IDEAS home Printed from https://ideas.repec.org/a/igg/jkm000/v15y2019i2p97-109.html
   My bibliography  Save this article

The Evolution of Data Science: A New Mode of Knowledge Production

Author

Listed:
  • Jennifer Lewis Priestley

    (Kennesaw State University, USA)

  • Robert J. McGrath

    (University of New Hampshire, USA)

Abstract

Is data science a new field of study or simply an extension or specialization of a discipline that already exists, such as statistics, computer science, or mathematics? This article explores the evolution of data science as a potentially new academic discipline, which has evolved as a function of new problem sets that established disciplines have been ill-prepared to address. The authors find that this newly-evolved discipline can be viewed through the lens of a new mode of knowledge production and is characterized by transdisciplinarity collaboration with the private sector and increased accountability. Lessons from this evolution can inform knowledge production in other traditional academic disciplines as well as inform established knowledge management practices grappling with the emerging challenges of Big Data.

Suggested Citation

  • Jennifer Lewis Priestley & Robert J. McGrath, 2019. "The Evolution of Data Science: A New Mode of Knowledge Production," International Journal of Knowledge Management (IJKM), IGI Global, vol. 15(2), pages 97-109, April.
    • Handle: RePEc:igg:jkm000:v:15:y:2019:i:2:p:97-109
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJKM.2019040106
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. William S. Cleveland, 2001. "Data Science: an Action Plan for Expanding the Technical Areas of the Field of Statistics," International Statistical Review, International Statistical Institute, vol. 69(1), pages 21-26, April.
    2. Seth Cooper & Firas Khatib & Adrien Treuille & Janos Barbero & Jeehyung Lee & Michael Beenen & Andrew Leaver-Fay & David Baker & Zoran Popović & Foldit players, 2010. "Predicting protein structures with a multiplayer online game," Nature, Nature, vol. 466(7307), pages 756-760, August.
    3. Cat Ferguson & Adam Marcus & Ivan Oransky, 2014. "Publishing: The peer-review scam," Nature, Nature, vol. 515(7528), pages 480-482, November.
    4. Eric Bender, 2016. "Challenges: Crowdsourced solutions," Nature, Nature, vol. 533(7602), pages 62-64, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Iva Golubi´c & Janko Marovt, 2020. "On Some Applications of Matrix Partial Orders in Statistics," International Journal of Management, Knowledge and Learning, International School for Social and Business Studies, Celje, Slovenia, vol. 9(2), pages 223-235.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Christoph Safferling & Aaron Lowen, 2011. "Economics in the Kingdom of Loathing: Analysis of Virtual Market Data," Working Paper Series of the Department of Economics, University of Konstanz 2011-30, Department of Economics, University of Konstanz.
    2. Prpić, John & Shukla, Prashant P. & Kietzmann, Jan H. & McCarthy, Ian P., 2015. "How to work a crowd: Developing crowd capital through crowdsourcing," Business Horizons, Elsevier, vol. 58(1), pages 77-85.
    3. Marcel Knöchelmann, 2019. "Open Science in the Humanities, or: Open Humanities?," Publications, MDPI, vol. 7(4), pages 1-17, November.
    4. Situngkir, Hokky, 2015. "Indonesia embraces the Data Science," MPRA Paper 66048, University Library of Munich, Germany.
    5. Claudiu Herteliu & Marcel Ausloos & Bogdan Vasile Ileanu & Giulia Rotundo & Tudorel Andrei, 2017. "Quantitative and Qualitative Analysis of Editor Behavior through Potentially Coercive Citations," Publications, MDPI, vol. 5(2), pages 1-16, June.
    6. Kovacs, Attila, 2018. "Gender Differences in Equity Crowdfunding," OSF Preprints 5pcmb, Center for Open Science.
    7. Costa, Carlos & Santos, Maribel Yasmina, 2017. "The data scientist profile and its representativeness in the European e-Competence framework and the skills framework for the information age," International Journal of Information Management, Elsevier, vol. 37(6), pages 726-734.
    8. Daphne R. Raban & Avishag Gordon, 2020. "The evolution of data science and big data research: A bibliometric analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(3), pages 1563-1581, March.
    9. Claude E. Concolato & Li M. Chen, 2017. "Data Science: A New Paradigm in the Age of Big-Data Science and Analytics," New Mathematics and Natural Computation (NMNC), World Scientific Publishing Co. Pte. Ltd., vol. 13(02), pages 119-143, July.
    10. Nils Hachmeister & Katharina Weiß & Juliane Theiß & Reinhold Decker, 2021. "Balancing Plurality and Educational Essence: Higher Education Between Data-Competent Professionals and Data Self-Empowered Citizens," Data, MDPI, vol. 6(2), pages 1-15, January.
    11. Hamilton, Daniel George & Fraser, Hannah & Hoekstra, Rink & Fidler, Fiona, 2020. "Journal policies and editors’ opinions on peer review," MetaArXiv qkjy4, Center for Open Science.
    12. Serena Ng, 2017. "Opportunities and Challenges: Lessons from Analyzing Terabytes of Scanner Data," NBER Working Papers 23673, National Bureau of Economic Research, Inc.
    13. Naihui Zhou & Zachary D Siegel & Scott Zarecor & Nigel Lee & Darwin A Campbell & Carson M Andorf & Dan Nettleton & Carolyn J Lawrence-Dill & Baskar Ganapathysubramanian & Jonathan W Kelly & Iddo Fried, 2018. "Crowdsourcing image analysis for plant phenomics to generate ground truth data for machine learning," PLOS Computational Biology, Public Library of Science, vol. 14(7), pages 1-16, July.
    14. Ulrich Rendtel & Willi Seidel & Christine Müller & Florian Meinfelder & Joachim Wagner & Jürgen Chlumsky & Markus Zwick, 2022. "Statistik zwischen Data Science, Artificial Intelligence und Big Data: Beiträge aus dem Kolloquium „Make Statistics great again“ [Statistics between data science, artificial intelligence and big da," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 16(2), pages 97-147, June.
    15. Spartaco Albertarelli & Piero Fraternali & Sergio Herrera & Mark Melenhorst & Jasminko Novak & Chiara Pasini & Andrea-Emilio Rizzoli & Cristina Rottondi, 2018. "A Survey on the Design of Gamified Systems for Energy and Water Sustainability," Games, MDPI, vol. 9(3), pages 1-34, June.
    16. Ahmad Yaman Abdin & Muhammad Jawad Nasim & Yannick Ney & Claus Jacob, 2021. "The Pioneering Role of Sci in Post Publication Public Peer Review (P4R)," Publications, MDPI, vol. 9(1), pages 1-12, March.
    17. Stephan R. Sain, 2023. "Data science and climate risk analytics," Environmetrics, John Wiley & Sons, Ltd., vol. 34(2), March.
    18. Hassani, Hossein & Beneki, Christina & Silva, Emmanuel Sirimal & Vandeput, Nicolas & Madsen, Dag Øivind, 2021. "The science of statistics versus data science: What is the future?," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    19. Shalini R. Urs & Mohamed Minhaj, 2023. "Evolution of data science and its education in iSchools: An impressionistic study using curriculum analysis," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 74(6), pages 606-622, June.
    20. Robert Swain & Alex Berger & Josh Bongard & Paul Hines, 2015. "Participation and Contribution in Crowdsourced Surveys," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-21, April.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:igg:jkm000:v:15:y:2019:i:2:p:97-109. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.