Semantic Entities

The Semantic Fusion™ Model is built around two primary types of semantic entities:

Concepts: Core business objects or entities (e.g., Customer, Product, Order)
Metrics: Quantifiable business measurements or KPIs (e.g., Revenue, Conversion Rate, Customer Lifetime Value)

Both entity types follow the same fundamental structure with base queries, attributes, dimensions, and relationships, but they serve different purposes in the semantic model and answer different types of business questions.

What is a Concept?

Concepts represent the fundamental building blocks of your data ecosystem. In the Semantic Fusion™ Model, a concept corresponds to a core business entity or domain object that users commonly analyze and query. As shown in the image, concepts in this sales platform include Opportunity, Account, Asset, Quote, License, Contract, etc.

Each concept has a rich contextual definition that explains its business meaning and characteristics. For example, the Opportunity concept (as seen in the image) is defined as "A potential sale or business transaction, created from an approved quote and associated with a specific Nimbus contract and asset. It includes details such as the unique identifier, associated account and asset, contract reference, classification (e.g., New, Expansion), sale type (e.g., Acquisition, Expansion), current stage in the sales process, potential value, expected or actual close date, and win status (where a closed opportunity is considered 'won' if successful or 'lost' if unsuccessful)."
Key characteristics of concepts include:

Multiple concepts can be defined on a single table, providing different perspectives, examples include: User, Account, Product
Concepts encapsulate the essential characteristics and behaviors of business objects
They maintain relationships with other concepts (as shown by the connecting lines in the image)
They contain SQL definitions that determine how data is retrieved from your data sources

When working with concepts, you're establishing the semantic foundation that enables natural language understanding within your data model. Properly defined concepts make it possible for users to ask questions using familiar business terminology rather than technical database language.

What is a Metric?

Metrics represent quantifiable business measurements that users analyze to evaluate performance, track progress, and make data-driven decisions. In the Semantic Fusion™ Model, a metric is a specialized entity that calculates and returns numerical values, typically aggregating or analyzing data across concepts.

Each metric has a detailed contextual definition that explains its business significance, calculation methodology, and appropriate use cases. For example, a "Win Rate" metric might be defined as "The percentage of closed opportunities that were won within a specified time period. This metric reflects sales effectiveness and is calculated by dividing the number of won opportunities by the total number of closed opportunities (both won and lost). It excludes open opportunities and is typically analyzed by time period, sales team, product line, or territory to identify performance patterns." Key characteristics of metrics include:

They quantify business performance or activity
They often involve calculations across multiple records (SUM, COUNT, AVG, etc.)
Examples include Revenue, Conversion Rate, Customer Lifetime Value, and Churn Rate
Metrics can be analyzed across various dimensions (time, geography, product category)
They may have targets, benchmarks, or thresholds for evaluation
They contain SQL definitions that implement the precise calculation logic

Metrics make it possible for users to ask questions about business performance and trends using natural language. Well-defined metrics ensure consistent calculation methodologies across the organization, eliminating the "multiple versions of the truth" problem that plagues many analytics environments.
The structure of a metric mirrors that of a concept, with:

A base query that defines the data scope and fundamental calculation
Attributes that provide additional context or component calculations
Dimensions that allow the metric to be sliced and analyzed from different perspectives
Relations that connect the metric to relevant concepts or other metrics

This consistent structure makes it easy for data experts to maintain and extend both concepts and metrics within the Semantic Fusion™ Model.

Difference Between Concepts and Metrics

While concepts and metrics share the same structural components, they serve distinct purposes in the semantic model:
Concepts:

Represent business entities or objects (things)
Focus on characteristics, properties, and relationships
Answer "what" and "who" questions
Typically have one record per business entity instance

Metrics:

Represent business measurements and KPIs (values)
Focus on quantification, aggregation, and trends
Answer "how much," "how many," and "how well" questions
Typically aggregate data across multiple records

Understanding this fundamental difference helps when designing your semantic model and deciding whether a particular business entity should be implemented as a concept or a metric.