Data Quality Monitor
Numlytics' Data Quality Monitor catches failing data before it reaches a report
One governed check registry, configurable SQL-based validation rules, detailed issue tracking, and a live Power BI dashboard. Every check is written in plain SQL, enabling the same framework to work across SQL Server, Microsoft Fabric, Oracle, and MySQL as your data estate grows.
Works on SQL Server, Microsoft Fabric, Oracle, and MySQL using SQL-based validation rules
Centralized check registry with configurable severity levels, thresholds, and ownership
Interactive Power BI dashboards for health monitoring, trends, drill-down, and alerts
Runs entirely within your environment—no agents, no third-party services, and no data movement
Platform At A Glance
4Live Power BI dashboard pages
4Severity levels, fully configurable
4+SQL platforms supported natively
100%
SQL-based validation framework
data quality monitoring
SQL Server data validation
Power BI data quality dashboard
Microsoft Fabric data quality
Oracle data quality checks
MySQL data quality monitoring
data quality check registry
See It In Action
Four Views Into the Health of Your Data
From daily checks running quietly on your database to record-level drill-down - every module below is a real page inside the Data Quality Monitor dashboard. Click a module to explore.
The Problem
Is Bad Data Reaching Your Reports First?
Most organisations find out about a data quality issue the same way - a stakeholder spots a wrong number in a report. If these challenges sound familiar, your data estate is running blind.
Average Data Quality Blind Spot Score
01
Bad Data Reaches Leadership Before It Reaches You
Without a governed data quality framework, issues such as missing keys, duplicate records, or invalid values often remain unnoticed until they appear in reports or dashboards, reducing confidence in business decisions.
Trust Risk
02
No Single Registry of What's Actually Being Checked
Validation logic lives in scattered scripts, one-off spreadsheets, and the memory of whoever wrote it last - not in a governed, auditable rule set anyone can review.
Tribal Knowledge
03
Recurring Failures Go Unnoticed
Without a trend view across runs, the same table can fail the same check week after week - quietly eroding trust - with nobody connecting the dots.
Silent Decay
04
Root-Cause Investigation Takes Days, Not Minutes
When an issue is finally found, analysts write investigation queries from scratch, hunting across tables for the specific failing records - every single time.
Lost Hours
05
Critical Issues Buried in the Noise
With no severity-based alerting, a Critical issue sits in the same inbox as a Low-priority one - and nobody knows what actually needs attention first.
Missed Priority
06
Every Platform Has Its Own Ad Hoc Fix - Or None
Oracle, SQL Server, MySQL and new Fabric infrastructure each get a different one-off quality check, or none at all - leaving no consistent governance layer across the estate.
Fragmented Coverage
The Solution
Data Quality Monitoring. Every Table, Every Day.
The Numlytics Data Quality Framework runs registered SQL checks against your database every day, captures every failing record, and surfaces it all in a live Power BI dashboard - so bad data is caught before it reaches a report, a dashboard, or a decision.
Platform Architecture
Built on SQL Server and Power BI - but every check is plain SQL, so the engine runs against any SQL-like platform: Microsoft Fabric, Oracle, MySQL, PostgreSQL, Synapse, and more. Point it at a new source and register a check in minutes - no new tooling, no agents.
Check Runs
Each registered data quality check is executed against your data source, with failing records captured and written to a permanent run history for reporting, investigation, and trend analysis.
Priority-Ranked Issue Detection
Every failing check is scored by severity, failing-row volume, and how long it's been open - so your team always knows which issue to fix first.
Record-Level Drill Down
Every issue drills through to the exact failing records, the field in error, and a copyable SQL query - ready to paste straight into a query tool for root-cause work.
Self-Validating Check Registry
New checks are schema-validated and compiled before they're saved - catching syntax errors and missing tables before they ever reach production.
Six Ways It Catches Bad Data
Every Layer, Every Platform, One Framework
From daily KPI monitoring to root-cause SQL - deploy the same registered-check model against Fabric, Oracle, MySQL, or SQL Server.
Data Quality Dashboard
Real-time KPI cards, issues by severity, and a priority score that ranks every failing check by urgency - so the most damaging issue always surfaces first.
Live Monitoring
Data Quality Trends
Daily issues by severity and failing records per table, tracked across every completed run. Spot a slow leak in a table before it becomes a Critical alert.
Trend Analysis
Record-Level Drill Down
Drill through any failing check to the individual bad records, the exact field in error, and a copyable SQL query to reproduce it instantly.
Root Cause
Check Registry & Open Alerts
One governed list of every active check plus every unacknowledged Critical or High alert - the single screen your data team checks each morning.
Alerting
Cross-Platform Check Engine
Every check is plain SQL. Point the same framework at Oracle, MySQL, Microsoft Fabric, or SQL Server - no rebuild, no new tooling.
