Project at a Glance
333Unique Tasks
3,996Weekly Snapshots
10Departments
95.27%Latest Snapshot Readiness
Data Analytics · Business Intelligence
Hospitality Pre-Opening Analytics
Power BI Dashboard for Pre-Opening Readiness & Operational Risk
An end-to-end hospitality analytics project designed to answer a critical pre-opening business question: is the hotel on track to open on time, and which departments, tasks, and vendors pose the greatest operational risk? Instead of using a ready-made dataset, I built a custom synthetic dataset from scratch using Python and Pandas, based on a fictional hotel pre-opening scenario. The data was cleaned, validated, structured, loaded into SQL Server for analysis, and visualized through a three-page Power BI dashboard.
PythonPandasSQL ServerPower BIDAXData Modeling
The Business Problem
Hotel pre-opening programs involve hundreds of interconnected tasks across multiple departments, vendors, deadlines, and operational dependencies.
Management needs more than a simple task list. They need visibility into overall readiness, delayed work, critical issues, departmental performance, vendor risk, and cost variance. This project was designed to transform that operational complexity into a decision-support dashboard for management.
“Is the hotel ready to open, and where should management focus attention?”
Building the Dataset
Rather than downloading a ready-made analytics dataset, I designed and generated a custom synthetic dataset using Python and Pandas. The dataset simulates a hotel pre-opening program across 10 departments and tracks task progress through 12 weekly snapshots.
Tasks
333 unique pre-opening tasks including ownership, priority, critical-path status, planned dates, vendors, and costs.
Task Snapshots
3,996 weekly observations tracking task status and progress over time.
Departments
10 operational and support departments.
Vendors
Vendor information covering internal and external delivery responsibilities.
Pre-Opening Framework
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Python / Pandas
↓
Synthetic Dataset Generation
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Data Cleaning & Validation
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SQL Server
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Power BI / DAX
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Management Insights
SQL & Data Analysis
After generating and validating the dataset, I loaded the analysis-ready data into SQL Server. SQL analysis was used to explore task performance, departmental readiness, operational risks, and cost patterns.
JOINsCASE statementsGROUP BYHAVINGAggregate functionsData validationBusiness KPI analysis
From Data to Decision Support
The cleaned data was modeled in Power BI using relationships between departments, tasks, task snapshots, vendors, and date dimensions. DAX measures were created to track operational KPIs including:
Overall ReadinessLatest Snapshot ReadinessDelayed TasksBlocked TasksHigh-Risk TasksCritical Open IssuesCritical Path ReadinessPlanned CostActual CostCost VarianceCost Variance %
The Dashboard
01 — Executive Overview
Provides management with a high-level view of hotel readiness, delayed work, blocked tasks, high-risk activities, critical issues, readiness trends, and departmental performance.
02 — Action Center
Turns the dashboard from passive reporting into an operational management tool by highlighting tasks requiring immediate attention and allowing management to focus on priority risks.
03 — Vendor & Cost Performance
Analyzes vendor performance and financial variance to identify potential delivery and cost risks before opening.
Key Results
95.27%Latest Snapshot Readiness
204Delayed Tasks
76High-Risk Tasks
34Critical Open Issues
594.78KCost Variance
4.3%Cost Variance %
These figures reflect the latest weekly snapshot of the synthetic pre-opening scenario, not a real hotel.
Tech Stack
PythonPandasSQL ServerPower BIDAXData ModelingData AnalysisData Visualization
Project Links
Portfolio Project Disclaimer
This portfolio project uses synthetic operational data informed by publicly available hotel pre-opening frameworks. It contains no confidential or operational data from my employer or any real property.