Scaling Data Analytics with a Semantic Model

When Do You Need a Semantic Model in Data Analytics?

With Zing’s low barrier to entry, you can start analyzing data without a semantic model. But as your team, data, and requirements grow, you can introduce a semantic model to any data source in Zing to manage complexity and ensure consistency. Terms that were once universally understood within a small team—like “daily active users” or “high-spend customers”—can lose clarity as more people interact with data. Meanwhile, broader access to analytics means more users without expertise in SQL or data structures need to extract insights. Additionally, the demand for real-time, mobile-friendly data access has surged, making usability beyond the traditional office setting essential.

Scaling Without Losing Shared Definitions

Today, business users expect to query data without knowing the technical intricacies of how metrics are calculated. However, non-technical users might struggle with:

• Constructing calculations with multiple joins and filters.
• Understanding data hierarchies, such as customer segments and product categories.
• Applying business rules correctly across multiple datasets.

A semantic model abstracts these complexities, offering a business-friendly layer where users can interact with intuitive terms like “revenue growth” or “customer retention rate” without needing to build formulas from scratch. This leads to faster decision-making and fewer miscalculations.

Use Case Example: Retail Chain Standardizing Sales Metrics

A nationwide retail chain struggled with inconsistent definitions of key sales metrics across its regional branches. Each branch had its own way of calculating “high-value customers” and “repeat purchases,” leading to discrepancies in reporting and decision-making.

By implementing a semantic model, the company:

• Standardized the definition of “high-value customers” based on a consistent formula across all locations.
• Enabled store managers to access intuitive, pre-defined metrics without SQL expertise.
• Provided mobile-friendly dashboards for real-time insights on sales performance.

This led to improved decision-making, consistent reporting, and better customer targeting strategies across all branches.

Enabling Smarter AI, NLP, and Guided Search

A semantic model in the context of LLMs (Large Language Models) improves responses by enhancing the model’s ability to:

• Understand context deeply: It moves beyond simple keyword matching and interprets the intent behind a question.
• Grasp meaning and relationships: It identifies semantic relationships between words, concepts, and structured/unstructured data.
• Improve response relevance: It retrieves and generates more precise and meaningful answers.
• Handle complex queries: It can process multi-step reasoning and make better inferences.

How this leads to better responses:

• Better question understanding: More accurate and context-aware responses.
• Data comprehension: When analyzing or summarizing structured/unstructured data, a semantic model ensures that responses align with the actual meaning of the content.
• Reduced ambiguity: It resolves vague queries by interpreting the intent rather than just responding to keywords.

Zing Data Example with a Semantic Model

1. Ask your question in natural language
2. Select your data source with a semantic model
3. For admins only: Quickly switch between semantic model mode and expert mode to bypass the semantic model for testing or other use cases
4. Zing Chat UI: Get fast answers to your questions

The modeling layer functionality is available on the Pro Plan and Enterprise Plan. To learn more and see how easy it is to upload your own model please visit our blog post semantic modeling layer.

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