Banks and other financial services organizations face continuing and growing pressures on their profitability. In addition to a significantly more complex regulatory environment, they also must keep pace with dramatic advances in technology and stave off mounting competition from nontraditional providers of financial services. These pressures make it imperative that banks operate with greater efficiency and hold costs to a minimum, while still maintaining the service levels their customers expect.
As they search for ways to offset rising costs and stabilize declining margins, management teams must recognize the important role that more effective data management can play in these efforts. By providing reliable data faster and with greater efficiency, improved data management can help financial services organizations reduce process costs, streamline operations, improve customer service, and make better, more informed decisions.
Performance Improvement Framework
Before considering specific examples of how better data management can improve bank performance, it’s helpful to take a step back and revisit the basics. Without a clear understanding of how a performance improvement program relates to long-term bank strategies and values, the improvement effort can degenerate into a series of sporadic and disconnected initiatives that lose sight of the ultimate goal.
Viewing performance improvement through a general framework can help avoid that outcome. The framework example in Exhibit 1 illustrates how performance improvement is encompassed within the overall strategy, vision, values, and culture of the organization. These factors provide the general environment in which the individual management groups – such as risk management, performance management, and profitability management – will operate.
Exhibit 1: Performance Improvement Framework Example
Source: Crowe analysis
This framework illustrates how the central focus of all improvement efforts must be the enhancement of the customer value exchange, with emphasis given to those customer segments that align with the organization’s strategy. That customer value is delivered through the bank’s people, products, processes, and platforms.
With that framework in mind, it starts to become apparent how various individual improvement tools and techniques – from automation and digital banking to staffing models, scorecards, and incentives – fit within the overall performance improvement approach (Exhibit 2).
Exhibit 2: Performance Improvement Program Examples
Source: Crowe analysis
Organizing performance improvement within the context of such a framework can help keep the various initiatives integrated and coordinated, and can help prevent these from deteriorating into a series of disconnected or unfocused programs.
Improvement Strategies and Data Needs
There are many possible avenues toward improved performance, and each avenue presents its own particular set of data requirements and challenges. Four of the most widely pursued improvement approaches illustrate this point.
1. Channel Optimization
The basic premise of channel optimization is to develop the most cost-effective combination of channels – such as branches, call centers, and digital access – that will deliver the optimal level of service to each organization’s particular customer base. To make this determination effectively, management must evaluate a number of metrics such as new account sales, transaction activity, call volumes, and branch profitability.
The data needed to develop these metrics comes from a variety of sources, including general ledger accounts and the organization’s core data and sales management systems, as well as various external sources such as demographic databases and regulatory agencies. By applying various analytic tools to this data, management can assess the effectiveness of its particular mix of customer channels, benchmarking its performance against other leading practices in order to develop a balanced combination of channels that is most appropriate to its strategy.
2. Process Efficiency
The objective in improving back-office operations is not necessarily to reduce absolute levels of expense, but rather to reduce unit costs – that is, the cost of a particular transaction or the delivery of a particular service. Concurrently, these costs must be evaluated in terms of the value they deliver to the customer and to the organization itself.
The financial services industry has drawn heavily on concepts developed in manufacturing and other industries, such as process mapping and various lean six sigma techniques, to eliminate potential bottlenecks, unnecessary steps, and duplication of effort. Again, these types of analyses are, by their very nature, data-driven and highly dependent on key metrics such as cycle times, unit costs, error rates, and other process controls.
3. Staff Productivity
Personnel costs are a major expense category in virtually every financial services company. As with channel optimization and process efficiency, the basic purpose of efficiency improvements in this area is relatively straightforward: to handle the current workload with fewer people, or to handle a greater workload without adding people. This not only requires establishing appropriate staffing models, but also means optimizing employee performance through changing job roles, improved training and supervision, and the use of well-designed incentives and rewards to attract and motivate the most capable and productive employees.
