Synthetic data is "artificially generated data that mimics real-world data, used when actual data is scarce or sensitive." Generating synthetic training images of people in the required poses allows you to rapidly augment your dataset without logistical, privacy, or labeling overhead.
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The Predictive Analytics & Decision Support pattern is defined as using historical data (past behavior) to forecast future events and provide decision support for human or automated processes. This is distinct from the Patterns & Anomalies pattern, which focuses on detecting unusual deviations rather than forecasting expected outcomes.
A CPMAI Glossary self-test question states that Predictive Analytics "uses historical data to forecast future outcomes" .
Another glossary question defines predictive analytics as aiming "to use historical data to forecast future outcomes" .
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In Phase VI: Model Operationalization, CPMAI specifies that a Model Governance Framework must be established before any model versioning or retraining occurs. This governance framework ensures proper version control, audit trails, and clear ownership for each model iteration, maintaining accountability and compliance throughout the model lifecycle.
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In Phase I's AI Skills Assessment, CPMAI directs teams to "List the cognitive skills you have available" and to identify "What expertise and skills you have available to you that you can use for this project" as well as any skills gaps to address . The methodology-and the CPMAI Glossary's definition of a data scientist- emphasizes core competencies in:
Data Engineering & Preparation (manipulating, collecting, transforming large data sets) Critical Thinking (interpreting insights to align with business goals) Algorithmic Understanding (selecting and applying the right statistical or ML models) Mathematical Proficiency (especially statistics and calculus underpinning model creation) By contrast, prompt engineering (A) is a specialized role for LLM interactions, not a general data-science core competency; and RPA-centric automation skills (E) fall outside the CPMAI focus on cognitive/ML capabilities.
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Tom Mitchell's widely adopted formulation captures ML's essence: improvement on task T, measured by P, through experience E. This aligns with CPMAI's view that ML enables systems to learn from data and improve over time ("The ability of a machine to learn from data, improve with experience, and apply that learning to make predictions.") .
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