Summary of "End to End Data Analytics Project | Power BI Project | Hospitality Domain"
Summary of "End to End Data Analytics Project | Power BI Project | Hospitality Domain"
This video is a comprehensive tutorial on executing a complete data analytics project using Power BI, centered around the Hospitality Domain. It simulates a real-life scenario where a data analyst works with a business stakeholder (a revenue manager) to build a revenue insights dashboard for a hotel chain. The project covers the entire analytics workflow, from understanding requirements to dashboard delivery and stakeholder feedback.
Main Ideas, Concepts, and Lessons
- Project Overview and Objectives:
- Learn Power BI from scratch with no prior experience needed.
- Understand key business concepts specific to the hotel industry.
- Execute a real-world data analytics project step-by-step:
- Requirement gathering from a domain expert.
- Data exploration and understanding.
- Data transformation and modeling.
- Dashboard design and creation.
- Stakeholder review and iterative improvements.
- Problem Statement and Dataset:
- Build a dashboard to generate revenue insights for a fictional hotel chain "Atle GRS" with multiple properties across India.
- Dataset includes multiple CSV files: hotel details, room types, booking data, date tables, and aggregated booking data.
- Business context such as weekends vs weekdays, booking channels, and room capacities are critical.
- Key Business Metrics Explained by Domain Expert (Abishek Anand):
- RevPAR (Revenue per Available Room): Total revenue divided by total rooms available.
- ADR (Average Daily Rate): Average rate at which rooms are sold.
- Occupancy Percentage: Successful bookings divided by total room capacity.
- Sellable Room Nights (SRN) / Daily Sellable Room Nights (DSRN): Number of rooms available to sell daily or monthly.
- Realization: Ratio of utilized room nights (actual stays) to booked room nights (including cancellations and no-shows).
- Explanation of cancellations, no-shows, and their impact on revenue recognition.
- Importance of differentiating weekends (Friday-Saturday) and weekdays (Sunday-Thursday) in hotel analytics.
- Analytics Levels:
- Level 1 Analysis: High-level metrics to identify if there is a problem (e.g., overall revenue, occupancy).
- Level 2 Analysis: Drill-down by city, property, room type, or booking channel to identify root causes.
- The "Onion" analogy: peel layers to find where the problem lies.
- Importance of enabling at least three levels of drill-down in dashboards.
- Power BI Data Preparation and Modeling:
- Importing multiple CSV files as a folder source.
- Using Power Query for data transformation: promoting headers, removing unnecessary columns, correcting weekend definitions.
- Creating relationships between dimension tables (hotels, rooms, dates) and fact tables (bookings, aggregated bookings) using star schema.
- Creating calculated columns (e.g., custom weekend/weekday classification) and measures (e.g., total revenue, total bookings) using DAX.
- Best practices for verifying data correctness and relationships.
- Dashboard Creation:
- Building tables and visuals with proper formatting (e.g., revenue in millions, percentages).
- Using slicers for filtering by city, room type, month, and week number.
- Adding KPIs as card visuals with week-on-week change indicators and conditional formatting (up/down arrows).
- Creating tooltips with line charts for trend analysis.
- Adding donut charts for category-wise occupancy.
- Emphasizing the importance of iterative design and stakeholder feedback.
- Stakeholder Review and Business Insights:
- Presentation of an 80% complete dashboard to the revenue manager for feedback.
- Discussion on the importance of weekday/weekend splits and channel-level analysis.
- Insights derived:
- Hotel chain is not using dynamic or weekend/weekday pricing strategies.
- Flat ADR (Average Daily Rate) indicates missed revenue opportunities.
- Pricing strategies in hotels: flat pricing, weekend/weekday pricing, dynamic pricing.
- Correlation between occupancy and average rating.
- Cancellation rates and customer behavior insights.
- Channel-level pricing and promotion strategies.
- Emphasis on collaboration between data analysts and business stakeholders for effective dashboard design.
- Final Exercise and Challenge:
- Viewers are encouraged to complete the dashboard, customize it (colors, styles), and add two new visuals based on stakeholder needs.
- Share the project on LinkedIn with a provided template and hashtag to win prizes.
- Community support available via Discord channel.
Methodology / Step-by-Step Instructions
- Step 1: Understand Problem Statement and Dataset
- Download dataset from provided source.
- Review problem statement and dashboard mockup.
- Understand business domain and key metrics with domain expert input.
- Step 2: Import and Transform Data in Power BI
- Import all CSV files as
Category
Educational
