Currently, there are a few trends and topics in tech without which the talk around technology and innovation is incomplete — analytics, artificial intelligence, blockchain to name a few. Augmented analytics is an extension of analytics that focuses on three main areas — Machine Learning, Natural language generation (NLP) and, Insight automation. The basic premise of augmented analytics is the elimination of painstaking tasks in the process of data analysis and, replacing them by automation thus, refocusing human attention on modern analytics, business process, and business value generation.

As per predictions made by Gartner, over 40% of tasks involved in data science will be automated thus, increasing productivity, quickening the process, and initiating broader usage of data and analytics.

Augmented analytics is touted as the subsequent wave of disruption in the field of data and analytics. The field will experience further adoption by data analysts and data scientists as more use-cases and value-generation is seen. Furthermore, Gartner suggests that automated analytics will be a “bridge between mainstream analytics done by business users and the advanced analytics techniques of data science.”

Through the use of statistical models and linguistic techniques to improve data management and performance, the core focus of augmented analytics is providing assistance in painstaking and time-consuming tasks in the stages of data analysis, data sharing, and business intelligence. This technology is not there to replace humans but supports them through enhanced interpretation capabilities. Additionally, it will enable companies to revolutionize the way business intelligence is produced and consumed.

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