What you'll learn

Organizations increasingly depend on analytics and AI to drive decisions, but these initiatives often fail, not because of tool limitations, but because the underlying data is not managed well enough to support them. Reports show conflicting numbers, AI models inherit bias from poor training data, and stakeholders lose trust in data-driven outcomes.

In this course, Managing Enterprise Data for Analytics and AI, you’ll gain the ability to evaluate whether enterprise data is fit for analytics and AI use cases, and to recommend practical improvements. First, you’ll explore how enterprise data management supports trustworthy reporting, business intelligence, and AI outcomes, distinguishing between data that is merely available and data that is genuinely trusted. Next, you’ll discover how to assess data fitness for analytics, applying a structured evaluation of quality, lineage, documentation, and ownership against real enterprise scenarios. Then, you’ll learn how to evaluate AI-specific readiness, including risks around bias, labeling, privacy, and governance that go beyond what analytics requires. Finally, you’ll understand how to recommend and prioritize data management improvements by building a stakeholder-aligned roadmap based on business value, risk, and feasibility.

When you’re finished with this course, you’ll have the skills and knowledge of enterprise data management needed to assess any organization’s data readiness and propose a concrete plan for improvement.