Data analytics is no longer available only to massive technology corporations and big banks. In fact, with access to enough data, even smaller credit unions can join the fun. With several data analytics platforms that are accessible and affordable, credit unions of nearly any size can start taking advantage of integrated access to data. Plus, it’s surprisingly easy to start your own credit union data analytics journey.
At first, it might seem outlandish. Can you really keep up with the Amazons, Googles, Netflixes, and Facebooks of the world? Maybe not at their global level, but in many cases, credit unions have more data about each individual member than Amazon has about each of its customers. Well, sort of. But you can definitely catch up to the Wells Fargos and the JP Morgan Chases out there.
So, how does it happen? How does it all start?
Step One: Find a Project
If you want to establish your own credit union data analytics program, your first step is to find a project. Your project can’t be vague. It’s not enough to choose “use analytics” or “become more data-driven” as a goal. Analytics itself isn’t a goal—it’s the tool you use to accomplish one.
When you choose a goal or project, choose one that addresses a need at your credit union. Some examples that credit unions have had success with are:
- Creating a dealer dashboard for indirect auto loans
- Implementing predictive analytics for CECL models
- Developing more insightful KPIs for loan officers
- Personalizing Creating targeted marketing campaigns
Once you’ve chosen a project for your credit union, you’re ready to move onto step two.
Step Two: Assemble a Team
The Avenger franchise didn’t succeed because of only one hero. Nor should your credit union’s data analytics project. Don’t just choose a headliner to run the project – assemble an entire team.
When you put together your team, resist the urge to include only members from specific business units. Instead, incorporate members from many different departments. Each team member will bring their own expertise and departmental insight to the table. All those different perspectives contribute to well-rounded, impactful analytics solutions.
Reaching across business units also helps to improve organization-wide buy-in. The more cross-functional you make the team, and the more people who can take ownership of your credit union’s success, the more momentum you’ll be able to build. Also, when you cultivate organization-wide buy-in, remember to include top-level staff in that movement.
Step Three: Evaluate Your Data Source
Some organizations buy bundled infrastructure that simplifies their technological ecosystem at the expense of performance and flexibility. However, most credit unions have an eye for quality—they choose the best CRM, the best core software, the best loan origination system and so on.
While putting together all the best systems is great for performance, there are a few drawbacks. The biggest drawback has to do with data access and integration.
Each of those systems produces its own data, and each of those data sets has value. The real power of data analytics is unlocked when you can bring multiple data sets together into one integrated view of your members. In addition to bringing the disparate sources of data together, you also have to make sure it’s properly stored and accessible.
The starting point is a data warehousing option to manage your disparate data sources. That will ensure that you have access to data from each source. Subsequently, you will want to consider combining the discrete data sources into a single source. Doing so would make it easier to access and use multiple data sources on one subject.
Step Four: Break the Inertia
In step one, you’ve already identified a project (or two!), step two you assembled a team, and in step three you gave users access to the data. Now, what do you do with that data?
Don’t worry if you don’t have a plan yet. During Step four, the starting point is to get comfortable with your data. Open some files, move some data around, and start playing with it. Get your hands dirty. Get comfortable in there.
Step four is about breaking the inertia that people have in their credit union data analytics journeys. If you worry too much about following a step-by-step plan toward your solution, you could be shooting yourself in the foot. Here’s how:
- Start small. Focus on the one or two projects you identified up front. Starting to build something is a sticking point. Playing around with data will keep your momentum high to get you over that hump.
- Your goals and needs are unique to your credit union, your credit union’s needs, and your members. Your data analytics plan will be similarly unique. While you should have a set of plans to follow, you should not expect to have clear-cut, specific, immutable plans. There will be hiccups and speed bumps. Embrace it! Modify your plan and proceed.
- Playing around with data may yield unexpected results. Sometimes, unexpected means really, really good things that you hadn’t previously considered.
If you’re not sure where to start playing with the data, you could try playing with purpose through a little data cleanup. By reducing redundancies, obvious errors, etc., you make the data more easily understandable and usable.
Credit unions of all sizes have more tools affordable and available to them today than ever before. Still, many are hesitant to take that first step.
But if you take the first step in this guide, you have already begun your credit union’s data analytics journey. And the best part is that you did not have to pay for a thing, or even get approval. Just by brainstorming possible analytics solutions to your credit union’s needs, you have gotten the ball rolling.
Plus, when you see how easy it is to take that first step, you have to ask yourself: Which is better, beginning your data analytics journey without a plan, or waiting until your competitors already have a solution?
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