Big data stands to transform today’s modern enterprises by helping companies run smoothly, monitor processes, and avoid major issues. Operations generate masses of data, whether taken from machinery, procurement, transport systems, or elsewhere around the business. Every piece of modern equipment is covered with sensors, ready to alert users when there is a problem needing to be addressed__just as a printer will notify the user when it’s time to put more ink in the machine.
All sensors are engineered to raise this type of alarm, but rarely are they used to predict failures or meaningfully enhance the operation. Big data technologies can change this by making it possible for professionals to go beyond warning signals to drawing useful conclusions. In this way, supply chain and operations management professionals can more proactively prevent problems and optimize the business.
Applying big data to predictive analytics enables companies to improve their operational systems. For example, by monitoring drivers’ braking and acceleration patterns and fuel usage, trucking companies make key adjustments to create more efficient and eco-friendly driving. This not only saves resources, but also minimizes driver fatigue and risky behavior. Mining historical data also can help users predict trends in real time, monitor external and internal conditions in order to enhance capacity, and develop more efficient operations for reduced costs and greater revenue.
Similarly, airlines have been able to predict future needs by monitoring historical data and using sensors to both raise alarms and collect data for predictive analysis. For example, if a backup generator fails during a flight from Seattle, its replacement will be waiting at the hangar by the time the airplane lands in New York. Airlines are on the leading edge in this regard.
In addition, the collection, aggregation, and monitoring of data can help predict inventory needs and enterprise operations. Research indicates the top business driver for big data is the goal of increasing the accuracy and depth of predictive analytics. Going back to the truck example, one could envision cases where a manufacturer would use aggregated data from thousands of fleets worldwide to identify typical patterns, provide best practices, and provide likely-failure condition warnings to clients.
Big data, big results
Technologies have changed, and even small companies can follow the lead of the airlines__mining historical data to predict trends in real time, monitoring external conditions to make sure they are able to deliver the correct capacity, and driving more efficient operations. Real-time insights are invaluable. Big data enables businesses to maximize revenue given a fixed capacity based on the best allocation of resources. It’s truly a matter of proactively monitoring and analyzing the data already being collected.
Many businesses are unknowingly wasting the data they create and store every day. The smarter strategy is to use it to greatly improve processes and operations. Consider exploring the available tools and technologies that can help integrate, manipulate, manage, and analyze big data at a reasonable cost. Big data isn’t easy, but it brings many benefits__some immediately tangible and some that will significantly advance the business in the long run.
Yves de Montcheuil is the vice president of marketing at Talend, a provider of open-source integration services. He is also a presenter, author, and blogger. De Montcheuil may be contacted at firstname.lastname@example.org.