Data warehouse and it's stages

Data warehousing combines data from multiple, usually varied, sources into one comprehensive and easily manipulated database. Different methods can then be used by a company or organization to access this data for a wide range of purposes. Analysis can be performed to determine trends over time and to create plans based on this information. Data warehouse usually store detail data of an enterpirse.

Companies commonly use data warehousing to analyze trends over time. They might use it to view day-to-day operations, but its primary function is often strategic planning based on long-term data overviews. From such reports, companies make business models, forecasts, and other projections. Routinely, because the data stored in data warehouses is intended to provide more overview-like reporting, the data is read-only.

There has been an information evolution happening in the data warehouse environment in recent past. Changing business requirements have placed demands on data warehousing technology to do more things faster. Now Data warehouses have evolved from back room strategic decision support systems to operational, business-critical components of the enterprise. Expectation from data warehouse has evolved to real-time decision.

Various stages of data warehouse evaluation can be summarized as below

Stage 1 Reporting: The initial stage typically focuses on reporting from a single view of the business to drive decision-making across functional and/or product boundaries. Questions are usually known in advance, such as a weekly sales report.

Stage 2 Analyzing: Focuses on why something happened, such as why sales went down or discovering patterns in customer buying habits. Users perform ad-hoc analysis, slicing and dicing the data at a detail level, and questions are not known in advance.

Stage 3 Predicting: Analysts utilize the system to leverage information to predict what will happen next in the business to proactively manage the organization's strategy. This stage requires data mining tools and building predictive models using historical detail. As an example, users can model customer demographics for target marketing.

Stage 4 Operationalizing: Providing access to information for immediate decision-making in the field enters the realm of active data warehousing. Stages 1 to 3 focus on strategic decision-making within an organization. Stage 4 focuses on tactical decision support. Tactical decision support is not focused on developing corporate strategy, but rather on supporting the people in the field who execute it.

Examples:

• Inventory management with just-in-time replenishment.
• Scheduling and routing for package delivery.
• Altering a campaign based on current results.

Stage 5 Active Data Warehousing: The larger the role an ADW plays in the operational aspects of decision support, the more incentive the business has to automate the decision processes. You can automate decision-making when a customer interacts with a web site. Interactive customer relationship management (CRM) on a web site or at an ATM is about making decisions to optimize the customer relationship through individualized product offers, pricing, content delivery and so on. As technology evolves, more and more decisions become executed with event-driven triggers to initiate fully automated decision processes.

Example: determine the best offer for a specific customer based on a real-time event, such as a significant ATM deposit.