I get asked why in the prime of my career I went back to working for a startup company, run by young talent, in a field on the cutting edge of analytics. It was because, for the first time, I felt like an owner had a vision I could get behind. He wanted to be something better, do something different, and wanted me to help him create something magnificent. I saw it as a unique opportunity because, for the first time, I found a true entrepreneur.
I have been married for almost 10 years. I have gotten good at buying gifts, even clothes. I can match blouses and jewelry, dresses and belts, shoes and jeans. My secret? I look at the mannequins. Mannequins were designed to attract your attention in a store window and to lure you into the store. Then inside, they are designed to show you some of the combinations you could make with their clothes. Essentially they are designed to get you to buy more than one thing. This is perfect for guys. All we have to do is point to the mannequin and ask a store sales person where we can find those certain pieces of clothing. We can buy the mannequin lock stock and barrel and end up with a complete outfit for our brides.
Branden Collingsworth has officially joined CAN's team as a data scientist. Branden interned at Contemporary Analysis this summer as a data scientist. In December, he just graduated with an MBA and Law Degree from the University of Nebraska at Lincoln. This is on top of a bachelor's degree in economics from UNL.
Contemporary Analysis (CAN) is a global data science company based in Omaha, NE that provide predictive analytics to multiple Fortune 500 companies and small businesses in the United States, Europe and Asia. CAN is focused on making analytics accessible to companies of all sizes and industries, and offers standard products and professional services.
We invest a lot of time and energy communicating our research, because unless we can effectively communicate our findings they are useless. When the goal is to communicate the most valuable information with the least amount of ink that can be understood with the least amount of effort. For your reference, our major influences are Deirdre McCloskey on writing, Stephen Few on dashboard design, and Edward Tufte on data visualization.
The nature of forecasting the future makes presenting predictive analytics unique and challenging. There is no flashy server or dashboard that will make presenting analytics any easier. There is only a model that tells a story about the future of users' business, customers, non-customers and competitors. While models are very valuable they are not your typical business intelligence artifacts. To produce a meaningful return on investment you need to translate the details of the story into results that can be applied to a specific business question.
We love predictive analytics and if we are not careful, it is easy to start reducing everything into predictive models. I have even caught Tadd standing at the window collecting primary data on the smoking habits of the people in our building. To make sure that CAN and predictive analytics experience continued success, we have developed a guide for when to apply predictive analytics.
First, predictive analytics should not be applied if:
The cost of being wrong is low.
You should not apply predictive analytics if reducing uncertainty does not provide enough value. Predictive models should only be applied in situations with a high cost and/or probability of being wrong and where predictive analytics can provide information to reduce uncertainty. To determine if predictive analytics is worth applying to a decision you need to calculate the expected value of information. In the book How to Measure Anything, Hubbard provides the following formula, expected value of information is equal to the difference between the expected opportunity loss before and after information. The expected opportunity loss is equal to the chance of being wrong multiplied by the cost of being wrong.
The value of dashboards and visualizations are that they allow users to shift from serial to parallel processing. When reading a block of text you can only process the information serially by starting at the top left of the text and finishing the bottom right. Dashboards and data visualizations allow you to absorb information in parallel making it easier to absorb information quickly, identify relationships and trends.