Augmented analytics refers to using AI and machine learning to automate parts of the analytics workflow, including data preparation, model selection, insight discovery, and narrative explanation. Instead of analysts manually preparing datasets and exploring dashboards, augmented analytics handles much of the heavy lifting.

Augmented analytics includes:

• Automated data cleansing

• Smart data profiling

• Outlier and anomaly detection

• Automated insights and pattern discovery

• Natural language query (“Show sales by region”)

• Natural language generation (NLG) summaries

• Auto-suggested transformations

• AI-driven root cause analysis

Tools like Power BI, Tableau Pulse, Qlik AutoML, ThoughtSpot, and Upsolve AI embed these features directly into BI interfaces. This makes analytics more accessible to business users who may not know SQL or statistical methods.

Technically, augmented analytics relies on:

• Statistical models

• Machine learning algorithms

• Semantic analysis

• LLM-powered interfaces

• Pattern recognition

• Seasonality detection

A major advantage is speed: insights appear instantly without requiring dashboard building. For example, an augmented analytics engine might tell you:

“Revenue dropped 14% last week due to lower conversions in the US West region, driven by checkout failures on mobile Safari.”

This type of insight could take hours for an analyst to discover manually.

However, augmented analytics does not replace analysts. Instead, it enhances their productivity by automating routine tasks so they can focus on strategic analysis and decision-making.

The future of BI is deeply tied to augmented analytics, shifting analytics from a reactive dashboard-driven approach to a proactive, insight-delivering system.