Accounting and Big Data: Trends, opportunities and direction for practitioners and researchers

Author/s Gianluca Gabrielli, Alice Medioli, Paolo Andrei
Publishing Year 2022 Issue 2022/2 Language English
Pages 24 P. 89-112 File size 104 KB
DOI 10.3280/FR2022-002004
DOI is like a bar code for intellectual property: to have more infomation click here

Below, you can see the article first page

If you want to buy this article in PDF format, you can do it, following the instructions to buy download credits

Article preview

FrancoAngeli is member of Publishers International Linking Association, Inc (PILA), a not-for-profit association which run the CrossRef service enabling links to and from online scholarly content.

Big Data, the Internet of Things and Machine Learning are only today starting to be widely used but are already attracting interest. They can generate a significant impact on business management. This article analyses use and exploitation of Big Data by business management, focusing on its role in reshaping accounting information systems. The Internet of Things and Machine Learning play a key role in obtaining insights and value in this complex world. Like other areas of business, the accounting function is showing growing interest in their possible applications. We analyze, from three perspectives, how big data impacts on the accounting role in supporting managers and decision-making process, also with the aim to define future research lines that scholars could explore. An internal perspective focuses on how big data can impact management accounting; an external perspective focuses on a new dimension of financial accounting and disclosure of information; and a third perspective, the control one, fo- cuses on the impact of big data on internal and external audit procedures.

Keywords: big data, accounting, big data analytics, management.

