Abstract
Intelligent analytics is an emerging paradigm in the age of big data, analytics, and artificial intelligence (AI). This chapter explores the nature of intelligent analytics. More specifically, this chapter identifies the foundations, cores, and applications of intelligent big data analytics based on the investigation into the state-of-the-art scholars' publications and market analysis of advanced analytics. Then it presents a workflow-based approach to big data analytics and technological foundations for intelligent big data analytics through examining intelligent big data analytics as an integration of AI and big data analytics. The chapter also presents a novel approach to extend intelligent big data analytics to intelligent analytics. The proposed approach in this chapter might facilitate research and development of intelligent analytics, big data analytics, business analytics, business intelligence, AI, and data science.
Top1 Introduction
Big data, analytics, Artificial Intelligence (AI) and their integration are at the frontier for revolutionizing our work, life, business, management, and organization as well as healthcare, finance, e-commerce, and web services (Henke & Bughin, 2016) (Lohr, 2012 February 11) (John, 2013) (Sun & Huo, 2019) (Chen & Zhang, 2014) (Laney & Jain, 2017) (Russell & Norvig, 2010). 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, Zou, & Strang, 2015; McAfee & Brynjolfsson, 2012). Big data analytics has big market opportunities. For example, International Data Corporation (IDC) forecasts that big data and business analytics (BDA) revenue will be $274.3 billion BY 2022 with a five-year compound annual growth rate (CAGR) of 13.2% from 2018 to 2022 (IDC, 2019).
AI and business intelligence (BI) have penetrated into modern analytics that at least includes augmented analytics, embedded analytics, mobile analytics, and cloud analytics (Eiloart, 2018) (Howson, Richardson, Sallam, & Kronz, 2019). For example, Amazon Web Services (AWS): Amazon QuickSight is a cloud analytics and BI service for performing ad hoc analysis and publishing interactive dashboards (Howson, Richardson, Sallam, & Kronz, 2019). Gartner predicts that 30% of new revenue growth from industry-specific solutions will include AI technology by 2021 (Laney & Jain, 2017). AI-derived business value is forecasted to increase to $US3.9 trillion in 2022 from $US1.2 trillion of 2018 (Pettey & van der Meulen, 2018), 325% jump! IDC predicted global spending on AI systems will more than double to $79.2 billion in 2022 with a compound annual growth rate (CAGR) of 38.0% over the 2018-2022 forecast period (IDC, 2019).
Intelligent big data analytics is an emerging science and technology based on AI (Russell & Norvig, 2010), and is becoming a mainstream market adopted broadly across industries, organizations, and geographic regions and among individuals to facilitate decision making for businesses and individuals to achieve desired business outcomes (Laney & Jain, 2017) (Sun, Sun, & Strang, 2018) (Sun Z., 2019) (INFORMS, 2014). Intelligent big data analytics in particular and intelligent analytics in general have become a disruptive technology for effective innovation and decision making in the digital age (Holsapplea, Lee-Postb, & Pakath, 2014) (Davis, 2014). However, the following issues have still been ignored to some extent in academia, industries, and governments.
- 1.
What are fundamentals of intelligent analytics?
- 2.
What is the relationship between big data analytics and intelligent analytics?
- 3.
How can we integrate big data analytics and AI?
This chapter will address these three research issues through exploring the nature of intelligent analytics. More specifically, this chapter identifies the theoretical and technological foundations, cores, and applications of intelligent big data analytics through an investigation into the state-of-the-art scholars’ publications and market analysis of advanced analytics. Then it examines intelligent big data analytics as an integration of AI and big data analytics through presenting a workflow-based approach to big data analytics and technological foundations for intelligent big data analytics. The chapter uses a multidisciplinary approach to significantly extend intelligent big data analytics to intelligent analytics and looks at augmented analytics as a kind of intelligent analytics.
The remainder of this chapter is organized as follows: Section 2 identifies foundations, cores, applications as fundamentals of intelligent big data analytics. Section 3 argues that intelligent big data analytics = big data analytics + AI. This is a basis and motivation for Section 4. Section 4 presents an inclusive approach to intelligent analytics. Section 5 examines augmented analytics as intelligent analytics. It is an example of intelligent analytics taking into account the state-of-the-art advanced analytics in the global market of analytics. Sections 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
Data Mining: Is a process of discovering various models, summaries, and derived values, knowledge from a given collection of data. Another definition is that it is the process of using statistical, mathematical, logical, AI methods and tools to extract useful information from large databases.
Artificial Intelligence (AI): Is science and technology concerned with imitating, extending, augmenting, automating intelligent behaviors of human beings.
Intelligent Analytics: Is science and technology about collecting, organizing and analyzing big data, big information, big knowledge and big wisdom to transform them to intelligent information, intelligent knowledge, and intelligent wisdom based on artificial intelligence and analytical algorithms and technologies.
Big Data: Is 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: Is 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.
Machine Learning: Is concerned about how computers can adapt to new circumstances and to detect and extrapolate patterns.
Data Science: Is 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: Is a system that can imitate, automate some intelligent behaviors of human beings. Expert systems and knowledge-based systems are examples of intelligent systems.