Essential Guide

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Editor's note

Augmented analytics tools are designed to simplify the BI and analytics process for business users and citizen data scientists. They've become featured elements in new software releases from BI vendors of all stripes looking to keep up with rivals in automating data discovery, preparation and analysis functions.

The emerging technologies include machine learning algorithms that can find relevant data and prep it for analysis, natural language processing and querying functionality, and software that suggests charts and other data visualizations. Augmented analytics "accelerates the time it takes to get accurate insights" from data sets, Gartner analyst Austin Kronz wrote in an October 2019 report.

Kronz said organizations should try out augmented analytics tools and monitor their continued development. This guide offers insights into the technology's potential uses and critical issues.

1Augmented analytics adoption, uses and issues

Gartner identified augmented analytics as a technology category in 2017. It's still an emerging part of the BI market, but augmented analytics tools are being deployed by early adopters. We report on how they're being used, their benefits and the hurdles that users face.

2Augmented analytics tools and related technologies

BI and analytics vendors have made augmented analytics functionality a centerpiece of their product updates. We explore the new capabilities that are being added to BI platforms as well as associated technologies for automating data science and data management processes.

3NLP's growing role in BI and analytics applications

Natural language processing (NLP) technology incorporated in augmented analytics tools enables end users who lack programming skills to run plain-text or voice queries of data sets. We examine how NLP expands self-service BI capabilities and current limitations in what it can do.