Originally published October 14, 2022, in CU Times
Gartner’s 100 data analytics predictions for 2025 offered up a roadmap for data-driven transformation. Data-driven transformation does not start with hiring or purchasing a tool. Let me repeat that: It does not begin with purchasing any resource at all. It does start with understanding the value of data and selling that value inside the organization, then envisioning the value in order to progress to an assessment and then education.
Data is one of the most robust assets any enterprise can have and credit unions are in a fortunate position to have a plethora of it. For a credit union to succeed with data, it must acknowledge its organizational data knowledge gaps. It is difficult to move forward when no one is speaking the same language. Understanding core competency in data knowledge will only strengthen a credit union’s success in launching a data effort.
The Five Elements Needed for Success
To harness and leverage their data to improve their members’ lives, credit unions need to have the following:
1. A straightforward data vision and strategy;
2. A member-centric use case;
3. Data maturity (with rock star data governance);
4. A data-centric culture; and
5. A road map for workplace adoption.
To get started, ask your credit union’s leaders these five questions:
1. Is the enterprise data vision relevant? Take a moment to review the organization’s data vision. What was the business problem identified that data would solve? Does it seem relevant? How should it be adjusted, or should it just be completely scraped?
2. What friction do our members experience doing business with us? Your members are engaging with your organization in ways they may not have in the past. The iterative changes your organization can make to reduce friction will prove beneficial both in the short and long term.
3. What is the current state of our data culture? Taking a moment to identify the good, bad and ugly of your “new normal” will help bring clarity to the positive aspects of your organization’s culture and what you should continue to encourage, foster and feed.
4. What is the current state of our organizational data maturity? Take a moment to review the current state of your organization’s data maturity. What is the current state of your organization’s data? Do you have a formal data governance program? If data maturity feels like a low priority, please take a moment to adjust your thinking. Data maturity is the foundation, the blueprint and the architectural renderings to your dream data home. Most home building experts will never head to an open piece of land and dig, hoping to create a home. Why would you do that with your data?
5. What does your workplace adoption road map look like? What are the time horizons? Do they include strategy, culture, data maturity and member-centric use case development workflows?
What to Watch Out For
There are a number of significant issues that commonly limit credit unions’ efforts to make data a more valuable, usable asset in their decision-making processes. Because of these challenges, it is not surprising to see that initial results are often not enough to overcome built-in resistance to change. It then becomes easy for people to shift their focus elsewhere before cultural adoption can occur.
First, it is helpful to understand that making the transition to using data as a key element of decision-making is a multiple phase approach that will extend beyond the strategy development phase. Focused attention on execution and cultural adoption is the key to turning your data strategy into long-term results that consistently increase member value and give you a competitive advantage.
The three critical phases of any data project:
Phase 1: Data Strategy. Set a clear direction for your strategy and determine how you can link data to member use cases to drive positive growth. It is critical that teams gain an understanding of how to leverage data to their improve decision-making processes and drive continuous improvement.
Phase 2: Launch New Processes. New processes for gathering, organizing, maintaining and using data are required when executing new data strategies. Formal projects usually become a vehicle for the organization to implement the strategy. Unfortunately, project delays and unanticipated issues weaken buy-in and fuel resistance against organizational change. It is important to have a solid framework in place for project leadership to minimize delays, and proactively identify and overcome roadblocks.
Phase 3: Cultural Adoption. Long-term integration into culture and day-to-day practices is required to achieve recurring benefits into the future. An enormous amount of time and dollars can be invested, and without integration into the organization’s day-to-day structure, long-term success won’t materialize.
The primary challenge is to build and maintain enthusiasm and buy-in throughout all three phases and avoid the let-down that commonly occurs at critical points in the journey.
Three major issues that prevent long-term success:
1. Impact on culture. The most significant issue is not fully appreciating the enormous impact on individuals and the culture of the organization when data becomes a driving factor in decision-making. Elevating the importance of data changes the way people do their jobs and comes with consequences. Not addressing this leaves embedded passive and active resistance throughout the organization.
2. Projects that are too narrow in scope. Projects that drive new data processes, structures and teams often do not adequately include the leadership and team development actions necessary to cement long-term alignment and buy-in. After the launch, a let-down in focus and attention can occur because a transitionary leadership and structure has not been fully formed.
3. Inexperience leveraging data. At an organizational level, teams may lack experience working together to align themselves when it comes to leveraging data and linking it to growth objectives. This makes it difficult for them to go beyond the initial use cases, and confidently identify new issues and applicable data solutions.
Considerations for Achieving Long-term Success
Focused Learning: Build up skills in the people and teams responsible for applying data in decision-making and determining how to link data to actions that drive business growth. Be able to articulate how using data supports continuous improvement efforts. Make it a priority to improve group decision-making processes with a better understanding of how group dynamics influence outcomes to minimize misaligned actions.
Improved Project Delivery: When establishing projects to execute your data strategy, make sure the plan is designed to overcome both the capability and motivational barriers. Many projects don’t adequately address the motivational barriers to change in organizations, which is a critical foundation for long-term success. Work with intention to optimize organizational alignment with the data projects initiated to create a support structure for gathering, organizing, maintaining and using data.
Better Transitionary Leadership: Establish a leadership development program for people and teams that will be responsible for maintaining and building data programs into the future. Create clear responsibilities and accountability for people, and provide support structures and resources to boost long-term success.
Jeff and Holly Karpinske are the founders of LOTUS ADVISORS, a business performance improvement advisory firm located in Mesa, Ariz.
Curious about the current state of data at your
Take this quick
to find out!
Please click the link and scroll to the bottom of the page.
Fill knowledge gaps to
We believe that data activation doesn't have to feel overwhelming or expensive to be impactful. After helping over 600 credit union leaders launch their data journeys, we have identified several consistent knowledge gaps. We have worked hard to fill these gaps with a variety of educational artifacts:
Here is how you can fill these gaps with a variety of educational artifacts:
Find out how successful your credit union will be with its data.