If you’re collecting bad data you are guaranteed to get, well, bad results.
More specifically, if there’s an inherent flaw in the way your plants collect data or the type of data they are collecting, your process can appear to be under control while still having critical flaws. Those flaws can rear their ugly head at any time.
Don’t believe me?
Look at these well known examples:
Nearly 12MM lbs of chicken strips recalled due to foreign material (metal) contamination, discovered when consumers complained. (What was missed as part of metal detection validation?)
Outbreak and recalls of par-fried chicken strips due to contamination with Salmonella Enteritidis. (Were cooking instructions validated?)
Outbreak and recall of cut melons due to contamination with Salmonella Carrau. (Was the antimicrobial wash treatment properly validated?)
Recall of chicken noodle soup for containing undeclared allergen, milk. (What was lacking in validation of the allergen control program?)
My guess is you could add to this list with more horror stories of food production processes failing.
Data validation is a critical part of your food production or processing business. A validation exercise tells you whether or not your system will truly control the hazards when everything is operating within control.
Get validation right
Get data validation right, and operate the system within control, and food safety and quality systems will result in production of safe, wholesome food. It’s that simple, but not necessarily that easy.
The CODEX definition for validation is: obtaining evidence that a control measure or combination of control measures, if properly implemented, is capable of controlling the hazard to a specified outcome.
I like to tell my clients that validation of food production or processing systems is a lot like a science project:
You set variables and controls, you identify your null hypothesis (remember 7th grade science?), then you challenge the system and use objective measurements to see how it performs.