The Decision Maker

The Purpose of Analytics

Posted by Nate Wentzlaff on Nov 1, 2016 12:04:37 PM

The Purpose of Analytics

As credit unions continue to invest in analytics solutions, they should focus on the purpose of analytics; Making data-driven decisions to better serve members.

Big data and analytics are a couple of the most used buzzwords throughout the credit union movement.  You can’t avoid these terms no matter where you try to hide. Many vendors promise analytics that will be a panacea to the movement. They continue to make bold claims that are sure to perk an executive’s ears (and drive sales for the vendor). Although there are many powerful products available to credit unions, they must understand the purpose of analytics before they begin their journey.

The Movement has Shifted!

Many credit unions have been great at making decisions in the past because they were able to know their members intimately. While these relationships are still essential today, the credit union movement is shifting into a new age of data. As credit unions expand their membership base, encourage digital banking, and face a new wave of regulations that expect a robust amount of data, they no longer have the luxury of relying on personal relationships and intuition as they have in the past. Data is now one of the most valuable assets credit unions have, and they should begin harnessing it to begin learning about their members and tailoring service to them.

Data, Data Everywhere, Not a Drop of Insight

Data is being produced at an alarming rate. Every device throughout the credit union movement is now gathering an ever-expanding array of data. From the core to call centers, mobile banking to ATM’s, credit unions are swimming in an ocean of data. Unfortunately, data is only as valuable as the decisions it influences. A robust data warehouse that sits on the shelf won’t assist credit unions in their effort to better serve their membership. In order to marry data with decisions, credit unions must understand their data.

Analytics to the Rescue?

Simply building (or buying) an analytics solution will not produce value for credit unions. Many times, analytics solutions sit in IT as another system to maintain. Without executives’ involvement, a credit union’s analytics program will just be another shiny tool in the toolbox. Beginning with the end in mind, credit unions must establish the reason they are building an analytics program, and the decisions that will be changed through the insights produced.

Data-Driven Decisions

Knowing what to do and acting on it are two very different things. Humans are always making lists of what they are going to do that usually end up in the trash the next time they clean out the “junk pile”. Analytics are only able to establish what a credit union should do; making decisions is up to the credit union employees. If credit unions don’t have a plan for implementing insights derived from the analytics solutions, they would be better off investing elsewhere. Implementing analytics is not a technical initiative, it is a cultural one. Once credit union leaders begin harnessing data to make better decisions, they will be able to revolutionize the business processes that make up their credit union.

Machine Learning

As credit unions establish analytics programs and make decisions based on the insights gathered, they will be able to establish machine learning. When credit unions consistently make decisions utilizing an analytic data model (ADM), the model will be able to learn from past decisions and adjust accordingly (while considering an ever-changing economic environment). With models learning from models, credit unions will become smarter as they journey into a financial services industry that is in the middle of being revolutionized.

Analytics in Action

After assessing all the data and insights gathered from their analytics program, it is up to the credit union leaders to put this data into action. Empowering middle management and front-line employees with a single version of the truth (SVOT), and a strategy to put it into action, will bring credit unions into a new era where they use data to better serve their members. The same spirit of collaboration and community that began the credit union movement will be stirred up again through analytics in action.

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Topics: Data Analytics, Machine Learning, Data-Driven