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.
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.