Big Data, Big Data Analytics, and Policymaking During a Global Pandemic

Big data and data analytics are currently the buzzwords in both academia and industry to become data driven. Big data has been the trending topic in the accounting industry also. Big data and data analytics will have an important impact on accounting and accountants. Big data will improve the quality of accounting information and the accounting profession will continue to provide real-time and dynamic information to assist in decision-making. The purpose of this research is to investigate the impact of big data and big data analytics in accounting. Data analytics is one of the most recent developments in the accounting context. This study is qualitative in nature and adopted a literature review methodology to gain a better understanding of the study area. This literature review seeks to provide a description and evaluation of the impact of big data analytics on accounting. This research found that big data presents great opportunities for decision making in accounting and risks analysis, which indicated that companies could improve their performance, measure performance, manage risks and allow effective real-time decision-making with data analytics. This research revealed that accountants can create more value in a world of big data analytics and encourages accountants to get started with big data to find answers to risks in business operations as well as understand financial performance. It shows that relying on big data analytics will open new possibilities for accountants. This study contributes to the research literature in the area of big data analytics on accounting. The limitations of this study are that it utilizes few recent peer reviewed articles in the general accounting practice, therefore not exhaustive in describing how big data and big data analytics impacts accounting.

The unprecedented proliferation of miniaturized sensors and intelligent communication, computing, and control technologies have paved the way for the development of the Industrial Internet of Things. The IIoT incorporates machine learning and massively parallel distributed systems such as clouds, clusters, and grids for big data storage, processing, and analytics. In IIoT, end devices continuously generate and transmit data streams, resulting in increased network traffic between device-cloud communication. Moreover, it increases in-network data transmissions. requiring additional efforts for big data processing, management, and analytics. To cope with these engendered issues, this article first introduces a novel concentric computing model (CCM) paradigm composed of sensing systems, outer and inner gateway processors, and central processors (outer and inner) for the deployment of big data analytics applications in IIoT. Second, we investigate, highlight, and report recent research efforts directed at the IIoT paradigm with respect to big data analytics. Third, we identify and discuss indispensable challenges that remain to be addressed for employing CCM in the IIoT paradigm. Lastly, we provide several future research directions (e.g., real-time data analytics, data integration, transmission of meaningful data, edge analytics, real-time fusion of streaming data, and security and privacy).

Chapter 16 - Big Data Analytics and Social Media in Disaster Management

Online social networking is one of the most prominent features and main reasons of the big data era. In this paper, we first present the immense opportunities brought by big data in: advancing the technical level of the Chinese IT industry, leading development of the new Internet economy, and accelerating interdisciplinary research between sociology and information science. Next we analyze key issues in online social networks and point out new challenges of big social data research on semantic understanding and analysis, multi-modal association and fusion, group behavior analysis and mining, multidimensional analysis and visualization, and system development and integration. We then focus on introducing key international and domestic achievements in big data and online social networks. We conclude with a look at future trends in big social data analytics and mining.

The field of public health provides a multidisciplinary area of research, and new challenges in analytics and operations management are reshaping the strategic decision-making dynamics. Big data is an emerging and promising area of research that has great potential to transform public health. Developed countries are designing big data tools and analytic techniques and using them to provide leadership. In this respect, low- and middle-income countries (LMICs) need to become more engaged in the big data ecosystem and to become more aware of how to use new analytical techniques. This chapter provides an overview of big data analytic techniques in public health and their potential to transform strategic public health management in LMICs. To achieve this goal, this chapter explains big data analytics and big data tools in public health and discusses development strategies for LMIC's big public data potential to ensure a better knowledge base for LMIC health policymakers.

