Each of the phases in the lifecycle is dependent on the success of the previous phase. As such, successful execution of each phase in the lifecycle is necessary in order to achieve effective data-driven decision-making.
Central to all of it — and necessary for successful completion of each phase — is training. Not only must the appropriate processes and tools be in place; stakeholders throughout the organization must in turn be trained on running the processes, managing the tools, and interpreting the outputs.
The data lifecycle is a framework that organizations can apply in many ways. It provides a framework for assessment of organizational data usage. It provides a roadmap for developing an analytics center of excellence. And it informs analytics staffing and team development.
The data lifecycle manifests differently at every organization. Some organizations have not yet broached every phase. Others have opportunity to deepen their maturity on particular phases.
In order to best understand how the data lifecycle may be applied to your organization, you need to envision what “great” looks like at every phase. If your overall goal is to develop into a more data-driven organization, these more focused, lifecycle phase-specific goals, are a helpful in prioritizing your analytics needs.