Any SQL Engine
Enterprise & Medallion-Ready
Runs against Oracle-based enterprise applications and Fabric medallion architectures alike - flagging exactly which layer, Bronze, Silver, or Gold, introduced the issue.
Direct Business Impact
Direct Business Impact
Where This Actually Shows Up in Your Business
Oracle Enterprise Applications
A bad record in your ERP doesn't wait for month-end to cause damage.
Most ERP, CRM, and finance systems run on Oracle. A null cost centre, a duplicate vendor ID, or a broken foreign key created today normally surfaces weeks later, during finance close or an audit. Point the framework at the Oracle schema and the same issue is flagged in the report the next day - while it's still cheap to fix.
Direct impact: issues caught at the source system, not discovered downstream in finance close.
Fabric Medallion Architecture
Know which layer broke - Bronze, Silver, or Gold - not just that a number looks wrong.
New infrastructure built on Microsoft Fabric almost always follows a medallion architecture. Register checks at each layer and the dashboard tells your team exactly where the break happened - a raw ingestion problem in Bronze versus a transformation bug introduced in Silver - instead of only seeing a wrong total in Gold.
Direct impact: cuts root-cause time from days of layer-by-layer tracing to one dashboard filter.
Key Benefits
Data Quality Outcomes You Can Measure
Every check, alert, and drill-down is designed to catch a real data issue faster - not just render another dashboard.
100%
Daily Check Coverage
Every registered check runs automatically each day - no manual sampling, no skipped tables, no forgotten spreadsheet QA.
Same-Day
Issue Detection Before It Reaches a Report
Failing records are flagged the day they occur - not discovered weeks later during finance close or a board review.
2 Wks
Phase 1 Go-Live
The check registry, daily runner, and live Power BI dashboard are running within two weeks. Start catching bad data immediately.
4+
SQL Platforms Supported
SQL Server, Oracle, MySQL, and Microsoft Fabric all run the identical check engine unmodified - no per-platform rebuild.
Zero
New Tooling Required
No new agents, no new licenses, no separate QA platform - just SQL checks and the Power BI dashboard your team already knows.
Layer-Level
Root Cause Precision
Pinpoint exactly which table, field, or medallion layer - Bronze, Silver, Gold - introduced the issue, instead of only seeing a bad number downstream.
Implementation Roadmap
Your Proven 4-Phase Data Quality Delivery Plan
Each phase delivers standalone data quality value while building toward the full framework. No big-bang risk. Bad data starts getting caught from Week 1.
Phase 1 · Weeks 1–2
Foundation & Check Registry
The dq schema is deployed, your first checks are registered in the Check Registry, and the daily Agent Job is scheduled. Validation triggers go live immediately, blocking any check that isn't schema-correct.
A governed foundation for managing and executing data quality checks.
2 weeks
Phase 2 · Weeks 3–6
Live Dashboard & Trends
The five Power BI views are built and connected. The main Data Quality Dashboard goes live with KPI cards and priority scoring, alongside the Trends page tracking daily issues by severity.
Data leaders see real-time issue volume and priority-ranked failures for the first time.
Weeks 3–6
Phase 3 · Weeks 7–10
Drill Down & Alerting
Record-level Drill Down is activated, with Copyable Check Queries ready to paste into SSMS. Critical and High severity checks are wired to automatic alerting via the Open Alerts view.
Engineers go from "a number looks wrong" to the exact failing record in one drill-through.
Weeks 7–10
Phase 4 · Weeks 11–14
Cross-Platform Rollout & Full Launch
The same check registry is extended to any additional SQL-like sources in your estate - Oracle, MySQL, or Microsoft Fabric - with the Check Registry & Alerts page live for daily use.
Full data quality framework live across every SQL platform you run, with board-ready reporting.
Weeks 11–14
Use Cases
Real Data Quality Challenges, Solved
Purpose-built for the data quality scenarios our clients face - regardless of which SQL platform their data lives on.
Use Case 01
Finance - Oracle ERP Data Quality
✓
Bad records caught the next day, not at month-end close
Cross-Platform Check Engine
A finance team runs its ERP on Oracle. A null cost centre or duplicate vendor ID used to surface weeks later during close. With checks registered directly against the Oracle schema, the same issue is flagged in the daily report the next morning - while it's still cheap to fix.
Use Case 02
Manufacturing & Retail - MySQL Transactional Systems
✓
Inventory and order errors caught before they hit reporting
Priority-Ranked Detection
An operations team runs inventory and order management on MySQL. The same registered-check model runs unmodified against it, ranking failures like negative stock counts or orphaned order lines by severity - so the highest-impact issue always surfaces first.
Use Case 03
Healthcare & Insurance - SQL Server Records
✓
Record-level root cause in one drill-through
Record-Level Drill Down
A healthcare provider needs to trace a duplicate patient ID or a missing required field back to its source. The Drill Down page shows the exact failing record and field, plus a copyable SQL query to reproduce it instantly - no manual query writing under pressure.
