A Review of Data Analytics Adoption in Business Industry

Chong, Fong Kim and Deshinta, Arrova Dewi (2019) A Review of Data Analytics Adoption in Business Industry. INTI JOURNAL, 2019 (34). ISSN e2600-7320

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Abstract

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.

Item Type: Article
Uncontrolled Keywords: Descriptive analytics, Predictive analytics, Big data
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Information Technology
Depositing User: Unnamed user with email masilah.mansor@newinti.edu.my
Date Deposited: 19 Nov 2019 03:08
Last Modified: 17 Apr 2024 04:56
URI: http://eprints.intimal.edu.my/id/eprint/1307

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