What is the process of identifying an issue that should be addressed and putting it in understandable and actionable terms called?

The process of identifying an issue that should be addressed and putting it into understandable and actionable terms is known as problem formulation. This stage is crucial in data science because it sets the direction for the entire project. During problem formulation, the analyst defines the specific problem to be solved, elucidates the goals of the analysis, and determines the data and methods required to address the problem effectively.

By articulating the issue clearly, practitioners can ensure that all stakeholders have a shared understanding of the problem and its implications, which fosters better collaboration and more effective solutions. This phase is foundational, as a well-formulated problem will guide the subsequent stages of data collection, analysis, and interpretation.

In contrast, data analysis, hypothesis testing, and root cause analysis are subsequent steps in the data science workflow. Data analysis refers to exploring and interpreting the data to find insights, hypothesis testing is used to validate or disprove specific assumptions based on statistical methods, and root cause analysis involves identifying the underlying causes of problems that may arise after the initial problem has been defined. Each of these processes plays a role after the initial problem formulation has provided a clear focus for the work.