Improvement initiatives that touch on individuals’ job descriptions, performance, compensation, and incentives are inherently sensitive – which means having accurate and reliable data for decision-making is especially critical. In addition to payroll, sales data, and other conventional data sources, many organizations also draw on internal metrics such as customer surveys, and external metrics such as salary surveys and industry benchmarking data.
4. Technology Enhancements
Technology affects all aspects of performance and efficiency improvement, including those discussed earlier. New ways of accessing services obviously have an impact on channel optimization, and technology is instrumental in helping to reduce process costs and improve staff productivity. Technology’s benefits are twofold. Not only can it automate workflows, routing work quickly and accurately without manual effort, it also can reduce the time it takes people to find the information they need. This second benefit applies to employees and customers alike.
Some of today’s most widely used technology-driven improvements include the increased use of imaging technology and signature pads to replace paper records, infrastructure that supports more flexible employee work arrangements, and increased use of customer self-service applications. Industry leaders also are making more effective use of company intranet channels to communicate to employees and to help them better manage their own workflows. Inevitably, all such technology-driven improvements are highly data-dependent.
Putting the Data to Work
Recognizing the data that is needed to drive various types of performance improvement is an important first step, but it is just a first step. Actually putting that data to work is a more involved process that involves at least four additional steps:
- Capturing performance metrics. This includes not only one-time capture of performance measures for analysis purposes, but also ongoing monitoring of performance to track the effectiveness of improvement efforts.
- Reporting performance. Performance metrics are of little value until the information they contain is delivered – in a format that is meaningful and understandable – to those who are empowered to take action.
- Comparing to benchmarks. Analysis begins when current performance data is compared to recognized benchmarks including internal historical trends, recognized industry standards, and, if available, the performance of comparable peer organizations.
- Analyzing root causes. After identifying desired improvement targets, it is time to take a step back and identify the conditions or practices that are keeping the organization from reaching those targets.
The entire performance improvement effort is considerably more involved, but these four steps establish a strong direction. When carried out effectively, these steps can be used to develop useful tools that drive sound decision-making.
For example, a Branch Value Analysis tool (Exhibit 3) applies data in a way that helps management make more effective decisions regarding the potential opening, closing, or consolidation of various branch locations. It does this by comparing performance metrics, costs, and relative value, as measured against the branch’s contribution to strategic objectives such as sales goals, customer service metrics, and customer retention.
Exhibit 3: Branch Value Analysis Example
Source: Crowe analysis
Another example is line management analysis, which helps improve process efficiency by identifying bottlenecks in the workflow, as well as sources of errors and rework. This type of analysis involves dividing a workflow process into a series of distinct steps, which may be performed by different individuals. Data is gathered about the performance at each step in the process, including the number of items processed, the number of errors received that require rework, the number of errors made and identified in later steps of the process, and the number of hours worked on those steps. The analysis uses this data to identify the steps in the process that are bottlenecks and slowing down the overall workflow, or that are experiencing higher error rates and wasting time due to required rework. By focusing attention on the sources of process slowdowns, an effective line management analysis can identify changes to staffing, training, or procedures that improve the overall efficiency of the process.
Tools such as these depend on the capture, reporting, and analysis of key performance data from a variety of bank systems.
As the examples illustrate, having access to reliable, up-to-date performance data is essential to reducing unit costs and improving operating efficiency. The underlying foundation for such access is a sound data governance program, which will help users to trust that the data that is collected, reported, and analyzed is accurate – without question or hesitation.
Effective data governance also will help to reinforce the importance of developing a single, consistent source for each data point, rather than relying on localized or detached versions of the data. Ultimately, effective data governance is essential in order to establish an enterprisewide commitment to maintaining data quality and availability across the organization – not just within an individual project or department.
As banks and other financial services organizations address today’s profitability challenges, the ability to make sound, objective, fact-based decisions is increasingly important to their success. Having ready access to timely and reliable data is a component of that effort.