IDEAS home Printed from https://ideas.repec.org/a/eee/tefoso/v174y2022ics0040162521006983.html
   My bibliography  Save this article

Data science roadmapping: An architectural framework for facilitating transformation towards a data-driven organization

Author

Listed:
  • Kayabay, Kerem
  • Gökalp, Mert Onuralp
  • Gökalp, Ebru
  • Erhan Eren, P.
  • Koçyiğit, Altan

Abstract

Leveraging data science can enable businesses to exploit data for competitive advantage by generating valuable insights. However, many industries cannot effectively incorporate data science into their business processes, as there is no comprehensive approach that allows strategic planning for organization-wide data science efforts and data assets. Accordingly, this study explores the Data Science Roadmapping (DSR) to guide organizations in aligning their business strategies with data-related, technological, and organizational resources. The proposed approach is built on the widely adopted technology roadmapping framework and customizes its context, architecture, and process by synthesizing data science, big data, and data-driven organization literature. Based on industry collaborations, the framework provides a hybrid and agile methodology comprising the recommended steps. We applied DSR with a research group with sector experience to create a comprehensive data science roadmap to validate and refine the framework. The results indicate that the framework facilitates DSR initiatives by creating a comprehensive roadmap capturing strategy, data, technology, and organizational perspectives. The contemporary literature illustrates prebuilt roadmaps to help businesses become data-driven. However, becoming data-driven also necessitates significant social change toward openness and trust. The DSR initiative can facilitate this social change by opening communication channels, aligning perspectives, and generating consensus among stakeholders.

