When we talk to credit unions about their plans for big data/analytics we try to explain that the word “data warehouse” is a loosely defined term and that a true enterprise data warehouse requires a significant amount of planning and a robust architecture to meet the needs of the users. The architecture we have chosen for our OnApproach M360™ data model is the star schema developed by Ralph Kimball.
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?
According to the whitepaper “Amazon vs Borders: A Lesson for Credit Unions”, written by Founder and CEO of OnApproach, Paul Ablack, Amazon has thrived on fostering loyal and intimate relationships with its customers. These relationships are the cornerstone of Amazon’s success. With utilization of data analytics, Amazon has information readily available to pursue and enrich these relationships.
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.
“…wallets may generate substantial data that will lead to better member insights and possibly also profits.” – Kirk Drake, founder and CEO of Ongoing Operations, LLC
In a highly competitive financial services industry, credit unions are turning to mobile to survive and meet the ever changing demand of members. Credit union members are flocking to their mobile devices to do everything from check balances to deposit checks because they find it more convenient than the “old way” of banking. As credit unions realize their members demand to go mobile, they begin to shift their focus from expanding physically to expanding virtually. Investments in mobile applications will likely dominate credit unions’ budgets in the coming years as they attempt to stay competitive. One of these investments will likely be enhancing mobile payments. Mobile payments will not only make life more convenient for credit union members but it will change the way credit unions interact with their members as they use Big Data and Analytics to gain better member insights.
Congratulations! You’ve been tasked by your credit union’s leadership team to plan out the first steps of a Business Intelligence (BI) initiative.
The good news is your organization is convinced that performance improvement will be driven by better, faster information. Now for the hard part: where to begin?
“True intuitive expertise is learned from prolonged experience with good feedback on mistakes.” –Daniel Kahneman, Nobel Prize in Economic Sciences.
In my most recent article, 7 Challenges to Consider when Building a Data Warehouse, I wrote about a few major complexities associated with building a data warehouse that are often overlooked by the ambitious credit union do-it-yourselfer. The do-it-yourselfer has a mentality that hiring someone to do the project will increase the cost significantly. In some cases this is true, but in the case of building a data warehouse they are at a significant disadvantage to an experienced business intelligence (BI) firm. The challenges of building a data warehouse can be extremely overwhelming. Fortunately, there are professional BI firms dedicated to building data warehouses which have experience solving these complexities.
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.
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: