How can financial institutions harness big data to their advantage?
“Simply adding ‘big data’ to someone’s job description and buying a software package is like giving a person a magic wand and expecting miracles,” Legg says in a CUNA white paper on data analytics.
In the report, “Big Data: Telling Powerful Stories to Expand Growth & Relationship Opportunities,” Legg and other credit union experts explore how financial institutions can make sense of this troublesome topic by examining their data and asking the right questions.
Legg says the challenge for most financial institutions is that the data already exists… it is simply waiting for someone to find the story it tells.
“You have to look at your data and see the stories it tells you,” Legg explains. “How can you pull stories out of your data?”
Where to start? Legg recommends keeping these key points in mind while marching down the path to big data:
- Technology will be an important tool for mining the data
- Consumer behaviors — across all channels — will provide many clues
- Team members need a wide variety of skills and talents to make sense of the data
- Data can reveal new business opportunities, and strategic planning is more powerful with the right insights
- Small steps toward bigger goals will bring impressive results
Cathy Graham, VP/Marketing, $3.9 billion asset Desert Schools Federal Credit Union agrees that it’s important to think of data a little differently now. “The social piece can provide insights into how people are thinking and behaving and what they’re saying about you,” she says. Now, it’s not just demographics, but also data based on groups, preferences, and individuals. “It can possibly be even more powerful than demographic data.
If you’re just starting down the path toward a big data initiative (or you’re looking to expand into new areas of insights), start with the data you already have, suggests Graham.
“Continually seek out ways to understand it, apply it, and add to it,” she says. “If you don’t have an MCIF, start with core data. Then make plans to add an MCIF and, more importantly, someone to run it. Then add in profit numbers, demographics, credit scores, and other more specific data.”
Graham offers these tips to get the program going:
- Start with data you already have
- Identify what’s doable in the next six months
- Approach it in bite-size chunks
- Identify some smaller steps
- Constantly clean up the data
- Add to the data
- Start with small goals, not the end goal
When establishing a big data strategy, PricewaterhouseCoopers urges financial institutions to set specific upfront and ongoing goals:
- Recognize that big data is not a technology problem. Rather, it’s a business opportunity. Look beyond technology challenges and objectives, alone, to include business needs and goals in your organization’s strategy to implement and leverage big data.
- Prepare your organization to face the big data storm that’s a whirlwind of data, technology, skills, business models, and economies. The analyst community will need to shift its thinking to ask new or different questions, and IT will need to shift its role from primarily data movers to idea enablers.
- Educate your organization’s business leaders around both the value and the how-to of making big data-driven, fact-based business decisions. Relying on gut instinct can result in erroneous actions that can be very costly, in terms of poor ROI and competitive positioning in the marketplace.
Increased spending, advances in computing power and software upgrades in software will mean nothing without investing in the right people to interpret the data, says Graham.
When assigning big data responsibilities, you have to give your entire team permission to make it happen, adds Legg. Sometimes that might mean changing people’s job descriptions and prioritizing responsibilities.
Dale Davaz, Director of eBusiness for $1.8 billion Spokane Teachers Credit Union, thinks it is very unlikely most credit unions have datasets so large that traditional database tools would be ineffective. They may not need a new generation of tools, he says.
“Most credit unions have only just scratched the surface of using traditional database tools to leverage their very real business intelligence potential,” Davas wrote in a white paper titled “The Data Craze.” “A larger percentage of credit unions larger than $500 million in assets have engaged in more modest business intelligence initiatives… with varying results to show for it.”
“Bringing together a single credit union’s transaction data — albeit from multiple and varied sources — is probably best characterized by terms like ‘small data’ or (generously) ‘medium data,’” says Davaz. “We’d suggest that years of additional work in ‘small data’ should be dedicated to squeezing better and better views of our operations and our members.”
Graham at Desert Schools FCU agrees. “Big data is like other marketing tools of the past,” she says. “The newest tool is always considered a panacea that will save us. But you need to have dedicated people whose jobs are to analyze the data — that’s when you get the results.”