Data Monetization Strategies: The Construction Industry Market Analysis
DOI:
https://doi.org/10.11113/ijbes.v11.n3.1351Keywords:
Big Data Analytics, Data Monetization, Construction Project Management, Construction ITAbstract
Data monetization is leveraging data to obtain economic benefits. In the context of Big Data Analytics (BDA), data serves as a fundamental asset, enabling the transformation into valuable insights throughout various construction phases. Unfortunately, there is lack of studies focusing on data monetization within the construction industry, given the unique supply and demand characteristics among construction stakeholders. This study aims to address the objective of identifying data monetization strategies applicable to the construction industry, particularly for data sellers and data buyers. Data was gathered through a quantitative survey among 100 construction practitioners in Malaysia, encompassing developers, contractors, consultants, government agencies, technology providers, and academia. Respondents were further categorized into data sellers and data buyers. The data were analyzed using mean analysis, t-test and Spearman's rank correlation coefficient. The study identified six data monetization strategies, comprising 19 determinants. The analysis revealed high preference on all data monetization strategies and moderate differences in the preferred strategies between data sellers and buyers. Significant differences were found in 3 determinants which are (1) trading data to facilitate decision-making, (2) trading data for construction reports, benchmarks, and indices, and (3) selling visualized data on real-time platforms. The t-test indicated that data sellers are more inclined towards the three strategies for effective monetization. Furthermore, Spearman’s correlation coefficient revealed the 3 determinants also positively influence another 3 determinants of (1) data wrapping to reflect better service from data provider, (2) buying raw data with its inherit information and (3) monetizing internal data to optimize organization’s work process. The insights enable stakeholders to implement mechanisms that foster data monetization within project cultures accelerating BDA undertakings. Future recommendations include using larger sample sizes to enhance generalizability and to explore more areas such as construction contracts, cost, health, and safety.
References
Abioye, S. O., Oyedele, L. O., Akanbi, L., Ajayi, A., Delgado, J. M. D., Bilal, M., Akinade, O. O., & Ahmed, A. (2021). Artificial Intelligence in the Construction Industry: A Review of Present Status, Opportunities and Future Challenges. Journal of Building Engineering, 44. https://doi.org/10.1016/j.jobe.2021.103299
Acciarini, C., Cappa, F., Boccardelli, P., & Oriani, R. (2023). How can organizations leverage big data to innovate their business models? A systematic literature review. Technovation, 123, 102713. https://doi.org/10.1016/j.technovation.2023.102713
Adel, K., Elhakeem, A., & Marzouk, M. (2023). Decentralized System for Construction Projects Data Management Using Blockchain and Ipfs. Journal of Civil Engineering and Management, 29(4), 342–359. https://doi.org/10.3846/jcem.2023.18646
Ahmed, V., Tezel, A., Aziz, Z., & Sibley, M. (2017). The future of Big Data in facilities management: opportunities and challenges. Facilities, 35(13–14), 725–745. https://doi.org/10.1108/F-06-2016-0064
Akinosho, T. D., Oyedele, L. O., Bilal, M., Ajayi, A. O., Delgado, M. D., Akinade, O. O., & Ahmed, A. A. (2020). Deep learning in the construction industry: A review of present status and future innovations. Journal of Building Engineering, 32. https://doi.org/10.1016/j.jobe.2020.101827
Alfaro, E., Bressan, M., Girardin, F., Murillo, J., Someh, I., & Wixom, B. H. (2019). BBVA’s data monetization journey. MIS Quarterly Executive, 18(2), 111–128. https://doi.org/10.17705/2msqe.00011
Arcadis. (2024, March 26). Construction cost Handbook. Arcadis. https://www.arcadis.com/en/knowledge-hub/perspectives/asia/research-and-publications/construction-cost-handbook
Arent van’t Spijker. (2014). The New Oil: Using Innovative Business Models to turn Data Into Profit (1st ed.). Technics Publications.
