A Reproducible Data-Quality and Exception-Management Framework for Resource-Constrained Organizations

References

[1] Alles, M.G., Kogan, A. and Vasarhelyi, M.A. (2002) ‘Feasibility and economics of continuous assurance’, Auditing: A Journal of Practice & Theory, 21(1), pp. 125–138. Crossref

[2] Appelbaum, D., Kogan, A. and Vasarhelyi, M.A. (2017) ‘Big Data and analytics in the modern audit engagement: Research needs’, Auditing: A Journal of Practice & Theory, 36(4), pp. 1–27. Crossref

[3] Bolton, R.J. and Hand, D.J. (2002) ‘Statistical fraud detection: A review’, Statistical Science, 17(3), pp. 235–255. Crossref

[4] Chandola, V., Banerjee, A. and Kumar, V. (2009) ‘Anomaly detection: A survey’, ACM Computing Surveys, 41(3), Article 15. ACM

[5] Chingezi, E., Chingezi, L., Ganyani, L., Yelduora, P.G. and Mupa, M.N. (2026) ‘Reducing repeat findings and control failures in operationally intensive U.S. enterprises: A data- driven internal audit framework for remediation, loss prevention, and governance improvement’, World Journal of Advanced Research and Reviews, 30(3), pp. 2188–2199. Crossref

[6] ISO (2009) ISO/IEC 25012:2008 Software engineering—Software product Quality Requirements and Evaluation (SQuaRE)—Data quality model. Geneva: International Organization for Standardization. ISO (Accessed: 2 September 2026).

[7] Jans, M., Alles, M.G. and Vasarhelyi, M.A. (2014) ‘A field study on the use of process mining of event logs as an analytical procedure in auditing’, The Accounting Review, 89(5), pp. 1751–1773. Crossref

[8] Leys, C., Ley, C., Klein, O., Bernard, P. and Licata, L. (2013) ‘Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median’, Journal of Experimental Social Psychology, 49(4), pp. 764–766. ScienceDirect

[9] Machekera, G., Mtemeli, P.S., Dongo, M.M. et al. (2026) ‘From trench safety to lifecycle reliability: A project-control framework for water and sewer reticulation delivery in U.S. communities’, World Journal of Advanced Research and Reviews . ResearchGate (Accessed: 2 September 2026).

[10] Miller, R. (2024) ‘A framework for current and new data quality dimensions’, Data, 9(12), 151. MDPI

[12] Okebu, D.T. and Mupa, M.N. (2026) ‘Predictive training analytics for competency-based pilot instruction: Early detection of checkride risk, procedural noncompliance and safety-relevant learning gaps’, World Journal of Advanced Research and Reviews . ResearchGate (Accessed: 2 September 2026).

[13] Olist (2018) Brazilian E-Commerce Public Dataset by Olist, version 7. Kaggle. Kaggle (Accessed: 2 September 2026).

[14] Papastergios, V., Ehrlinger, L. and Gounaris, A. (2025) ‘Unfolding data quality dimensions in practice: A survey’, arXiv:2507.17507. arXiv

[15] Pipino, L.L., Lee, Y.W. and Wang, R.Y. (2002) ‘Data quality assessment’, Communications of the ACM, 45(4), pp. 211–218. ACM

[16] Ridzuan, F. and Zainon, W.M.N.W. (2024) ‘A review on data quality dimensions for big data’, Procedia Computer Science, 234, pp. 341–348. ScienceDirect

[17] Svanberg, J., Öhman, P., Samsten, I., Neidermeyer, P.E., Rana, T. and Danielson, M. (2025) ‘Addressing the exception prioritization problem in continuous auditing systems with thresholding’, Intelligent Systems in Accounting, Finance and Management, 32(4), e70022. Wiley

[18] Taanisa, T., Mukwata, N.A., Sydney, J., Ganyani, L., Maturure, R.N., Chingezi, E., Yelduora, P.G. and Mupa, M.N. (2026) ‘AI- enabled audit analytics for SME financial reporting and anomaly detection: A risk-based framework for early irregularity identification and control strengthening’, World Journal of Advanced Research and Reviews, 30(3), pp. 1113–1126. Crossref

[19] Vasarhelyi, M.A., Alles, M.G. and Kogan, A. (2004) ‘Principles of analytic monitoring for continuous assurance’, Journal of Emerging Technologies in Accounting, 1(1), pp. 1–21. Crossref

[20] Wang, J., Liu, Y., Li, P., Lin, Z., Sindakis, S. and Aggarwal, S. (2024) ‘Overview of data quality: Examining the dimensions, antecedents and impacts of data quality’, Journal of the Knowledge Economy, 15(1), pp. 1159–1178. Springer

[21] Wang, R.Y. and Strong, D.M. (1996) ‘Beyond accuracy: What data quality means to data consumers’, Journal of Management Information Systems, 12(4), pp. 5–33. Taylor & Francis

@article{1723143,
      author = {Allen Teerahumba, Emmanuel Hagan, Flora Phiri, Trevor Kauyu, Munashe Naphtali Mupa},
      title = {A Reproducible Data-Quality and Exception-Management Framework for Resource-Constrained Organizations},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {3},
      pages = {1893-1907},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1723143.pdf},
      abstract = {Resource-constrained small and medium-sized enterprises (SMEs) and nonprofit organizations require reliable operational reporting but often lack dedicated data-quality teams, enterprise observability platforms and continuous-audit capacity. This study develops and validates a Reproducible Data-Quality and Exception-Management Framework (RDEMF) that converts a small control library into a governed sequence of detection, risk scoring, root-cause coding, ownership, remediation and closure verification. The empirical demonstration uses the Brazilian E-Commerce Public Dataset by Olist, distributed through Kaggle, comprising 99,441 orders, 112,650 order lines, 103,886 payments, 32,951 products, 99,441 customers, 3,095 sellers and 99,224 reviews. Twelve order-level controls and complementary table-level tests assess completeness, uniqueness, validity, referential integrity, temporal consistency, reconciliation, timeliness and robust statistical anomalies. Overall, 21,715 orders (21.84%) triggered at least one rule; 6,530 (6.57%) accumulated a risk score of four or more. Late delivery affected 7,826 delivered orders (8.11%); 1,359 orders (1.37%) recorded carrier hand-off before approval; 775 orders (0.78%) had no item record; and 381 (0.38%) had an absolute payment-to-item reconciliation difference above R$0.01. Robust outlier rules flagged unusual values or durations but were treated as review candidates rather than errors. A transparent queue simulation, based on stated service-time assumptions rather than observed case handling, reduced mean completion time for high-severity exceptions from 5,275.0 to 381.4 hours under risk-priority sequencing, while total workload remained unchanged. The study contributes an auditable rule schema, entity-by-dimension and co-occurrence heat maps, an exception register, a scoring model, a minimum viable dashboard and a closure playbook. Findings demonstrate that low-cost controls can reveal concentrated reliability risks, but causal claims about remediation performance require prospective organizational pilots.},
      keywords = {data quality; exception management; SMEs; nonprofit organizations; reconciliation; anomaly detection; continuous auditing; data governance; operational resilience},
      month = {September},
  }