Suggested Citation

  • Kayabay, Kerem & Gökalp, Mert Onuralp & Gökalp, Ebru & Erhan Eren, P. & Koçyiğit, Altan, 2022. "Data science roadmapping: An architectural framework for facilitating transformation towards a data-driven organization," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    • Handle: RePEc:eee:tefoso:v:174:y:2022:i:c:s0040162521006983
    DOI: 10.1016/j.techfore.2021.121264
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0040162521006983
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.techfore.2021.121264?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Sivarajah, Uthayasankar & Kamal, Muhammad Mustafa & Irani, Zahir & Weerakkody, Vishanth, 2017. "Critical analysis of Big Data challenges and analytical methods," Journal of Business Research, Elsevier, vol. 70(C), pages 263-286.
    2. Kaibo Liu & Jianjun Shi, 2015. "A Systematic Approach for Business Data Analytics with a Real Case Study," International Journal of Business Analytics (IJBAN), IGI Global, vol. 2(4), pages 23-44, October.
    3. Park, Hyunkyu & Phaal, Rob & Ho, Jae-Yun & O'Sullivan, Eoin, 2020. "Twenty years of technology and strategic roadmapping research: A school of thought perspective," Technological Forecasting and Social Change, Elsevier, vol. 154(C).
    4. Pape, Tom, 2016. "Prioritising data items for business analytics: Framework and application to human resources," European Journal of Operational Research, Elsevier, vol. 252(2), pages 687-698.
    5. Fosso Wamba, Samuel & Akter, Shahriar & Edwards, Andrew & Chopin, Geoffrey & Gnanzou, Denis, 2015. "How ‘big data’ can make big impact: Findings from a systematic review and a longitudinal case study," International Journal of Production Economics, Elsevier, vol. 165(C), pages 234-246.
    6. L'industria, 2021. "Call for Papers," L'industria, Società editrice il Mulino, issue 1, pages 175-189.
    7. Pearson, R.J. & Costley, A.E. & Phaal, R. & Nuttall, W.J., 2020. "Technology Roadmapping for mission-led agile hardware development: a case study of a commercial fusion energy start-up," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
    8. Geum, Youngjung & Lee, HyeonJeong & Lee, Youngjo & Park, Yongtae, 2015. "Development of data-driven technology roadmap considering dependency: An ARM-based technology roadmapping," Technological Forecasting and Social Change, Elsevier, vol. 91(C), pages 264-279.
    9. Santosh B. Rane & Nandkumar Mishra, 2018. "Roadmap for business analytics implementation using DIPPS model for sustainable business excellence: case studies from the multiple fields," International Journal of Business Excellence, Inderscience Enterprises Ltd, vol. 15(3), pages 308-334.
    10. Lismont, Jasmien & Vanthienen, Jan & Baesens, Bart & Lemahieu, Wilfried, 2017. "Defining analytics maturity indicators: A survey approach," International Journal of Information Management, Elsevier, vol. 37(3), pages 114-124.
    11. Dutta, Debprotim & Bose, Indranil, 2015. "Managing a Big Data project: The case of Ramco Cements Limited," International Journal of Production Economics, Elsevier, vol. 165(C), pages 293-306.
    12. Daim, Tugrul U. & Yoon, Byung-Sung & Lindenberg, John & Grizzi, Robert & Estep, Judith & Oliver, Terry, 2018. "Strategic roadmapping of robotics technologies for the power industry: A multicriteria technology assessment," Technological Forecasting and Social Change, Elsevier, vol. 131(C), pages 49-66.
    13. Mohammad Daradkeh, 2019. "Visual Analytics Adoption in Business Enterprises: An Integrated Model of Technology Acceptance and Task-Technology Fit," International Journal of Information Systems in the Service Sector (IJISSS), IGI Global, vol. 11(1), pages 68-89, January.
    14. Patrick Mikalef & Ilias O. Pappas & John Krogstie & Michail Giannakos, 2018. "Big data analytics capabilities: a systematic literature review and research agenda," Information Systems and e-Business Management, Springer, vol. 16(3), pages 547-578, August.
    15. L'industria, 2021. "Call for papers," L'industria, Società editrice il Mulino, issue 4, pages 771-786.
    16. Benjamin T. Hazen & Joseph B. Skipper & Christopher A. Boone & Raymond R. Hill, 2018. "Back in business: operations research in support of big data analytics for operations and supply chain management," Annals of Operations Research, Springer, vol. 270(1), pages 201-211, November.
    17. Vidgen, Richard & Shaw, Sarah & Grant, David B., 2017. "Management challenges in creating value from business analytics," European Journal of Operational Research, Elsevier, vol. 261(2), pages 626-639.
    18. Lee, Jung Hoon & Phaal, Robert & Lee, Sang-Ho, 2013. "An integrated service-device-technology roadmap for smart city development," Technological Forecasting and Social Change, Elsevier, vol. 80(2), pages 286-306.
    19. Kerr, Clive & Farrukh, Clare & Phaal, Robert & Probert, David, 2013. "Key principles for developing industrially relevant strategic technology management toolkits," Technological Forecasting and Social Change, Elsevier, vol. 80(6), pages 1050-1070.
    20. David Golightly & Genovefa Kefalidou & Sarah Sharples, 2018. "A cross-sector analysis of human and organisational factors in the deployment of data-driven predictive maintenance," Information Systems and e-Business Management, Springer, vol. 16(3), pages 627-648, August.
    21. L'industria, 2021. "Call for Papers," L'industria, Società editrice il Mulino, issue 2, pages 377-391.
    Full references (including those not matched with items on IDEAS)

