The restaurateur was a good chef in her time, but now she devotes her attention to the books and marketing of the business. And the restaurants are doing well.
However, the other day she walked the kitchen of one of her bistros and stopped at the rotisserie oven. Then she observed the chicken cooking at 130 degrees F. She concluded the cook was incompetent and didn’t know the oven needs to be at or above 375.
She wrote a formal letter to the head chef and demanded the cook be reprimanded.
The cook had the oven at 130 because the chickens were cooling down to be served. They were already fully cooked.
The bad data?
The restaurateur erroneously concluded the chickens were in their first cook.
What data do you gather on walk-throughs? What conclusions do you/can you make? What errors could also be made in that data?
Data-driven is not good enough. It must be smart data.
Don’t assume the chicken needs to be at 375 when it’s already cooked.
[pullquote align=”left”]”Bad” data is worse than no data because it can burn the chicken. Don’t burn the chicken. [/pullquote]