Management And Accounting Web

Data Mining, Analytics and Big Data Bibliography

Provided by James R. Martin, Ph.D., CMA
Professor Emeritus, University of South Florida

Data Mining

Data Mining Main Page | Quantitative Methods Main Page

Agrawal, A., J. Gans and A. Goldfarb. 2020. How to win with machine learning. Harvard Business Review (September/October): 126-133. (Artificial intelligence in business, data mining and prediction models).

Aldhizer, G. R. III. 2017. Visual and text analytics: The next step in forensic auditing and accounting. The CPA Journal (June): 30-33.

Allen, E., D. E. O'Leary, H. Qu and C. W. Swenson. 2021. Tax specific versus generic accounting-based textual analysis and the relationship with effective tax rates: Building context. Journal of Information Systems (Summer): 115-147.

Alles, M. and G. L. Gray. 2016. Incorporating big data in audits: Identifying inhibitors and a research agenda to address those inhibitors. International Journal of Accounting Information Systems (22): 44-59.

Alles, M., G. Brennan, A. Kogan and M. A. Vasarhelyi. 2006. Continuous monitoring of business process controls: A pilot implementation of a continuous auditing system at Siemens. International Journal of Accounting Information Systems 7(2): 137-161.

Alzamil, Z., D. Appelbaum and R. Nehmer. 2020. An ontological artifact for classifying social media: Text mining analysis for financial data. International Journal of Accounting Information Systems (38): 100469.

Amani, F. A. and A. M. Fadlalla. 2017. Data mining applications in accounting: A review of the literature and organizing framework. International Journal of Accounting Information Systems (24): 32-58.

Amato, R. M. 2022. Getting data literacy right. Strategic Finance (November): 62-63.

Anders, S. B. 2017. Audit data analytics resources. The CPA Journal (June): 72-73.

Andiola, L. M., E. Masters and C. Norman. 2020. Integrating technology and data analytic skills into the accounting curriculum: Accounting department leaders' experiences and insights. Journal of Accounting Education (50): 100655.

Angelo, B., D. Ayres and J. Stanfield. 2018. Power from the ground up: Using data analytics in capital budgeting. Journal of Accounting Education (42): 27-39.

Anthony, C. 2021. When knowledge work and analytical technologies collide: The practices and consequences of black boxing algorithmic technologies. Administrative Science Quarterly 66(4): 1173-1212.

Appelbaum, D. 2016. Securing big data provenance for auditors: The big data provenance black box as reliable evidence. Journal of Emerging Technologies in Accounting 13(1): 17-36.

Appelbaum, D., A. Kogan and M. A. Vasarhelyi. 2017. An introduction to data analysis for auditors and accountants. The CPA Journal (February): 32-37. (Summary).

Appelbaum, D., A. Kogan and M. A. Vasarhelyi. 2017. Big data and analytics in the modern audit engagement: Research needs. Auditing: A Journal of Practice & Theory 36(4): 1-27.

Appelbaum, D., A. Kogan, M. Vasarhelyi and Z. Yan. 2017. Impact of business analytics and enterprise systems on managerial accounting. International Journal of Accounting Information Systems (25): 29-44. (Summary).

Araz, O. M., T. Choi, D. L. Olson and F. S. Salman. 2020. Data analytics for operational risk management. Decision Sciences 51(6): 1316-1319.

Araz, O. M., T. Choi, D. L. Olson and F. S. Salman. 2020. Role of analytics for operational risk management in the era of big data. Decision Sciences 51(6): 132-1346.

Baader, G. and H. Krcmar. 2018. Reducing false positives in fraud detection: Combining the red flag approach with process mining. International Journal of Accounting Information Systems (31): 1-16.

Bakarich, K. M. 2022. Using data visualization to help uncover fraud. The CPA Journal (March/April): 24-49.

Ballou, B., J. H. Grenier and A. Reffett. 2021. Stakeholder perceptions of data and analytics based auditing techniques. Accounting Horizons (September): 47-68.

Barton, D. and D. Court. 2012. Making advanced analytics work for you: A Practical guide to capitalizing on big data. Harvard Business Review (October): 78-83. (Choose the right data, Build models that predict and optimize business outcomes, and Transform your company's capabilities).

Basu, A., 2013. Executive edge: Five pillars of prescriptive analytics success. Analytics Magazine. (March/April): 8-12. (Hybrid data, integrated predictions and prescriptions, prescriptions and side effects, adaptive algorithms, and feedback mechanism).

Beaulieu, P. R. 2020. Contract-based cost analytics. Journal of Emerging Technologies in Accounting 17(1): 11-19.

Benson, B. and A. Aljabr. 2022. Artificial intelligence and data science: Next wave change. Cost Management (September/October): 18-22.

Berinato, S. 2014. With big data comes big responsibility. Harvard Business Review (November): 100-104.

Berinato, S. 2019. Data science and the art of persuasion. Harvard Business Review (January/February): 126-137.

Berry, M. J. A. and G. S. Linoff. 2004. Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management. Wiley Computer Publishing.

Blix, L. H., M. A. Edmonds and K. B. Sorensen. 2021. How well do audit textbooks currently integrate data analytics. Journal of Accounting Education (55): 100717.

Blyakhman, A. 2022. Even accountants benefit from experiments. Strategic Finance (September): 12.

Blyakhman, A. 2022. Technology workbook: Selecting data visualization tools. Strategic Finance (June): 62-63.

Bogaert, M., M. Ballings, R. Bergmans and D. Van den Poel. 2021. Predicting self-declared movie watching behavior using Facebook data and information-fusion sensitivity analysis. Decision Sciences 52(3): 776-810.

Bojinov, I., G. Saint-Jacques and M. Tingley. 2020. Avoid the pitfalls of A/B testing: Make sure your experiments recognize customers varying needs. Harvard Business Review (March/April): 48-53.

Borthick, A. F. and L. N. Smeal. 2020. Data analytics in tax research: Analyzing worker agreements. and compensation data to distinguish between independent contractors and employees using IRS factors. Issues in Accounting Education (August): 1-23.

Borthick, A. F. and R. R. Pennington. 2017. When data become ubiquitous, what becomes of accounting and assurance? Journal of Information Systems (Fall): 1-4.

Borthick A. F., G. P. Schneider and T. R. Viscelli. 2017. Analyzing data for decision making: Integrating spreadsheet modeling and database querying. Issues in Accounting Education (February): 59-66.

Bouwens, J. 2018. Data science: The contribution of the financial manager. Cost Management (November/December): 44-47.

Bradford, M., E. Taylor and M. Seymore. 2021. The critical first step to data security: Management accountants are equipped to apply business performance measurement skills in identifying KPIs for data security and classification. Strategic Finance (December): 26-33.

Bramer, M. 2007. Principles of Data Mining (Undergraduate Topics in Computer Science). Springer.

Brown-Liburd, H., A. Cheong, M. A. Vasarhelyi and X. Wang. 2019. Measuring with exogenous data (MED), and government economic monitoring (GEM). Journal of Emerging Technologies in Accounting 16(1): 1-19.

Brown-Liburd, H., H. Issa and D. Lombardi. 2015. Behavioral implications of big data's impact on audit judgment and decision making and future research directions. Accounting Horizons (June): 451-468.

Byrnes, P. E. 2019. Automated clustering for data analytics. Journal of Emerging Technologies in Accounting 16(2): 43-58.

Cainas, J. M., W. M. Tietz and T. Miller-Nobles. 2021. KAT insurance: Data analytics cases for introductory accounting using Excel, Power BI, and/or Tableau. Journal of Emerging Technologies in Accounting 18(1): 77-85.

Calderon, T. G. and C. G. Onita. 2017. Big data and the perceived expectations gap in digital authentication processes. Journal of Forensic & Investigative Accounting 9(2): 736-750.

