Plain-English reference
Understand the terminology behind AI-assisted reporting without vendor jargon or exaggerated claims.
Software that uses artificial intelligence to turn a user's request into a data query and a report, chart, or dashboard.
An AI report builder typically reads database metadata, interprets a plain-English question, generates a query, and presents the result visually. It reduces—but does not eliminate—the need to validate data definitions and results.
A way to explore data by asking questions in everyday language instead of writing a query or configuring every chart manually.
The system maps business language to tables, columns, relationships, filters, and calculations before producing an answer.
The use of an AI model to translate a natural-language question into a SQL query.
Reliable text-to-SQL depends on database dialect, schema context, business definitions, relationships, and validation—not only the language model.
The structure of a database, including its tables, columns, data types, keys, and relationships.
Schema understanding is critical to AI analytics because it tells the system where information lives and how records can be joined safely.
A governed mapping between raw database structures and shared business concepts such as revenue, customer, or active subscription.
A semantic layer can improve consistency by defining metrics once. AI analytics tools may use explicit definitions, schema metadata, or both.
The processes and software used to transform organizational data into reports, dashboards, and decision-support information.
Traditional BI often relies on analysts to model data and create dashboards. AI-assisted BI can make portions of that workflow accessible to non-technical users.
The graphical presentation of data using charts, tables, maps, and other visual forms.
The right visualization depends on the question, data types, number of categories, and whether the goal is comparison, trend, composition, distribution, or relationship.
Analytics that business users can perform directly with limited dependence on a centralized data or BI team.
Good self-service analytics combines ease of use with governed access, clear metric definitions, and a way to inspect or validate results.