Monitor data quality with Notion rules, SQL checks & AI-powered alerts

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Important notice

This workflow is provided as-is. Please review and test before using in production.

1. Workflow Overview

Description This workflow continuously validates data quality using rules stored in Notion, runs anomaly checks against your SQL database, generates AI powered diagnostics, and alerts your team onl...

Best for

Document Extraction automation workflows
AI Summarization automation workflows
advanced n8n builders looking for reusable templates

Tools used

n8n-nodes-base.postgres, n8n-nodes-base.code, n8n-nodes-base.set, n8n-nodes-base.notion, @n8n/n8n-nodes-langchain.openai, n8n-nodes-base.splitinbatches, n8n-nodes-base.stickynote, n8n-nodes-base.scheduletrigger

Source and attribution

This workflow is cataloged by N8N Workflows and links back to its original n8n.io source page by Yassin Zehar.

Original n8n.io source

1.1 Workflow description

Title: Monitor data quality with Notion rules, SQL checks & AI-powered alerts
Workflow name: Monitor data quality with Notion rules, SQL checks & AI-powered alerts

Description

This workflow continuously validates data quality using rules stored in Notion, runs anomaly checks against your SQL database, generates AI-powered diagnostics, and alerts your team only when real issues occur.

Notion holds all data quality rules (source, field, condition, severity). n8n reads them on schedule, converts them into live SQL queries, and aggregates anomalies into a global run summary.

The workflow then scores data health, creates a Notion run record, optionally opens a Jira issue, and sends a Slack/email alert including AI-generated root cause & recommended fixes.

Target users

Perfect for:

DataOps
Analytics
Product Data
BI
Compliance
ETL/ELT pipelines
Platform reliability teams.

Workflow steps

How it works

Notion → Rules Database Each entry defines a check (table, field, condition, severity).
n8n → Dynamic Query Execution Rules are converted into SQL and checked automatically.
Summary Engine Aggregates anomalies, computes data quality score.
AI Diagnostic Layer Root cause analysis + recommended fix plan.
Incident Handling Notion Run Page + optional Slack/Email/Jira escalation. Silent exit when no anomaly = zero noise.

Setup Instructions

Create two Notion databases:
- Data Quality Rules → source / field / rule / severity / owner
- Data Quality Runs → run_id / timestamp / score / anomalies / trend / AI summary/recommendation

Connect SQL database (Postgres / Supabase / Redshift etc.)
Add OpenAI credentials for AI analysis
Connect Slack + Gmail + Jira for incident alerts
Set your execution schedule (daily/weekly)

Expected outcomes

Fully automated, rule-based data quality monitoring with minimal maintenance and zero manual checking.
When everything is healthy, runs remain silent.
When data breaks, the team is notified instantly: with context, root cause insight, and a structured remediation output.

Tutorial video

Watch the Youtube Tutorial video

About me :

I’m Yassin a Project & Product Manager Scaling tech products with data-driven project management. 📬 Feel free to connect with me on Linkedin

1.2 Logical Blocks

This catalog entry is organized from the workflow JSON. The node-level section below shows the executable blocks available for review before importing the template.

2. Block-by-Block Analysis

Block 1 - Product anomalies

Type / Role: n8n-nodes-base.postgres - postgres
Config choices: Version 2.6

Block 2 - check orders

Type / Role: n8n-nodes-base.postgres - postgres
Config choices: Version 2.6

Block 3 - build summary

Type / Role: n8n-nodes-base.code - code
Config choices: Version 2

Block 4 - run metadata

Type / Role: n8n-nodes-base.set - set
Config choices: Version 3.4

Block 5 - Data quality run page

Type / Role: n8n-nodes-base.notion - notion
Config choices: Version 2.2

Block 6 - AI analysis and recommendation

Type / Role: @n8n/n8n-nodes-langchain.openAi - openAi
Config choices: Version 2

Block 7 - calculate trend

Type / Role: n8n-nodes-base.code - code
Config choices: Version 2

Block 8 - Autofix simulation

Type / Role: n8n-nodes-base.code - code
Config choices: Version 2

Block 9 - split batch

Type / Role: n8n-nodes-base.splitInBatches - splitInBatches
Config choices: Version 3