Calderon, T. G., J. J. Cheh and I. Kim. 2003. How large corporations use data mining to create value. Management Accounting Quarterly (Winter): 1-11.

Canace, T. G., A. Jaffer and P. Juras. 2019. The CFO as asset manager. Management accountants have the opportunity to use data analytics to align asset management with their company's strategic direction. Strategic Finance (December): 24-31.

Cao, M., R. Chychyla and T. Stewart. 2015. Big data analytics in financial statement audits. Accounting Horizons (June): 423-429.

Castillo, A. 2021. Natural language processing: Machine learning methods in forensic accounting. The CPA Journal (June/July): 16-19. (Case study).

Cataldi, B., B. Callahan, J. F. Sander and A. S. Kelly. 2017. Cutting through the numbers: How data mining was used to uncover multiple frauds at a hospital system medical center. Journal of Forensic & Investigative Accounting 9(3): 936-940.

Cefaratti, M. A. and H. Lin. 2018. Exploring data center migration: A case study. Journal of Information Systems (Spring): 1-17.

Chae, B. and D. L. Olson. 2013. Business analytics for supply chain: A dynamic-capabilities framework. International Journal of Information Technology & Decision Making 12 (01): 9-26.

Chae, B. K., C. Yang, D. Olson, and C. Sheu. 2014. The impact of advanced analytics and data accuracy on operational performance: A contingent resource based theory (RBT) perspective. Decision Support Systems (59): 119–126.

Chai, S. and W. Shih. 2017. Why big data isn't enough. MIT Sloan Management Review (Winter): 57-61.

Chan, C. and S. P. Landry. 2019. Financial statements too good to be true? An instructional case assessing that question using analytical procedures and Beneish's M-Score. Journal of Forensic & Investigative Accounting 11(2): 380-394.

Chaudhuri, S., U. Dayal, and V. Narasayya. 2011. An overview of business intelligence technology. Communications of the ACM 54(8): 88-98.

Cheng, C. and A. Varadharajan. 2021. Using data analytics to evaluate policy implications of migration patterns: Application for analytics, AIS, and tax classes. Issues in Accounting Education (May): 111-128.

Cheng, C., S. Rhoades-Catanach and L. Watson. 2023. Data analytics for undergraduate tax students: A Alteryx case study for MACRS depreciation. Issues in Accounting Education (November): 145-164.

Cheong, A., K. Yoon, S. Cho and W. G. No. 2021. Classifying the contents of cybersecurity risk disclosure through textual analysis and factor analysis. Journal of Information Systems (Summer): 179-194.

Chiu, T., H. Brown-Liburd and M. A. Vasarhelyi. 2019. Performing test of internal controls using process mining: What could go wrong? The CPA Journal (June): 54-57.

Chiu, T., Y. Wang and M. A. Vasarhelvi. 2020. The automation of financial statement fraud detection: A framework using process mining. Journal of Forensic & Investigative Accounting 12(1): 86-108.

Cho, S., M. A. Vasarhelyi and C. Zhang. 2019. Editorial: The forthcoming data ecosystem for business measurement and assurance. Journal of Emerging Technologies in Accounting 16(2): 1-21.

Chugh, R. and S. Grandhi. 2013. Why business intelligence? Significance of business intelligence tools and integrating BI governance with corporate governance. International Journal of E-Entrepreneurship and Innovation (IJEEI) 4 (2), 1-14.

Churyk, N. T., A. Dzuranin and P. J. Schmidt. 2019. Special issue on preparing accounting students for careers using big data. Journal of Accounting Education (48): 48-49.

Churyk, N. T., D. Janvrin and M. W. Watson. 2017. Special issue on Big Data. Journal of Accounting Education (38): 1-2.

Cokins, G. 2013. Top 7 trends in management accounting. Strategic Finance (December): 20-29.

Collins, J. C. 2017. Data mining your general ledger with Excel. Journal of Accountancy (January): 27-32.

Collins, V. and J. Lanz. 2019. Managing data as an asset. The CPA Journal (June): 22-27.

Corban, T. 2021. Technology workbook: Data as a strategic asset. Strategic Finance (April): 60-61.

Cosic, R., G. Shanks and S. Maynard. 2012. Towards a business analytics capability maturity model. Location, location, location. Proceedings of the 23rd Australasian Conference on Information Systems: 1-11.

Daniel, S. J., Y. Xiao and T. Yeh. 2022. Improving business resilience: Part of a management accountant's job is to help guide the company through challenging economic times. Data analytics can help. Strategic Finance (October): 36-43.

Davenport, T., 2014. Big Data at Work: Dispelling the myths, Uncovering the Opportunities. Harvard Business Review Press.

Davenport, T. H. 2006. Competing on analytics. Harvard Business Review (January): 98-107. ("Some companies have built their very businesses on their ability to collect, analyze, and act on data. Every company can learn from what these firms do." Some applications include: 1) Simulating and optimizing supply chain flows, reducing inventory and stock-outs, 2) Identifying customers with the greatest profit potential, 3) Identifying the price that will maximize yield or profit, 4) Selecting the best employees for tasks or jobs, 5) Detecting and minimizing quality problems, 6) Proving a better understanding of the drivers of financial performance including nonfinancial factors, 7) Improving quality, efficacy and safety of products and services).

Davenport, T. H. 2013. Analytics 3.0. Harvard Business Review (December): 64-72.

Davenport, T. H. 2014. What businesses can learn from sports analytics. MIT Sloan Management Review (Summer): 10-13.

Davenport, T. H. and J. G. Harris. 2007. Competing on Analytics: The New Science of Winning. Harvard Business School Press.

Davenport, T. H., J. G. Harris and R. Morison. 2010. Analytics at Work: Smarter Decisions, Better Results. Harvard Business Press.

Davenport, T. H. and S. Kudyba. 2016. Designing and developing analytics-based data products. MIT Sloan Management Review (Fall): 82-89.

Davenport, T. H., J. G. Harris and Robert Morison. 2010. Analytics at Work: Smarter Decisions, Better Results. Harvard Business Press.

Davenport, T. H., P. Barth and R. Bean. 2012. How 'big data' is different. MIT Sloan Management Review (Fall): 43-46.

Debreceny, R. and G. L. Gray. 2004. Grab your picks and shovels! There's gold in your data. Strategic Finance (January): 24-28.

Decision Sciences. 2021. Special issue on data mining & decision analytics. Decision Sciences 52(3): 542.

Desai, V., T. Fountaine and K. Rowshankish. 2022. A better way to put your data to work: Package it the way you would a product. Harvard Business Review (July/August): 100-107. (What is a data product and how are they used?)

Dilla, W., D. J. Janvrin and R. Raschke. 2010. Interactive data visualization: New directions for accounting information systems research. Journal of Information Systems (Fall): 1-37.

Dilla, W. N. and R. L. Raschke. 2015. Data visualization for fraud detection: Practice implications and a call for future research. International Journal of Accounting Information Systems (16): 1-22.

Domino, M. A., D. Schrag, M. Webinger and C. Troy. 2021. Linking data analytics to real-world business issues: The power of the pivot table. Journal of Accounting Education (57): 100744.

Dow, K. E., N. Jacknis and M. W. Watson. 2021. A framework and resources to create data analytics-infused accounting curriculum. Issues in Accounting Education (November): 183-205.

Drew, J. 2018. Merging accounting with 'big data' science. Journal of Accountancy (July): 48-52.

Drew, J. 2018. Paving the way for a new digital world. Journal of Accountancy (June): 18-22.

Druică, E., B. Oancea and C. Vâlsan. 2018. Benford's law and the limits of digit analysis. International Journal of Accounting Information Systems (31): 75-82.

