Data Quality: Core Concepts

Course details

This theoretical course is designed for data professionals, stakeholders at data organizations, data leadership, and professionals interested in data quality. Mark Freeman—a data engineer and tech lead—presents a high-level overview of data quality, a measure of how well data meets a company's expectations for accuracy, completeness, consistency, reliability, and validity. In addition to course content, complete a final project that involves a provided code repository to review and an analytics report with data quality issues. Identify data quality issues and make recommendations on how to fix them. This course equips you with a strong understanding of the concepts underpinning data quality.

Concepts

Introduction

The importance of data quality

Data Quality Fundamentals

Data quality introduction
Impact of poor data quality
Defining data quality
Data quality dimensions - Intro
DQ dimensions - Validity, completeness, consistency
DQ dimensions - Integrity, timeliness, currency
DQ dimensions - Reasonableness, uniqueness, accuracy
Common data quality assessment frameworks
Connecting data quality to business outcomes - Intro
Thought exercise - Ecommerce
Thought exercise - Understand the business model
Thought exercise - Map your data lifecycle
Thought exercise - Identify your stakeholders
Thought exercise - Evaluate how stakeholders drive revenue
Thought exercise - Assess how DQ impacts revenue and risk
Thought exercise - Synthesize research and communicate ROI

Data Quality Across the Data Lifecycle

Data lifecycle introduction
Data lifecycle overview
Data lifecycle stakeholders - Business
Data lifecycle stakeholders - Engineers
Data lifecycle stakeholders - Data
DQ across the lifecycle - Business strategy
DQ across the lifecycle - Data creation
DQ across the lifecycle - Data acquisition
DQ across the lifecycle - Transactional databases
DQ across the lifecycle - ETL ELT pipelines
DQ across the lifecycle - Analytical databases
DQ across the lifecycle - Data analytics and data products
DQ across the lifecycle - Data insights consumption

Common Data Quality Issues and How to Measure Them

Introduction to issues and measurements
Root cause analysis - RCA
Null rates
Data freshness and timeliness
Schema changes
Data transformation bugs
Data drift
Measuring data quality

Data Quality Tooling

Introduction to tooling
Data dictionaries
Data catalogs
Data lineage
Data monitoring and observability
Data contracts

Conclusion

Next steps