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Data Warehouse

What is a Data Warehouse?

Operational vs Informational data

Data Warehouse vs Data Mart

Components of a Data Warehouse

Learn more about Data Warehousing

What is a Data Warehouse?

The primary concept behind data warehousing is that the data stored for business analysis can most effectively be accessed by separating it from the data in the operational systems. A data warehouse, therefore, is a collection of data gathered from one or more data repositories to create a new, central database. For example a hospital may create a data warehouse by extracting the operational data it has accumulated concerning patient information, lab results, drug use, length of stay, disease state, etc,. Data Warehousing is not just the data in the warehouse, but also the architecture and tools to collect, query, analyze and present information.

The characteristics of a data warehouse were first defined by W.H. Inmon who stated, “a data warehouse is subject-oriented, integrated, time-variant and non-volatile [data] collection in support of management decision making processes”. Let’s break that definition down:

• Subject-oriented: all relevant data concerning a subject http://www.thearling.comis gathered and stored in a single database.
• Integrated: all data in the warehouse must be compatible with each other regardless of type or location.
• Time-variant: all data contains a reference to time so that the age of each piece of data can be determined.
• Non-volatile: the data does not change once it has been collected.

Operational / informational data:

Operational data is the data you use to run your business. This data is what is typically stored, retrieved, and updated by your Online Transactional Processing (OLTP) system. An OLTP system may be, for example, a reservations system, an accounting application, or an order entry application.

Informational data is created from the wealth of operational data that exists in your business and some external data useful to analyze your business. Informational data is what makes up a data warehouse. Once data has been extracted from the operational systems into the new database it is then referred to as historical data.

Data Warehouse vs. Data Mart

A data mart is simply a mini-data warehouse. More specifically it is a data warehouse designed for a specific purpose or to be analyzed by a particular group within the institution. Take the hospital example from above, a data mart could be created collecting various financial data specifically for analytical use by the accounting department. The data collected can be extracted either directly from the data warehouse or from individual repositories (which is where a data warehouse extracts it's data). Basically, a data warehouse collects a wide range of data types, while a data mart specifically involves only data the user will want.

Components of a Data Warehouse

• Database Servers: Operational data accumulated during standard business must be extracted and stored into a database. Most companies use a relational database stored on a mainframe server. Oracle, Sybase, SQL server and DB2 are just a few of the database systems available.

• Queries/Reports: Querying is a broad term that encompasses all the activities of requesting data from a data warehouse for analysis. Reports are then generated to display the results for the specified query. Querying, obviously, is the whole point of using the data warehouse.

• OLAP/ Multi-dimensional analysis: Relational databases store data in a two dimensional format; tables of data represented by rows and columns. Multi-dimensional analysis, commonly referred to as On-Line Analytical Processing (OLAP), offer an extension to the relational model to provide a multi-dimensional view of the data. These tools allow users to drill down from summary data sets into the specific data underlying the summaries. Statistical analysis tools provide summary information and help determine the degree of relationship between two factors.

• Data Mining: Data mining is the process of analyzing business data in the data warehouse to find unknown patterns or rules of information that you can use to tailor business operations. Data mining predicts future trends and behaviors, allowing businesses to make proactive, knowledge driven decisions.

More info...

Data Warehousing Information Center: Extensive site with a wide range of info.

Darwin Magazine : Provides a quick overview on the concepts behind data warehousing.

Data Warehouse Institute : Association for data warehouse professionals.

Data Warehouse.com : Focused mainly on the business application and management of a data warehouse.

National Center for Health Statistics : Center for disease control uses a data warehouse to analyze national health-related data.