Fiserv: How Data-Driven Organizations Outperform Their Peers
Posted: 1 June 2015 | Author: Jon Nordhausen | Source: Fiserv
The average financial institution stores up to one exabyte – 1 quintillion bytes – of data. Operating on razor-thin margins, top performing banks and credit unions are increasingly focused on the potential of this data to create useful insights and better outcomes. Big data can mean big business for financial institutions.
McKinsey & Company cites big-data analytics as a top-five catalyst to drive job growth and boost the U.S. economy by 2021. As data exponentially expands and becomes more comprehensive, organizations are increasingly using integrated technology to make sense of all this data, turning it into actionable insights that drive better business decisions.
Financial institutions typically use data analytics in tandem with intuition and experience to better understand the comprehensive relationship they have with their customers, including the impact of each banking channel. This analysis can reveal new ways of attacking an issue, addressing key market segments or identifying potential blind spots, which ultimately improves financial performance and solidifies business strategies. Big data isn’t about creating a data warehouse. Instead, it’s about leveraging data to drive better outcomes.
Despite its potential, many financial institutions are hesitant to harness the information they have at their fingertips. We asked clients attending a Fiserv Forum 2014 data analytics session what the biggest obstacle was to making data-driven decisions. Half said data analytics were simply not an executive priority at their organization. Perhaps the reluctance is based on a nagging question: do data-driven organizations really outperform their peers?
In a Bank Intelligence study of 256 high-performing banks, Fiserv found top performers do rely on data to make informed decisions, conducting in-depth footprint and customer analysis to manage their capacity and leverage their strengths. They’re not working harder; they’re working on the right things.
When a $15 billion Midwest bank needed to significantly increase earnings and enhance its digital offerings, data analytics revealed the bank’s cash outlay for its branch network was out of line, especially with increasing consumer demand for digital channels. Using data analytics, the bank considered market position, market growth potential, profit and other indicators for each branch. The findings influenced the decision to strategically close or sell more than 50 branches.
Although a difficult decision, closing the branches freed up funds to upgrade the bank’s digital and mobile offerings, and enabled the organization to move into more advantageous markets, based on the data. As a result, the bank reported revenues improved by 22 percent per office.
Because of results like these, more and more financial institutions are re-evaluating their analytic capabilities as a way to better understand customer relationships, optimize channels, manage risk and fraud, pinpoint reasons for acquisition and attrition, and find gaps in service. To do it right, they’re making investments in personnel and infrastructure. Bain & Company projects the financial services industry will spend $6.4 billion in hardware, software and services related to data analytics in 2015.
Expect data analytics to soon become a core competency – and a key differentiator – for most financial institutions. However, the learning curve for data analysis can be steep. Most banks and credit unions will need help from experts in data analytics and financial services to provide necessary tools and know-how. Making necessary investments will help ensure the influx of big data doesn’t become useless noise.