Analytical Technologies and Business Consulting: A Systematic Analysis of Literature

Tecnologías analíticas y consultoría empresarial: un análisis sistemático de literatura

Published 2026-06-24


https://doi.org/10.30554/lumina.v27.n1.5624.2026
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Vol. 27 No. 1 (2026): Lúmina 27(1)
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Artículos de revisión
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LOZANO CHICUE , G. A. ., CORREA CRUZ, L., & Fajardo, M. Y. (2026). Analytical Technologies and Business Consulting: A Systematic Analysis of Literature. Lúmina, 27(1), 27-1. https://doi.org/10.30554/lumina.v27.n1.5624.2026
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LOZANO CHICUE , G. A. ., CORREA CRUZ, L., & Fajardo, M. Y. (2026). Analytical Technologies and Business Consulting: A Systematic Analysis of Literature. Lúmina, 27(1), 27-1. https://doi.org/10.30554/lumina.v27.n1.5624.2026

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GERMAN ALFONSO LOZANO CHICUE
Universidad de la Amazonia
https://orcid.org/0009-0006-0988-4166 ORCID
LUCELLY CORREA CRUZ
Universidad de la Amazonia
https://orcid.org/0000-0002-4781-0305 ORCID
Maria Yeny Fajardo
Universidad de la Amazonia
https://orcid.org/0000-0002-3846-149X ORCID
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LUCELLY CORREA CRUZ,

Doctor en Educación y Cultura Ambiental, Universidad de la Amazonia

Integrante grupo de investigación GEMA

Docente de tiempo completo Universidad de la Amazonia

Business consulting faces challenges related to digital transformation arising from the convergence of Big Data Analytics (BDA), Artificial Intelligence (AI), and Business Intelligence (BI), which are reconfiguring consulting practice and providing added value to organizations. This study was developed through a systematic review using the PRISMA methodology, analyzing 50 research documents published between 2020 and 2025. The results identified the following critical dimensions: business intelligence in decision-making, digital transformation strategies, and effective business consulting models articulated with data analysis. Likewise, a conceptual model is proposed as an alternative to address critical gaps related to methodological standardization and adaptation for MSMEs, and to bring new perspectives to consulting services through the integration of technological capabilities, data governance, and organizational capabilities. Based on empirical evidence, it is concluded that business consultancies that articulate technological tools within strategic frameworks generate sustainable dynamics of organizational transformation.

