As you’ve probably heard, while Hillary Clinton won the majority of the popular vote, Donald Trump was awarded more than 270 votes in the Electoral College. Many, particularly on social media, were incredulous, partly at the candidates but also at the pollsters — in particular, famous polling analysts like Nate Silver from Disney’s FiveThirtyEight blog. Silver rose to fame as the guy who successfully predicted the past few presidential elections. In 2008, he correctly predicted 49 of 50 states, and in 2012 he nailed all 50. With that, plus an impressive showing in the midterms, a legend was born. The 2016 presidential election was not so kind to FiveThirtyEight, with misses in the battlegrounds of Florida, North Carolina, Pennsylvania, Michigan and Wisconsin turning the odds quickly in favor of Trump. But throughout nearly the entire general election campaign, Mrs. Clinton was an overwhelming favorite. This Election Proved You’re Only As Good As Your Data. Engadget.com, November 10, 2016.
I always wanted to poll project teams to get an idea of how well everyone thought we were doing. I never did do this because we always had so many strong opinions on what needed to be done and what needed to be changed. I never saw anyone or any one group that was consistently accurate at predicting how things were going to turn out. I did readily see, however, defect trends, feature completion trends, and the like that always looked similar from project to project. The details (e.g., actual features, actual defects) would vary by project but the aggregate trends remained noticeably similar.
For me, my data was always my touchstone on how my project was going. The data reflected what people were doing and that was always a better predictor, in my experience than asking people directly how things were going. Of course, I always did ask how things were going and we always responded to any issues but the predictive insights always came from the hard project data. I’ve concluded that both sources are essential because if we didn’t hear and react to the issues then the project data would most probably have failed to be predictive of our project’s progress.
How good has your data been at predicting the success of your projects?