When planning for a day of business, how do you calculate the numerous factors that may affect your bottom-line revenue? For Serco, a company which operates a bike-sharing service throughout London, the answer was in their data. In order to find the most efficient and cost-effective way to manage and maintain 12,000 shared bicycles across 800 stations throughout London, Serco teamed up with IBM Partner DecisionBrain to analyze their customer data and usage patterns. For this project, DecisionBrain used IBM Decision Optimization to calculate the optimal number of bikes needed at each station at any given time, and also to plan efficient routes for maintenance teams to repair and redistribute bikes accordingly. The results were seen in a decrease in company costs and an overall more efficient bike sharing service.

Today, machine learning (ML), artificial intelligence (AI) and decision optimization are not just buzzwords found all over the news. They are urgent requirements for many companies that fear disruption, want to perform pragmatic analysis and make better decisions with their data. Data has been called the next natural resource, like oil. But just as with oil, it must be refined to be valuable, and its end value must exceed the cost of refining it. With data, the value of AI is the cost of investing in collecting, organizing and analyzing all that data.