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  1. Abu-AlSondos, I. A. (2023). The impact of business intelligence system (BIS) on quality of strategic decision-making. International Journal of Data and Network Science, 7(4), 1901–1912. https://doi.org/10.5267/j.ijdns.2023.7.003
  2. Ahmed, R., Shaheen, S., & Philbin, S. P. (2022). The role of big data analytics and decision-making in achieving project success. Journal of Engineering and Technology Management, 65, Article 101697. https://doi.org/10.1016/j.jengtecman.2022.101697
  3. Azevedo, A., & Almeida, A. H. (2021). Grasp the challenge of digital transition in SMEs—A training course geared towards decision-makers. Education Sciences, 11(4), Article 151. https://doi.org/10.3390/educsci11040151
  4. Barbieri, G., Laserna, J., & Mateus, L. M. (2024). Towards a business intelligence application for evidence-based maintenance. IFAC-PapersOnLine, 58(8), 37–42. https://doi.org/10.1016/j.ifacol.2024.08.047
  5. Basile, L. J., Carbonara, N., Panniello, U., & Pellegrino, R. (2024). How can technological resources improve the quality of healthcare service? The enabling role of big data analytics capabilities. IEEE Transactions on Engineering Management, 71(9), 5771–5781. https://doi.org/10.1109/TEM.2024.3366313
  6. Bernardo, B. M. V., São Mamede, H., Barroso, J. M. P., & dos Santos, V. M. P. D. (2024). Data governance & quality management—Innovation and breakthroughs across different fields. Journal of Innovation & Knowledge, 9(4), Article 100598. https://doi.org/10.1016/j.jik.2024.100598
  7. Brous, P., Janssen, M., & Krans, R. (2020). Data governance as success factor for data science. In M. Hattingh, M. Matthee, H. Smuts, I. Pappas, Y. K. Dwivedi, & M. Mäntymäki (Eds.), Responsible design, implementation and use of information and communication technology (pp. 431–442). Springer. https://doi.org/10.1007/978-3-030-44999-5_36
  8. Chatterjee, S., Chaudhuri, R., Gupta, S., Sivarajah, U., & Bag, S. (2023). Assessing the impact of big data analytics on decision-making processes, forecasting, and performance of a firm. Technological Forecasting and Social Change, 196, Article 122824. https://doi.org/10.1016/j.techfore.2023.122824
  9. Cheng, L. (2022). Decision modeling and evaluation of enterprise digital transformation using data mining. Mobile Information Systems, 2022, Article 2380100. https://doi.org/10.1155/2022/2380100
  10. Chenic, A. Ș., Burlacu, A., Dobrea, R. C., Tescan, L., Crețu, A. I., Stanef-Puica, M. R., Godeanu, T. N., Manole, A. M., Virjan, D., & Moroianu, N. (2023). The impact of digitalization on macroeconomic indicators in the new industrial age. Electronics, 12(7), Article 1612. https://doi.org/10.3390/electronics12071612
  11. Dal Forno, A. J., Bauler, L. M., Soares, S. de S. S., & Sayão, M. L. M. dos R. (2024). Indicator automation with Power BI in the innovation sector of a transformer company. International Journal of Information Engineering and Electronic Business, 16(4), 16–27. https://doi.org/10.5815/ijieeb.2024.04.02
  12. de Kerckhove, D. (2021). The personal digital twin, ethical considerations. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 379(2207), Article 20200367. https://doi.org/10.1098/rsta.2020.0367
  13. El Hilali, W., El Manouar, A., & Janati Idrissi, M. A. (2021). The mediating role of big data analytics in enhancing firms’ commitment to sustainability. International Journal of Advanced Technology and Engineering Exploration, 8(80), 932–944. https://doi.org/10.19101/IJATEE.2021.874114
  14. Guo, H., & Polak, P. (2024). Finance centralization—Research on enterprise intelligence. Humanities and Social Sciences Communications, 11, Article 1536. https://doi.org/10.1057/s41599-024-04098-8
  15. Hsu, C. H., He, X., Zhang, T. Y., Chang, A. Y., Liu, W. L., & Lin, Z. Q. (2022). Enhancing supply chain agility with Industry 4.0 enablers to mitigate ripple effects based on integrated QFD-MCDM: An empirical study of new energy materials manufacturers. Mathematics, 10(10), Article 1635. https://doi.org/10.3390/math10101635
  16. Hussain, F., Nauman, M., Alghuried, A., Alhudhaif, A., & Akhtar, N. (2023). Leveraging big data analytics for enhanced clinical decision-making in healthcare. IEEE Access, 11, 127817–127836. https://doi.org/10.1109/ACCESS.2023.3332030
  17. Janík, S., Szabó, P., Mĺkva, M., & Mareček-Kolibiský, M. (2022). Effective data utilization in the context of Industry 4.0 technology integration. Applied Sciences, 12(20), Article 10517. https://doi.org/10.3390/app122010517
