Perfect Data turns data quality into a repeatable operating rhythm: define rules that detect defects, run scans on a schedule, route notifications to owners, and track trends until issues stop recurring.
The essential building blocks of a data quality program.
Encode “what good looks like.” Rules typically return exception rows (bad data) so teams can focus on actionable defects.
Run rules automatically—hourly, nightly, or aligned to operational windows like close, batch loads, or reporting deadlines.
Route issues to the teams who can fix root causes. Reduce “downstream heroics” and repeated breaks.
Create a shared operating model: who analyzes, who fixes, who oversees—and what “done” looks like.
Track error counts and recurrence over time. Turn quality into a measurable KPI, not a subjective debate.
Maintain a durable scan history and outcomes to support audits, governance meetings, and leadership reporting.
A simple loop that scales from a handful of checks to an enterprise program.
Perfect Data is often applied to reporting stores, warehouses, and operational marts—where issues become visible and impactful.
Choose the model that matches your security and scale requirements.
We’ll identify your first high-impact rules and propose a rollout plan that matches your team structure and constraints.