Credit unions aiming to build Big Data & Analytics capabilities have a lot of decisions to make. One of the most fundamental decisions is how much source data to capture. The two dimensions of “how much” are depth and breadth.
As digital strategies continue to proliferate throughout the credit union industry, the contact center has become essential.
Calling a company about an issue can be a miserable experience. Being transferred to four departments and having to explain an issue, not to mention basic account information, four different times will frustrate the most patient consumers. These experiences have given the call center a bad image in the minds of consumers. Stereotypes of call center agents who do not speak fluent English failing to understand a problem have been burned into the American culture. However, the necessity for remotely assisting consumers has never been greater. Redesigning the call center into a contact center will enable credit unions to give their members excellent service. It will also empower credit unions to continue learning about their members through every interaction.
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?
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.