Eenie, Meenie, Miney, Mo …

After hours of brainstorming, debating, and surveying the data, the executive team has narrowed its choices to a select few. It’s decision time. The CEO jots the top options on the white board and intones: “Eenie, meenie, miney, mo…”

Amazon lists more than 7,000 books on decision making, but it’s a safe bet that a discussion on the efficacy of childhood chants is not among them. Instead, we have a lot of heady research around those high-stakes choices where we’re under a lot of pressure, dealing with high degrees of uncertainty, and feeling overwhelmed by mounds of data and options. The challenge for credit union leaders is balancing their intuition about what’s best for their organizations with what the data tells them.

“I think it’s very common that intuition conflicts with data,” says Jim Austin, principal with Decision Strategies International, Conshohocken, Pennsylvania, and learning director for CUES’ CEO Institute I at the University of Pennsylvania’s Wharton School of Business. “In my experience, most decisions start at an emotional, intuitive level, and then we tend to look for data to rationalize it with analytics.”

A 2011 Harvard Business Review article points out several obstacles that can distort reasoning in business decisions: confirmation bias, or relying on data sources that reinforce our perspectives; anchoring, an overreliance on one data source; and loss aversion, taking caution to the extreme. Four guiding questions can help avoid these “decision traps,” Austin suggests: 

• Do we understand the problem? “We tend to zero in much too quickly before we understand the problem we’re dealing with,” he cautions.
• Where does our information come from? Is it objective and reliable?
• Do we have a decision-making process that starts with surfacing data and ideas and then goes to decisions?
• What can we learn from past mistakes?

Early in the process, leaders should spend a lot more time listening than directing and encourage other members of the team to speak out—especially if they are offering contrary and diverse perspectives.

“Everyone should feel empowered to bring ideas to the table. It’s the leader’s job to surface different ideas and points of view so that he or she can make the best decision,” Austin says. He cites the advice of Honeywell CEO David Cote: “Your job as a leader is to be right at the end of the meeting, not at the beginning of the meeting.”

To get it right at the end of the meeting, leaders should pay special attention to ideas that contradict their own, and they should widen their information channels to include people from all ranks of the organization, advises Kathy Pearson, Ph.D., president of Enterprise Learning Solutions, Philadelphia, and a faculty member for CEO Institute I.

“Decisions made by heterogeneous groups with diverse viewpoints are of higher quality and provide better controls for biases,” Pearson notes. “Intuition has its place, but we want to temper it by getting differing points of view.”

Put Data in Its Place

Data can provide key insights for strategic decisions, but executives need to put the story it tells in proper perspective. “Everyone loves big data, but it only tells you what’s happening today, not what’s going to happen in the future,” Pearson cautions. “You have to think about how valid the data is for the time period and the situation you’re considering—and recognize that there are some questions data can’t answer for you.”

She cites research by McKinsey & Co. about factors that had the biggest impact on strategic business decisions on entering a new market, for example, or making a large capital investment. Only 8 percent of the outcome of those decisions correlated directly with data analytics, while 39 percent related to company and industry variables, such as the range of available investment opportunities, capital, and resources and the predictability of evolving consumer preferences.

The most significant factor in business outcomes was the quality of the decision-making process to critically examine and apply the data analysis, to ferret out ideas from throughout the organization, and to recognize uncertainties. In short, the report concludes, “superb analysis is useless unless the decision process gives it a fair hearing.”

But there is an argument to be made on the increasing importance of business analytics. Paul Ablack, CEO of OnApproach, Plymouth, Minnesota, cites the research amassed in Thomas Davenport’s Competing on Analytics that companies embracing big data outperform their peers. Businesses that have harnessed all the available information they can about customer patterns and preferences—think Amazon—have transformed their industries, and new entrants in financial services, like Google Wallet and Apple Pay, are poised to do the same, he cautions.

Faced with new, more nimble and data-driven competitors, CU leaders might need “to get more serious about challenging staff to do something with mountains of data at their fingertips,” Ablack says. As just a few examples, data analytics could help monitor and adjust underwriting standards; establish criteria to identify the most profitable members and bundle products to retain and grow their ranks; investigate channel preferences; and dissect transaction data.

The chief obstacle to deploying all that data to optimize decision making is that most credit unions don’t have a “single source of truth,” he says. Instead, it is siloed in core, loan origination, credit card, marketing, and other systems. By implementing a data warehouse solution, managers could swap the current balance of spending 80 percent of their time gathering and preparing data and 20 percent analyzing it.

Data tells a story if you look at all the available information, not just bits and pieces, says CUES member Troy Hall, chief operations officer for $1.3 billion, 140,000-member South Carolina Federal Credit Union, based in North Charleston.

Executives at South Carolina FCU apply a grounded theory model to decision making, which emphasizes a “data-first” approach. Developed by social scientists, this model calls for gathering information, analyzing it and categorizing it, and then formulating conclusions about what it means for your organization. That order is the reverse of developing a hypothesis and then looking for data to support it.

“Allowing themes to emerge from the data is the safest way to safeguard against manipulating it,” Hall says.

Whenever possible, decision makers at South Carolina FCU aim to consider multiple data points from a variety of sources. For example, executives study lending data in the context of the experiences of peer institutions and economic conditions at the local, regional, and national level.

“We know that a national average for housing forecasts may not be true for Charleston. We’re a coastal community with a large influx of population,” Hall says. “However, when we look at auto sales and lending, the national trends are more on par with what we’re seeing locally.”

