Combining all this data at the lowest level of granularity provides better insight into cash inflows and outflows—as well as your key ratios—to better manage risk and drive profitability.

Financial institutions also need a comprehensive view of all this granular data in a real-time dashboard. For instance, a branch or lending manager could use a credit risk dashboard that details loan charge-offs and delinquencies to determine what changes need to be made to meet revenue goals or to identify a disruptive pattern. Or, a financial institution could use benchmarking capabilities to understand how they compare to companies of a similar size or industry, helping them understand their strengths and weaknesses in relation to other organizations.

Data can be a blessing or a burden. Don’t hoard your data. With the right strategy and the technology to support it, your organization can hone in on the right data to make decisions that will move the needle forward.