If you are a credit union still waiting on the data analytics sidelines, you’re already too late. Data analytics is not a fad – it is a major opportunity for credit unions to gain deeper insights and improve decision making to create a strong and competitive future. However, it is not always clear where credit unions should begin. To help answer these questions, John Best recently spoke with Clay Yearsley, SVP of Data Analytics at Texas Trust Credit Union about getting started on the analytics journey, the skills needed, and the value of data in the podcast, “Catching a Unicorn – Discussing Data Analytics with Clay Yearsley”.
The message has been ringing load and clear throughout the credit union industry for years: make better use of data and analytics or lose “member share” to more progressive CU peers or (horrors!) banks and fintech startups.
Despite the warning cries, the proportion of credit unions embracing this trend is (horrifyingly!) low.
In my previous blog, “What is a Data Lake? Part 1”, I discussed how to define a data lake, and how it differs from a data warehouse. To briefly recap, a data lake is a massive data repository for raw data in its native format. To better understand the idea, let’s dive a bit deeper and get to know the advantages and disadvantages surrounding data lakes.
To start, there are a number of advantages data lakes serve for financial institutions:
Financial institutions all over are working to build effective data strategies and improve decision-making. With so many new technologies and innovations out there, it can get very difficult to keep up with the industry and even keep straight the buzzwords we hear throughout the day. In this piece, let’s dive in to better understand what makes a data lake.
When my brother and I were kids, we liked to build things. We built forts, ramps and anything else we could fashion out of scrap wood. Typically, our projects served a specific function, to ward off rival “street gangs” of preteens from another block, to propel our dirt bikes into the air, or whatever else we decided could or would result in our becoming temporarily disabled. We thought we were good builders, but the greatest evidence that we were not, is that our work does not exist in any form today.
In the fourth Data Analytics Series BIGcast, From Questions to Answers: Becoming a Data-Driven Organization, John Best speaks with Brewster Knowlton of The Knowlton Group about data-driven decisions, data warehousing and successful data integrations.
The Six Characteristics of a Data-Driven Organization
According to Brewster, there are six characteristics to determine whether your organization is really data-driven:
Let’s face it: we live in a world where a strong data and analytics competency is becoming a “must have” for successful companies. Despite the growing significance of analytics, the majority of banks and credit unions are not “data-driven” organizations.
We’ve uncovered a number of common reasons why investment in data and analytics has been pushed off or outright rejected. Despite these challenges, most of the common reasons against data and analytics are driven by inaccuracies or misinformation.
In this post, we will address the common pushbacks against data and analytics projects and how to overcome those challenges.
Relying solely on a data warehouse, without an enterprise data management strategy, is a recipe for disaster.
Credit unions are beginning to invest heavily in big data and analytics. When deciding how to allocate funds in this space, leaders are awash with buzzwords and conflicting advice. One of the most common terms used within big data and analytics is: data warehouse. Deciding whether to build or buy a data warehouse is an important strategic decision for credit unions. Unfortunately, many decision-makers get lost in discussions about storage capacity, data processing, data visualization, etc. All of these concepts are important. However, data warehousing is not the solution. It is a powerful tool in an enterprise data management (EDM) strategy.
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.