Envisioning Precise Risk Models through Big Data Analytics

Successful Service Delivery largely depends on how end-to-end Risk Management practices are embedded. Most risks assessed today are largely driven by one’s perception of how an event can affect the status quo. The deciphering risk resulting from these soft parameters are practically immeasurable and intangible. The involvement of “perception” in such a critical process makes it difficult to understand the holistic view of the business or the services leading to being unprepared at those crucial unknown moments that can take a well-run service delivery into catastrophic consequences.

In this Knowledge Sharing article, Pranali Humne discusses ways to identify and capture risk by leveraging Big Data analytics to evaluate potential threats. A major premise of the article is that the ability to predict risk is enhanced by employing the right implementation practices that enable correct use of accurate statistical models and technologies.

Using Big Data Analytics, risks can be analyzed as a combination of historical data and various environmental/cultural parameters. Multiple Risk assessment techniques with diversified data are used to arrive at an accurate risk assessment.

This article discusses the following techniques for mitigating risk:

• Structured “What-if” Technique

• Markov Analysis

• Monte Carlo Simulation

• Evaluation of Alternatives (EoA)

Sample simulations are illustrated against each of these, extrapolated into occurrences from different scenarios with their predictability. The identified risks—classified as quantitative or qualitative risks—are then prioritized understanding the nature, magnitude, and impact of the risks. The identified risks can also be prioritized based on organizational risk appetite. A mathematical model is developed based on the probability of occurrence, impact, and exposure of risks.

Pranali posits that exploring and presenting the collective intelligence gathered to successfully mitigate risk will enable a Service Delivery Organization to earn trust, instill greater business sense, and make the business, its customers, and its employees, successful by efficient practices of prevention fueled by continuous learning.

Read the article.