We are entering into a new era where systems are enabled by live, real-time data such as GPS, sensors, mobile, social media, and a connected web of streaming data. Enterprise system designers and operational decision makers are beginning to understand the potential of this continuous intelligence, and think about how to use it in operational decision-making.

Operational decisions"”in manufacturing, logistics, procurement, security, risk/disaster management, or other operational domains"”are made on a variety of different time frames. Discussions about operational decision-making processes and systems often focus on planning (strategic and tactical) decisions driving through to execution. These are critical to success. But no matter how good you are at planning and executing, "stuff happens."

This is where real-time decision-making comes in"”the moment-by-moment judgment calls made by dispatchers, plant workers, first responders, warfighters, law enforcement officers, logisticians, and their leaders in the "heat of battle," often out in the field or on the plant floor.

Making the right moment-by-moment decisions requires good instincts and intuition, but just as critically, it requires continuous situational awareness"”a clear, accurate, current and full understanding of what is going on and what is most important so you can figure out the best course of action. Traditional enterprise systems are not designed to provide continuous real-time situational awareness. Operational decision-making and the enterprise systems that support it usually suffer from the equivalent of the "fog of war""”having an incomplete, imprecise, or out-of-date picture of what is actually happening on the ground. Furthermore, these systems lack the ability to escalate those data points that are truly important for real-time decisions. Sometimes the critical pieces of information are missing, or too late. but at other times they are simply lost in a sea of information overload.

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Keywords: real-time data, information dashboards, data mining, Big Data, data analytics, supply chain solutions, supply chain IT, supply chain management