  1. American Institute of Certified Public Accountants (1988), Statement on Auditing Standards No. 59: The Auditor’s Consideration of an Entity’s Ability to Continue as a Going Concern. (New York: AICPA).
  2. Association of Certified Fraud Examiners (ACFE), 2021. 2020 Global Fraud Study. -- Available at:
  3. Barton D., & Court D. (2012), Making advanced analytics work for you, Harvard Business Review, 90, p. 78.
  4. Basoglu K. A., & Hess T. J. (2014), Online Business Reporting: A Signaling Theory Perspective, Journal of Information Systems, 28(2), pp. 67-101,
  5. Belfo F., & Trigo A. (2013), Accounting Information Systems: Tradition and Future Directions, Procedia Technology, 9, pp. 536-546,
  6. Bhimani A., & Willcocks L. (2014), Digitisation, Big Data and the transformation of accounting information, Accounting and Business Research, 44(4), pp. 469- 490, DOI: 10.1080/00014788.2014.910051
  7. Bollen J., Mao H., & Zeng X. (2011), Twitter mood predicts the stock market, Journal of Computational Science, 2(1), pp. 1-8,
  8. 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), pp. 196-211,
  9. Brynjolfsson E., & McAfee A. (2017), The business of artificial intelligence. -- available on the internet at ofartificial-intelligence.
  10. Canadian Institute of Chartered Accountants/American Institute of Certified Public Accountants (CICA/AICPA) (1999), Research Report: Continuous Auditing. Toronto, (Canada: CICA, AICPA).
  11. Cao M., Chychyla R., & Stewart T. (2015), Big data analytics in financial statement audits, Accounting Horizons, 29(2), pp. 423-429,
  12. Chae B., Yang C., Olson D., & Sheu C. (2014), The impact of advanced analytics and data accuracy on operational performance: A contingent resource based theory (RBT) perspective, Decision Support Systems, 59(1), pp. 119-126,
  13. Chui M., Loffler M., & Roberts R. (2010), The internet of things. McKinsey Q., pp. 70-79.
  14. Cowie R., Douglas-Cowie E., Tsapatsoulis N., Votsis G., Kollias S., Fellenz W., & Taylor J. (2001), Emotion recognition in human-computer interaction, IEEE Signal Processing Magazine, 18(1), pp. 32-80.
  15. Crawley M., & Wahlen J. (2014), Analytics in empirical/archival financial accounting research, Business Horizons, 57(5), pp. 583-593.
  16. Davenport T. H., Harris J., & Shapiro J. (2010), Competing on talent analytics. Harvard Business Review, 88(10), pp. 52-58.
  17. Earley C. E. (2015), Data analytics in auditing: Opportunities and challenges, Business Horizons, 58(5), pp. 493-500,
  18. Gal G. (2008), Query Issues in Continuous Reporting Systems, Journal of Emerging Technologies in Accounting, 5(1), pp. 81-97,
  19. Gartner (2016), IT glossary. -- Available on-line at: it-glossary/big-data/.
  20. Ghahramani Z. (2015), Probabilistic machine learning and artificial intelligence, Nature, 521(7553), pp. 452-459,
  21. 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, pp. 580-587, DOI: 10.1109/CVPR.2014.81
  22. Gorla N., Somers T. M., & Wong B. (2010), Organizational impact of system quality, information quality, and service quality, Journal of Strategic Information Systems, 19(3), pp. 207-228,
  23. Grabski S. V., Leech S. A., & Schmidt P. J. (2011), A review of ERP research: A future agenda for accounting information systems, Journal of Information Systems, 25(1), pp. 37-78,
  24. Griffin P. A., & Wright A. M. (2015), Commentaries on big data’s importance for accounting and auditing, Accounting Horizons, 29(2), pp. 377-379,
  25. Groomer S. M., & Murthy U. S. (2003), Monitoring High Volume On-line Transaction Processing Systems Using a Continuous Sampling Approach. International Journal of Auditing, 7(1), pp. 3-19,
  26. Hardgrave B., Aloysius J., and Goyal S. (2013), RFID-enabled visibility and retail inventory record inaccuracy: Experiments in the field, Production and Operations Management, 22(4), pp. 843-856.
  27. Haverson A. (2014), Why Predictive Analytics Should Be "A CPA Thing". (New York, NY: AICPA).
  28. 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), pp. 853-864,
  29. Huerta E., & Jensen S. (2017), An accounting information systems perspective on data analytics and big data, Journal of Information Systems, 31(3), pp. 101-114,
  30. Kaplan A., & Haenlein M. (2019), Digital transformation and disruption: On big data, blockchain, artificial intelligence, and other things, Business Horizons, 62(6), pp. 679-681,
  31. Kaplan R. S., & Norton D. P. (1996), Strategic Learning & the Balanced Scorecard, Strategy & Leadership, 24(5), pp. 18-24,
  32. Krahel J. P., & Titera W. R. (2015), Consequences of big data and formalization on accounting and auditing standards, Accounting Horizons, 29(2), pp. 409-422,
  33. Kuhn J. R., & Sutton S. G. (2010), Continuous auditing in ERP system environments: The current state and future directions, Journal of Information Systems, 24(1), pp. 91-112,
  34. Laney D. (2001, February 6), 3D data management: Controlling data volume, velocity, and variety. META Group. -- Retrieved from http://blogs.gartner. com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf.
  35. Lee I. (2017), Big data: Dimensions, evolution, impacts, and challenges, Business Horizons, 60(3), pp. 293-303,
  36. Lee I., & Shin Y. J. (2020), Machine learning for enterprises: Applications, algorithm selection, and challenges, Business Horizons, 63(2), pp. 157-170¸
  37. Lev B., & Gu F. (2016), The end of accounting and the path forward for investors and managers.
  38. Lindqvist U., & Neumann P. G. (2017), The future of the internet of things, Communications of the ACM, 60(2), pp. 26-30, DOI: 10.1145/3029589
  39. Mittermayer M. A. (2004), Forecasting intraday stock price trends with text mining techniques, Proceedings of the Hawaii International Conference on System Sciences, 37(C), pp, 1029-1038,
  40. Moffitt K. C., & Vasarhelyi M. A. (2013), AIS in an age of big data, Journal of Information Systems, 27(2), pp. 1-19,
  41. Mullainathan S., & Spiess J. (2017), Machine learning: An applied econometric approach, Journal of Economic Perspectives, 31(2), pp. 87-106,
  42. Payne R. (2014), Discussion of Digitisation, Big Data and the transformation of accounting information by Alnoor Bhimani and Leslie Willcocks (2014), Accounting and Business Research, 44(4), pp. 491-495, DOI: 10.1080/00014788.2014.910053
  43. Quattrone P. (2016), Management accounting goes digital: Will the move make it wiser?, Management Accounting Research, 31, pp. 118-122,
  44. Redman T. C. (1998) The impact of poor data quality on the typical enterprise Commun. ACM 41, 2 (Feb. 1998), pp. 79-82, DOI: 10.1145/269012.269025
  45. Redman T. C. (2013), Data's credibility problem, Harvard business review: HBR, 91(12).
  46. Reiner J., and Sullivan M. (2005), RFID in healthcare, Healthcare Purchasing News, 29(6): pp. 74-76.
  47. Torpey D., Walden V., and Sherrod M. (2009), Fraud triangle analytics, Fraud Magazine.
  48. Torralba A., Fergus R., & Freeman W. T. (2008), 80 million tiny images: A large data set for nonparametric object and scene recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(11), pp. 1958-1970, DOI: 10.1109/TPAMI.2008.128
  49. Turnbull L. (2006), Why I Can’t Wait for the RFID Dream to Become a Full-Blown Reality. -- Available at: wait-for-the-rfid-dream-to-become-a-fullblown-reality/.
  50. Vasarhelyi M. A. (2012), AIS in a more rapidly evolving Era, Journal of Information Systems, 26(1), pp. 1-5,
  51. Vasarhelyi M. A., Alles M. G., & Kogan A. (2004), Principles of Analytic Monitoring for Continuous Assurance, Journal of Emerging Technologies in Accounting, 1(1), pp. 1-21,
  52. Vasarhelyi M. A., Kogan A., & Tuttle B. M. (2015), Big data in accounting: An overview, Accounting Horizons, 29(2), pp. 381-396,
  53. Vijayanarasimhan S., & Grauman K. (2014), Large-scale live active learning: Training object detectors with crawled data and crowds, International Journal of Computer Vision, 108(1-2), pp. 97-114,
  54. Warren J. D., Moffitt K. C., & Byrnes P. (2015), How big data will change accounting, Accounting Horizons, 29(2), pp. 397-407,
  55. Yoon K., Hoogduin L., & Zhang L. (2015), Big data as complementary audit evidence, Accounting Horizons, 29(2), pp. 431-438,
  56. Zicari R. V. (2015). From classical analytics to big data analytics. -- Available on- line on December 21, 2016 at -- analytics-to-big-data-analytics/.

Gianluca Gabrielli, Alice Medioli, Paolo Andrei, Accounting and Big Data: Trends, opportunities and direction for practitioners and researchers in "FINANCIAL REPORTING" 2/2022, pp 89-112, DOI: 10.3280/FR2022-002004