One of my favorite topics is the very human tendency to misuse and misunderstand the information that is provided by business intelligence systems.
Here’s a link to a TED Talk by Peter Donnelly, an Oxford statistician, pointing out some of the common errors that people make, and the serious consequences that can result.
Here’s a taste of the material:
(1) Imagine you’re sequentially tossing a coin. On average, which of these sequences will take longer to achieve?:
They have to be the same, right? (hint: wrong)
(2) Imagine you take a medical test for a disease that has a 99% success rate, and you have a positive result. How likely is it that you have the disease? (hint: no, it’s not 99%)
Peter goes on to talk about a case where a pediatrician gave statistically-invalid expert testimony at a trial that resulted in a UK mother being convicted of murdering her children (later thankfully overturned on appeal).
We should all take account of these kinds of frightening tales when implementing business intelligence:
- Just providing information doesn’t mean that it will be used effectively, and IT organizations should take responsibility for the end result of BI — better corporate performance — not just think of themselves as tools providers.
- Analytic expertise and data expertise is as important as technology expertise, and should be an explicit part of any business intelligence competency center.
- Sharing information widely and providing collaboration will make it more likely for mistakes to be spotted and corrected.
Does anybody else have any good stories and examples of data misuse?