When was the last time you were presented with data that led to incorrect conclusions?
In our digital world companies are amassing data: about our personal preferences, what sites we visits, where we purchase, what we buy, where we travel.
Case at hand: Any time you visit a site that has a "like" button you are transmitting data to facebook about having visited that page. Assuming you checked the "keep me logged in" box - the code included with the "like" button uses your loging authentication information on facebook's servers. Who is to tell how that data can be used? What conclusions might be made based on patterns emerging from this data? If my wife vists Etsy.com using my PC's desktop will an advertiser start pushing me ads for arts & crafts in my facebook account? Will a psychographic tagging service start making assumptions about my preferences by drawing conclusions from a subset of data realized through my online behavior?
The holy grail of advertising will be when the full circle of conversion can be traversed: from the moment you are a "visitor", as you become "prospect" for a product/service by visiting related site/page and ultimately closing the loop by purchasing the product that was originally advertised-for when you were just a "visitor". This information is powerful and is highly valuable to advertisers.
Back to the topic at hand: I will be speaking at a conference in January about analytics and dashboards and would like to enlist your help. Do you have examples where data (facts) were incorrectly used? misquotes? resulted in severe consequences? Or when the right data led to wrong conclusions ("all roses are flowers so all flowers are therefor roses"). Can you share examples of poorly designed dashboards? Data gathering/dashboarding initiatives that died due to politics?
Examples can be from any area/field (media, financial services, manufacturing, etc).
If I use your example you will get the credit!