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What tangible benefits do you hope to achieve through your Big Data initiatives? (rank all that apply) Source: [Big Data Executive Survey 2013].
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The goal of the paper is to present the application of big data solutions in the process of organizations’ management especially concerning healthcare subjects. It raises the issue of big data application in multiple areas, including supporting decisions and the improvement of efficiency and efficacy of the whole decision-making process. Big data t...
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... High income countries such as the United States (U.S.) have experienced a big increase in the rate of growth of data in their health care system. It is reported that in 2011 alone, the data in the healthcare system of U.S. had reached 150 exabytes (Chluski and Ziora 2015) and (Cottle et al. 2013); and therefore, expected to have greatly increased as of today. On the other hand, low/middle income countries are experiencing demographic (including population aging) and epidemiological changes which are causing a disease burden shift from communicable to noncommunicable diseases. ...
Machine Learning for Phenotyping is composed of three chapters and aims to introduce clinicians to machine learning (ML). It provides a guideline through the basic concepts underlying machine learning and the tools needed to easily implement it using the Python programming language and Jupyter notebook documents. It is divided into three main parts: part 1—data preparation and analysis; part 2—unsupervised learning for clustering and part 3—supervised learning for classification.KeywordsMachine learningPhenotypingData preparationData analysisUnsupervised learningClusteringSupervised learningClassificationClinical informatics
... High income countries such as the United States (U.S.) have experienced a big increase in the rate of growth of data in their health care system. It is reported that in 2011 alone, the data in the healthcare system of U.S. had reached 150 exabytes (Chluski and Ziora 2015) and (Cottle et al. 2013); and therefore, expected to have greatly increased as of today. On the other hand, low/middle income countries are experiencing demographic (including population aging) and epidemiological changes which are causing a disease burden shift from communicable to noncommunicable diseases. ...
Health institutions are increasingly collecting vast amounts of patient data. However, mining data from those different institutions is not possible for various challenges. In this chapter, we will report on our experience on the trend of Data Science in Global Health in Uganda. The aim is to provide an insight into their challenges and limitations towards real-world implementation of a data science approach in global health. We also present a series of digital health projects that we implemented during the course of the project, and provide a critical assessment of the success and challenges of those implementations.
... While there are overt facilitative power relations of system integration, there are also covert ones. Carole Cadwalladr (2017a;2017b), in detailed investigative journalism, shows how big data derived from open systems can be gamed for political advantage, since knowledge gained from big data analytics creates a competitive advantage (Chluski & Ziora, 2015;Gobble, 2013;Kiron & Shockley, 2011;McAfee, Brynjolfsson, Davenport, Patil, & Barton, 2012;Prescott, 2014;Sharma, Mithas, & Kankanhalli, 2014;Vinod, 2013). We shall explore this issue next. ...
Recently, openness has become a new approach in strategizing as ownership and control of internal assets are no longer vital to achieving competitive advantage (Chesbrough & Appleyard, 2007). Nowadays, knowledge is widespread and open systems are generally regarded as beneficial in terms of organizational design and work culture. However, openness also comes with politics and it is not a practice that will necessarily be welcomed by all. Openness changes the power dynamics within an organization; there are critics as well as friends, as we shall explore. Openness is a process that can change over time, becoming more or less open as events occur and contingencies or actors change. We are interested in how dominant organizational actors can seemingly manipulate 'open systems' strategically.
... These tools are explained in detail in table 3. Data retrieval is a process of extracting file or valuable information from large healthcare databases. Big data analysis in healthcare frequently contains information recovery and data mining [51]. Wang et al. [52] mention that "information retrieval is the process of searching within large document collections, and in healthcare it mainly covers medical text retrieval and medical image retrieval. ...
A systematic literature review of papers on big data in healthcare published between 2010 and 2015 was conducted. This paper reviews the definition, process, and use of big data in healthcare management. Unstructured data are growing very faster than semi-structured and structured data. 90 percentages of the big data are in a form of unstructured data, major steps of big data management in healthcare industry are data acquisition, storage of data, managing the data, analysis on data and data visualization. Recent researches targets on big data visualization tools. In this paper the authors analysed the effective tools used for visualization of big data and suggesting new visualization tools to manage the big data in healthcare industry. This article will be helpful to understand the processes and use of big data in healthcare management.
... When explaining the effect of big data analytics on organisations, authors have pointed to three different types (or stages) of data analytical practices (Galbraith, 2014a;Berner et al., 2014;Grossman and Siegel, 2014;Porter and Heppelmann, 2015;Davenport et al., 2012;Davenport, 2006;Bughin et al., 2011): descriptive analytics, predictive analytics and prescriptive analytics (Vahn, 2014;Kaisler et al., 2013;Evans and Lindner, 2012;Delen and Demirkan, 2013;LaValle et al., 2011;Larson and Chang, 2016). Each stage offers insights that can improve and optimise performance and sustain competitive advantage (McAfee et al., 2012;Chluski and Ziora, 2015;Prescott, 2014;Vinod, 2013;Kiron and Shockley, 2011;Sharma et al., 2014;Gobble, 2013). Each stage increases in complexity, as does the value it may add to the business that employs it. ...
