Data Analytics - If You Build It, They Will Come - Not So Joe!


In the movie Field of Dreams, the main character, a farmer, is instructed to build a baseball field in the middle of his cornfield so Chicago White Sox baseball star, "shoeless" Joe Jackson can play on it. The famous quote from the movie is, "If you build it, they will come." While this might have worked in the movie, it doesn't work in enterprise data and analytics workplace adoption.

Workplace adoption is quite possibly the ultimate destination for data transformation. When an organization has the vision, strategy, and resources to achieve its data transformation goals, then, by default of transformation, it creates new capabilities and, ideally, a new culture. However, workplace adoption is not an organic development. Most organizations do build incredible data and analytic tools, but unfortunately, they are not organically adopted.

Successful organizations practice diligently to propel new capabilities and culture to continue the trajectory that the data transformation plan ignited. The following is a basic outline for a five-phase, data workplace adoption framework.


Phase 1: Determine

In the first phase, the credit union is reviewing the credit union's current data condition to determine the current state of data transformation.


Phase 2: Design

This phase and the remaining three focus on building and strengthening the data-driven operational capability.


Phase 3: Build

Once the member-centric use case has been identified, the solution is built using human-centered design and design-thinking process as defined in the creative data confidence capability.


Phase 4: Deploy

This phase is about how quickly, agilely, and iteratively this solution can be designed and introduced to the member.


Phase 5: Measure

After the solutions have been deployed, the measurement of success is an essential post-deployment phase.

While this is a high-level framework, it does provide a pathway for the organization to achieve success with their data and analytics effort.



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