Data Lifecycle

A framework for building an analytics organization.

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

The data lifecycle is a framework that considers how data initiatives work together from a people, processes and tools standpoint. Data lifecycle phases include:

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

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 within every organization. Some organizations have not yet engaged in every phase. Others have an opportunity to deepen their maturity within 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 helpful in prioritizing your analytics needs.

Learn more about our consulting services that support the Data Lifecycle framework >


Framework phases that consider how data initiatives work together from a people, process and tools standpoint.


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, is 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 martech decisions and reporting needs.


Lifecycle phase goal: Collected data is made readily available for reporting.  Data is 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 is 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 is 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.