About the Author
Jon Shepherd brings valuable knowledge and experience in Machine Learning/AI, RDBMS, enterprise application software, cloud computing, and much more to Vectice. Jon is a product strategist with a proven track record of bringing data products to market. He is a skilled coordinator between sales, product management, development, finance, marketing, and other executive functions. Previously, he has worked at Explorium, Anaconda, Julia Computing and Netezza in various sales-related roles.
Jon has 30 years of professional experience and witnessed the explosive growth of data science first-hand. He decided to write a paper for Vectice which chronicles the major turning points in the history of data science.
Introduction
On October 2, 2006, Netflix launched a million-dollar competition that would change the world of data science. The competition, dubbed “The Netflix Prize”, challenged anyone to improve their Cinematch movie recommendation system. Just 6 days after the announcement, a team called “WXYZConsulting” had already beaten Cinematch by a small margin.
However, to be awarded the grand prize of $1 million, an improvement on the margin of error of more than 10% was needed. While gradual improvements were made, this benchmark was not reached until September 18, 2009, when team “BellKor’s Pragmatic Chaos” achieved an improvement of 10.06%, and was awarded the prize money in a ceremony.
“The Netflix Prize” sparked public interest in the data science field and how this academic tool could be used to solve real-world business problems.
For many participants, the Netflix competition was their first exposure to big data and predictive analytics. While the internet was already growing strong and companies were collecting large amounts of data, the tools to analyze data and make predictions were still in their infancy. Let’s look at some historical trends that enabled the emergence of machine learning as an approach to help organizations make predictions and decisions based on insights from data.
Read the full white paper here ➡️ How Data Science Entered The Corporate World
