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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.”
NetSuite custom fields contain valuable business context for AI anomaly detection, but standard exports often display system IDs instead of readable values. This creates data quality issues that reduce the effectiveness of anomaly detection algorithms.
Here’s how to structure and extract NetSuite custom fields in formats that optimize AI anomaly detection input and model interpretability.
Convert custom field IDs into AI-readable data
Coefficient provides superior custom field handling compared to native NetSuite export methods. The automatic value conversion transforms custom field IDs into readable names, improving AI model interpretability and reducing preprocessing overhead.
Full custom field support ensures comprehensive data capture, while field selection capabilities allow precise control over which custom fields to include, reducing data noise that can interfere with anomaly detection algorithms.
How to make it work
Step 1. Use Records & Lists import to access all custom fields.
Select the record types relevant to your anomaly detection models – transactions, customers, or items. Records & Lists method provides access to all custom fields on these record types with proper value conversion.
Step 2. Apply field selection to include only anomaly-relevant custom fields.
Use the field selection capabilities to focus on custom fields that provide meaningful context for anomaly detection. Include date custom fields for temporal patterns, numeric fields for statistical analysis, and list/record fields for categorical variables.
Step 3. Validate custom field formatting with data preview.
Use the real-time data preview to verify that custom field values are properly converted from IDs to readable names. This ensures your AI models receive interpretable data instead of system identifiers.
Step 4. Configure automated refreshes for current custom field data.
Set up scheduled refreshes to maintain current custom field values as your business data changes. The system handles custom field updates automatically, ensuring anomaly detection models work with fresh data.
Step 5. Optimize field sequence with drag-and-drop reordering.
Use column reordering to arrange custom fields in the sequence your anomaly detection algorithms expect. Group related custom fields together to improve model training efficiency.
Clean custom field data for better anomaly detection
Properly structured NetSuite custom fields provide the business context that makes AI anomaly detection more accurate and interpretable. Automated value conversion eliminates the data quality issues that typically plague custom field exports. Start optimizing your custom field data today.