Ayodele, T. O., & Kajimo-Shakantu, K. (2022). Challenges and drivers to data sharing among stakeholders in the South African construction industry. Journal of Engineering, Design and Technology, 20(6), 1698–1715. https://doi.org/10.1108/JEDT-02-2021-0074
Bilal, M., Oyedele, L. O., Akinade, O. O., Ajayi, S. O., Alaka, H. A., Owolabi, H. A., Qadir, J., Pasha, M., & Bello, S. A. (2016). Big data architecture for construction waste analytics (CWA): A conceptual framework. Journal of Building Engineering, 6, 144–156. https://doi.org/10.1016/j.jobe.2016.03.002
Bilal, M., Oyedele, L. O., Kusimo, H. O., Owolabi, H. A., Akanbi, L. A., Ajayi, A. O., Akinade, O. O., & Davila Delgado, J. M. (2019). Investigating profitability performance of construction projects using big data: A project analytics approach. Journal of Building Engineering, 26. https://doi.org/10.1016/j.jobe.2019.100850
Bilal, M., Oyedele, L. O., Qadir, J., Munir, K., Ajayi, S. O., Akinade, O. O., Owolabi, H. A., Alaka, H. A., & Pasha, M. (2016). Big Data in the construction industry: A review of present status, opportunities, and future trends. In Advanced Engineering Informatics (Vol. 30, Issue 3, pp. 500–521). Elsevier Ltd. https://doi.org/10.1016/j.aei.2016.07.001
Bohé, A., Hong, M., Macdonald, C., & Paice, N. (2013). Data Monetization in the Age of Big Data. https://www.accenture.com/in-en/case-studies/communications-media/manzoni
Boyes, H. (2015). Security, Privacy, and the Built Environment.
Braun, T., Fung, B. C. M., Iqbal, F., & Shah, B. (2018). Security and privacy challenges in smart cities. Sustainable Cities and Society, 39, 499–507. https://doi.org/10.1016/j.scs.2018.02.039
Building Cost Information Service. (2024, March 26). Unlocking Data. Building Cost Information Service. https://bcis.co.uk/
Building Cost Information Services Malayia. (2024, March 26). N3C. Building Cost Information Services Malayia. https://n3c.cidb.gov.my/n3c/index.php
Chaurasia, S., & Verma, S. (2020). Strategic Determinants of Big Data Analytics in the AEC Sector: A Multi-Perspective Framework. Construction Economics and Building, 20(4), 63–81. https://doi.org/doi.org/10.5130/AJCEB.v20i4.6649
Construction Industry Development Board. (2020). Construction 4.0 Strategic Plan (2021-2025) Next Revolution of the Malaysian Construction Industry.
Dixon, T., Wetering, J. van de, Sexton, M., Lu, S.-L., Williams, D., Duman, D. U., & Chen, X. (2017). Smart Cities, Big Data and the Built Environment: What’s Required? www.rics.org
Domnina, S. V., Savoskina, E. V., & Shekhova, N. V. (2016). On Innovative Decisions in the Investment-construction Cycle. Procedia Engineering, 153, 741–746. https://doi.org/10.1016/j.proeng.2016.08.236
Faroukhi, A. Z., Alaoui, I. El, Gahi, Y., & Amine, A. (2020). An Adaptable Big Data Value Chain Framework for End-to-End Big Data Monetization. Big Data and Cognitive Computing, 4(4), 1–27. https://doi.org/10.3390/bdcc4040034
Faroukhi, A. Z., El Alaoui, I., Gahi, Y., & Amine, A. (2020). Big data monetization throughout Big Data Value Chain: a comprehensive review. Journal of Big Data, 7(1). https://doi.org/10.1186/s40537-019-0281-5
Fazeli, A., Dashti, M. S., Jalaei, F., & Khanzadi, M. (2021). An integrated BIM-based approach for cost estimation in construction projects. Engineering, Construction and Architectural Management, 28(9), 2828–2854. https://doi.org/10.1108/ECAM-01-2020-0027
Gad, G. M., & Shane, J. S. (2014). Trust in the Construction Industry: A Literature Review. Construction Research Congress 2014, 2136–2145. https://doi.org/10.1061/9780784413517.217
