Bill Inmon’s Corporate Information Factory (CIF) is one of the two primary patterns for data warehousing. The component parts of Inmon’s definition of a data warehouse, “a subject oriented, integrated, time variant, and nonvolatile collection of summary and detailed historical data,” describe the concepts that support the CIF and point to the differences between warehouses and operational systems.

• Subject-oriented: The data warehouse is organized based on major business entities, rather than focusing on a functional or application.

• Integrated: Data in the warehouse is unified and cohesive. The same key structures, encoding and decoding of structures, data definitions, naming conventions are applied consistently throughout the warehouse. Because data is integrated, Warehouse data is not simply a copy of operational data. Instead, the warehouse becomes a system of record for the data.

• Time variant: The data warehouse stores data as it exists in a set point in time. Records in the DW are like snapshots. Each one reflects the state of the data at a moment of time. This means that querying data based on a specific time period will always produce the same result, regardless of when the query is submitted.

• Non-volatile: In the DW, records are not normally updated as they are in operational systems. Instead, new data is appended to existing data. A set of records may represent different states of the same transaction.

• Aggregate and detail data: The data in the DW includes details of atomic level transactions, as well as summarized data. Operational systems rarely aggregate data. When warehouses were first established, cost and space considerations drove the need to summarize data. Summarized data can be persistent (stored in a table) or non-persistent (rendered in a view) in contemporary DW environments. The deciding factor in whether to persist data is usually performance.

• Historical: The focus of operational systems is current data. Warehouses contain historical data as well. Often they house vast amounts of it. 

Inmon, Claudia Imhoff and Ryan Sousa describe data warehousing in the context of the Corporate Information Factory (CIF). See this figure. CIF components include:

• Applications: Applications perform operational processes. Detail data from applications is brought into the data warehouse and the operational data stores (ODS) where it can be analyzed.

• Staging Area: A database that stands between the operational source databases and the target databases. The data staging area is where the extract, transform, and load effort takes place. It is not used by end users. Most data in the data staging area is transient, although typically there is some relatively small amount of persistent data.

• Integration and transformation: In the integration layer, data from disparate sources is transformed so that it can be integrated into the standard corporate representation / model in the DW and ODS.

• Operational Data Store (ODS): An ODS is integrated database of operational data. It may be sourced directly from applications or from other databases. ODS’s generally contain current or near term data (30-90 days), while a DW contains historical data as well (often several years of data). Data in ODS’s is volatile, while warehouse data is stable. Not all organizations use ODS’s. They evolved as to meet the need for low latency data. An ODS may serve as the primary source for a data warehouse; it may also be used to audit a data warehouse.

• Data marts: Data marts provide data prepared for analysis. This data is often a sub-set of warehouse data designed to support particular kinds of analysis or a specific group of data consumers. For example, marts can aggregate data to support faster analysis. Dimensional modeling (using denormalization techniques) is often used to design user-oriented data marts.

• Operational Data Mart (OpDM): An OpDM is a data mart focused on tactical decision support. It is sourced directly from an ODS, rather than from a DW. It shares characteristics of the ODS: it contains current or near-term data. Its contents are volatile.

• Data Warehouse: The DW provides a single integration point for corporate data to support management decision-making, and strategic analysis and planning. The data flows into a DW from the application systems and ODS, and flows out to the data marts, usually in one direction only. Data that needs correction is rejected, corrected at its source, and ideally re-fed through the system.

• Operational reports: Reports are output from the data stores.

• Reference, Master, and external data: In addition to transactional data from applications, the CIF also includes data required to understand transactions, such as reference and Master Data. Access to common data simplifies integration in the DW. While applications consume current master and Reference Data, the DW also requires historical values and the timeframes during which they were valid (see Chapter 10).

This figure depicts movement within the CIF, from data collection and creation via applications (on the left) to the creation of information via marts and analysis (on the right). Movement from left to right includes other changes. For example,

• The purpose shifts from execution of operational functions to analysis
• End users of systems move from front line workers to decision-makers
• System usage moves from fixed operations to ad hoc uses
• Response time requirements are relaxed (strategic decisions take more time than do daily operations)
• Much more data is involved in each operation, query, or process

The data in DW and marts differs from that in applications:

• Data is organized by subject rather than function
• Data is integrated data rather than ‘siloed’
• Data is time-variant vs. current-valued only
• Data has higher latency in DW than in applications
• Significantly more historical data is available in DW than in applications