Big Data Initiatives by Governments -- Issues and Challenges: A Review
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paper_title: Big data: The next frontier for innovation, competition, and productivity

paper_content:

The amount of data in our world has been exploding, and analyzing large data sets—so-called big data— will become a key basis of competition, underpinning new waves of productivity growth, innovation, and consumer surplus, according to research by MGI and McKinsey's Business Technology Office. Leaders in every sector will have to grapple with the implications of big data, not just a few data-oriented managers. The increasing volume and detail of information captured by enterprises, the rise of multimedia, social media, and the Internet of Things will fuel exponential growth in data for the foreseeable future.
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paper_title: Exploratory Visualization Involving Incremental, Approximate Database Queries and Uncertainty

paper_content:

Large datasets can mean slow queries, for which users must wait. Incremental visualization systems can give faster results at a cost of accuracy. This article asked analysts to use one and report on their results. Their feedback provides suggestions for alternative visualizations to represent a query still in progress.
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paper_title: BIG DATA PROCESSING: BIG CHALLENGES AND OPPORTUNITIES

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With the rapid growth of emerging applications like social network, semantic web, sensor networks and LBS (Location Based Service) applications, a variety of data to be processed continues to witness a quick increase. Effective management and processing of large-scale data poses an interesting but critical challenge. Recently, big data has attracted a lot of attention from academia, industry as well as government. This paper introduces several big data processing techniques from system and application aspects. First, from the view of cloud data management and big data processing mechanisms, we present the key issues of big data processing, including definition of big data, big data management platform, big data service models, distributed file system, data storage, data virtualization platform and distributed applications. Following the MapReduce parallel processing framework, we introduce some MapReduce optimization strategies reported in the literature. Finally, we discuss the open issues and challenges...
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paper_title: ERP and Big Data: The Inept Couple☆

paper_content:

Abstract The world is witnessing an unprecedented interest in big data. Big data is data that is big in size (volume), big in variety (structured; semi-structured; unstructured), and big in speed of change (velocity). It was reported that almost 90% of the data worldwide was just created in the past 2 years. Therefore, this paper is an attempt to align ERP systems with big data. The objective is to suggest a future research agenda to bring together big data and ERP. While almost everyone is talking about big data at the product or tool level, relationship with social media, relationship with Internet of things, etc. no one has tried to integrate big data and ERP. A research agenda is discussed and introduced in this paper.
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paper_title: Yesterday, Today and Tomorrow of Big Data

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Abstract Owing to the self-improvement desire, the human being always tries to reach to the current information and generate new ones from the data on hand. The practices are realized by processing and transforming the data, whose existence is broadly accepted, into information. Generating information from data is vitally important in terms of regulating the life. Especially firms need to store and transform data quickly and properly into information in order to achieve the objectives such as having a competitive edge, producing new products, moving the firm ahead and stabilizing the internal dynamics. The increase in the amount of data sources also increases the amount of the data acquired. Therefore storing and processing data become difficult and classical approaches remain incapable to do such transactions. By means of Big Data large amount of data with a wide range can be stored, managed and processed. Besides Big Data ensures proper information quickly and offers advantage and convenience to the firms, researchers and consumers by taking the properties of Volume, Value, Variety, Veracity and Velocity into consideration. This study consists of 5 parts. In the Introduction part the features, classification, the process, the areas of usage and the techniques of Big Data are explained. In the second part the appearance process and the advantages of the concept of Big Data are illustrated with examples. A detailed literature review is produced in the third part. The actual studies and the most interested areas of Big Data are told in this part. In the fourth part the future of the Big Data is evaluated. Besides the situation and distribution of the studies on Big Data in Turkey and all over the world is presented. In the Conclusion part, an overall assessment is included and probable troubles are mentioned.
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paper_title: How ‘Big Data’ Can Make Big Impact: Findings from a Systematic Review and a Longitudinal Case Study

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Big data has the potential to revolutionize the art of management. Despite the high operational and strategic impacts, there is a paucity of empirical research to assess the business value of big data. Drawing on a systematic review and case study findings, this paper presents an interpretive framework that analyzes the definitional perspectives and the applications of big data. The paper also provides a general taxonomy that helps broaden the understanding of big data and its role in capturing business value. The synthesis of the diverse concepts within the literature on big data provides deeper insights into achieving value through big data strategy and implementation.
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paper_title: Data-intensive applications, challenges, techniques and technologies: A survey on Big Data