Du, N., T. Wang and O. R. Whittington. 2021. Accounting data analytics exercise for intermediate accounting: Warranty expense and product liability. Journal of Emerging Technologies in Accounting 18(2): 201-208.

Duman, E. and M. H. Ozcelik. 2011. Detecting credit card fraud by genetic algorithm and scatter search. Expert Systems with Applications 38 (10): 13057-13063.

Dunn, C. L., G. J. Gerard and S. V. Grabski. 2017. The combined effects of user schemas and degree of cognitive fit on data retrieval performance. International Journal of Accounting Information Systems (26): 46-67.

Dyckman, T. R. 2010. Interpreting Economic and Social Data: A Foundation of Descriptive Statistics by Othmar W. Winkler. The Accounting Review (September): 1820-1822.

Dzuranin, A. C. 2021. Statement on Management Accounting: Data Visualization. Institute of Management Accounting.

Dzuranin, A. C. 2022. Explanatory data visualizations: Data visualizations can be used to effectively communicate the results of analyses and guide decision making. Strategic Finance (January): 42-49.

Dzuranin, A. C., J. R. Jones and R. M. Olvera. 2018. Infusing data analytics into the accounting curriculum: A framework and insights from faculty. Journal of Accounting Education (43): 24-39.

Eilifsen, A., F. Kinserdal, W. F. Messier, Jr. and T. E. McKee. 2020. An exploratory study into the use of audit data analytics on audit engagements. Accounting Horizons (December): 75-103.

El-Wakeel, F. 2019. Technology workbook: Agile project management in analytics: Agile project management is an iterative adaptive approach that helps ensure the project delivers what the customer truly needs. Strategic Finance (May): 66-67. (Summary).

El-Wakeel, F. 2020. Technology workbook: Considerations in data analytics problem structuring. Strategic Finance (October): 60-61.

El-Wakeel, F. 2022. Change management in disguise: When looking at scaling data analytics within your organization, remember that factoring in change management is essential. Strategic Finance (January): 62-63.

El-Wakeel, F. 2022. Storytelling in data strategy. Strategic Finance (September): 62-63.

El-Wakeel, F., L. Jiles and R. Lawson. 2020. Storytelling with data visualization: Leverage data visualization tools and techniques to tell the story behind the data and deliver greater strategic value. Strategic Finance (December): 34-39.

Enget, K., G. D. Soucedo and N. S. Wright. 2017. Mystery, Inc.: A Big Data case. Journal of Accounting Education (38): 9-22.

Fass, N. 2018. CFOs are making data and analytics top priorities. Strategic Finance (October): 9.

Fass, N. 2019. Mature analytics improve profit margins. Strategic Finance (October): 9.

Fay, R. and E. M. Negangard. 2017. Manual journal entry testing: Data analytics and the risk of fraud. Journal of Accounting Education (38): 37-49.

Ferguson, C. M. 2021. The IRS's big plans for big data. The CPA Journal (August/September): 73-74.

Fischetti, T. 2015. Data Analysis with R. Packt Publishing.

Fisher, I. E., M. R. Garnsey, S. Goel and K. Tam. 2010. The role of text analytics and information retrieval in the accounting domain. Journal of Emerging Technologies in Accounting (7): 1-24.

Fitzgerald, M. 2015. General Mills builds up big data to answer big questions. MIT Sloan Management Review (Summer): 34.

Fitzgerald, M. 2016. Better data brings a renewal at the Bank of England. MIT Sloan Management Review (Summer): 3-13.

Fitzgerald, M. 2016. Building a better car company with analytics. MIT Sloan Management Review (Summer): 40-44.

Fitzgerald, M. 2016. Data-driven city management. MIT Sloan Management Review (Summer): 3-10.

Fitzgerald, M. 2016. General Motors relies on IoT to keep its customers safe and secure. MIT Sloan Management Review (Summer): 86-91.

Fogarity, D. and P. C. Bell. 2014. Should you outsource analytics? MIT Sloan Management Review (Winter): 41-45.

Foreman, J. W. 2013. Data Smart: Using Data Science to Transform Information into Insight. Wiley.

Geerts, G. 2021. Drive business success with data analytics: Data comes from many sources and in many types. Your insights from this data are what will lead to better business performance. Journal of Accountancy (June): 37, 39, 41, 43, 45, 47, 49, 51.

Godin, S. 2019. Leaders don't hide behind data. MIT Sloan Management Review (Fall): 1-3.

Govindarajan, V. and N. V. Venkatraman. 2022. The next great digital advantage: Smart businesses are using datagraphs to reveal unique solutions to customer problems. Harvard Business Review (May/June): 56-63.

Gray, D. 2021. What makes successful frameworks rise above the rest: Business leaders can better assess and strengthen analytical frameworks using seven evaluation criteria. MIT Sloan Management Review (Summer): 1-6.

Gray, G. L. and R. S. Debreceny. 2014. A taxonomy to guide research on the application of data mining to fraud detection in financial statement audits. International Journal of Accounting Information Systems 15(4): 357-380.

Gregg, A. 2017. Start-ups embrace cryptocurrency to raise needed capital: 'Initial coin offerings' let companies raise money without ceding control. The Washington Post (December 4): A13. (Note).

Griffin, P. A. and A. M. Wright. 2015. Commentaries on big data's importance for accounting and auditing. Accounting Horizons (June): 377-379.

Grobart, J. 2020. Preparing for whistleblower complaints. AI and analytics can be useful tools when investigating whistleblower claims and rooting out fraud and other illegal behaviors. Strategic Finance (September): 34-39.

Grus, J. 2015. Data Science from Scratch: First Principles with Python. O'Reily Media.

Guan, J., A. S. Levitan and S. Goyal. 2018. Text mining using Latent Semantic Analysis: An illustration through examination of 30 years of research at JIS. Journal of Information Systems (Spring): 67-86.

Guerriero, E., R. L. Engebretson and C. W. Parker. 2019. Leveraging data analytics: Uncovering hidden opportunities, generating revenue, and serving clients. The CPA Journal (December): 70-75.

Hagel, J. 2013. Why accountants should own big data. Journal of Accountancy (November): 20-21. (Business intelligence).

Hagiu, A. and J. Wright. 2020. When data creates competitive advantage... and when it doesn't. Harvard Business Review (January/February): 94-101. ("To determine to what degree a competitive advantage provided by data-enabled learning is sustainable, companies should answer seven questions).

Han, J. and M. Kamber. 2006. Data Mining Concepts and Techniques. Morgan Kaufmann Publishers.

Haq, I., M. Abatemarco and J. Hoops. 2020. The development of machine learning and its implications for public accounting. The CPA Journal (June): 6-9.

Harmon, W. K., S. Mutlu and Z. Ye. 2023. Book review: Richardson, V. J., R. A. Teeter and K. L. Terrell. 2003. Data Analytics for Accounting (3rd edition). McGraw Hill. Accounting Horizons (December): 207-211.

Harper, C. and C. Dunn. 2018. Building better accounting curricula: Data management and data analytics, as well as the new technologies associated with them, are driving modern accounting. Higher learning needs to catch up. Strategic Finance (August): 46-53.

Harvard Business Review. 2017. How companies really use big data. Harvard Business Review (September/October): 26.

Harvard Business Review. 2017. How data science is disrupting the job market. Harvard Business Review (September/October): 24.

Harvard Business Review. 2019. People don't need as much data as they think. Harvard Business Review (May/June): 28.

Hastie, T., R. Tibshirani and J. Friedman. 2009. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, second edition. Springer.

Hawkins, S. R., J. Pickerd, S. L. Summers and D. A. Wood. 2023. The development of the process mining event log generator (PMELG) tool. Accounting Horizons (December): 89-95.

Hayashi, A. M. 2014. Thriving in a big data world. MIT Sloan Management Review (Winter): 35-39.

