Setting Data Validation Rules
Prevent bad data from reaching your dashboard by setting validation rules that flag or block imports with out-of-range values, missing fields, or inconsistencies.
Why validation rules matter
The most common source of dashboard inaccuracy is not a bug in AskBiz — it is bad data coming in from a connected source. A product with a £0 price, an order with a negative quantity, a date in the wrong timezone, or a duplicate transaction ID can all cause metric calculations to produce incorrect results. Validation rules let you define acceptable data boundaries and ensure that out-of-range values are either flagged for review or blocked entirely before they reach your dashboard.
Types of validation rules
Range rules: Define minimum and maximum acceptable values for numeric fields. Example: 'Order value must be between £0 and £50,000 — flag anything outside this range.' Null rules: Specify which fields must not be empty. Example: 'Order ID, date, and amount are required — reject rows where these are missing.' Format rules: Enforce data format requirements. Example: 'SKU must match the pattern [A-Z]{2}-[0-9]{4} — flag non-conforming values.' Duplicate rules: Flag records where a unique identifier (order ID, customer ID) appears more than once in the same import.
Setting up validation rules
Go to Settings → Data Sources → Validation Rules → Add Rule. Select the data source the rule applies to, the field, the rule type (range, null, format, or duplicate), and the action (flag for review, or block import). Rules are evaluated during every sync and every CSV upload — flagged records appear in the Data Quality log (Settings → Data Quality) for your review, and blocked records are never written to your dashboard. A daily data quality email summary (optional) lists all flags from the past 24 hours.
Monitoring data quality over time
Go to Settings → Data Quality → Quality Score to see an overall data quality score for your account — a percentage reflecting how many records in the last 30 days passed all validation rules without flags. A score above 98% is healthy. Below 95% indicates a systematic data problem worth investigating. The Quality Score log shows the trend over time, so you can see whether data quality is improving or degrading after a platform update, a CSV format change, or a new team member handling data entry.