Gartner’s 100 Data Analytics Predictions for 2025 offered a roadmap for data-driven transformation. Data-driven transformation not start by renting or buying a tool. Let me say it again: it is. not start by buying any resource. This Is start by understanding the value of data and selling that value within the organization, then consider the value in order to move on to an assessment, then to training.
Data is one of the strongest assets a business can have and credit unions are in a prime position to have a plethora of it. For a credit union to be successful with data, it must recognize its gaps in organizational data knowledge. It’s hard to move forward when no one speaks the same language. Understanding the basic competency in data literacy will only enhance a credit union’s success in launching a data effort.
The five elements necessary for success
To leverage and leverage their data to improve the lives of their members, credit unions need to have the following:
1. A simple 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 roadmap for workplace adoption.
To get started, ask these five questions of your credit union leaders:
1. Is the business data vision relevant? Take a moment to review the organization’s data vision. What was the identified business problem that the data would solve? Does this seem relevant to you? How should it fit, or should it just be scraped off completely?
2. What friction do our members experience when doing business with us? Your members are interacting with your organization in ways they may not have had in the past. The iterative changes your organization can make to reduce friction will prove beneficial 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 clarify the positive aspects of your organization’s culture and what you should continue to encourage, foster, and nurture.
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 seems like a low priority to you, please take a moment to adjust your thinking. Data maturity is the foundation, blueprint, and architectural renderings of your dream data house. Most home building experts will never go to open ground and dig in hopes of building a house. Why would you do that with your data?
5. What does your workplace adoption roadmap look like? What are the time horizons? Do they include strategy, culture, data maturity, and member-centric use case development workflows?
What to pay attention to
There are a number of significant issues that generally limit credit unions’ efforts to make data a more valuable and usable asset in their decision-making processes. Due to these challenges, it is not surprising to find that early results are often not enough to overcome the inherent resistance to change. It then becomes easy for people to focus elsewhere before cultural adoption can occur.
First, it’s helpful to understand that the transition to using data as a key part of decision-making is a multi-phased approach that will extend beyond the strategy development phase. Attention to execution and cultural adoption is key to transforming your data strategy into long-term results that consistently increase member value and give you a competitive edge.
The three critical phases of any data project:
Phase 1: Data Strategy. Set a clear direction for your strategy and determine how you can tie data to member use cases to drive positive growth. It’s critical that teams understand how to leverage data to improve their decision-making processes and drive continuous improvement.
Phase 2: Initiate new processes. New processes for collecting, organizing, maintaining, and using data are required when executing new data strategies. Formal projects usually become a way for the organization to implement strategy. Unfortunately, project delays and unforeseen problems weaken buy-in and fuel resistance to organizational change. It is important to have a solid framework in place for project management to minimize delays and proactively identify and overcome roadblocks.
Phase 3: Cultural adoption. Long-term integration into the culture and daily practices is necessary to obtain recurring benefits in the future. A tremendous amount of time and money can be invested, and without integration into the day-to-day structure of the organization, long-term success will not materialize.
The main challenge is to create and maintain enthusiasm and buy-in throughout the three phases and to avoid the disappointment that usually occurs at critical points in the journey.
Three major issues that prevent long-term success:
1. Impact on culture. The bigger problem is not fully appreciating the huge impact on individuals and organizational culture when data becomes a driving factor in decision-making. The increase in the importance of data is changing the way people do their jobs and has consequences. Failure to address this issue leaves passive and active resistance embedded throughout the organization.
2. Projects that are too narrow in scope. Projects that pilot new data processes, structures, and teams often don’t adequately include the leadership and team development actions needed to cement long-term alignment and buy-in. Post-launch, a drop in focus and attention may occur because a leadership and transition structure has not been fully formed.
3. Inexperience in data mining. At the organizational level, teams may lack experience working together to align when it comes to leveraging data and linking it to growth goals. This makes it difficult for them to go beyond the initial use cases and confidently identify new problems and applicable data solutions.
Considerations for long-term success
Targeted learning: Build the skills of 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 explain how the use of 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 setting plans to execute your data strategy, make sure the plan is designed to overcome both capacity and motivation barriers. Many projects fail to adequately address motivational barriers to change in organizations, which form a critical foundation for long-term success. Work with the intent to optimize organizational alignment with initiated data projects to create a support structure for the collection, organization, maintenance and use of data.
Best Transition Leadership: Establish a leadership development program for the people and teams who will be responsible for maintaining and building data programs in the future. Create clear responsibilities and accountability for people, and provide support structures and resources to drive long-term success.
Anne Legg is the founder of THRIVE, a data literacy and strategy consulting firm based in San Diego, California.
Jeff and Holly Karpinske are the founders of LOTUS ADVISORS, a business performance improvement consulting firm located in Mesa, Arizona.