I have also witnessed what happens when organizations try to shortcut these foundational steps. The damage is predictable and costly:
User Resistance and Misaligned Solutions: Building data quality systems without input from actual users creates solutions that don’t solve real problems, which results in resistance to adoption and wasted resources.
Reactive Crisis Management: Without proactive monitoring, data quality problems remain hidden until they cause business disruptions. By then, fixes are expensive, urgent, and often incomplete, leading to unreliable decision-making across the organization.
Persistent Data Quality Debt: When there is no clear resolution process, data quality issues accumulate like technical debt. Problems persist for months or years, gradually eroding trust in data systems and creating ongoing business friction.
