The Decision Maker

How Credit Unions Can Win the Big Data Play

Posted by Robert McGarvey on Jul 2, 2018 12:47:00 PM

Ask executives at the money center banks how they plan to win, against both fintechs and smaller institutions like credit unions, and they smirk as they say two words, big data.

Big data is today’s magic.  How does Amazon knows what book you want to read next, or what music you want to buy, or when you are about to run out of cat treats? Those are simple examples but the answer is big data. Amazon crunches a lot of data, in a blink of an eye, and it knows what you want, maybe before you know.

The race now is on inside financial institutions to crunch lots of data and to achieve similar predictive intimacy about their customers and members. 

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Topics: Big Data, Data warehouse, Data Pool, Data Lake, Data Ownership, Collaborative Analytics

Comparing Credit Unions to Craft Beer: What Can Your Organization Learn from a Brewery?

Posted by Mark Portz on Mar 20, 2018 12:14:00 PM

As collaborative community-based institutions, credit unions are very unique organizations. For many that have not been exposed to credit unions, it is difficult to see what makes this community so special, or even how a credit union differs from a bank, for that matter.

Since my time as an undergraduate at the University of Minnesota, I have been highly involved in another industry, or “community” – that of local craft beer. In fact, discovering the parallels between these two unique industries sparked my initial interest in the credit union movement, and is ultimately what has led me to my current role at a collaborative credit union service organization (CUSO).

In very recent years, the nation has witnessed the craft brewing community explode. In fact, according to the Brewers Association, the craft beer industry has been experiencing years of steady 10-15 percent growth, and now boasts over 6,000 U.S. craft breweries, for the first time in history. Despite the parallels between these communities outlined below, we are all aware of the declining number of U.S. credit unions, shrinking from over 7,200 in 2011, to less than 6,000 today. For community-based institutions focused on quality service, why do these trends differ?

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Topics: Data Pool, Shared Applications, predictive analytics, collaboration, CU Analytics Platform

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: Data Integration, Data Pool, Video, collaboration, Data Lake, Data Ownership, Webinar

Credit Unions and Data Lakes – The Next Wave

Posted by Peter Keers, PMP on Oct 5, 2017 12:03:00 PM

In two previous OnApproach blogs, the concept of a data lake was defined and differentiated from a traditional data warehouse. Yet, a key point was a data lake and a data warehouse are not mutually exclusive. In fact, a structured data warehouse could be a subset of an overall data lake architecture.

Simply stated, a data lake is an effective way to store and access very large quantities of data.

What does this mean for credit union decision makers?

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Topics: Data warehouse, Data Pool, Data Lake

What is a Data Lake? - Part 2: Sink or Swim

Posted by Mark Portz on Jul 26, 2017 11:07:00 AM

In my previous blog, “What is a Data Lake? Part 1”, I discussed how to define a data lake, and how it differs from a data warehouse. To briefly recap, a data lake is a massive data repository for raw data in its native format. To better understand the idea, let’s dive a bit deeper and get to know the advantages and disadvantages surrounding data lakes.

To start, there are a number of advantages data lakes serve for financial institutions:

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Topics: Data warehouse, Data Pool, Data Lake

What is a Data Lake? - Part 1: Testing the Waters

Posted by Mark Portz on Jul 18, 2017 11:03:00 AM

Financial institutions all over are working to build effective data strategies and improve decision-making. With so many new technologies and innovations out there, it can get very difficult to keep up with the industry and even keep straight the buzzwords we hear throughout the day. In this piece, let’s dive in to better understand what makes a data lake.

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Topics: Data warehouse, Data Pool, Data Lake

5 Reasons to Pool your Data

Posted by Mark Portz on Mar 7, 2017 1:04:00 PM

Data continues to prove itself as a necessity for decision-making in financial institutions. For years, major banks and innovative companies such as Google and Amazon have taken advantage of “Big Data” to gain better insights into their customer base and make business decisions to position themselves for the future. The credit union industry is finally beginning to take advantage of their data and utilize new technologies. However, credit unions are much smaller than major banks and simply don’t have the same quantity of data that banks are able to collect from their customers. Fortunately, data pooling serves as a great solution to this problem. Here are 5 reasons your credit union should participate in data pooling:

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Topics: Data Pool, predictive analytics, Data Analytics

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: Analytics, Data Integration, Data Pool, Cooperation, Data Visualization, Insight Platform

Descriptive, Prescriptive and Predictive Analytics, Oh My: The CECL Journey

Posted by Mark Portz on Jan 23, 2017 11:06:49 AM

In the third Data Analytics Series BIGcast, The CECL Effect, John Best speaks with Dr. Joseph Breeden of Deep Future Analytics about CECL, data pooling, and predictive analytics.

The Impact of CECL

As Joe Breeden explains in the podcast, CECL stands for Current Expected Credit Loss, and is the new accounting standard for how financial institutions will set loss reserves. Typically, organizations under $10 Billion assets have utilized moving averages to calculate loss reserves, but this model is backward-looking and will not be acceptable for the new regulations. A moving average model will always set your loss reserves too low moving into a recession and too high moving out of the recession.

When discussing how to meet CECL requirements and create a forward-looking model, Breeden states, “There is a lot of flexibility on how you implement it, but there are two things that are pretty much unavoidable”:

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Topics: Data Pool, predictive analytics, Data Analytics, CECL, BIGcast

Medtronic and Masimo agree to share de-identified patient data to improve patient safety; A lesson for the credit union industry

Posted by Paul Ablack on Nov 15, 2016 10:01:00 AM

In a Star Tribune article this week, “Medtronic agrees to share data in patient-safety effort”, we learned that “when it comes to patient safety, the leaders of the two companies are now sitting at the same table to discuss how they can share de-identified patient data with each other, as well as outside researchers and entrepreneurs, to predict health problems.”

The need to share data about patients and customers is hitting the mainstream as companies figure out that the pooling of this data is critical to the discovery of new insights that can help them develop better products that improve the lives of their customers. Competitors in many industries are realizing that they need to “get over themselves” and start figuring out ways to share this data for the greater good.

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Topics: Data Pool, collaboration

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You now have more information at hand about your credit union than ever before. But are you using it to "out-think" your rivals? If not, you may be missing out on a potent competitive tool.

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