... Yadav and Soni, 2008;Jagadish et al., 2014;Russell and Bennett, 2015;Sukumar and Ferrell, 2013;Höchtl et al., 2016;Viitanen and Pirttimaki, 2006;McBride, 2014;Hawking and Sellitto, 2015;Milolidakis et al., 2014;O'Leary, 2013;He et al., 2015;Moore et al., 2012;Raman, 2016; Direction, 2012;Gunnarsson et al., 2007;Venkatachari and Chandrasekaran, 2016;Verkooij and Spruit, 2013;Network_world, 2014;Shim et al., 2016;Prasad and Madhavi, 2012;He et al., 2013;Ghosh, 2016;Sinnott, 2016;Marine-Roig and Clavé, 2015;Mathias et al., 2011; Justel-Vázquez and Josep-LluísSánchez-Marín, 2016;Jetzek et al., 2014;Tao et al., 2014;Bruns et al., 2014)(Chluski and Ziora, 2015;Blackburn et al., 2015;Coussement et al., 2015;Park, 2014;Tarka and. Łobiński, 2014;Mookherjee et al., 2016;Sellitto and Hawking, 2015;Sun et al., 2014;Salzillo et al., 2012;Lozada, 2014;Garcia Martinez and Walton, 2014;Jin et al., 2016;Janssen and Haiko, 2017;Ciulla et al., 2012;Lewis et al., 2013; Cai et al., 2016; IM 2003_awards, 2004Stojanovic and Kessler, 2011;Barnea, 2014;Wixom et al., 2008;Paula et al., 2003;Luby and Whysel, 2013;Audzeyeva and Hudson, 2016;Weiner et al., 2015;Jermol et al., 2003;Wixom et al., 2013;Şerbănescu, 2012;Şerbănescu and Necşulescu, 2012;Foshay and Kuziemsky, 2014;Shollo and Galliers, 2016;Chongwatpol, 2016)(Gabel and Tokarski, 2014;O'Donoghue et al., 2016;Ferreira et al., 2016;Vera-Baquero et al., 2015;Miguel and Miller, 2015;Wang et al., 2011;Fihn et al., 2014;Osuszek et al., 2016;Dutta and Bose, 2015;Bonomo et al., 2014;Mondare et al., 2011;Souza, 2014;Liberatore and Wenhong, 2010;Doğan et al., 2015;Ashcroft, 2012;Bertsimas et al., 2016;Amatriain, 2013;Halamka, 2014;Kalakou et al., 2015;Papenfuss et al., 2015;Bekmamedova and Shanks, 2014;Fernández- Manzano et al., 2016)(Pajouh et al., 2013) Transforming(Galbraith, 2014a;Fitzgerald, 2016aFitzgerald, , 2016bFitzgerald, , 2016cMcAfee et al., 2012;Moorthy et al., 2015;Nudurupati et al., 2016;Sanders, 2016) Overview impact factor and publication date articles used. ...
In order for organisations to remain competitive in times of ambiguity and uncertainty, there is a need to detect and anticipate unknown unknowns, also called ‘black swans’. When these are ignored they may lead to competitive struggles. In this paper, we build on this view and suggest that big data analytics can provide necessary insights to help change strategy making. Research suggests that ambidextrous organisations should focus on developing and maintaining their dynamic capabilities. Following on from this, we take a dynamic capabilities perspective and propose a theoretical framework to explain the intricacies of big data analytics. This framework explains the ability of organisations to detect, anticipate and respond strategically in ambiguous and uncertain business environments. For a meta-synthesis of 101 cases of big data analytics, we employ a multi-method approach that incorporates Natural Language Processing, semantic analysis and case analysis, allowing extraction and analysis of structured information from unstructured data. Overall, we find evidence of big data analytics helping to detect, anticipate and respond to industry disruption. We offer six propositions about the relationships between the levels of data analytics capabilities and strategic dynamic capabilities. We find that descriptive data analytics improves the capability of an organisation to understand the business context (sensing) and that predictive data analytics aids in the realisation of business opportunities (seizing). This study contributes to an understanding of big data analytics as a dynamic organisational capability that supports strategic decision-making in times of ambiguity and uncertainty. We conclude by suggesting areas for further investigation, particularly in regard to the strategic application of prescriptive data analytics.
