Seize the Opportunities: Top 3 Challenges to Overcome for Advanced Analytics Success



The success of a credit union is increasingly becoming reliant on advanced analytics, but many a CU executive remains in the dark about all the benefits such analytics can provide. These tips will help you overcome the challenges to reap all the rewards.

Advanced analytics is redefining the financial services industry – and it is becoming a critical component in strategic planning, business growth and member acquisition. But today, many credit union executives are still unclear of all the benefits advanced analytics can deliver – and how to successfully weave the strategy into the credit union’s daily business operations. Some challenges and uncertainties include how to manage the data, how to mitigate compliance and security risks, and how to quantify the true ROI without flawed results. There are obvious obstacles and risks that can overshadow the tremendous benefits. Fortunately, the answers and a clear roadmap already exist.

Unlike mega banks that have a large budget to hire a chief data officer and an army of data scientists to build a data analytics program from the ground up, many credit unions are seeking out innovative experts who have spent years researching, developing, testing and refining a data analytics product offering. Why? Proven outside partners require a fraction of the time and budget of an in-house product. You receive the benefit of time-tested expertise, customized dashboards and support throughout the program implementation. Those credit unions on the fence as to whether or not to move ahead with an advanced analytics product should take the time to research an innovator with years of experience. A provider that has already combined the software development and implementation acumen with powerful data analytics is a plus.

Taking Action and Being Wary of Skewed Results

Flawed data leads to faulty conclusions and misleading business outcomes. It’s true that when it comes to analyzing big data, if you do not understand the data and misinterpret it, then you will risk getting flawed results. Also, after collecting and cleansing the data, some are left asking, “So now what do we do with it?” How do you overcome these challenges?

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