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Advanced · Self-paced learning

Data Governance for AI Transformation

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Assign business ownership, maintenance and technical provision for a data flow. In a case, compare whether the data supports broad capacity planning or decisions about individuals. Determine who decides on correction or restrictions on use.

In the course case, monthly case totals are available but complete individual records are not. Assess whether the totals support broad capacity planning and why they cannot support decisions about individuals. When a new source changes the definition of a case, reassess comparability and responsibilities.

You design a process for quality checks, error handling and decisions on data use, then work through it using the case. Refer unresolved privacy and rights questions to the responsible specialists.

What you will work on

  • Describe a data flow and business accountability boundaries
  • Set quality requirements for a purpose and check them using example data
  • Reassess comparability and responsibilities when a data source changes

Who it is for

  • Data owners and accountable business roles
  • Business and AI managers
  • Data governance professionals

Topics

  • Data origin and use context
  • Business ownership and operational stewardship
  • Quality requirements and evidence
  • Use approval and specialist assessment
  • Source changes and escalation

Your result

Your completed check sheet shows which quality rules apply to the intended use and how identified errors are corrected. Use it for the next review of the data flow.

Prerequisites

Knowledge of a business process or dataset and basic AI use. Metadata or a synthetic description is sufficient; raw data need not be supplied.

Format and approach

Planned as six to eight hours of self-study. You design a data flow and quality rules, with self-checks and explanations.

Planned learning time: 6–8 hours

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