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A clear link between data, informa- granularity and objectivity. Flatter organizations tion, and decision implies the absence of personal of the future will delegate not because managers and organizational biases.

Interested stakeholders have greater faith in the lower cadres of the orga- can take a closer look at the data and the associ- nization but because individuals at the lower ated inferences to understand the basis of conclu- levels are the ones that are likely to be best posi- sions.

Not only does this promise a greater buy-in tioned to make timely decisions. Thus, transparency favors the executives can make their call. Compare this context in which human interaction becomes eas- with the traditional top-down model of organiza- ier.

It paves the way for richer exchange of infor- tional decisions where strategic choices of the mation and ideas. But due to its very for the lower-level staff.

It mini- The time, energy, and other resources required to mizes the value of executive vision. The subjec- fully understand or reexamine the basis of choices tive process of environmental scanning allows makes transparency not an antecedent but a con- senior executives to imbibe their valued prefer- sequence of trust.

Participants are more likely to ences into organizational choices through selec- believe in transparency if they already trust the tive attention to information.

It enables decision makers, and those that are less receptive organizations to do what would be uninformed to the choices remain free to accuse the process as and at times, highly irrational. Dual decision model Comprehensive examination of Early choices can constrain later considerations alternatives 2.

Granularity In-depth understanding Critical information can be lost due to information overload 3. Bottom-up decision Prompt decisions Impairment of vision making.

In sum, the big data revolution promises a change in the way individuals and organiza- Further Readings tions make decisions. But it also brings with it a host of challenges. The opportunities and Boyd, D. They include the dual deci- Economist Intelligence Unit. Big data: The organizational decisions. The table above sum- management revolution.

Harvard Business Review, marizes how the promise of big data is an 90 10 , 61— Behavioral Analytics analytics put forth by Davenport and Harris as well as Kohavi and colleagues. Business analytics Lourdes S.

Martinez in turn is a subarea within business intelligence School of Communication, San Diego State and described by Negash and Gray as systems that University, San Diego, CA, USA integrate data processes with analytics tools to demonstrate insights relevant to business planners and decision-makers.

According to Montibeller Behavioral analytics can be conceptualized as a and Durbach, behavioral analytics differs from process involving the analysis of large datasets traditional descriptive analysis of behavioral data comprised of behavioral data in order to extract by focusing analyses on driving action and behavioral insights.

The purpose of this process is generate behavioral insights for the purposes of threefold. For example, a health website that tracks of users. Lastly, behavioral analytics informs ical process of extracting behavioral insights from decision-making at the individual level by datasets containing behavioral data.

A tion is derived from previous conceptualizations health website that is personalized to unique of the broader overarching idea of business health characteristics and demographics of Springer International Publishing AG L. Examples of these technology companies such as Google as well as sources of data include keyword searchers, navi- online gaming communities.

The sheer size of gation paths, and click-through patterns. By input- these datasets is giving rise to new methods, ting data from these sources into machine learning such as data visualization, for behavioral analyt- algorithms, computational social scientists are ics.

Fox and Hendler note the opportunity in able to map human factors of consumer behavior implementing data visualization as a tool for as it unfolds during purchases.

For example, Carneiro and Mylonakis through in-person face-to-face communication. In the realm of social media, Bollen their consumers with greater personalization that and colleagues have also demonstrated how anal- enhances the consumer experience. For example, a central role with commercial and academic between recommendation search engines for implications for game development. Although Brown reports on exploration behavioral analytics within the game industry is in the use of behavioral analytics to track cross- a challenge due to reluctance on the part of various border smuggling activity in the United Kingdom organizations to share analytic methods.

Along these lines analysis in this area due to a perception that these and in the context of an enormous amount of approaches represent a form of intellectual prop- available data, Jou discusses the possibilities for erty. Sifa further notes that to the extent that data implementing behavioral analytics techniques to mining, behavioral analytics, and the insights identify insider threats posed by individuals derived from these approaches provide a compet- within an organization.

These sources methods. Connecting data from various techniques to improve employee usability of inter- sources and aggregating them under a comprehen- nal systems. Given the burgeoning formance and decision-making practices. Google trends: A approaches for problem-solving and perfor- web-based tool for real-time surveillance of disease outbreaks. Clinical Infectious Diseases, 49 While concerns related Davenport, T.

Competing on analyt- to accuracy and ethical use of these insights ics: The new science of winning. Boston: Harvard remain to be addressed, behavioral analytics can Business School Press. Guns, swords and data: Clustering of player behavior in dented opportunities to enhance business, man- computer games in the wild. Proceedings of the IEEE agement, and operations.

