Using big data for generating firm-level innovation indicators - a literature review

Background: Digital data sources have become ubiquitous in modern culture in the era of digital technology but often tend to be under-researched because of restricted access to data sources due to fragmentation, privacy issues, or industry ownership, and the methodological complexity of demonstrating their measurable impact on human health. Even though new big data sources have shown unprecedented potential for disease diagnosis and outbreak detection, we need to investigate results in the existing literature to gain a comprehensive understanding of their impact on and benefits to human health.Objective: A systematic review of systematic reviews on identifying digital data sources and their impact area on people's health, including challenges, opportunities, and good practices.Methods: A multidatabase search was performed. Peer-reviewed papers published between January 2010 and November 2020 relevant to digital data sources on health were extracted, assessed, and reviewed.Results: The 64 reviews are covered by three domains, that is, universal health coverage (UHC), public health emergencies, and healthier populations, defined in WHO's General Programme of Work, 2019–2023, and the European Programme of Work, 2020–2025. In all three categories, social media platforms are the most popular digital data source, accounting for 47% (N = 8), 84% (N = 11), and 76% (N = 26) of studies, respectively. The second most utilized data source are electronic health records (EHRs) (N = 13), followed by websites (N = 7) and mass media (N = 5). In all three categories, the most studied impact of digital data sources is on prevention, management, and intervention of diseases (N = 40), and as a tool, there are also many studies (N = 10) on early warning systems for infectious diseases. However, they could also pose health hazards (N = 13), for instance, by exacerbating mental health issues and promoting smoking and drinking behavior among young people.Conclusions: The digital data sources presented are essential for collecting and mining information about human health. The key impact of social media, electronic health records, and websites is in the area of infectious diseases and early warning systems, and in the area of personal health, that is, on mental health and smoking and drinking prevention. However, further research is required to address privacy, trust, transparency, and interoperability to leverage the potential of data held in multiple datastores and systems. This study also identified the apparent gap in systematic reviews investigating the novel big data streams, Internet of Things (IoT) data streams, and sensor, mobile, and GPS data researched using artificial intelligence, complex network, and other computer science methods, as in this domain systematic reviews are not common.

The thesis concerns the use of big data in Official Statistics; the aim is to bring some new experimental studies into the big data literature. In particular, the purpose is to evaluate if and how big data could be used in Official Statistics. Only a few experiments exists on the use of big data for statistical purposes; it is a challenge task and a lot of experimentation is needed to find out evidence and solutions to use big data for statistical purposes. The analysis performed in the thesis goes into two different directions: 1. combining a traditional data source with a big data source to verify the potential of the latter to replicate official results; 2. analyzing a big data source per se and then trying to combine with an Official Statistics source to identify common patterns. The thesis initially proposes a literature review of definitions of big data and experiments, in particular concerning the use of the new sources combined with traditional data sources. Then, three original studies have been performed: the first two concern mobility in Lombardy region using mobile phone data. They both refer to the same issue (mobility patterns), but they differ in the traditional data source used: Origin/Destination matrix in the first case, an integrated version of the O/D matrix in the second. The objective of these two studies is trying to put in a unique interpretative framework one traditional statistical source and one typical kind of big data in order to evaluate some informative potentialities of this approach. In particular, we wanted to check if the two sources show common patterns, to evaluate future uses of the big data source in Official Statistics. The third study shows the pilot that was carried out during the traineeship I had the opportunity to attend at Eurostat, in collaboration with the Task Force Big Data. It concerns the use of Wikipedia, free online encyclopedia, for Tourism Statistics. The aim is to evaluate the use of Wikipedia page views as a source of information for the identification of factors that drive tourism to an area and whether it is possible to predict tourism flows using these data. A final chapter proposes conclusions and future remarks on the use of big data in Official Statistics. Two of the studies (the first on mobility patterns and the one on Wikipedia) have been or are being published, in a shorter and revised version. The three experiments show some potential in the use of big data in Official Statistics. The study needs more in-depth analysis, many more experiments and considerations will be necessary before we can achieve some definitive and convincing approaches.

