The Decision Maker

How Data Integration Helps Credit Union Analytics Platforms

Posted by CU 2.0 on Dec 11, 2018 10:59:00 AM

The immediate future of banking and the financial industry is in analytics. The ability to draw conclusions from massive sets of data helps financial institutions improve their advertisement targeting, their ability to underwrite loans, and a whole host of other things. One of the sticking points in the machinery is in credit unions’ ability to perform adequate data integration.

Data integration sounds relatively simple on its own. Data integration is the practice of combining multiple streams or forms of data into a single readable format. The extent of data integration needed increases as the amount of data—or the number of data sources—increases.

Why do Credit Unions Need Data Integration?

This will be a long answer, and so I’ll break it up into three parts. The first part will address the many sources of data that credit unions deal with on a daily basis. The second part will introduce the necessity of analytics platforms in finance. The third section will explain the role of credit union data integration in the grand scheme of things.

1.     Credit Unions Generate Lots of Data

Credit unions exist in the financial sector, which is technologically fast-moving. Partially because of this, and partially because credit unions must record financial and member data, credit unions are inundated with a massive amount of data daily.

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Topics: Data Integration, Insight Platform, Data Analytics

4 Challenges and the Opportunity for Credit Unions [Video]

Posted by Mark Portz on Feb 6, 2018 11:07:00 AM

In the webinar, "Fueling a Bright Future for Credit Union Analytics", Austin Wentzlaff, VP Business Development, OnApproach, presents the challenges and opportunities for credit unions regarding topics including data analytics, digital transformation, and collaboration. 

Credit unions are facing several unique challenges. As an industry, credit unions have fallen behind competing fintech startups and major retail banks. It is vital for financial institutions to understand the problems they are facing and how they are possible to overcome. In 2018, the credit union industry must work together to push past these challenges and remain relevant in the age of digital transformation.

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Topics: Video, Data Ownership, Data Lake, Data Integration, Collaboration, Data Pool

Lagging Contenders: How Credit Unions Can Catch Up in Data and Analytics - Part 1

Posted by Peter Keers, PMP on Aug 8, 2017 11:18:43 AM

The message has been ringing load and clear throughout the credit union industry for years: make better use of data and analytics or lose “member share” to more progressive CU peers or (horrors!) banks and fintech startups.

Despite the warning cries, the proportion of credit unions embracing this trend is (horrifyingly!) low.

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Topics: Data Quality, Data Integration, Data Analytics, Analytic Data Model

Stop Worrying about Millennials, Say Hello to Generation Z: Part 2

Posted by Mark Portz on May 9, 2017 1:04:00 PM

In the first part of this blog, we learned about who Generation Z is. Now that we have a better understanding of who we are talking about and realize that we need to be prepared, let’s look at what it means for your financial institution.

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Topics: Data Integration, Digital

Prescriptive Analytics for Credit Unions: Healthcare Style

Posted by Nate Wentzlaff on Mar 2, 2017 12:03:00 PM

Just as healthcare is developing robust analytics for patients, credit unions have a great opportunity to empower members to track their financial health and take actions to improve it.

Being raised in a small town, I never thought about the healthcare I received. I had the same doctor from birth until I moved to college. As long as nothing seemed wrong to him, I felt confident that I was healthy. However, when I moved to a bigger city, everything changed. I was no longer able to rely on my hometown doctor, and I needed a way to monitor and maintain my health. At the same time, the healthcare industry was going through a data revolution. The traditional relationships between doctors and patients were changed forever. In shopping for my new healthcare provider, I felt the most comfortable with the one that had the best analytics and enabled me to make data-driven decisions to improve my health.

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Topics: Data Analytics, Data Integration

Credit Union Cooperation: Google Maps Style

Posted by Nate Wentzlaff on Feb 7, 2017 12:02:00 PM

As credit unions begin their journey into the future, they must rely on an industry standard analytics platform to guide them to their destinations.

Google Maps has revolutionized how we navigate our lives. It saves us from headaches caused by unnecessary traffic and other challenges in traveling. My journey from work to home has many different routes depending on traffic patterns. During days with slower traffic (i.e. - winter snowstorms), the Google Maps recommended route will change every 5 – 10 minutes. Using an analytics engine that informs me of the best route allows me to spend extra time on more important things in life. Credit unions have a similar opportunity when navigating their institutions into the uncertain future of financial services. Establishing an industry standard analytics platform will enable credit unions to cooperate on analytics and guide them to their desired destinations.

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Topics: Insight Platform, Data Integration, Data Pool, Data Visualization, Collaboration, Data Analytics

The Power of Semantics in Credit Union Analytics

Posted by Nate Wentzlaff on Aug 23, 2016 11:06:00 AM

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.

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Topics: Data Integration, Semantic Layer, Data Standards, Analytic Data Model

Sharpen Collateral Valuation through Data Integration

Posted by Nate Wentzlaff on Jul 13, 2016 11:30:00 AM

Collateral Valuations are essential while serving members and maintaining a healthy credit union. However, credit unions are relying on inaccurate valuations of their members’ collateral values because of disintegrated data.

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Topics: Data Integration, Credit Unions, Big Data

Having It All: The Keys to Comprehensive Data Capture

Posted by Peter Keers, PMP on Jul 1, 2015 9:57:33 AM

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.

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Topics: Big Data, Data Integration, Analytic Data Model, Data Standards

Why Analytics Requires Standard Data Sets

Posted by Nate Wentzlaff on Mar 30, 2015 12:43:00 PM

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.

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Topics: Data Integration, Analytic Data Model, Data Standards