ACQUIRING KNOWLEDGE FROM BIG DATA IN THE DIGITAL AGE : A SYSTEMATIC LITERATURE REVIEW
DOI:
https://doi.org/10.47111/jti.v20i1.23282Keywords:
Big Data, Knowledge Acquisition, Strategic Knowledge, Critical Resources, Data-Driven Decision-MakingAbstract
Big Data has become a critical driver of innovation and competitiveness, yet many organizations continue to struggle to transform data into actionable knowledge. This challenge persists because firms lack clarity on the critical resources and integrated processes required to effectively acquire and apply knowledge from Big Data. This study conducts a PRISMA-guided systematic literature review to synthesize how firms acquire knowledge from Big Data, the resources that enable this capability, and how the resulting knowledge enhances decision quality. The findings reveal that Big Data knowledge acquisition operates through a three-stage cycle, data integration, analytical knowledge generation, and organizational absorption and application, supported by six interdependent resource dimensions: technology, human, data, organization, knowledge, and environment. Each dimension plays a distinct but complementary role in enabling firms to capture, integrate, and transform Big Data into meaningful knowledge. The study identifies actionable patterns that demonstrate how these resources can be configured to strengthen evidence-based decision-making, strategic foresight, innovation, and continuous learning. By clarifying the dynamic interplay between methods, processes, and resources, this study provides both theoretical insights and practical guidance for organizations seeking to develop sustainable data-driven capabilities.
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References
[1] P. Chauhan and M. Sood, “Big Data: Present and Future,” Computer (Long. Beach. Calif)., vol. 54, no. 4, pp. 59–65, 2021, doi: 10.1109/MC.2021.3057442.
[2] Z. Lashkaripour, “The Era Of Big Data: A Thorough Inspection In The Building Blocks Of Future Generation Data Management,” Int. J. Sci. Technol. Res., vol. 9, no. 10, pp. 321–330, 2020, [Online]. Available: https://www.ijstr.org/final-print/oct2020/The-Era-Of-Big-Data-A-Thorough-Inspection-In-The-Building-Blocks-Of-Future-Generation-Data-Management.pdf
[3] A. H. Shathi and B. Ahmed, “Privacy Preservation for Big Data Publishing: Applying K-Anonymity and Differentially Private Synthetic Data Generation with DP-CTGAN,” J. Hunan Univ. Nat. Sci., vol. 62, no. 07, pp. 1–17, 2025, doi: 10.5281/zenodo.15803650.
[4] P. Sakshi and G. Malwadkar, “Big Data Analytics: A Literature Review Paper,” Int. J. Adv. Res. Sci. Commun. Technol., vol. 3, no. 3, pp. 304–309, 2023, doi: 10.48175/ijarsct-8160.
[5] K. R. Gade and J. P. M. Chase, “Data-Driven Decision Making in a Complex World,” J. Comput. Innov., vol. 1, no. 1, pp. 1–18, 2021, [Online]. Available: https://researchworkx.com/index.php/jci/article/view/2
[6] J. Holstein, P. Spitzer, M. Hoell, M. Vössing, and N. Kühl, “Understanding Data Understanding : a Framework to Navigate the Intricacies of Data Analytics,” in 32nd European Conference on Information Systems, 2024. doi: https://doi.org/10.48550/arXiv.2405.07658.
[7] M. A. Rauf, S. A. Shorna, Z. H. Joy, and M. M. Rahman, “Data-Driven Transformation: Optimizing Enterprise Financial Management and Decision-Making With Big Data,” Acad. J. Bus. Adm. Innov. Sustain., vol. 4, no. 2, pp. 94–106, 2024, doi: 10.69593/ajbais.v4i2.75.
[8] T. Wibowo and Y. Christian, “Usage of Blockchain to Ensure Audit Data Integrity,” EQUITY, vol. 24, no. 1, pp. 47–58, 2021, doi: 10.34209/equ.v24i1.2357.
[9] E. L. Ferreira, M. A. Terlizzi, D. Administração, E. D. R. Francisco, and D. Administração, “Practices and Barriers for Big Data Projects : A Case Study On A Large Insurance Company,” Rev. Gestão E Proj., vol. 15, no. 1, pp. 1–35, 2024, doi: https://doi.org/10.5585/gep.v15i1.24673.
[10] F. Shaikh and G. L. Kharade, “The Value of Analytics for Big Data in Business Intelligence,” Int. J. Res. Trends Innov., vol. 10, no. 5, pp. 686–695, 2025, [Online]. Available: https://www.ijrti.org/papers/IJRTI2505082.pdf
[11] M. Ghasemaghaei, “Does data analytics use improve firm decision making quality? The role of knowledge sharing and data analytics competency,” Decis. Support Syst., vol. 120, no. January, pp. 14–24, 2019, doi: 10.1016/j.dss.2019.03.004.
