Data cleanup

Purpose

• Detect and flag missing, inconsistent, or incorrectly formatted values before processing begins
• Standardize dates, currencies, and any other identifiers across sources into a consistent format
• Produce clean, structured datasets ready for reconciliation, reporting, or accounting workflows

Inputs

• Raw data files from connected sources
• Validation rules defined within your workflow
• Data conventions that set the expected format and values for each field

Outputs

• Cleaned and standardized datasets, ready to pass to the next node in your workflow
• Validation reports documenting changes made and issues flagged
• Error flags for records that couldn't be cleaned automatically and require review

Typical use cases

Data from different sources is often formatted differently and needs to be unified before it can be processed in the next steps.

• Standardize dates and currencies across payment processors before payment service provider reconciliation
• Standardize stock symbols across different trading platforms