Elementor Page #236

Conversational Dashboard

AI | Data Viz | Tableau | AI & NLP

E-commerce leaders need instant answers, but waiting on data teams for ad-hoc reports creates bottlenecks. This two-part Business Intelligence module solves that by pairing a robust executive Tableau dashboard for daily monitoring with a generative AI chat interface that builds custom charts on the fly.

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Key features

Executive view

A strategic Tableau interface consolidating revenue, channel performance, and product KPIs.

Conversational BI

A Streamlit web app that translates natural language questions into custom data visualizations.

Secure Querying

AI generates Python code to query a controlled data structure, protecting raw database access and saving token costs.

The how and the results

To eliminate reporting bottlenecks and empower leadership with instant answers, a dual-layer analytics system was built using Python, Tableau, and Streamlit.

1. Taming the Data (Data Engineering) Handling over 285 million raw behavioral events requires serious processing power. Using Python, this massive dataset was cleaned, filtered, and transformed into a highly focused analytical table, isolating actual purchase intent and revenue drivers from the noise of general web traffic.

2. The Executive View (Tableau) The processed data feeds directly into a centralized Tableau dashboard. This gives leadership a stable, interactive environment to track critical KPIs—such as revenue trends, channel comparisons, and category breakdowns—at a glance, without needing to write a single database query.

3. Chatting with the Data (Streamlit & Generative AI) For unpredictable, ad-hoc questions that aren’t on the standard dashboard, users can simply ask a custom Streamlit web app in plain English (e.g., “Show me a bar chart of the top 10 products” ). The AI dynamically translates this business question into executable Python code on the fly to build the requested visualization.

4. Securing the System (Dictionary-Based Architecture) Giving an AI model unrestricted access to a company’s raw database is a massive security risk. Instead, this app queries a controlled, structured dictionary representation of the data. This architecture drastically lowers API token costs, secures the underlying raw data, and explicitly displays the generated Python code in the UI for total transparency.

The Results

This dual-layer approach provides the best of both worlds: structured reporting and on-demand conversational analytics:

  • Zero-Wait Reporting: Stakeholders can generate custom visual reports instantly, drastically reducing their dependency on the data team.

  • Unified Visibility: Channel performance and revenue drivers are monitored centrally via the Tableau executive view.

  • Cost & Security Efficiency: The dictionary-based query architecture ensures the underlying dataset is never fully exposed to the LLM, keeping computing costs low and enterprise security high.

Background

Multichannel e-commerce generates massive amounts of behavioral data across websites, apps, and social platforms. While executives need to understand this data to drive strategy, they shouldn’t need a data science degree to ask simple questions about top-selling brands or revenue trends.

Key Challenges:

  • Leadership struggles to consolidate performance indicators across different sales channels.

  • Data analysts waste hours building ad-hoc, one-off reports for basic business questions.

  • Giving an AI model unrestricted access to raw company databases presents significant security and computing cost risks.