Root Cause Analysis
Audience: Professionals responsible for data quality, analytics, reporting, data engineering, or operational decision‑making who need a structured approach to diagnosing and resolving data‑related problems.
Duration: 1 Day
Delivery: Virtual (1 full day) or in-person (2 × 0.5 day)
Course description:
This course provides a practical, structured approach to identifying, diagnosing, and resolving the root causes of data issues. Participants learn how to distinguish symptoms from underlying problems, trace issues across systems, pipelines and data asset. Participants will lear to apply proven RCA techniques to prevent recurrence. The course focuses on real‑world data challenges: inconsistent values, broken pipelines, incorrect logic, missing data, model drift, and governance gaps.
Course overview:
1. Understanding Data Failures
Types of data issues: quality, timeliness, lineage, logic, structure, governance
How data issues propagate through pipelines, dashboards, and decision systems
Distinguishing symptoms (e.g., wrong numbers) from structural causes (e.g., upstream schema change)
2. RCA Foundations for Data
Adapting classic RCA tools (5 Whys, Fishbone, Fault Trees) to data ecosystems
Mapping data flows, dependencies, and failure points
Identifying where in the lifecycle the issue originates: ingestion, transformation, modeling, visualization, or governance
3. Data Quality Dimensions
Different data dimensions
Availability of data (accessibility, timelines)
Value of data (relevance, consistency)
Trust in data (accuracy, completeness, interdependency, uniqueness)
Examples of how each dimension can fail and how to diagnose it
Using dimensions to build data quality profililes to detect anomalies and patterns
4. Investigating Data Pipelines
Tracing issues across ETL/ELT processes and broader systems
Identifying logic errors, transformation failures, and schema drift
Understanding how small upstream changes create large downstream impacts
5. RCA for Analytical and Reporting Errors
Diagnosing incorrect metrics, broken logic, and misaligned business rules
Identifying issues caused by ambiguous definitions or poor documentation
Validating assumptions and confirming expected behaviour
6. Governance and Process‑Driven Causes
How unclear ownership, weak controls, and missing standards create recurring data issues
RCA for human‑driven errors: manual processes, inconsistent updates, versioning problems
Linking RCA outcomes to governance improvements
7. Fixing Issues and Preventing Recurrence
Designing corrective and preventive actions (CAPA) for data ecosystems
Strengthening monitoring, alerts, and data quality checks
Embedding RCA into data operations and analytics workflows
By the end of this course, participants will be able to:
Identify and classify different types of data issues and their impacts
Apply structured RCA techniques to diagnose the true source of data failures
Trace issues across data pipelines, transformations, and reporting layers
Evaluate data quality using profiling, validation, and anomaly detection
Distinguish between logic errors, process failures, and governance gaps
Develop corrective and preventive actions that address root causes, not symptoms
Strengthen data reliability through improved controls, documentation, and monitoring