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It is already true that Big Data has drawn huge attention from researchers in information sciences, policy and decision makers in governments and enterprises. As the speed of information growth exceeds Moore’s Law at the beginning of this new century, excessive data is making great troubles to human beings. However, there are so much potential and highly useful values hidden in the huge volume of data. A new scientific paradigm is born as dataintensive scientific discovery (DISD), also known as Big Data problems. A large number of fields and sectors, ranging from economic and business activities to public administration, from national security to scientific researches in many areas, involve with Big Data problems. On the one hand, Big Data is extremely valuable to produce productivity in businesses and evolutionary breakthroughs in scientific disciplines, which give us a lot of opportunities to make great progresses in many fields. There is no doubt that the future competitions in business productivity and technologies will surely converge into the Big Data explorations. On the other hand, Big Data also arises with many challenges, such as difficulties in data capture, data storage, data analysis and data visualization. This paper is aimed to demonstrate a close-up view about Big Data, including Big Data applications, Big Data opportunities and challenges, as well as the state-of-the-art techniques and technologies we currently adopt to deal with the Big Data problems. We also discuss several underlying methodologies to handle the data deluge, for example, granular computing, cloud computing, bio-inspired computing, and quantum computing.
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paper_title: Beyond the hype: Big data concepts, methods, and analytics

paper_content:

We define what is meant by big data.We review analytics techniques for text, audio, video, and social media data.We make the case for new statistical techniques for big data.We highlight the expected future developments in big data analytics. Size is the first, and at times, the only dimension that leaps out at the mention of big data. This paper attempts to offer a broader definition of big data that captures its other unique and defining characteristics. The rapid evolution and adoption of big data by industry has leapfrogged the discourse to popular outlets, forcing the academic press to catch up. Academic journals in numerous disciplines, which will benefit from a relevant discussion of big data, have yet to cover the topic. This paper presents a consolidated description of big data by integrating definitions from practitioners and academics. The paper's primary focus is on the analytic methods used for big data. A particular distinguishing feature of this paper is its focus on analytics related to unstructured data, which constitute 95% of big data. This paper highlights the need to develop appropriate and efficient analytical methods to leverage massive volumes of heterogeneous data in unstructured text, audio, and video formats. This paper also reinforces the need to devise new tools for predictive analytics for structured big data. The statistical methods in practice were devised to infer from sample data. The heterogeneity, noise, and the massive size of structured big data calls for developing computationally efficient algorithms that may avoid big data pitfalls, such as spurious correlation.
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paper_title: ERP and Big Data: The Inept Couple☆

paper_content:

Abstract The world is witnessing an unprecedented interest in big data. Big data is data that is big in size (volume), big in variety (structured; semi-structured; unstructured), and big in speed of change (velocity). It was reported that almost 90% of the data worldwide was just created in the past 2 years. Therefore, this paper is an attempt to align ERP systems with big data. The objective is to suggest a future research agenda to bring together big data and ERP. While almost everyone is talking about big data at the product or tool level, relationship with social media, relationship with Internet of things, etc. no one has tried to integrate big data and ERP. A research agenda is discussed and introduced in this paper.
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paper_title: A conceptual framework for delivering cost effective business intelligence solutions as a service

paper_content:

Smart use of business intelligence (BI) can allow organizations to leverage the huge amounts of transactional data at their disposal and turn it into a powerful decision support mechanism that gives them competitive advantage. Despite the potential benefits of an effective BI system, the adoption and use of BI systems within the enterprise remains low, especially among smaller companies with resource constraints. This can partly be explained by the predominant deployment approach available today in which a firm needs to procure, install, configure and operate a BI system in-house. Barriers of high cost, complexity and lack of in-house expertise discourage many firms from adopting BI systems. This paper argues that adopting a cloud computing model, where BI is offered as a service over the Internet can lower these barriers and accelerate the pace of BI adoption. However, migrating BI systems from traditional on-premise environments to the cloud presents huge challenges. There are technical, economic, organizational and regulatory hurdles to overcome. Further, BI systems are multi-component (ETL, Data warehouse, data marts, OLAP, reporting, data mining etc.) and deciding which component(s) to move to the cloud, and which ones to leave on-premise needs careful consideration. In addition, the fact that cloud computing is still in its infancy means there is a general lack of conceptual and architectural frameworks to guide companies considering migrating enterprise systems to the cloud. This paper takes a closer look into traditional BI and proposes a conceptual framework that companies can use to chart an adoption path for cloud BI. The framework combines attributes of IT outsourcing, traditional BI, cloud computing as well as decision theory to present a consolidated view of cloud BI. The domain of South African Higher Education was chosen as the target in which the framework will be tested.
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paper_title: Competing on analytics.

