Data Validator Pro: Because "Garbage In, Garbage Out" Is a Real Problem
Bad data costs real money and real time. I built a tool that catches problems before they cascade into disasters.
Here's a scenario that plays out in businesses every day: someone exports data from one system, formats it in Excel, and imports it into another system. Somewhere in that chain, an email address is missing its "@", a phone number has letters in it, a date is in the wrong format.
The downstream system accepts it anyway. Months later, the marketing team wonders why their emails are bouncing. The CRM has contacts with no phone numbers. Reports are broken because date calculations fail.
Data Validator Pro exists to catch that before it happens. Upload your CSV, define your rules — "this column must be a valid email", "this one must be a number between 0 and 100", "this date must be in DD/MM/YYYY format" — and it tells you every row that fails, why it fails, and what the actual value was.
The hardest part was building the rule engine to be both powerful and intuitive. I didn't want to force users to write regex patterns (though you can, if you want to). I wanted plain-language rules: "must be a phone number", "must not be empty", "must be unique in this column".
It took six weekends to build the rule engine properly. Worth every hour.