Reframing Open Big Data

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

    Abstract

    Recent developments in the techniques and technologies of collecting, sharing and analysing data are challenging the field of information systems (IS) research let alone the boundaries of organizations and the established practices of decision-making. Coined ‘open data’ and ‘big data’, these developments introduce an unprecedented level of societal and organizational engagement with the potential of computational data to generate new insights and information. Based on the commonalities shared by open data and big data, we develop a research framework that we refer to as open big data (OBD) by employing the dimensions of ‘order’ and ‘relationality’. We argue that these dimensions offer a viable approach for IS research on open and big data because they address one of the core value propositions of IS; i.e. how to support organizing with computational data. We contrast these dimensions with two other categories that stem from computer science and engineering, namely
    ‘big/small’ and ‘open/closed’ to address the complex interplay between people and data, social interaction and technological operations. Thus conceived, this paper contributes an alternative approach for the study of open and big data as well as laying the theoretical groundwork for its future empirical research.
    Recent developments in the techniques and technologies of collecting, sharing and analysing data are challenging the field of information systems (IS) research let alone the boundaries of organizations and the established practices of decision-making. Coined ‘open data’ and ‘big data’, these developments introduce an unprecedented level of societal and organizational engagement with the potential of computational data to generate new insights and information. Based on the commonalities shared by open data and big data, we develop a research framework that we refer to as open big data (OBD) by employing the dimensions of ‘order’ and ‘relationality’. We argue that these dimensions offer a viable approach for IS research on open and big data because they address one of the core value propositions of IS; i.e. how to support organizing with computational data. We contrast these dimensions with two other categories that stem from computer science and engineering, namely
    ‘big/small’ and ‘open/closed’ to address the complex interplay between people and data, social interaction and technological operations. Thus conceived, this paper contributes an alternative approach for the study of open and big data as well as laying the theoretical groundwork for its future empirical research.

    Conference

    ConferenceThe 21st European Conference on Information Systems (ECIS) 2013
    Number21
    LocationUtrecht University, Utrecht Science Park 'de Uithof'
    CountryNetherlands
    CityUtrecht
    Period05/06/201308/06/2013
    Internet address
    SeriesProceedings of the European Conference on Information Systems
    ISSN0000-0034

    Keywords

      Cite this

      Marton, A., Avital, M., & Jensen, T. B. (2013). Reframing Open Big Data. In ECIS 2013 Proceedings [Paper 169] Atlanta, GA: Association for Information Systems. AIS Electronic Library (AISeL). Proceedings of the European Conference on Information Systems
      Marton, Attila ; Avital, Michel ; Jensen, Tina Blegind. / Reframing Open Big Data. ECIS 2013 Proceedings. Atlanta, GA : Association for Information Systems. AIS Electronic Library (AISeL), 2013. (Proceedings of the European Conference on Information Systems).
      @inproceedings{7edc48c589d24b1a8c31bdb8ab6c735e,
      title = "Reframing Open Big Data",
      abstract = "Recent developments in the techniques and technologies of collecting, sharing and analysing data are challenging the field of information systems (IS) research let alone the boundaries of organizations and the established practices of decision-making. Coined ‘open data’ and ‘big data’, these developments introduce an unprecedented level of societal and organizational engagement with the potential of computational data to generate new insights and information. Based on the commonalities shared by open data and big data, we develop a research framework that we refer to as open big data (OBD) by employing the dimensions of ‘order’ and ‘relationality’. We argue that these dimensions offer a viable approach for IS research on open and big data because they address one of the core value propositions of IS; i.e. how to support organizing with computational data. We contrast these dimensions with two other categories that stem from computer science and engineering, namely‘big/small’ and ‘open/closed’ to address the complex interplay between people and data, social interaction and technological operations. Thus conceived, this paper contributes an alternative approach for the study of open and big data as well as laying the theoretical groundwork for its future empirical research.",
      keywords = "Open big data (OBD), Openness, Order, Relationality, IS research",
      author = "Attila Marton and Michel Avital and Jensen, {Tina Blegind}",
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      publisher = "Association for Information Systems. AIS Electronic Library (AISeL)",

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      Marton, A, Avital, M & Jensen, TB 2013, Reframing Open Big Data. in ECIS 2013 Proceedings., Paper 169, Association for Information Systems. AIS Electronic Library (AISeL), Atlanta, GA, Proceedings of the European Conference on Information Systems, Utrecht, Netherlands, 05/06/2013.

      Reframing Open Big Data. / Marton, Attila; Avital, Michel; Jensen, Tina Blegind.

      ECIS 2013 Proceedings. Atlanta, GA : Association for Information Systems. AIS Electronic Library (AISeL), 2013. Paper 169.

      Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

      TY - GEN

      T1 - Reframing Open Big Data

      AU - Marton,Attila

      AU - Avital,Michel

      AU - Jensen,Tina Blegind

      PY - 2013

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      N2 - Recent developments in the techniques and technologies of collecting, sharing and analysing data are challenging the field of information systems (IS) research let alone the boundaries of organizations and the established practices of decision-making. Coined ‘open data’ and ‘big data’, these developments introduce an unprecedented level of societal and organizational engagement with the potential of computational data to generate new insights and information. Based on the commonalities shared by open data and big data, we develop a research framework that we refer to as open big data (OBD) by employing the dimensions of ‘order’ and ‘relationality’. We argue that these dimensions offer a viable approach for IS research on open and big data because they address one of the core value propositions of IS; i.e. how to support organizing with computational data. We contrast these dimensions with two other categories that stem from computer science and engineering, namely‘big/small’ and ‘open/closed’ to address the complex interplay between people and data, social interaction and technological operations. Thus conceived, this paper contributes an alternative approach for the study of open and big data as well as laying the theoretical groundwork for its future empirical research.

      AB - Recent developments in the techniques and technologies of collecting, sharing and analysing data are challenging the field of information systems (IS) research let alone the boundaries of organizations and the established practices of decision-making. Coined ‘open data’ and ‘big data’, these developments introduce an unprecedented level of societal and organizational engagement with the potential of computational data to generate new insights and information. Based on the commonalities shared by open data and big data, we develop a research framework that we refer to as open big data (OBD) by employing the dimensions of ‘order’ and ‘relationality’. We argue that these dimensions offer a viable approach for IS research on open and big data because they address one of the core value propositions of IS; i.e. how to support organizing with computational data. We contrast these dimensions with two other categories that stem from computer science and engineering, namely‘big/small’ and ‘open/closed’ to address the complex interplay between people and data, social interaction and technological operations. Thus conceived, this paper contributes an alternative approach for the study of open and big data as well as laying the theoretical groundwork for its future empirical research.

      KW - Open big data (OBD)

      KW - Openness

      KW - Order

      KW - Relationality

      KW - IS research

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      Marton A, Avital M, Jensen TB. Reframing Open Big Data. In ECIS 2013 Proceedings. Atlanta, GA: Association for Information Systems. AIS Electronic Library (AISeL). 2013. Paper 169. (Proceedings of the European Conference on Information Systems).