Cloud computing and big data are the two important technology innovations which have the potential to restructure the value chain of telecom service providers. Apart from total cost saving, there are many operational and business objectives while adopting cloud computing technology into the telecom domain. Telecom service providers typically have terabytes of operational data available. When effectively analyzed, this information can help them to maintain consistent and appropriate service delivery across the seasonal peaks and valleys. Big data analytics offer telecom operators a real opportunity to gain a broad image of their day to day operations, customers, and more over organizations and innovative efforts to be taken. In this research paper we identify and list the key factors affecting the operational performance of a telecom contact center and the potential role of big data analytics and provenance in overcoming the performance bottlenecks. We present a case study evaluating how telecom companies can employ big data analytics in inbound and outbound contact centers for enhancement of operational performance. Results indicate that, despite of many challenges including lack of well-defined big data processing strategies, potential security threats and the need for workforce re-skilling, the use of opportunities of Big Data analytics in telecom domain is unprecedented.

Public health researchers increasingly recognize that to advance their field they must grapple with the availability of increasingly large (i.e., thousands of variables) traditional population-level datasets (e.g., electronic medical records), while at the same time integrating additional large datasets (e.g., data on genomics, the microbiome, environmental exposures, socioeconomic factors, and health behaviors). Leveraging these multiple forms of data might well provide unique and unexpected discoveries about the determinants of health and wellbeing. However, we are in the very early stages of advancing the techniques required to understand and analyze big population-level data for public health research. To address this problem, this paper describes how we propose that big data can be efficiently used for public health discoveries. We show that data analytics techniques traditionally employed in public health studies are not up to the task of the data we now have in hand. Instead we present techniques adapted from big data visualization and analytics approaches used in other domains that can be used to answer important public health questions utilizing these existing and new datasets. Our findings are based on an exploratory big data case study carried out in San Diego County, California where we analyzed thousands of variables related to health to gain interesting insights on the determinants of several health outcomes, including life expectancy and anxiety disorders. These findings provide a promising early indication that public health research will benefit from the larger set of activities in contemporary big data research.

Businesses, government sectors, academia and society have been flooded with data which nowadays have become valuable economic assets. Data and Big Data Analytics (BDA) are vital for the succession of any organization. Big Data Analytics (BDA) delineates the approaches utilized to examine, process, analyze and expose hidden underlying patterns, interesting relations, and intelligence from huge datasets for different purposes. Despite the growth and widespread use of data analytics, the study on this research area is still at the nascent stage. The scenario is seen particularly for the developing countries like Malaysia. Previous literature has recognized data analytic development and its effect but has not yet provided a comprehensive review. This paper intends to conceptualize BDA manifestation, its application, and its challenges epistemology. This study employs a comprehensive review of past literature to serve the purpose. The findings of this paper will enhance the understanding of BDA conception epistemology (definition and application). This concept paper also intends to shed some light on BDA research areas by identifying the challenges surrounding BDA deployment. From the theoretical point of view, this paper contributes to providing BDA taxonomy based on the application in various industries. From the practical perspective, this paper contributes to BDA research field by identifying the obstacles that need to be managed by the BDA adopter for optimum utilization and decision making. From the government perspective, it contributes to Malaysian national agenda to becoming a leading regional BDA solution hub for all sectors and developed nation by the year 2025.

Advancing total quality management in higher education through AI and big data analytics

