Transforming the Member Experience Using Amazon Redshift with Together Credit Union

As a community-focused, not-for-profit financial institution, Together Credit Union provides financial services and banking products at 30 branches across the United States. It holds more than 2 billion dollars in assets. To understand how their branches were performing, branch managers had to wait for monthly reports, a process that involved manual data collection and queries on transactional systems. By the time they received their branches’ key performance indicators, the period was over, and they could not react.

“By the time this data was delivered, it was nearly 2 months old,” says Paul Scheibal, director of data architecture at Together Credit Union. “It’s hard to define your goals and adjust internal processes to meet them when you don’t know where you’re at in the moment.”

To deliver near real-time insights, Together Credit Union required scalable, powerful technology. It also wanted a self-service solution so that branch managers could access reports without having to rely on IT teams. Finally, the solution needed to be secure so that the credit union could protect its member data.

To meet these goals, Together Credit Union chose to build a centralized data lake on Amazon Redshift. It engaged AWS Professional Services, a global team of experts that helps companies achieve their desired business outcomes on AWS, to assess the security of its system. “Data security is of the utmost importance,” says Scheibal. “AWS Professional Services conducted an audit of our system and validated our security architecture.”

In 4–6 months, Together Credit Union defined the data lake architecture, set up its cloud infrastructure, and trained its team on the AWS services that it was planning to use. The company engaged in eight AWS Solutions-Focused Immersion Days to educate its team about AWS services relevant to the project. Then, it began to build the data lake, connecting sources such as customer surveys, account information, loan data, and more to the Amazon Redshift database.

“I am very impressed with the performance of Amazon Redshift,” says Scheibal. “I compared querying times between another database that we use for our transactional system and the data lake on Amazon Redshift. The queries that took 15–20 minutes on the other database were taking 10–15 seconds using Amazon Redshift.”

The data is ingested using APIs, change data capture, and flat files using AWS DataSync—a secure, online service that automates and accelerates moving data between on premises and AWS Storage services—connected to Together Credit Union’s core and other systems, including Corelation KeyStone. The credit union also implemented site-to-site VPNs to facilitate local communications and achieve near real-time data access. It also adopted Python and the database engine power of Amazon Redshift to turn this information into insights for branch managers.

At every stage of development, Together Credit Union adopted a range of AWS services to protect the member information that is hosted on the data lake. These services include AWS Security Hub, a service that automates AWS security checks and centralizes security alerts; AWS WAF, a service for protecting web applications from common bugs and bots; and Amazon Detective, which analyzes and visualizes security data to investigate security issues.

Together Credit Union now stores 1.1 TB of data on the data lake, which business users can access through their branch dashboards in near real time. Branch managers can view data at varying levels on their dashboards, from a high-level view of their operations to more granular information about their employees, members, and products. With this information, managers can make decisions to enhance operational efficiency and member satisfaction.

“We’re starting to see a lot of positive collaboration between branch managers and their employees and collaboration between different branches,” says Scheibal. “These interactions and competitions are wonderful to see.”

Through their dashboards, branch managers can query data by themselves without having to rely on Together Credit Union’s technical teams. These self-service capabilities have removed hours of manual work and provide branch managers with information that is much more relevant. “Now, branch managers can see new members in near real time,” says Scheibal. “We know that our members are happy and that they are getting the products that they need from us.”

Now that Together Credit Union has established its data lake, it plans to explore new use cases across different parts of its organization. Having a strong data foundation in place, the credit union is in a better position to adopt advanced technologies, including artificial intelligence and machine learning. For example, it is developing propensity models, which will help branch locations understand the needs of their customers at a deeper level and deliver more personalized services.

“We’re still reaping benefits from this project,” says Scheibal. “It is going to have a positive impact on both membership growth and each member’s product portfolio as they come on board.”