Assessing the methodological quality of big data: an introduction to the Total Error framework The availability and use of big data sources is increasing exponentially. The variety of new and emerging data sources offers opportunities to complement, replace, improve or add to conventional data sources. Survey data are one kind of conventional data sources. In survey research, a framework to assess the accuracy of survey data already existed for quite some time. This framework is known as the Total Survey Error (TSE) framework. The philosophy behind this framework has only recently been universalized to (big) data in general in the form of the Total Error (TE) framework. This generic framework, which allows for assessing the accuracy of (big) data, is outlined in this article. Additionally, the TE framework is applied to big data sources that could be relevant for policing: police-registered crime data, Twitter data and mobile phone data.

Taking a byte out of big data

The use of "Big Data" to explain fluctuations in the broader economy or guide the business decisions of a firm is now so commonplace that in some instances it has even begun to rival more traditional government statistics and business analytics. Big data sources can very often provide advantages when compared to these more traditional data sources, but with these advantages also comes the potential for pitfalls. We lay out a framework called SMALL that we have developed in order to help interested parties as they navigate the big data minefield. Based on a set of five questions, the SMALL framework should help users of big data spot concerns in their own work and that of others who rely on such data to draw conclusions with actionable public policy or business implications. To demonstrate, we provide several case studies that show a healthy dose of skepticism can be warranted when working with and interpreting these new big data sources.

Attention on “big data” spans nursing and the health sciences, and extends as well to engineering/computer sciences through to the liberal arts in professional literature. A current Google search (3 Nov 2016) of “big data” yields 288 million entries. A focused search of “big data and nursing” yields more than 3.9 million entries. Thus we ask, “Why big data? Why nursing?” The focus of this chapter is to provide an overview of why big data has emerged now and to make the case for how big data has the capacity to change health, healthcare systems, and nursing. This chapter lays a foundation for the chapters and case studies to follow that explore what data, knowledge, and transformation processes are needed to put information and knowledge into the hands of nursing wherever nurses are working. In this chapter we examine the big data sources within and beyond nursing and healthcare that can be collected and analyzed to improve nursing and patient, family and community health. This chapter entices the reader to examine “Why big data now?” and “Why big data in the future?” This chapter is meant to stir curiosity for “Why should I be knowledgeable?” Whether the reader’s role is in clinical practice, education, research, industry, or policy, the applied uses of big data analytics are empowering change at an exponential speed across all domains. Big data has the capacity to illuminate nursing’s discovery of new knowledge and best practices that are safe, effective and lead to improved outcomes including well-being of providers; it also can expand nursing’s vision and future possibilities through increasing awareness of what nursing doesn’t know. The importance of nursing’s lens on the new discoveries obtained through big data and data science is critical to the transformation of health and healthcare systems. This transformation completes the challenge of placing the person at the center of all care initiatives and actions.

