Abstract
This chapter explores the elements of intelligent business analytics and addresses the following research questions: What are the elements, principles, technologies, and tools of intelligent business analytics? How can one incorporate the latest intelligent technologies into business analytics? This research highlights that big data and big DIKIW are the elements for intelligent business analytics; big data analytics and intelligent big data analytics and big DIKIW and intelligent big DIKIW analytics are the foundation of intelligent business analytics; the principles, technologies, and tools of intelligent business analytics are emerging strategic sources for organizations and individuals in the competitive global environment. This chapter presents a framework for DIKIW-driven intelligent business analytics through incorporating DIKIW analytics into intelligent business analytics. The proposed approach might facilitate research and development of intelligent business, big data analytics, business intelligence, AI, and data science.
TopIntroduction
Intelligent business analytics has drawn increasing attention in industries. Google search engine for “intelligent business analytics” shows about 36,700 results (retrieved on August 26, 2021). Some companies have used intelligent business analytics as their marketing mission. For example, Exigy has promoted intelligent business analytics as a cutting-edge technology that can digitally transform business and solve business challenges through providing “accurate insights, on-demand everywhere” (https://www.exigy.com/intelligent-business-analytics/).
Artificial intelligence (AI) and business intelligence (BI) have been already applied in business analytics for decades (Eiloart, 2018; Richardson, Schlegel, Sallam, Kronz, & Sun, 2021). Gartner predicts that 30% of new revenue growth from industry-specific solutions will include AI technology in 2021 (Laney & Jain, 2017). AI-derived business value is forecasted to increase from $US1.2 trillion in 2018 to $US3.9 trillion in 2022 (Pettey & van der Meulen, 2018). International Data Corporation (IDC) forecasts that big data and business analytics revenue will increase to $274.3 billion by 2022 with a five-year compound annual growth rate (CAGR) of 13.2% from 2018 (IDC, 2019).
Furthermore, a Google Scholar search for “intelligent business analytics” (August 27, 2021) found only 89 results. This implies that intelligent business analytics is still an emerging research area in academia although business analytics has been around us for decades (Davenport, December 2013; Delena & Demirkanb, 2013) and drawn increasing attention in the past decade (Sun Z., 2020). The following are still pressing issues for the academia, industries, and governments, based on our preliminary analysis:
- 1.
What are the elements, principles, techniques, and tools of intelligent business analytics and their interrelationships?
- 2.
What is the relationship between intelligent big data analytics and intelligent business analytics?
- 3.
How can we incorporate the latest intelligent techniques into business analytics applications?
This research addresses the above-mentioned research issues. More specifically, this chapter identifies and explores the elements, principles, technologies, and tools of intelligent business analytics through an investigation into the state-of-the-art scholars’ publications and market analysis of advanced analytics. It examines intelligent business analytics as integration of AI, other intelligent techniques, and business big data analytics. This research demonstrates that not only big data and big data analytics but also DIKIW (data, information, knowledge, intelligence, wisdom) and DIKIW analytics constitute the foundation of intelligent business analytics. The chapter presents a framework for DIKIW-driven intelligent business analytics through incorporating DIKIW analytics into intelligent business analytics.
The remainder of this chapter is organized as follows: Section 2 identifies and looks at the elements of intelligent business analytics. Section 3 examines the principles of intelligent business analytics. Sections 4 and 5 discuss the technologies and tools for intelligent business analytics. Section 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 work.
Key Terms in this Chapter
SMACS Technology: Includes social technology, mobile technology, analytics technology, cloud technology, and service technology.
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 Analytics: science and technology about collecting, organizing, and analyzing big data, big information, big knowledge, and big wisdom to transform them into intelligent information, intelligent knowledge, and intelligent wisdom based on artificial intelligence and analytical algorithms and technologies. Intelligent analytics consists of big DIKIW analytics and intelligent big DIKIW analytics.
DIKIW: An abbreviation of data, information, knowledge, intelligence, and wisdom. The latter are the elements of many disciplines such as computing, AI, business, and management.
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.
Artificial Intelligence (AI): Science and technology concerned with understand, imitate, extend, augment, and automate intelligent behaviors of human beings and others including machines.
Machine Learning: Concerned about how computer can adapt to new circumstances and to detect and extrapolate patterns.
Data Mining: A process of discovering various models, summaries, and derived values, knowledge from a given collection of data. Alternatively, it is the process of using statistical, mathematical, logical, AI methods and tools to extract useful information, knowledge, and intelligence from large database.