History of Data Warehousing
Data warehouses extend the transformation of data into information
In the 1990’s executives became less concerned with the day-to-day business operations and more concerned with overall business functions
The data warehouse provided the ability to support decision making without disrupting the day-to-day operations
Data Warehouse Fundamentals
Data warehouse – a logical collection of information – gathered from many different operational databases – that supports business analysis activities and decision-making tasks
The primary purpose of a data warehouse is to aggregate information throughout an organization into a single repository for decision-making purposes
Extraction, transformation, and loading (ETL) – a process that extracts information from internal and external databases, transforms the information using a common set of enterprise definitions, and loads the information into a data warehouse
Data mart – contains a subset of data warehouse information
Multidimensional Analysis and Data Mining
Databases contain information in a series of two-dimensional tables
In a data warehouse and data mart, information is multidimensional, it contains layers of columns and rows
Dimension – a particular attribute of information
Data mining – the process of analyzing data to extract information not offered by the raw data alone
To perform data mining users need data-mining tools
Data-mining tool – uses a variety of techniques to find patterns and relationships in large volumes of information and infers rules that predict future behavior and guide decision making
Information Cleansing or Scrubbing
An organization must maintain high-quality data in the data warehouse
Information cleansing or scrubbing – a process that weeds out and fixes or discards inconsistent, incorrect, or incomplete information
Business intelligence – information that people use to support their decision-making efforts
Principle BI enablers include:
Even the smallest company with BI software can do sophisticated analyses today that were unavailable to the largest organizations a generation ago.
The largest companies today can create enterprisewide BI systems that compute and monitor metrics on virtually every variable important for managing the company.
How is this possible? The answer is technology—the most significant enabler of business intelligence.
A key responsibility of executives is to shape and manage corporate culture.
The extent to which the BI attitude flourishes in an organization depends in large part on the organization’s culture.
Perhaps the most important step an organization can take to encourage BI is to measure the performance of the organization against a set of key indicators.