Business intelligence in enterprise computing environment

A Review of Research Relevant to the Emerging Industry Trends: Industry 4.0, IoT, Blockchain, and Business Analytics

Industry 4.0, Internet of Things, Blockchain, and Business Analytics are the hot research topics and have attracted much attention from scholars and practitioners in recent years. In order to identify the forces driving their development and to promote their development, this paper reviews the extant studies on these topics. The review provides a comprehensive overview of state-of-the-art researches on Industry 4.0, Internet of Things, Blockchain, and Business Analytics. The results assist scholars to figure out the directions of future studies on these topics.

Artificial Intelligence powered Internet of Things and smart public service

Purpose

The Internet of Things (IoT) has attracted a lot of attention in both industrial and academic fields for recent years. Artificial intelligence (AI) has developed rapidly in recent years as well. AI naturally combines with the Internet of Things in various ways, enabling big data applications, machine learning algorithms, deep learning, knowledge discovery, neural networks and other technologies. The purpose of this paper is to provide state of the art in AI powered IoT and study smart public services in China.

Design/methodology/approach

This paper reviewed the articles published on AI powered IoT from 2009 to 2018. Case study as a research method has been chosen.

Findings

The AI powered IoT has been found in the areas of smart cities, healthcare, intelligent manufacturing and so on. First, this study summarizes recent research on AI powered IoT systematically; and second, this study identifies key research topics related to the field and real-world applications.

Originality/value

This research is of importance and significance to both industrial and academic fields researchers who need to understand the current and future development of intelligence in IoT. To the best of authors’ knowledge, this is the first study to review the literature on AI powered IoT from 2009 to 2018. This is also the first literature review on AI powered IoT with a case study of smart public service in China.

Leveraging Fine-Grained Sentiment Analysis for Competitivity

The surge in the use of social media tools by most businesses and corporate society for varied purposes cannot be over emphasised. The two top social media sites heavily patronised by businesses are Facebook and Twitter. For companies to harness the business potential of social media to increase competitive advantage, sentiments behind textual data of their customers, fans and competitors must be monitored and analysed with keen interest. This paper demonstrates how companies in the Telecommunication industry can understand consumer opinions, frustrations and satisfaction through opinion mining analyses and interpret customers’ textual data to enhance competitiveness. Sentiment analysis that classifies positive, negative and neutral sentiments of customers of the top three telecommunication companies in Ghana (MTN, Vodafone and Tigo) is studied. The proposed method extracts “intelligence” from the classified customers’ comments and compares it with responses from the companies. The results show how customer sentiments can be harnessed into successful online advertising projects. Companies can use the results to enhance their responsiveness to customer-centred, improve on the quality of their service, integrate social sentiments into PR plan, develop a strategy for social media marketing and leverage on the advantages of online advertising.

Rationalising Business Intelligence Systems and Explicit Knowledge Objects: Improving Evidence-Based Management in Government Programs

Public sector programs often fail to leverage their business intelligence systems and explicit knowledge objects to drive efficiency and effectiveness. Given the current federal fiscal environment and the need for effective government — a catalyst to the requirement to use "evidence and rigorous evaluation in budget, management, and policy decisions" (OMB Memorandum M-12-14) — federal programs look to business intelligence as an evidence-based decision-making practice leading to a more lean government, improving efficiency and effectiveness. However, cost overruns, technical obstacles, and next-generation information challenges stemming from pervasive computing can reduce any perceived value of utilising explicit knowledge systems to support evidence in decision making. Through the evaluation of five diverse projects tasked to address the use of evidence in decision-making practices, this research shows that achieving contextualisation of information requirements, stakeholder alignment, and the complexity/feasibility of information integration are key factors that should be analysed to improve the evidence-based decision-making practice in government programs, and may be accomplished through a systematic approach, such as the rationalisation of business intelligence systems. Thus, a rationalisation framework is provided to facilitate the management of business intelligence systems geared towards a more efficient and effective use of explicit knowledge.