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The high-level process provides the controlling lifecycle for the Power BI End-to-End Delivery Framework.
It connects the activities required to take an analytics requirement from the initial business problem through requirements definition, data design and preparation, semantic modelling, report development, validation, deployment and operational support.
The process is intentionally broader than report development alone. It provides a structured delivery path in which business analysis, data analysis, technical implementation, validation and governance activities remain connected throughout the lifecycle.

Complete high-level Power BI analytics delivery lifecycle. © 2026 Carl Patten. Portfolio copy.
The editable process model and source files are retained privately.
Each high-level activity represents a defined stage within the wider delivery lifecycle.
Many of these stages are implemented as detailed subprocesses containing:
The high-level process therefore provides the control structure, while the detailed subprocesses define how individual stages are performed.
The framework does not treat process activities and project documentation as separate artefacts.
Individual delivery tasks are associated with the documents and document sections that:
This creates traceability between the delivery process and the controlled project documentation supporting it.
The complete framework contains detailed subprocess models beneath the applicable high-level activities.
Selected subprocesses are published separately as portfolio evidence to demonstrate how the high-level lifecycle is decomposed into detailed activities and decisions.
The complete subprocess library and editable BPMN source models are retained privately.
The purpose of the framework is to provide more than a sequence of development tasks.
It is designed to support:
The complete high-level lifecycle is shown publicly because it demonstrates the breadth and structure of the framework.
Detailed process logic is shown only through selected representative subprocesses.
The following remain private:
This provides sufficient evidence to assess the methodology while avoiding publication of the complete reusable framework.
© 2026 Carl Patten. All rights reserved.