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"The concept of data warehousing goes back to the 1980s, when Oracle was transforming data from its transactional databases for analysis, and Teradata introduced the concept of a database machine dedicated to decision support." (Source: Jill Dyche, Baseline-Consulting). Soon after, IBM researchers Barry Devlin and Paul Murphy, in the quest to tackle the problem of enterprise integration, came up with the 'business data warehouse' in 1988. Around the mid-1990s, because of support systems such as symmetric multiprocessors, massively parallel processors, graphical user interface tools, and improvements in RDBMS (a DBMS in which data is stored in the form of tables), DW grew explosively.

Within two decades, DW has become indispensable for most organizations. From its traditional role of data storage, DW today embraces storage, retrieval, and analysis. It makes a lot of business sense for producers who want answers to various questions, such as, who are my most profitable customers, what is the most profitable product, what affects my revenues and margins, etc. A data warehouse that compiles data from various sources and keeps it in a unified, standardized, and integrated form is fast growing and maturing as it answers all the above questions on data management and control.

Moreover, DW, which can be in the form of digital libraries, scientific databases, personal databases, or over the Web, has shrunk the quantum of information that would have gone to piles of CDs and disk drives. Data management has witnessed a complete revolution with the development of DW; data which was maintained in tabular form can now be accessed with a click on the computer using hypertext transfer protocol (http).

A DW implementation involves substantial costs in terms of its set up, maintenance, and upgradation (DW technology gets obsolete sooner than other software used by organizations), as well as human costs on hiring and training of staff; these costs make DW implementation difficult for small organizations. However, small organizations would do well to undertake a DW cost-benefit analysis before opting out of it. The many benefits of an effective DW implementation include integrated internal and external information, support to strategic decisions and planning, long-term cost effectiveness, enhanced customer satisfaction, competitive advantage, customer tracking, and a focus on high revenue products, among others, empowering the organization to earn an ROI of 100 percent in the first year itself.

Moreover, DW is time variable (historic as well as current data), non-volatile (cannot be manipulated unlike physical books), and subject oriented (not on the basis of functions such as finance and marketing), which makes it even more advantageous. With all these merits, DW has become a strategic tool for decision makers.

Data Warehousing (DW) refers to electronically storing, retrieving, and analyzing an organization's data. It is the planning and strategic reporting in the organization and helps in effectively managing and controlling business. A data warehouse consolidates and reconciles information from various business divisions into one integrated database and helps measure performance, compliance, and profitability.

DW is a necessity in today's organizational set up and acts as a foundation for business intelligence (BI) architecture. It is very useful for data integration; data reporting and analysis; knowledge discovery and data mining; and, decision support and performance management. Owing to its advantages, companies are adopting DW and updating existing DW software, leading to market growth.

At the same time, DW has various shortcomings, some of which include cost concerns, inflexibility due to lack of full customization, and the inability to yield real-time data due to downtime. Despite these shortcomings, DW has become an integral part of organizations and is being widely used.

Our partners at Analysts Perspectives present an overview of analyst observations about DW and its evolution and their opinions and predictions about its future prospects.

Some key findings include the fact that tightening regulatory and compliance environment and value of maintaining more historical data online longer for better analytics and decision-making purposes are driving the growth of data warehouses; human costs — that is costs incurred during the manual processes of selecting, implementing, and training — is an important consideration when budgeting for DW; and current database scaling technologies are too cost prohibitive or inflexible to meet the ever-increasing demands of the business.