A contextual parsing of big data values to quantity surveyors

Zafira Nadia Maaz, Shamsulhadi Bandi, Roslan Amirudin

Abstract


Big data is the new generation of technology designed for organizations to economically extract value from large volumes of a wide variety of data through high velocity capture, discovery, storage and analysis. Manifest as the frontier of 21st century technology, big data instigate superior business return. This lure businesses to zealously capitalize big data. In correspond, professionals too are charting their way to improve customer value with big data. Leading research in this area accede maximization on big data; revolutionized the norm of medical and accounting profession. Despite the substantial value, big data uptake from the quantity surveying profession recognized subtle. Contrarily, construction stakeholders swiftly embrace modern technology in their construction value chain. This invoke a change in data landscape thus, present an urgent call for professionals, especially quantity surveyors to recognize the change, embrace and reap the big data benefit. This paper aims to expand big data knowledge from the context of quantity surveying profession as an approach to soothe the big data and quantity surveying gap. This paper identifies generic big data value from professional perspective and explore big data value from the quantity surveying context. Aligning to the blurry big data paradigm in the quantity surveying context, this research adopts quantitative research with desk study on 28 papers and framework analysis through 15 semi-structured interviews with big data industry expert with quantity surveying background. This research found that big data values are consistent across profession albeit the difference on how big data is maximized. Other than that, the paucity of quantity surveying big data pursuance seen as repercussion of infancy big data state in the construction industry. However, this research insinuate quantity surveying profession are in strategic position to move forward with big data.


Keywords


Big data; Big data value; Construction Industry; Framework Analysis; Quantity Surveyors

Full Text:

PDF

References


Addo-Tenkorang, R., & Helo, P. T. (2016). Big Data Applications in Operations/Supply Chain Management: A Literature Review. Computers and Industrial Engineering, 101, 528–543. https://doi.org/10.1016/j.cie.2016.09.023

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

Association of Chartered Certified Accountants. (2013). Big data : its power and perils. Retrieved from http://www.accaglobal.com/content/dam/acca/global/PDF-technical/futures/pol-afa-bdpap.pdf

Basoglu, K. A., & Hess, T. J. (2014). Online Business Reporting: A Signaling

Theory Perspective. Journal of Information Systems, 28(2), 67–101.

Benson, R. J., Bugnitz, T. L., & Walton, W. (2004). From Business Strategy to IT Action: Right Decisions for a Better Bottom Line. Hoboken, New Jersey: John Wiley & Sons.

Bilal, M., Oyedele, L. O., Qadir, J., Munir, K., Ajayi, S. O., Akinade, O. O., … Pasha, M. (2016). Big Data in the construction industry: A review of present status, opportunities, and future trends. Advanced Engineering Informatics, 30(3), 500–521. https://doi.org/10.1016/j.aei.2016.07.001

Bourmistrov, A., & Kaarbøe, K. (2013). From comfort to stretch zones: A field study of two multinational companies applying “beyond budgeting” ideas. Management Accounting Research, 24(3), 196–211. https://doi.org/10.1016/j.mar.2013.04.001

Braun, V., & Clarke, V. (2006). Using Thematic Analysis is Psychology. Qualitative Research in Psychology, 3(2), 77–101.

Brown, B., Chui, M., & Manyika, J. (2011). Are you Ready for the Era of Big Data. McKinsey Quarterly, (October).

Bryman, A. (2004). Social Research Methods (2nd ed.). New York: Oxford University Press.

Cao, M., Chychyla, R., & Stewart, T. (2015). Big data analytics in financial statement audits. Accounting Horizons, 29(2), 423–429. https://doi.org/10.2308/acch-51068

CEBR. (2012). Data equity Unlocking the Value of Big Data. Centre for Economics and Business Research. https://doi.org/10.1108/MIP-05-2012-0055

Chan, W. C. (2003). Stock price reaction to news and no-news: Drift and reversal after headlines. Journal of Financial Economics, 70(2), 223–260. https://doi.org/10.1016/S0304-405X(03)00146-6

Chang, R. M., Kauffman, R. J., & Kwon, Y. (2014). Understanding the Paradigm Shift to Computational Social Science in the Presence of Big Data. Decision Support Systems, 63, 67–80. https://doi.org/10.1016/j.dss.2013.08.008

Chawla, N. V, & Davis, D. A. (2013). Bringing Big Data to Personalized Healthcare:Patient-centered framework. Journal of General Internal Medicine, 28(3), 660–665. https://doi.org/10.1007/s11606-013-2455-8

Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business Intelligence and Analytics: From Big Data to Big Impact. MIS Quarterly, 36(4), 1165–1188. https://doi.org/10.1145/2463676.2463712

