The credit union industry is on the cusp of significant challenges with the potential to disrupt the financial services landscape as we know it. Big Data and Analytics is driving a new breed of competitor into what has been a very traditional marketplace. The industry will need to envision and build out the “Next Big Idea” for credit unions to stay competitive and successfully navigate the next 10 years.
Analytics is top-of-mind for many credit union executives. Yet, as with all new technologies, there is a concern that it won’t work. The concern is well justified. There are many technologies that promise to make organizations more successful but fail to yield much for the company besides higher cost every month.
The failure of these technologies isn’t always the fault of the technology itself or the company providing the technology. Rather, it is the failure to properly integrate the new technology into the organization. In the case of analytics, there are several factors that will make or break the technology. Here are 5 factors to consider when implementing analytics:
- Integrate Analytics Across the Organization
In an industry where data is the most valuable asset, data integrity is essential. Building a successful credit union begins with data integrity.
OnApproach's Founder and CEO Paul Ablack discusses today's evolution of Big Data and how credit unions can benefit from this increasingly refined information to provide more specific products and services for enhanced value.
Topics: Reporting and Analytics, Analytics, Mobile Banking, Business Intelligence, Big Data, Credit Unions, Mobile Payments, Data warehouse, Data Integration, Marketing, Data Pool, Video, Mobile, Shared Applications, Big data/analytics, predictive analytics, Lending Clubs, Cooperation, Podcast
Credit unions seeking to improve their Big Data/Analytics capabilities face a classic choice: build (DIY) or buy?
The credit union industry is living in a wilderness of untamed data. Cultivating this “data wilderness” will require a vast array of analytics tools.
Credit unions are in the middle of an information revolution. As data becomes cheaper and easier to store, it is growing exponentially. This phenomenon is known as Big Data, and it is creating a thick wilderness for credit unions to clear. However, with the right mix of analytics tools and strategy this data growth can be leveraged. By transforming the wilderness into a field, credit unions can plant an abundance of new products and services. Similar to a farmer tilling his land, credit unions have the opportunity to leverage analytics tools to cultivate their data for a financial harvest.
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.
When starting your Big Data/Analytics journey, there are many project characteristics to consider. The first, before considering analytics, is how to integrate all of the data into a “single source of truth.” That is, how to design a data warehouse that will fulfill your needs and integrate all the necessary disparate data sources at your credit union.
A true enterprise data warehouse requires a significant amount of planning and a robust architecture to meet the needs of the end users. The architecture seen most fit for the complex nature of credit union data sources is the star schema developed by Ralph Kimball. While this might be one of the best solutions for credit unions, architecturally speaking, it presents a few challenges that hinder the desired end result, reporting and analytics.
Supreme Court Justice Potter Stewart famously said about obscenity, "I know it when I see it". This often seems to be the case with many credit union decision makers when asked to define Business Intelligence (BI) requirements.
Consider this common scenario: an innovative credit union executive champions the BI concept. The executive points out all the flaws in organization’s current reporting and analytics. Then, showing examples of how BI is revolutionizing performance in other industries, secures budget dollars for a BI initiative.