Data Governance manages and oversees an organization’s availability, usability, integrity, and security. It involves the implementation of policies, procedures, standards, and guidelines to ensure that data is managed consistently and compliant.
A key aspect of data governance is understanding the organization and structure of data within a system. This is where physical, logical, and conceptual data models come into play.
A physical data model describes the actual storage and organization of data within a system. This includes the specific database tables and fields used to store data and the relationships between those tables.
For example, a physical data model for a retail website might include tables for products, customers, and orders, with relationships established between these tables to indicate how they are related. Database administrators and developers often use physical data models to understand how data is stored and accessed within a system.
A logical data model, however, describes how data is organized and related in a system, independent of the specific technology or physical storage used.
It defines the logical relationships between data elements and their attributes. Business analysts and other stakeholders often use logical data models to understand the organization and structure of data within a system without knowing how it is stored or accessed.
On the other hand, a conceptual data model provides a high-level view of the overall concepts and relationships within a system without specifying the details of how the data is stored or accessed.
It defines the overall structure of the data and the relationship of the data elements with the business. Conceptual data models are used by business leaders, stakeholders, and data governance teams to understand the organization and structure of data within a system and how it aligns with the business.
It is important to note that these three types of data models are not mutually exclusive. They often work together to provide a complete understanding of a system’s data organization and structure.
For example, a conceptual data model might identify a system’s overall business entities and relationships. In contrast, a logical data model is used to refine and specify those entities and relationships in more detail. The physical data model is then used to implement the logical data model in a specific technology or database.
In Data Governance, these models are critical for understanding data organization, communication within the organization, and compliance and security. Physical data models are used to ensure that data is stored consistently and securely, while logical and conceptual data models are used to ensure that data is used in a consistent and compliant manner.
Physical data models are also used to understand the data lineage, which is the history of data from its creation to its deletion. This is particularly important in regulated industries, where data lineage is used to demonstrate compliance with regulations such as GDPR and HIPAA.
Logical and conceptual data models are used to understand the data governance policies and procedures that should be in place to ensure data is used in a compliant manner.
In summary, physical, logical, and conceptual data models play a critical role in Data Governance by providing a detailed understanding of the organization and structure of data within a system. Physical data models are used to ensure data is stored consistently and securely.
In contrast, logical and conceptual data models are used to ensure data is used in a consistent and compliant manner. By using these models together, organizations can ensure that their data is managed consistently and compliant while also facilitating communication and understanding within the organization.
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