Better forecasting is here: Get to know IBM’s Hybrid Cloud strategy
Whether inclement weather or a wildly fluctuating economy, planning for the future is heavily dependent on understanding a complex interplay of factors. Every business craves a crystal ball, but to predict and shape future outcomes, enterprises need to successfully collect, organize and analyze their data—which is a challenge that most companies struggle to do or do well.
Modernize your data: Clearing hurdles to get to AI
DecisionBrain has had a long and unique relationship with IBM. As a former IBMer, I worked in the ILOG division for 15 years before leaving to start my own venture. DecisionBrain’s employees boast global expertise in IBM Data and AI solutions, with some having also come from IBM.
Our relationship with IBM has offered us a clearer path to success with its hybrid cloud strategy with IBM Cloud Pak for Data at its core—an information architecture for AI that is designed to automate the often complex and messy steps that are needed to get to AI successfully.
How we’re keeping London’s Cycle Hire scheme humming
Take our work with Serco, a company which operates a bike-sharing service throughout London. The organization wanted most efficient and cost-effective way to manage and maintain 12,000 shared bicycles across 800 stations throughout London. For this project, we needed to analyze the bicycles usage patterns, so we deployed a predictive and prescriptive solution to calculate the optimal number of bikes needed at each station at any given time. We also needed to plan efficient routes for maintenance teams to repair and redistribute bikes accordingly.
After training the solution on two years of historical data, the tool was ready to provide real-time predictions for where bikes will be needed, and to provide real-time recommendations on how to get them there, every day. The solution runs online and is available 24/7. The results were realized in a decrease in company costs and an overall more efficiently run bike sharing service.
Today’s predictive models forecast the number of bike pick-ups and drop-offs of each station throughout the day. The model considers circumstantial data, such as historical bike demand, historical weather data and current forecasts, to make a unique prediction for every day.
As an IBM Business Partner, one of the biggest advantages of Cloud Pak for Data is that it brings everything we need into one single place, seamlessly collecting, organizing and analyzing data regardless of its type or where it lives. This makes our operations much easier and helps reduce the cost of building our projects because we have predictive capabilities, decision optimization and machine learning technology—all in one centralized location, from which we can easily deploy to any cloud.
IBM’s IBM Cloud Pak for Data can help you scale the value of your clients’ data to help them become a more predictive enterprise by seamlessly collecting, organizing and analyzing data regardless of its type or where it lives.
Register for this webinar to learn more about IBM’s new hybrid cloud strategy and how you can deliver powerful predictive capabilities to your clients.
Go deeper—visit Seismic for client presentations, summaries and more.