Augmented analytics

Augmented analytics describes the use of AI and machine learning to automate steps in the analytics workflow that previously required a skilled analyst: cleaning and preparing data, automatically surfacing statistically notable patterns, generating plain-language explanations of a chart, or answering a question typed in natural language. It is less a single technique than a layer of automation applied across the existing analytics stack.

Typical capabilities include automated insight generation, which flags a metric change and estimates its likely drivers, natural language query and Text-to-SQL interfaces, auto-generated narratives, and anomaly flags surfaced without a person explicitly asking for them. Under the hood these features are built on machine learning models applied to the organization's own data and metric definitions.

Augmented analytics matters because it extends Self-service BI beyond people trained in statistics or SQL, and it can surface patterns a busy analyst might not think to look for. Its main risk is over-trust: an automatically generated explanation or "significant" insight can look authoritative while resting on a spurious correlation or a small sample, so its output should be treated as a starting hypothesis to verify, not a finished conclusion—particularly for anyone without the data literacy to judge whether the automated explanation actually holds up.