Predicting Business Success:Going beyond simple analysis to develop expert systems that advise future course of action, such as how to shift in response to a supply chain disruption.
By Matt Sarrel
Organizations at the forefront of big data analytics continuously re-evaluate and re-define the strategic decisions that have gotten the company where it is today. More than three-quarters of survey respondents indicated that they use analytics to guide future strategy. Many organizations manage strategic risk using big data analytics programs to provide better line of sight into the organization and its markets. This allows them to develop the ability and processes to anticipate and act ahead of events that might derail corporate progress.
Enterprises rely on analytics to monitor, detect and anticipate events in order to avoid unnecessary risk. They arm themselves with real time information in order to monitor supply levels to help minimize disruptions. Many advanced analytics programs have developed hundreds or thousands of automated tasks, such as moving inventory from one location to another when the warehouse is getting full.
Companies that are really on the cutting edge of big data analytics are developing predictive models to anticipate business needs based on dynamic variables like weather, investor sentiment, or political upheavals. Such modeling allows companies to adopt risk-based pricing models and to anticipate regulatory involvement, enabling them to bring a product to market before regulatory constraints can be applied.
Let’s take a look at a program like this in action. Chevron Corp., a global energy company, understands the link between risk and performance. Each drilling miss can cost the company upward of US$100 million. But the seismic surveys it uses to evaluate potential drilling sites – each up to 50 terabytes of data – take an enormous amount of time and computing power to analyze. Chevron’s geologists always knew they wanted to do more, but were restrained by one of the biggest challenges organizations face in using analytics: a lack of bandwidth to focus on analytics.
In the summer of 2010, the U.S. federal government temporarily suspended all deep water drilling permits in the Gulf of Mexico, regulation that essentially shut down all oil exploration in the region for nine months. Rather than sit idle, geologists at Chevron seized the opportunity. Using recent advances in computing power and data storage capabilities, as well as refinements to their already advanced computer models, geologists were able to improve the odds of drilling a successful well at certain of its deep-water prospects to nearly 1 in 3, up from odds of 1 in 5 or worse. The intensive review led the company to change the next year’s drilling schedule to explore several higher-probability wells first.
For more insight on how companies are using analytics to shape their futures, join IBM on November 7, 2012 at 1:00 p.m. ET. For a webinar: Analytics: The real-world use of big data / How innovative enterprises extract value from uncertain data.