Chen, X., Lu, W., & Liao, S. (2017). A Framework of Developing a Big Data Platform for Construction Waste Management: A Hong Kong Study. Proceedings of the 20th International Symposium on Advancement of Construction Management and Real Estate, 1069–1076. https://doi.org/10.1007/978-981-10-0855-9_94

Comuzzi, M., & Patel, A. (2016). How Organisations Leverage Big Data: A Maturity Model. Industrial Management & Data Systems, 116(8), 1468–1492. https://doi.org/10.1108/IMDS-12-2015-0495

Cox, M., & Ellsworth, D. (1997). Managing Big Data for Scientific Visualization. ACM Siggraph, MRJ/NASA Ames Research Center.

Digital News Asia. (2013). Big Data Spells Big Opportunity for Finance Profession: ACCA. Retrieved November 18, 2017, from https://www.digitalnewsasia.com/tech-at-work/big-data-spells-big-opportunity-for-finance-profession-acca

Du, D., Li, A., & Zhang, L. (2014). Survey on the applications of big data in Chinese real estate enterprise. Procedia Computer Science, 30, 24–33. https://doi.org/10.1016/j.procs.2014.05.377

Dynamic Markets. (2012). Data and the CFO: A Love/Hate Relationship. Retrieved October 19, 2017, from www.sas.com/reg/gen/uk/big-data?page =dynamic%3E

Elkins, A. C., Derrick, D. C., Burgoon, J. K., & Nunamaker, J. F. (2012). Predicting users’ perceived trust in Embodied Conversational Agents using vocal dynamics. Proceedings of the Annual Hawaii International Conference on System Sciences, 579–588. https://doi.org/10.1109/HICSS.2012.483

Forbes Insights. (2015). Betting on Big Data: How the Right Culture, Strategy and Investments Can Help You Leapfrog the Competition. Forbes Insights, 30. Retrieved from http://images.forbes.com/forbesinsights/StudyPDFs/Teradata-BettingOnBigData-REPORT.pdf

Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 580–587. https://doi.org/10.1109/CVPR.2014.81

Groves, P., Kayyali, B., Knott, D., & Van Kuiken, S. (2013a). The “big data” revolution in healthcare: accelerating value and innovation. McKinsey Global Institute. https://doi.org/10.1145/2537052.2537073

Groves, P., Kayyali, B., Knott, D., & Van Kuiken, S. (2013b). The Big Data Revolution in Healthcare. McKinsey & Company. Retrieved from https://www.ghdonline.org/uploads/Big_Data_Revolution_in_health_care_2013_McKinsey_Report.pdf

Hafiz, A., Lukumon, O., Muhammad, B., Olugbenga, A., Hakeem, O., & Saheed, A. (2015). Bankruptcy Prediction of Construction Businesses: Towards a Big Data Analytics Approach. In 2015 IEEE 1st International Conference on Big Data Computing Service and Applications (pp. 347–352). https://doi.org/10.1109/BigDataService.2015.30

Hathaway, S. (2014). ACCA Comment: Why big data matters for accountants. Retrieved October 16, 2017, from http://www.cityam.com/article/1397690791/acca-comment-why-big-data-matters-accountants

Holton, C. (2009). Identifying disgruntled employee systems fraud risk through text mining: A simple solution for a multi-billion dollar problem. Decision Support Systems, 46(4), 853–864. https://doi.org/10.1016/j.dss.2008.11.013

IBM. (2012). IBM big data platform for healthcare.

IBM. (2013). Data-Driven Healthcare Organizations Use Big Data Analytics for Big Gains. Retrieved from http://www03.ibm.com/industries/ca/en/healthcare/ documents/Data_driven_healthcare_organizations_use_big_data_analytics_ for_big_gains.pdf

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 & Sustainability, 5(2), 152–161. https://doi.org/10.11113/ijbes.v5.2.274

Kiron, D., Prentice, P. K., & Ferguson, R. B. (2014). Raising the Bar With Analytics. MIT Sloan Management Review, 55(2), 28–34.

Knowledgent. (2014). Big Data and Healthcare Payers. Retrieved from https://knowledgent.com/wp-content/uploads/2014/07/Big-Data-and-Healthcare-Payers-Whitepaper.pdf

KPMG. (2016). Building a Technology Advantage. Harnessing the Potential of Technology to Improve the Performance of Major Projects. Global Construction Survey 2016. Retrieved from https://assets.kpmg.com/content/dam/kpmg/tr/pdf/2016/11/tr-global-construction-survey-2016.pdf

Lasrado, J. (2018). Five Technologies That Are Changing The Construction Industry. Retrieved September 18, 2018, from https://www.forbesmiddleeast.com/en/five-technologies-that-are-changing-the-construction-industry/

Maaz, Z., Bandi, S., & Amirudin, R. (2018). Potential Opportunities and Future Directions of Big Data in The Construction Industry. In South East Asian Technical University Consortium 2018 (pp. 1–8). Yogjakarta: IEEE.

