Like building a home, designing a credit union’s ideal analytics strategy can easily exceed budget and time estimates if the people in charge do not have an adequate blueprint. Just as homebuilders would never begin buying materials, pouring concrete, or picking paint colors without a well-established plan, credit unions shouldn’t integrate analytics people, processes, or technology without a strategic roadmap.
Marching methodically through three distinct phases, credit unions can greatly improve the chances of standing up the dream home equivalent of an analytics strategy.
Phase 1: Designing The Blueprint
An effective analytics blueprint lays out milestones in each of the six core elements. As strategists think through the nuances of each element, it’s helpful to remember there’s no finality to an analytics blueprint. In fact, the best strategies are made up of plans nimble enough to evolve with emerging market realities and changing goals of the business.
People: Determining roles, responsibilities, and an operating model for data and analytics.
Process: Building out the procedures that enable data and analytics execution.
Technology: Identifying the platforms and tools that allow data and analytics to function.
Governance: Establishing processes that support data analytics.
Analytics: Practicing the disciplines of reporting, exploratory research, and predictive analytics.
Architecture: Creating processes capable of scaling data and supporting future design and build.
The purpose of the analytics blueprint is to create a structure that ultimately accomplishes three things: 1) analyzes the current state of data and analytics; 2) formulates a vision of target end-state; and 3) establishes a ‘north star’ for full analytics maturity.
Phase 2: Building The Foundation & Framing Business Problems
Once the blueprint has been completed and socialized, the credit union is ready to begin pouring the foundation of the analytics dream home. Doing so starts with six questions leadership should aim to answer in a cross-functional way, tackling the solutions with as diverse a group as possible:
Which of our business problems can data analytics help solve?
What product or service capabilities are we trying to elevate with the help of data?
What insights are we trying to understand?
How can we use data to learn about our membership?
What kind of data would help, and where can we source it from?
How will we implement our processes across teams?
To be sure, the above questions are merely a starting point. However, they address some of the initial elements that will eventually become the foundation to strengthen and sustain a credit union-wide culture of analytics.
Phase 3: Optimizing Spaces & Planning or Expansion
The third phase of building a credit union’s analytics strategy is focused on achieving speed to value. Not only does this help team members across the organization see and feel the impact of data analytics, it generates unrivaled momentum. When credit unions start small, earn quick wins, and socialize those wins with intention, they generate excitement and enthusiasm for data and its ability to further the credit union’s highest purpose.
Phase two revealed the business problems and opportunities data was most capable of solving. Phase three makes space for credit union teams to experiment with use cases designed to ferret out that potential. If, for instance, a credit union determined in phase two that indirect memberships represented a sizable growth opportunity, phase three may involve the creation of a predictive model that identifies those indirect members most likely to covert to active members. The model need not be perfect; in fact, it’s likely it will not be. Testing, experimenting, even failing, leads to learning, and the appreciation for continuous learning is at the heart of every effective analytics culture.
Breaking ground on your analytics dream home is not something that can be done overnight. It may also require expertise and assistance from outside the walls of the credit union. Just as you would select a general contractor with a shared vision for a new house, you want an analytics partner that appreciates and understands the movement. That is how a credit union ensures its analytics strategy is built with, and maintains, the kind of people-centricity members expect.
Aaron Grossman is a consulting sales specialist for CUNA Mutual Group’s AdvantEdge Digital where he guides credit unions through the data transformation journey. He can be reached at firstname.lastname@example.org.