Nowadays a lot of massive data is stored and typically, the data itself contains a lot of non-trivial but useful information. Data mining techniques can be used to discover this information which can help the companies for decision-making. However, in real life applications, data is massive and is stored over distributed sites. One of my major research topics is to protect privacy over this kind of data. Previously, the important characteristics, issues and challenges related to management of the large amount of data has been explored. Various open source data analytics frameworks that deal with large amount of Data analytics workloads have been discussed. Comparative study between the given frameworks and suitability of the same has been proposed. Digital universe is flooded with huge amount of data generated by number of users worldwide. These data are of diverse in nature, come from various sources and in many forms. To keep with the desire to store and analyze ever larger volumes of complex data, relational databases vendors have delivered specialized analytical platforms that come in many shapes and sizes from software only to analytical services that run in third party hosted environments. In addition new technologies have emerged to address exploding volumes of complex data, including web traffic, social media content and machine generated data including sensor data, global positioning system data.Big data is defined as large amount of data which requires new technologies and architectures so that it becomes possible to extract value from it by capturing and analysis process. Due to such large size of data it becomes very difficult to perform effective analysis using the existing traditional techniques. Big data has become a prominent research field, especially when it comes to decision making and data analysis. However, Big data due to its various properties like volume, velocity, variety, variability, value and complexity put forward many challenges. Since Big data is a recent upcoming technology in the market which can bring huge benefits to the business organizations, it becomes necessary that various challenges and issues associated in bringing and adapting to this technology are brought into light. Another challenge is that data collection may not have enough accuracy which will lead to a non-consistent analysis that can critically affect the decision based on this analysis. Moreover, it is clearly apparent that organizations need to employ data-driven decision making to gain competitive advantage. Processing, integrating and interacting with more data should make it better data, providing both more panoramic and more granular views to aid strategic decision making. This is made possible via Big Data exploiting affordable and usable Computational and Storage Resources. Many offerings are based on the Map-Reduce and Hadoop paradigms and most focus solely on the analytical side. Nonetheless, in many respects it remains unclear what Big Data actually is; current offerings appear as isolated silos that are difficult to integrate and/or make it difficult to better utilize existing data and systems. Since data is growing at a huge speed making it difficult to handle such large amount of data (Exabyte). The main difficulty in handling such large amount of data is because that the volume is increasing rapidly in comparison to the computing resources. The Big data term which is being used now a days is kind of misnomer as it points out only the size of the data not putting too much of attention to its other existing properties.If data is to be used to make accurate decisions in time it becomes necessary that it should be available in accurate, complete and timely manner. This makes the data management and governance process bit complex adding the necessity to make Data open and make it available to government agencies in standardized manner with standardized APIs, metadata and formats thus leading to better decision making, business intelligence and productivity improvements.This paper presents a discussion and evaluation for the most prominent techniques used in the processes of data collection and analysis in order to identify the privacy defects in them that affects the accuracy of big data. Depending on the results of this analysis, recommendations were provided for improving data collection and analysis techniques that will help to avoid if not all then most of the problems facing the use of big data in decision making. Keywords: Big Data, Big Data Challenges, Big Data Accuracy, Big Data Collection, Big Data Analytics.

A large quantity of statistics is generated because of the explosive boom inside the wide variety of devices connected to the Internet of Things. However, such data large amount of data is not useful without the power of analytics. Different IOT, big data, and analytics have enable individuals to get significant understanding into vast measure of information produced by IoT gadgets. Nonetheless, these arrangements are still in their underlying state, and the area comes up short on a far reaching overview. The contribution of this paper is to purpose a five layer architecture for big IOT data. The functionality of five different layers of big IOT data architecture is explained. The opportunities for big IoT data architecture are discussed. Further more challenges face by the architecture are also explained.

Massive amounts of building operational data are collected and stored in modern buildings, which provide rich information for in-depth investigation and assessment of actual building operational performance. However, the current utilization of big building operational data is far from being effective due to the gaps between building engineering and advanced big data analytics. Data mining is a promising technology for extracting previously unknown yet potentially useful insights from big data. This paper aims to explore the potential application of advanced data mining techniques for effective utilization of big building operational data. A case study of mining the operational data of an educational building for performance improvement is presented. Decision tree, clustering analysis, and association rule mining are adopted to analyze the operational data. The results show that useful knowledge can be extracted for identifying typical building operation patterns, detecting operation deficiencies, and spotting energy conservation opportunities. Practical application:The current utilization of big building operational data in the building industry is rather limited due to the lack in experience of using advanced big data analytics. This study presents a data mining-based method for analyzing massive building operational data. The case study results validate the efficiency and effectiveness of the method proposed. It can help building professionals to discover valuable insights into building operation patterns and thereby developing strategies for improving building energy efficiency. The method can be fully realized using the open-source software R, which provides great flexibilities in its integration with building automation systems.

