Journal of International Business and Management (JIBM)

Print ISSN: 2616-5163

 Online ISSN: 2616-4655

Aims and Scope |  Author Guidelines |  Current Issue |  Archive |  Indexing |  Editorial Board |  Contact Us


Volume 3 | Issue 3 | 2020


Title: Moving from Big Data to Smart Data for Enhanced Performance, Business Efficiency, and New Business Models

Author(S): Marcel Schramm*; Mathew Shafaghi

Corresponding Author Affiliation*: University of Bolton, United Kingdom (UK)

Abstract:

In recent years the use and application of data including; Big Data, AI, Intelligent Data, Forecasting, Planning, and Warehousing tools have provided businesses with significant opportunities and potential for competitive advantages. However, this is the beginning of the journey. The notion that data is used for generating intuition is gradually being rejected by organisation, and the focus is more on how to leverage the full potential of data. The term Big Data was introduced in 1980 and contains a number of different fields including messages, images, posts on social media networks, GPS signals from phones, and business data. Whilst Big Data can assist in improving performance, efficient operations, customised promotion, and new business models, the success of Big Data relies heavily on Smart Data. Smart Data first appeared in the literature in 2009 and defined as use of relevant data for supporting decision making process. Smart Data is viewed by some as a foundation for business intelligence and bring many benefits to data driven organisations including; Data Modelling, Data, Analytics, Access Control in line with data governance, and Data Aggregation. This paper provides a critical review of literature dealing with both Big Data and Smart Data with a view to recommend a conceptual framework for Smart Data Framework based on four levels of Strategy, Resources, Operations, and Design.

Keywords: Smart Data, Big Data, Business Performance, Data Analytics

DOI: https://doi.org/10.37227/jibm-2020-02-16

Full-Text [PDF]


 

Disclaimer| Privacy Policy| Terms & Conditions| This work is licensed under a Creative Commons | Attribution-NonCommercial 3.0 Unported License.
Copyright © 2018, RPA Journals. All Right Reseverd