  18. Klein, L., Guilhem, A. P. S., de Sousa, H. A., & de Oliveira, E. L. S. (2023). Institutional pressures on setting up big data analytics capability. Revista Contabilidade & Finanças, 34(92), Article e1591. https://doi.org/10.1590/1808-057x20231591.en
  19. Kusmin, K.-L., Normak, P., & Ley, T. (2024). A methodology for planning, implementation and evaluation of skills intelligence management—Results of a design science project in technology organisations. Frontiers in Artificial Intelligence, 7, Article 1424924. https://doi.org/10.3389/frai.2024.1424924
  20. Liagkou, V., Stylios, C., Pappa, L., & Petunin, A. (2021). Challenges and opportunities in Industry 4.0 for mechatronics, artificial intelligence and cybernetics. Electronics, 10(16), Article 2001. https://doi.org/10.3390/electronics10162001
  21. Lu, H., & Shaharudin, M. S. (2024). Role of digital transformation for sustainable competitive advantage of SMEs: A systematic literature review. Cogent Business & Management, 11(1), Article 2419489. https://doi.org/10.1080/23311975.2024.2419489
  22. Madyatmadja, E. D., Adiba, C. N. A., Sembiring, D. J. M., Pristinella, D., & Putra, A. M. (2021). The positive impact of implementation business intelligence and big data in hospitality and tourism sector. International Journal of Emerging Technology and Advanced Engineering, 11(6), 59–71. https://doi.org/10.46338/ijetae0621_07
  23. Maghsoudi, M., & Nezafati, N. (2023). Navigating the acceptance of implementing business intelligence in organizations: A system dynamics approach. Telematics and Informatics Reports, 11, Article 100070. https://doi.org/10.1016/j.teler.2023.100070
  24. Marcelo, A. B. (2020). A practical approach to digital transformation: A guide to health institutions in developing countries. In L. A. Celi, M. S. Majumder, P. Ordóñez, J. S. Osorio, K. E. Paik, & M. Somai (Eds.), Leveraging data science for global health (pp. 307–314). Springer. https://doi.org/10.1007/978-3-030-47994-7_18
  25. Martínez-Peláez, R., Ochoa-Brust, A., Rivera, S., Félix, V. G., Ostos, R., Brito, H., Félix, R. A., & Mena, L. J. (2023). Role of digital transformation for achieving sustainability: Mediated role of stakeholders, key capabilities, and technology. Sustainability, 15(14), Article 11221. https://doi.org/10.3390/su151411221
  26. Medlama, M., Vianney, L. S., Piring, R., Ilham, S., & Annamalah, S. (2025). The impact of artificial intelligence on leadership decision-making: Opportunities and challenges. Communications on Applied Nonlinear Analysis, 32(9S), 1878–1885. https://doi.org/10.52783/cana.v32.4359
  27. Mikalef, P., & Gupta, M. (2021). Artificial intelligence capability: Conceptualization, measurement calibration, and empirical study on its impact on organizational creativity and firm performance. Information & Management, 58(3), Article 103434. https://doi.org/10.1016/j.im.2021.103434
  28. Mikalef, P., Boura, M., Lekakos, G., & Krogstie, J. (2020). The role of information governance in big data analytics driven innovation. Information & Management, 57(7), Article 103361. https://doi.org/10.1016/j.im.2020.103361
  29. Mitrofanova, Y. S., Glukhova, L. V., Burenina, V. I., Evstafeva, O. A., & Popova, T. N. (2021). Smart production: Features of assessing the level of personnel digital readiness. Procedia Computer Science, 192, 2962–2970. https://doi.org/10.1016/j.procs.2021.09.068
  30. Nyanga, C., Pansiri, J., & Chatibura, D. (2020). Enhancing competitiveness in the tourism industry through the use of business intelligence: A literature review. Journal of Tourism Futures, 6(2), 139–151. https://doi.org/10.1108/JTF-11-2018-0069
  31. Osman, A. M. S., Elragal, A. A., & Ståhlbröst, A. (2022). Data-driven decisions in smart cities: A digital transformation case study. Applied Sciences, 12(3), Article 1732. https://doi.org/10.3390/app12031732
  32. Persaud, A., & Zare, J. (2024). Beyond technological capabilities: The mediating effects of analytics culture and absorptive capacity on big data analytics value creation in small- and medium-sized enterprises. IEEE Transactions on Engineering Management, 71, 7147–7159. https://doi.org/10.1109/TEM.2023.3249415
  33. Pour, P. D., Ahmed, A. A., Nazzal, M. A., & Darras, B. M. (2023). An Industry 4.0 technology selection framework for manufacturing systems and firms using fuzzy AHP and fuzzy TOPSIS methods. Systems, 11(4), Article 192. https://doi.org/10.3390/systems11040192
