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

0. Introduction

01 - The importance of data quality

1. Data Quality Fundamentals

02 - Data quality introduction
03 - Impact of poor data quality
04 - Defining data quality
05 - Data quality dimensions - Intro
06 - DQ dimensions - Validity, completeness, consistency
07 - DQ dimensions - Integrity, timeliness, currency
08 - DQ dimensions - Reasonableness, uniqueness, accuracy
09 - Common data quality assessment frameworks
10 - Connecting data quality to business outcomes - Intro
11 - Thought exercise - Ecommerce
12 - Thought exercise - Understand the business model
13 - Thought exercise - Map your data lifecycle
14 - Thought exercise - Identify your stakeholders
15 - Thought exercise - Evaluate how stakeholders drive revenue
16 - Thought exercise - Assess how DQ impacts revenue and risk
17 - Thought exercise - Synthesize research and communicate ROI

2. Data Quality Across the Data Lifecycle

18 - Data lifecycle introduction
19 - Data lifecycle overview
20 - Data lifecycle stakeholders - Business
21 - Data lifecycle stakeholders - Engineers
22 - Data lifecycle stakeholders - Data
23 - DQ across the lifecycle - Business strategy
24 - DQ across the lifecycle - Data creation
25 - DQ across the lifecycle - Data acquisition
26 - DQ across the lifecycle - Transactional databases
27 - DQ across the lifecycle - ETL ELT pipelines
28 - DQ across the lifecycle - Analytical databases
29 - DQ across the lifecycle - Data analytics and data products
30 - DQ across the lifecycle - Data insights consumption

3. Common Data Quality Issues and How to Measure Them

31 - Introduction to issues and measurements
32 - Root cause analysis - RCA
33 - Null rates
34 - Data freshness and timeliness
35 - Schema changes
36 - Data transformation bugs
37 - Data drift
38 - Measuring data quality

4. Data Quality Tooling

39 - Introduction to tooling
40 - Data dictionaries
41 - Data catalogs
42 - Data lineage
43 - Data monitoring and observability
44 - Data contracts

Conclusion

45 - Next steps

Data Quality: Core Concepts

Authors

Mark Freeman