Augmented analytics trends leverage data science and artificial intelligence

Augmented analytics is originally defined by global advisory firm Gartner. With the help of augmented analytics data preparation, data discovery, insight generation, and insight explanation are done, this helps everyone to explore, understand, analyze and act on data in an improved way. It also caters to the transformation, automation of all the data used by the users. Augmented analytics in simple terms is the technology used for using artificial intelligence and machine learning in order to transmute the construction and consumption of analytics. 

As soon as the banking sector realised that there was a gap in their insights while targeting older customers for management services, the banking sector started using augmented analytics to mechanize the analysis process of their high-volume data. Augmented analytics helps the agricultural industry to in reducing the time it took for building models. Using augmented analytics, it became easier for the agricultural sector to search for the top hybrid seed combinations to sell to farmers that come within a database of thousands. Augmented analytics also enables the industry to bring time-to-insight down from months to days. 

Top augmented analytics trends:

• With augmented analytics traditional dashboards become less relevant

• Augmented analytics caters to increased AI and machine learning services

• With augmented analytics Graph analytics will become more pervasive

• Augmented analytics capabilities simplify tasks such as data preparation and querying

• Augmented analytics accelerates IoT to play an increasingly central role