Despite the importance of risk containment, most C-suites are ill equipped to address it. Today’s risk management operations are not enabled to assist company leadership in making optimal decisions around the total cost of risk.
Frequently, data is the disconnect culprit: its timeliness, accuracy, and the way it’s presented and interpreted. Though companies are now more data driven, few can keep up with the demands of big data, due to ineffective data models and siloing.
Leveraging New Technologies
Now, we’re at a tipping point in enterprise risk management where converging technologies—such as cloud computing, the Internet of Things (IoT), and predictive analytics—are changing the way companies identify and manage risk.
Big Change #1: Cloud-based Enterprise Risk Management
The cloud helps companies overcome this data fragmentation by standardizing and centralizing information so it can be shared and reviewed anytime, from anywhere, by all stakeholders.
Cloud-based systems enable risk managers to elevate data to the C-suite quickly and succinctly for executive action. Cloud-based data warehouses make it possible to benchmark risk performance against peer groups adding context to information. The key is having high-quality, real-time aggregated data, facilitated by cloud-based platforms.
The cloud also is elastic, enabling companies to scale storage capacity and processing power, allowing companies to mine and analyze large and variable datasets without investing in massive on-premise IT infrastructure or compromising security.
Big Change #2: Dynamic Data from IoT & Social Media
Risk managers have traditionally collected and analyzed historical data to predict future risk exposure. But, time lags from data collection to aggregation and analysis have hampered decision-making. With the proliferation of sensor-equipped devices, like infrastructure sensors monitoring property integrity, risk operations are now empowered to monitor in real time the status of property, equipment and events that affect risk. IoT provides risk operations unprecedented agility to anticipate and respond to perilous events.
Social media is also an important tool for monitoring potential reputational, location-based, and safety risks. Integrating social media alerts about global, adverse events into a risk management system provides risk managers with another data source for providing the C-Suite with real-time perspectives on potential risk exposures.
The challenge for the C-suite now becomes data overload. The key is analyzing all this unstructured data, filtering out the “noise,” and converting impactful data into actionable insight.
Big Change #3: Big Data & Predictive Analytics
Today, companies monitor multiple data sources in real time (dynamic data), enabling faster, more informed decisions. To effectively unlock and use big data, companies must have predictive analytics. Predictive analytics applications that support risk management efforts include:
Claims: Predictive model scoring can determine the probability of fraudulent claims so resources can be focused on high-risk transactions. Using predictive analytics, potential high severity or high-frequency claims can be identified early in the claims process, providing the opportunity to mitigate their financial impact.
Location-Based Risk: Identifying where incidents are most likely to occur can help companies more accurately focus resources and allocate costs. Monitoring social media and regional sensors can inform companies of safety issues or the potential for incidents in specific geographies.
Anticipating & Responding to Crises or Disasters: Weather and seismic sensors can inform companies of the probability of a natural disaster, granting time to move people and resources from damage zones. Real-time data from sensors and social media can maximize situational awareness during and after a disaster, and minimize continued risk. In 2012, researchers from the University of Tokyo examined how Twitter could be used for earthquake detection. They referred to Twitter users as “social sensors and regard each tweet as sensory information.” 
If a company has offices in an earthquake-prone area for example, sensors can immediately inform risk managers of a quake and prompt emergency procedures before the people on the ground can even react. Social media data might raise a red flag about rising civil unrest that would necessitate locking down facilities or evacuating personnel.
New Platforms Poised to Reinvent Risk Management
Managing risk is now a continuous process. Risk managers can keep C-suite executives abreast of changing risk conditions in real time for key decisions. Dynamic dashboards, collaborative tools, and on-demand reporting will replace static reports. Risk management platforms that bring it all together will close the capability gap. As a result, the C-suite will no longer be looking in the rear-view mirror with respect to their risk exposures, but will have real-time navigation technology enabling dynamic risk management.
 Takeshi Sakaki and Yutaka Matsuo (2012), “Earthquake Observation by Social Sensors,” Earthquake Research and Analysis—Statistical Studies, Observations and Planning, Dr. Sebastiano D’Amico (Ed.), ISBN: 978-953-51-0134-5