Use Case 04
New Fabric Builds - Medallion Architecture
✓
Root-cause time cut from days of tracing to one filter
Enterprise & Medallion-Ready
A team migrating to Microsoft Fabric lands data through Bronze, Silver, and Gold layers. Checks registered at each layer show exactly where a bad number was introduced - a raw ingestion issue in Bronze versus a transformation bug in Silver - instead of only spotting it downstream in Gold.
Client Success
What Data Teams Say About Our Data Quality Framework
We used to find bad data during month-end close, when it was already too late to fix quietly. Now the framework flags a failing record the day it lands in Oracle - before it ever reaches Finance. That alone changed how our team works.
The Drill Down page is the feature our engineers actually use every day. Copy the query, paste it into SSMS, see the exact failing rows - no more writing ad hoc diagnostic SQL under pressure during an incident.
We were migrating to Fabric and had no idea whether a bad number was an ingestion problem or a transformation bug. Registering checks at each medallion layer told us exactly where the break was - that alone saved days of tracing.
Beyond Data Quality
Data Quality is One Part of What We Do.
Numlytics delivers end-to-end Microsoft data analytics solutions - from cloud migration and data engineering to BI consulting and AI-ready architecture. One partner. Your entire data estate.
Microsoft Fabric Migration
Move your data estate to Microsoft Fabric - the unified analytics platform that replaces fragmented tools with one governed, AI-ready environment. We carry your data quality checks with you, mapped to each medallion layer.
Explore Microsoft Fabric Migration
Power BI Consulting & Dashboard Development
From executive scorecards to operational dashboards, our Power BI specialists design and build reporting solutions that drive real decisions - all on data your quality framework has already validated.
Explore Power BI Consulting
Azure DataOps & Cloud Data Engineering
We design and deliver scalable Azure data pipelines, lakehouse architectures, and DataOps frameworks that keep your data flowing reliably - with data quality checks built into every layer.
Explore Azure DataOps
Snowflake Implementation & Management
For organisations running Snowflake as their cloud data platform, we deliver full implementation, optimisation, and ongoing management, with the same registered-check model watching your Snowflake schema.
Explore Snowflake Services
Not Sure Which Solution You Need?
Our team works across the full Microsoft data stack - Power BI, Fabric, Azure, and Snowflake, plus Oracle and MySQL sources. Tell us your challenge, and we'll point you in the right direction. No commitment required.
Common Questions
Common Questions About Our Data Quality Framework
The Numlytics Data Quality Framework is a data quality solution that helps organisations define, execute, and monitor configurable validation checks across their data sources. Results are stored for historical analysis and surfaced through interactive Power BI dashboards with KPI summaries, trends, and record-level investigation.
It's built for data engineering teams, data quality analysts, BI managers, and CDOs who need to know when data breaks before it reaches a report - rather than finding out from a confused stakeholder after the fact. It's especially valuable for teams running enterprise applications on Oracle or building new infrastructure on Microsoft Fabric.
Implementation is tailored to your environment and priorities. The framework can be introduced in phases, allowing you to begin with core validation checks and reporting before expanding to additional rules, dashboards, and supported data platforms.
Every check is a plain SQL query registered in the Check Registry. It must return three aliased columns - RecordKey, FieldInError, and IssueDetail - identifying exactly which record failed, which field caused it, and why. A validation trigger blocks any check that doesn't follow this structure, so a broken check can never be saved.
Data quality checks are designed to be efficient and can be executed in a way that minimises impact on operational workloads. The implementation approach is tailored to your environment and data platform.
Yes. Because every check is plain SQL, the same framework runs against any SQL-like platform - Microsoft Fabric, Oracle, MySQL, PostgreSQL, Synapse, and more. This matters most for organisations running enterprise applications on Oracle, where a data issue can be caught immediately instead of surfacing weeks later in a report, and for teams building new infrastructure on Fabric, where checks can flag exactly which medallion layer - Bronze, Silver, or Gold - introduced a problem.
Right-click any check on the dashboard and drill through to the Drill Down page. It shows the individual failing records, the exact field in error, and a Copyable Check Queries table with the exact SQL behind the check - ready to paste into SSMS for deeper investigation.
Every check is assigned a severity - Critical, High, Medium, or Low - when it's registered. The dashboard's Priority Score then ranks every failing check by severity weight, failing-row volume, and how long the issue has been open, so your team always knows which one to fix first.
New validation rules are added through the Check Registry, where they can be configured with categories, severity levels, ownership, and thresholds. Each check should be reviewed and tested before being used in production to ensure it produces the expected results.
Get Started
Ready to Catch Bad Data Before It Reaches a Report?
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