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.
