This project simulates a BI Engineer's workflow in a professional environment, following industry best practices from requirement gathering through implementation. By collecting stakeholder requirements and translating them into appropriate data visualizations, I built insightful and interactive Tableau dashboards that provide trend monitoring and actionable insights for key stakeholders.
The project consists of two interactive dashboards:
- Sales Dashboard: Empowers sales managers and executives to track Sales KPIs, monitor year-over-year performance, analyze sales and profit by subcategory, and identify trends. This enables data-driven decisions to optimize sales strategies and achieve revenue targets.
- Customer Dashboard: Equips marketing teams and executives with tools to understand customer segmentation, analyze behavioral trends, and track engagement metrics. This facilitates targeted marketing strategies to enhance customer satisfaction and drive revenue growth.
Here is a demo of the project in action: 
You can also watch the full demo video here: Demo Video
The initial phase focused on understanding stakeholder needs and defining business requirements.
- Requirement Collection: Conducted detailed analysis to gather data visualization requirements, simulating the workflow of a BI engineer.
- Translation into BI Metrics: Converted abstract business requirements into measurable data metrics and determined appropriate granularity. Refer to Sales_Dashboard_Requirement_Analysis.pdf
- Chart Selection: Identified suitable visualization types for each metric. Refer to the highlighted sections in Sales_Dashboard_Requirement_Analysis.pdf.
Designed the dashboard structure and interactivity to meet business objectives.
- Mockup Creation: Developed mockups to outline the dashboard layout and align with stakeholder expectations.
- Formatting Standards: Defined a consistent color scheme, spacing, and layout for enhanced readability and branding.
- Interactive Features: Designed filters for year, product category, and location data, and added intuitive navigation between dashboards.
Prepared and modeled the dataset to ensure integrity and usability.
- Table Relationships: Connected data sources, analyzed structures, and identified dimensions and facts.
- Data Modeling: Established logical relationships and created a comprehensive data model.
- Field Standardization: Renamed fields and tables for clarity, ensuring consistent and correct data types.
- Data Exploration: Validated data logic, granularity, and relationships by testing rows and columns.
Built dashboards following the defined requirements and designs.
- Calculated Fields and Testing: Created and validated calculated fields and parameters.
- Chart Development:
- KPI Overview: Presented year-over-year comparisons of sales, profit, and quantity.
- Sales Trends: Visualized monthly trends with highlights for peak and low points.
- Product Subcategory Comparison: Designed dual-axis charts to compare category-level performance.
- Weekly Trends: Built dynamic weekly trend analysis with benchmark indicators.
- Dashboard Assembly: Integrated charts and structured containers to form a cohesive dashboard.
- Formatting: Polished visuals with adjustments to colors, legends, fonts, and tooltips.
- Applied a similar workflow as the Sales Dashboard, with a focus on customer-specific KPIs, customer distribution by order frequency, and top 10 customer metrics.
Enhanced user engagement with dynamic and interactive elements:
- Filters: Implemented floating filter panels for year, product, and location selections across charts.
- Navigation: Added interactive buttons and icons for seamless transitions between dashboards.
- Predictive Analysis: Integrate predictive analytics to forecast sales trends and customer behavior patterns.
- Sales KPI Tracking: Incorporate weekly/monthly/yearly revenue goals with progress tracking to adjust marketing and sales strategy.
- Customer Lifecycle Analysis: Develop cohort analysis features to monitor customer lifecycle patterns and identify opportunities for customer retention and growth.
- Automated Data Refresh: Establish automated processes for data updates to enable real-time analytics and insights.
- AI-Generated Insights: Explore the integration of AI-powered insights through cloud deployment and large language model (LLM) capabilities to enhance data interpretation and decision support.