At Evolytics we think of analytics in terms of the data lifecycle.

The data lifecycle is a framework that considers organizational data strategy, collection, reporting, interpretation, and usage.  In short:

  • Align: measurement planning, KPI development, data auditing
  • Acquire: data collection and data collection tools’ implementation
  • Aggregate: data engineering, data blending and summarization
  • Access: reporting, data visualization
  • Analyze: data storytelling, optimization recommendations, predictive analytics
  • Act: A/B testing, personalization

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.


Lifecycle phase goal: All organizational stakeholders contribute to – and come to consensus on – key objectives and the associated measurement plan.  There is a clear understanding of how data will be used for decision-making, including key business questions, supporting KPIs and contextual metrics, and potential optimizations or program change use cases.


Lifecycle phase goal: All the necessary data, as defined by the measurement plan, are collected accurately and efficiently.  With the implemented data collection systems, stakeholders can rely on the data’s availability and accuracy.  Data collection methodologies are based on the business’s data and reporting needs.


Lifecycle phase goal: Collected data is made readily available for reporting.  Data are processed, cleansed, combined, linked, and centralized to create a single source of truth.  Data can be extracted and queried efficiently. Data architecture is documented so all users know how and where to find the data they need.


Lifecycle phase goal: Stakeholders across the organization can access the data they need for decision-making at an appropriate frequency.  Reporting focuses on agreed-upon measures, KPIs, and business questions. Stakeholders understand how to use and interpret the reporting tools to uncover the insights they need.  Time is spent interpreting results rather than pulling reports.


Lifecycle phase goal: Data are not taken at face value; insights, implications, and context are added to tell a story.  Data stories are not only past-looking but also forward-thinking (not only what happened, but what will happen and what we should do).  Statistics are applied when needed. Benchmarking is in place to provide context to results. Strategic relevance guides analysis prioritization.


Lifecycle phase goal: There is a culture of learning and optimization.  Data are used to make operational, marketing, or product changes.  A formal testing process ensures that changes are rolled out strategically and results are measurable.  Learnings build upon one another and experiences are increasingly personalized accordingly.

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