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Information Repositories Component

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Information Repositories Supported by the SAFE Architecture

Shared Information Repositories typically support reporting and analysis across the enterprise. Typically they are populated by hundreds of downstream feeds from the production environments. Most of these downstream feeds have just evolved over time with little documentation to support maintenance. They are a major barrier to the transition from legacy systems to new production environments.

Reference data and master data can be used to support operational or analytical processes. This information is typically represented across many systems; data mastering rules for how this information is synchronised are critical to ensuring the integrity of the overall information environment.

Many organisations have a warehouse environment that is an example of a shared repository of corporate information. It will receive feeds from both private (application) data stored and shared repositories.

Contents

Application Data Store

Application Data Stores are databases associated with specific enterprise applications. They may also be accessible by other applications, or they may provide an API layer to access data or functionality associated with the application. Generally, they are not common or shared data stores but may provide this role in some cases.

Integrated Operational Data Store

The Integrated Operational Data Store (iODS) is used to store data that is common to the enterprise and utilized in real-time on a transactional basis. Additionally used for operational reporting. Master Data Stores can provide iODS, transactional and anlaytical roles. The primary role of the iODS is provide a mechanism to relate data from a number of federated producer systems (ensuring referential integrity across systems), provide a means to improve other aspects of data quality such as accuracy and to provide a set of common data elements to simplify integration.

The properties of the iODS are as folows:

  • Integrated: Data from disparate producer systems is consolidated into a consistent view of the enterprise to ensure referential integrity across systems
  • Subject-Oriented: Data is stored grouped by business subject areas, rather then optimal transactional processing
  • Detailed-Oriented: Data in the iODS is at the same level of granularity as the operational systems, with no additional aggregates or summaries.
  • Volatile: data is permanently added, updated and deleted to the iODS to provide a snapshot of the current business environment;
  • Current-valued: there is no long term history in the iODS; it usually stores one day/week/month worth of data;

At this very high level, the above description aligns with the definition of an Operation Data Store in the “Corporate Information Factory".[1]

The High Level Solution Architecture of the iODS describes this component in more detail.

Data Warehouse

A Data Warehouse is used to store data that is common to the enterprise and utilized for reporting and analytical purposes. It consists of data that has been copied from other sources (application data stores and operational data stores) on a periodic basis. Data warehouses are created to support reporting and analytical research. They employ data models that are different from application and operational data stores to support these requirements. They also allow reporting and analytics to be done without adversely affecting transactional performance in the other data stores.

Common properties of the Data Warehouse as described by the SAFE Architecture are as follows:

  • Integrated: Data from disparate producer systems is consolidated into a consistent view of the enterprise to ensure referential integrity across systems
  • Subject-Oriented: Data is stored grouped by business subject areas, rather then optimal transactional processing
  • Detailed-Oriented: Data in the Data Warehouse is at the same level of granularity as the operational systems. It may also contain aggregate and summary data.
  • Non-Volatile: data is non-volatile in that is can always be tracked to a certain point in time; all data is measurable against a time dimension
  • Current-valued: The Data Warehouse stores long-term historical data

Data Mart

Data Marts are data stores that are built from extracts from the data warehouse and sometimes from application data stores. These employ specific data models for the specific domains, reports or analytics they are intended to support. Typical domains may be related to either a business area or a specific focus such as profit, sales, marketing or revenue.

References

  1. The Corporate Information Factory, W. H. Inmon, Claudia Imhoff, Ryan Sousa (Wiley, 2001).
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