Appendix: The Five Types of Analytics for Risk Management
Descriptive Analytics – What has happened?
Descriptive analytics lets risk managers look at the past by analyzing historical data. It is
traditionally used in the day-to-day operations of the organization. Using simple math and
statistical tools, this type of analytics collects and organizes historical data -- which can
include sales numbers, revenue figures, inventory, equipment purchases, etc. -- to provide a
report or snapshot of the company's operations.
Predictive Analytics – What will happen?
As the name implies, predictive analytics uses data and trends to predict how likely it is that
an event or outcome will happen (based on the current situation). This enables company
executives to take a proactive, data-driven approach to business decision making. For risk
managers, this helps them identify which areas to focus their attention on to reduce certain
risks or occurrences.
Diagnostic Analytics – Why did it happen?
Diagnostic analytics is also known as root cause analysis. It utilizes drill down techniques, data
discovery, and data mining to help determine why a particular event or occurrence happened.
Insights derived from these tools are valuable for analyzing historical trends and developing
forecasts.
Prescriptive Analytics – What should we do next?
Prescriptive analytics uses data to determine the best course of action. With the ability to
comb through huge amounts of data, It makes recommendations for next steps in the
decision-making process.
Cognitive Analytics - the Human Intelligence Factor
Considered the newest -- and most advanced form of analytics -- Cognitive analytics
combines intelligent technologies like artificial intelligence, machine learning algorithms, and
deep learning models to draw inferences from existing data, relationships, and patterns to
make conclusions. By attempting to imitate human thinking, this type of analytics is expected
to make cognitive applications smarter and more effective over time.
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