... A report delivered to the U.S. Congress in August 2012 defines big data as "a term that describes large volumes of high velocity, complex, and variable data that require advanced techniques and technologies to enable the capture, storage, distribution, management, and analysis of the information" (Chluski & Ziora, 2015). Big Data analysis is already being used successfully in several countries including the United States, which has incorporated the concept in almost all its productive sectors. ...
The last several years have revealed information technology and scientific data to be important allies. However, the most important scientific ally is that which can assist in the complex task of identifying, collecting and treating the exponential amount of data added to the Web every day in the XXI century. Moreover, the volume of this data exceeds 2.5 x 1018 new bytes per day and arrives on the Web at a rapid speed. Thus, it is necessary to assess the veracity of these data and their value to decision-making. Although approximately 43% of the data are related to health and about one million scientific articles published per year are in the health field, it is important to think beyond trivial models to solve problems of local health with a global focus. Thus, this work aims to contribute to the reflection on the use of free tools as well as to discussions of collaborative and effective partnerships for action in the field of Public Health. This study shows opportunities to use open science for global health innovation, mainly, to the countries with difficulty in managing the problems of their ills. Open science in times of Big Data are much more agile than the old model of closed science, i.e., isolated groups that either did not share data or did share but at prices that were unaffordable for developing or underdeveloped countries. Technological development for new chemical entities can be facilitated by using the open science of the Scientific’s Big Data.
... While there are overt facilitative power relations of system integration, there are also covert ones. Carole Cadwalladr (2017a;2017b), in detailed investigative journalism, shows how big data derived from open systems can be gamed for political advantage, since knowledge gained from big data analytics creates a competitive advantage (Chluski & Ziora, 2015;Gobble, 2013;Kiron & Shockley, 2011;McAfee, Brynjolfsson, Davenport, Patil, & Barton, 2012;Prescott, 2014;Sharma, Mithas, & Kankanhalli, 2014;Vinod, 2013). We shall explore this issue next. ...
The concern with openness is well established in organization theory providing a common language for observing, understanding and predicting system behaviours. Beside more conventional views of systems, which favour an objectivised view of relations between organizations and, therefore, recommendations for setting the conditions of their mutual openness, Luhmann’s theoretical framework shows that openness is problematic per se for social systems as organizations. Systems endogenously construct their differentiation from other systems through closure. Any systemic society is based on closure and specific cognitive rules, not on openness and objectivised communication. In the language of systems theory, openness is a lure as a systemic analysis of the fragmentation of power shows. We use Clegg’s (1989) ‘circuits’ approach to a systems theory of power to make connections with Luhmann (1979): there are many points of comparison between them, including the key role of events, the centrality of social constructions and the autopoietic nature of the circuits of power.
... Big data "embraces multiple methods, techniques and tools enabling conduct of different business analyses for the purpose of enterprises management. It may be deployed at strategic, tactical and operational level of management in different branches of companies" [5]. The crucial is the fact that data for the purpose of analyses may be structured, semi-structured or unstructured. ...
Nowadays big data solutions are widely applied in different types of organizations. Such solutions bring multiple advantages in business activity of contemporary enterprises and especially in logistics area. The aim of the paper is to present the notion of big data solutions and advantages resulting from its application in different areas of logistics such as supply chain or inventory optimization. The paper also presents review of selected practical examples and case studies regarding big data application in logistics area.
Social media, such as Twitter, typically stores a large amount of user-generated content regarding different aspects of society. These contents include social events, e-commerce products, healthcare, etc. This chapter proposes a best-fitted clustering method to classify sentiment samples related to healthcare topics. Thus, we examine other clustering models with keyword extraction methods on the real healthcare datasets collected from Twitter. The experiment results indicate that self-organized map model with the TF-IDF extraction method can achieve the best clustering accuracy. Moreover, the optimized model can have great potential to handle large-scale data in real practice.
This review paper aims at providing a systematic analysis of articles published in various journals and related to the uses and business applications of big data. The goal is to provide a holistic picture of the place of big data in the tourism industry. The reviewed articles have been selected for the period 2013-2020 and have been classified into 8 broad categories namely business strategy and firm performance; banking and finance; healthcare; hospitality; networks and telecommunications; urbanism and infrastructures; law and legal regulations; and government. While the categories are reflective of components of tourism industries and infrastructures, the meta-analysis is organized around 3 broad themes: preferred research contexts, conceptual developments, and methods used to research big data business applications. Main findings revealed that firm performance and healthcare remain popular contexts of research in the big data realm, but also demonstrated a prominence of qualitative methods over mixed and quantitative methods for the period 2013-2020. Scholars have also investigated topics involving the notions of competitive advantage, supply chain management, smart cities, but also ethics and privacy issues as related to the use of big data.
