Big data analytics: transforming data to action

PurposeBig pharma, which comprise the most important companies in the pharmaceutical sector, are ambidextrous organizations by nature. Big data can heavily influence this characteristic by simultaneously requiring adequate business process management. In fact, the impact of big data on business process management can assist big pharma in increasing process efficiency (which is related to the research and development pipeline) and process efficacy (related to product portfolio management). The purpose of this paper is to investigate this possibility and opportunity.Design/methodology/approachIn the absence of specific scientific studies, as indicated by a review of the existing literature, the authors have adopted a grounded theory approach. This research has observed multiple cases (the 15 most important big pharma companies worldwide) through an electronic survey conducted on secondary data. The study has allowed the generation of a theoretical framework based on the (direct) relationship between knowledge process standardization (as the dependent variable) and big data (as the independent variable) in organizations oriented toward ambidexterity, such as big pharma in the specific scope of this research.FindingsAs big data utilization becomes widespread along the pipeline (or even along the value chain/supply chain), business process management increasingly uses (or tends to use) standardization, adopting process standardization as the main coordination mechanism to manage big knowledge. This theory is even more true when considering the moderating role of ambidexterity. An enterprise oriented toward structural ambidexterity (such as big pharma) that uses big data will require increased process standardization to manage big knowledge. Alternatively, an enterprise oriented toward contextual ambidexterity that uses big data will require increased output standardization.Originality/valueBased on an analytical literature review, no research to date has focused strict attention on the influence that big data can have on business process management to improve the natural ambidexterity of big pharma. The main unique feature of this research relies on this point. The main value of the research originates from the theoretical framework reconstructed by grounded theory, which constitutes a powerful strategic tool to support executives and managers of big pharma in organizing business process management for their ambidextrous organizations using big data.

Implementation of Electronic Health Record (EHR) systems continues to expand. The massive number of patient encounters results in high amounts of stored data. Transforming clinical data into knowledge to improve patient care has been the goal of biomedical informatics professionals for many decades, and this work is now increasingly recognized outside our field. In reviewing the literature for the past three years, we focus on "big data" in the context of EHR systems and we report on some examples of how secondary use of data has been put into practice. We searched PubMed database for articles from January 1, 2011 to November 1, 2013. We initiated the search with keywords related to "big data" and EHR. We identified relevant articles and additional keywords from the retrieved articles were added. Based on the new keywords, more articles were retrieved and we manually narrowed down the set utilizing predefined inclusion and exclusion criteria. Our final review includes articles categorized into the themes of data mining (pharmacovigilance, phenotyping, natural language processing), data application and integration (clinical decision support, personal monitoring, social media), and privacy and security. The increasing adoption of EHR systems worldwide makes it possible to capture large amounts of clinical data. There is an increasing number of articles addressing the theme of "big data", and the concepts associated with these articles vary. The next step is to transform healthcare big data into actionable knowledge.

Introduction. In the context of modern challenges and needs of the global business environment, effective management of business processes and ensuring economic security are extremely important for the success of IT enterprises. This article examines the key aspects of economic security and business process management in the context of information technology, enabling analysis, evaluation, and optimization of enterprise activities in this sector. Research Objective. Studying key aspects of economic security and business process management in the context of their optimization in the IT sector, as well as researching methods of business process management and providing practical recommendations that can be useful for IT enterprises to improve their efficiency and competitiveness. Method (methodology). The research methodology involves literature analysis, empirical studies, and the use of analytical, synthesis, modeling, and forecasting methods to gain a deeper understanding of economic security and business process management in the IT sector. General scientific techniques have been utilized, including the analysis of scientific literature addressing issues of economic security and business process management. Empirical studies include the analysis of practical situations in the IT sector, allowing for the examination of specific cases of success or challenges in the field of economic security and business process management. This combination of methods will enable a comprehensive analysis and provide recommendations for improving practices in the IT sector. The results. The research methodology encompasses literature analysis, empirical studies, and the utilization of analysis, synthesis, modeling, and forecasting methods to gain a deeper understanding of economic security and business process management in the IT sector. Empirical research involves analyzing practical situations in the IT sector, allowing for the examination of specific cases of success or challenges in the realm of economic security and business process management. This combination of methodologies enables a comprehensive analysis and provides recommendations for improving business process management practices within the context of economic security in the IT sector Conclusions. The issues of achieving economic security status by IT businesses have been studied and summarized, taking into account their specificities. Methods for assessing the effectiveness of business process management through the use of indicator systems adapted to the IT industry have been generalized. Suggestions have been made for developing performance indicators for IT enterprise business processes to enhance management practices and ensure stability and competitiveness of IT companies within the context of economic security.

