Data Quality Insights

What is Data Quality?

Data quality refers to the overall usability of data for its intended purpose. It encompasses various dimensions like accuracy, completeness, consistency, validity, timeliness, and uniqueness. Poor data quality can lead to flawed analysis, incorrect decision-making, operational inefficiencies, and compliance issues. Data quality management involves profiling, cleansing, standardizing, and monitoring data to ensure it meets defined quality standards. Commonly used techniques include data validation rules, data deduplication, data profiling tools, and data governance frameworks.

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What other technologies are related to Data Quality?

Data Quality Complementary Technologies

Data Governance

Data Governance establishes policies and procedures that ensure data quality. It's complementary because data quality initiatives operate within the data governance framework.

mentioned alongside Data Quality in 24% (1.7k) of relevant job posts

Metadata Management

Metadata Management provides context and information about data, which is crucial for understanding and improving data quality. Data quality processes rely on metadata to identify and resolve data issues.

mentioned alongside Data Quality in 36% (437) of relevant job posts

Master Data Management

Master Data Management (MDM) aims to create a single, consistent view of critical data entities. Data quality is essential for MDM to be effective, and MDM improves overall data quality by standardizing key data.

mentioned alongside Data Quality in 17% (796) of relevant job posts