For banks, the surest path to digital transformation excellence

A decade after the global financial crisis, organizations find themselves at another point of disruptive change. Digital transformation sits high on the agenda for financial institutions as a way to achieve many important goals: improve customer engagement, reduce costs, and defend against new and traditional competitors.

More than 85 percent of banks cited implementing a digital transformation project as a critical priority, according to EY’s 2018 Global Banking Outlook. The objectives include: improve client experience across all touchpoints; automate business processes; gain insights into business opportunities; optimize operational efficiency; and keep pace with nimble fintech competitors.

To succeed, FIs are modernizing front-, middle- and back-office applications both on-premise and in the cloud. And to accelerate business insights and improve decision-making, they’re investing in next-generation analytics and “data lakes” (a centralized repository that allows you to store structured and unstructured data at any scale). In fact, firms risk failing to realize their full potential from lack of investment in data management, and governance capabilities that integrate data, systems and people.

Four things financial services organizations should bear in mind

1. Simplify data management. Seamless integration of data regardless of type, volume, latency, or location ensures it’s available and accessible where FIs need it most—across all operational, analytical and transactional systems.
2. Greater investments in advanced analytics and data lakes to improve risk control. FIs need to become more proactive to identify, fix and monitor data quality. They can lower their risk costs through analytics-aided techniques such as digital credit assessment, advanced early-warning systems, next-generation stress testing and credit-collection analytics.
3. A centralized “single source of truth.” A single source of truth (SSOT) is a trusted data source that gives a complete picture of the data object as a whole. With a centralized SSOT for all product, customer and employee data, FIs can ensure that each record is unique, complete, interconnected and accessible to and from all systems across the enterprise.
4. Treat and govern data as a business asset. Financial institutions must consider the strategic value of their data: They can tie it to a business outcome measured in hard dollars. Existing data governance policies and processes also need standardization across the organization.

What to do if your data strategy needs work

1. Stop throwing smart bodies at your complex data problems. Financial institutions too often opt to manually integrate, validate and manage data through data engineers and programmers who write lines of code versus adopting purpose-built software solutions to automate these processes. Data demand is growing exponentially across financial services and IT budgets are increasing at the same pace. Chief information officers (CIOs) and enterprise architects must become smarter and more efficient in how they spend developer resources. 
2. Get the business to grasp the importance of modernizing data management. As new business application investments get funded, CIOs, data architects and chief data officers (CDOs) must understand the business goals. If they neglect scalable data management and governance foundation to feed trusted data to those new investments, they risk falling short.
3. Operationalize data governance across the enterprise. Data stewards on spreadsheets via email, or file-sharing applications, can no longer maintain data governance processes, policies, standards and measurements. CDOs need solutions that create and relate critical data elements to existing business processes, policies, people, regulations and systems—and govern that information in a way that’s collaborative, automated and verifiable.
4. Don’t rely on business applications and analytical systems to manage and govern data. A CRM system is not designed to update other applications when a customer has a new email. Nor will it care if risk applications contain duplicates of the same customer, which results in higher or incorrect credit risk estimates. The average bank runs on 50 systems or more, so firms must consider master data management solutions. This allows them to access and share accurate, consistent and holistic views of their customers and employees.
5. Measure the data’s business impact and value. Nothing compares to correlating measurable outcomes to data. As a starter, consider Net Income Per Employee. Every business function—from commercial loan origination to new customer onboarding—requires access to the right data. Research has found that poor data quality can impact 30 percent of a company’s gross revenue. That spans losses in business productivity and revenue opportunities; such as a higher rate of mortgage buybacks due to incorrect underwriting decisions caused by data quality errors.

Putting it all together: Banking’s best investment

Strong economic growth and rising revenues have brightened horizons across the financial services industry. The strategic focus on data-driven digital transformation promises to build on those successes with bold innovations and stronger core capabilities. Financial institutions that invest in these capabilities will realize value from one of their most important assets—their data. In this way we can see the acronym ROI, return on investment, in a whole new light: the Riches of Information.

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Peter Ku is vice president and head of industry consulting/financial services strategist, Americas for Informatica.