What Is Augmented Analytics?

Augmented analytics uses AI, machine learning, and natural language processes to analyze and interpret large datasets. Learn more.

By Linda Pophal

2M Read

How do augmented analytics work?

Augmented analytics uses machine learning and natural-language generation to help make sense of massive amounts of data, minimizing the need for trained data scientists. In essence, augmented analytics democratizes data, offering untrained users the ability to make sense of the numbers—and to make decisions based on the insights they gain.

What’s the difference between predictive analytics and augmented analytics?

Predictive analytics, as the name suggests, analyzes data to predict future outcomes. Augmented analytics, on the other hand, analyzes data for current-day insights.

What are the advantages and disadvantages of augmented analytics?

The big advantage of augmented analytics is that it makes possible what was previously impossible. Humans simply don’t have the same capacity as modern technology to analyze large amounts of data and draw relevant conclusions.

However, one disadvantage is that the insights gleaned depend on the data provided. If the data is low quality or lacks integrity, the assumptions will be flawed.

Used effectively, though, augmented analytics can reap the benefits of human and AI cognitive collaboration to produce insightful recommendations.

What are some examples of how augmented analytics is used?

Augmented analytics can be used in a variety of ways to help HR leaders and managers make informed people-related decisions like who to hire, who to promote, who needs training in specific competencies, and more.

Augmented analytics can also help companies be more proactive in their people practices. Merck KGaA, for instance, used advanced analytics to help it identify employees who might be flight risks. They also used augmented analytics to predict future talent needs.