[12] R. Fernando and H. Sama, “Information Technology Management System of Sharing Knowledge Between Universities Using Systematic Literature Review,” J. Teknol. Dan Sist. Inf. Bisnis, vol. 7, no. 1, pp. 82–90, 2025, doi: https://doi.org/10.47233/jteksis.v5i1.1733.
[13] B. T. Atuahene, S. Kanjanabootra, and T. Gajendran, “Transformative role of big data through enabling capability recognition in construction,” Constr. Manag. Econ., vol. 41, no. 3, pp. 208–231, 2023, doi: 10.1080/01446193.2022.2132523.
[14] S. Wang and H. Wang, “Big data for small and medium-sized enterprises (SME): a knowledge management model,” J. Knowl. Manag., vol. 24, no. 4, pp. 881–897, 2020, doi: 10.1108/JKM-02-2020-0081.
[15] S. Tyagi, V. Bansal, and D. Saxena, “Big Data Analytics Adoption Framework and its Verification Using a Case Study,” in IFIP Advances in Information and Communication Technology, 2024, pp. 259–270. doi: 10.1007/978-3-031-50204-0_22.
[16] S. Saide and M. L. Sheng, “Toward Business Process Innovation in the Big Data Era: A Mediating Roles of Big Data Knowledge Management,” Big Data, vol. 8, no. 6, pp. 464–477, 2020, doi: 10.1089/big.2020.0140.
[17] P. Del Vecchio, G. Mele, G. Passiante, D. Vrontis, and C. Fanuli, “Detecting customers knowledge from social media big data: toward an integrated methodological framework based on netnography and business analytics,” J. Knowl. Manag., vol. 24, no. 4, pp. 799–821, 2020, doi: 10.1108/JKM-11-2019-0637.
[18] M. M. Babu, M. Rahman, A. Alam, and B. L. Dey, “Exploring big data-driven innovation in the manufacturing sector: evidence from UK firms,” Ann. Oper. Res., vol. 333, no. 2–3, pp. 689–716, 2021, doi: 10.1007/s10479-021-04077-1.
[19] C. Lin, A. S. Kunnathur, and L. Li, “Conceptualizing big data practices,” Int. J. Account. Inf. Manag., vol. 28, no. 2, pp. 205–222, 2020, doi: 10.1108/IJAIM-12-2018-0154.
[20] C. Eze, J. R C Nurse, and H. Jassim, “Big data management capabilities in the hospitality sector: Service innovation and customer generated online quality ratings,” Comput. Human Behav., vol. 121, p. 106777, 2021, doi: https://doi.org/10.1016/j.chb.2021.106777.
[21] T. Cadden, J. Weerawardena, G. Cao, Y. Duan, and R. McIvor, “Examining the role of big data and marketing analytics in SMEs innovation and competitive advantage: A knowledge integration perspective,” J. Bus. Res., vol. 168, p. 114225, 2023, doi: 10.1016/j.jbusres.2023.114225.
[22] S. Pathak, V. Krishnaswamy, and M. Sharma, “A dynamic capability perspective on the impact of big data analytics and enterprise architecture on innovation: an empirical study,” J. Enterp. Inf. Manag., vol. 38, no. 2, pp. 532–563, 2025, doi: 10.1108/JEIM-01-2024-0059.
[23] D. S. Johnson, D. Sihi, and L. Muzellec, “Implementing big data analytics in marketing departments: Mixing organic and administered approaches to increase data-driven decision making,” Informatics, vol. 8, no. 4, 2021, doi: 10.3390/informatics8040066.
[24] C. Brewis, S. Dibb, and M. Meadows, “Leveraging big data for strategic marketing: A dynamic capabilities model for incumbent firms,” Technol. Forecast. Soc. Change, vol. 190, p. 122402, 2023, doi: 10.1016/j.techfore.2023.122402.
[25] C.-L. Chong, S. Z. Abdul Rasid, H. Khalid, and T. Ramayah, “Big Data Analytics Capability for Competitive Advantage and Firm Performance in Malaysian Manufacturing Firms,” Int. J. Product. Perform. Manag., vol. 73, no. 7, pp. 2305–2328, 2023, doi: https://doi.org/10.1108/IJPPM-11-2022-0567.
[26] M. Ertz, I. Latrous, A. Dakhlaoui, and S. Sun, “The impact of Big Data Analytics on firm sustainable performance,” Corp. Soc. Responsib. Environ. Manag., vol. 32, no. 1, pp. 1261–1278, 2025, doi: 10.1002/csr.2990.
