May 29, 2018

Using SAM’s Knowledge Engine to Detect Business Continuity Crises

Almar Sheikh by Almar Sheikh

Behind the scenes, organizations are constantly trying to account for, and adjust to, a number of variables that have the potential to lead to any length of business downtime. Some variables, like the weather, are known and others, like fires, shootings, or terror attacks, are much more unknown. Having an alerting system for both plays a key role any business continuity team. According to a study done on the impact of disasters on businesses, downtime can cost up to $17,244 per minute and this doesn’t take into account any larger ramifications (PR, brand damage, ripple effects,). A big challenge for any business continuity team is being able to monitor both known unknowns and unknown unknowns fast enough to mitigate damage and course correct. Every business has their unique risks, but the BCI puts adverse weather, fire, acts of terror, supply chain disruptions, and transport delays as major business continuity issues. Some of these known issues are easier to track and get ahead of with traditional tools, others are near impossible to predict. On top of the challenge of monitoring for unpredictable disruption events, delayed information can compound the impact.

At SAM, we believe social media acts as a sensory network of micro updates and signals, able to detect potential business disruptions before any other source. Our Knowledge Engine uses artificial intelligence to leverage these real-time signals, giving organizations the advantage of speed and situational awareness. SAM’s AI works much like your own analyst, aiming to provide a complete picture of unfolding events. With relevant social updates, location, media headlines, and on-the-ground visuals, users are able to quickly assess if an event is interfering with their business operations and how they can act to reduce the impact.

The impact of some events are more obvious than others, like a recent gas leak that lead to the evacuation of a McDonald’s in London. SAM alerted to this event at 9:43am GMT, as bystanders started to notice a flurry of activity. Social media showed our users the scale of the impact as nearby markets and stores began evacuation procedures an hour before local media reports.

Similarly, SAM detected a bomb scare at a Westfield mall in Los Angeles. Initially, the impact only affected Bloomingdale’s operations, but once the bomb squad arrived, panic escalated, halting all businesses operations within the mall.  

In other instances, the cause of business impact may not always be so obvious. SAM detected a car explosion in Aliso Viejo, California, at 21:29pm GMT, social reported that the disruption had lead to a shutdown of the connecting highway. This event quickly shifted from a seemingly inconsequential traffic issue to one that had impact on the operations of the Target and other businesses near by.

Regardless of your operations and/or unique risks, the speed at which teams are able to detect disruption events is key to limiting the costs of downtime. In the past, our users relied on human analysts to manually monitor and parse through media reports, Tweetdeck, or waited too long for official sources. SAM aims to be your AI Analyst, detecting disruption events at their earliest moments and quickly distilling data into a simple stream of key information.

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