The Decision Maker

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

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

The Data Warehouse is not Enough

Posted by Nate Wentzlaff on Oct 27, 2015 2:05:49 PM

Relying solely on a data warehouse, without an enterprise data management strategy, is a recipe for disaster.

 

Credit unions are beginning to invest heavily in big data and analytics.  When deciding how to allocate funds in this space, leaders are awash with buzzwords and conflicting advice.  One of the most common terms used within big data and analytics is: data warehouseDeciding whether to build or buy a data warehouse is an important strategic decision for credit unions.  Unfortunately, many decision-makers get lost in discussions about storage capacity, data processing, data visualization, etc.  All of these concepts are important.  However, data warehousing is not the solution.  It is a powerful tool in an enterprise data management (EDM) strategy. 

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Topics: Semantic Layer, Enterprise Data Management, Analytic Data Model, Data Analytics

6 Steps to Develop Member-Centric Models

Posted by Nate Wentzlaff on Jul 22, 2015 11:11:08 AM

As financial technologies become more complex, credit unions must design their business and data models around the member.

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Topics: Big Data, Analytic Data Model, Membership, Collaboration, Data Analytics

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

Contact Analytics: Going Beyond the Call Center

Posted by Nate Wentzlaff on Jun 10, 2015 12:00:00 PM

As digital strategies continue to proliferate throughout the credit union industry, the contact center has become essential.

Calling a company about an issue can be a miserable experience.  Being transferred to four departments and having to explain an issue, not to mention basic account information, four different times will frustrate the most patient consumers.  These experiences have given the call center a bad image in the minds of consumers.  Stereotypes of call center agents who do not speak fluent English failing to understand a problem have been burned into the American culture.  However, the necessity for remotely assisting consumers has never been greater.  Redesigning the call center into a contact center will enable credit unions to give their members excellent service.   It will also empower credit unions to continue learning about their members through every interaction.

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Topics: Membership, Analytic Data Model, Data Analytics

Cooperative Analytics: The Standard Data Model

Posted by Nate Wentzlaff on Apr 16, 2015 12:30:00 PM

In order to cooperate more effectively, credit unions must “read from the same book” by agreeing on common meanings for their business terms. 
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Topics: Big Data, Analytic Data Model, Semantic Layer, Collaboration

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

Credit Unions and Big Data/Analytics [VIDEO]

Posted by Austin Wentzlaff on Mar 16, 2015 12:13:00 PM



 

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Topics: Business Intelligence, Big Data, Credit Unions, Data Integration, Data Pool, Analytic Data Model, Data Analytics

DNA of the Credit Union

Posted by Nate Wentzlaff on Feb 26, 2015 12:50:00 PM

The DNA of any organism is the model of its identity.  Analytical models of data (the building blocks of the credit union) will allow the industry to make better decisions that reflect their character.

As data pours in from multiple facets, credit unions must be able to mold it into a model.  Similar to DNA code for each living organism, data needs to have a code.  Analysis built on raw data makes organization-wide decisions difficult.  Therefore, a certain data structure is necessary.  Defining this structure is how each credit union defines their unique character.  Every organism has a unique code for their life; DNA.  Genetic code instructs every cell and allows an organism to function harmoniously, even when it is not consciously in control. This is the way that the credit union of the future must be designed.  The analytical data model (ADM) is a credit union’s DNA code.

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Topics: Big Data, Analytic Data Model, Data Analytics