Becoming a Data Driven Credit Union Isn’t a Straight Line

-By Stef Luck, Trellance
Most credit unions don’t need to be convinced about why becoming data driven should be a priority. They understand that data is key to serving today’s digital member in the ways they expect. But how to become a data-driven credit union isn’t always straightforward. Most credit unions are surprised to discover there isn’t a sequential series of steps or “right way” to move forward on the journey. 

Data Maturity: A Journey with No Final Destination 

Even the phrase “data maturity journey” can be a misnomer if it leads someone to believe it starts at “point A” and ends at “point B.” This makes for a nice, neat visual – but in our experience, reality is much different. 

1. It can be overwhelming to feel a long list of steps must be taken before your credit union can begin to use more sophisticated data analytics. We have seen firsthand that it’s possible for eager credit unions to quickly begin using advanced analytical models.
2. Every credit union is different. Even within the same credit union, data needs will be multi-dimensional and dynamic across the organization. As needs and goals differ, there simply won’t be a template process that works universally.
3. A linear journey implies a final destination: once you reach it, you’re done. Not only is this unrealistic, it can actually be detrimental.  
4. A linear path is process-based and not outcome-based. The best path for each credit union will be unique and determined by identifying goals – not working through a set series of steps and hoping you end in the right spot.
5. Becoming data-driven isn’t solely a technological shift – it’s a cultural one.

The Four Data Pillars You May Encounter on Your Journey
Though every credit union’s data journey will be unique, most will encounter four data pillars along the way. There are no hard-and-fast rules about approaching the pillars in a certain order – credit unions can even work within multiple pillars at the same time.

Following are descriptions of each pillar.

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Pillar 1: Data Management
Data management is a framework of data disciplines that organize a credit union’s data to maximize value. It creates a foundation to ensure data is accurate, up-to-date, and accessible.

Pillar 2: Descriptive Analytics
Descriptive analytics makes use of large amounts of historical data, providing insights into what has happened by aggregating information and summarizing the findings.

Pillar 3: Predictive Analytics
Predictive analytics makes predictions about the future to enable proactive decisions. Algorithms are used to identify historical trends and accurately anticipate what might happen in the future.

Pillar 4: Prescriptive Analytics
Prescriptive analytics helps choose the best course of future action by advising on possible outcomes. Based on the data and scenarios analyzed, prescriptive analytics recommends specific actions and strategies by employing advanced analytical techniques.

The process of becoming data-driven is less of a straight line, and more of a “choose your own adventure.” Your credit union can chart its own path, and paint its own picture.

This article has been reduced for space. Read the full article at https://www.trellance.com/becoming-a-data-driven-credit-union-isnt-a-straight/ ​