Last year IBM estimated that bad data costs the US economy $3.1 trillion annually. The costs stem from making poor decisions based on incorrect data, hiring employees to retroactively fix data inconsistencies, debating which version of the data is most accurate, and much more.
It’s clear that companies can’t just ignore the problem. Amazon, Netflix, and Google have consistently (and more or less conclusively) proven the value of using data to enhance the user experience. Without data, personalization vanishes and consumers realize they can get better digital service elsewhere.
All of this innovation forces credit unions into a difficult dilemma. When budgets are already strained by branch upkeep and high regulation, should credit unions invest heavily in fixing their data problems or should they plow ahead like normal?
Given IBM’s estimates, credit unions might not have a choice. Having bad data is so expensive that those who refuse to fix the problem today will likely face an insurmountable hurdle a decade from now.
Here are some ideas to make the process more efficient.
1. Be a stickler in defining every term and logging every data point with precision.
Above all, gathering good data requires discipline. If each department defines terms differently and logs information according to their own subjective viewpoints, your data will be nearly useless. It’s difficult, but getting data right requires a full buy-in from all employees involved in the process. Everyone has to agree on strict definitions for each term as well as the ground rules for the entire process. What’s more, everyone needs the discipline to stick to the plan without fail.
2. Hire a data specialist to oversee the process.
If you’re part of a small credit union with an extremely tight budget, you might consider limiting this hire to a temporary consultant. However you do it, you’ll want someone experienced (preferably with a Ph.D.) to set up the process and train your team on data entry and analysis. There are so many ways to go wrong in setting up data analytics that six months from launch you’ll be wishing you started with a data specialist. Spend the money upfront to save money down the road.
3. Make sure those who enter data regularly interact with those who analyze data.
After you launch your process for gathering and refining data, it’s easy for various departments to exclusively focus on their individual role. When this happens, you unwittingly risk communication breakdown between departments.
To fix this problem, it’s critical to invite employees who enter data to shadow employees who analyze data (and vice versa). Each employee must regularly watch exactly how the process actually works from beginning to end. Only then will employees be able to flag parts of the process that have deviated from standard procedure. In addition, this one-on-one interaction is the perfect opportunity for leaders to keep things consistent.
4. Collaborate with technology providers to slice the data in interesting ways.
No single institution can access all the data they need to edge out the competition. Because of this, it helps to partner with a variety of technology providers to gather and refine additional data. Some companies let you see internal and external data and then refine that data in ways that help your consumers make sense of their financial situation. Whatever option you choose, it’s useful to find ways to complement your data strategy.
5. Revisit the process quarterly with an eye on ROI.
After you’ve launched your process to fix bad data, it’s crucial to revisit it quarterly to make sure everything is clearly tied to ROI. Bring in employees from a variety of teams, show them the process, and ask them whether they can tie the data even more directly to ROI. Ask attendees to play devil’s advocate, looking for ways the data might be misconstrued or useless. If you find that everyone leaves these meetings with a greater conviction for what you’re doing with data, you know you’re doing it right.
As technology continues to improve, good data will increasingly become a centerpiece of successful credit unions. Those that master this messy process now will find that they’ve built a foundation for the next decade and beyond. They’ll be able to see which of their users have accounts with their competitors and offer hyper-personalized offers to win the full loyalty of these users. As a result, they’ll have the upperhand when it comes to market share and better serve their members in the process.
Jon Ogden is director of content marketing for MX, a provider of data-driven money-management solutions based in Lehi, Utah. He can be reached at email@example.com. Or go to www.mx.com.