Case study • Real Estate • Data & Analytics
200% Product Usage Growth with Unified Data & Interactive Dashboards
We partnered with a leading rental real estate platform managing nearly 9 million listings to unify fragmented data systems and enhance decision-making. By building integrated data pipelines and modernizing dashboards, we enabled real-time insights, improved data reliability, and significantly boosted product usage and revenue.
Success Highlights
190% revenue growth driven by better data utilization
5% increase in subscription revenue with enhanced reporting
Key Details
Industry: Real Estate / PropTech Geography: United States
Platform: Amazon Web Services (AWS), Tableau
Business Challenge
The client struggled with fragmented data systems and inefficient reporting, limiting their ability to make timely, data-driven decisions.

Our Solution Approach
We implemented a data integration and visualization strategy to unify data sources, improve data quality, and enable real-time insights.
1 · Discover
Identify Data Silos & Visibility Gaps
Analyzed existing data sources, reporting tools, and dashboard usage to identify inefficiencies and inconsistencies.
2 · Consolidate
Build Unified Data Pipelines
Developed pipelines to ingest and transform data from multiple platforms into a centralized, analytics-ready format.
3 · Automate
Enable Data Processing & Quality Checks
Automated data ingestion, cleaning, and validation processes to ensure accuracy and reduce manual effort.
4 · Accelerate
Deliver Interactive Dashboards & Insights
Modernized Tableau dashboards and analytics applications to provide real-time, user-friendly insights.
Technical Highlights
Data ingestion pipelines into AWS S3 for centralized storage and scalable data processing ETL workflows for multi-source data integration enabling unified analytics across platforms
Automated data validation scripts for anomaly detection and data quality assurance Interactive dashboard development in Tableau Interactive dashboard development in Tableau Ad-hoc analytics workflows for custom reporting and business insight generation
// Python – Data Pipeline Validation Logic
def process_data(batch):
cleaned_data = clean(batch)
if validate(cleaned_data):
store_in_s3(cleaned_data)
update_dashboard()
else:
log_error(batch)
Business Outcomes
Transformed fragmented data into a unified analytics ecosystem, enabling faster decisions, improved visibility, and measurable business growth.
200%
Increase in Product Usage:
Improved visibility and user experience drove higher adoption across the platform.
190%
Revenue Growth:
Enhanced data-driven decision-making led to better customer engagement and increased revenue.
5%
Growth in Subscription Revenue:
Improved reporting and insights enabled optimized subscription models and upselling opportunities.
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