The effects of data quality on decision quality are well-known and documented; hence, the adage, garbage in, garbage out. Poor quality data could lead to flawed decisions, heighten risk exposure, increase operational costs, and reduce the ability to design and execute strategy. Despite the general awareness of the effects of poor data quality, ensuring high-quality data remains a significant challenge. Most organizations rely on the strategy of finding and fixing data issues, which is costly and unsustainable. Researchers and practitioners approach data quality as an end goal rather than a means of achieving business objectives. This approach is flawed because it is the quality of the decisions, not the data, that ultimately matters. Thus, data quality programs and frameworks should focus on improving the quality of decisions, which impacts business profitability and sustainable growth.
Business decision-makers can confidently make a well-informed decision if they understand the data quality issues and severity of the issues underlying the insights they rely on. In other words, sharing information about data quality issues with decision-makers would mitigate the effects of poor-quality data on decision quality. It would be worthwhile to compare the impact of data quality on data-driven decisions when the decision-maker was aware of the issues and when they were not. In my days at Target Corporation, we approached data quality as a team with a mandate to gain decision-makers trust and ensure they rely on enterprise data for decision-making. Over time, data quality transcended into an organizational mindset and became the responsibility of each target associate (team member). High-level executives recognized the importance of data quality and were keen on obtaining information about the quality of the data, which they relied on for decision-making.
Data quality is a core tenet of an organizational mindset and should be embraced. It is defined as the capability of data to be used effectively, economically, and rapidly to inform and evaluate decisions (Karr et al., 2006). It further emphasizes that data quality is not an end goal. Three hyper-dimensions are essential to ensuring high-quality data: process (reliability, security, and confidentiality), data (accuracy, completeness, consistency, and integrability), and user (accessibility, interpretability, metadata, relevance, and timeliness).
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