Data Warehouse Architectures -- <One> Inmon’s Corporate Information Factory

A highly simplified depiction of the Corporate Information Factory appears in Figure.

To understand this architecture, start by looking at the left side of the diagram. There, you will find the operational systems, or transaction systems, that support the business. The data stores associated with these systems may take a number of different forms, including hierarchical data, relational data, and even simple spreadsheets. For the sake of simplicity, only four operational systems are depicted.

These systems feed a process labeled ETL for “extract, transform, load.” This process consolidates information from the various operational systems, integrates it, and loads it into a single repository called the enterprise data warehouse. This processing step is nontrivial. It may require accessing information in a variety of different formats, resolving differing representations of similar things, and significant restructuring of data. Some organizations refer to this process as data integration. It may be a batch process that runs periodically or a transaction-based process that occurs in near real time. The final result is the same: the enterprise data warehouse.

The enterprise data warehouse is the hub of the corporate information factory. It is an integrated repository of atomic data. Integrated from the various operational systems, it contains a definitive and consistent representation of business activities in a single place. Atomic in nature, the data in this repository is captured at the lowest level of detail possible.

In the Corporate Information Factory architecture, the enterprise data warehouse is not intended to be queried directly by analytic applications, business intelligence tools, or the like. Instead, its purpose is to feed additional data stores dedicated to a variety of analytic systems.

Data marts are databases that support a departmental view of information. With a subject area focus, each data mart takes information from the enterprise data warehouse and readies it for analysis. As the earlier quotation suggests, Inmon advocates the use of dimensional design for these data marts. The data marts may aggregate data from the atomic representation in the enterprise data warehouse.

The data marts serve as the focus for analytic activities, which may include queries, reports, and a number of other activities. These activities are enabled by a variety of different tools, including some that are commonly referred to as business intelligence tools and reporting tools.