Data Warehouses

"A Data Warehouse is a repository of integrated information, available for queries and analysis. Data and information are extracted from heterogeneous sources as they are generated....This makes it much easier and more efficient to run queries over data that originally came from different sources." (Data Warehousing, 205)

There are two fundamentally different types of information systems in all organizations: operational systems and informational systems.

"Operational systems" are just what their name implies; they are the systems that help us run the enterprise operation day-to-day. These are the backbone systems of any enterprise, our "order entry', "inventory", "manufacturing", "payroll" and "accounting" systems. Because of their importance to the organization, operational systems were almost always the first parts of the enterprise to be computerized. Over the years, these operational systems have been extended and rewritten, enhanced and maintained to the point that they are completely integrated into the organization. Indeed, most large organizations around the world today couldn't operate without their operational systems and the data that these systems maintain.

On the other hand, there are other functions that go on within the enterprise that have to do with planning, forecasting and managing the organization. These functions are also critical to the survival of the organization, especially in our current fast-paced world. Functions like "marketing planning", "engineering planning" and "financial analysis" also require information systems to support them. But these functions are different from operational ones, and the types of systems and information required are also different. The knowledge-based functions are informational systems.

"Informational systems" have to do with analyzing data and making decisions, often major decisions, about how the enterprise will operate, now and in the future. And not only do informational systems have a different focus from operational ones, they often have a different scope. Where operational data needs are normally focused upon a single area, informational data needs often span a number of different areas and need large amounts of related operational data.

A Data Warehouse Architecture (DWA) is a way of representing the overall structure of data, communication, processing and presentation that exists for end-user computing within the enterprise. The architecture is made up of a number of interconnected parts:

• Operational Database / External Database Layer

• Information Access Layer

• Data Access Layer

• Data Directory (Metadata) Layer

• Process Management Layer

• Application Messaging Layer

• Data Warehouse Layer

• Data Staging Layer

Designing data warehouses is very different from designing traditional operational systems. For one thing, data warehouse users typically don't know nearly as much about their wants and needs as operational users. Second, designing a data warehouse often involves thinking in terms of much broader, and more difficult to define, business concepts than does designing an operational system. In this respect, data warehousing is quite close to Business Process Reengineering (BPR).