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.
As an industry in the middle of a massive shift, credit unions must steer in the best direction to enter a prosperous future.
The Financial services industry has traditionally been an industry where change happens gradually at the direction of a few large institutions. Since the introduction of the internet in the 1990’s, finance has been in a state of exponential change. The digitalization of money is causing an even more powerful catalyst of change: the proliferation of data at a pace most credit unions cannot fathom. Consequently, credit unions must form a data-driven vision to act as a strong rudder to navigate through the data storm.
In an industry where data is the most valuable asset, data integrity is essential. Building a successful credit union begins with data integrity.
By utilizing analytics-enabled alerts, credit unions will be able to make strategic decisions on a daily basis.
The amount of activities occurring at a credit union every day can be intimidating when building data-driven solutions. With complex business processes and tasks requiring manual intervention, credit unions are hesitant in utilizing analytics software to improve decision-making. In order to effectively leverage analytics insights, they must implement alerts tools. Alerts are a common feature in consumer technology. Notifications on social media websites, texting, emails and online news are just a few of the examples of alerts that are controlling our everyday actions. Utilizing analytics-enabled alerts in the busin
ess setting, executive management will be able to build an alerts strategy to delegate tasks throughout the credit union.
Step 1) Business Strategy
It all begins with the business strategy. In order for an alerts solution to be effective, credit unions must have an analytics (data-driven) business strategy. The executive team must develop and solidify a clear analytics strategy and communicate it to the entire organization. Establishing certain KPIs (Key Performance Indicators) and establishing specific goals for each one will be the foundation for an effective alerts strategy.
A frequently asked question in the credit union movement is, “what is the data most credit unions have that they should be gathering, monitoring and reporting?”
In short, all of your data can be a critical component in gaining valuable insight into your members and optimizing business practices. The big question is . . . How accessible, comprehensive and useful is the data?
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:
Credit unions rely on quality mortgage loans to establish a stable asset base for their operations. Quality assets allow them to fulfill their purpose of serving members with innovative and personalized products and services. High quality mortgage origination and investor relations with 3rd party mortgage investors such as Freddie Mac allow credit unions to leverage mortgage loans to drive their business. Originating and selling sound mortgage loans enables credit unions to secure cash flow in exchange for their value-producing mortgage loan processes. To ensure this process is carried out effectively it is vital that credit unions implement high quality business analytics (BA) throughout the mortgage loan life cycle (MLLC).
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:
“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.