The Trellance Data Blog

The “Yin and Yang” of Credit Union Reporting/Analytics Software: 3 Factors to Consider

Posted by Paul Ablack on Jan 8, 2015 1:34:07 PM


As a veteran of the Business Intelligence (BI) industry, which is now being eclipsed by Big Data and Analytics, I have witnessed many organizations looking for the “perfect BI software”.

For at least a decade now, BI software companies have been striving for leadership in the coveted Gartner Magic Quadrant for Business Intelligence. The Magic Quadrant evaluates BI software vendors on two dimensions: (1) Completeness of Vision and (2) Ability To Execute. While these two dimensions do provide very good insight into the capabilities of each vendor’s product offering, they don’t tell the whole story.

Many IT departments over the years that have gone through the process of acquiring expensive BI software because they were led to believe by the BI vendors that the software was a “Silver Bullet”. Rather than making reporting & analytics easy and accessible across the enterprise, the promise of the Silver Bullet never materialized and it was often relegated to the class of “Failed IT Implementations”.

If you have been charged with the responsibility for choosing the right Reporting/Analytics (R/A) software for your credit union, here are three key factors that must be considered before making a purchasing decision and setting expectations within your credit union.

  1. One R/A Software Package May Not Be Enough - The users of the R/A software can be categorized into four groups: (1) Executive, (2) Operations, (3) Power Users and, (4) Information consumers. (See diagram below – The link to the full article is: Information needs and reporting functionality required by each group needs to be understood when selecting a vendor. For example, a Power User will want the ability to pivot data “on the fly” to get different variations of reports without the need to involve a report developer.


  1. Be Careful When Setting Expectations With Your User Community – Once the purchase decision regarding R/A software has been made, those who approved the purchase will be anticipating their cool reports and dashboards. They will not be expecting to hear that the reports still have to be written and it could take a while. Why? The report creation process is not nearly as simple as most people perceive it to be.  It requires a certain level of technical knowledge combined with an ability to understand user requirements. These requirements must be then transformed into design elements that can be understood by a report developer. All R/A software requires these two levels of knowledge in order to produce meaningful visualizations that can be easily consumed by the user community.
  2. Implementation is KEY - Understand the Yin and Yang of “R/A Software” and “The Data Model” before you begin – The Yin (the dark portion) and Yang (the white portion) symbol is an excellent representation of how two complementary forces come together in balance and harmony to produce true equilibrium or “happiness”. Happiness in the world of reporting and analytics can be defined as the point at which all levels of user needs are being met, the usage of the R/A software is very high, and people are talking about how much they love their new found access to information.


Applying this analogy to reporting & analytics where the Data Model is the “Yin” and R/A Software the is the “Yang”, it becomes clear that both carry equal importance in the pursuit of organizational “happiness”. If you plan to successfully stand up an R/A software application you will have to consider both the Yin and the Yang. The Gartner Magic Quadrant does a good job of profiling the different R/A software vendors (the “Yang”) so let me shed some light on the other side which is the Data Model (the “Yin”).

Data Model – The “Yin”

The Data Model is a database that is architected specifically for reporting and analytics. It is vastly different from the architecture found in conventional transactional system like a core processor. In the data architecture world, the dominant data model standard is called the Kimball methodology ( and it can be applied to any industry.

Nevertheless, building an industry specific Data Model implementation is a significant undertaking. Fortunately, a credit union data model based on Kimball methodology has been developed by a CUSO (credit union service organization) over the last three year and is currently in production at a number of credit unions across the United States. Below is a table that summarizes the two foundational features of the credit union Data Model and how it impacts credit union reporting & analytics:

Data Model Feature

Benefits to Credit Union Reporting & Analytics

Industry Standard Architecture

  • Interfaces with ALL R/A Software packages
  • Core system and Ancillary system agnostic
  • Significantly improves the accuracy and performance of ALL R/A Software packages
  • Report development is faster and simpler. For example, it supports “power users” that know how to use the R/A Software but don’t know SQL.
  • Creates a “Standard” data model platform that enables credit unions to (1) share R/A Software applications (ie. templates) and (2) create large industry data pools for improved predictive analytics
  • Creates a Single Source of Truth for all Reporting and Analytics at the credit union
  • Stores data at the transaction level which allows easy drilling to the detail for quick verification of totals and access to actionable information (e.g. -  marketing lists)

Automated Data Integration Processes

  • Data can be integrated from any database readable source and connected in the data model.
  • The use of Conformed Dimensional data (e. g. - Member, Employee, Branch) ensures that all reports will tie out regardless of the R/A Software used to create the report.
  • Error tracking is automated and can be logically traced back to the source.

 New Call-to-action

Topics: Reporting and Analytics, Big Data, Data Integration, Analytic Data Model