The more adept that companies become at utilizing their big data, the more value they can derive from the effort. This value initially comes from the easiest return on investments (ROIs) that organizations can obtain from big data. There are ways to save either money or time by using big data and analytics and applying those methods to corporate operational areas.
Examples of this range from avoiding expensive downtime for repairs on commuter trams through predictive (and preventative) maintenance, to monitoring thermostats throughout buildings in order to regulate temperatures and save on energy costs, to attaching sensors to shipping containers to ensure that perishable goods remain properly refrigerated.
When I recently visited with Deon Newman, vice president of IoT marketing at IBM Watson, he pointed to facilities management as an area where many companies are investing in analytics. "Forty five percent of worldwide energy is consumed by buildings," said Newman. "So when you have a large enterprise like Siemens that has 300,000 buildings around the globe, the company estimates that it can take out approximately $140 million per year in energy costs and 10,000 million tons per year of emissions. This enables it to significantly reduce its carbon footprint through the use of Internet of Things analytics."
Another company that is adopting IoT analytics is Kone, which produces elevators and people- moving equipment that move roughly one billion people each day. The Kone example is particularly interesting because it demonstrates how companies initially use analytics and IoT data to take out costs and save time, and then move big data and analytics further up the value chain by finding ways to further leverage the data and to generate revenue.