Bad data becomes easier to fund when buyers can translate it into operational and financial consequences. The conversation shifts from abstract quality scores to rework, missed revenue, delayed decisions, customer friction and avoidable risk.

What buyers try to measure

• Manual reconciliation and duplicated effort.
• Decision delays caused by conflicting numbers.
• Customer and revenue impact from inaccurate records.
• Compliance and reporting exposure.
• AI and analytics projects slowed by remediation work.

What this means for vendors

Data quality vendors should help buyers establish a baseline cost before promising improvement. A credible business case shows which failure modes matter, how the solution changes them and how the organisation will measure the difference after implementation.

Our buyer intelligence is informed by ongoing conversations with senior enterprise leaders through roundtables and leadership communities.