How to Improve Risk Management with Big Data Analytics
Our understanding of risk has come a long way. It’s no longer a four-letter word synonymous with disaster. A Guide to the Project Management Body of Knowledge (PMBOK® Guide) and related resources provide frameworks and tools to define and manage risk. We have risk registers, risk workshops and other options. Yet many projects are still derailed by risks that result in delays, loss and public criticism.
Big Data—a term that took off in the 2000s—is part of the answer. At first, Big Data focused on marketing and customer behavior: Amazon’s purchase recommendation engine is a classic example.
“Big Data analytics first became popular in marketing and customer service. Over the past few years, it has been applied to risk management. In the aftermath of the financial crisis of 2008, regulatory agencies requested reports that required global financial institutions to develop analytics capabilities fast,” Anshu Prasad, partner in the analytics practice at consulting firm A.T. Kearney.
Regulation is on the rise in many industries, and keeping up to date with these requirements from a risk standpoint is important to maintain your business awareness.
What’s in Your Big Data Analytics Toolbox?
Finding the right tool for the job matters when you get started with Big Data analytics. However, there may be no need to pull out your
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