    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. Ashrafi, Amir & Zare Ravasan, Ahad & Trkman, Peter & Afshari, Samira, 2019. "The role of business analytics capabilities in bolstering firms’ agility and performance," International Journal of Information Management, Elsevier, vol. 47(C), pages 1-15.
    2. Brinch, Morten & Gunasekaran, Angappa & Fosso Wamba, Samuel, 2021. "Firm-level capabilities towards big data value creation," Journal of Business Research, Elsevier, vol. 131(C), pages 539-548.
    3. Zhang, Hao & Daim, Tugrul & Zhang, Yunqiu (Peggy), 2021. "Integrating patent analysis into technology roadmapping: A latent dirichlet allocation based technology assessment and roadmapping in the field of Blockchain," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    4. Park, Hyunkyu & Phaal, Rob & Ho, Jae-Yun & O'Sullivan, Eoin, 2020. "Twenty years of technology and strategic roadmapping research: A school of thought perspective," Technological Forecasting and Social Change, Elsevier, vol. 154(C).
    5. Kim, Junhan & Geum, Youngjung, 2021. "How to develop data-driven technology roadmaps:The integration of topic modeling and link prediction," Technological Forecasting and Social Change, Elsevier, vol. 171(C).
    6. Tabesh, Pooya & Mousavidin, Elham & Hasani, Sona, 2019. "Implementing big data strategies: A managerial perspective," Business Horizons, Elsevier, vol. 62(3), pages 347-358.
    7. Benedikt Berger & Martin Adam & Alexander Rühr & Alexander Benlian, 2021. "Watch Me Improve—Algorithm Aversion and Demonstrating the Ability to Learn," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 63(1), pages 55-68, February.
    8. Harkaran Kava & Konstantina Spanaki & Thanos Papadopoulos & Stella Despoudi & Oscar Rodriguez-Espindola & Masoud Fakhimi, 2021. "Data Analytics Diffusion in the UK Renewable Energy Sector: An Innovation Perspective," Post-Print hal-03781046, HAL.
    9. Roßmann, Bernhard & Canzaniello, Angelo & von der Gracht, Heiko & Hartmann, Evi, 2018. "The future and social impact of Big Data Analytics in Supply Chain Management: Results from a Delphi study," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 135-149.
    10. Carole L. Crumley, 2021. "Historical Ecology: A Robust Bridge between Archaeology and Ecology," Sustainability, MDPI, vol. 13(15), pages 1-12, July.
    11. Ben Oldfrey & Giulia Barbareschi & Priya Morjaria & Tamara Giltsoff & Jessica Massie & Mark Miodownik & Catherine Holloway, 2021. "Could Assistive Technology Provision Models Help Pave the Way for More Environmentally Sustainable Models of Product Design, Manufacture and Service in a Post-COVID World?," Sustainability, MDPI, vol. 13(19), pages 1-18, September.
    12. Morimura, Fumikazu & Sakagawa, Yuji, 2023. "The intermediating role of big data analytics capability between responsive and proactive market orientations and firm performance in the retail industry," Journal of Retailing and Consumer Services, Elsevier, vol. 71(C).
    13. Christoph Doerffel, 2021. "The Poverty Effect of Democratization: Disaggregating Democratic Institutions," Jena Economics Research Papers 2021-018, Friedrich-Schiller-University Jena.
    14. Heckelei, Thomas & Huettel, Silke & Odening, Martin & Rommel, Jens, 2021. "The replicability crisis and the p-value debate – what are the consequences for the agricultural and food economics community?," Discussion Papers 316369, University of Bonn, Institute for Food and Resource Economics.
    15. Carlos Moreno-Leguizamon & Marcela Tovar-Restrepo, 2022. "Transbordering assemblages: Power, agency and autonomy (re)producing health infrastructures in the South East of England," Urban Studies, Urban Studies Journal Limited, vol. 59(3), pages 624-640, February.
    16. Huang, Tseng-Lung & Liu, Ben S.C., 2021. "Augmented reality is human-like: How the humanizing experience inspires destination brand love," Technological Forecasting and Social Change, Elsevier, vol. 170(C).
    17. Kumar, Satish & Sahoo, Saumyaranjan & Lim, Weng Marc & Dana, Léo-Paul, 2022. "Religion as a social shaping force in entrepreneurship and business: Insights from a technology-empowered systematic literature review," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    18. Duan, Yanqing & Cao, Guangming & Edwards, John S., 2020. "Understanding the impact of business analytics on innovation," European Journal of Operational Research, Elsevier, vol. 281(3), pages 673-686.
    19. Disney Leite Ramos & Shouming Chen & Ahmed Rabeeu & Abdul Basit Abdul Rahim, 2022. "Does SDG Coverage Influence Firm Performance?," Sustainability, MDPI, vol. 14(9), pages 1-10, April.
    20. Jan Brocke & Mieke Jans & Jan Mendling & Hajo A. Reijers, 2021. "A Five-Level Framework for Research on Process Mining," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 63(5), pages 483-490, October.

    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:eee:tefoso:v:174:y:2022:i:c:s0040162521006983. 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: Catherine Liu (email available below). General contact details of provider: http://www.sciencedirect.com/science/journal/00401625 .

    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.