In 2017, Kaggle announced they had surpassed 1million active monthly users. Kaggle, for those outside the data science world, is a subsidiary of Google where Machine Learning Specialists and Data Scientists can hone their skills and contribute to open source data science projects. The platform hosts competitions and allows teams to get together and come up with the best data science solutions for real world problems. Recently the NFL hosted a competition and encouraged contrtibutors to build the best model for predicting how many yards a running-back would gain on a given play. These competitions have become a breeding ground for amateurs to build their skills, experts to share their knowledge, and allows people from all backgrounds and experiences to come together for a common goal. While developing an AI Transformation Strategy, companies should take an open-source approach and include a plan to make their data available throughout the organization and allow all team members to contribute.
Open-source competitions have a history of surprising contributions to science and technology. One of the most compelling examples comes from much earlier than the world of the internet. In 1714 the English Parliament offered a 10,000 Lira pound prize to anyone who designed a method to measure longitude. Some of the leading scientific minds focused their considerable talents on this problem, but the purse was eventually claimed by the self-taught clockmaker John Harrison. Harrison used increasingly accurate clocks that could help ships determine their East-West orientation while at sea. This was a huge advancement for sea-faring ships as they could use the stars to determine North-South orientation, but there was no reliable way to determine longitude-which informs East-West orientation- and therefore resulted in ships going off course.
What Kaggle competitions and John Harrison show us is that by opening up a network and encouraging all people to contribute, organizations have a much better chance of reaching meaningful solutions. The first step is putting up the proper security and privacy controls to mitigate the risks of sharing data. Once this is complete and the data is safe to release, companies must arm their workforce with new AI experimentation tools that allow those with non-technical expertise to participate. Several companies are developing new tools for those with no programming expertise so they can apply Machine Learning models to data and derive useful insights. These platforms cannot replace the experience and expertise of highly-educated data scientists, but they allow companies to unleash the creativity and problem-solving skills of their entire employee base and give them a better chance to become a sophisticated, data-driven company in the future.
Many companies have decided to pursue digital transformations and are migrating data to sources that allow for the application of AI. The decision to make this large investment of dollars and time is a step in the right direction and will set these companies up for success in the future. To go further, and gain a powerful advantage over the competition, management teams should embrace the open-source and arm their employees with the tools they need to test the waters of an unknown future.
