Overview
A major U.S. telecommunications provider partnered with Analytic Vizion to prove that a semantic layer could apply business logic to the sales metrics its reporting teams rely on. Through a focused proof of concept, Analytic Vizion built one minimum viable semantic model in an approved environment, enabled one agentic analytics capability, and delivered an hour of hands-on reporting-team training. As a result, the client gained a documented foundation for trusted sales reporting, governed access, and future self-service analytics.
The Challenge
A major U.S. telecommunications provider wanted to determine whether a semantic layer could support AI-enabled sales analytics. The team needed to apply agreed-upon business logic to priority sales metrics so reporting users could explore data, build dashboards, and ask meaningful questions.
However, the organization did not yet have the documented metrics, aligned calculations, and configured semantic-model foundation needed for a practical proof of concept.
This created several barriers:
- Priority sales metrics and business logic required alignment and documentation
- The initial POC environment lacked the required data objects and semantic-model setup
- Licensing and permissions delayed platform access and administrative configuration
- Reporting users had not yet tested how semantic-model-driven analytics could support their workflows
The client needed a focused proof of concept that could turn those open questions into a usable semantic-model foundation and support a future licensing decision.
Our Approach
Analytic Vizion guided the client through a proof of concept designed to test a semantic layer for AI-enabled sales analytics. The work combined stakeholder alignment, a focused delivery plan, and hands-on technical implementation.
People
Analytic Vizion worked with reporting, business-logic, platform-administration, and dashboarding stakeholders to define what the POC needed to prove. The team met with reporting leaders to understand the sales metrics their teams use and the questions they need to answer.
Throughout the POC, Analytic Vizion kept stakeholders aligned on priorities, blockers, and next steps. The team also delivered a one-hour working session for reporting users. During the session, users explored data, built visualizations, created dashboards, and tested agentic analytics against the MVP semantic model.
Process
First, Analytic Vizion confirmed the available POC environment, data, data objects, semantic-model visibility, access requirements, workstreams, and ownership structure. This assessment created a clear view of the dependencies required to move forward.
Next, the team established a focused MVP framework. An MVP, or minimum viable product, is the smallest practical version of a solution needed to test a real business use case. Analytic Vizion used this framework to prioritize the sales metrics and reporting functionality that mattered most.
The team then documented priority sales metrics, dashboard calculations, business logic, semantic-model mappings, business context, permission sets, permission-set groups, and administrative dependencies. As a result, the client gained a clearer foundation for access governance and future self-service reporting.
Technology
After the client selected an approved POC environment, Analytic Vizion rebuilt the MVP semantic model within it. The team added documented sales metrics and calculations from critical sales dashboards, creating a business-friendly structure for data exploration.
Analytic Vizion also enabled the out-of-the-box Analytics and Visualization Agent to work with the MVP semantic model. This allowed reporting users to ask questions of sales data, explore insights, develop visualizations, and create dashboards using agreed-upon business logic.
The team began dashboard and visualization development against the semantic model. As a result, the client could assess how a semantic layer could support more consistent sales reporting, agentic analytics, and future self-service capabilities.
Tools & Technology Stack
- Tableau Next
- Salesforce / Data360
- Semantic Data Model (SDM)
- Data Model Objects (DMOs)
- CBTrain environment
The Results
The proof of concept demonstrated that the client could build a semantic layer that applies business logic to priority sales metrics.
The team established one approved POC environment and created one MVP semantic model within it. This model included documented sales metrics and calculations based on existing critical sales dashboards.
The team also enabled and tested one agentic analytics capability against the MVP semantic model. As a result, reporting users could ask questions of the data and assess the experience using business-relevant sales metrics.
Analytic Vizion delivered one hands-on reporting-team enablement session. During the session, users explored data, developed visualizations, created dashboards, and tested agentic analytics.
In addition, the client received reusable documentation for semantic-model mapping, metric definitions, business context, permissions, permission-set groups, and agent setup considerations. Therefore, leadership had stronger evidence for future licensing and investment decisions.
The POC gave the client a clear foundation for next-step planning. It showed how a semantic layer can apply business logic to priority sales metrics, support agentic data exploration, and guide future self-service reporting and governance decisions.
Key Takeaways
AI needs business context
AI-enabled analytics can speed up data exploration. However, it needs clear metrics, calculations, and business rules to provide useful answers. A semantic layer gives AI tools and reporting users a shared understanding of sales data.
A focused MVP reduces risk
The client did not try to model every sales metric at once. Instead, it built a minimum viable semantic layer around the metrics that mattered most for the POC. As a result, the team tested the business case while identifying gaps early.
Governance enables self-service reporting
Self-service reporting requires more than accessible data. Teams also need clear permissions, business context, administrative processes, and consistent metric definitions. By documenting these dependencies early, the client created a stronger foundation for future production planning.
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