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.

Mayew, W. J., & Venkatachalam, M. (2012). The power of voice: Managerial affective states and future firm performance. Journal of Finance, 67(1), 1–44. https://doi.org/10.1111/j.1540-6261.2011.01705.x

McAfee, A., & Brynjolfsson, E. (2012). Big Data: The Management Revolution. Harvard Buiness Review, 90(10), 61–68. https://doi.org/10.1007/s12599-013-0249-5

Metaxas, D., & Zhang, S. (2013). A review of motion analysis methods for human nonverbal communication computing. Image and Vision Computing, 31(6–7), 421–433. https://doi.org/10.1016/j.imavis.2013.03.005

Mittermayer, M.-A. (2004). Forecasting Intraday stock price trends with text mining techniques. 37th Annual Hawaii International Conference on System Sciences, 2004. Proceedings of The, 00(C), 1–10. https://doi.org/10.1109/HICSS.2004.1265201

Murdoch, T. B. T. B., & Detsky, A. S. A. S. (2013). The inevitable application of big data to health care. Jama, 309(13), 1351–1352. https://doi.org/10.1001/jama.2013.393

New Vantage Partners. (2012). Big Data Executive Survey 2012. Big Data Executive Survey.

Radhakrishnan, R., Divakaran, A., & Smaragdis, P. (2005). Regunathan Radhakrishnan , Ajay Divakaran and Paris Smaragdis Mitsubishi Electric Research Labs. Signal Processing, 158–161.

Raghupathi, W., & Raghupathi, V. (2014). Big data analytics in healthcare: promise and potential. Health Information Science and Systems, 2(1), 3. https://doi.org/10.1186/2047-2501-2-3

Ritchie, J., & Spencer, L. (1994). Qualitative Data Analysis for Applied Policy Research. In Analyzing Qualitative Data (pp. 173–194). London: Routledge.

Saldaña, J. (2009). The Coding Manual for Qualitative Researchers. London: SAGE Publications.

Savage, N. (2012). Digging for drug facts. Communications of the ACM, 55(10), 11. https://doi.org/10.1145/2347736.2347741

Singh, P. (2017). Big Data Can Transform The Construction Industry. Here’s How. Retrieved July 12, 2018, from https://analyticsindiamag.com/big-data-can-transform-construction-industry-heres/

The Economist. (2011). Building With Big Data.

Torpey, D., Walden, V., & Sherrod, M. (2009). Fraud Triangle Analytics. Fraud Magazine.

Torralba, a, Fergus, R., & W., F. (2008). 80 Millions Tiny Images: a Large Dataset for Non-Parametric Object and Scene Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(11), 1958–1970. https://doi.org/10.1109/TPAMI.2008.128

Ularu, E. G., Puican, F. C., Apostu, A., & Velicanu, M. (2012). Perspectives on Big Data and Big Data Analytics. Database Systems Journal, III(4), 3–14. https://doi.org/10.5406/jaesteduc.46.4.iii

Vasarhelyi, M. A., Kogan, A., & Tuttle, B. M. (2015). Big data in accounting: An overview. Accounting Horizons, 29(2), 381–396. https://doi.org/10.2308/acch-51071

Warren, J. D., Moffitt, K. C., & Byrnes, P. (2015). How big data will change accounting. Accounting Horizons, 29(2), 397–407. https://doi.org/10.2308/acch-51069

World Economic Forum. (2011). Personal Data : The Emergence of a New Asset Class. World Economic Forum. Retrieved from http://www.weforum.org/

reports/personal-data-emergence-new-asset-class

Zenger, B. (2012). Can Big Data Solve Healthcare’s Big Problems?




DOI: https://doi.org/10.11113/ijbes.v5.n3.311

Refbacks

  • There are currently no refbacks.


Copyright (c) 2018 International Journal of Built Environment and Sustainability

License URL: https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode

 

Copyright © 2018 Penerbit UTM Press, Universiti Teknologi Malaysia.

Disclaimer : This website has been updated to the best of our knowledge to be accurate. However, Universiti Teknologi Malaysia shall not be liable for any loss or damage caused by the usage of any information obtained from this web site.

Best viewed: Mozilla Firefox 4.0 & Google Chrome at 1024 × 768 resolution.