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Some companies have built their very businesses on their ability to collect, analyze, and act on data.
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paper_title: Business Intelligence and Analytics: From Big Data to Big Impact

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Business intelligence and analytics (BI&A) has emerged as an important area of study for both practitioners and researchers, reflecting the magnitude and impact of data-related problems to be solved in contemporary business organizations. This introduction to the MIS Quarterly Special Issue on Business Intelligence Research first provides a framework that identifies the evolution, applications, and emerging research areas of BI&A. BI&A 1.0, BI&A 2.0, and BI&A 3.0 are defined and described in terms of their key characteristics and capabilities. Current research in BI&A is analyzed and challenges and opportunities associated with BI&A research and education are identified. We also report a bibliometric study of critical BI&A publications, researchers, and research topics based on more than a decade of related academic and industry publications. Finally, the six articles that comprise this special issue are introduced and characterized in terms of the proposed BI&A research framework.
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paper_title: ELTA: New Approach in Designing Business Intelligence Solutions in Era of Big Data

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Abstract Current work presents new approach for designing business intelligence solutions. In the era of big data, former and robust analytical concepts and utilities needs to adopt themselves to changed market circumstances. Main focus of the work is to address acceleration of building process of a “data-centric” business intelligence solution on one side, and also prepare business intelligence solutions for big data utilization. Research is addressing following goals: (a) reduce time spent during business intelligence solution designing phase; (b) achieve flexibility of business intelligence solution by removing problems with adding new data sources; (c) prepare business intelligence solution for utilization of big data concepts. Research proposes extension of existing Extract, Load and Transform (ELT) approach to the new one Extract, Load, Transform and Analyse (ELTA).
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paper_title: Big data: The next frontier for innovation, competition, and productivity

paper_content:

The amount of data in our world has been exploding, and analyzing large data sets—so-called big data— will become a key basis of competition, underpinning new waves of productivity growth, innovation, and consumer surplus, according to research by MGI and McKinsey's Business Technology Office. Leaders in every sector will have to grapple with the implications of big data, not just a few data-oriented managers. The increasing volume and detail of information captured by enterprises, the rise of multimedia, social media, and the Internet of Things will fuel exponential growth in data for the foreseeable future.
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paper_title: What can big data and text analytics tell us about hotel guest experience and satisfaction

paper_content:

Abstract The tremendous growth of social media and consumer-generated content on the Internet has inspired the development of the so-called big data analytics to understand and solve real-life problems. However, while a handful of studies have employed new data sources to tackle important research problems in hospitality, there has not been a systematic application of big data analytic techniques in these studies. This study aims to explore and demonstrate the utility of big data analytics to better understand important hospitality issues, namely the relationship between hotel guest experience and satisfaction. Specifically, this study applies a text analytical approach to a large quantity of consumer reviews extracted from Expedia.com to deconstruct hotel guest experience and examine its association with satisfaction ratings. The findings reveal several dimensions of guest experience that carried varying weights and, more importantly, have novel, meaningful semantic compositions. The association between guest experience and satisfaction appears strong, suggesting that these two domains of consumer behavior are inherently connected. This study reveals that big data analytics can generate new insights into variables that have been extensively studied in existing hospitality literature. In addition, implications for theory and practice as well as directions for future research are discussed.
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paper_title: The Role of Cloud Computing Architecture in Big Data