Computational Intelligence and Games. El-Nasr, M. Game analytics: Maximizing the value of player data. New York: Springer Publishers. Cross-References Fields, T. Social game design: Monetization methods and mechanics. Science, Kohavi, R. Emerg- ing trends in business analytics. Communications of the ACM, 45 8. Further Readings Mitchell, T. Computer science: Mining our reality.

Montibeller, G. Behavioral analyt- Bollen, J. Modeling public ics: A framework for exploring judgments and choices mood and emotion: Twitter sentiment and socio-eco- in large data sets. ISSN nomic phenomena. Proceedings of the Fifth Interna- Business intelligence.

Brown, G. Use of kohonen self-organizing Sifa, R. Behavior evolution in tomb raider smuggling activity. Proceedings of the World Congress underworld. Intelligence and Games. Big Humanities Project to the digitalized portfolios. On the other hand they relate to the computerized philology tools Ramon Reichert for the application of secondary data or results.

Beyond the research between IT, cultural studies, and human- narrow discussions about the methods, ities in general. Recently, because of higher avail- computer-based measuring within humanities ability of digital data, they gained even more and cultural studies claims the media-like pos- importance.

These terms were related broadened to history of art, culture and sociol- mostly to the methodological and practical devel- ogy, media studies, technology, archaeology, opment of digital tools, infrastructures, and history and musicology Gold According to the second phase, in addition to In addition to the theoretical explorations on the quantitative digitalization of texts, the science according to Davidson , Svensson research practices are being developed in , Anne et al.

Ramsey and studies are in the center of Digital humanities. Rockwell , pp. The development from humanities 1. Roberto Busa research and development. On the basis of the — , an Italian Jesuit priest, is considered new digital infrastructure of social web as a pioneer of Digital Humanities. This project hypertext systems, Wiki tools, Crowd funding enabled the achievement of uniformity in histori- software etc. Watson, ging Humanities work on digital publications the founder of IBM, Busa , , and mediation in peer-to-peer networks and pp.

In this context one should searched through with a telephone coupler. Since the introduction of punch card methods, they have been dealing with quantitative Big Humanities have been considered only occa- and IT procedures for facilities of knowledge sionally from the perspective of science and management.

Historical approach to the have been without its predecessors, so they can interdependent relation between humanities and be seen as a part of a larger and longer history of cultural studies and the usage of computer-based knowledge and media archeology. The archive of large text corpora appeared, publishing the and library studies took part directly in the histor- computer-based analysis about word indexing, ical change of paradigm of information pro- word frequency, and word groups.

Until the late s, it was processing and analysis of information can be the automatized method of processing large text systematically removed. The technical procedure of its university facilities, its specialist journals punch cards changed the lecture practice of text Journal of Literary and Linguistic Computing, analysis by transforming a book into a database Computing in the Humanities , discussion panels and by turning the linear-syntagmatic structure of HUMANIST , and conference activities.

The text into a factual and term-based system. This debate remains regarded in the work reports less than an saw the possible applications of the punch card autonomous method, and it is seen mostly as a system as largely positive and placed them into tool for critical text examination and as a simpli- the context of economically motivated rationality. He talked about software-driven programs for text processing. In the s and s, the technical channel of research, writing, and publication development and the text reception were domi- resources without any power but also a govern- nated by the paradigm of a database.

Computers did voting. In addition to these epistemological to reorganize the knowledge of the society. In this context, Bernhard Rieder structivist actor-network-theory. Fernschau, 4, — Thomae Aquinatis Hymnorum research process even as a substitution for a sub- Ritualium Varia Specimina Concordantiarum.

Primo saggio di indici di parole automaticamentecomposti e stantial theory construction. That means that the stampati da macchine IBM a schede perforate. Milano: research interests are almost completely data Bocca. This evidence-based concentration on the Busa, R. The annals of humanities computing: The data possibilities can deceive the researcher to index Thomisticus.

Computers and the Humanities, 14 2 , 83— Big Humanities Project 5. Davidson, C. Humanities 2. Publications of the Modern Language stream. Debates in the digital humani- Pietsch, E. Neue Methoden zur Erfassung des ties. Minneapolis: University of Minnesota Press. Haber, P. Notes toward an epistemology of building in the digital Hagner, M. Editorial Digital humanities. Gold Ed. Minneapolis: University of 9, 7— Minnesota Press. Hockey, S. History of humanities computing. In Rieder, B. Digital methods: Five S.

Schreibman, R. Unsworth Eds. Berry Ed. Oxford: Blackwell. London: Palgrave. Jarausch, K. In: Dokumentation auf dem Gebiete der Philosophie. Schreibman, S. Digital humanities: Centres and Krajewski, M. In Formation.