A USAF sponsored MITRE research team undertook four separate, domain-specific case studies about Big Data applications. Those case studies were initial investigations into the question of whether or not data quality issues encountered in Big Data collections are substantially different in cause, manifestation, or detection than those data quality issues encountered in more traditionally sized data collections. The study addresses several factors affecting Big Data Quality at multiple levels, including collection, processing, and storage. Though not unexpected, the key findings of this study reinforce that the primary factors affecting Big Data reside in the limitations and complexities involved with handling Big Data while maintaining its integrity. These concerns are of a higher magnitude than the provenance of the data, the processing, and the tools used to prepare, manipulate, and store the data. Data quality is extremely important for all data analytics problems. From the study's findings, the "truth about Big Data" is there are no fundamentally new DQ issues in Big Data analytics projects. Some DQ issues exhibit return-s-to-scale effects, and become more or less pronounced in Big Data analytics, though. Big Data Quality varies from one type of Big Data to another and from one Big Data technology to another.

This chapter provides a brief overview of the growing role played by big data, data analytics, and artificial intelligence (AI) in the accounting profession. The term 'Big Data' along with other trending topics such as 'Data Analytics' and 'Artificial Intelligence (AI)' have become buzzwords in the accounting profession in recent years. The skills of accounting professionals have evolved as technology has made rapid advances from using pencil and paper to typewriters and calculators, and eventually spreadsheets and accounting software. Data analytics in accounting is a relatively new skill set that is growing significantly in all areas of the accounting profession. Accounting professionals who can discern patterns and trends in big data and translate them into compelling strategic narratives will find themselves at the center of the twenty-first-century business world. Therefore, accounting professionals can capitalize on numerous opportunities in this rapidly evolving, disruptive, but ultimately advantageous environment by embracing big data, data analytics, and artificial intelligence (AI) to stay ahead of the competition.

Data Science is associated with new discoveries, the discovery of value from the data. It is a practice of deriving insights and developing business strategies through transformation of data in to useful information. It has been evaluated as a scientific field and research evolution in disciplines like statistics, computing science, intelligence science, and practical transformation in the domains like science, engineering, public sector, business and lifestyle. The field encompasses the larger areas of artificial intelligence, data analytics, machine learning, pattern recognition, natural language understanding, and big data manipulation. It also tackles related new scientific challenges, ranging from data capture, creation, storage, retrieval, sharing, analysis, optimization, and visualization, to integrative analysis across heterogeneous and interdependent complex resources for better decision-making, collaboration, and, ultimately, value creation. In this paper we entitled epicycles of analysis, formal modeling, from data analysis to data science, data analytics -A keystone of data science, The Big data is not a single technology but an amalgamation of old and new technologies that assistance companies gain actionable awareness. The big data is vital because it manages, store and manipulates large amount of data at the desirable speed and time. Big data addresses detached requirements, in other words the amalgamate of multiple un-associated datasets, processing of large amounts of amorphous data and harvesting of unseen information in a time-sensitive generation.
 As businesses struggle to stay up with changing market requirements, some companies are finding creative ways to use Big Data to their growing business needs and increasingly complex problems. As organizations evolve their processes and see the opportunities that Big Data can provide, they struggle to beyond traditional Business Intelligence activities, like using data to populate reports and dashboards, and move toward Data Science- driven projects that plan to answer more open-ended and sophisticated questions.
 Although some organizations are fortunate to have data scientists, most are not, because there is a growing talent gap that makes finding and hiring data scientists in a timely manner is difficult. This paper, aimed to demonstrate a close view about Data science, big data, including big data concepts like data storage, data processing, and data analysis of these technological developments, we also provide brief description about big data analytics and its characteristics , data structures, data analytics life cycle, emphasizes critical points on these issues.