Creating a Digital Data Analyst

How we built a natural language interface to complex financial data, enabling analysts to query SSAS cubes without learning DAX or ordering PowerBI Reports.

Spillwave Team wins-losses business-process-insourcing, bpi, ai-web-integration, developer-productivity, information-retrieval

Wins and Losses: Building a Digital Data Analyst in 8 Weeks

At Spillwave Solutions, we recently undertook the ambitious task of creating an AI-powered Digital Data Analyst in just eight weeks for a financial services client. The goal was simple yet challenging: enable financial analysts to interact with complex financial data using natural language questions, bypassing the steep learning curve of the DAX query language or cost and timeline of ordering custom PowerBI reports.

Here’s a quick rundown of the wins and losses from our journey:

Wins

  1. Rapid Deployment and Immediate Value: Within the tight timeframe, we delivered a production-ready tool that allows analysts to query data directly, significantly reducing the need for custom reports and dramatically shortening data retrieval times from days to seconds.

  2. Robust Technical Solution: We built a sophisticated Retrieval-Augmented Generation (RAG) system utilizing Azure OpenAI, pgVector for semantic searches, and FastAPI. This enabled a conversational AI to translate natural language questions into accurate DAX queries effectively, achieving a 70-80% success rate.

  3. Democratizing Data Analysis: Our solution empowered business users without technical expertise to independently explore financial data, leading to deeper insights, faster decision-making, and reduced technical dependency.

  4. Innovative Prompt Engineering: The project succeeded largely due to advanced prompt engineering techniques, including dynamically selected few-shot examples, specialized DAX syntax rules, and sophisticated error-correction mechanisms.

Losses

  1. Underestimating DAX Complexity: Initially, our team underestimated the complexities associated with DAX compared to traditional SQL. Early prototypes struggled significantly, requiring a strategic pivot to a more deterministic workflow rather than a purely agentic approach.

  2. Data Context Challenges: Obtaining detailed business-specific metadata proved difficult. Clients were understandably reluctant to manually document hundreds of data fields. To mitigate this, we adopted AI-assisted metadata generation and prioritized critical fields for human refinement.

  3. Limited Training Data: Foundation language models lacked extensive training in DAX, presenting significant knowledge gaps. Our response was to heavily leverage query examples and context metadata to bridge this gap.

  4. Non-Linear Development Progress: Development did not follow a smooth, incremental improvement path. Instead, progress occurred in substantial leaps, which required resilience and persistence from the team during early, less visibly productive phases.

Key Learnings

Real Query Examples are Gold: Concrete examples of good queries provided the greatest improvement, surpassing abstract guidelines.

Domain-Specific Augmentation is Essential: Specialized tasks like financial analysis need significant knowledge augmentation beyond foundational AI models.

Persistence Pays Off: Initial slow phases often give way to rapid progress, emphasizing the importance of patience and continuous experimentation.

Overall, our experience showed that significant business value could be rapidly delivered despite technical challenges, provided the right strategies, tools, and tenacity are applied. We’re excited to build upon these wins and tackle the identified losses in our future iterations.