Page 102 - DCAP208_Management Support Systems

Unit 7: Business Analytics and Data Visualization

Notes
7.5.11 Water Resources
7.5.12 GIS in Agriculture and Soil

7.6 Summary
7.7 Keywords
7.8 Review Questions
7.9 Further Readings

Objectives

After studying this unit, you will be able to:
Discuss the Concept of Business Analytics
Explain Data Visualization

Discuss the Concept of Online Analytical Processing (OLAP)
Explain the Concept of GIS

Introduction

Business analytics is a term that refers to the applications, practices, skills, and technologies that
are necessary for a complete investigation of a company’s past business performance. Data
visualization is a general term used to describe any technology that lets corporate executives
and other end users “see” data in order to help them better understand the information and put
it in a business context. Data analysis is a process that requires the reviewing of data to determine
trends and abnormalities for a business. This is a daunting task because large data sets are
typically presented in a spread sheet format. Data visualization is the process of converting
standard textual data into pictures that are easily understood by a general audience. These
pictures typically include graphs, shapes, and abstract objects that are color coded based on the
significances of the data. In this unit, we will discuss the concept of business analytics and data
visualization.

7.1 An Overview of Business Analytics and Data Visualization

In this section, we will discuss an overview of Business Analytics and Data Visualization.

7.1.1 Business Analytics

Business Analytics (BA) is the practice of iterative, methodical exploration of an organization’s
data with emphasis on statistical analysis. Business analytics is used by companies committed to
data-driven decision making.
BA is used to gain insights that inform business decisions and can be used to automate and
optimize business processes. Data-driven companies treat their data as a corporate asset and
leverage it for competitive advantage. Successful business analytics depends on data quality,
skilled analysts who understand the technologies and the business and an organizational
commitment to data-driven decision making.
Examples of BA uses include:
Exploring data to find new patterns and relationships (data mining)

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