Credit unions need advanced analytics in their data operations, without question.
Imagine being able to automatically present products and services to your members at exactly the right time and place, just like big retailers such as Amazon or Zappos.
Imagine being able to spot a trend quickly and push it across the organization, giving each employee the right information to act on the trend and make an informed decision.
A distant dream? It doesn’t have to be.
There’s a definite progression on the journey to advanced analytics. So long as you ask the right questions along the way, you can build a coherent data analytics strategy that will take your credit union to the next level.
Starting your journey
The first question you need to ask of your organization is, “Can we access the data?”
With legacy systems and many disparate point solutions in need of integration, data fragmentation is the No. 1 challenge for most credit unions. It’s hard to know where the data “lives” or even its quality and breadth. Is it clean data? Is it relevant? Timely?
Getting to the core of these questions will enable you to formulate your data management strategy. Once you’ve collected the data you need in one place, as in a data warehouse, you can start accessing it.
The next question is, “What is the business value of the data?” Most credit unions look to data to provide self-service and reporting, using snapshots of data as a guide for making business decisions.
These take the form of standard reports, ad hoc reports, or selective drill-downs—all of which tell you what’s happening in particular areas of the business. These backward-looking reporting capabilities certainly provide some member insights.
But to get to the level where you start using data to make future decisions, we enter “analytics proper,” or what’s commonly referred to as guided analytics.
In the guided analytics stage, you need to ask, “What does the data mean?” Statistical analysis tells you why certain circumstances occur. Forecasting tells you what will happen if the trends continue.
Here, business users start to get a clearer picture of the importance of particular trends, and understand the impact certain decisions will have on the organization.
The last level—predictive and prescriptive analytics—help credit unions arrive at the ultimate goal: insights that directly improve the member experience. Through data science and advanced tools such as machine learning, you’re able to take all available data and start analyzing future outcomes.
You need to ask:
These are the questions that get credit unions to the level of Amazon and Zappos. This is where you start anticipating what your members will need.
At that point, you can design programs to increase member satisfaction and engagement.
You can deliver products and services that ultimately provide the member experience and commitment to service for which all of us in the credit union industry strive.
If member experience is the end goal, your advanced analytics strategy will most certainly succeed.