The Trellance Data Blog

Data insights start, naturally, with data. Get the data wrong, and your insights will be wrong.  Who wants to take business actions on insights based on bad data?

The real problem is when you’re acting on data insights that you don’t know are based on bad data. You need to prevent that from happening because you don’t want to make incorrect assumptions. If you’ve done nothing to build useful data, why should you expect it to be good? 

The data industry has been around a long time and has proven techniques for building that solid foundation upon which to base your insights. Using these techniques and following a structured methodology provide the discipline needed to make that foundation. But building a good foundation that produces valuable insights takes time. Don’t try to cut corners and don’t assume your data is useful.

This week’s BIGcast brings in OnApproach’s CEO, Paul Ablack, to talk with John Best about his vision for the credit union industry. The second episode of the Data Lake BIGcast Series, Data Lake: What It Takes, discusses how the creation of an industry-standard data lake could completely revolutionize the credit union industry. Paul goes into depth on the capabilities of a data lake and how this transformation is not only beneficial for credit unions, but the entire community.

Laying the Groundwork for the Future

OnApproach is a credit union service organization (CUSO) that is focused on collaborative analytics for the credit union industry. OnApproach provides credit unions with a middleware solution for efficiently organizing and maximizing the full potential of that data. Paul goes on to explain:

Data is extremely valuable for all the initiatives underway. The big challenge for credit unions is the integration of that data and putting it to the correct use. Our mission is to bring all that data into one place and make it easily accessible for not just credit unions, but the industry as a whole. We call this the CU App Store, where app developers and vendors can come in and create an app that credit unions can use to perform analysis (i.e. models of attrition, outreach, and predictive analytics).

Credit unions are awash in data, but until recently there were few options for leveraging this data for better decision making. That has changed with the emergence of two major innovations. 

A common language forms a bond between humans and strengthens their ability to cooperate. Credit unions must agree on common semantics to establish powerful business analytics throughout the industry.

Language is very powerful tool. The ability for people to understand each other forms one of the strongest bonds known to man. The credit union industry has remained strong through the power of collaboration. However, new challenges have been mounting against the industry such as big banks and fintech competitors, stricter regulations and the increasing complexity of products and channels. Therefore, being on the “same page” is more important than ever. Currently, credit unions are speaking different languages and trying to collaborate. This results in miscommunication throughout the industry which depends on unity for strength. The ability to communicate easily and effectively is the foundation of credit union analytics.

Credit unions aiming to build Big Data & Analytics capabilities have a lot of decisions to make. One of the most fundamental decisions is how much source data to capture. The two dimensions of “how much” are depth and breadth.

With systems designed to capture a robust amount of data on every transaction that occurs throughout the credit union, standard data sets are required to form strong analytics.

Credit union leaders must develop a data-driven vision.  The ideals cast in this vision give clear direction to employees.  That being said, without standard data sets to monitor business processes, credit unions will fall into a worse condition than before establishing an analytics program.  Employees will bring data to meetings but will have different versions of the truth.  Standard data sets are essential for a solid analytics foundation.