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In this data-driven society, we are collecting a massive amount of data from people, actions, sensors, algorithms and the web; handling “Big Data” has become a major challenge. A question still exists regarding when data may be called big data. How large is big data? What is the correlation between big data and business intelligence? What is the optimal solution for storing, editing, retrieving, analyzing, maintaining, and recovering big data? How can cloud computing help in handling big data issues? What is the role of a cloud architecture in handling big data? How important is big data in business intelligence? This chapter attempts to answer these questions. First, we review a definition of big data. Second, we describe the important challenges of storing, analyzing, maintaining, recovering and retrieving a big data. Third, we address the role of Cloud Computing Architecture as a solution for these important issues that deal with big data. We also discuss the definition and major features of cloud computing systems. Then we explain how cloud computing can provide a solution for big data with cloud services and open-source cloud software tools for handling big data issues. Finally, we explain the role of cloud architecture in big data, the role of major cloud service layers in big data, and the role of cloud computing systems in handling big data in business intelligence models.
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paper_title: A Survey of Big Data Analytics in Healthcare and Government

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Abstract This paper gives an insight of how we can uncover additional value from the data generated by healthcare and government. Large amount of heterogeneous data is generated by these agencies. But without proper data analytics methods these data became useless. Big Data Analytics using Hadoop plays an effective role in performing meaningful real-time analysis on the huge volume of data and able to predict the emergency situations before it happens. It describes about the big data use cases in healthcare and government.
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paper_title: Big data and e-government: issues, policies, and recommendations

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The promises and potential of Big Data in transforming digital government services, governments, and the interaction between governments, citizens, and the business sector, are substantial. From "smart" government to transformational government, Big Data can foster collaboration; create real-time solutions to challenges in agriculture, health, transportation, and more; and usher in a new era of policy- and decision-making. There are, however, a range of policy challenges to address regarding Big Data, including access and dissemination; digital asset management, archiving and preservation; privacy; and security. This paper selectively reviews and analyzes the U.S. policy context regarding Big Data and offers recommendations aimed at facilitating Big Data initiatives.
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paper_title: Big data: Issues, challenges, tools and Good practices

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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 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. This paper introduces the Big data technology along with its importance in the modern world and existing projects which are effective and important in changing the concept of science into big science and society too. The various challenges and issues in adapting and accepting Big data technology, its tools (Hadoop) are also discussed in detail along with the problems Hadoop is facing. The paper concludes with the Good Big data practices to be followed.
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paper_title: Big data: Issues, challenges, tools and Good practices

paper_content:

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 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. This paper introduces the Big data technology along with its importance in the modern world and existing projects which are effective and important in changing the concept of science into big science and society too. The various challenges and issues in adapting and accepting Big data technology, its tools (Hadoop) are also discussed in detail along with the problems Hadoop is facing. The paper concludes with the Good Big data practices to be followed.
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Title: Big Data Initiatives by Governments -- Issues and Challenges: A Review

Section 1: INTRODUCTION  
Description 1: Introduce the popularity of big data among organizations, particularly in the government and public service sector. Outline the purpose of the review, which is to examine big data initiatives and challenges faced by governments.

Section 2: UNDERSTANDING BIG DATA  
Description 2: Provide an overview of big data, its significance, and common definitions in the context of both public and private sectors.

Section 3: Definitions of Big Data  
Description 3: Present various definitions of big data from different organizations and researchers, highlighting the key characteristics of big data.

Section 4: The 5v's of Big Data  
Description 4: Discuss the five characteristics of big data - Volume, Variety, Velocity, Veracity, and Value - and their relevance to the public sector.

Section 5: Big Data vs. Business Intelligence  
Description 5: Explore the relationship between big data and business intelligence, emphasizing how big data analytics supports business intelligence and decision-making processes.

Section 6: BIG DATA AND E-GOVERNMENT  
Description 6: Describe the importance of big data in e-government, including how it enhances efficiency and effectiveness, and supports various public sector functions.

Section 7: BIG DATA INITIATIVES IN SEVERAL COUNTRIES  
Description 7: Review and summarize big data initiatives implemented by governments in leading countries, noting trends and developments in these initiatives.

Section 8: ISSUES AND CHALLENGES IN BIG DATA INITIATIVES  
Description 8: Outline the issues and challenges related to the implementation of big data initiatives by governments, including policy, security, and data sharing concerns.

Section 9: CONCLUSION  
Description 9: Summarize the benefits and challenges of big data for government agencies and emphasize the need for effective planning and implementation to ensure successful big data initiatives.