\uD83D\uDCD8 Chapter Learning Objectives
Describe the role of business intelligence in providing comprehensive business decision support
Describe the architecture, reporting styles, evolution, and benefits of business intelligence
Differentiate between operational data and decision support data
Identify the purpose, characteristics, and components of a data warehouse
Develop star and snowflake schemas for decision-making purposes
Describe the characteristics and capabilities of online analytical processing (OLAP)
Describe the role and functions of data analytics and data mining
Explain how SQL analytic functions are used to support data analytics
Define data visualization and explain how it supports business intelligence
Define a data lake
Business intelligence (BI) is the collection of best practices and software tools developed to support business decision making in this age of globalization, emerging markets, rapid change, and increasing regulation. The complexity and range of information required to support business decisions has increased, and operational database structures were unable to support all of these requirements. Therefore, a new data storage facility, called a data warehouse, developed. The data warehouse extracts its data from operational databases as well as from external sources, providing a more comprehensive data pool.
Additionally, new ways to analyze and present decision support data were developed. Online analytical processing (OLAP) provides advanced data analysis and visualization tools, including multidimensional data analysis. This chapter explores the main concepts and components of business intelligence and decision support systems that gather, generate, and present information for business decision makers, focusing especially on the use of data warehouses, data analytics, and data visualization.