Hayes, L. and J. E. Boritz. 2021. Classifying restatements: An application of machine learning and textual analytics. Journal of Information Systems (Fall): 107-131.

Heichler, E. 2018. Why the data marketplaces of the future will sell insights, not data. MIT Sloan Management Review (Fall): 1-4.

Heister, S., M. Kaufman and K. Yuthas. 2021. Blockchain and the future of business data analytics. Journal of Emerging Technologies in Accounting 18(1): 87-98.

Hermanson, D. R., J. G. Lawson and D. S. Street. 2022. Detecting and resolving 'dirty' data: Ten steps to better business insights. The CPA Journal (July/August): 36-41. (Dirty data refers to invalid, incomplete, or inaccurate data).

Hernandez, M., R. Raveendhran, E. Weingarten and M. Barnett. 2019. How algorithms can diversify the startup pool: Data-driven approaches can help venture capital firms limit gender bias and make better, fairer investment decisions. MIT Sloan Management Review (Fall): 71-78.

Hey, T. 2010. The next scientific revolution. Harvard Business Review (November): 56-63.

Hoelscher, J. and A. Mortimer. 2018. Using Tableau to visualize data and drive decision-making. Journal of Accounting Education (44): 49-59.

Hoelscher, J. L. and T. Shonhiwa. 2021. Not so fuzzy auditing analytics. Journal of Emerging Technologies in Accounting 18(1): 99-112.

Hoffman, R. 2016. Using artificial intelligence to set information free. MIT Sloan Management Review (Fall): 1-15.

Hogarth, R. M. and E. Soyer. 2015. Using simulated experience to make sense of big data. MIT Sloan Management Review (Winter): 49-54.

Holsapple, C., A. Lee-Post and R. Pakath. 2014. A unified foundation for business analytics. Decision Support. Systems. 64, 130-141.

Holt, M. and B. Lang. 2021. GADGET: An accounting data generator. Journal of Emerging Technologies in Accounting 18(1): 113-129.

Holt, M., B. Lang and S. G. Sutton. 2017. Potential employees' ethical perceptions of active monitoring: The dark side of data analytics. Journal of Information Systems (Summer): 107-124.

Holt, T. P. and T. M. Loraas. 2021. A potential unintended consequence of big data: Does information structure lead to suboptimal auditor judgment and decision-making? Accounting Horizons (September): 161-186.

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.

Hopper, G. 2021. Deep Finance: Corporate Finance in the Information Age. Leaders Press.

Hua, Z., Y. Wang, X. Xu, B. Zhang and L. Liang. 2007. Predicting corporate financial distress based on integration of support vector machine and logistic regression. Expert Systems with Applications 33(2): 434-440.

Huerta, E. and S. Jensen. 2017. An accounting information systems perspective on data analytics and big data. Journal of Information Systems (Fall): 101-114.

Hume, E. and A. West. 2020. Becoming a data-driven decision making organization. The CPA Journal (April): 32-35.

Janert, P. K. 2010. Data Analysis with Open Source Tools. O'Reilly Media.

Jans, M. and M. Hosseinpour. 2019. How active learning and process mining can act as continuous auditing catalyst. International Journal of Accounting Information Systems (32): 44-58.

Jans, M., M. Alles and M. Vasarhelyi. 2013. The case for process mining in auditing: Sources of value added and areas of application. International Journal of Accounting Information Systems 14(1): 1-20.

Jans, M., M. G. Alles and M. A. Vasarhelyi. 2014. A field study on the use of process mining of event logs as an analytical procedure in auditing. The Accounting Review (September): 1751-1773.

Janvrin, D. and I. Fisher. 2021. Textual analysis for accountants: Accountants are using textual analysis for everything from revenue recognition and contract analysis to privacy compliance and video recordings. Strategic Finance (June): 46-53.

Janvrin, D. J. and M. W. Watson. 2017. "Big data": A new twist to accounting. Journal of Accounting Education (38): 3-8.

Jernigan, S., S. Ransbotham and D. Kiron. 2016. Data sharing and analytics drive success with IoT. MIT Sloan Management Review (Fall): 1-17.

Joshi, M. P., N. Su, R. D. Austin and A. K. Sundaram. 2021. Why so many data science projects fail to deliver: Organizations can gain more business value from advanced analytics by recognizing and overcoming five common obstacles. MIT Sloan Management Review (Spring): 85-89.

Journal of Accountancy. 2016. 6 ethical questions about big data. Journal of Accountancy (October): 24.

Journal of Accountancy. 2021. Gaps identified in systems, analytics training for students; PEEC addresses Records requests: Limits on lending staff to attest clients. Journal of Accountancy (April/May): 5.

Journal of Forensic & Investigative Accounting. 2021. Book review: Harford, T. 2021. The Data Detective: Ten Easy Rules to Make Sense of Statistics. Riverhead Books.   Journal of Forensic & Investigative Accounting 13(3): Not numbered.

Kabacoff, R. 2015. R in Action: Data Analysis and Graphics with R. Manning Publications.

Kane, G. C. 2015. How digital transformation is making health care safer, faster and cheaper. MIT Sloan Management Review (Fall): 41-47.

Kane, G. C. 2017. Big data and IT talent drive improved patient outcomes at Schumacher Clinical Partners. MIT Sloan Management Review (Fall): 96.

Karim, K. E., K. J. Lin, R. E. Pinsker and H. Zhu. 2019. Using linguistics to mine unstructured data from FASB exposure drafts. Journal of Information Systems (Spring): 67-83.

Khalifa, M. and I. Zabani. 2016. Utilizing health analytics in improving the performance of healthcare services: A case study on a tertiary care hospital. Journal of Infection and Public Health (November-December): 757-765.

Kim, R., J. Gangolly and P. Elsas. 2017. A framework for analytics and simulation of accounting information systems: A Petri net modeling primer. International Journal of Accounting Information Systems (27): 30-54.

Kirkos, E., C. Spathis and Y. Manolopoulos. 2007. Data mining techniques for the detection of fraudulent financial statements. Expert Systems with Applications 32 (4), 995-1003.

Koch, R. 2015. Big data or big empathy? Strategic Finance (December): 62-63.

Kogan, A., B. W. Mayhew and M. A. Vasarhelyi. 2019. Audit data analytics research - An application of design science methodology. Accounting Horizons (September): 69-73.

Kogan, G., N. Myers, D. J. Gaydon and D. M. Boyle. 2021. Advancing digital transformation: RPA and self-service data analytics are time-saving technologies that can transform your organization. Here's what you should know to begin using them. Strategic Finance (December): 34-41.

Kohavi, R., L. Mason, R. Parekh and Z. Zheng. 2004. Lessons and challenges from mining retail e-commerce data. Machine Learning 57 (1-2): 83-113.

Kokina, J., D. Pachamanova and A. Corbett. 2017. The role of data visualization and analytics in performance management: Guiding entrepreneurial growth decisions. Journal of Accounting Education (38): 50-62. (A case that addresses the growing need for accountants to develop compentency in predictive analytics).

Koreff, J., M. Weisner and S. G. Sutton. 2021. Data analytics (ab) use in healthcare fraud audits. International Journal of Accounting Information Systems (42): 100523.

Kovalerchuk, B., E. Vityaev and R. Holtfreter. 2007. Correlation of complex evidence in forensic accounting using data mining. Journal of Forensic Accounting 8(1-2): 53-88.

Kozlowski, S., H. Issa and D. Appelbaum. 2018. Making government data valuable for constituents: The case for the advanced data analytics capabilities of the ENHANCE framework. Journal of Emerging Technologies in Accounting 15(1): 155-167.

Kozyrkov, C. 2020. To recognize risks earlier, invest in analytics. It helps you ask the right questions and learn faster. Harvard Business Review (November/December): 53-55.

