Automate, Analyze, and Validate Data Quality Course

Lesson 1 • Data Quality Maturity Models

Presents staged maturity frameworks to benchmark an organisation's current state. Guides prioritisation of improvement initiatives.

Lesson 2 • Regulatory and Compliance Context

Surveys data governance obligations driven by privacy, financial reporting, and industry standards. Frames compliance as a quality driver.

Lesson 3 • Root Causes of Data Quality Issues

Categorises sources of data defects across people, processes, and systems. Enables targeted remediation rather than symptomatic fixes.

Lesson 4 • Business Impact of Poor Data Quality

Quantifies the operational, financial, and reputational costs of bad data. Connects quality failures to real decision-making consequences.

Lesson 1 • Pattern and Format Analysis

Identifies format patterns using regular expressions and cardinality checks. Uncovers hidden inconsistencies in free-text and coded fields.

Lesson 2 • Profiling Report Design and Communication

Structures profiling findings into actionable reports for technical and business audiences. Bridges discovery output to rule-writing in subsequent chapters.

Lesson 3 • Statistical Summaries and Distributions

Computes descriptive statistics—mean, median, standard deviation, and percentiles—to detect anomalies. Reveals distributional patterns that inform threshold setting.

Lesson 4 • Relationship and Referential Profiling

Examines foreign-key relationships, join keys, and cross-table dependencies. Surfaces orphaned records and referential integrity violations.

Lesson 1 • Consistency and Referential Integrity Rules

Builds rules that enforce agreement across fields, tables, and systems. Addresses cross-system synchronisation and derived-field consistency.

Lesson 2 • Rule Taxonomy and Classification

Organises rules by dimension, scope, and enforcement level. Provides a classification system that scales across domains and datasets.

Lesson 3 • Uniqueness and Deduplication Rules

Designs rules to detect exact and fuzzy duplicates at record and entity levels. Introduces matching keys and similarity thresholds.

Lesson 4 • Rule Documentation and Governance

Establishes a rule catalog with metadata, ownership, and change history. Ensures rules remain auditable and maintainable over time.

Lesson 1 • Testing and Validating the Automation Suite

Applies unit and integration testing to validation code to ensure rule logic is correct. Prevents false positives and negatives from undermining trust.

Lesson 2 • Automation Architecture Patterns

Surveys checkpoint, sidecar, and inline validation architectures for pipelines. Selects the right pattern based on latency, volume, and failure tolerance.

Lesson 3 • Scheduling and Orchestration

Schedules validation jobs using workflow orchestrators and cron-based triggers. Manages dependencies between profiling, validation, and alerting tasks.

Lesson 4 • Integrating Checks into Data Pipelines

Embeds quality gates at ingestion, transformation, and serving layers of a pipeline. Defines pass, warn, and fail thresholds that control data flow.

Lesson 1 • Distribution Shift Detection

Applies statistical tests to detect when data distributions change significantly over time. Distinguishes legitimate business changes from data quality failures.

Lesson 2 • Volume and Schema Change Detection

Monitors row counts, column counts, and schema diffs to detect structural drift. Catches upstream changes before they corrupt downstream consumers.

Lesson 3 • Statistical Thresholds and Control Charts

Uses z-scores, interquartile ranges, and control chart limits to flag outliers. Establishes dynamic thresholds that adapt to data distributions.

Lesson 4 • Alert Design and Noise Reduction

Designs alert routing, severity tiers, and suppression rules to minimise alert fatigue. Ensures actionable signals reach the right owners promptly.

Lesson 1 • Data Lineage Fundamentals

Defines column-level and dataset-level lineage and explains how it is captured. Establishes lineage as the foundation for impact and root cause analysis.

Lesson 2 • Incident Management and Post-Mortems

Structures quality incident response from detection through resolution and retrospective. Produces post-mortem artefacts that prevent recurrence.

Lesson 3 • Automated Lineage Tracking in Pipelines

Instruments pipelines to emit lineage metadata automatically at each transformation step. Reduces manual lineage maintenance and improves accuracy.

Lesson 4 • Root Cause Investigation Techniques

Applies five-whys, fishbone diagrams, and timeline analysis to isolate defect origins. Distinguishes systemic causes from one-time incidents.

Lesson 1 • Continuous Improvement Feedback Loops

Uses dashboard trends to prioritise remediation backlogs and track fix effectiveness. Closes the loop between measurement and action.

Lesson 2 • Defining Meaningful Quality Metrics

Selects KPIs that reflect business impact rather than technical counts alone. Aligns metrics to the six quality dimensions and stakeholder priorities.

Lesson 3 • Dashboard Implementation and Tooling

Builds interactive dashboards using BI tools connected to quality metric tables. Covers data model design, refresh scheduling, and access control.

Lesson 4 • SLA and Threshold Management

Defines quality SLAs with owners, measurement windows, and breach escalation paths. Embeds SLA tracking directly into dashboard views.

Lesson 1 • Data Quality in DataOps and DevOps

Embeds quality gates into CI/CD pipelines so that data changes are validated before promotion. Treats data quality as code with version control and testing.

Lesson 2 • Data Governance Structures for Quality

Defines data stewardship roles, councils, and decision rights that enforce quality standards. Aligns governance to organisational structure and data domains.

Lesson 3 • Automating Governance Workflows

Automates certification, approval, and change-management workflows for rules and datasets. Reduces manual overhead while maintaining auditability.

Lesson 4 • Scaling Rule Management Across Domains

Implements centralised rule repositories with domain-specific extensions and inheritance. Prevents rule sprawl while enabling domain autonomy.