Gbadamosi, A. Q., Oyedele, L., Mahamadu, A. M., Kusimo, H., Bilal, M., Davila Delgado, J. M., & Muhammed-Yakubu, N. (2020). Big data for Design Options Repository: Towards a DFMA approach for offsite construction. Automation in Construction, 120. https://doi.org/10.1016/j.autcon.2020.103388
Ghasemaghaei, M., Ebrahimi, S., & Hassanein, K. (2018). Data analytics competency for improving firm decision making performance. The Journal of Strategic Information Systems, 27(1), 101–113. https://doi.org/10.1016/j.jsis.2017.10.001
Ghorbany, S., Noorzai, E., & Yousefi, S. (2023). BIM-based solution to enhance the performance of public-private partnership construction projects using copula bayesian network. Expert Systems with Applications, 216. https://doi.org/10.1016/j.eswa.2023.119501
Günther, W. A., Rezazade Mehrizi, M. H., Huysman, M., & Feldberg, F. (2017). Debating big data: A literature review on realizing value from big data. Journal of Strategic Information Systems, 26(3), 191–209. https://doi.org/10.1016/j.jsis.2017.07.003
Hanafizadeh, P., & Harati Nik, M. R. (2020). Configuration of Data Monetization: A Review of Literature with Thematic Analysis. Global Journal of Flexible Systems Management, 21(1), 17–34. https://doi.org/10.1007/s40171-019-00228-3
Hartmann, P. M., Zaki, M., Feldmann, N., & Neely, A. (2016). Capturing value from big data – a taxonomy of data-driven business models used by start-up firms. International Journal of Operations & Production Management, 36(10), 1382–1406. https://doi.org/10.1108/IJOPM-02-2014-0098
Hashim, M. A., Che Ibrahim, C. K. I., Jaafar, N. A. L., Kordi, N. E., Haron, A. T., & Umeokafor, N. (2024). Building data driven culture for digital competitiveness in construction industry: a theoretical exploration. International Journal of Construction Management, 1–13. https://doi.org/10.1080/15623599.2024.2322237
Huang, Y., Shi, Q., Zuo, J., Pena-Mora, F., & Chen, J. (2021). Research Status and Challenges of Data-Driven Construction Project Management in the Big Data Context. Advances in Civil Engineering, 2021. https://doi.org/10.1155/2021/6674980
Inoua, S. M., & Smith, V. L. (2020). The Classical Theory of Supply and Demand (ESI Working Paper 20-11).
Ismail, S. A., Bandi, S., & Maaz, Z. N. (2018). An Appraisal into the Potential Application of Big Data in the Construction Industry. International Journal of Built Environment and Sustainability, 5(2). https://doi.org/10.11113/ijbes.v5.n2.274
Jiang, J., & Gallupe, R. B. (2015). Environmental Scanning and Business Insight Capability: The Role of Business Analytics and Knowledge Integration. Twenty-First Americas Conference on Information Systems, 1–28.
Khalfan, M. M. A., McDermott, P., & Swan, W. (2007). Building trust in construction projects. Supply Chain Management: An International Journal, 12(6), 385–391. https://doi.org/10.1108/13598540710826308
Kiu, M. S., Chia, F. C., & Wong, P. F. (2020). Exploring the potentials of blockchain application in construction industry: a systematic review. International Journal of Construction Management , 22(15), 2931–2940. https://doi.org/https://doi.org/10.1080/15623599.2020.1833436
Koseleva, N., & Ropaite, G. (2017). Big Data in Building Energy Efficiency: Understanding of Big Data and Main Challenges. Procedia Engineering, 172, 544–549. https://doi.org/10.1016/j.proeng.2017.02.064
Laitila, M. (2017). Data monetization: Utilizing data as an asset to generate new revenues for firms. Aalto University.
