Best practices for proactive enterprise risk management: “Let the (big) data tell you”
The relationship between enterprise risk management and data culture is simple, linear and just a bit deceptive. The greater an organization’s reliance on effective data practices, the greater its chances are for increased productivity, efficiency and revenue generation.
Unfortunately, that same reliance translates into equally higher levels of risk.
Enterprise risk is oftentimes horizontal, encompassing myriad aspects of downtime, disaster recovery, business continuity, fraud detection, cybersecurity and regulatory compliance. Accounting for each of those factors across business units is a job in itself; neglecting to do so could nullify any data-driven value whatsoever.
Steve Bennett, director of global government practice at SAS, spent more than 10 years employed by the U.S. Department of Homeland Security. He worked as director of the National Biosurveillance Integration Center, assistant director of its Risk Analytics Division, and risk assessment program manager, Integrated Chemical, Biological, Radiological and Nuclear (CBRN). He specializes in proactive risk management by extracting insights from big data—which could potentially increase risk for many organizations—as a fundamental means of mitigating (if not eradicating) risk.
According to Bennett, a core component of that approach is detection and investigation for “any sort of data problem where you’re looking for a signal and then you’re trying to surface that signal for investigative action. That’s true in law enforcement, that’s true in cyber [security], it’s true in insider threats.”
It’s also true in various domains of enterprise risk. Proactive risk assessment is applicable to any form of risk organizations might encounter, from fraudulent transactions to network availability. Although law enforcement risk threatens lives whereas enterprise risk threatens funds, the same shrewd combination of big data management and machine learning techniques can empower organizations to proactively detect, investigate and minimize risk while maximizing big data’s ROI.