Major challenges ranging from – advanced technologies, inflexible legacy footprints, heavy budget constraints, the introduction of product models, and the battle for talent are making it harder for financial institutions to deliver on the promise of large-scale transformation. Ailing transformation initiatives are not only costly but can also have an impact on market competitiveness.

One of the key reasons for failed transformations continues to be alignment across Business, Technology, and Operations teams. Given the role Operations teams play as key enablers across the enterprise, we are witnessing growing demand and pressure for senior Operations executives to take more leadership in overall transformation outcomes.

Operations leaders can successfully drive transformation and create value for their company by embracing new imperatives:

• Become the connective tissue that supports programs and delivers outcomes across multiple divisions
• Deliver faster returns on investment to establish proof of success and help programs self-fund
• Create business value through modernized data analysis, metrics, and reporting

Operations as connective tissue in financial institutions

Operations functions need to become the connective tissue across all businesses and functions within a financial institution. To do this, they must:

• Drive, define, and enable comprehensive data ownership across business and operations
• Establish deeper technical awareness and partnerships with technology teammates with a “technology first” mindset
• Be accountable to break down barriers across functions and divisions that impede collaboration on mission-critical outcomes

Industry example: One firm had an unresolved regulatory issue regarding the preparation of regulatory reporting for over five years. Reporting ownership was siloed, and the Businesses, Operations, and Finance teams lacked data linkages and coordinated operational processes.

To resolve the regulatory issue, the Operations team established data ownership across all functions with “handshakes'' between the appropriate process steps. This created a data lineage and reduced data errors. The plan to address the regulatory issue led to a broader strategy that is underway, which includes centralizing firmwide data management and creating a Chief Information and Operations Office to unify process, data, and technology ownership.

Transforming operations for successful financial change

According to our research, 79% of large-scale transformations within financial institutions do not fully achieve their transformation objectives. Operations can buck this trend by treating the delivery of complex transformations as a core competency and remembering that transformation needs to be grounded in data flow automation, not just people-based process optimization. Because these large-scale transformations take years, leading Operations teams focus first on quick, low-tech solutions to capture initial value (typically aiming for 20% or more of the anticipated benefit) and grow on these successes for broader impact.

Industry example: One bank had many critical Know Your Customer (KYC) issues that could not wait for a planned three-year transformation. Their periodic KYC reviews were delayed, the KYC records failed quality control checks, and the KYC officers were not able to perform to their highest levels. Unnecessary rework plagued the firm.

The Operations team analyzed the issue to pinpoint areas with the highest delays and errors. With this analysis, the team found low-tech solutions and process ownership changes that doubled KYC office productivity and reduced costs by 25% within the first year of the transformation. This alleviated the pressure from critical KYC issues for the rest of the transformation.

Drive business value through modernization

Adopting a modernized approach to data analysis and metrics can help create transparency for improved decision-making and business value during times of transformation and business-as-usual. Leading Operations teams create more value by:

• Producing atomic metrics by process step and customer segment, better informing profitability and a wide range of other use cases
• Focusing on metrics beyond typical financial and operational measures, including cultural and workforce behaviors, resiliency traits, and environmental, social, and governance impacts
• Automating the production of metrics but retaining flexibility for ongoing changes

Industry example: One bank struggled to understand its costs in a key line of business. The business was growing, but profitability was flat. The bank did not understand the relationship between the drivers of operational costs and the clients they were serving.

The Operations team re-engineered metrics to examine profitability by client segment and other characteristics. Through this delineation, they found that the largest and smallest client segments were not profitable. The business immediately changed its approach to pricing by segment. Meanwhile, the Operations team began a transformation to attack the drivers of cost hampering the profitability of the largest and smallest client segments, including through more self-service capabilities and automated reporting, in order to improve the overall profit margin by 17 percent.