<p>Agenda 2030 goal 11 commits towards making disaster risk reduction an integral part of sustainable social and economic development. Flooding poses some of the most serious challenges in front of developing nations by hitting hardest to the most vulnerable. Focussing on the urban poor, frequently at highest risk are characterised by inadequate housing, lack of services and infrastructure with high population growth and spatial expansion in dense, lower quality urban structures. Use of big data from within these low-quality urban settlement areas can be a useful step forward in generating information to have a better understanding of their vulnerabilities. Big data for resilience is a recent field of research which offers tremendous potential for increasing disaster resilience especially in the context of social resilience. This research focusses to unleash the unrealised opportunities of big data through the differential social and economic frames that can contribute towards better-targeted information generation in disaster management. The scoping study aims to contribute to the understanding of the potential of big data in developing particularly in low-income countries to empower the vulnerable population against natural hazards such as floods. Recognising the potential of providing real-time and long-term information for emergency management in flood-affected large urban settlements this research concentrates on flood hazard and use of remotely sensed data (NASA, TRMM, LANDSAT) as the big data source for quick disaster response (and recovery) in targeted areas. The research question for the scoping study is: Can big data source provide real-time and long- term information to improve emergency disaster management in urban settlements against floods in developing countries?  Previous research has identified several potentials that big data has on faster response to the affected population but few attempts have been made to integrate the factors to develop an aggregated conceptual output . An international review of multi-discipline research, grey literature, grass-root projects, and emerging online social discourse will appraise the concepts and scope of big data to highlight the four objectives of the research and answer the specific questions around existing and future potentials of big data, operationalising and capacity building by agencies, risk associated and prospects of maximising impact. The research proposes a concept design for undertaking a thematic review of existing secondary data sources which will  be used to provide a holistic picture of how big data can support in resilience through technological change within the specific scope of social and environmental contexts of developing countries. The implications of the study lie in the system integration and understanding of the socio-economics, political, legal and ethical contexts essential for investment decision making for strategic impact and resilience-building in developing nations.</p>

Big data is a term that is used to describe data that is high volume, high velocity, and/or high variety; requires new technologies and techniques to capture, store, and analyze it; and is used to enhance decision making, provide insight and discovery, and support and optimize processes. With regard to the definition of big data, IBM Company uses volume, velocity, variety, value and veracity as 5Vs to summarize the concept of big data. There are different types of big data, for example, structured, semi-structured and un-structured data. The contents of big data can be text data, audio data, video data and still image and it indicates that the big data may have diverse data types as well as data qualities. Big data has variety of sources such as healthcare center, commercial system, industries, social media, telecommunication, transportation, sensor machines and others. In this paper, I reviewed three the most security challenging perspectives and I studied lack of concentrations in these areas by most research works. To confirm security in the big data platforms, it is critical to ascertain the data rendering points and their security techniques to safeguard the data in this pacing digital world. Then I envisage directions for the future research. In this paper, I have reviewed the big data sources and its security issues in the three directions such as data at rest, data at communication and data in process/use. Keywords: Big Data, Big Data Security, Big Data source, Attribute based encryption, storage path, Transport layer security, secure shell, Cloud service Provider DOI: 10.7176/CEIS/12-3-01 Publication date: November 30 th 2021

The smart grid (SG) allows integration of renewable energy sources, distributed generation (DG) and storage systems. This chapter builds on the concepts of data management and analytics in SG to build the foundation needed for data analytics to transform Big Data for high‐value action. Big data sources in SG generally fall into two main categories; electric utility data sources and supplementary data sources. The Big Data system will store, process, and mine information in an efficient manner to enhance different SG services. The chapter presents a list of the most common big data tools utilized by successful analytics developers to store, manage, and analyze big data. Big Data needs additional convincing methods to handle the massive amount of information in a limited time span. Any data exchange in SG must be effectively protected through specific “Privacy Concerns” which have potential privacy impacts of SG and smart meter systems.

Artificial intelligence and technological advancements are exceptionally influenced the entire society and mankind. Unprecendented and extensive use of social media, mobile phones and the internet has resulted in accumulation of huge amount of data. Most of this big data are available in unstructured form and it is beyond the capability of traditional systems to manage, maintain, supervise, keeping and analyse the data within a limited time span. Effective analysis and interpretation of health care data provides new insights in the condition of patients and suggest the most appropriate treatment opportunities. Discovery and invention of vital information in medical data helps the health care professionals to arrive at appropriate clinical decisions and improvement of quality of life in a variety of patients. In this article, we have discussed various issues and addressed them with the updated information on big data sources, big data management, big data processing and big data analysis through various tools and techniques. We have also analysed and interpreted the recent applications and advancements in artificial intelligence and big data in the health care technology and m-Health domain.

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.

