Augmented Analytics is an approach of data analytics that uses machine learning and natural language processing to automate analysis processes usually performed by specialists or data scientists. Augmented analytics is a next-generation data and analytics concept that employs machine learning to automate data preparation, insight revelation, and insight sharing for a large spectrum of business users, operational workers, and citizen data scientists. Augmented analytics empower specialist data scientists to concentrate on technical problems. Users consume less time investigating data and more time acting on the multiple applicable insights with less bias than manual approaches.
Augmented analytics uses Artificial Intelligence (AI) with state of the art machine learning techniques, including in part Automated Machine Learning. Thanks to these data analysis strategies, it becomes possible to identify patterns in complex data sets. Indeed, it empowers anybody to extract value, i.e., insights, from their data sets, regardless of the amount of data they have. Insights on business data mean business intelligence. Business users, who are not data scientists, are empowered to anticipate business issues. For example, they can predict the likelihood of a click on a link or the churn of a customer.