Data governance and data quality vocabulary

Data Governance:

• Data Governance: The overall management of the availability, usability, integrity, and security of the data in an enterprise. It encompasses policies, processes, roles, responsibilities, and standards.
• Data Steward: A person responsible for the quality and management of a specific data domain. They act as a liaison between IT and business users.
• Data Owner: A business executive who has ultimate responsibility for a specific data asset, even if they don't directly manage it.
• Data Custodian: Typically an IT role responsible for the technical management and maintenance of data and its related infrastructure. They implement the policies set by data owners and stewards.
• Data Governance Council/Committee: A group of stakeholders representing different business areas who make decisions about data governance policies and priorities.
• Data Policy: A documented rule or guideline that governs how data is collected, stored, used, and shared.
• Data Architecture: The overall structure and organization of data assets within an organization, including how data is stored, processed, and accessed.
• Metadata: Data about data. It provides context and meaning to data, making it easier to understand and use. Examples include data definitions, data lineage, and business glossaries.
• Data Lineage: The documented path that data takes from its origin to its current location. Essential for understanding data transformations and ensuring data quality.
• Data Dictionary/Business Glossary: A centralized repository of definitions for data elements, business terms, and other data-related concepts. Promotes consistency and understanding.
• Data Security: Protecting data from unauthorized access, use, disclosure, disruption, modification, or destruction.
• Data Privacy: Managing personal data in compliance with relevant regulations and protecting individuals' privacy rights.
• Compliance: Adhering to relevant laws, regulations, and industry standards related to data management.
• Data Audit: A formal review of data governance processes and data quality to ensure compliance and identify areas for improvement.

Data Quality:

• Data Quality: The degree to which data is fit for its intended use. Encompasses various dimensions.
• Accuracy: The extent to which data is free from errors and correctly reflects the real world.
• Completeness: The degree to which data is complete and contains all necessary values.
• Consistency: The extent to which data is consistent across different systems and databases.
• Timeliness: The degree to which data is up-to-date and reflects the current state of affairs.
• Validity: The extent to which data conforms to defined rules and constraints.
• Uniqueness: The degree to which data is free from duplicates.
• Data Profiling: The process of examining data to understand its content, structure, and quality.
• Data Cleansing/Scrubbing: The process of identifying and correcting errors, inconsistencies, and other data quality issues.
• Data Standardization: The process of converting data into a standard format.
• Data Enrichment: The process of adding value to data by incorporating data from other sources.
• Data Quality Metrics: Quantifiable measures used to track and monitor data quality. Examples include error rates, completeness rates, and consistency rates.
• Root Cause Analysis: Investigating the underlying causes of data quality problems.

Related Concepts:

• Master Data Management (MDM): A technology and process used to create and maintain a single, authoritative source of master data (e.g., customer, product, location).
• Data Lake: A centralized repository for storing raw data in its native format.
• Data Warehouse: A centralized repository for storing structured data that has been processed and transformed for analysis.
• Big Data: Extremely large and complex datasets that require specialized tools and techniques for processing and analysis.

This vocabulary provides a solid foundation for understanding and discussing data governance and data quality. Remember that these concepts are interconnected and contribute to the overall effectiveness of data management within an organization.

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By Jerry Ramonyai