Addressing the policy challenges and opportunities of “Big data”

How can service providers turn their big data into actionable knowledge that drives profitable business results? Using the real-world case of China Southern Airlines, this chapter illustrates how big data analytics can help airline companies to develop a comprehensive 360-degree view of the passengers. This chapter introduces a number of data mining techniques, including Weibo customer value modeling, social network analysis, website click-stream analysis, customer activity analysis, clustering analysis, Recency-Frequency-Monetary (RFM) analysis, and principle component analysis. Using the sample dataset provided by the airline company, this chapter demonstrates how to apply big data techniques to explore passengers’ travel pattern and social network, predict how many times the passengers will travel in the future, and segment customer groups based on customer lifetime value. In addition, this chapter introduces a multi-channel intelligence customer marketing platform for airlines. The findings of this study provide airline companies useful insights to better understand the passenger behavior and develop effective strategies for customer relationship management.

Big Data Analytics (BDA) in accounting, play an important role in strategies for business management and can help in all decision-making processes. Most companies must therefore be seen as adopting BDA in accounting system, which is critical for a company to organize, manage and operate its processes. In order to implement BDA in accounting systems successfully, it is important to understand the underlying factors that influence BDA in accounting. Therefore, this research intends to study this perspective of factors that influence and affect successful BDA in accounting and related accounting performance. For this research, case study and questionnaire methods have been adopted. Case studies have been conducted in two Thai organizations. The findings of the two key case studies indicated 19 variables that could have an impact in accounting in BDA, which contributed to the preliminary framework being developed. Moreover, based on the findings of case studies, a survey instrument was created. Survey questionnaires from 213 respondents were obtained from two large-scale surveys were sent to selected members of Thailand accountant, and Thailand computer to test the research framework. The results indicate that the top five critical factors for ensuring BDA in accounting were: 1) information technology strategists; 2) top management commitment, 3skills development in BDA, 4) technology capability, and; 5) competitive environments. All factors were statistically significant at the 0.05 level. Therefore, it is now clear which factors are influencing BDA in accounting system and which of those factors are critical success factors for ensuring DBA in accounting system successes.

As we are living in the era of big data, huge volumes of a wide variety of complex data-which can be of different levels of veracity-are generated or collected at a high velocity from rich sources of data in various real-life applications. A rich source of these big data sources is the Internet of Things (IoT), which include a collection of sensors, smartphones and other mobile devices, wearable devices, as well as other "things" that are capable to operate within the existing Internet infrastructure. Embedded in these big data are valuable knowledge and useful information. Hence, the research problem of data mining from big IoT data have drawn attention of many researchers as it aims to discover implicit, previously unknown and potentially useful information and knowledge from the data. For instance, frequent pattern mining finds sets of frequently co-occurring items in the IoT domains. Associative classification discovers rules revealing relationships among items within the frequent patterns and their associations with the corresponding class labels. Induction based classification uses decision tree or random forest to learn from old big IoT for classifying or making predictions on new data. Over the past quarter of a century, many serial, distributed, parallel, and MapReduce-based (Hadook-based and Spark-based) big data mining algorithms have been proposed. These algorithms are run in local computers, distributed and parallel environments, clusters, grids, clouds and/or data centers. In this paper, we review some of these algorithms, discuss issues and research prospective in mining classification patterns from these big IoT data in fog. Our case study on a real-life application shows the feasibility on classifying real-life big IoT data over fog for urban analytics.

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

The management of big data is mainly affected by the size of the big graph data that represents the huge volumes of data. The size of this structure may increase with the size of data to be handled over the time. Facing this issue, the querying time may be affected and the introduced delay may not be tolerated by running applications. Moreover, the investigation of attacks through the collected massive data could not be ensured using traditional approaches, which do not support big data constraints. In this context, we propose in this paper, a novel temporal conceptual graph to represent the big data and to optimize the size of the derived graph. The proposed scheme built on this novel graph structure enables tracing back of attacks using big data. The efficiency of the proposed scheme for the reconstruction of attack scenarios is illustrated using a case study in addition to a conducted comparative analysis showing how smart big graph data is obtained through the optimization of the graph size.

Our environment becomes permeated by intelligent devices and systems that directly interact with physical objects, including our bodies. Such cyber-physical systems produce immense amount of data that can be stored and analysed. Those data are of value as 'actionable knowledge' only if the process of collection, cleaning, and analysis is well managed and unbiased. The system has to be well understood and the statistical questions properly formulated, otherwise spurious correlations can be misinterpreted as causation. A major challenge is the changing nature of the technical systems, of the environment in which they operate and of the humans who use them. Those interacting subsystems manifest novel dynamic behaviour, different from purely digital systems. We will discuss the general problems of assessing the quality of cyber-physical systems and use smart medical devices as a case study.

