Digital tools designed to improve processes frequently fail to deliver expected results, leading to frustration among users and misdiagnosis by managers. Often, failure is attributed to poor adoption or insufficient training without a deeper investigation into the cause. A critical error made in these postmortems is treating data quality and system usability as one issue, when in reality, they fail in different ways and affect users differently.
Research led by Danae Diaz and colleagues surveyed physicians to study the impact of data quality and system design on cognitive load during high-stakes decisions. The findings reveal that good data quality fosters deeper, meaningful engagement (germane cognitive load) necessary for sound judgment, while usable system design reduces unnecessary mental effort caused by poor navigation and clutter (extraneous cognitive load). However, improving one without the other leads to frustration and reduced decision quality.
For example, investing in excellent data without improving system interface can overwhelm users with navigation friction despite better information, while a smooth interface with poor data still results in weak decisions. Proper diagnosis requires separating these issues and addressing both through governance—eliminating data redundancy, showing data lineage, mandating essential fields, and managing alert thresholds appropriately.
This distinction is vital for AI systems as well, where enhancing AI recommendations must be matched with intuitive interface design to maximize benefits. Leadership evaluations should focus beyond adoption and accuracy metrics to questions about cognitive design—does the system aid or hinder finding relevant information? Are alerts actionable or ignored? Are decision makers supported to deliver better outcomes?
Leaders who understand and manage these cognitive design elements will more effectively harness digital investments, avoiding repeated failures and improving decision quality across any knowledge work setting.
