Data Validation Center

The Data Validation Center empowers users to review and verify customer data efficiently, ensuring accuracy and consistency across your datasets. Cascade's validation system categorizes data concerns into actionable areas, helping your team identify issues, investigate discrepancies, and maintain high data quality. 

Validation Views

1. Summary View

The Summary section provides an overview of all validation activity across the four categories: 

• Business Logic 

• Missing or Duplicates 

• Reconciliation 

• Data Drift 

This view displays the count of issues detected in each category, further broken down by warning or quarantine flags. It allows users to quickly gauge the overall health of the data. 

Interactions: 

• Clicking on any count within the Summary will navigate to the Details view, showing all flagged items in that category. 
• Users can also directly access the full list of flagged items in the Details view for comprehensive review. 

2. Detail View

The Details section lists every flagged item identified during validation. It provides granular details, including: 

• Type of issue (warning or quarantine) 

• Specific data points involved 

• Context or explanation for the flag 

This detailed view allows users to investigate individual items, assess their validity, and take corrective actions as necessary. 

Validation Categories

1. Business Logic

Cascade reviews data for logical consistency, flagging items such as: 

• Transactions dated in the future or extremely distant past (e.g., year 1900) 

• Schedules occurring before disbursement dates 

Flags are classified as: 

• Warnings: Likely data concerns that should be verified by the customer (e.g., "Payment before Disbursement Date"). While unusual, some business cases may permit these. 

• Quarantine: Clear errors that must be corrected before analysis (e.g., Transaction Date in the Future).

Cascade detects missing data and duplicate entries, flagging them as warnings or quarantine depending on severity.

3. Reconciliation

Cascade compares customer data (e.g., DPD, Outstanding Balance) with internally calculated figures. Discrepancies are flagged for review, prompting investigations and communications with the customer.

4. Data Drift

Cascade monitors changes across uploads for key variables: 

• Disbursement dates 

• Payment dates 

• Pledge amounts 

• Interest rates 

Significant shifts are flagged to help identify unintended data alterations over time.