Removing “human middleware” in analytics
Affinity Federal Credit Union — in the top 2% of credit unions by asset size — is continually looking for opportunities to better leverage their data assets to improve service to their more than 185,000 members.
Historically, they had been relying on legacy analytics infrastructure tools like ModelMax or Dundas BI. These solutions required too much manual effort — effectively requiring a layer of “human middleware” to operationalize data. It took too much time and effort to make informed, data-driven decisions. AFCU had been partnered with a Credit Union Service Organization (CUSO) that provided analytics-as-a-service. This approach was both slow and uncontrollable, often getting in the way of decision-making and making it difficult to grow internal understanding of data.
AFCU realized they couldn’t remain reliant on an outsourced analytics team and legacy processes to unearth insights from their data. It was time to transition to a modern, self-service BI program to allow faster data-backed decision-making at scale.
AtScale’s Semantic Layer underwrites self-service BI
AFCU saw the importance of a semantic layer to establish analytics governance policies while establishing the level of flexibility needed to scale self-service BI. A semantic layer would allow for unified data access across all stakeholders in their business, technical and otherwise.
AtScale was chosen as an independent semantic layer that enabled open connection to different BI platforms and different cloud services. This approach expanded access to data for both seasoned data scientists and to non-technical business users. By expanding data science programs, AFCU was able to incorporate advanced prescriptive and predictive analytics to their business, powering growth and smart decision-making.
Building out the right semantic layer strategy was important to enabling outcome-based decision-making and gaining leverage from a treasure trove of customer and financial data. The AFCU team was able to leverage a flexible modeling environment to build views of raw data that addressed a wider range of business needs. The ability to quickly create new views of data, without relying on complex ETL, enabled the team to more rapidly iterate analytics.
AFCU was able to harness the power of dimensional modeling with AtScale, standardizing dimensions, hierarchies, and attributes to present a unified set of data regardless of the analytics toolset being used to access. By shielding users from the complexity of data wrangling and engineering, this organization has given their internal teams a leg up and made self-service BI a reality.
More for your money: Self-service BI in action
By applying AtScale’s semantic layer, this organization gained the ability to manage data models, calculations, dimension definitions, access controls, and governance in a single location — all integrated with business tools like Excel and Tableau. This ultimately allows the team to understand their customers better and provide better services and products based on meaningful data.
Improved business outcomes are a natural consequence of applying a semantic layer, and the organization’s citizen data scientists benefit from being able to do their jobs more effectively with self-service BI.
“AtScale plays a very heavy role in our overall data program,” said John A., VP of Data and Analytics at the credit union. “We’re just now starting to deploy Tableau across most of our user base [thanks to AtScale], and we’re seeing great value coming from that.”
With a semantic layer, this organization is able to use business tools the team is already comfortable with to access deeper and more relevant insights, all while retaining autonomy and building up knowledge capital for the organization. This has a positive ripple effect throughout the entire organization, right down to their most important constituents, their members.