  34. Ramos, M. E., Azevedo, A., Meira, D., & Curado Malta, M. (2023). Cooperatives and the use of artificial intelligence: A critical view. Sustainability, 15(1), Article 329. https://doi.org/10.3390/su15010329
  35. Redondo, R., Herrero, Á., Corchado, E., & Sedano, J. (2020). A decision-making tool based on exploratory visualization for the automotive industry. Applied Sciences, 10(12), Article 4355. https://doi.org/10.3390/app10124355
  36. Ryalat, M., Franco, E., Elmoaqet, H., Almtireen, N., & Al-Refai, G. (2024). The integration of advanced mechatronic systems into Industry 4.0 for smart manufacturing. Sustainability, 16(19), Article 8504. https://doi.org/10.3390/su16198504
  37. Sánchez-Serrano, S., Pedraza-Navarro, I., & Donoso-González, M. (2022). ¿Cómo hacer una revisión sistemática siguiendo el protocolo PRISMA? Usos y estrategias fundamentales para su aplicación en el ámbito educativo a través de un caso práctico. Bordón. Revista de Pedagogía, 74(3), 51–66. https://doi.org/10.13042/Bordon.2022.95090
  38. Santos, G., Sá, J. C., Félix, M. J., Barreto, L., Carvalho, F., Doiro, M., Zgodavová, K., & Stefanović, M. (2021). New needed quality management skills for Industry 4.0. Sustainability, 13(11), Article 6149. https://doi.org/10.3390/su13116149
  39. Schmidt, D. H., van Dierendonck, D., & Weber, U. (2023). The data-driven leader: Developing a big data analytics leadership competency framework. Journal of Management Development, 42(4), 297–326. https://doi.org/10.1108/JMD-12-2022-0306
  40. Shapovalova, A., Kuzmenko, O., Polishchuk, O., Larikova, T., & Myronchuk, Z. (2023). Modernization of the national accounting and auditing system using digital transformation tools. Financial and Credit Activity Problems of Theory and Practice, 4(51), 33–52. https://doi.org/10.55643/fcaptp.4.51.2023.4102
  41. Siddiqui, A. A., Lahmar, A., Singh, P., Arora, K., Samadhiya, A., & Kumar, A. (2024). Unlocking circular supply chain 4.0: Identifying key barriers through bibliometrics and TISM-MICMAC. International Journal of Logistics Research and Applications, 27(6), 912–937. https://doi.org/10.1080/13675567.2023.2213286
  42. Sivarajah, U., Kumar, S., Kumar, V., Chatterjee, S., & Li, J. (2024). A study on big data analytics and innovation: From technological and business cycle perspectives. Technological Forecasting and Social Change, 202, Article 123328. https://doi.org/10.1016/j.techfore.2024.123328
  43. Sorger, M., Ralph, B. J., Hartl, K., Woschank, M., & Stockinger, M. (2021). Big data in the metal processing value chain: A systematic digitalization approach under special consideration of standardization and SMEs. Applied Sciences, 11(19), Article 9021. https://doi.org/10.3390/app11199021
  44. Stachová, K., Stacho, Z., Šamalík, P., & Sekan, F. (2024). The impact of E-HRM tools on employee engagement. Administrative Sciences, 14(11), Article 303. https://doi.org/10.3390/admsci14110303
  45. Sun, Q., & Shi, Y. (2023). Will digital disclosure affect corporate stocks? Based on stakeholders’ cognition. Engineering Economics, 34(2), 223–229. https://doi.org/10.5755/j01.ee.34.2.31514
  46. Tsahat, C. O. O., Ngoulou-A-Ndzeli, & Nkouka Moukengue, C. G. L. (2024). Opportunities for using machine learning and artificial intelligence in business analytics. Computer and Information Science, 17(2), Article 1. https://doi.org/10.5539/cis.v17n2p1
  47. Vysotska, V., Berko, A., Burov, Y., Uhryn, D., Hu, Z., & Dvorzhak, V. (2024). Information technology for the data integration in intelligent systems of business analytics. International Journal of Information Engineering and Electronic Business, 16(4), 66–92. https://doi.org/10.5815/ijieeb.2024.04.05
  48. Wang, Z. (2025). Enhancing business intelligence through AI and big data: A focus on precision mining and real-time analysis. Applied and Computational Engineering, 119(1), 61–66. https://doi.org/10.54254/2755-2721/2025.21643
  49. Yitmen, I., Alizadehsalehi, S., Akıner, İ., & Akıner, M. E. (2021). An adapted model of cognitive digital twins for building lifecycle management. Applied Sciences, 11(9), Article 4276. https://doi.org/10.3390/app11094276
  50. Zaid, M., Farooqi, R., & Azmi, S. N. (2025). Driving sustainable supply chain performance through digital transformation: The role of information exchange and responsiveness. Cogent Business & Management, 12(1), Article 2443047. https://doi.org/10.1080/23311975.2024.2443047

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