30 Data Governance in Business Essentials

In the world of business, data is power. From customer insights to market trends, having accurate and reliable data can make or break a company’s success. However, with great power comes great responsibility – and that’s where data governance comes into play.

Data governance refers to the overall management of data within an organization. It involves establishing processes, policies, and controls to ensure that data is accurate, secure, and used effectively. This is especially important as businesses collect more and more data from various sources.

1. Data Governance: The overall management of the availability, usability, integrity, and security of the data employed in an enterprise.

    

2. Data Stewardship: The responsibility and authority for managing data assets.

    

3. Data Quality: The degree to which data is accurate, complete, timely, consistent, and reliable.

    

4. Master Data Management (MDM): The process of linking all critical data to one file, called a master file, to provide a common point of reference.

    

5. Data Privacy: The handling of sensitive data to ensure that a person’s privacy is not violated.

    

6. Data Security: Protecting data from unauthorized access and data corruption throughout its lifecycle.

    

7. Metadata Management: The administration of data that describes other data, which is used for understanding and managing information.

    

8. Data Compliance: Adhering to data-related regulations and standards.

    

9. Data Lifecycle Management (DLM): The process of managing the flow of an information system’s data and associated metadata from creation and initial storage to the time when it becomes obsolete and is deleted.

    

10. Data Warehouse: A system used for reporting and data analysis, and is considered a core component of business intelligence.

     

11. Data Architecture: The models, policies, rules, or standards that govern which data is collected, and how it is stored, arranged, integrated, and put to use in data systems and in organizations.

     

12. Data Integration: Combining data from different sources and providing a unified view of these data.

     

13. Big Data: Extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions.

     

14. Business Intelligence (BI): Technologies, applications, and practices for the collection, integration, analysis, and presentation of business information.

     

15. Data Mining: The process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.

     

16. Data Model: An abstract model that organizes elements of data and standardizes how they relate to one another and to the properties of real-world entities.

     

17. Data Ethics: The branch of ethics that focuses on the moral problems related to data (including generation, recording, curation, processing, dissemination, sharing and use), algorithms (as well as the related ethical implications of artificial intelligence) and corresponding practices, systems, and technologies.

     

18. Data Literacy: The ability to read, understand, create, and communicate data as information.

     

19. Data Cleansing: The process of detecting and correcting (or removing) corrupt or inaccurate records from a record set, table, or database.

     

20. Data Governance Framework: A model or structure for establishing authority, policies, and procedures to manage data.

     

21. Data Policy: Guidelines that govern the collection, storage, management, quality, and integrity of data.

     

22. Data Audit: A systematic assessment of a data set or data management system to ensure compliance with regulations, policies, and standards.

     

23. Information Lifecycle Management (ILM): The policies, processes, practices, and tools used to align the business value of information with the most appropriate and cost-effective IT infrastructure from the time information is conceived through its final disposition.

     

24. Data Lineage: The life cycle of data, including its origins, what happens to it, and how it is used over time.

     

25. Data Lake: A storage repository that holds a vast amount of raw data in its native format until it is needed.

     

26. Data Protection Officer (DPO): An enterprise security leadership role required by the General Data Protection Regulation (GDPR), responsible for overseeing data protection strategy and implementation to ensure compliance with GDPR requirements.

     

27. Data Sovereignty: The concept that data is subject to the laws and governance structures of the country in which it is located.

     

28. Cloud Data Management: The process of storing and managing data within cloud computing environments.

     

29. Data Analytics: The science of analyzing raw data to make conclusions about that information.

     

30. Data Visualization: The graphical representation of information and data using visual elements like charts, graphs, and maps.

These terms cover various aspects of data governance and management, encompassing the strategies and tools businesses use to handle data effectively and responsibly. Understanding these concepts is crucial for anyone involved in data management, IT, or business decision-making processes.