What is augmented analytics?

Augmented analytics is an industry term for a shift in how analytics gets done: instead of a specialist manually preparing data, writing queries and building every chart, AI takes on the heavy lifting. In practice that means automating data preparation, surfacing patterns and outliers automatically, and — most visibly — letting people ask questions in plain language rather than learning SQL or a BI tool. The goal is to widen who can actually get answers from data, beyond the analysts and engineers.

iDBQuery is a concrete embodiment of the idea:

• Natural-language questions. Anyone can ask in plain English and get an answer, no SQL required.
• Automated query writing. iDBQuery writes and runs the SQL for you against one live model of your sources.
• Autonomous investigation. An Analyst agent can pursue a question on its own — forming hypotheses, querying, and reporting back — which is augmented analytics taken a step further.
• Automatic understanding of messy schemas. A semantic layer interprets cryptic tables and columns so you don't have to.

Where iDBQuery pushes past the typical augmented-analytics tool is trust: every figure it produces is a cited answer, traced to the exact source rows, so the automation doesn't come at the cost of verifiability. Augmented analytics broadens access to insight; iDBQuery broadens it while keeping every result auditable — and lets you deploy the whole thing in your own cloud, on-premise, or fully air-gapped.