Understanding Big Data for Industrial Innovation and Design: The Missing Information Systems Perspective

Author : Miguel Baptista Nunes

This paper identifies a need to complement the current rich technical and mathematical research agenda on big data with a more information systems and information science strand, which focuses on the business value of big data.

An agenda of research for information systems would explore motives for using big data in real organizational contexts, and consider proposed benefits, such as increased effectiveness and efficiency, production of high-quality products/services, creation of added business value, and stimulation of innovation and design.

Impacts of such research on the academic community, the industrial and business world, and policy-makers are discussed.

URL : Understanding Big Data for Industrial Innovation and Design: The Missing Information Systems Perspective

DOI : https://doi.org/10.1515/jdis-2017-0017


Big Data and Data Science: Opportunities and Challenges of iSchools

Authors : Il-Yeol Song, Yongjun Zhu

Due to the recent explosion of big data, our society has been rapidly going through digital transformation and entering a new world with numerous eye-opening developments. These new trends impact the society and future jobs, and thus student careers.

At the heart of this digital transformation is data science, the discipline that makes sense of big data. With many rapidly emerging digital challenges ahead of us, this article discusses perspectives on iSchools’ opportunities and suggestions in data science education.

We argue that iSchools should empower their students with “information computing” disciplines, which we define as the ability to solve problems and create values, information, and knowledge using tools in application domains.

As specific approaches to enforcing information computing disciplines in data science education, we suggest the three foci of user-based, tool-based, and application-based. These three foci will serve to differentiate the data science education of iSchools from that of computer science or business schools.

We present a layered Data Science Education Framework (DSEF) with building blocks that include the three pillars of data science (people, technology, and data), computational thinking, data-driven paradigms, and data science lifecycles.

Data science courses built on the top of this framework should thus be executed with user-based, tool-based, and application-based approaches.

This framework will help our students think about data science problems from the big picture perspective and foster appropriate problem-solving skills in conjunction with broad perspectives of data science lifecycles. We hope the DSEF discussed in this article will help fellow iSchools in their design of new data science curricula.

URL : Big Data and Data Science: Opportunities and Challenges of iSchools

DOI : https://doi.org/10.1515/jdis-2017-0011

Digitising Cultural Complexity: Representing Rich Cultural Data in a Big Data environment

Authors : Jennifer Edmond, Georgina Nugent Folan

One of the major terminological forces driving ICT integration in research today is that of « big data. » While the phrase sounds inclusive and integrative, « big data » approaches are highly selective, excluding input that cannot be effectively structured, represented, or digitised.

Data of this complex sort is precisely the kind that human activity produces, but the technological imperative to enhance signal through the reduction of noise does not accommodate this richness.

Data and the computational approaches that facilitate “big data” have acquired a perceived objectivity that belies their curated, malleable, reactive, and performative nature. In an input environment where anything can “be data” once it is entered into the system as “data,” data cleaning and processing, together with the metadata and information architectures that structure and facilitate our cultural archives acquire a capacity to delimit what data are.

This engenders a process of simplification that has major implications for the potential for future innovation within research environments that depend on rich material yet are increasingly mediated by digital technologies.

This paper presents the preliminary findings of the European-funded KPLEX (Knowledge Complexity) project which investigates the delimiting effect digital mediation and datafication has on rich, complex cultural data.

The paper presents a systematic review of existing implicit definitions of data, elaborating on the implications of these definitions and highlighting the ways in which metadata and computational technologies can restrict the interpretative potential of data.

It sheds light on the gap between analogue or augmented digital practices and fully computational ones, and the strategies researchers have developed to deal with this gap.

The paper proposes a reconceptualisation of data as it is functionally employed within digitally-mediated research so as to incorporate and acknowledge the richness and complexity of our source materials.

URL : https://hal.archives-ouvertes.fr/hal-01629459

D’abord les données, ensuite la méthode ? Big data et déterminisme en sciences sociales

Auteurs/Authors : Jean-Christophe Plantin, Federica Russo

Si les chercheurs en sciences sociales ont depuis longtemps recours à de larges quantités de données, par exemple avec les enquêtes par questionnaire, le recours à des données numériques massives et hétérogènes, ou « big data », est de plus en plus fréquent.

À travers un abandon de la théorie pour la recherche de corrélations, cette multitude de données suscite-t-elle une nouvelle forme de déterminisme ?

L’histoire des sciences sociales indique au contraire que l’accroissement des données disponibles a entraîné un rejet progressif d’une hypothèse déterministe héritée des sciences de la nature, au profit d’une autonomisation méthodologique fondée sur la modélisation statistique.

Dans ce contexte, cet article montre que l’accent mis sur la taille des big data ne signifie pas tant un retour au déterminisme, mais est davantage révélateur du désajustement actuel entre les caractéristiques de ces données massives et les méthodes et infrastructures en sciences sociales.

URL : https://socio.revues.org/2328

Learning Analytics and the Academic Library: Professional Ethics Commitments at a Crossroads

Authors : Kyle M.L. Jones, Dorothea Salo

In this paper, the authors address learning analytics and the ways academic libraries are beginning to participate in wider institutional learning analytics initiatives. Since there are moral issues associated with learning analytics, the authors consider how data mining practices run counter to ethical principles in the American Library Association’s “Code of Ethics.”

Specifically, the authors address how learning analytics implicates professional commitments to promote intellectual freedom; protect patron privacy and confidentiality; and balance intellectual property interests between library users, their institution, and content creators and vendors.

The authors recommend that librarians should embed their ethical positions in technological designs, practices, and governance mechanisms.

URL : Learning Analytics and the Academic Library: Professional Ethics Commitments at a Crossroads

Alternative location : http://crl.acrl.org/index.php/crl/article/view/16603

Big data is not about size: when data transform scholarship

Authors : Jean-Christophe Plantin, Carl Lagoze, Paul N. Edwards, Christian Sandvig

“Big data” discussions typically focus on scale, i.e. the problems and potentials inherent in very large collections. Here, we argue that the most important consequences of “big data” for scholarship stem not from the increasing size of datasets, but instead from a loss of control over the sources of data.

The breakdown of the “control zone” due to the uncertain provenance of data has implications for data integrity, and can be disruptive to scholarship in multiple ways. A retrospective look at the introduction of larger datasets in weather forecasting and epidemiology shows that more data can at times be counter-productive, or destabilize already existing methods.

Based on these examples, we look at two implications of “big data” for scholarship: when the presence of large datasets transforms the traditional disciplinary structure of sciences, as well as the infrastructure for scholarly communication.

URL : https://books.openedition.org/editionsmsh/9103