Krahel, J. P. and W. R. Titera. 2015. Consequences of big data and formalization on accounting and auditing standards. Accounting Horizons (June): 409-422.

Kramer, J., D. Schnurr and M. Wohlfarth. 2019. Trapped in the data-sharing dilemma. MIT Sloan Management Review (Winter): 22-23.

Krieger, F., P. Drews and P. Velte. 2021. Explaining the (non-) adoption of advanced data analytics in auditing: A process theory. International Journal of Accounting Information Systems (41): 100511.

Krotov, V. and M. Tennyson. 2018. Research note: Scraping financial data from the web using the R language. Journal of Emerging Technologies in Accounting 15(1): 169-181.

Krumwiede, K., L. Serven and R. Liou. 2021. Forecasting the future: Five ways organizations can implement predictive analytics to improve the FP&A function. Strategic Finance (September): 30-37.

Kwon, O., N. Lee and B. Shin. 2014. Data quality management, data usage experience and acquisition intention of big data analytics. International Journal of Information Management. 34 (3), 387-394.

Laplante, S. K. and M. E. Vernon. 2021. Incorporating data analytics in a technical tax setting: A case using Excel and Tableau to examine a firm's schedule M-3 and tax risk. Issues in Accounting Education (May): 129-139.

Larose, D. T. 2004. Discovering Knowledge in Data: An Introduction to Data Mining. Wiley-Interscience.

Laursen, G. H. N. and J. Thorlund. 2010. Business Analytics for Managers: Taking Business Intelligence Beyond Reporting. Wiley.

Lawson, J. G. and D. A. Street. 2021. Detecting dirty data using SQL: Rigorous house insurance case. Journal of Accounting Education (55): 100714.

Lawson, R. 2019. New competencies for management accountants. Strategic Finance (March): 40-47.

Lawson, R. 2019. New competencies for management accountants. The CPA Journal (September): 18-21.

Lawson, R. and T. Hatch. 2020. The impact of big data on finance. Strategic Finance (February): 46-52.

Lee, L. S., G. Casterella and B. Wray. 2021. Preparing for audit data analytics with the AICPA general ledger audit data standards. Journal of Emerging Technologies in Accounting 18(1): 131-157.

Leidner, D., O. Tona, B. H. Wixom and I. A. Someh. 2021. Putting dignity at the core of employee data use. MIT Sloan Management Review (Fall): 1-7.

Leonardi, P. and N. Contractor. 2018. Better people analytics. Harvard Business Review (November/December): 70-81.

Li, H., J. Dai, T. Gershberg and M. A. Vasarhelyi. 2018. Understanding usage and value of audit analytics for internal auditors: An organizational approach. International Journal of Accounting Information Systems (28): 59-76.

Li, M., Y. Wu, Y. He, S. Huang and A. Nair. 2020. Sparse inverse covariance estimation: A data mining technique to unravel holistic patterns among business practices in firms. Decision Sciences 51(4): 1046-1073.

Libby, T., J. M. Schwebke and P. M. Goldwater. 2022. Using data analytics to evaluate the drivers of revenue: An introductory case study using Microsoft Power Pivot and Power BI. Issues in Accounting Education (November): 97-105.

Lim, S., D. Yim, J. Khuntia and M. Tanniru. 2020. A continuous-time Markov chain model - Based business analytics approach for estimating patient transition states in online health infomediary. Decision Sciences 51(1): 181-208.

Lin, P. P. 2014. What CPAs need to know about big data. The CPA Journal (November): 50-55.

Liu, B. 2007 and 2010. Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data. Springer.

Liu, Q. 2016. Textual analysis: A burgeoning research area in accounting. Journal of Emerging Technologies in Accounting 13(2): 89-91.

Liu, Q., V. Chiu, B. W. Muehlmann and A. A. Baldwin. 2021. Bringing scholarly data analytics knowledge using emerging technology tools in accounting classrooms: A bibliometric approach. Issues in Accounting Education (November): 153-181.

Liu, Y. and K. C. Moffitt. 2016. Text mining to uncover the intensity of SEC comment letters and its association with the probability of 10-K restatement. Journal of Emerging Technologies in Accounting 13(1): 85-94.

Loftus, S., S. S. McCoy, E. G. Valentine and T. West. 2023. Labor cost visualization at Carescript: An introductory data analytics case for management accounting. Issues in Accounting Education (November): 199-209.

Losi, H. J., E. V. Isaacson and D. M. Boyle. 2022. Integrating data analytics into the accounting curriculum: Faculty perceptions and insights. Issues in Accounting Education (November): 1-23.

Loveman, G. 2003. Diamonds in the data mine. Harvard Business Review (May): 109-123.

Markov, Z. and D. T. Larose. 2007. Data Mining the Web: Uncovering Patterns in Web Content, Structure, and Usage. Wiley-Interscience.

Martin, J. R. Not dated. What is data mining? Management And Accounting Web. DataMining

Matignon, R. 2007. Data Mining Using SAS Enterprise Miner. Wiley-Interscience.

May, T. 2009. The New Know: Innovation Powered by Analytics. Wiley.

Mayer-Schonberger, V. and T. Ramge. 2022. The data boom is here - It's just not evenly distributed. MIT Sloan Management Review (Spring): 7-9.

McAfee, A. and E. Brynjolfsson. 2012. Big data: The management revolution: Exploiting vast new flows of information can radically improve your company's performance. But first you'll have to change your decision-making culture. Harvard Business Review (October): 60-68.

McCue, C. 2007. Data Mining and Predictive Analysis: Intelligence Gathering and Crime Analysis. Butterworth-Heinemann.

McKinney, E. Jr., C. J. Yoos II and K. Snead. 2017. The need for 'skeptical' accountants in the era of Big Data. Journal of Accounting Education (38): 63-80.

Mesa, W. B. 2019. Accounting students' learning processes in analytics: A sensemaking perspective. Journal of Accounting Education (48): 50-68.

Milton, M. 2009. Head First Data Analysis: A Learner's Guide to Big Numbers, Statistics, and Good Decisions. O'Reilly Media.

MIT Sloan Management Review. 2017. Lessons from becoming a data-driven organization. MIT Sloan Management Review (Winter): 3-13.

Mitchell-Guthrie, P. 2020. Beyond buzzwork bingo: Driving digital transformation and shareholder value. Cost Management (May/June): 31-36.

Moffit, K. C. and M. A. Vasarhelyi. 2013. Editorial. AIS in a age of big data. Journal of Information Systems (Fall): 1-19.

Monterio, B. J. 2019. The future of technology and analytics. Strategic Finance (June): 76-77.

Mukhopadhyay, S., S. Samaddar, A. O. Solis and A. Roy. 2021. Disease detection analytics: A simple linear convex programming algorithm for breast cancer and diabetes incidence decisions. Decision Sciences 52(3): 661-698.

Murolo, S. B. 2019. Planning for data security. Journal of Accountancy (October): 62-63.

Murthy, U. S. and G. L. Geerts. 2017. An REA ontology-based model for mapping big data to accounting information systems elements. Journal of Information Systems (Fall): 45-61.

Neeley, T. and P. Leonardi. 2022. Developing a digital mindset: How to lead your organization into the age of data, algorithms, and AI. Harvard Business Review (May/June): 50-55.

Negangard, E. M. and R. G. Fay. 2020. Electronic discovery (ediscovery): Performing the early stages of the Enron investigation. Issues in Accounting Education (February): 43-58.

Nestor, J. 2020. Analyzing data and strategies to improve management. Cost Management (March/April): 3-4.

Nichols, W. 2013. Advertising analytics 2.0: Marketers now have and unprecedented ability to fine-tune their allocation decisions while making course corrections in real time. Harvard Business Review (March): 60-68.

Nielsen, S. 2015. The impact of business analytics on management accounting.

