Module 3
Building the Analytics Foundation
The unglamorous plumbing everything rests on: the ingestion-to-warehouse pipeline, the ongoing discipline of data quality and governance, and the identity and attribution layer beneath every metric.
By the end of this module, you will be able to:
- Describe the pipeline that turns raw feeds into analyzable, integrated data
- Explain why data quality requires continuous ownership rather than a one-time cleanup
- Identify how patient matching and attribution errors corrupt every downstream metric
- The Data Pipeline Raw feeds become usable analytics through a pipeline. This lesson covers ingestion, integration, and the warehouse that holds it together. About 4 min
- Data Quality and Governance Analytics built on bad data produce confident wrong answers. This lesson covers the quality and governance discipline that makes data trustworthy. About 4 min
- Identity and Attribution in Data Before you can analyze a patient, you have to know who they are and whether they are yours. This lesson covers patient matching and attribution logic. About 4 min
Module quiz
Answer all questions to see your score.
1. Why is integrating multiple sources into one place the foundational analytics task?
The value comes from combining complementary sources; integration is where the real work and most failures live.
2. Why is data quality an ongoing program, not a one-time cleanup?
Data sources change and degrade constantly, so quality requires continuous monitoring and ownership, not a single fix.
3. Why is patient identity matching foundational and error-prone?
With no universal patient identifier, matching is essential and imperfect; errors split or merge histories and distort every metric.