Impact of Poor Customer Data Quality on Business Performance

Abstract

In the digital era, the ubiquity of data presents organizations with unparalleled opportunities for growth and innovation. Yet, the benefits of this data proliferation are intricately tied to its quality, positioning data integrity as a critical determinant of organizational success. This dissertation delves into the profound impact of customer data quality on business performance, a nexus increasingly pivotal in an age of digital transformation. Through empirical analysis, this research meticulously evaluates the
correlation between data quality and key business performance metrics, thereby illuminating the strategic importance of data quality enhancement. Adopting a mixedmethods approach, the study employs quantitative analysis supplemented by qualitative
insights to explore the multifaceted consequences of suboptimal data quality, including strategic misalignments, operational inefficiencies, and compromised decision-making processes. The findings unequivocally advocate for the implementation of robust data governance frameworks, underscoring superior data quality as not merely a technical requisite but a fundamental pillar for strategic growth and competitive advantage. This research contributes to the burgeoning discourse on data management, offering empirical evidence and strategic insights to guide organizations in navigating the complexities of data quality optimization.
This abstract aims to succinctly encapsulate the essence of your research, highlighting its relevance, methodology, key findings, and the broader implications for the field of business management and data quality governance.