In a data-driven environment, you invest in only what you can prove. What happens if the data is later shown to be bogus and the real data produces a negative result? Do you remove that successful product immediately? Do you change the data processing so that the bad data would have had produced a positive result? Do you question the usefulness of the process and throw it out? Do you do nothing?
That design cannot be disproved is one of its strengths.
by Steve Weller — Mar 23, 09
In a data-driven environment, you invest in only what you can prove. What happens if the data is later shown to be bogus and the real data produces a negative result? Do you remove that successful product immediately? Do you change the data processing so that the bad data would have had produced a positive result? Do you question the usefulness of the process and throw it out? Do you do nothing?
That design cannot be disproved is one of its strengths.