What is Data Science? Applying Analytics to Risk Management

What is Data Science?

Applying Data Analytics to Risk Management

Today, our customers face a diverse set of operational challenges. How do you know you're operating at maximum efficiency? Do you understand your risk profile? Are you minimizing exposure to accidents and disruptions? How can you be sure you're doing enough? The answer to those questions may lie within the data you already collect.

Historically, the methods developed for modeling risk and reliability issues had to be simplified; limited by computing power and the types and amount of data available for analysis. Today, traditional methods that use engineering models, actuarial science, and statistics are being enhanced with data science. But what exactly is data science?

Simply put, data science uses methods, processes and systems to extract knowledge and insights from structured and unstructured datasets.

Our customers are collecting more data than ever before, and we now have the ability to gather, integrate and analyze the data far more efficiently and effectively. Our new tools allow us to perform analytics that uncover meaningful insights. Artificial Intelligence tools, such as image recognition and natural language processing, give us access to more varied types of data. Machine learning delivers faster and better predictions. And by using data visualization tools, we can quickly and easily connect the dots.

These tools are helping us reveal insights far beyond the data's original intended purposes, leading to a shift in thinking, from "Do I have the right data to answer the question?", to "What questions can I answer with the data I collect?" Ultimately, leveraging these tools allows us to more efficiently and effectively assess the risk and reliability issues that are most important to operational success—giving us confidence in managing industry's toughest operational challenges.