Data Quality: Core Concepts

linkedin.com/learning/data-quality-core-concepts

8,072 learners | 1h 28m | Advanced | Released Dec 2024 | 4.7/5 stars

A comprehensive overview of data quality — a measure of how well data meets a company's expectations for accuracy, completeness, consistency, reliability, and validity. This theoretical course is designed for data professionals, stakeholders, leadership, and anyone interested in data quality.

What You'll Learn

  • The nine dimensions of data quality (validity, completeness, consistency, integrity, timeliness, currency, reasonableness, uniqueness, accuracy)
  • How to connect data quality initiatives to business outcomes and ROI
  • Common data quality issues and measurement techniques
  • Tools that support data quality initiatives
  • The complete data lifecycle and stakeholder roles

Course Sections

  1. Data Quality Fundamentals — Nine quality dimensions, assessment frameworks, business impact analysis
  2. Data Lifecycle — How data quality applies across creation, acquisition, processing, storage, and consumption
  3. Common Issues & Measurement — Root cause analysis, null rates, data freshness, schema changes, transformation bugs, data drift
  4. Tooling — Data dictionaries, catalogs, lineage tracking, monitoring, observability, and data contracts
1 # Data Quality: Core Concepts
2
3 [linkedin.com/learning/data-quality-core-concepts](https://www.linkedin.com/learning/data-quality-core-concepts)
4
5 **8,072 learners** | 1h 28m | Advanced | Released Dec 2024 | 4.7/5 stars
6
7 ---
8
9 A comprehensive overview of data quality — a measure of how well data meets a company's expectations for accuracy, completeness, consistency, reliability, and validity. This theoretical course is designed for data professionals, stakeholders, leadership, and anyone interested in data quality.
10
11 ## What You'll Learn
12
13 - The nine dimensions of data quality (validity, completeness, consistency, integrity, timeliness, currency, reasonableness, uniqueness, accuracy)
14 - How to connect data quality initiatives to business outcomes and ROI
15 - Common data quality issues and measurement techniques
16 - Tools that support data quality initiatives
17 - The complete data lifecycle and stakeholder roles
18
19 ## Course Sections
20
21 1. **Data Quality Fundamentals** — Nine quality dimensions, assessment frameworks, business impact analysis
22 2. **Data Lifecycle** — How data quality applies across creation, acquisition, processing, storage, and consumption
23 3. **Common Issues & Measurement** — Root cause analysis, null rates, data freshness, schema changes, transformation bugs, data drift
24 4. **Tooling** — Data dictionaries, catalogs, lineage tracking, monitoring, observability, and data contracts

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