From Data Defects to Decision Failure: A Practical Framework for Data Quality Management in Emerging Markets

Abstract

Data quality is a persistent constraint on the reliability of information systems and analytics, yet much of the dominant guidance on data quality management implicitly assumes stable infrastructure, high automation, and mature governance capacity. In many emerging market organisations, data is produced and maintained through hybrid arrangements that combine manual capture, spreadsheet mediation, partial system integration, and locally improvised routines that compensate for gaps in process design and institutional capacity. In these settings, data quality failure is not only a technical defect in stored records. It functions as a decision risk pathway through which duplication, incompleteness, ambiguity, inconsistency, and delayed updates propagate into operational error, managerial misjudgment, and accountability breakdown. This paper develops a coherent, publication-oriented first draft that links established data quality theory and governance principles to the institutional realities of resource-constrained implementation environments. The manuscript is grounded in consumer-oriented data quality theory (Wang & Strong, 1996; Strong et al., 1997), the ontological anchoring of intrinsic data quality (Wand & Wang, 1996), data governance and stewardship principles as articulated in DAMA DMBOK (DAMA International, 2017), and the ICT4D lens on design actuality gaps and local improvisation (Heeks, 2002). Methodologically, the paper adopts a design science research stance (Hevner et al., 2004; Peffers et al., 2007) and treats PRO DQ as the central artifact. PRO DQ is a circular framework comprising Prevent, Responsibilise, Observe, Diagnose, and Fix. The framework is designed to be implementable under constrained budgets and mixed digital maturity, while remaining auditable and governance aware. The paper contributes a defect taxonomy, a defect-to-decision propagation model, and a practical implementation logic that can be evaluated in future field studies through case-based deployment, pilot interventions, and longitudinal monitoring.