Big Data or Bust: The Credit Union Wakeup Call

 

By Robert McGarvey

 

Ask credit union executives about big data and what you are most likely to hear aren’t cheers of approval but grumbles and that is because many institutions have shoveled money into big data initiatives but have enjoyed scant benefits.

That’s fact: big data plays require a lot of thought, planning and sophistication to do successfully. It is very easy to get this all wrong.

But also know that from Amazon to the money center banks there’s a rush to harness big data in order to better serve customers and in that way make more sales.

The whole concept of big data may in fact seem to fly in the face of a lot of what credit union executives believe their businesses are based on, that is, face to face interactions with friends and neighbors whom we know and therefore who needs big data?

Credit unions do.

I’m looking at a ranking of credit unions by membership and the leader has 4.7 million members. Over 50 credit unions have more than 200,000 members.  These numbers are way past the old beliefs about friends and neighbors.

Nobody is saying to ignore personal interactions. Cherish them.  They are indeed some of what can make credit unions special. But also embrace the big data driven insights that let an Amazon recommend exactly the book you had been wanting to read but didn’t know it existed.

For a credit union, the right data analytics can deliver powerful results. In a white paper sponsored by Filene, Philipp Kallerhoff observed: “What and when people buy and how much debt they’re carrying can indicate how likely they are to upgrade (or close) their accounts. The cluster analysis undertaken here can predict the next best product with 30% accuracy. “

In other words: credit unions can enjoy the same kinds of data analytics triumphs as Amazon.

That’s easy to say but many credit unions will tell you hard to do.

But tune into what experts at OnApproach – a data focused CUSO – are saying. Read this stark observation in a recent OnApproach blog: “The financial services industry is facing a period of sweeping changes in the forms of fintech disruption, challenging regulations, and evolving member preferences and expectations. In order to remain relevant in this new arena, credit unions must be able to integrate and optimize data.”

Harness your data – or maybe face extinction.

You face many challenges.  Data may be the cure.

Where do big data initiatives go awry?  You remember that old saying: Garbage in, garbage out.  

OnApproach’s Austin Wentzlaff blogged this: “ In a credit union, data is coming from many disparate sources from all facets of the organization. In order to overcome this, a data warehouse is essential. However, when a data warehouse tries to combine inconsistent data from disparate sources, it encounters errors. Inconsistent data, duplicates, logic conflicts, and missing data all result in data quality challenges.”

Data comes in two piles.  There’s what you might call little data – this is structured data that comes in known forms, such as the data created in a core system.  It’s rather easy to process but the problem is that it gives only a one dimensional view of a member.

Enter big data which, by definition, is unstructured and voluminous.  Think all the data on Facebook or Instagram or Twitter, plus SMS messages, maybe email, and a whole lot more.

Marrying those two heaps of data is the rub – but it also is critical. Austin Wentzlaff noted, “In the world of data and data analytics, credit unions must leverage all the data accessible to them.”

Yep.  Insights come to those that gather all the information they can.

But making use of it isn’t easy.  Austin Wentzlaff sketched a how-to road map: “Credit unions should start with the structured data within their own operational systems by developing the data infrastructure to manage, store, and analyze the data. Once the credit union has all of their structured data in a single repository, planning should begin to leverage unstructured data from available data sources.”

He added:  “If the data warehouse does not support the move from structured data to unstructured data there will be a serious loss of value. While both Big Data and Little Data are extremely powerful, the marriage of the two is where the real value lies.”

One estimate is that maybe 5% of credit unions have their data in good enough shape to get real value from big data analytics.

That mean 95% don’t.

Big pushes in 2017, at least at the money center banks, are the rise of machine intelligence aka artificial intelligence.  But getting there requires rich data the machines can readily process – and for many credit unions that takes them back to square one.

What needs to happen to actually make use of all the data a credit union has?  The data – basically – has to be massaged into a usable form.  Frequently that means putting the data into a data warehouse and the core concept is that data can be manipulated – rearranged and cleaned up – without fundamentally distorting its meaning.

Huge progress has been made into this regard.

Many projects are still failing but, said experts, the primary cause now is because they are underfunded.  Good data scientists and their tools don’t come cheap.

Does that mean only the very largest credit unions can make use of data analytics? Nope.  Data analytics skills are become more widespread and at the same time there are more options available.  Some credit unions are hiring internal data scientists.  Others are contracting with large vendors. Still others are working with CUSOs to attempt to keep costs down.  Either way, there are many more data focused companies aimed at credit unions and community banks.

The best advice: start now to set big data goals. Such as? A common goal is how to get smarter in offering members new products – which Filene already tells us is prime for a big data revolution.  Know too that the money center banks are investing heavily in exactly this. They want to offer a customer a car loan minutes before he or she walks onto a dealer’s lot.

A credit union needs to stay determined to develop that same sort of timely pertinence and, very probably, the answers are in fact already in the data that’s on hand.

Ditto for when is the right time to offer a member a CD, a new credit card, maybe an upgraded share draft account.

The answers are in the data when you know how to harvest and analyze it.

Make 2017 the year you put big data to work for you and your members.

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