Nielsen, S., E. H. Nielsen, A. Jacobsen and L. Bjern Pedersen. 2014. Management accounting and business analytics: An example of system dynamics modelling's use in the design of a balanced scorecard. Danish Journal of Management & Business 78(3-4): 31-44. DJoMB

Nisbet, R., J. Eder IV and G. Miner. 2009. Handbook of Statistical Analysis and Data Mining Applications. Academic Press.

No, W. G., K. Lee, F. Huang and Q. Li. 2019. Multidimensional audit data selection (MADS): A framework for using data analytics in the audit data selection process. Accounting Horizons (September): 127-140.

O'Brien, A. and D. N. Stone. 2021. A case study in managing the analytics "Iceberg": Data cleaning and management using Alteryx. Journal of Emerging Technologies in Accounting 18(2): 221-245.

Ott, R. L and M. T. Longnecker. 2015. An Introduciton to Statistical Methods and Data Analysis. 7th Edition. Brooks Cole.

Padmanabhan, B. and A. Tuzhilin. 2002. Knowledge refinement based on the discovery of unexpected patterns in data mining. Decision Support Systems 33(3): 309-321.

Padmanabhan, B. and A. Tuzhilin. 2003. On the use of optimization for data mining: Theoretical interactions and eCRM opportunities. Management Science (October): 1327-1343.

Parra-Moyano, J., K. Schmedders and A. Pentland. 2020. What managers need to know about data exchanges. MIT Sloan Management Review (Summer): 39-44.

Patelli, L. 2021. Ethics maps for AL analytics. Strategic Finance (January): 13-14.

Pei, D. and M. A. Vasarhelyi. 2020. Big data and algorithmic trading against periodic and tangible asset reporting: The need for U-XBRL. International Journal of Accounting Information Systems (37): 100453.

Perols, J. L. and A. C. Dzuranin. 2021. A picture is worth a thousand words: Using interactive data visualization to assess fraud risk. Journal of Forensic Accounting Research 6(1): 461-490.

Perols, J. L., R. M. Bowen, C. Zimmermann and B. Samba. 2017. Finding needles in a haystack: Using data analytics to improve fraud prediction. The Accounting Review (March): 221-245.

Peters, M. D., B. Wieder, S. G. Sutton and J. Wakefield. 2016. Business intelligence systems use in performance capabilities: Implications for enhanced competitive advantage. International Journal of Accounting Information Systems (21): 1-17.

Plumlee, R. D., B. A. Rixom and A. J. Rosman. 2015. Training auditors to perform analytical procedures using metacognitive skills. The Accounting Review (January): 351-369.

Polimeni, R. S. and J. A. Burke. 2021. Integrating emerging accounting digital technologies and analytics into an undergraduate accounting curriculum - A case study. Journal of Emerging Technologies in Accounting 18(1): 159-173.

Pope, K. R. and L. Jiles. 2022. Management accountants and corporate compliance: Data analytics capabilities, incentives for reporting wrongdoing, and innovative training can help accounting professionals mitigate the risk of corporate misconduct. Strategic Finance (December): 28-33.

Porter, M. E. and J. E. Heppelmann. 2017. Why every organization needs an augmented reality strategy. Harvard Business Review (November/December): 46-57. (Augmented reality or AR "transforms volumes of data and analytics into images or animations that are overlaid on the real world." ..."By superimposing digital information directly on real objects or environments, AR allows people to process the physical and digital simultaneously, eliminating the need to mentally bridge the two. That improves our ability to rapidly and accurately absorb information, make decisions, and execute required tasks quickly and efficiently."..."Every company needs an implementation road map that lays out how the organization will start to capture the benefits of AR in its business while building the capabilities needed to expand its use."... "It will profoundly change training and skill development, allowing people to perform sophisticated work without protracted and expensive conventional instruction - a model that is inaccessible to so many today. AR, then, enables people to better tap into the digital revolution and all it has to offer.").

Porter, M. E. and J. E. Heppelmann. 2018. Why every organization needs an augmented reality strategy: Interaction. Harvard Business Review (March/April): 19.

Presley, T. J. 2019. A risk based approach to large datasets: Analysis of time series data for a large merchandising firm. Journal of Accounting Education (49): 100639.

Prokesch, S. 2017. Reinventing talent management: How GE uses analytics to guide a more digital, farflung workforce. Harvard Business Review (September/October): 54-55.

Provost, F. and T. Dawcett. 2013. Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking.  O'Reily Media.

Qasim, A. and F. F. Kharbat. 2020. Blockchain technology, business data analytics, and artificial intelligence: Use in the accounting profession and ideas for inclusion into the accounting curriculum. Journal of Emerging Technologies in Accounting 17(1): 107-117.

Qasim, A., H. Issa, G. A. El Refae and A. J. Sannella. 2020. A model to integrate data analytics in the undergraduate accounting curriculum. Journal of Emerging Technologies in Accounting 17(2): 31-44.

Quang, C. 2019. Developing the right skill sets: In response to accountants' lack of data analytics expertise, Singapore Management University revamped its curriculum to develop versatile accounting professionals. Strategic Finance (August): 54-59.

Rajaraman, A., J. Leskovec and J. D. Ullman. 2012. Mining of Massive Datasets.

Ransbotham, S. and D. Kiron. 2017. Analytics as a source of business innovation. MIT Sloan Management Review (Spring): 1-16.

Ransbotham, S. 2017. The subtle sources of sampling bias hiding in your data. MIT Sloan Management Review (Fall): 20-22.

Ransbotham, S., D. Kiron, P. Gerbert and M. Reeves. 2017. Reshaping business with artificial intelligence. MIT Sloan Management Review (Fall): 1-17.

Raschke, R. L. and K. F. Charron. 2021. Review of data analytic teaching cases, have we covered enough? Journal of Emerging Technologies in Accounting 18(2): 247-255.

Redman, T. C. 2008. Data Driven: Profit from Your Most Important Business Asset. Harvard Business School Press.

Redman, T. C. 2021. What's holding your data program back? To deliver on the promise of data-backed technology, such as AI, companies must address underlying restraining forces. MIT Sloan Management Review (Fall): 1-10.

Rezaee, Z., A. Dorestani and S. Aliabadi. 2018. Application of time series analyses in big data: Practical, research, and education implications. Journal of Emerging Technologies in Accounting 15(1): 183-197.

Rezaee, Z., J. Wang and L. M. Brian. 2018. Toward the integration of big data into forensic accounting education. Journal of Forensic & Investigative Accounting 10(1): 87-99.

Richardson, V. J. and M. W. Watson. 2021. Act or be acted upon: Revolutionizing accounting curriculums with data analytics. Accounting Horizons (June): 129-144.

Richins, G., A. Stapleton, T. C. Stratopoulos and C. Wong. 2017. Big data analytics: Opportunity or threat for the accounting profession? Journal of Information Systems (Fall): 63-79.

Riggins, F. J. and B. K. Klamm. 2017. Data governance case at KrauseMcMahon LLP in an ear of self-service BI and Big Data. Journal of Accounting Education (38): 23-36.

Rikhardsson, P. and O. Yigitbasioglu. 2018. Business intelligence & analytics in management accounting research: Status and future focus. International Journal of Accounting Information Systems (29): 37-58.

Riley, J., P. J. Schmidt and K. S. Church. 2022. An SME approach to data analytics. Strategic Finance (May): 26-33.

Roberts-Witt, S. L. 2001. Gold diggers: Let customers and partners mine your data using new e-business intelligence tools. It could turn into a gold rush. PC Magazine (February, 20): ibiz 6-ibiz 10.

Roberts-Witt, S. L. 2002. Data mining: What lies beneath? Finding patterns in customer behavior can deliver profitable insights into your business. PC Magazine (November, 19): iBiz 1-6. (Note).

