Augmented Analytics Explained

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Augmented Analytics, just like the application of Augmented Reality, is meant to assist and guide the users in analysing the data by giving more insights to data processing and providing automation from data ingestion to data analysis.

Using machine learning and natural language processing that humans could understand, Augmented Analytics aims to automate the process of collating data from various sources, analyse it and present it to the user in a manner that is understandable even from a perspective of a non-technical person.

With Augmented Analytics, there is already no need for data scientists to spend too much time on processing data from the start, as data collection is already automated. Also, non-technical people or business executives can have the ability to look into the data analysis and gain business intelligence without having a need to consult with data experts for interpretation.

This does not mean that data experts will lose their jobs, but in fact, Augmented Analytics will help them by providing more insights and strategising business intelligence as Augmented Analytics cuts off the time consuming manual data gathering and analysis.

For example, a company finds out that one of their products is doing well and performing better than their other products by a certain percentage. This insight requires more analysis such as asking if the product is better in quality, is it because of a certain period in a year and how it compares to other companies. Without Augmented Analytics, this insight will just remain an insight and will hold no value to the company.

Such data point needs more analysis in a less time-consuming and resource-intensive way, compared to hiring data scientists. Augmented Analytics provides automation in the data processing portion of the analysis and assists the business executives in finding more insights effectively and efficiently.

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