NexAura

Case Study: AI-Powered Semantic Model Documentation

An innovative solution that leverages AI to automatically generate intelligent, self-updating data dictionaries for Power BI semantic models.

Project Type

AI & Automation

Core Technologies

Power BI, TMDL, LLMs

Sector

Commercial / Govt.

Impact

75% Reduction in Documentation Time

The Business Challenge

In most organisations, Power BI model documentation is a major pain point. It's a manual, time-consuming process that is often neglected, leading to "black box" data models that are difficult to understand, maintain, and trust. This lack of clear documentation results in:

  • Wasted hours reverse-engineering complex DAX measures.
  • Inconsistent or incorrect use of data fields by report builders.
  • A significant barrier to onboarding new team members.
  • A lack of trust in the data, as the underlying business logic is unclear.

The Solution: An AI-Powered Documentation Engine

I developed a solution that automates the creation of a comprehensive data dictionary by integrating Power BI's metadata capabilities with the analytical power of Large Language Models (LLMs). This system extracts the model's structure, sends it to an AI for analysis, and then uses the generated insights to build a user-friendly data dictionary.

The Automated Workflow:

1. Metadata & TMDL Extraction

The process begins by programmatically extracting all model metadata—tables, columns, measures, and relationships—using Power BI's built-in `INFO.VIEW` functions and the Tabular Model Definition Language (TMDL).

2. AI Analysis & Description Generation

This structured metadata is then passed to an AI model (like GPT-4 or Claude) with a carefully crafted prompt. The AI analyses the DAX expressions, column names, and relationships to generate business-friendly descriptions, explain calculation logic, and provide usage recommendations.

3. Creation of an Interactive Data Dictionary

The AI-generated content is used to populate a detailed data dictionary, typically in an interactive format like a Power BI report or a formatted Excel file. This serves as a central reference for all model users.

4. Building an AI Q&A Interface

As a final step, the enriched data dictionary is used as a knowledge base for a Q&A bot. This allows users to ask natural language questions about the data model (e.g., "How is customer lifetime value calculated?") and receive instant, context-aware answers.

Key Outcomes & Business Impact

  • Massive Efficiency Gains: Reduced the time required to document a complex data model by over 75%, freeing up developers to focus on creating value.
  • Data Understanding: Empowered business users to understand and correctly use the semantic model without needing to read complex DAX code.
  • Improved Data Governance: Created a "living document" that can be automatically updated as the data model evolves, ensuring documentation is never out of date.
  • Accelerated Development: Report builders can now develop new reports and analytics up to 50% faster, as they can quickly discover and understand the available data fields and measures.

Have a challenge for us?

Let's discuss how our expertise can be applied to solve your organisation's unique data and AI challenges. Schedule a complimentary call today.

Book Your Free Discovery Call