Getting started in data analytics includes careful strategic thinking
By Karen Epper Hoffman
Data analytic systems are touted as the next best technology to productively transform business operations in virtually every industry, including relationship-based community banks. When it comes to embracing a data-driven approach to their various operations, community banks can quickly become overwhelmed, technology consultants and providers say. However, the biggest challenge for community banks, consultants say, isn’t necessarily collecting data but rather building and maintaining a data analytics program that is efficient and effective across a bank’s overall activities and operations.
Some technology consultants recommend that community banks first carefully consider their overall goals before launching a bankwide data analytics program. “You need to go through a process of defining the information you would like to have, or need to have, to manage your bank in the best possible way,” says Jeff Morris, managing director and principal at Austin Associates LLC, a software sales and technology consulting firm in Austin, Texas.
That initial assessment should include identifying the gaps of information that need to be addressed first for the most strategic benefit, Morris says. Typically the most valuable information community banks seek is how to make individual customers or product lines more profitable, he adds.
Top executives and directors should “understand and embrace that changes are … important because if they are pushing back and being negative at every turn, nothing will get done,” says David Peterson, principal for the technology consulting firm i7 Strategies in Hahira, Ga. For executives or directors coming up through lending or investments, as opposed to operations, manipulating and analyzing data to influence the bank’s direction “is a whole new world that can be very foreign to them,” he adds. “Management buy-in is an absolute start.”
Leaders also should make sure that sufficient resources are available to achieve their objectives, according to Morris. This includes systems, staff, a budget and a plan to accomplish their goals. “All information has a cost, and the cost of ‘perfect’ information is very high,” he says. An appropriate plan can produce meaningful information based on a bank’s resources.
Assembling a centralized system for capturing data “systematically and consistently” is an essential early step in launching a bankwide data analytic initiative, according to Nancy Michael, senior director for product strategy at Moody’s Analytics, ICBA’s Preferred Service Provider for analytics. Such data should include financials, qualitative customer data, and behavioral information including payment and credit information, Michaels says. “Establishing common metrics across these groups ensures reporting consistency throughout the organization.”
Consider what data should be captured first to provide the most strategic advantage.
Peterson suggests that community banks diving into data analytics first take a careful and detailed look at the current and changing demographics in their service areas. Understanding the makeup of a customer base—whether in urban, suburban or rural markets—can be critical to mapping data to determine consumer habits and preferences, Peterson adds. “You need to be thinking, ‘Who are my customers now, and who will they be?’”
Because most community banks rely heavily on third-party service providers for various operational functions, conducting early conversations with core banking providers is important, says Tom Frale, director of business development for RLR Management Consulting Inc., a technology software firm in Palm Desert, Calif. Evaluation of what staffing is needed to properly delve into data analytics and stay on task is also appropriate, he says.
“If an organization is going down this path, they must understand the time and the effort to do it, and make sure it’s done right,” Frale says. “It’s a healthy exercise to look at your third parties, especially if you want to really dive deep and get a good look at your data.”
A data-driven operational approach, according to Michael, requires an alignment of strategy, risk and technology, as well as operations. It requires investments in systems that centralize data and standardize processes, reinforced with policy and supporting training, she adds.
“Beginning to build the culture for data-driven decision making is the first step,” Morris says. “Putting systems in place to provide the relevant information is the second step, and putting the process into practice with management modeling the behavior is the third.”
12 Starting Steps
Industry insiders offer a dozen recommendations for community banks to consider as first steps toward becoming more data-driven in their operations.
- Establish a strong data governance program.
- Align top goals and priorities with usable and actionable insights.
- Establish and monitor benchmarks and relevant dashboards on an ongoing basis, including ones that measure performance against both past performance and industry peers.
- Strive for data quality and consistency while standardizing processes.
- Understand best practices in collecting and organizing data.
- Learn the details and challenges with today’s tools, processes and frameworks.
- Apply rules-based systems to ensure that the credit and business policies are followed.
- Select systems that can help capture, organize and use key data points inside processes to achieve strategic goals, such as making faster quality loan decisions, identifying new customers or reducing expenses.
- Leverage customer data to analyze product use and customer needs to drive cross-selling.
- Evaluate loan processing, workflow reporting and portfolio metrics to target process inefficiencies.
- Learn vendors’ recommendations on how to use their software to validate data used in processes.
- Review how multiple systems will integrate to avoid data gaps and incomplete data sets.
—Karen Epper Hoffman
Karen Epper Hoffman is a financial writer in Washington state.