Faster processing power fuels ‘bigger’ data
Analyze larger datasets to glean insights on greater member engagement.
Big data is now bigger than ever. Faster, too. The sheer power of contemporary tools for manipulating data has changed the game.
“The biggest advances have been in large datasets,” says John Best, president/CEO of Best Innovation Group, a CUNA consulting partner. “We’ve seen great progress in both software and processing power, which allows us to process bigger datasets faster and more efficiently.”
“In the past five to 10 years, we’ve seen an explosion in client and customer engagement tools, with significant advances in their quality and breadth,” he says. “Today, we have countless options for analyzing data. That has empowered credit unions, using their own resources, to acquire, grow, and retain customers.”
Hansen says “lower storage costs and the ability to link data sources have been significant contributors. When we say ‘big data’ we’re really referring to ‘big storage.’ ”
Credit unions unable to afford big data storage can take advantage of co-op datasets, Hansen advises.
Becoming savvier about big data
Not every credit union is aware of the strides big data has made. “Many credit unions are still working on transitioning to analytics as a discipline,” Best says. “This means having analytics become a separate, distinct department inside the credit union. Once a credit union views data analytics as a distinct function, the door opens to go beyond standard reporting and gain insights into its membership.”
Notable results occur, including:
- Improved performance: Knowing the right service to provide at the right time and to the right members.
- Improved efficiencies: Analytics allows institutions to deploy their resources more effectively.
- Improved planning: Prescriptive and predictive analytics can be game-changers. The primary purpose is identifying trends unique to your business that result in a benefit for the member and the institution.
Looking at best practices, Best suggests an approach that addresses handling the data and organizing the personnel who work with it.
“First, curate your data,” he says. “Become data-driven and expect data-driven results. Manage, measure, and monitor all your channels, assets, and resources with predetermined key performance indicators. Know before a project starts what success or failure looks like.
“Ask interesting questions and prove/disprove them with data,” Best adds. “For example, ‘Why do we have such a surge on the digital channels on Fridays at 8 a.m.?’ The answer might be that your home banking data show people are looking to see if payroll has posted. In response, you create an alert for members when their payrolls post so they don’t overrun the system. Determine your members’ pain points before they do.”
“The key driver for success remains the same for credit unions over time: Understand member behavior,” Hansen says. “What are members’ profiles? What are their needs? Are they happy with you? These simple elements have been talked about for years, but still not everyone has a clear picture. The question is, how much data do you need to understand your members?”
The organizational aspect of analytics, Best says, involves four steps:
- Create a data governance committee. “This group is responsible for normalizing and curating the data in your credit union,” he says, “so when you need to run reports or get details from disparate systems, you don’t have mismatches that take valuable employee time to reconcile.”
- Hire someone to start your data analytics group and centralize it so the person in charge of governance ensures system compatibility.
- Collaborate. “Data analytics depends on volume, variety, and velocity,” he says. “Smaller credit unions that collaborate with others will have better outcomes if they have more data to draw from. This opportunity is unique to the credit union industry due to its noncompetitive nature. It’s our secret weapon.”
- Security awareness. “Data warehouses are high-value targets for cyber criminals,” Best says. “Guard them with the highest levels of security. Encrypt any data that’s personal in nature.”
Big data, small CUs
Credit union size isn’t a determining factor in big data utilization rates.
“The level of sophistication varies from credit union to credit union,” Hansen says. “A small credit union might have a tightly integrated core system, making analysis easier than for a large credit union that operates on an older legacy system.
“But smaller credit unions often lack one or two of the three critical areas—scale of data, analytical software, and analytical talent—which hinder their ability to create real analytical insight,” he adds.
Hansen offers three rules for successful data analytics:
- Find the right talent that fits the software you’re using or investing in.
- Connect that talent to tools with which they’re proficient.
- Accumulate, analyze, and take action on the data. One best practice is to avoid getting lost in the data. Have clear objectives or you’ll be analyzing forever.
“Also,” he adds, “engage your compliance and legal teams to ensure lawful use of big data.”
Beyond the legal pitfalls, credit unions should caution against turning their focus on data analysis too far inward, Hansen advises.
“When credit unions focus on their own world, they might miss the bigger picture of audience behaviors,” he says. “That becomes even more critical when you’re dealing with a smaller member base. Strong data analytics requires volume. The bigger the dataset, the more improved your odds of correctly predicting what an audience is likely or not likely to do.”