Robinson, C. C., J. Cherfoli and K. Tysiac. 2019. Data and the deep bleu sea: Georgia Aquarium uses AI and predictive analytics to improve the guest experience. Journal of Accountancy (June): 30-32.

Roozen, F., B. Steens and L. Spoor. 2019. Technology: Transforming the finance function and the competencies management accountants need. Management Accounting Quarterly (Fall): 1-14.

Rose, A. M, J. M. Rose, K. Sanderson and J. C. Thibodeau. 2017. When should audit firms introduce analyses of big data into the audit process? Journal of Information Systems (Fall): 81-99.

Rosenbaum, D. 2012. Digging out from big data: Unstructured data is piling up in corporate computers, making compliance and other tasks more difficult. CFO (July/August): 32-33.

Ross, J. W., C. M. Beath and A. Quaadgras. 2013. You may not need big data after all. Harvard Business Review (December): 90-98.

Saeedi, A. 2021. Audit opinion prediction: A comparison of data mining techniques. Journal of Emerging Technologies in Accounting 18(2): 125-147.

Sarkar, S., J. Gray, S. R. Boss and E. Daly. 2021. Developing institutional skills for addressing big data: Experiences in implementation of AACSB Standard 5. Journal of Accounting Education (54): 100708.

Schaper, R. and K. Matsushita. 2020. Data bias and diversity and inclusion. Strategic Finance (December): 16, 18.

Schmidt, P. J., J. Riley and K. S. Church. 2020. Investigating accountants' resistance to move beyond Excel and adopt new data analytics technology. Accounting Horizons (December): 165-180.

Schmidt, P. J., K. S. Church and J. Riley. 2020. Clinging to Excel as a security blanket: Investigating accountants' resistance to emerging data analytics technology. Journal of Emerging Technologies in Accounting 17(1): 33-39. (Use of Status Quo Bias Theory).

Schneider, G. P., J. Dai, D. J. Janvrin, K. Ajayi and R. L. Raschke. 2015. Infer, predict, and assure: Accounting opportunities in data analytics. Accounting Horizons (September): 719-742.

Schuele, K. and E. Felski. 2021. Comprehensive data analytics project using Excel and Tableau for the sales and purchases cycles. Journal of Emerging Technologies in Accounting 18(2): 257-268.

Schymik, G., K. Corral, D. Schuff and R. St. Louis. 2015. The benefits and costs of using metadata to improve enterprise document searches. Decision Sciences 46(6): 1049-1075.

Scott, J. 2015. Optimizing big data. Strategic Finance (November): 12.

Seow, P., G. Pan and T. Suwardy. 2016. Data mining journal entries for fraud detection: A replication of Debreceny and Gray's (2010) techniques. Journal of Forensic & Investigative Accounting 8(3): 501-514.

Shah, H. and L. Jiles. 2020. A data-driven approach to the pandemic. Management accountants and other professionals are leveraging data analytics to react effectively to the Covid-19 pandemic. Strategic Finance (September): 26-33.

Sharda, R., D. Delen and E. Turban. 2017. Business Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th Edition. Pearson. (Contents: Chapter 1: An Overview of Business Intelligence, Analytics, and Data Science. Chapter 2: Descriptive Analytics I: Nature of Data, Statistical Modeling, and Visualization. Chapter 3: Descriptive Analytics II: Business Intelligence and Data Warehousing. Chapter 4: Predictive Analytics I: Data Mining Process, Methods, and Algorithms. Chapter 5: Predictive Analytics II: Text, Web, and Social Media Analytics. Chapter 6: Prescriptive Analytics: Optimization and Simulation. Chapter 7: Big Data Concepts and Tools. Chapter 8: Future Trends, Privacy and Managerial Considerations in Analytics).

Shawver, T. J. and T. A. Shawver. 2020. Teaching data analytics in a collaborative team environment. Journal of Emerging Technologies in Accounting 17(2): 57-62.

Shi, Y., T. Wang and L. C. Alwan. 2020. Analytics for cross-border e-commerce: Inventory risk management of an online fashion retailer. Decision Sciences 51(6): 1347-1376.

Shields, B. 2018. Integrating analytics in your organization: Lessons from the sports industry. MIT Sloan Management Review (Winter): 108-115.

Shirata, C. Y. and M. Sakagami. 2008. An analysis of the “going concern assumption”: Text mining from Japanese financial reports. Journal of Emerging Technologies in Accounting (5): 1-16.

Shirata, C. Y., H. Takeuchi, S. Ogino and H. Watanabe. 2011. Extracting key phrases as predictors of corporate bankruptcy: Empirical analysis of annual reports by text mining. Journal of Emerging Technologies in Accounting (8): 31-44.

Short, J. E. and S. Todd. 2017. What's your data worth? MIT Sloan Management Review (Spring): 17-19.

Siano, F. and P. Wysocki. 2021. Transfer learning and textual analysis of accounting disclosures: Applying big data methods to small(er) datasets. Accounting Horizons (September): 217-244.

Silvi, R., K. Moeller and M. Schlacfke. 2010. Performance management analytics - The next extension in managerial accounting.

Singh, K. and P. Best. 2019. Anti-money laundering: Using data visualization to identify suspicious activity. International Journal of Accounting Information Systems (34): 100418.

Skiena, S. S. 2017. The Data Science Design Manual. Springer. (Contents: Chapter 1: What is Data Science? Chapter 2: Mathematical Preliminaries. Chapter 3: Data Munging. Chapter 4: Scores and Ranking. Chapter 5: Statistical Analysis. Chapter 6: Visualizing Data. Chapter 7: Mathematical Models. Chapter 8: Linear Algebra. Chapter 9: Linear and Logistic Regression. Chapter 10: Distance and Network Methods. Chapter 11: Machine Learning. Chapter 12: Big Data: Achieving Scale. Chapter 13: Coda.)

Sledgianowski, D., M. Gomaa and C. Tan. 2017. Toward integration of Big Data, technology and information systems competencies into the accounting curriculum. Journal of Accounting Education (38): 81-93.

Smith, D. 2019. Technology workbook: AI in data governance. Strategic Finance (September): 60-61.

Smith, D. and F. El-Wakeel. 2019. What is a data science model? Strategic Finance (November): 60-61.

Smith, D. and L. Heffernan. 2019. Technology workbook: Transforming analytics through data governance. Strategic Finance (January): 58-59.

Stephenson, S. S. 2023. CPA perceptions of big data analytics. Cost Management (March/April): 10-17.

Strategic Finance. 2020. Data analytics tools beyond Excel. Strategic Finance (March): 58. (Alteryx, iDashboards, Microsoft Power Bl, Sisense, Tableau, Vena Solutions).

Stuerke, P. S. 2023. Book review: King, T. A. 2022. The Numerate Leader: How to Pull Game-Changing Insights from Statistical Data. Wiley. Accounting Horizons (September): 279-280.

Sun, L., X. Zheng, Y. Jin, M. Jiang and H. Wang. 2019. Estimating promotion effects using big data: A partially profiled LASSO model with endogeneity correction. Decision Sciences 50(4): 816-846.

Tadesse, A. F. and N. E. Vincent. 2022. Combining data analytics with XBRL: The ViewDrive case. Issues in Accounting Education (February): 197-215.

Tan, P., M. Steinbach and V. Kumar. 2005. Introduction to Data Mining. Addison Wesley.

Tang, J. and K. E. Karim. 2017. Big data in business analytics: Implications for the audit profession. The CPA Journal (June): 34-39.

Tapis, G. P. and K. Priya. 2020. Developing and assessing data analytics courses: A continuous proposal for responding to AACSB Standard A5. Journal of Emerging Technologies in Accounting 17(1): 133-141.

