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AI agent for chief digital officers

Data Quality Monitoring Agent

Key datasets passing quality checks with fast fixes

MonitoringQuality controlExecutives and strategy
See how it worksGet access
Data Quality Monitoring Agent: what goes in, what the agent does and what you get

What it does

Decisions depend on clean data, but errors in key datasets often go unnoticed until a report is wrong. Every day this agent runs quality checks on key datasets: completeness, valid values, duplicates and freshness. Data older than 24 hours or with duplicates over 1% fails. When a check fails, it traces the failure to the source system, identifies the likely cause, and alerts the data owner with examples. It tracks the fix and reruns the check to confirm; if the rerun still fails, it escalates to the owner's manager. It publishes a weekly quality score per dataset. The CDO approves changes to quality rules and escalations. Edge case: a seasonal pattern looks like an error, so the agent adjusts the rule only after the owner confirms it is expected.

How it works

Follow the arrows from top to bottom. The orange dashed arrow is the loop: when a check fails, the agent goes back and tries again.

Start and resultWhat it doesA check on its own workWaits for your OKGoes back and retries
Yes, continueYes, continueApprovedNoNo 1 STARTS WHEN Daily run 2 USES A TOOL Run completeness, validity, duplicate and freshnesschecks 3 DOES Trace failures to source system and likely cause 4 CHECKS THE RESULT Is the failure a real error rather than an expectedseasonal pattern? If not: ask the owner to confirm and propose a ruleadjustment. Back to step 3. 5 USES A TOOL Alert data owner with examples 6 CHECKS THE RESULT Does the rerun pass after the fix? If not: escalate to owner's manager. Back to step 5. 7 YOU APPROVE CDO approves rule changes and escalations 8 DOES Update weekly quality score per dataset 9 RESULT Quality report
Read the steps as a list
  1. Daily run
  2. Run completeness, validity, duplicate and freshness checks
  3. Trace failures to source system and likely cause
  4. Is the failure a real error rather than an expected seasonal pattern?If not: ask the owner to confirm and propose a rule adjustment. Back to step 3.
  5. Alert data owner with examples
  6. Does the rerun pass after the fix?If not: escalate to owner's manager. Back to step 5.
  7. CDO approves rule changes and escalationsThe agent waits here for your OK.
  8. Update weekly quality score per dataset
  9. Quality report

How it decides

It runs set rules and tracks failures to fixed status.

  • Freshness over 24 hours fails
  • Duplicates over 1% fail
  • Rule changes need confirmation

Make it yours

Every agent is a starting point. You choose these settings for your own situation.

  • Datasets
  • Rules
  • Thresholds
  • Report frequency

What keeps you in control

It always asks you first

  • Rules
  • Escalations

Hard limits

  • Never edits data
  • No personal data in alerts

It stops when

  • Done: all pass
  • Stop: source down

Set it up

We guide you through the set-up, step by step

Members get the full set-up guide for this agent. No technical skills needed: you copy, paste and upload.

10 minto set it up in your AI
5 AIsChatGPT, Claude, Copilot, Gemini, Grok
  • One set of instructions to paste into your AI, with the clicks for ChatGPT, Claude, Microsoft 365 Copilot, Gemini and Grok
  • The agent then walks you through connecting your own data, one source at a time
  • A downloadable copy with the flow chart, the rules and the full guide
Get access to this agent

An example run

What happensAfter a June migration, the customer table showed 3% duplicates, failing the 1% rule. The agent traced them to the old and new IDs both loading, and alerted the owner. The owner applied a fix, but the rerun still showed 1.4%, so the check failed again. The agent escalated to the owner's manager, and a second fix brought it to 0.2%. The CDO approved the escalation.

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