360 Degrees: A Big Picture View of Data Analytics Strategy

 

Good business relies on smart strategy, across industries and from top to bottom in organizations. Looking to increase employee engagement? Start with strategy. Working toward growth goals? Start with strategy. Trying to figure out how to use the ever-increasing amount of data available about credit unions and credit union members? Right again. Strategy. Without a solid data analytics strategy guiding the way, even the most compelling data can amount to nothing more than a missed opportunity.

An understanding of the following areas can help credit union leaders develop a 360-degree strategy for effectively incorporating data analytics into their businesses.

 defining strategy and gaining buy-in

  1. Defining Strategy and Gaining Buy-In. Setting a specific data analytics strategy may seem daunting at the start, but it doesn’t need to be. Why? Because it aligns with and flows from a credit union’s overall business strategy. And, just like a broader strategic plan or initiative, it requires support and buy-in throughout the organization. 
  2. Identifying What You Want from the Data. A natural next step that follows strategy setting is to ask, “How are we going to get there?” This step asks credit union leaders to think backward about what answers would be helpful to have as a way to form the right questions.
  3. Capturing and Modeling the Data. Once strategy is set and questions are formed, available data can be captured, mined and organized. And, if it hasn’t happened yet, it’s critical at this point in the process that IT and business strategy be fully aligned to ensure the right tools and technologies are being deployed. Another consideration is to ensure that collected data is “democratized,” or accessible to the average end user who needs to see and interpret it.
  4. Identifying Insights and Opportunities. The insights and opportunities data delivers are nothing without the human touch of evaluation and analysis. Data “translators” can tie the data back to a credit union’s specific strategy and business goals and communicate opportunities organization-wide.
  5. Workflow Integration and Change Management. Depending on what insights are gleaned and what opportunities are uncovered, it isn’t uncommon for things to get a little sticky at this point. If systems need to shift or new capabilities need to be developed, having that earlier buy-in becomes invaluable to fully leverage what the data reveals.
  6. Continuous Improvement. The trick to continuous improvement is to simultaneously be looking forward–keeping goals in sight–while thoughtfully reflecting at what has worked well and what might need to be tweaked.

It’s important to remember that these six steps are not a checklist but an ongoing, evolving process. Success can mean embracing an increased agile culture. It’s worth the effort. The expanding world of data and analytics is a place where organizations reap enormous tangible benefits quickly.

Read our whitepaper, Predictive Analytics for Credit Unions, to learn more about the 360-degree view of data analytics strategy as well as the broader offerings and applications of predictive analytics for the credit union industry.