Block 10 - Sticky Note

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

Block 11 - Sticky Note1

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

Block 12 - Sticky Note2

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

Block 13 - Sticky Note3

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

Block 14 - Daily trigger

Type / Role: n8n-nodes-base.scheduleTrigger - scheduleTrigger
Config choices: Version 1.2

Block 15 - Get database data

Type / Role: n8n-nodes-base.notion - notion
Config choices: Version 2.2

Block 16 - Switch module

Type / Role: n8n-nodes-base.switch - switch
Config choices: Version 3.3

Block 17 - Issue condition

Type / Role: n8n-nodes-base.if - if
Config choices: Version 2.2

Block 18 - Data condition

Type / Role: n8n-nodes-base.if - if
Config choices: Version 2.2

Block 19 - Jira issue

Type / Role: n8n-nodes-base.jira - jira
Config choices: Version 1

Block 20 - Slack message alert

Type / Role: n8n-nodes-base.slack - slack
Config choices: Version 2.3

Block 21 - Email reporting

Type / Role: n8n-nodes-base.gmail - gmail
Config choices: Version 2.1

3. Summary Table

Workflow	Monitor data quality with Notion rules, SQL checks & AI-powered alerts
Complexity	advanced
Nodes	21
Categories	Document Extraction, AI Summarization
Author	Yassin Zehar
Published	26 Nov 2025

4. Reproducing the Workflow from Scratch

1. Download the workflow JSON

Use the JSON export at /data/workflows/11256/11256.json as the source template for this automation.
2. Import the template into n8n

Open n8n, import the downloaded JSON, and review each node before activating the workflow.
3. Configure credentials and variables

Replace placeholder credentials, API keys, webhook URLs, account IDs, and environment-specific values with your own settings.
4. Test with sample data

Run the workflow manually or in a staging workspace, inspect node output, and confirm downstream systems receive the expected data.
5. Activate and monitor

Enable the workflow only after testing, then monitor executions, errors, and rate limits during the first production runs.

5. General Notes & Resources

Review imported nodes carefully before activation. This catalog entry is intended to help you inspect the workflow structure, understand required services, and find related templates faster.

Node names, credentials, schedules, webhook paths, and external service limits may need adjustment for your workspace.

Download workflow JSON Original n8n.io source Document Extraction workflows AI Summarization workflows

Frequently asked questions

What does Monitor data quality with Notion rules, SQL checks & AI-powered alerts do?

What do I need before importing this workflow?

Review the workflow JSON, configure any required credentials in n8n, and test the automation in a safe workspace before using it in production.

Can I customize this workflow?

Yes. Use the block-by-block analysis and the downloadable JSON to inspect each node, then adjust credentials, prompts, schedules, filters, or destinations for your Document Extraction, AI Summarization use case.

Yassin Zehar

19 workflows

Nodes

n8n-nodes-base.postgres n8n-nodes-base.code n8n-nodes-base.set n8n-nodes-base.notion @n8n/n8n-nodes-langchain.openai n8n-nodes-base.splitinbatches n8n-nodes-base.stickynote n8n-nodes-base.scheduletrigger

Complexity

advanced

Published 26 Nov 2025

Likes 0

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Install path: /data/workflows/11256/11256.json

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Monitor data quality with Notion rules, SQL checks & AI-powered alerts

Workflow preview

Important notice

1. Workflow Overview

Best for

Tools used

Source and attribution

1.1 Workflow description

Description

Target users

Workflow steps

How it works

Setup Instructions

Expected outcomes

Tutorial video

About me :

1.2 Logical Blocks

2. Block-by-Block Analysis

Block 1 - Product anomalies

Block 2 - check orders

Block 3 - build summary

Block 4 - run metadata

Block 5 - Data quality run page

Block 6 - AI analysis and recommendation

Block 7 - calculate trend

Block 8 - Autofix simulation

Block 9 - split batch

Block 10 - Sticky Note

Block 11 - Sticky Note1

Block 12 - Sticky Note2

Block 13 - Sticky Note3

Block 14 - Daily trigger

Block 15 - Get database data

Block 16 - Switch module

Block 17 - Issue condition

Block 18 - Data condition

Block 19 - Jira issue

Block 20 - Slack message alert

Block 21 - Email reporting

3. Summary Table

4. Reproducing the Workflow from Scratch

1. Download the workflow JSON

2. Import the template into n8n

3. Configure credentials and variables

4. Test with sample data

5. Activate and monitor

5. General Notes & Resources

Frequently asked questions