The Inmon methodology, also known as the Inmon approach or the Inmon model, is a data warehousing methodology developed by Bill Inmon. It provides a structured framework for designing and implementing data warehouses that focus on integrating data from various sources into a centralized repository.

The Inmon methodology emphasizes the importance of building a single, unified view of data, commonly referred to as the data warehouse. This approach stands in contrast to the Kimball methodology, which advocates for building data marts that are focused on specific business areas.

Here are the key components and principles of the Inmon methodology:

1. Enterprise Data Warehouse (EDW): The cornerstone of the Inmon methodology is the creation of an enterprise data warehouse (EDW). The EDW is a comprehensive and centralized repository that stores all relevant data from various sources within an organization. It serves as a single source of truth for reporting, analysis, and decision-making.
2. Data Integration: The Inmon methodology emphasizes the integration of data from multiple sources into the EDW. Data from operational systems, such as transactional databases, spreadsheets, and external sources, is transformed and loaded into the data warehouse. This consolidation process ensures that data is consistent, accurate, and accessible to users across the organization.
3. Data Granularity: Inmon advocates for storing data at the most granular level possible within the data warehouse. This means that individual transactions or events are captured and stored in the EDW. Storing data at a granular level enables detailed analysis and provides flexibility for reporting and querying.
4. Data Normalization: The Inmon methodology promotes the use of normalized data structures within the data warehouse. Normalization involves organizing data into multiple tables based on logical relationships and dependencies. This approach reduces data redundancy and improves data integrity, but it can result in more complex queries compared to denormalized structures.
5. Data Governance: Data governance is a key principle in the Inmon methodology. It emphasizes the need for clear data definitions, data stewardship, and data quality management. Data governance processes and policies ensure that data within the EDW is accurate, consistent, and trustworthy.
6. Bottom-Up Approach: The Inmon methodology follows a bottom-up approach to data warehousing. It starts with building a strong foundation by integrating and consolidating data from various sources into the EDW. Once the EDW is established, data marts can be created on top of it to serve specific business needs.
7. Iterative Development: The Inmon methodology supports an iterative development approach. It recognizes that building a data warehouse is an ongoing process that evolves over time. It encourages incremental development, allowing for continuous improvement and adaptation to changing business requirements.
8. Business Orientation: The Inmon methodology places a strong emphasis on aligning the data warehouse with the needs of the business. It aims to support business intelligence, reporting, and analytics to enable informed decision-making. The data warehouse is designed to be business-friendly, providing a user-friendly interface and delivering relevant insights to business users.

Overall, the Inmon methodology emphasizes the importance of a centralized and integrated data warehouse that serves as a foundation for robust data analysis and reporting. By following this approach, organizations can achieve a comprehensive view of their data and derive meaningful insights to drive business success.