LaValle, S., Lesser, E., Shockley, R., Hopkins, M. S., & Kruschwitz, N. (2010, December 21). Big Data, Analytics and the Path From Insights to Value. MIT Sloan Management Review. https://sloanreview.mit.edu/article/big-data-analytics-and-the-path-from-insights-to-value/
Lewis, A., & McKone, D. (2016, May 10). So Many M&A Deals Fail Because Companies Overlook This Simple Strategy. Harvard Business Review.
Liu, C.-H., & Chen, C.-L. (2015). A Review Of Data Monetization: Strategic Use Of Big Data. 15th International Conference on Electronic Business, 12–18. https://aisel.aisnet.org/iceb2015
Lu, W., Webster, C., Peng, Y., Chen, X., & Chen, K. (2018). Big Data in Construction Waste Management: Prospects and Challenges. Detritus, 4(December), 129–139. https://doi.org/10.31025/2611-4135/2018.13737
Lu, Y., & Zhang, J. (2021). Bibliometric analysis and critical review of the research on big data in the construction industry. Engineering, Construction and Architectural Management. https://doi.org/10.1108/ECAM-01-2021-0005
Maaz, Z. N., Bandi, S., & Amirudin, R. (2018). A contextual parsing of big data values to quantity surveyors. International Journal Of Built Environment And Sustainability, 5(3), 241–250.
Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H. (2011). Big data: The next frontier for innovation, competition, and productivity. www.mckinsey.com/mgi.
Mauri, M. (2022). Information Visualization for the Construction Industry (pp. 185–206). https://doi.org/10.1007/978-3-030-82430-3_8
Mehta, S., Dawande, M., Janakiraman, G., & Mookerjee, V. (2019). How to Sell a Dataset? Pricing Policies for Data Monetization. Proceedings of the 2019 ACM Conference on Economics and Computation, 679–679. https://doi.org/10.1145/3328526.3329587
Mehta, S., Dawande, M., Janakiraman, G., & Mookerjee, V. (2022). An Approximation Scheme for Data Monetization. Production and Operations Management, 31(6), 2412–2428. https://doi.org/10.1111/poms.13676
Mendoza, R. M. (2023). Project Management Skills, People Management Skills, and Operational Excellence of Construction Project Managers in Private Construction Management Services. International Journal of Social Science and Education Research Studies, 03(06), 1019–1038. https://doi.org/10.55677/ijssers/v03i6y2023-08
Meng, Q., Peng, Q., Li, Z., & Hu, X. (2022). Big Data Technology in Construction Safety Management: Application Status, Trend and Challenge. Buildings, 12(533). https://doi.org/https://doi.org/10.3390/ buildings12050533
Ministry of Works Malaysia. (2024, March 26). KKR Publications. Ministry of Works Malaysia. https://www.kkr.gov.my/en/gallery/list-kkr-publications?page=0
Moro-Visconti, R. (2020). Big Data Valuation. In The Valuation of Digital Intangibles (pp. 345–360). Springer International Publishing.
Moura, J., & Serrão, C. (2015). Security and Privacy Issues of Big Data. In Handbook of Research on Trends and Future Directions in Big Data and Web Intelligence (pp. 20–52). IGI Global.
Najjar, M. S., & Kettinger, W. J. (2013). Data Monetization: Lessons from a Retailer’s Journey. MIS Quarterly Executive, 12(4), 213–225.
Nash, S., Chinyio, E., Gameson, R., & Suresh, S. (2010). The Dynamism of Stakeholders’ Power in Construction Projects. Proceeding 26th Annual ARCOM Conference, 471–480.
