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Big data reduction framework for value creation in sustainable enterprises

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  • Rehman, Muhammad Habib ur
  • Chang, Victor
  • Batool, Aisha
  • Wah, Teh Ying

Abstract

Value creation is a major sustainability factor for enterprises, in addition to profit maximization and revenue generation. Modern enterprises collect big data from various inbound and outbound data sources. The inbound data sources handle data generated from the results of business operations, such as manufacturing, supply chain management, marketing, and human resource management, among others. Outbound data sources handle customer-generated data which are acquired directly or indirectly from customers, market analysis, surveys, product reviews, and transactional histories. However, cloud service utilization costs increase because of big data analytics and value creation activities for enterprises and customers. This article presents a novel concept of big data reduction at the customer end in which early data reduction operations are performed to achieve multiple objectives, such as (a) lowering the service utilization cost, (b) enhancing the trust between customers and enterprises, (c) preserving privacy of customers, (d) enabling secure data sharing, and (e) delegating data sharing control to customers. We also propose a framework for early data reduction at customer end and present a business model for end-to-end data reduction in enterprise applications. The article further presents a business model canvas and maps the future application areas with its nine components. Finally, the article discusses the technology adoption challenges for value creation through big data reduction in enterprise applications.

Suggested Citation

  • Rehman, Muhammad Habib ur & Chang, Victor & Batool, Aisha & Wah, Teh Ying, 2016. "Big data reduction framework for value creation in sustainable enterprises," International Journal of Information Management, Elsevier, vol. 36(6), pages 917-928.
    • Handle: RePEc:eee:ininma:v:36:y:2016:i:6:p:917-928
    DOI: 10.1016/j.ijinfomgt.2016.05.013
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    References listed on IDEAS

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    Cited by:

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    2. Qinglan Liu & Adriana Hofmann Trevisan & Miying Yang & Janaina Mascarenhas, 2022. "A framework of digital technologies for the circular economy: Digital functions and mechanisms," Business Strategy and the Environment, Wiley Blackwell, vol. 31(5), pages 2171-2192, July.
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    4. Bimba, Andrew Thomas & Idris, Norisma & Al-Hunaiyyan, Ahmed & Mahmud, Rohana Binti & Abdelaziz, Ahmed & Khan, Suleman & Chang, Victor, 2016. "Towards knowledge modeling and manipulation technologies: A survey," International Journal of Information Management, Elsevier, vol. 36(6), pages 857-871.
    5. Gupta, Shivam & Kar, Arpan Kumar & Baabdullah, Abdullah & Al-Khowaiter, Wassan A.A., 2018. "Big data with cognitive computing: A review for the future," International Journal of Information Management, Elsevier, vol. 42(C), pages 78-89.
    6. Piera Centobelli & Roberto Cerchione & Eugenio Oropallo & Wael Hassan El‐Garaihy & Tamer Farag & Khalid Hassan Al Shehri, 2022. "Towards a sustainable development assessment framework to bridge supply chain practices and technologies," Sustainable Development, John Wiley & Sons, Ltd., vol. 30(4), pages 647-663, August.
    7. Maniyassouwe Amana & Pingfeng Liu & Mona Alariqi, 2022. "Value Creation and Capture with Big Data in Smart Phones Companies," Sustainability, MDPI, vol. 14(23), pages 1-22, November.
    8. Lim, Chiehyeon & Kim, Ki-Hun & Kim, Min-Jun & Heo, Jun-Yeon & Kim, Kwang-Jae & Maglio, Paul P., 2018. "From data to value: A nine-factor framework for data-based value creation in information-intensive services," International Journal of Information Management, Elsevier, vol. 39(C), pages 121-135.
    9. Jimenez-Marquez, Jose Luis & Gonzalez-Carrasco, Israel & Lopez-Cuadrado, Jose Luis & Ruiz-Mezcua, Belen, 2019. "Towards a big data framework for analyzing social media content," International Journal of Information Management, Elsevier, vol. 44(C), pages 1-12.
    10. Jaber Alwidian & Sana Abdel Rahman & Maram Gnaim & Fatima Al-Taharwah, 2020. "Big Data Ingestion and Preparation Tools," Modern Applied Science, Canadian Center of Science and Education, vol. 14(9), pages 1-12, September.
    11. Yang, Li & Zou, Haobo & Shang, Chao & Ye, Xiaoming & Rani, Pratibha, 2023. "Adoption of information and digital technologies for sustainable smart manufacturing systems for industry 4.0 in small, medium, and micro enterprises (SMMEs)," Technological Forecasting and Social Change, Elsevier, vol. 188(C).
    12. Sehrish Atif, 2023. "Mapping circular economy principles and servitisation approach in business model canvas: an integrated literature review," Future Business Journal, Springer, vol. 9(1), pages 1-21, December.
    13. Micus, Christian & Schramm, Simon & Boehm, Markus & Krcmar, Helmut, 2023. "Methods to analyze customer usage data in a product decision process:A systematic literature review," Operations Research Perspectives, Elsevier, vol. 10(C).
    14. Hani Bani-Salameh & Mona Al-Qawaqneh & Salah Taamneh, 2021. "Investigating the Adoption of Big Data Management in Healthcare in Jordan," Data, MDPI, vol. 6(2), pages 1-16, February.
    15. Mihalis Giannakis & Rameshwar Dubey & Shishi Yan & Konstantina Spanaki & Thanos Papadopoulos, 2022. "Social media and sensemaking patterns in new product development: demystifying the customer sentiment," Annals of Operations Research, Springer, vol. 308(1), pages 145-175, January.
    16. Xiaohui Chen & Kongbiao Lu, 2024. "How does digital technology administrative penalty affect big data technology innovation: evidence from China," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-13, December.

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