A Enterprise Risk Management definition
A definition of Enterprise Risk Management that I like is this one from Wikipedia, which refers to it as a framework for “identifying events or circumstances relevant to the organization’s objectives, assessing them in terms of likelihood and magnitude of impact, determining a response strategy, and monitoring progress.”
COSO (a committee of private sector organizations that provide thought leadership on various aspects of corporate governance), elaborates on this definition to include members of the organization, such as the board and management, who are responsible for applying this process throughout the organization.
From the definition of Enterprise Risk Management to the concrete process
In practice, Enterprise Risk Management is characterized by several important elements:
- Executives and directors are involved in this process
- All corporate departments and the ecosystem, including the supply chain, can expose the organization to potential risks and threats
- Any activity (financial, legal, operational, marketing etc.) can produce risks for the enterprise
It follows that quickly identifying all the phenomena is a crucial element to help companies mitigate (or completely avoid) risks. Managing risks allows companies to achieve their goals and objectives and, more importantly, to add value.
One area where ERM can be applied is in CEO / Executive support, where intelligence tools can be used to identify important information about prospective new talent, potential partners, third party suppliers or anyone that the company interacts with, and use it to assess the risks of meeting with them or doing business with them and ensure a successful (and risk free) outcome.
How semantic technology can support Enterprise Risk Management
Given the definition of Enterprise Risk Management, let’s look at how semantic technology can contribute to this process.
Thanks to its ability to read and understand written language, semantic technology can support enterprise risk analysts in acquiring a wide variety of data from internal and external sources (print media, internal databases, company data, web sources and social media, etc.) to track and identify issues of relevance and critical events. Based on custom targets, semantic technology acquires, normalizes and indexes the data, explores and analyzes the information by extracting entities, and transform them into actionable results.
This is not a theoretical approach: semantic technology is ready and available and it can be easily integrated in existing systems. Even better, several of the world’s largest companies are already using it to prevent risks.
Want to learn more?