Ngo, J., Hwang, B., & Zhang, C. (2020). Factor-based big data and predictive analytics capability assessment tool for the construction industry. Automation in Construction, 110, 1–12. https://doi.org/10.1016/j.autcon.2019.103042
O’brien, W., London, K., & Vrijhoef, R. (2004). Construction supply chain modeling: a research review and interdisciplinary agenda. ICFAI Journal of Operations Management, 3(3), 64–84. https://hdl.handle.net/10536/DRO/DU:30020890
Ofulue, J., & Benyoucef, M. (2022). Data monetization: insights from a technology-enabled literature review and research agenda. Management Review Quarterly. https://doi.org/10.1007/s11301-022-00309-1
Owolabi, H. A., Bilal, M., Oyedele, L. O., Alaka, H. A., Ajayi, S. O., & Akinade, O. O. (2020). Predicting Completion Risk in PPP Projects Using Big Data Analytics. IEEE Transactions on Engineering Management, 67(2), 430–453. https://doi.org/10.1109/TEM.2018.2876321
Parvinen, P., Pöyry, E., Gustafsson, R., Laitila, M., & Rossi, M. (2020). Advancing data monetization and the creation of data-based business models. Communications of the Association for Information Systems, 47, 25–49. https://doi.org/10.17705/1CAIS.04702
Patel, T., & Patel, V. (2020). Data Privacy in Construction Industry by Privacy-Preserving Data Mining (PPDM) Approach. Asian Journal of Civil Engineering, 21(3), 505–515. https://doi.org/10.1007/s42107-020-00225-3
Preidel, C., Borrmann, A., Oberender, C., & Tretheway, M. (2016). Seamless integration of common data environment access into BIM authoring applications: The BIM integration framework. In S. Christodoulou & R. Scherer (Eds.), eWork and eBusiness in Architecture, Engineering and Construction: ECPPM 2016 (1st Edition). CRC Press.
Public Works Department Malaysia. (2024, March 26). Technical Documents. Public Works Department Malaysia. https://www.jkr.gov.my/my/page/dokumen-teknikal-0
Qian, X. (Alice), & Papadonikolaki, E. (2020). Shifting trust in construction supply chains through blockchain technology. Engineering, Construction and Architectural Management, 28(2), 584–602. https://doi.org/10.1108/ECAM-12-2019-0676
Raddats, C., Naik, P., & Ziaee Bigdeli, A. (2022). Creating value in servitization through digital service innovations. Industrial Marketing Management, 104, 1–13. https://doi.org/10.1016/j.indmarman.2022.04.002
Radl, J., & Kaiser, J. (2019). Benefits of Implementation of Common Data Environment (CDE) into Construction Projects. IOP Conference Series: Materials Science and Engineering , 1–4. https://doi.org/doi:10.1088/1757-899X/471/2/022021
Ram, J., Afridi, N. K., & Khan, K. A. (2019). Adoption of Big Data Analytics in Construction: Development of a Conceptual Model. Built Environment Project and Asset Management, 9(4), 564–579. https://doi.org/10.1108/BEPAM-05-2018-0077
Reyes Veras, P., Renukappa, S., & Suresh, S. (2022). Awareness of Big Data concept in the Dominican Republic construction industry: an empirical study. Construction Innovation, 22(3), 465–486. https://doi.org/10.1108/CI-05-2021-0090
Reyes-Veras, P. F., Renukappa, S., Suresh, S., & Algahtani, K. (2022). Strategies for Implementing Big Data Concept in the Construction Industry of the Dominican Republic. https://wlv.openrepository.com/handle/2436/624487
Ritala, P., Keränen, J., Fishburn, J., & Ruokonen, M. (2024). Selling and monetizing data in B2B markets: Four data-driven value propositions. Technovation, 130. https://doi.org/10.1016/j.technovation.2023.102935
Sawhney, A., & Knight, A. (2023). Digitalisation in Construction Report 2023. www.rics.org
Schober, P., & Schwarte, L. A. (2018). Correlation coefficients: Appropriate use and interpretation. Anesthesia and Analgesia, 126(5), 1763–1768. https://doi.org/10.1213/ANE.0000000000002864
Shubham Shubham, Saloni Saloni, & Sidra-Tul-Muntaha. (2023). Optimizing construction processes and improving building performance through data engineering and computation. World Journal of Advanced Research and Reviews, 18(1), 390–398. https://doi.org/10.30574/wjarr.2023.18.1.0614
Tan, Y. J., Maaz, Z. N., Bandi, S., & Palis, P. A. (2023). Common Data Environment: Bridging the Digital Data Sharing Gap Among Construction Organizations (pp. 333–342). https://doi.org/10.1007/978-3-031-25274-7_27
Tanga, O., Akinradewo, O., Aigbavboa, C., Oke, A., & Adekunle, S. (2022). Data Management Risks: A Bane of Construction Project Performance. Sustainability (Switzerland), 14(19). https://doi.org/10.3390/su141912793
Taylan, O., Kabli, M. R., Porcel, C., & Herrera-Viedma, E. (2017). Contractor Selection for Construction Projects Using Consensus Tools and Big Data. International Journal of Fuzzy Systems.
