Broken CSVs, Solved: Automating the Cleanup Process

A cross-functional data operations team supporting multiple departments was constantly battling broken CSV imports. Externally submitted files often riddled with commas inside job titles, descriptions, or notes caused column misalignments and ingestion failures. What should have been automated turned into 5–6 hours per week of manual triage and cleaning.

The Problem: 

Improper punctuation handling in CSV files led to:

• Column Misalignment: Rows split incorrectly due to textual commas in fields

• Ingestion Failures: Pipelines broke from inconsistent field counts

• Manual Cleanup Time: Analysts spent hours fixing files before use

• Delayed Insights: Time to value suffered across analytics functions

• Ongoing Risk: No scalable way to ensure structural integrity from varied sources

The Solution: 

To eliminate the ambiguity and chaos, the team implemented a structured preprocessing solution that:

• Analyzed high-risk fields (e.g., Title, Description) for punctuation

• Applied contextual logic to distinguish structural vs. natural-language commas

• Standardized quoting across all text fields

• Validated and tested each file for consistent structure before ingestion

• Automated the process and embedded it into the pipeline to scale effortlessly

The Benefits of Solving This Problem: 

🕒 Manual Cleanup Time cut from 5–6 hours/week to <1 hour/week
📈 File Import Success Rate improved from ~65% to 100%
❌ Column Mismatches fully eliminated
⚡ Analyst Time to Value reduced from delayed to immediate

By embedding intelligent preprocessing into the ingestion workflow, the organization fully automated a once-manual, error-prone task. This not only stabilized analytics delivery but created a scalable foundation for future data growth. The result? Clean files, fewer errors, and faster decisions across the board.