Business Process Management (BPM) is the factual criteria for business process modeling. Although it is widely used in various fields, when required When using business processes to express big data business processes, the existing business process specifications do not support it. Big data business can only be simulated as multiple manual tasks before execution, which is not friendly to non-big data technology practitioners, and will The non-standardized business process and poor reusability lead to a reduction in the efficiency of business completion. On the other hand, while supporting big data technology brings flexibility to BPM, it consumes a lot of process engine resources when executing big data business processes, and the execution efficiency is low, which brings greater burden to the process engine. Aiming at the above deficiencies, this paper carries out the following work: (1) Extend the existing business process modeling, and define a new category of " Big Data Service Task" by extending the activity elements in BPMN2.0 (2) Propose a compatible big data technology The task scheduling strategy in the business process includes a single big data task scheduling decision and an intensive big data task scheduling decision.

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.

PurposeBig Data has become crucial for fulfilling the vision of smart cities; however, there are several challenges associated with its use. This study aims to provide a holistic view of challenges of using Big Data in smart cities and the solutions to deal with them by systematically selecting, reviewing and synthesizing the literature in this area.Design/methodology/approachA systematic literature review was conducted by following a rigorous search protocol that identified a total of 310 peer-reviewed academic research papers. These papers were subsequently filtered and finally 38 relevant papers were selected.FindingsA number of major challenges (human dynamics, privacy, security, volume, velocity, variety, veracity and value) associated with Big Data in a smart city and the respective solutions to address them were identified. This study primarily contributes in providing the process of effectively managing Big Data in smart cities.Practical implicationsThe study provides valuable information to the Big Data practitioners by illustrating the process of effective management of Big Data in smart cities. This study further provides the directions to the future researchers.Originality/valueThe study is original and is based on the existing literature and its interpretation.

Purpose: This study aims to investigate the impact of Big Data Analytics Capabilities (BDAC) on Indonesian Firm Performance (FPER), addressing the lack of empirical research in this area despite Indonesia's significant Big Data expenditure growth. The study also proposes a conceptual model as a framework for developing BDAC. Method: This is a quantitative study conducted in Indonesia using purposive and snowball sampling techniques to collect data from 51 Indonesian companies. The research model is based on the IT Capability framework, Sociomaterialism Theory, and incorporates Business Process Agility (BPA) as a mediating variable, as suggested by previous research. Findings: The results demonstrate a significant positive impact of BDAC on FPER. Furthermore, the study confirms that BPA significantly mediates the relationship between BDAC and FPER, highlighting the crucial role of agile business processes in leveraging the benefits of Big Data Analytics for improved firm performance. Practical Implications: This research provides valuable insights for Indonesian companies by: 1) demonstrating the significant positive impact of investing in BDAC on firm performance; 2) highlighting the importance of developing agile business processes to maximize the return on Big Data Analytics investments; and 3) offering a conceptual framework for developing and enhancing BDAC within Indonesian organizations. Originality: This study contributes to the existing literature by: 1) providing empirical evidence on the impact of BDAC on FPER specifically within the Indonesian context; 2) proposing a novel conceptual model for developing BDAC based on a combination of IT Capability framework, Sociomaterialism Theory, and the mediating role of BPA; and 3) addressing the gap in research on the relationship between BDAC and FPER in the Indonesian market. Paper Type: This research can be classified as an empirical study within the field of Information Systems, specifically focusing on Big Data Analytics, firm performance, and business process management. Keywords: Big Data, Firm Performance, Business Process Agility

Purpose. The aim of the article is to identify the key features of the implementation of digital solutions in the business process management system of modern enterprises. Methodology of research. The following research methods were used to achieve the set goal: methods of system analysis (to analyse problems during the planning of the implementation of digital solutions in the business process management system of modern enterprises); methods of scientific abstraction, deduction and induction (for formulating a scientific hypothesis of work, laws and principles), analysis and synthesis – to analyse the state of development of the business process management system of modern enterprises; abstract and logical (for making theoretical generalizations, substantiating research methodology, formulating conclusions). The tabular method clearly reflects the current state of the investigated problem. Findings. It has been established that the transition to a digital economy is based on technologies that define the Fourth Industrial Revolution, which consists of digitization and includes the saturation of the physical world with electronic and digital devices. The study also found that the process of digital transformation of business processes consists of elements such as customer interaction, operational business processes and enterprise models. It is determined that the implementation of digital solutions in each of these parts of the model opens up new opportunities for the development of enterprises. It is substantiated that the introduction of digital solutions in the management of business processes of enterprises has a positive impact on efficiency, flexibility and adaptability to changes in the external environment. It is noted that in order to maintain competitiveness, it is important not only to implement technology, but also to change the corporate culture and adapt employees to new tools. The study also highlights the most common digital solutions that are often implemented by modern enterprises: artificial intelligence, big data, blockchain, Internet of Things and cloud computing, which provide transparency, security and integration in business process management. Originality. A modern systematic overview of the most common digital solutions used to manage business processes at the enterprise has been further developed. Practical value. The results of the study can be useful for business managers and business process management specialists, as they provide a clear idea of the advantages and peculiarities of using digital solutions in the practice of enterprises. Key words: artificial intelligence, blockchain, big data, cloud technologies, fourth industrial revolution, digitalization.