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 One – 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 Two – 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 Three –
Execution: Where the Rubber Meets 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.
