Outcome
A database clean enough for automations and reporting to trust, maintained on a recurring schedule instead of decaying between manual one-off cleanups.
High Automation system
Data Cleanup Job
A scheduled hygiene job that normalizes fields, merges duplicates, and fixes formatting across a contact database on a recurring basis, using dry runs and backups so it never destroys good data.
- 3 days to 2 weeks
- timeline
- High
- complexity
- 5
- tools
- 4
- steps
Built with real HMX tool paths
MMake
nn8n
AAirtable
SSupabase
GGoogle Sheets
MMake
nn8n
AAirtable
SSupabase
GGoogle Sheets
System facts
Data Cleanup Job uses an event-driven automation layer for AI Automation. A scheduled hygiene job that normalizes fields, merges duplicates, and fixes formatting across a contact database on a recurring basis, using dry r... The architecture connects scan the dataset for issues, make, n8n, and completed workflow with an explicit control path.
Main risk
Aggressive merge or normalization rules delete or overwrite useful records and the damage is hard to reverse.
Prevention
Run dry runs, export a backup before each pass, review change samples, and only auto-merge on high-confidence deterministic keys.
Fallback
Keep a reversible change batch and quarantine uncertain records for human review rather than auto-merging them.
System architecture
Data Cleanup Job Architecture
6 nodes
Scan the dataset for issues
Apply normalization rules
Make
n8n
Exception Path
Completed Workflow
- 01Scan the dataset for issues
A scheduled hygiene job that normalizes fields, merges duplicates, and fixes formatting across a contact database on a recurring basis, using dry r...
- 02Apply normalization rules
Apply normalization rules (E.164 phones, lowercased emails, standardized tags) against a backup snapshot
- 03Make
Make carries Data Cleanup Job through validated triggers, branches, writebacks, and exception paths.
- 04n8n
Merge duplicates by a deterministic key, keeping the most complete record and preserving history
- 05Exception Path
Keep a reversible change batch and quarantine uncertain records for human review rather than auto-merging them.
- 06Completed Workflow
A database clean enough for automations and reporting to trust, maintained on a recurring schedule instead of decaying between manual one-off clean...
How it is built
A scheduled hygiene job that normalizes fields, merges duplicates, and fixes formatting across a contact database on a recurring basis, using dry runs and backups so it never destroys good data.
- 01Scan the dataset for issues: duplicates, inconsistent casing, bad phone/email formats, and empty required fields
- 02Apply normalization rules (E.164 phones, lowercased emails, standardized tags) against a backup snapshot
- 03Merge duplicates by a deterministic key, keeping the most complete record and preserving history
- 04Output a QA report of changes made and quarantine records that are too ambiguous to auto-fix
Tools
Workflow surface
- Make
- n8n
- Airtable
- Supabase
- Google Sheets
- Event layer: Scan the dataset for issues: duplicates, inconsistent casing, bad phone/email formats, and empty required fields
- Validation layer: Apply normalization rules (E.164 phones, lowercased emails, standardized tags) against a backup snapshot
- Branching layer: Make carries Data Cleanup Job through validated triggers, branches, writebacks, and exception paths.
- Writeback layer: n8n handles routine steps while run dry runs, export a backup before each pass, review change samples, and only auto-merge on high-confidence deterministic keys.
- Exception layer: A database clean enough for automations and reporting to trust, maintained on a recurring schedule instead of decaying between manual one-off clean...
Data flow
- 01Scan the dataset for issues: duplicates, inconsistent casing, bad phone/email formats, and empty required fields
- 02Apply normalization rules (E.164 phones, lowercased emails, standardized tags) against a backup snapshot
- 03Merge duplicates by a deterministic key, keeping the most complete record and preserving history
- 04Output a QA report of changes made and quarantine records that are too ambiguous to auto-fix
Controls and fallbacks
- Aggressive merge or normalization rules delete or overwrite useful records and the damage is hard to reverse.
- Run dry runs, export a backup before each pass, review change samples, and only auto-merge on high-confidence deterministic keys.
- Keep a reversible change batch and quarantine uncertain records for human review rather than auto-merging them.