The Inmon methodology, also known as the Inmon approach or the Corporate Information Factory (CIF), is a data warehousing methodology developed by Bill Inmon. It focuses on creating a centralized data model called the Data Warehouse (DW) and emphasizes the importance of data integration and consistency.
Data transformations in the Inmon methodology typically occur during the extraction, transformation, and loading (ETL) process, which populates the Data Warehouse. Here are some common data transformations that can be applied within the Inmon methodology:
- Data Extraction: Data is extracted from various operational systems, such as transactional databases, flat files, or external sources. The extraction process involves identifying the relevant data and extracting it into a staging area for further processing.
- Data Cleansing: Data cleansing involves removing or correcting any errors, inconsistencies, or inaccuracies in the extracted data. This may include removing duplicate records, handling missing values, standardizing formats, and resolving conflicts or discrepancies.
- Data Integration: Data integration combines data from multiple sources into a unified format. It involves mapping and transforming the data to a common structure, ensuring consistency in data types, formats, and definitions across different data sources. Integration may also involve resolving semantic differences and harmonizing data values.
- Data Conforming: Data conforming aligns the integrated data with the predefined Data Warehouse model. This step involves mapping and transforming the data to match the structure, schema, and business rules of the Data Warehouse. It ensures that the data is in a consistent and standardized format suitable for analysis.
- Data Aggregation: Data aggregation involves summarizing and consolidating data at different levels of granularity. Aggregation can be performed to create higher-level summaries or pre-calculated measures that support efficient querying and reporting. Aggregations can help improve query performance and reduce the complexity of analytical queries.
- Data Enrichment: Data enrichment involves enhancing the extracted data by adding additional relevant information from external sources or by deriving new attributes. This may include appending demographic data, geolocation data, or performing calculations to derive new metrics or indicators.
- Data Loading: After transformation, the cleansed, integrated, conformed, and enriched data is loaded into the Data Warehouse. This step involves inserting or updating the transformed data into the appropriate tables or structures of the Data Warehouse.
These data transformations are iterative and may be performed in multiple stages as part of the ETL process. The goal is to transform raw, heterogeneous, and inconsistent data into a consistent, integrated, and reliable format suitable for reporting, analysis, and decision-making within the Inmon methodology.