The Decision Maker

Peter Keers, PMP

Recent Posts

To Build or Not to Build (Buy) – That is the Question for Credit Unions

Posted by Peter Keers, PMP on Jan 31, 2019 1:52:49 PM

As the Age of Analytics for credit unions rolls forward, the question of “Build or Buy” is faced almost daily by decisionmakers. It comes at all stages in the data and analytics journey, so credit unions must understand the tradeoffs in deciding to Build or Buy.

First, however, consider the question itself: Build or Buy. “Build” means the credit union uses its own resources to design, construct, launch, and maintain an application or capability. “Buy” means acquiring these same elements from an entity outside the organization.

The fast pace of technological evolution has added an innovative dimension the definition of “Buy”. Increasingly, “Buy” includes Software as a Service (SaaS) as well as on-premises implementations.

The Build Option

The perceived advantages of Build are customization and control. By keeping projects in-house, the Credit Union can design a system tailored to its unique requirements. Although all credit unions are chartered to do a specific set of services, each has its own flavor for delivering these services.

These Build option advantages favor larger credit unions with greater resources. Having the team depth of a larger organization enables greater possibilities for having both the skills and numbers to take on Build projects.

The major disadvantage of Build is cost. A custom-tailored suit is more expensive than an off-the-rack brand. Another, subtle but important disadvantage is strategic focus. A credit union is wired to be a member-oriented financial services organization. Though it may have gifted technologists on its staff, most credit unions are unlikely to have the technical breadth and depth to build a truly industrial grade application. There is also a big risk of knowledge experts leaving the organization in the current low unemployment environment.

Another cost concern is ongoing maintenance and enhancements. Experience shows custom-built applications are notoriously expensive to keep up-to-date and in efficient working order. The credit union is saddled with this ongoing burden for its data and analytics capability to keep pace with new industry trends.

See 7 Challenges to Consider When Building a Data Warehouse: http://blog.onapproach.com/7-challenges-consider-building-data-warehouse

The Buy Option

At first glance, it might be assumed the Buy option is the mirror opposite of Build. A purchased product will not be exactly customized to the credit union’s specific requirements nor will the organization have as much control over the project. However, this is a game of trade-offs driven by primarily by the size of the credit union. In order to survive, all credit unions must embark on the data and analytics journey. Those ignoring this trend will ultimately be acquired by credit unions that do take data and analytics seriously or simply become obsolete.

For the majority of credit unions, the Buy option holds significant advantages. By giving up some customization and control, the organization gains significant data and analytics capabilities at a more affordable price. In fact, not only is a tested commercial product liable to cost less up front, it also has the advantage of having the bugs worked out as the result of use at multiple sites. Therefore, the cost and headaches of the inevitable errors in complex programming code are avoided. If fact, the perception that a Build project results in a more tailored outcome may be overstated. Most commercial products are very configurable to meet specific credit union requirements.

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Topics: Insight Platform, Credit Unions, Data Integration

Millennials in the House: Leveraging Data about Your Young Members to Attract More

Posted by Peter Keers, PMP on Jul 17, 2018 10:03:00 AM

“Where are the millennials?”. This question has been echoing throughout the credit union industry since this age group (18-36 year olds) was first seen on the horizon in the post-9/11 landscape.

After the Great Recession of 2008, millennials were expected to flock to credit unions due to their well-documented distrust of banks. Yet, it hasn’t happened.  There are estimated to be 71 million millennials but too few are becoming credit unions members. About a third of older age groups (Baby Boomers, Gen-X, etc.) are credit union members but only about 25% of millennials are.

This is a problem since the conventional wisdom is that once a young person becomes a member, he or she is liable to stay as a credit union member for a long time.

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Topics: Millennials, Marketing

Data “De-Identification”: The Stairway to Big Data Heaven

Posted by Peter Keers, PMP on Feb 13, 2018 12:03:00 PM

Credit union interest in Big Data is at an all-time high. The promise of predictive analytics and other Big Data opportunities will be a key part of helping the industry compete more effectively with traditional banks and fintech upstarts.

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Topics: Encryption, Security, Collaboration, Big Data, Digital

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

Lagging Contenders: How Credit Unions Can Catch Up in Data and Analytics - Part 2

Posted by Peter Keers, PMP on Aug 15, 2017 10:16:00 AM

This is Part 2 of 2 in a blog series on how credit unions can catch up in data and analytics. In Part 1, we discussed which questions credit unions need to be asking to get off the bench, the issue with data silos, and what it will take to move forward with data analytics. In Part 2, we will further discuss the concept of big data, staffing for data analytics, and creating value from the data.

"A recent McKinsey & Company report emphasizes the fact that many industries are achieving only a fraction of their “digital potential”. However, the report observes, “In the United States, the information and communications technology sector, media, financial services, and professional services are surging ahead…”. This means other players in the marketplace served by credit unions have a big head start.

Credit unions that have been sitting on the sidelines can wait no longer. To get off the bench, these organizations need to ask:

  • What are the basic questions about the organization’s strategic direction that cannot be answered today?
  • How can existing data be better “generated, collected, and organized”?
  • What data outside the organization would be useful?
  • What skillsets are missing internally and to what degree can they (or should they) be outsourced?
  • Once “insights” are uncovered from analytics, what are the practical steps to leveraging them to create value?"
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Topics: Digital, Data, Big Data

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

Get Your Game On: Criteria for Evaluating Analytics Tools

Posted by Peter Keers, PMP on Jul 11, 2017 11:07:00 AM

Forward-thinking credit unions are tuning their internal data for improved decision making. Previously, data was locked up in multiple, “siloed” transactional systems. Now, innovative credit unions organize their critical information within integrated data warehouses.

 However, once the data is made available, how does a credit union make best use of it? An apt analogy is the XBOX and other gaming platforms. The game console by itself is a marvelous piece of technology. Yet, without the games, it is not very useful. By the same token, video games without a console are useless. Put the two together and wonderful things happen for video game aficionados.

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Topics: Data Analytics

Leveraging the Data Advantage

Posted by Peter Keers, PMP on Jun 26, 2017 12:03:00 PM

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. 

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Topics: Data Standards, CU Analytics Ecosystem, Data Analytics

Robo-Analytics: Artificial Intelligence in the Credit Union of the Future

Posted by Peter Keers, PMP on Jun 12, 2017 12:01:00 PM

A recent Forbes magazine article by Randy Bean and Thomas H. Davenport notes how General Electric (GE) is making a bold transformation into a “digital industrial” company. In the past ten years, GE has taken important steps to capture massive amounts of data (massive = “Big Data”) from devices throughout the enterprise. At first, it seems GE applied conventional analytics to find ways to increase revenue, cut cost, and many other beneficial outcomes. While analytics continues to be a critical part of GE’s evolution into being a digital industrial company, GE is taking a further step forward into the emerging areas of artificial intelligence and machine learning.

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Topics: Machine Learning, Artificial Intelligence, Digital

Credit Union Analytics Comes of Age: 9 Essential Guidelines

Posted by Peter Keers, PMP on Jun 6, 2017 11:04:00 AM

A fascinating new report by McKinsey & Company highlights that credit unions can drive organizational value by creating an analytics culture.

The report recognizes that financial services analytics has reached a point where marketing was in the 1970’s for the banking sector. Prior to that time, sales and marketing initiatives for credit unions and banks were rare. In 2017, a credit union would find it difficult to survive without some level of marketing effort.

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Topics: Leadership, Data Analytics