Glossary

Find here a list of key terms used throughout the Wisdom AI product and documentation. Use it as a quick reference to ensure a shared vocabulary across Admins, Data Admins and Explorers.

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A

Administrator / Admin: Highest-privilege user role with full access to connect data sources, configure domains, manage security, and govern the rollout of Wisdom AI. Also responsible for foundational setup and governance, including user, domain, data source, data model, and knowledge management, as well as accessing dashboards and reports, monitoring, and auditing. Agentic Analytics: A multi-step analytic workflow executed by an internal planning agent that decomposes complex questions into sub-queries and composes the final answer automatically. Analysis Plan: The intermediate set of steps (SQL, transformations, visualisations) generated by Wisdom AI when executing Agentic Analytics. Answer Accuracy Score: A system metric that tracks the percentage of answers receiving positive feedback (👍) versus total answers served, used to judge readiness for roll-out. Audit Logs: Records of events within the WisdomAI system, used to track usage and monitor platform health.

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B

Build Stories (Dashboards): The capability to create and maintain Stories (Wisdom AI’s term for dashboards), including dashboards and domain-specific assets.

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C

Chat Analytics: Conversational, natural-language interaction mode where users ask questions and receive answers in real time. Column-Level Security: A granular access control mechanism that restricts user access to specific columns within a data table. Connector: Reusable integration module that handles authentication and data extraction for a specific database, warehouse, or SaaS application. Continuous Learning: Wisdom AI’s process of improving answer quality over time based on feedback, reviewed queries, and added context. Context Enhancement: The act of supplying extra business logic, synonyms, or documentation so the NLQ engine produces domain-specific answers.

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D

Data Administrator: A role with privileges focused on domain and data-model maintenance but without full system-wide admin rights. They can connect domain-level data, model business context, manage content, apply data policies, and collaborate with Explorers. Data Dictionary: Human-readable catalogue of domain tables, columns, metrics, and their business definitions. Data Model Augmentation: Enhancing the semantic model with derived fields, entities, or metrics to create richer answers. Data Source: Any origin from which Wisdom AI ingests data (database, warehouse, CSV, SaaS API, etc.). Data Warehouse: Centralised analytical database (e.g., Snowflake, BigQuery) commonly used as a primary data source. Derived Column: A calculated column added to a table or view within a domain, not present in the raw source. Derived Table: A table produced from transformations or joins on raw tables, materialised or virtual, and added to the semantic model. Domain: A dedicated, configurable space within the platform designed to represent a specific business area or dataset. It functions as the core container for your data sources, business knowledge (including context, metrics, and entities), and settings, enabling you to connect data, apply business logic, and fine-tune the AI for accurate and relevant data analysis responses. Domain Management: The capability to configure and maintain organizational domains within WisdomAI. Domain-Level Permissions: Fine-grained access rules that determine who can query or edit each domain.

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E

Embedded Analytics: Mechanism for embedding Wisdom AI Stories or charts into external applications via iframe or public link. Entity: A business object represented in the semantic model (e.g., Customer, Order); usually maps to a primary-key table. Explorer: End-user role focused on asking questions, creating Stories, and sharing insights; cannot access admin configuration. They can ask questions, explore stories, use playbooks, share insights, save views, and utilize knowledge management, including access to dashboards and reports.

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F

Feedback Loop: Cycle in which user feedback (👍/👎, edits, reviewed queries) trains Wisdom AI to improve future answers. Fork (Chats): Create an independent copy of a chat or Story so it can be modified without affecting the original.

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G

Go/No-Go Demo: Validation checkpoint where Admins present curated dashboards to stakeholders to decide if Wisdom AI is ready for wider release. Governance: Set of policies and controls that maintain data integrity, security, and compliance within Wisdom AI.

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L

Limits & Quotas: System-enforced maximums on rows processed, file sizes, API calls, or concurrent queries.

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M

Metric: Quantitative measure (e.g., total revenue, active users) defined in the semantic model and calculated by Wisdom AI. Monitoring Dashboard: Admin-only Story that surfaces operational KPIs such as sync latency and answer accuracy.

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N

Natural-Language Query (NLQ): An end-user question expressed in everyday language, parsed and converted to SQL by Wisdom AI.

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P

Phased Roll-out: Controlled deployment approach that starts with a pilot domain and gradually expands to more data and users. Playbook: Reusable, guided workflow that automates multi-step analyses or recurring business questions. Prompt Template: Preset wording used to influence the NLQ engine’s tone, terminology, or calculation logic. Python-Powered Analysis (beta): Feature (in limited release) that lets users run Python code inside a Story for advanced transformations.

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Q

Query Pattern: Reusable example or phrasing that teaches users how to ask effective questions (e.g., “Show metric by dimension for last n days”).

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R

Reports: Formal presentations of data and analysis, accessible by various roles. Reviewed Query: An NLQ that has been checked by an Admin, corrected if necessary, and saved as a trusted reference. Role-Based Access Control (RBAC): A security model where permissions are organized around roles, and users are assigned to roles, ensuring appropriate access based on responsibilities. Row-Level Access Control (RLAC): Rules that restrict data visibility at the row level based on user attributes (e.g., region).

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S

Scheduled Story: Story or chart automatically sent at a defined cadence via email or Slack. Schemas: The structure of tables and relationships within a database or data source. Semantic Model: Layer that abstracts raw tables into business-friendly entities, metrics, relationships, and dimensions. Sharing Model: Framework that governs who can view, edit, fork, or subscribe to content within Wisdom AI. Stories: Wisdom AI’s term for a dashboard: a collection of answer cards and visualisations saved with a title and description. Subscription: User opt-in to receive scheduled Story deliveries or notifications. Sync Latency: Elapsed time between the source data change and its availability in Wisdom AI after the latest sync.

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T

Tenant / Environment: Isolated instance of Wisdom AI (with its own domains, users, and settings) provisioned for a single customer or business unit. Thumbs-Up / Thumbs-Down: Simple feedback buttons that signal whether an answer was helpful and drive the Continuous Learning process.

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U

Usage Metrics: Data points that measure how the WisdomAI platform is being used, contributing to monitoring system health.

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W

Webhook: HTTP callback that triggers an external system when certain events occur in Wisdom AI (e.g., Story published, data sync complete).