Augmented Analytics: Concepts, Key Technologies, and Practical Applications

Last year Gartner listed Augmented Analytics among the top ten strategic trends, predicting it would disrupt current data analysis models and become the third wave of data and BI capabilities.

Augmented analytics is defined as a next‑generation data and analytics paradigm that uses machine learning to automate data preparation, insight discovery, and insight sharing for a broad range of users, including business analysts, operational workers, and citizen data scientists.

The data analysis workflow is abstracted into three stages—data preparation, insight discovery, and result sharing—and augmented analytics aims to make each stage more usable (no‑code) and automated.

Key technology areas are grouped into three categories: enhanced data preparation (visual interaction and algorithmic assistance), enhanced data analysis (natural language query, natural language generation, automated insights, and automated visualization), and enhanced machine learning (AutoML covering feature engineering, model selection & hyper‑parameter optimization, and neural architecture search).

Natural Language Query (NLQ) translates user questions into SQL (NL2SQL) using semantic parsing; modern solutions combine deep learning with rule‑based features and are evaluated on datasets such as WikiSQL and Spider.

Natural Language Generation (NLG) converts structured data into human‑readable text, evolving from template‑based systems to neural models like Turing‑NLG, and is used in BI tools for automated report narration.

AutoML automates the entire machine‑learning pipeline, addressing feature engineering, model selection, hyper‑parameter tuning, and neural architecture search, with commercial products from Google, Microsoft, Amazon, and Alibaba’s PAI AutoLearning.

Alibaba’s Data Robot exemplifies a practical deployment of augmented analytics, offering conversational analysis, dynamic alerts, intelligent insights, and automated reporting through NLQ, NLG, and automated insight technologies.

Gartner forecasts that by 2025 data storytelling will be the dominant analysis method, with 75% of stories generated by augmented analytics, underscoring the growing importance of these technologies for both business users and data scientists.