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데이터 품질 입문

기초기술 수준

업데이트됨 2026. 2.

데이터 품질 관리의 기본을 살펴보세요. 데이터 품질을 모니터링하고 개선하기 위한 핵심 개념, 차원 및 기법을 학습하십시오.

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1

Defining Data Quality Terms

Chapter 1 introduces basic data quality terms, including data quality dimensions and data quality roles and responsibilities. You will also learn the importance and value of data quality in a business context.

Importance of data quality

Value of data quality

Data quality questions

Data quality terms and concepts

Data quality definition in context

Bonus data quality dimensions

Data quality dimension practice

Importance of consistency

Data quality roles and responsibilities

The Data Governance Team

Which data quality role fits

Which role is responsible

2

Data Quality Processes and Components

You’ll start chapter 2 by identifying data quality rules for each data quality dimension using data profiles. You’ll also learn about metadata and data lineage before exploring the overall data quality process for triaging and remediating issues.

Data quality rules using dimensions

Data quality rules

Data quality scenarios

Data profiles

Data quality rules based on a data profile

Data profile conclusions

Metadata and data quality

Identifying metadata

Order of data lineage

Data quality issues triage

Using data lineage and metadata

Executing data quality processes

3

Data Quality Rules In Action

In chapter 3, you’ll learn about the different types of data quality rules and the concept of data quality alert thresholds. You’ll finish the chapter with an exercise that puts dimensions, data quality rules, data quality processes, and data quality alerts together.

Detective Data Quality Rules

Identifying detective rules

Remediating detected issues

Preventative Data Quality Rules

When to use a preventative rule

Detective versus preventative data quality rules

Anomaly detection

When to use anomaly detection

Triaging anomalies

Data quality thresholds

Threshold selection

Matching threshold to criticality

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