Introduction - Data Warehouse Systems: Design and Implementation - page 2

Chapter 1
Introduction
Organizations today are facing increasingly complex challenges in terms of
management and problem solving in order to achieve their operational goals.
This situation compels people in those organizations to utilize analysis tools
that can better support their decisions. Business intelligence comprises a
collection of methodologies, processes, architectures, and technologies that
transform raw data into meaningful and useful information for decision
making. Business intelligence and decision-support systems provide
assistance to managers at various organizational levels for analyzing strategic
information. These systems collect vast amounts of data and reduce them
to a form that can be used to analyze organizational behavior. This data
transformation comprises a set of tasks that take the data from the sources
and, through extraction, transformation, integration, and cleansing processes,
store the data in a common repository called a data warehouse .Data
warehouses have been developed and deployed as an integral part of decision-
support systems to provide an infrastructure that enables users to obtain
ecient and accurate responses to complex queries.
A wide variety of systems and tools can be used for accessing, analyzing,
and exploiting the data contained in data warehouses. From the early days
of data warehousing, the typical mechanism for those tasks has been online
analytical processing ( OLAP ). OLAP systems allow users to interactively
query and automatically aggregate the data contained in a data warehouse.
In this way, decision makers can easily access the required information and
analyze it at various levels of detail. Data mining tools have also been used
since the 1990s to infer and extract interesting knowledge hidden in data
warehouses. From the beginning of the twenty-first century, a large number
of new business intelligence techniques have been developed and used to assist
decision making. Thus, the business intelligence market is shifting to provide
sophisticated analysis tools that go beyond the data navigation techniques
that popularized the OLAP paradigm. This new paradigm is generically
called data analytics . The business intelligence techniques used to exploit a