Julian Assange

Data Culture

Election Day Surprise – Did Candidates Kick all Three Buckets?

The results of the 2016 presidential election came as a massive surprise to pundits, pollsters and both parties alike. After hindsight analysis, everyone recognized that there was some unknown data that the data-analytics focused Clinton Campaign did not know was out there. This came to bite them in the end.

For any decision or plan we recommend considering three different kinds of data. We call them three different “buckets” of data.

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The Green Bucket – This is the data you already have and understand.

The Yellow Bucket – This is data that you know is available, but that you judge is not important enough to spend the resources to gather and analyze.

The Red Bucket –  This is data that you don’t know is out there, but that potentially could have a big effect on your plan or decision making.

The 2016 Presidential Race offers us contrasting examples. For the Clinton Campaign the Green Bucket was really big. They invested heavily in big data and opposition research.

Their data operation was so extensive that they had actually mapped every potential voter and what their issues might be. We can assume that for them the Yellow Bucket was small. There was probably not much data that they felt was worthwhile to go out and get if they suspected it existed.

Prior to Election Day Tuesday, we would have put the WikiLeaks emails In the Red Bucket for the Clinton Campaign. It was something that came out of left field and had enormous potential to change the race. The data that really turned out to be in the Red Bucket, however, was the unprecedented number of Democratic voters who stayed home- enough to change the results in several key swing states. This data was out there but the Democrats did not realize it was out there and/or did not know how to get their hands on it.

For the Trump campaign the Green Bucket was small. They did make some investment in data, but they relied most on their rallies. Their Yellow Bucket was big, there was a lot about the voters that they did not know and that they judged that it would not be worth while to try and find out. The Trump Campaign’s Red Bucket turned out to have a lot of surprises linked to his past, which they were not prepared for since they made a conscious decision to not do operational research against their own candidate. Trump’s base was clearly not influenced but this bucket of data.

The three buckets are always there, but human beings have a hard time staying aware of them. The human mind has a limited capacity for attention, but is great at fooling itself into thinking that it is aware of everything that is going on. The bucket metaphor is a tool for forcing yourself to open up your awareness around critical decisions.  The Clinton Campaign was widely recognized as using data as a key tool, but it obviously was not looking at all of the important data. There was a big bucket of data that they never factored into their polling equations. Just like a pilot who goes through his checklist everything he takes off, you can’t trust yourself to “naturally” think of all the options.  You will not always succeed when you use this technique. But you will get your head out of the task at hand to see what might block your success.

I highly recommend this exercise using actual physical buckets when you are faced with a major decision or doing strategic planning.

At CloudBase Services, we believe in the power of data as key to making the most informed decisions. We help our clients build database platforms and dashboard analytics tools, as a Premier Tableau Partner. We know a strong Data Culture is essential for the success of any data implementation. Therefore, we developed a series of Data Fluency survey apps and data mapping tools for our clients to access how they are doing with data and identify areas to improve.

Feel free to Contact The CloudBase Services Team with any questions or if you would like to give any of our survey apps a try.

Blog by Ken Taylor:

Ken TaylorKen’s varied talents and interests have led him down diverse educational and career paths including theology, welding and computer programming. Ken has been a computer programmer for over 25 years and even founded his own programming business in the Bay Area a number of years ago. As a programmer, Ken enjoys interacting with a variety of people and helping them solve their unique business problems. Ken’s interest in business applications has evolved into a deep interest in the organizational culture of businesses and how that culture interacts with and affects their ability to master their own data. Ken earned his undergraduate degree from Stanford. Learn more info on his site Databetter.org.

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