Teece, D. J., & Linden, G. (2017). Business models, value capture, and the digital enterprise. Journal of Organization Design, 6(1), 8. https://doi.org/10.1186/s41469-017-0018-x
Thomas, L. D. W., & Leiponen, A. (2016). Big Data Commercialization. IEEE Engineering Management Review, 44(2), 74–90. https://doi.org/10.1109/EMR.2016.2568798
Vaske, J. J., Beaman, J., & Sponarski, C. C. (2017). Rethinking Internal Consistency in Cronbach’s Alpha. Leisure Sciences, 39(2), 163–173. https://doi.org/10.1080/01490400.2015.1127189
Wixom, B. H. (2014, August 21). Cashing in Your Data. MIT Centre for Information Systems Research. https://cisr.mit.edu/publication/2014_0801_DataMonetization_Wixom
Wixom, B. H., & Ross, J. W. (2018). How to Monetize Your Data. In How to Go Digital (pp. 39–48). The MIT Press. https://doi.org/10.7551/mitpress/11633.003.0009
Woerner, S. L., & Wixom, B. H. (2015). Big data: Extending the business strategy toolbox. Journal of Information Technology, 30(1), 60–62. https://doi.org/10.1057/jit.2014.31
Woroch, R., & Strobel, G. (2022). Show me the Money: How to monetize data in data-driven business models? Wirtschaftsinformatik 2022 Proceedings, 1–16. https://aisel.aisnet.org/wi2022
Wu, L., & Abourizk, S. M. (2021). Towards Construction ’ S Digital Future : A Roadmap For Enhancing Csce 2021 Annual Conference Towards Construction ’ S Digital Future : A Roadmap For. CSCE 2021 Annual Conference Inspired, May. https://doi.org/10.13140/RG.2.2.29564.67205
Wu, Z., Chen, C., Cai, Y., Lu, C., Wang, H., & Yu, T. (2019). BIM-Based Visualization Research in the Construction Industry: A Network Analysis. International Journal of Environmental Research and Public Health, 16(18), 3473. https://doi.org/10.3390/ijerph16183473
Yuan, Z., Yu, X., Jiang, Y., Sun, J., Liu, Z., & Li, B. (2024). Current status and governance of data assets monetization in the global maritime industry: A comparative study of the United States, Europe, and China. Ocean and Coastal Management, 251. https://doi.org/10.1016/j.ocecoaman.2024.107078
Zhang, X., Yue, W. T., Yu, Y., & Zhang, X. (2023). How to monetize data: An economic analysis of data monetization strategies under competition. Decision Support Systems, 173. https://doi.org/10.1016/j.dss.2023.114012
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 International Journal of Built Environment and Sustainability
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Copyright of articles that appear in International Journal of Built Environment and Sustainability belongs exclusively to Penerbit Universiti Teknologi Malaysia (Penerbit UTM Press). This copyright covers the rights to reproduce the article, including reprints, electronic reproductions or any other reproductions of similar nature.
Authors who publish with this journal agree to the following terms:
- This Journal applies Creative Commons Licenses of CC-BY-NC-SA
- Authors retain copyright and grant the journal right of publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
