The Components of Big Data and Knowledge Management Will Change Radically How People Collaborate and Develop Complex Research

Emerging as a rapidly growing field, big data is already known for promising success and having considerable synergies with knowledge management. The common goal of this collaboration is to improve and facilitate decision making, fueling the competition, fostering innovation, and achieving economic success through acquisition of knowledge to various applications. Knowledge in the entire world or inside any organization has already expanded itself in various directions and is exponentially increasing with time. To withstand the current competitive environment, an intensive collaboration of knowledge management with different approaches and algorithms of big data is required. Classical structuring is becoming obsolete with the increasing amount of knowledge components.

Big Data, Semantics, and Policy-Making

At the heart of all policy design and implementation, there is a need to understand how well decisions are made. It is evidently known that the quality of decision making depends significantly on the quality of the analyses and advice provided to the associated actors. Over decades, organizations were highly diligent in gathering and processing vast amounts of data, but they have given less emphasis on how these data can be used in policy argument. With the arrival of big data, attention has been focused on whether it could be used to inform policy-making. This chapter aims to bridge this gap, to understand variations in how big data could yield usable evidence, and how policymakers can make better use of those evidence in policy choices. An integrated and holistic look at how solving complex problems could be conducted on the basis of semantic technologies and big data is presented in this chapter.

Optimization of Aerospace Big Data Including Integrated Health Monitoring With the Help of Data Analytics

The objective of this chapter is to present the role and advantages of big data governance in the optimal use of integrated health monitoring systems with a specific reference to the aerospace industry. Aerospace manufacturers and many passenger airlines have realized the benefits of sharing and analyzing the huge amounts of data being collected by their latest generation airliners and engines. While aero engines are already equipped with integrated engine health monitoring concepts, aircraft systems are now being introduced with integrated vehicle health monitoring concepts which require large number of sensors. The data generated by these sensors is enormously high and grows over a period of time to constitute a big data to be monitored and analyzed. This chapter aims to give an overview of various systems and their data logging processes, simulations, and data analysis. Various sensors that are required to be used in important systems of a typical fighter aircraft and their functionalities emphasizing the huge volume of data generated for the analysis are presented in this chapter.

Big Data Governance in Agile and Data-Driven Software Development

Constructing a big data governance framework is important when a company performs data-driven software development. The most important aspects of big data governance are data privacy, security, availability, usability, and integrity. In this chapter, the authors present a business case where a framework for big data governance has been built. The business case is about the development and continuous improvement of a new mobile application that is targeted for consumers. In this context, big data is used in product development, in building predictive modes related to the users and for personalization of the product. The main finding of the study is a novel big data governance framework and that a proper framework for big data governance is useful when building and maintaining trustworthy and value adding big data-driven predictive models in an authentic business environment.

The Big Data Research Ecosystem

Big data is the emerging field where innovative technology offers new ways to extract value from an unequivocal plethora of available information. By its fundamental characteristic, the big data ecosystem is highly conjectural and is susceptible to continuous and rapid evolution in line with developments in technology and opportunities, a situation that predisposes the field to research in very brief time spans. Against this background, both academics and practitioners oddly have a limited understanding of how organizations translate potential into actual social and economic value. This chapter conducts an in-depth systematic review of existing penchants in the rapidly developing field of big data research and, thereafter, systematically reviewed these studies to identify some of their weaknesses and challenges. The authors argue that, in practice, most of big data surveys do not focus on technologies, and instead present algorithms and approaches employed to process big data.

Big Data and Official Statistics

Over recent years, the potential of big data for government, for business, for society has excited much comment, debate, and even evangelism. But are big data really the panacea to all our data problems or is this just hype and hubris? This is the question facing official statisticians: Are big data worth the investment of time and resources? While the statistical possibilities appear endless, big data also present enormous challenges and potential pitfalls: legal, ethical, technical, and reputational. This chapter examines the opportunities and challenges presented by big data and also discusses some governance issues arising for official statistics.

The Link Between Innovation and Prosperity

The digital era accelerates the growth of knowledge to such an extent that it is a challenge for individuals and society to manage it traditionally. Innovative tools are introduced to analyze massive data sets for extracting business value cost-effectively and efficiently. These tools help extract business intelligence from explicit information, so that tacit knowledge can be transferred into actionable insights. Big data are relevantly fashionable because of their accuracy and the capability of predicting future trends. They show their mightiness of bringing business prosperity from supermarket giants to businesses and disciplines of all kinds. However, with data widely spreading, people are concerning their potential risk of increasing inequality and threatening democracy. Big data governance is needed, if people want to keep their private right. This chapter explores how big data can be governed for maintaining the benefits of the individual and society. It aims to allow technology to humanize the digital era, so that people can be benefited from living in the present.

Making the Most of Big Data for Financial Stability Purposes

Big data has become a key topic in data creation, storage, retrieval, methodology, and analysis in the financial stability area. The flexibility and real-time availability of big data have opened up the possibility of extracting more timely economic signals, applying new statistical methodologies, enhancing economic forecasts and financial stability assessments, and obtaining rapid feedback on policy impacts. But, while public financial authorities appear increasingly interested in big data, their actual use has remained limited, reflecting a number of operational challenges. Moreover, using big data for policy purposes is not without risks, such as that of generating a false sense of certainty and precision. Exploring big data is thus a complex, multifaceted task, and a general and regular production of big data-based information would take time. Looking ahead, it is key to focus on concrete pilot projects and share these experiences. International cooperation would certainly add value in this endeavor.

Big Data for Prediction

Technical field of big data for prediction lures the attention of different stakeholders. The reasons are related to the potentials of the big data, which allows for learning from past behavior, discovering patterns and values, and optimizing business processes based on new insights from large databases. However, in order to fully utilize the potentials of big data, its stakeholders need to understand the scope and volume of patenting related to big data usage for prediction. Therefore, this chapter aims to perform an analysis of patenting activities related to big data usage for prediction. This is done by (1) exploring the timeline and geographic distribution of patenting activities, (2) exploring the most active assignees of technical content of interest, (3) detecting the type of the protected technical according to the international patent classification system, and (4) performing text-mining analysis to discover the topics emerging most often in patents' abstracts.

Big Data and People Management

This chapter investigates the role of big data (BD) in human resource management (HRM). The interest is related to the strategic relevance of human resources (HR) and to the increasing importance of BD in every dimension of a company's life. The analysis focuses on the perception of the HR managers on the impact that BD and BD analytics may have on the HRM and the possible problems the HR departments may encounter when implementing human resources analytics (HRA). The authors' opinion is that attention to the perceptions shown by the HR managers is the more important element conditioning their attitude towards BD and it is the first feature influencing the possibility that BD can become a positive challenge. After the presentation of the topic and of the state of the art, the study is introduced. The main findings are discussed and commented to offer suggestion for HR managers and to underline some key points for future research in this field.