One particularly perfidious example of this problem comes when incorrect data is ‘corrected’ to be more accurate.
A fictionalized conversation:
Data Vendor: We’ve heard that Enron [1]falsified their revenue data[2]. They claimed to make eleven trillion dollars last year, and we put that in our data at the time, but on closer examination their total revenue was six dollars and one Angolan Kwanza, worth one-tenth of a penny.
Me: Oh my! Thank you for letting us know.
DV: We’ve corrected Enron’s historical data in our database to reflect this upd-
Me: You what??
DV: W-we assumed that you would want corrected data...
Me: We absolutely do not want that! Do not correct it! Go back and...incorrect...the historical data immediately!
One particularly perfidious example of this problem comes when incorrect data is ‘corrected’ to be more accurate.
A fictionalized conversation:
Not the actual company
Not the actual data