IDEAS home Printed from https://ideas.repec.org/a/spr/comaot/v28y2022i3d10.1007_s10588-021-09330-3.html
   My bibliography  Save this article

BigData oriented to business decision making: a real case study in constructel

Author

Listed:
  • Anthony Martins

    (CISeD Research Centre in Digital Services, Polytechnic of Viseu)

  • Maryam Abbasi

    (CISUC - Centre for Informatics and Systems of the University of Coimbra)

  • Pedro Martins

    (CISeD Research Centre in Digital Services, Polytechnic of Viseu)

  • Filipe Sá

    (CISeD Research Centre in Digital Services, Polytechnic of Viseu)

Abstract

Analyze and understand how to combine data warehouse with business intelligence tools, and other useful information or tools to visualize KPIs are critical factors in achieving the goal of raising competencies and business results of an organization The main objective of this paper is to present the development of a BI platform, using DW tools to create graphs and detailed reports for the Constructel company. The development of this work was thought and developed in stages, starting with the analysis of the theme, analyzing the literature review to support the case study; used tools; requirements gathering; architectural design; and ending with the development and implementation of a platform with dashboards and reports for the organization’s management. With the availability of this platform, it is intended that business managers will be able to identify solutions and anomalies in a more insightful and faster way, thus allowing to improve productivity and business quality, without neglecting the satisfaction of the organization’ internal employees, greater flexibility and availability so that managers can deal with other situations that are more technical and linked to the business itself.

Suggested Citation

  • Anthony Martins & Maryam Abbasi & Pedro Martins & Filipe Sá, 2022. "BigData oriented to business decision making: a real case study in constructel," Computational and Mathematical Organization Theory, Springer, vol. 28(3), pages 271-291, September.
  • Handle: RePEc:spr:comaot:v:28:y:2022:i:3:d:10.1007_s10588-021-09330-3
    DOI: 10.1007/s10588-021-09330-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10588-021-09330-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10588-021-09330-3?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. Sandro Bimonte & Omar Boussaid & Michel Schneider & Fabien Ruelle, 2019. "Design and Implementation of Active Stream Data Warehouses," International Journal of Data Warehousing and Mining (IJDWM), IGI Global, vol. 15(2), pages 1-21, April.
    3. Georgia Garani & Sven Helmer, 2012. "Integrating Star and Snowflake Schemas in Data Warehouses," International Journal of Data Warehousing and Mining (IJDWM), IGI Global, vol. 8(4), pages 22-40, October.
    4. Steven Ji-fan Ren & Samuel Fosso Wamba & Shahriar Akter & Rameshwar Dubey & Stephen J. Childe, 2017. "Modelling quality dynamics, business value and firm performance in a big data analytics environment," International Journal of Production Research, Taylor & Francis Journals, vol. 55(17), pages 5011-5026, September.
    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. 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.
    2. Aljumah, Ahmad Ibrahim & Nuseir, Mohammed T. & Alam, Md. Mahmudul, 2021. "Traditional Marketing Analytics, Big Data Analytics, Big Data System Quality and the Success of New Product Development," OSF Preprints 9auec, Center for Open Science.
    3. Dubey, Rameshwar & Gunasekaran, Angappa & Childe, Stephen J. & Bryde, David J. & Giannakis, Mihalis & Foropon, Cyril & Roubaud, David & Hazen, Benjamin T., 2020. "Big data analytics and artificial intelligence pathway to operational performance under the effects of entrepreneurial orientation and environmental dynamism: A study of manufacturing organisations," International Journal of Production Economics, Elsevier, vol. 226(C).
    4. Erkan Bayraktar & Ekrem Tatoglu & Arafat Salih Aydiner & Dursun Delen, 2024. "Business Analytics Adoption and Technological Intensity: An Efficiency Analysis," Information Systems Frontiers, Springer, vol. 26(4), pages 1509-1526, August.
    5. Rajesh Chidananda Reddy & Biplab Bhattacharjee & Debasisha Mishra & Anandadeep Mandal, 2022. "A systematic literature review towards a conceptual framework for enablers and barriers of an enterprise data science strategy," Information Systems and e-Business Management, Springer, vol. 20(1), pages 223-255, March.
    6. S. Vijayakumar Bharathi, 2017. "Prioritizing and Ranking the Big Data Information Security Risk Spectrum," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 18(3), pages 183-201, September.
    7. 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.
    8. Fernando ALMEIDA & Samantha LOW-CHOY, 2021. "Exploring The Relationship Between Big Data And Firm Performance," Management Research and Practice, Research Centre in Public Administration and Public Services, Bucharest, Romania, vol. 13(3), pages 43-57, September.
    9. Ghasemaghaei, Maryam & Calic, Goran, 2020. "Assessing the impact of big data on firm innovation performance: Big data is not always better data," Journal of Business Research, Elsevier, vol. 108(C), pages 147-162.
    10. Tabesh, Pooya & Mousavidin, Elham & Hasani, Sona, 2019. "Implementing big data strategies: A managerial perspective," Business Horizons, Elsevier, vol. 62(3), pages 347-358.
    11. de Camargo Fiorini, Paula & Roman Pais Seles, Bruno Michel & Chiappetta Jabbour, Charbel Jose & Barberio Mariano, Enzo & de Sousa Jabbour, Ana Beatriz Lopes, 2018. "Management theory and big data literature: From a review to a research agenda," International Journal of Information Management, Elsevier, vol. 43(C), pages 112-129.
    12. Aaltonen, Aleksi Ville & Alaimo, Cristina & Kallinikos, Jannis, 2021. "The making of data commodities: data analytics as an embedded process," LSE Research Online Documents on Economics 110296, London School of Economics and Political Science, LSE Library.
    13. Sidney Anderson, 2024. "Expanding data literacy to include data preparation: building a sound marketing analytics foundation," Journal of Marketing Analytics, Palgrave Macmillan, vol. 12(2), pages 227-234, June.
    14. Michela Arnaboldi, 2018. "The Missing Variable in Big Data for Social Sciences: The Decision-Maker," Sustainability, MDPI, vol. 10(10), pages 1-18, September.
    15. Anike Sult & Janice Wobst & Rainer Lueg, 2024. "The role of training in implementing corporate sustainability: A systematic literature review," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 31(1), pages 1-30, January.
    16. Brewis, Claire & Dibb, Sally & Meadows, Maureen, 2023. "Leveraging big data for strategic marketing: A dynamic capabilities model for incumbent firms," Technological Forecasting and Social Change, Elsevier, vol. 190(C).
    17. Leogrande, Angelo, 2021. "The Destruction of Price-Representativeness," MPRA Paper 111239, University Library of Munich, Germany.
    18. Damminda Alahakoon & Rashmika Nawaratne & Yan Xu & Daswin Silva & Uthayasankar Sivarajah & Bhumika Gupta, 2023. "Self-Building Artificial Intelligence and Machine Learning to Empower Big Data Analytics in Smart Cities," Information Systems Frontiers, Springer, vol. 25(1), pages 221-240, February.
    19. Taiwen Feng & Hongyan Sheng, 2023. "Identifying the equifinal configurations of prompting green supply chain integration and subsequent performance outcome," Business Strategy and the Environment, Wiley Blackwell, vol. 32(8), pages 5234-5251, December.
    20. 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.

    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:spr:comaot:v:28:y:2022:i:3:d:10.1007_s10588-021-09330-3. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.