Many credit unions are under the impression they must have perfect data before beginning a Business Analytics (BA) initiative. While high quality data is the foundation for BA, one of the best ways to determine the quality of the credit union’s data is by jumping right into an analytics project. Business problems are the basis for beginning any new project. Viewing credit union data from the lens of BA software allows credit unions to assess their data quality and improve their underlying systems. In order to grasp the concept of analytics, and how it can benefit business decisions, starting an analytics project (with a top down view) is the first step.
In my previous blog, 3 Steps to Build Business Analytics in Mortgage Lending, I explained the steps a credit union should take to implement Business Analytics (BA). A great entry point for credit unions to begin establishing BA is mortgage lending. Once BA has been implemented, opportunities for deepening the cooperative culture of a credit union arise. Cooperation is the DNA of the Credit Union Movement. BA gives credit unions the ability to establish clear communication to strengthen their cooperative-oriented culture. Data-driven decisions and effective communication delivered by BA (embedded in business processes) will reinforce the cooperative strategy of credit unions.
Credit unions, as member-owned financial cooperatives, track and report regularly the number of members they have. Membership is reported to the National Credit Union Administration (NCUA), the credit union board, credit union management and in the annual report to members, among others.
Often the number of members reported varies depending on who at the credit union does the reporting. This can lead to confusion and concern. There may be someone in finance, someone in marketing and someone in administration all reporting on how many members the credit union has, and all reporting a different number of members!
Obviously the total member count needs to be consistent and accurate. However, there are several challenges that must be overcome:
According to a recent study by SunGard Consulting Services, a majority of organizations surveyed were using outdated reporting and analytics techniques. The study concluded that while advanced data handling and reporting processes are widely available, they were not being acquired and implemented.
This phenomenon has been a big problem in the credit union industry for years. Backward-looking, spreadsheet-based system (SBS) reporting and analytics continues to be the norm in many institutions.
Most credit union leaders are familiar with the concept of Big Data and business intelligence, but they may fail to fully understand the significance they have on their credit union and its future. Big Data can provide credit unions with the ability to make better decisions that positively affect member relationships and ultimately their top and bottom lines. An essential piece of any business intelligence (BI) strategy is a data warehouse. Data warehouses provide credit unions with the ability to integrate data from many disparate sources to create a single source of truth. From this single source of truth, credit unions are able to generate reporting and analytics tools that leverage data to make the most informed business decisions possible. A data warehouse project seems simple: find all disparate sources of data and consolidate them into a single source of truth. In all actuality, building a data warehouse is a complex process that could end in disaster if handled improperly. There are several obstacles in the process that need to be overcome in order to achieve success. These obstacles typically take an extensive amount of time to conquer, especially the first time they’re encountered. Credit union leaders should consider the following data warehouse challenges before building a data warehouse:
“Midsize banks are falling behind in meeting the needs of the fastest growing demographic groups, millennials and minorities, especially in online, mobile and problem resolution,” –Jim Miller, Director of Banking Services at J.D. Power
Mobile banking is top-of-mind for most credit unions in 2014 as they begin to realize the power it has on member interaction and satisfaction. Mobile Banking apps give members the ability to conduct several transactions that in certain ways replace the need for brick-and-mortar branch locations. Most recent advancements provide mobile app enabled credit unions with the ability to promote other banking products and services, attract millennials with perks and incentives, allow members to make mobile payments (also known as mobile Peer-to-Peer (P2P) payments), and even deposit checks through their mobile device. The benefits of mobile banking are undeniable but many midsize banks are not making it a top priority because they don’t have a way of justifying the investment. Measuring return on investment (ROI) for mobile banking apps, or an advancements to apps, can be extremely difficult as there is no real cash flow or revenue numbers associated. With big data reporting and analytics; however, credit unions can observe the effectiveness of an app on its members with ease and as a result be able to measure its ROI.
“The factor perhaps most determinant of success with an analytical strategy is the degree of engagement from the executive ranks.”
This quote from the recent Aberdeen Group report Executive’s Guide to Effective Analytics (http://bit.ly/1e7nZFH) sums up one of the key drivers to an effective credit union analytics effort.
Organizational change sometimes can grow from a “bottom up” effort. Indeed, analytics champions frequently arise from functional areas within the enterprise. However, unless the credit union C-Suite understands, believes in, and pushes the analytics agenda, such initiatives have little chance of succeeding.
Credit Union leaders are painfully aware that new ideas in technology often explode on the scene in excited waves of hyperbole (hence, “hype”). Big Data certainly fell into this pattern. Not only were there unending media reports about the subject (“buzz”), but most credit union decision makers were hard-pressed to define precisely what was meant by the term Big Data and how it would help their organizations.
In the December 2013 issue of the Harvard Business Review, reporting and analytics guru Thomas Davenport writes about how far the innovators in the analytics field have advanced.
Davenport divides the evolution of analytics into three eras.
Analytics 1.0 – This first era was marked by the first widespread use of data to support fact-based decision making. The idea of data warehouses became more main stream as enterprises began to organize their internal data specifically for the purpose of better analyzing historical data.
Many credit unions are launching business intelligence initiatives with high hopes for improving performance. However there are two big challenges to overcome.
First, important data is usually bottled up in unrelated “silos”. It is essential to build a universal data model to connect all the various data sources into a cohesive repository. This becomes the “single source of truth” on which all subsequent analytical efforts depend.
Second, an effective analytics capability needs to be developed. This requires careful consideration. A recent Forbes magazine article provides some great insight to those taking on the second challenge.