Intelligent Big Data Analytics: A Managerial Perspective

Intelligent Big Data Analytics: A Managerial Perspective

Zhaohao Sun (Papua New Guinea University of Technology, Papua New Guinea)
Copyright: © 2019 |Pages: 19
DOI: 10.4018/978-1-5225-7277-0.ch001

Abstract

Intelligent big data analytics is an emerging paradigm in the age of big data, analytics, and artificial intelligence (AI). This chapter explores intelligent big data analytics from a managerial perspective. More specifically, it first looks at the age of trinity and argues that intelligent big data analytics is at the center of the age of trinity. This chapter then proposes a managerial framework of intelligent big data analytics, which consists of intelligent big data analytics as a science, technology, system, service, and management for improving business decision making. Then it examines intelligent big data analytics for management taking into account four managerial functions: planning, organizing, leading, and controlling. The proposed approach in this chapter might facilitate the research and development of intelligent big data analytics, big data analytics, business intelligence, artificial intelligence, and data science.
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Introduction

Big data and big data analytics have become the important frontier for innovation, research and development (Chen & Zhang, 2014) (Laney & Jain, 2017). Big data analytics has big market opportunities. For example, IDC forecasts that big data and analytics-related services marketing in Asia/Pacific (Excluding Japan) region will grow from US$3.8 billion in 2016 to US$7.0 billion in 2019 at a 16.3% CAGR (compound annual growth rate) (Roche, 2016). Big data and its emerging technologies including big data analytics have been not only making big changes in the way the business operates but also making traditional data analytics and business analytics bring forth new big opportunities for academia and enterprises (Sun, Sun, & Strang, 2016, Sun, Strang, & Yearwood, 2014; Sun, Zou, & Strang, 2015; McAfee & Brynjolfsson, 2012).

Artificial intelligence (AI) is becoming a core business and analytic competency to transform business processes, reconfigure workforces, optimize infrastructure and blend industries (Laney & Jain, 2017). Gartner predicts that 30% of new revenue growth from industry-specific solutions will include AI technology by 2021 (Laney & Jain, 2017).

Intelligent big data analytics is an emerging science and technology based on AI, and is becoming a mainstream market adopted broadly across industries, organizations, and geographic regions and among individuals to facilitate decision making for business and individual to achieve desired business outcomes (Laney & Jain, 2017) (Sun, Sun, & Strang, 2018) (Howson, Sallam, & Richa, 2018). However, the following issues have not been drawn significant attention in both academia and industries.

  • What is the foundation of intelligent big data analytics?

  • What is a managerial perspective on intelligent big data analytics?

This chapter will address these two research questions through exploring intelligent big data analytics from a managerial perspective. The first key contribution of this paper is to propose that intelligent big data analytics is at the center of the age of trinity. The second key contribution of this paper is to present a managerial framework of intelligent big data analytics. The framework demonstrates intelligent big data analytics as a science, technology, system, service and management. The third key contribution of this paper is to examine intelligent big data analytics for management as intelligent big data analytics for planning, organizing, leading and controlling.

The remainder of this chapter is organized as follows: Section 2 explores the age of trinity: age of big data, the age of analytics, and the age of AI, and shows that intelligent big data analytics is at the center in the age of trinity. Section 3 proposes a managerial framework of intelligent big data analytics. Section 4 discusses intelligent big data analytics as a management. Sections 5 and 6 provides discussion and implications as well as future research directions of this research. The final section ends this chapter with some concluding remarks and future research directions.

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An Age Of Trinity: Big Data, Analytics, And Artificial Intelligence

This section first briefly overviews the age of big data, the age of analytics, and the age of AI, and then looks at the age of trinity and shows that intelligent big data analytics plays a central role in the age of trinity.

Key Terms in this Chapter

Artificial Intelligence (AI): Science and technology concerned with imitating, extending, augmenting, and automating the intelligent behaviors of human beings.

Big Data: Data with at least one of the ten big characteristics consisting of big volume, big velocity, big variety, big veracity, big intelligence, big analytics, big infrastructure, big service, big value, and big market.

Intelligent Big Data Analytics: Science and technology about collecting, organizing, and analyzing big data to discover patterns, knowledge, and intelligence as well as other information within the big data based on artificial intelligence and intelligent systems.

Management: The process of manager’s coordinating and overseeing the work activities of others so that their activities are completed.

Machine Learning: Is concerned with how computer can adapt to new circumstances and to detect and extrapolate patterns.

Data Science: A field that builds on and synthesizes a number of relevant disciplines and bodies of knowledge, including statistics, informatics, computing, communication, management, and sociology to translate data into information, knowledge, insight, and intelligence for improving innovation, productivity, and decision making.

Intelligent System: A system that can imitate, automate some intelligent behaviors of human beings. Expert systems and knowledge-based systems are examples of intelligent systems.

Data Mining: A process of discovering various models, summaries, and derived values, knowledge from a given collection of data.

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