›› Free Data Maturity Scorecard — Discover your analytics score in 3 minutes MY SCORECARD →

Data Quality Monitoring Explained

Data Quality Monitoring Explained

Most data quality problems are discovered the wrong way round: a stakeholder notices a number that looks off in Power BI, and only then does anyone go looking for the bad record sitting three tables upstream. The Numlytics Data Quality Monitor flips that – a governed check registry and live Power BI dashboard that catches failing records before they reach a report, running on SQL Server, Oracle, MySQL, or Microsoft Fabric with no external service and no data leaving your environment.

The Real Cost of Bad Data: What It Actually Costs Across Oracle, SQL Server, and Microsoft Fabric

The Real Cost of Bad Data: Oracle, SQL Server & Fabric

The cost of bad data never shows up as a single line item. It shows up as a finance team quietly re-running a report before a board meeting, an analyst spending an afternoon reconciling two numbers that should already match, and a sales forecast leadership stopped trusting months ago without ever saying so. This article walks through where those costs actually hide – inside an Oracle ERP and across a Microsoft Fabric medallion architecture and what a governed data quality framework changes about that pattern.

Power BI Desktop Bridge

Power BI Desktop Bridge

AI agents can edit Power BI reports. What they couldn’t do – until now is check whether the edit actually worked. The Power BI Desktop Bridge adds a validate-reload-screenshot loop that makes an AI agent look at its own output before calling the job done. Here’s where it earns its keep, and where it still needs a human in the room.