Three Challenges and Three Solutions for Data Management

In today’s data-driven world, credit unions understand the value of insights that can be gained from member data. Yet, for many, there’s uncertainty on how to make that happen, along with the usual technology growing pains. Creating a data platform is a great first step, and segmentation and predicting member needs is even better.

But how do you unlock the full potential of data? How do you open it up to the organization -- so your managers can readily access it and your marketing team can act on it? You’ll need to overcome three primary challenges credit unions face today.

Challenge #1 – Organization

The devil is in the details. 

If your business users lack the information they need, it’s likely due to fragmentation, a lagging IT infrastructure, or the big expense of getting and storing big data. The complexities of running multiple systems – mortgage LOS, consumer LOS, core, others – often result in a wide variety of disjointed perspectives… not that helpful.

What’s needed to solve fragmentation and other similar data issues is a framework, architecture and collective strategy that brings multiple systems together. This is where the devil is in the details. This strategy should include a data warehouse solution that sorts and “cleans” data, along with today’s best tools for presentation, such as OLAP cubes and application programming interfaces (APIs). These readily-available tools can help you efficiently organize data around business value, and pave the way to mining actionable insights.

Challenge #2 – Translation

Where the fun begins. 

Many business users spend a lot of time collecting data, and not enough time analyzing it for a clear view of how to improve performance or make a sound decision. Without solid analytics that provide a complete picture of all data (versus ad hoc queries and reports that only tell part of the story), you simply can’t get the in-depth, enterprise-wide view you need.

A solution that incorporates structured data and models in a dynamic presentation layer is the answer, and where the fun begins. This allows your business users to translate, visualize and manipulate the data to test different theories (without affecting the underlying operational data). Being able to creatively work with the data in this way often leads to new ideas and business-boosting insights.

Challenge #3 – Execution

Where the rubber hits the road. 

Advanced analytics is where the rubber meets the road. Here data becomes truly actionable and yields reliable forecasting. Yet, most credit unions lack this capability. Adding to the difficulties is unstructured data, such as call center texts, spreadsheets, web log content and more. These data assets have high value, but take too much time and effort to extract manually.

Infrastructure systems that can support machine learning and predictive analytics can elevate your credit union’s capabilities, efficiently mine unstructured assets, and unlock the true potential of your data. It’s a powerful investment that can help your credit union:

  • Create accurate lifecycle stages among your members
  • Predict products and services they’ll add to their portfolio
  • Scale and allocate resources where they will do the most good
  • Roll out promotions when and where they’ll get the best results

For even more helpful tips on data management, read our whitepaper: Data and Analytics Toolkit: Practical Success Factors for Your Data Management Solution.