Overall Analysis Process
=========================

The data analytics process on the platform typically follows a well-defined iterative procedure. This includes the formulation of goals, planning implementations, executing pipelines, and interpreting results. The entire process can also be automated.

The standard steps are as follows:

1. **Goals and Subgoals**: Define the overall objective of the analysis and break it down into manageable subgoals. See :doc:`Goals and Subgoals <goals_and_subgoals>`.
2. **Implementation Plan**: Each goal is associated with a set of implementation steps, automatically generated and editable. See :doc:`Implementation Plans <implementation_plans>`.
3. **Pipeline Generation**: Based on the implementation plan, the system generates a Python analysis pipeline. See :doc:`Pipelines <pipelines>`.
4. **Pipeline Execution**: The pipeline is executed, and results are captured and displayed. See :doc:`Pipeline Execution and Results <pipeline_execution_and_results>`.
5. **Result Interpretation**: Results are analyzed in the context of domain knowledge. See :doc:`Result Interpretation <result_interpretation>`.

Each goal may be executed through multiple iterations. Each iteration investigates a single goal but multiple iterations can be run for comparison and refinement.