Aufstieg und Fall der peripheries. In: M. Thaller Ed. In the digital humanities Historical social research, Vol. Gugerli, M. Hagner, M. Hampe, B. Orland, 37 3 , pp. Tanner Eds. Svensson, P. The landscape of digital humanities. Online: Lauer, G. Die digitale Vermessung der Kultur. In Moorstedt Eds. Das Thaller, M. Controversies around the digital neue Versprechen der Allwissenheit pp. Research, 37 3 , 7— McCarty, W. Humanities computing.

London: Vanhoutte, E. Terras, J. Dynamic vernaculars: Emergent dig- E. Vanhoutte Eds. Lecture presented pp. Farnham: Ashgate.

Electronic health records 2 Department of Communication Studies, EHRs , an example of biomedical big data, can University of Alabama at Birmingham, provide timely data for assisting monitoring of Birmingham, AL, USA infectious diseases, disease outbreaks, and chronic illnesses, which could be particularly valuable during public health emergencies.

By Thanks to the development of modern data col- collecting and extracting data from EHRs, public lection and analytic techniques, biomedical health organizations and authorities could receive research generates increasingly large amounts of extraordinary amount of information.

By analyz- data in various formats and at all levels, which is ing the massive data from EHRs, public health referred to as big data. Big data is a collection of researchers could conduct comprehensive obser- data sets, which are large in volume and complex vational studies with uncountable patients who in structure. To illustrate, the data managed by are treated in real clinical settings over years. Such sion-making Hoffman and Podgurski Therefore, to further knowledge and big data, the National Institutes of Health NIH uncover hidden value, there is an increasing need has initiated a Big Data to Knowledge BD2K to better understand and mine biomedical big data initiative to maximize the use of biomedical big by innovative techniques and new approaches, data.

Processing biomed- science, ical big data one hundred times faster than d the establishment of centers of excellence in conventional data analytic tools, MAP demon- data science Margolis et al. Third, given multidimensional: some big data, such as EHRs, the complex questions posed by the generation of provide depth by including multiple types of data large amounts of data requiring interdisciplinary e.

Moreover, design studies and perform subsequent data ana- social media, credit cards, census records, and a lyses Margolis et al. In the United States, the linkage algorithms based on other information, national cyberinfrastructure CI refers to an such as demographics. By requiring enough vari- existing system of research supercomputer centers ables to match, hospitals and clinics are able to and high-speed networks that connect them reduce the risk of linkage errors to an acceptable LeDuc et al.

CI has been widely used by level even though two different patients share the physical and earth scientists, and more recently same characteristics e. In addition, the same techniques used to researchers. It has been argued that more compre- match patients across different EHRs can be hensive adoption of CI could facilitate many chal- extended to data sources outside of health care, lenges in biomedical area.

One example of which is an advantage of probabilistic linkage. This Further Readings process raises important issues of consent and protection of patient privacy Institute of Medi- Hoffman, S. Big bad data: Law, cine Possible constructive responses could public health, and biomedical databases. Beyond the HIPAA privacy rule: Enhancing privacy, improving health through decision-making process, and to give patients research. Additionally, changes in Press.

Leveraging the national cyberinfrastructure for biomedical research. Improved approaches to patient con- tion, 21 2 , — Journal of the gies for analyzing clinical data are all needed for American Medical Informatics Association, 21 6 , ethical and informed use of biomedical big data. Weber, G. Finding the missing link for big biomedical data.

Journal of American Medical Association, 4 , Biosurveillance infrastructures of social media, participatory sources, and non-text-based sources. In Health Governance. While traditional approaches particular the areas of sensor technology, biomet- to health prognosis operated with data collected in ric recognition process, and the general tendency the clinical diagnosis, Internet biosurveillance toward convergence of information and commu- studies use the methods and infrastructures of nication technologies are stimulating the Big Data Health Informatics.

That means, more precisely, research. The conquest of mass markets through that they use unstructured data from different sensor and biometric recognition processes can web-based sources and targets using the collected sometimes be explained by the fact that mobile, and processed data and information about changes web-based terminals are equipped with a large in health-related behavior.

The two main tasks of variety of different sensors. More and more users the Internet biosurveillance are 1 the early detec- come this way into contact with the sensor tech- tion of epidemic diseases, biochemical, radiolog- nology or with the measurement of individual ical, and nuclear threats Brownstein et.

Due to the more stable and and 2 the implementation of strategies and mea- faster mobile networks, many people are perma- sures of sustainable governance in the target areas nently connected to the Internet using their mobile of health promotion and health education Walters devices, providing connectivity an extra boost. Biosurveillance has established itself With the development of apps, application as an independent discipline in the mids, as software for mobile devices such as smartphones military and civilian agencies began to get inter- iPhone, Android, BlackBerry, Windows Phone ested in automatic monitoring systems.

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