In today’s competitive global business environment, understanding and managing enterprisewide information is crucial for making timely decisions and responding to changing business conditions. Many companies are realizing a business advantage by leveraging one of their key assets - business data. There is a tremendous amount of data generated by day-to-day business operational applications. In addition there is valuable data available from external sources such as market research organizations, independent surveys and quality testing labs. Studies indicate that the amount of data in a given organization doubles every five years. Data Warehousing has emerged as an increasingly popular and powerful concept of applying information technology to turn this huge islands of data into meaningful information for better business decisions. Meta Group, Inc., a leading consultant in the data warehousing environment, suggests that over 90% of the Fortune 2000 businesses will put into place a data warehouse by the end of 1996.

According to Bill Inmon, known as the father of Data Warehousing, a data warehouse is a subject oriented, integrated, time-variant, nonvolatile collection of data in support of management decisions.

• Subject-oriented means that all relevant data about a subject is gathered and stored as a single set in a useful format;
• Integrated refers to data being stored in a globally accepted fashion with consistent naming conventions, measurements, encoding structures, and physical attributes, even when the underlying operational systems store the data differently;
• Non-volatile means the data warehouse is read-only: data is loaded into the data warehouse and accessed there;
• Time-variant data represents long-term data--from five to ten years as opposed to the 30 to 60 days time periods of operational 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. Informational data is typically:

Relational databases store data in a two dimensional format: tables of data represented by rows and columns. Multi-dimensional analysis solutions, commonly referred to as On-Line Analytical Processing (OLAP) solutions, offer an extension to the relational model to provide a multi-dimensional view of the data. For example, in multi-dimensional analysis, data entities such as products, geographies, time periods, store locations, promotions and sales channels may all represent different dimensions. Multi-dimensional solutions provide the ability to:

• analyze potentially large amounts of data with very fast response times
• "slice and dice" through the data, and drill down or roll up through various dimensions as defined by the data structure
• quickly identify trends or problem areas that would otherwise be missed

Multi-dimensional data structures can be implemented with multidimensional databases or extended RDBMSs. Relational databases can support this structure through specific database designs (schema), such as "star-schema", intended for multi-dimensional analysis and highly indexed or summarized designs. These structures are sometimes referred to as relational OLAP (ROLAP)-based structures.

Data mining predict future trends and behaviors, allowing businesses to make proactive, knowledge driven decisions. 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. For instance, data mining can find patterns in your data to answer questions like:

• what item purchased in a given transaction triggers the purchase of additional related items?
• how do purchasing patterns change with store location?
• what items tend to be purchased using credit cards, cash, or check?
• how would the typical customer likely to purchase these items be described?
• did the same customer purchase related items at another time?

Fig 1: Data Warehousing Architecture Model

The following components should be considered for a successful implementation of a Data Warehousing solution:

• Open Data Warehousing architecture with common interfaces for product integration
• Data Modeling with ability to model star-schema and multi-dimemsionality
• Extraction and Transformation/propagation tools to load the data warehouse
• Data warehouse database server
• Analysis/end-user tools: OLAP/multidimensional analysis, Report and query
• Tools to manage information about the warehouse (Metadata)
• Tools to manage the Data Warehouse environment

Creating the informational data, that is, the data warehouse, from the operational systems is a key part of the overall data warehousing solution. Building the informational database is done with the use of transformation or propagation tools. These tools not only move the data from multiple operational systems, but often manipulate the data into a more appropriate format for the warehouse. This could mean:

• The creation of new fields that are derived from existing operational data
• Summarizing data to the most appropriate level needed for analysis
• Denormalizing the data for performance purposes
• Cleansing of the data to ensure that integrity is preserved.

Even with the use of automated tools, however, the time and costs required for data conversion are often significant. Bill Inmon has estimated 80% of the time required to build a data warehouse is typically consumed in the conversion process.

Once ready, data is loaded into a relational database management system (RDBMS) which acts as the data warehouse. Some of the requirements of database servers for data warehousing include: Performance, Capacity, Scalability, Open interfaces, Multiple-data structures, optimizer to support for star-schema, and Bitmapped indexing . Some of the popular data stores for data warehousing are relational databases like Oracle, DB2, Informix or specialized Data Warehouse databases like RedBrick, SAS.

A well designed and implemented data warehouse can be used to:

• Understand business trends and make better forecasting decisions
• Bring better products to market in a more timely manner
• Analyze daily sales information and make quick decisions that can significantly affect your company's performance

Data warehousing can be a key differentiator in many different industries. At present, some of the most popular Data warehouse applications include:

Data Warehousing provides the means to change raw data into information for making effective business decisions--the emphasis on information, not data. The data warehouse is the hub for decision support data. A good data warehouse will... provide the RIGHT data... to the RIGHT people... at the RIGHT time: RIGHT NOW! While data warehouse organizes data for business analysis, Internet has emerged as the standard for information sharing. So the future of data warehousing lies in their accessibility from the Internet. Successful implementation of a data warehouse requires a high-performance, scaleable combination of hardware and software which can integrate easily with existing systems, so customers can use data warehouses to improve their decision-making--and their competitive advantage.