The CPMAI Method for Data equips teams to manage, govern, and prepare data for AI success:
Task 1: Managing Data Fundamentals and Big Data Concepts
- Explain how data fuels AI intelligence initiatives
- Define Big Data and its relationship to AI systems
- Extract value from unstructured data resources
- Apply lessons learned from Big Data implementations
- Evaluate the role of data science in analytics processes
- Apply Big Data approaches to enhance AI capabilities
Task 2: Implementing Data Governance and Management
- Design comprehensive data lifecycles for AI applications
- Establish data stewardship roles and responsibilities
- Establish data management plans for AI initiatives
- Document data lineage throughout the AI pipeline
- Implement master data management practices
Task 3: Engineering Data Pipelines for AI
- Design data feed mechanisms for continuous data flow
- Construct data pipelines optimized for AI workloads
- Apply data engineering principles to AI infrastructure
- Create separate training and inference data pipelines
- Design scalable data architectures for growing datasets
- Create automated documentation of data pipeline components
Task 4: Executing Data Preparation and Transformation
- Validate the “garbage in, garbage out” principle in AI contexts
- Apply methods to improve data quality and accuracy
- Address AI-specific needs in data preparation
- Clean and enhance data for optimal AI performance
- Apply data augmentation to increase dataset robustness
- Balance datasets to prevent bias in model training