[27] P. Mikalef and J. Krogstie, “Examining the interplay between big data analytics and contextual factors in driving process innovation capabilities,” Eur. J. Inf. Syst., vol. 29, no. 3, pp. 260–287, 2020, doi: 10.1080/0960085X.2020.1740618.
[28] R. Capurro, R. Fiorentino, S. Garzella, and A. Giudici, “Big data analytics in innovation processes: which forms of dynamic capabilities should be developed and how to embrace digitization?,” Eur. J. Innov. Manag., vol. 25, no. 6, pp. 273–294, 2021, doi: 10.1108/EJIM-05-2021-0256.
[29] X. Xiao, Q. Tian, and H. Mao, “How the interaction of big data analytics capabilities and digital platform capabilities affects service innovation: A dynamic capabilities view,” IEEE Access, vol. 8, pp. 18778–18796, 2020, doi: 10.1109/ACCESS.2020.2968734.
[30] N. Hajli, M. Tajvidi, A. Gbadamosi, and W. Nadeem, “Understanding market agility for new product success with big data analytics,” Ind. Mark. Manag., vol. 86, pp. 135–143, 2020, doi: 10.1016/j.indmarman.2019.09.010.
[31] M. Brinch, A. Gunasekaran, and S. Fosso Wamba, “Firm-level capabilities towards big data value creation,” J. Bus. Res., vol. 131, pp. 539–548, 2021, doi: 10.1016/j.jbusres.2020.07.036.
[32] M. Al-Okaily and A. Al-Okaily, “Financial data modeling: an analysis of factors influencing big data analytics-driven financial decision quality,” J. Model. Manag., vol. 20, no. 2, pp. 301–321, 2025, doi: 10.1108/JM2-08-2023-0183.
[33] S. Akter, A. Gunasekaran, S. F. Wamba, M. M. Babu, and U. Hani, “Reshaping competitive advantages with analytics capabilities in service systems,” Technol. Forecast. Soc. Change, vol. 159, p. 120180, 2020, doi: 10.1016/j.techfore.2020.120180.
[34] Y. Jiang, T. Feng, and Y. Huang, “Antecedent configurations toward supply chain resilience: The joint impact of supply chain integration and big data analytics capability,” J. Oper. Manag., vol. 70, no. 2, pp. 257–284, 2024, doi: 10.1002/joom.1282.
[35] J. Ranjan and C. Foropon, “Big Data Analytics in Building the Competitive Intelligence of Organizations,” Int. J. Inf. Manage., vol. 56, p. 102231, 2021, doi: 10.1016/j.ijinfomgt.2020.102231.
[36] M. Garmaki, R. K. Gharib, and I. Boughzala, “Big data analytics capability and contribution to firm performance: the mediating effect of organizational learning on firm performance,” J. Enterp. Inf. Manag., vol. 36, no. 5, pp. 1161–1184, 2023, doi: 10.1108/JEIM-06-2021-0247.
[37] M. L. Khalil, N. A. Aziz, F. Long, and H. Zhang, “What factors affect firm performance in the hotel industry post-Covid-19 pandemic? Examining the impacts of big data analytics capability, organizational agility and innovation,” J. Open Innov. Technol. Mark. Complex., vol. 9, no. 2, p. 100081, 2023, doi: 10.1016/j.joitmc.2023.100081.
[38] S. Gupta, T. Justy, S. Kamboj, A. Kumar, and E. Kristoffersen, “Big data and firm marketing performance: Findings from knowledge-based view,” Technol. Forecast. Soc. Change, vol. 171, no. June, 2021, doi: 10.1016/j.techfore.2021.120986.
[39] U. Awan, S. Shamim, Z. Khan, N. U. Zia, S. M. Shariq, and M. N. Khan, “Big data analytics capability and decision-making: The role of data-driven insight on circular economy performance,” Technol. Forecast. Soc. Change, vol. 168, p. 120766, 2021, doi: 10.1016/j.techfore.2021.120766.
[40] R. Rosati et al., “From knowledge-based to big data analytic model: a novel IoT and machine learning based decision support system for predictive maintenance in Industry 4.0,” J. Intell. Manuf., vol. 34, no. 1, pp. 107–121, 2023, doi: 10.1007/s10845-022-01960-x.
[41] M. Ghasemaghaei and O. Turel, “Possible negative effects of big data on decision quality in firms: The role of knowledge hiding behaviours,” Inf. Syst. J., vol. 31, no. 2, pp. 268–293, 2021, doi: 10.1111/isj.12310.
