A Review of Data Analytics Adoption in Business Industry

This paper presents a review of data analytics adoption in various companies from the different business industry. An introductory analysis on how these companies solve problems encountered and relate their action plans with business analytics and/or business intelligent solutions especially in Cust...

Full description

Saved in:
Bibliographic Details
Main Authors: Chong, Fong Kim, Deshinta, Arrova Dewi
Format: Article
Language:English
Published: INTI International University 2019
Subjects:
Online Access:http://eprints.intimal.edu.my/1307/1/ij2019_34.pdf
http://eprints.intimal.edu.my/1307/
http://intijournal.intimal.edu.my
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper presents a review of data analytics adoption in various companies from the different business industry. An introductory analysis on how these companies solve problems encountered and relate their action plans with business analytics and/or business intelligent solutions especially in Customer Relationship Management (CRM), Supply Chain Management (SCM) and Decision Support Systems (DSS). Many business organizations and communities are finding and getting advantages from powerful knowledge by analyzing the resource of data analytics. Data analytics actually is only part of what big data can offers. By using analytics, an organization gets even greater value in order to serve its customer better through changing how they think, work and interact with them. Those new insights come from the powerful knowledge of big data and resulted in powerful actions to gain competitive advantages. Some companies manage to transform their business models by driving growth in new sectors using new ways with the help of big data and its analytics. This paper used three major companies in the study i.e. Starbucks, Heineken N.V and Electronic Arts (EA). The studies found that all those companies are able to meet their market supply and demands opportunities by utilizing their past historical data in the data warehouse to understand the underlying hidden trends and patterns that will enable them to make better decisions.