Advances in the field of technology enabled individuals and businesses to collect large amounts of data (structured and unstructured) from various sources like never before. Data from social media, user-generated, internet, health care, manufacturing, supply chain, financial institution, and sensors have grown exponentially. This paper’s objective is to review how big data drive and impact innovation, competitive advantage, productivity, and decision support. Methodology: A comprehensive literature review on big data and identifying the impact of big data analytics on innovation, competitive advantage, productivity, and decision support are studied. The reviewed literature created the foundation for studying, a model that was developed based on an extensive review of literature as well as case studies and future forecast by market leaders. Big data is the latest buzzword among businesses. A new model is suggested identifying big data and the correlation between innovation, competitive advantage, productivity, and decision support. Findings: A review of scholarly literature and existing case studies finds that there is a gap between existing frameworks and the integration of big data into various business and management functions and objectives. The findings are interesting that literature is rich with concepts and frameworks for achieving the end goal for business or management function along with framework but very little is available in the literature on the question of how to integrate big data analytics into those frameworks. This paper finds that a key question is missing i.e., what are the essential steps that businesses should perform to implement and integrate big data analytics into existing frameworks to fully exploit the big data potential. Research Limitations/Implications: The research was limited to a review of selective literature focused on in-depth understanding of big data. Additionally, it focuses on how big data leads to innovation, competitive advantage, productivity, and decision support. Although there are many other related fields of studies where big data impact can be studied but is not part of this study effort. Future studies can lead to more in-depth studies of other related areas of studies. Practical Implications: The review of the literature suggested that “Big Data” is playing an important role in innovations, creating competitive advantage, enhancing productivity, and assisting in data-driven decisions. Businesses are taking advantage of the customer insights that are innovating products and services which are very customer-centric, keeping the competition on the run, improving productivity at all levels, and making educated decisions every day. The future will be driven by smarter big data solutions and insights. Originality/Value: The study provides evidence that big data is the catalyst for innovation, creates competitive advantage, enhances productivity, and assists in decision making. The methodology is to review scholarly literature and case studies. It supports the need for developing new models, implementation frameworks for better insights, and patterns. The big data implementation methodologies, framework, and governance have been ignored in empirical research.

Network analytics in the big graph database era: A research framework and agenda for business and innovation

Nowadays, big data enables to discover many aspects in agriculture sector such as finding unknown crop patterns or predicting the price of products. However, these massive data are often complex and heterogeneous which includes both structured (e.g., farm information) and unstructured data (e.g., image data, sensor data). It is required new techniques and tools to extract and represent valuable information in the form of human understanding to improve decision making for enhancing farm management. In this paper, we propose a big data knowledge acquisition platform which consists of efficient knowledge acquisition techniques integrated with an intuitive visualization tool supporting decision making applications. Firstly, we deploy open source big data frameworks (e.g., Flume, Hive, HBase) to support developing of multiple methods for collecting and storing data. Secondly, we implement distributed machine learning techniques on Hadoop and Spark to acquire knowledge from big data sources. Finally, we provide a visualization tool on web interface which can display extracted knowledge in multiple views (e.g., charts, tables) to support decision making applications. Experiments with real datasets show that the proposed platform is efficient and effective to answer important questions in smart farming.

This paper provides a discussion of the use of Big Data for economic forecasting and a critical review of recent empirical studies drawing on Big Data sources, including those using internet search, social media and financial transactions related data. A broad conclusion is that whilst Big Data sources may provide new and unique insights into high frequency macroeconomic activities, their uses for macroeconomic forecasting are relatively limited and have met with varying degrees of success. Specific issues arise from the limitations of these data sets, the qualitative nature of the information they incorporate and the empirical testing frameworks used. The most successful applications appear to be those which seek to embed this class of information within a coherent economic framework, as opposed to a naive black box statistical approach. This suggests that future work using Big Data should focus on improving the quality and accessibility of the relevant data sets and in developing more appropriate economic modelling frameworks for their future use.