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: Data Lake, Data Ownership, Big Data, Data Pool, Collaboration, Analytic Data Model

Leveraging Data to Create Exceptional Member Experiences at Ideal Credit Union [VIDEO]

Posted by Mark Portz on Apr 17, 2018 1:02:00 PM

 

MnCUN Interviews: Ideal CU and OnApproach Work Together to Leverage Data Analytics' Potential... from CUbroadcast on Vimeo.

At the Minnesota Credit Union Network (MnCUN) Annual Conference, Paul Ablack, CEO, OnApproach and Alisha Johnson, Executive Vice President of Operations for Ideal Credit Union, joined Mike Lawson, Host of CUbroadcast, to discuss data access, member profitability, member engagement, data lakes, timely and targeted marketing, chatbots, real-time analytics, and credit union collaboration.  

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Topics: Video, Case Study, Membership, Analytic Data Model

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 Lake, Data Pool, Analytic Data Model

A Day in the Life of a Data Analytics SVP: Making Use of Your Credit Union’s Data

Posted by Mark Portz on Aug 24, 2017 11:17:42 AM

If you are a credit union still waiting on the data analytics sidelines, you’re already too late. Data analytics is not a fad – it is a major opportunity for credit unions to gain deeper insights and improve decision making to create a strong and competitive future. However, it is not always clear where credit unions should begin. To help answer these questions, John Best recently spoke with Clay Yearsley, SVP of Data Analytics at Texas Trust Credit Union about getting started on the analytics journey, the skills needed, and the value of data in the podcast, “Catching a Unicorn – Discussing Data Analytics with Clay Yearsley”.

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Topics: Data Analytics, Podcast, Leadership, Analytic Data Model

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

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 Lake, Data Pool, Analytic Data Model

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 Lake, Data Pool, Analytic Data Model

Building for the Future

Posted by Michael Cochrum on Apr 5, 2017 11:15:00 AM

When my brother and I were kids, we liked to build things. We built forts, ramps and anything else we could fashion out of scrap wood.  Typically, our projects served a specific function, to ward off rival “street gangs” of preteens from another block, to propel our dirt bikes into the air, or whatever else we decided could or would result in our becoming temporarily disabled.  We thought we were good builders, but the greatest evidence that we were not, is that our work does not exist in any form today.

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Topics: FinTech, Big Data, Analytic Data Model

Looking to the Future of Data Warehousing

Posted by Mark Portz on Jan 30, 2017 11:01:00 AM

In the fourth Data Analytics Series BIGcast, From Questions to Answers: Becoming a Data-Driven Organization, John Best speaks with Brewster Knowlton of The Knowlton Group about data-driven decisions, data warehousing and successful data integrations.

The Six Characteristics of a Data-Driven Organization

According to Brewster, there are six characteristics to determine whether your organization is really data-driven:

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Topics: Data-Driven, Podcast, Analytic Data Model

What’s Holding You Back from Being Data-Driven?

Posted by Brewster Knowlton (The Knowlton Group) on Sep 20, 2016 11:08:00 AM

Let’s face it: we live in a world where a strong data and analytics competency is becoming a “must have” for successful companies. Despite the growing significance of analytics, the majority of banks and credit unions are not “data-driven” organizations.

We’ve uncovered a number of common reasons why investment in data and analytics has been pushed off or outright rejected. Despite these challenges, most of the common reasons against data and analytics are driven by inaccuracies or misinformation.

In this post, we will address the common pushbacks against data and analytics projects and how to overcome those challenges.

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Topics: Analytic Data Model, Big Data