Albert Einstein once said, "If I had an hour to solve a problem, I'd spend 55 minutes thinking about the problem and 5 minutes thinking about solutions."
I wish credit unions would adopt a similar attitude when they confront the challenges and opportunities related to analytics. But too often, that's just not the case. Instead of analyzing the most critical needs and defining clear objectives, discussions center on technical solutions and how to implement them.
Unfortunately, time and again these "solutions" only serve to create new and additional problems.
Craving A Data Panacea
The craving for a data panacea is understandable. Credit unions are looking for ways to cut costs and increase revenue. And while most have rightly recognized that data analytics can play a key role in achieving both, many are still scrambling to get a handle on data; and consequently, misfire in their initial attempts with analytics.
The two most typical scenarios I see:
Silver bullet — Credit unions, in comparison to the large banks, are often handicapped from making large investments in enterprise solutions due to the overall expense. So instead, they go with a specific technology solution — likely positioned as a silver bullet by a partnering IT vendor — which on the surface seems like the most cost-effective way to address the business need. The problem is that it results in tunnel vision, with the focus more on the technology and less on the specific deliverables tied to its implementation.
Boiling the ocean — There are cases where a credit union does commit to a comprehensive, scalable analytics solution to deploy across the entire organization. This is a huge undertaking — highly expensive, time consuming, and fraught with risk. Problems ensue when the credit union gets caught up in perfecting the solution — resulting in repeated cycles of testing and tweaking; and a behemoth, unwieldy system that's outdated before ever being fully functional.
Both these scenarios obviously end in frustration and dissatisfaction, and extract a heavy toll in both dollars and opportunity cost.
Four Essential Components
So, how can a credit union find success with data analytics? Following are essential components of a successful initiative:
1. Internal expertise and skill. Credit unions already have vast amounts of data at their disposal. However, it remains untapped and unrefined due to the absence of key personnel — data scientists with the skill set to extract, interpret, and leverage data, and the data-savvy leadership, at the highest level, to champion the data management effort. This internal expertise is critical for any data analytic initiative to succeed. Dollars spent to attract and/or train existing staff — and then retain these valuable employees — is arguably the smartest data investment a credit union can make.
Be forewarned however, these professionals are in heavy demand. So, credit unions would be wise to address the personnel issue sooner rather than later.
2. Situation and needs analysis. The time spent upfront — from reviewing existing data collected to identifying business needs and opportunities — will ensure that the analytics initiative is sound, focused, and manageable. And, aligned with corporate strategy.
3. Go small. Be strategic. Invest prudently. Analytics design/ implementation and corresponding funding allocation should be prioritized based on the most pressing business needs and the most promising focus areas. To illustrate: If a credit union has a goal to grow its mortgage portfolio, it should develop analytics which will deliver insight into factors such as how growth is trending, how the overall lending portfolio is evolving, how the shift is impacting other lending areas, etc.
4. Fail fast. The sophistication and speed of new technology enables solutions to be installed and assessed in a dramatically condensed time frame. A given solution must deliver value within the first few months, i.e. provide meaningful data useful in real world decision making, and if it doesn't, it should be scrutinized and potentially replaced by a different solution. In other words, if the solution fails to deliver, it needs to fail fast. This allows a credit union to reevaluate and course correct, and keep the implementation on schedule and on budget. Agility, nimbleness, and responsiveness are the critical characteristics of an effective analytics solution.
A Sense Of Urgency
I understand and share the sense of urgency credit unions feel when it comes to tapping into the potential of data analytics. However, credit unions need to look before they leap. Assess the current level of internal data expertise; and, if necessary, take action to ensure that the key personnel are in place to mine and manage data. Spend the time and energy up front to understand business needs and opportunities and develop focused analytics to address them.
Strategic and agile solutions based on thorough analysis and needs assessment: A winning approach even Albert Einstein would appreciate.
Drew McMullen is partner and managing director of the financial services division of Sense Corp, a management consulting firm based in St. Louis with offices in Texas.