Third-party partners are great sources of useful data, including marketing firms, credit life insurance vendors, credit bureaus, core providers, trade associations, and industry specialists like the Filene Research Institute, Madison, Wisconsin, and Raddon Financial Group, Lombard, Illinois, he suggests.

Learn From the ‘Best and Brightest’

A classic case study of the dos and don’ts of executive decision making compares the Kennedy Administration’s handling of the Bay of Pigs invasion and the Cuban missile crisis. “Kennedy was known for bringing together the best and brightest people as his advisors, but no one wanted to challenge the president,” Austin explains. “He took a different course in the Cuban missile crisis and left his advisors to work together to come up with the best options. And then he came into the meeting to consider their recommendations and make the decision.”

Democratic decision making is admirable for nations, but suboptimal for businesses, Austin contends. The process can get bogged down in a meeting of peers. “If you have to bring everyone along to agree on a decision, what tends to happen is that you get the least common denominator.” 

Austin cites a useful strategy adopted by the venture capital firm Kleiner Perkins Caufield & Byers: When considering major decisions, each partner must complete a “balance sheet” on the issues, summarizing the points for and against each decision from his or her perspective before the meeting. This approach guarantees that (1) everyone at the table has prepared to consider the issues, and (2) “the partners are on record as changing their points of view as a result of listening to what their colleagues have to say,” he notes.

“My grandmother used to say, ‘Jim, you have two ears and one mouth. Can you use them in that proportion?’” he adds.

Executives who have succeeded without the benefit of deliberate decision making in the past may just have been lucky so far. “There is a tendency to take a little bit of information and assume it helps us be better decision makers when, in fact, we are still very much in the dark,” Austin says.

Experiments as Data Sources

Austin suggests categorizing the decisions facing your executive team as a “portfolio” with varying levels of risk and need for analysis:

Core decisions are about operational necessities, with a focus on running as efficiently as possible to free up resources for other initiatives. For these decisions, return on investment and other financial metrics are the key data points. For example: How can we improve branch efficiency and still offer service that exceeds members’ expectations?

Then come new initiatives for which you have enough data to weigh the risks, costs, and likely returns. For example: What mobile services must we offer to keep pace with members’ changing expectations?

Finally, there are the “experiments,” where the most immediate decisions involve allotting small investments to research and try new things. In this category, give your team permission to “fail fast, fail often” at a small cost, with the aim of identifying potentially great new ideas. It’s too early to apply financial metrics to these decisions. Instead, you are focusing on strategic fit and feasibility. For example: Can we use social media to improve member satisfaction? Does this channel reach all members or just Millennials?

At South Carolina FCU, “we try to subscribe to the domain of being a learning organization, which means we tend to pilot a lot to increase people’s comfort level with a new initiative, rather than a mass rollout,” Hall explains.

For example, the credit union is changing its retail strategy to separate sales from service. As members increasingly rely on remote channels for routine transactions, “members will use our financial centers for more complex transactions—mortgages, insurance, and investments—that require a lot of learning and dynamic conversations,” Hall notes.

As a result, job descriptions and hiring and training processes are all changing. South Carolina FCU is moving to centralized loan decisioning. To do this, it is training other staff to close loans, and letting front-line employees focus on sales and conducting needs analysis for additional products and services that might benefit members.

“You don’t want to hire people with a great propensity to sell and then bury them in processes and paperwork,” Hall says.

Branch layouts are also changing to accommodate this new approach to sales and service, with a centralized space for member interactions, rather than individual assigned offices. The new layout is modeled on the way auto dealerships and mortgage lenders outside the CU movement serve customers.

South Carolina FCU piloted this new model first in a medium size office, then in a bigger office, and finally in a small branch before rolling it out to its other 14 financial centers. This incremental approach provides a proving ground to adjust new programs and a training ground so other staff can observe the changes in action, take notes, make suggestions, and put aside any doubts they might have.

Avoiding ‘Analysis Paralysis’

To avoid getting stalled in decision making, Hall recommends staying centered on strategic direction and objectives and relying on the data to shape tactical decisions. That approach should reduce the need to contemplate the “whys” and broad perspective on what to do and keep a narrower focus on tactics. It also ensures that the strategic structure is in place to support those tactics.

“Sometimes credit unions try something that they’ve not created a structure to support and then they can’t figure out why it doesn’t work,” he notes.

“It’s a balancing game, and there’s always the possibility of analyzing something to death,” Hall adds. “As a whole, our industry is so risk-averse, it makes us very challenged to qualify as learning organizations that are willing to experiment and try something new.”

Retired U.S. Army general and former Secretary of State Colin Powell put forth the 40/70 rule as a guide to make information-based decisions without getting bogged down by a need to know everything. Applying that rule to credit unions: If, based on your experience in financial services, you estimate that you have less than 40 percent of the data out there in the field of knowledge, acknowledge that you’re basically flipping a coin. Make the decision, but plan to monitor the results and revisit if necessary. On the other hand, if you have 70 percent of the data that is likely available, you should be able to make a fact-based decision.

If you try to get more than that 70 percent, you can become paralyzed while your competitors forge ahead. “But in fields that rely on analytics, like financial services or engineering, senior leaders who grow up in that space can have a hard time with that,” Pearson admits. “As an engineer, it bothers me to think there’s more data out there.

“The best decision-makers are the ones who recognize, ‘I have to make a decision even if I don’t have all the information,’” she adds. “I have to move ahead even if there’s uncertainty. There are no guarantees. That’s the hard work of being an executive.”

Karen Bankston is a long-time contributor to Credit Union Management who writes about credit unions, membership growth, marketing, operations and technology. She is the proprietor of Precision Prose, Eugene, Oregon.