Thomas, W. S. 2020. Power BI: An analytical view. Journal of Accountancy (March): 40-51.

Thomas, W. S. 2022. Using Power BI for advanced QuickBooks data analytics. Journal of Accountancy (December): 1-15.

Thomke, S. and J. Manzi. 2014. The discipline of business experimentation. Increase your chances of success with innovation test-drives. Harvard Business Review (December): 70-79. (Summary).

Tietz, W., J. M. Cainas and T. L. Miller-Nobles. 2019. Add data analytics to intro accounting. Strategic Finance (August): 36-41.

Tietz, W., T. Miller-Nobles and J. Cainas. 2022. Teaching the ETL process: Real-world data doesn't arrive clean and ready for analysis. Students need to learn the steps that go into extracting, transforming, and loading data for use. Strategic Finance (August): 34-41.

Topaloglu, O. and M. Dass. 2021. The impact of online review content and linguistic style matching on new product sales: The moderating role of review helpfulness. Decision Sciences 52(3): 749-775.

Torgo, L. 2010. Data Mining with R: Learning with Case Studies. Chapman and Hall/CRC.

Tschakert, N., J. Kokina, S. Kozlowski and M. Vasarhelyi. 2017. How business schools can integrate data analytics into the accounting curriculum. The CPA Journal (September): 10-12. (Summary).

Tsiptsis, K. and A. Chorianopoulos. 2010. Data Mining Techniques in CRM: Inside Customer Segmentation. Wiley.

Urban, G., A. Timoshenko, P. Dhillon and J. R. Hauser. 2020. Is deep learning a game changer for marketing analytics? MIT Sloan Management Review (Winter): 71-76.

Vasarhely, M. A. 2021. Applications of data analytics: Cluster analysis of not-for-profit data. Journal of Information Systems (Fall): 199-221.

Vasarhelyi, M. A., A. Kogan and B. M. Tuttle. 2015. Big data in accounting: An overview. Accounting Horizons (June): 381-396.

Vasarhelyi, M. A., M. G. Alles and A. Kogan. 2004. Principles of analytic monitoring for continuous assurance. Journal of Emerging Technologies in Accounting (1): 1-21.

Vercellis, C. 2009. Business Intelligence: Data Mining and Optimization for Decision Making. Wiley.

Vial, G., J. Jiang, T. Giannelia and A. Cameron. 2021. The data problem stalling AI: AI efforts can fail to move out of the lab if organizations don't carefully manage access to data throughout the development and production life cycle. MIT Sloan Management Review (Winter): 47-53.

Walkowiak, S. 2016. Big Data Analytics with R: Utilize R. to uncover patterns in your Big Data. Packt Publishing.

Wang, J. and J. G. S. Yang. 2009. Data mining techniques for auditing attest function and fraud detection. Journal of Forensic & Investigative Accounting 1(1): 1-24.

Wang, Y., T. Chiu and V. Chiu. 2020. Redesigning business process to comply with the new revenue recognition standard using process mining. Journal of Emerging Technologies in Accounting 17(1): 149-163.

Warren, J. D. Jr., K. C. Moffitt and P. Byrnes. 2015. How big data will change accounting. Accounting Horizons (June): 397-407.

Watkins, C., A. Ferreira, K. Rotaru and L. R. Gaerlan. 2020. Big data prioritization in SCM decision-making: Its role and performance implications. International Journal of Accounting Information Systems (38): 100470.

Weirich, T. R., N. Tschakert and S. Kozlowski. 2017. Teaching data analytics using ACL. Journal of Emerging Technologies in Accounting 14(2): 83-89.

Weirich, T. R., N. Tschakert and S. Kozlowski. 2018. Teaching data analytics skills in auditing classes using Tableau. Journal of Emerging Technologies in Accounting 15(2): 137-150.

Werner, M. 2017. Financial process mining - Accounting data structure dependent control flow inference. International Journal of Accounting Information Systems (25): 57-80.

Werner, M., M. Wiese and A. Maas. 2021. Embedding process mining into financial statement audits. International Journal of Accounting Information Systems (41): 100514.

Williams, S. 2011. 5 Barriers to BI success and how to overcome them. Strategic Finance (July): 26-33. (Note).

Winston, W. 2016. Microsoft Excel Data Analysis and Business Modeling, 5th Edition. Microsoft Press.

Witten, I. H. and E. Frank. 1999. Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufman.

Witten, I. H. and E. Frank. 2005. Data Mining: Practical Machine Learning Tools and Techniques, 2nd Edition. Morgan Kaufman.

Wixom, B. H. and J. W. Ross. 2017. How to monetize your data. MIT Sloan Management Review (Spring): 10-13.

Wixom, B. H., G. Piccoli and J. Rodriguez. 2021. Fast-track data monetization with strategic data assets. MIT Sloan Management Review (Summer): 1-4.

Wu, C. and R. B. Dull. 2021. Accessing cloud data to expand research and analytical opportunities: An example using IRS/AWS data for nonprofit organizations. Journal of Emerging Technologies in Accounting 18(2): 171-183.

Xu, M. and S. Bhattacharyya. 2021. Taste transitivity for collaborative filtering: A stochastic network dynamics approach. Decision Sciences 52(3): 629-660.

Yang, B., B. Ke, B. Li, Y. J. Yu and J. Zhang. 2020. Detecting accounting fraud in publicly traded U.S. firms using a machine learning approach. Journal of Accounting Research (March): 199-235.

Yang, F., B. Dolar and L. Mo. 2018. Textual analysis of corporate annual disclosures: A comparison between bankrupt and non-bankrupt companies. Journal of Emerging Technologies in Accounting 15(1): 45-55.

Yang, S. Y., F. Liu, X. Zhu and D. C. Yen. 2019. A graph mining approach to identify financial reporting patterns: An empirical examination of industry classifications. Decision Sciences 50(4): 847-876.

Yoon, K. and T. Pearce. 2021. Can substantive analytical procedures with data and data analytics replace sampling as tests of details? Journal of Emerging Technologies in Accounting 18(2): 185-199.

Yoon, K., L. Hoogduin and L. Zhang. 2015. Big data as complementary audit evidence. Accounting Horizons (June): 431-438.

Zengul, F. D., N. Oner, J. D. Byrd and A. Savage. 2021. Revealing research themes and trends in 30 top-ranking accounting journals: A text-mining approach. Abacus 57(3): 468-501.

Zhang, C. and D. N. Stone. 2023. Integrating Alteryx Designer and Tableau Desktop into the AIS course: An analytics mindset model. Issues in Accounting Education (May): 35-61.

Zhang, J., S. Porwal and T. V. Eaton. 2020. Data preparation for CPAs: Extract, transform, and load: ETL processes unearth the fuel needed to power the analytics and visualizations that unlock business insights. Journal of Accountancy (December): 50-59. (The authors focus on an application called Alteryx that can clean, join, and organize large amounts of data through an interactive and automated workflow).

Zhang, J., X. Yang and D. Appelbaum. 2015. Toward effective big data analysis in continuous auditing. Accounting Horizons (June): 469-476.

Zhang, Q., D. Koutmos, K. Chen and J. Zhu. 2021. Using operational and stock analytics to measure airline performance: A network DEA approach. Decision Sciences 52(3): 720-748.

Zhao, S. 2021. Thumb up or down? A text-mining approach of understanding consumers through reviews. Decision Sciences 52(3): 699-719.

Zhaokai, Y. and K. C. Moffitt. 2019. Contract analytics in auditing. Accounting Horizons (September): 111-126.

Zheng, Z., B. Padmanabhan and S. Kimbrough. 2003. On the existence and significance of data preprocessing biases in web usage mining. INFORMS Journal on Computing 15(2): 148-170.

Zumel, N., J. Mount and J. Porzak. 2014. Practical Data Science with R. Manning.