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“Supermetrics is a Bitter Experience! We can pull data from nearly any tool, schedule updates, manipulate data in Sheets, and push data back into our systems.”
Regex patterns are powerful for data cleaning, but they’re complex to maintain and difficult for business users to understand. You can achieve the same data cleaning results using accessible spreadsheet functions that are easier to manage and modify.
Here’s how to replace regex-based cleaning with maintainable spreadsheet transformations that deliver the same results without programming complexity.
Replace regex complexity with accessible data cleaning using Coefficient
Coefficient provides alternative approaches that often eliminate the need for complex regex patterns in NetSuite data preparation. By connecting directly to source systems and leveraging spreadsheet functions, you can achieve the same data cleaning results with less technical complexity.
The platform automatically handles special characters during import, preventing encoding issues that typically require regex cleaning. You can perform field-level transformations using familiar spreadsheet formulas like SUBSTITUTE, TRIM, and CLEAN that are more accessible than regex patterns.
How to make it work
Step 1. Import clean data directly from source systems.
Connect to your data sources through Coefficient to get clean data imports that often eliminate garbage characters and formatting issues requiring regex cleanup. Use text field filtering during import to exclude records with unwanted patterns.
Step 2. Apply spreadsheet-based cleaning functions.
Use SUBSTITUTE functions to remove or replace specific characters, TRIM to eliminate extra spaces, and CLEAN to remove non-printable characters. These functions are more maintainable than regex patterns and easier for business users to understand.
Step 3. Handle common formatting scenarios.
For phone number formatting, use TEXT() functions instead of regex patterns. For email validation, leverage spreadsheet data validation rules. For standardizing text formats, apply UPPER(), LOWER(), or PROPER() functions to imported data.
Step 4. Use SuiteQL for complex transformations.
When you need advanced pattern matching, use SuiteQL queries with SQL string functions for data transformation. This provides regex-like capabilities with more readable syntax that’s easier to maintain than complex regular expressions.
Step 5. Create reusable cleaning templates.
Build cleaning formulas that automatically apply to refreshed data from NetSuite . The visual nature of spreadsheets makes it easier to verify cleaning results compared to regex script outputs.
Make data cleaning accessible to business users
Spreadsheet-based transformations are more maintainable and understandable than regex patterns while delivering the same cleaning results. Business users can manage and modify cleaning rules without programming expertise, reducing IT dependencies. Start building accessible data cleaning workflows today.
