ThoughtSpot

This post introduces a conceptual model for understanding how data flows within an organizational analytics system. From raw source tables to contextual modeling and prompt-driven generation, the framework outlines each entity’s role in transforming data into actionable insights. Designed with scalability and clarity in mind, this structure supports intuitive exploration and visualization across platforms like ThoughtSpot.

Modeling Based on the Author's Perspective

Entity Name	Description
Primary Organization	The main organizational unit that governs data access, modeling, and visualization.
Source Database / Data Warehouse	The original data repository from which structured tables are extracted.
Source Table	A table within the source database containing raw data for analysis.
Source Table Column	Individual fields within the source table, representing specific data attributes.
Table	A contextualized data structure used for querying and joining within ThoughtSpot.
View	A logical subset or transformation of a table, often used to simplify queries.
Model (Work Sheet)	A curated configuration of data tables and views used for structured analysis.
Data Set	A defined collection of data elements used for modeling and visualization.
Column	A data field within a contextual table or view, used in queries and visualizations.
Synonym	A user-friendly alias that maps natural language terms to technical data fields.
Token	A semantic unit generated from prompts, used to interpret user intent.
Prompt	A user-generated query or instruction that initiates data generation or visualization.
Generation	The process of producing tokens, charts, or insights based on prompts and context.
Live Board	An interactive dashboard that displays real-time charts and insights.
Chart	A visualization unit defined by a script or configuration, representing specific data logic within the Live Board.

By mapping the full lifecycle of data—from ingestion to visualization—this model provides a clear foundation for building robust analytics systems. It highlights how semantic context and user prompts drive meaningful generation, enabling organizations to scale insight delivery.