In the Cold War techno-thriller WarGames, a marine monitoring a nuclear missile silo deep under the Nevada desert sees a red warning light blink on his console. “Just flick it with your finger,” his colleague tells him. He does, and the bulb goes out. Problem solved.

But what will their supervisor, looking at a report later, make of this brief alert? Without “situational awareness” of the event – handled with a quick verbal exchange in the room – the manager would be left to infer whether America was at risk of a nuclear incident or just had a bad solder joint. The risk that he could misinterpret the data, out of context, is no small matter.

This lack of context around data translates easily from Hollywood to oilfields. As Big Data has grown bigger, industrial operations are compiling and analyzing enormous volumes of information, usually in real time and often remotely. Much of the human interaction that gives meaning to data fails to accompany those metrics as they are compiled and analyzed.

I recently asked my colleague Moray Laing, an oil and gas expert here at SAS, to help me connect the dots. He told me that, without context, the quest to attain “a single source of the truth” from data will remain unfulfilled. “That’s the missing element. Although activity is partially handled in the existing data stream, situation-based information tends to be handled by the human being – not just what am I doing, but what is the intent of what I’m trying to achieve?”

Laing, a former long-time Baker Hughes engineer, reminded me that the Big Data dynamic has grown from original three V’s – volume, velocity, variety – to seven, including veracity. The term is a nod to the impact that imprecise or uncertain data can have on accurate analysis of data, especially when it's flowing quickly and in large volumes. Contextual analytics represent a new frontier for companies attempting to capture this new element of big data and turn it into useful information. “Knowledge management is a key piece of the oil and gas industry,” Laing says. “We have tons of data both in stream and stored at the enterprise, but how do I unlock the knowledge? I think it comes back to that fourth V, veracity. To have the data enriched by context is one of the best ways to do that.”

Examples for improving field operations with contextual analytics abound. Here’s one: Drilling is a complex operation fraught with hazards where multiple objectives are constantly being balanced. During an influx event a remote operations team, or even an embedded control algorithm needs both situation and intent to be able to provide not just the right advice, but the appropriate advice. The situation is “the driller is trying to control an influx” enrichment comes with the intent “the driller is using a two circulation procedure”.

Laing tells me that consistent standards in communicating the context of a situation in the field are the one of the biggest gaps in oil and gas companies’ ability to capture the veracity of big data. Once context is captured , calculated, categorized and stored in a consistent fashion as part of the data stream coming off an oil well operation, it opens the door to a level of automation that can reduce risk and cut the cost of drilling on those wells. Contextual analytics takes us one step closer to an intelligent drilling rig that reduces cost as well as mitigating risk.

Contextual analytics could also tell that missile silo manager in Nevada: Call an electrician.