Goal
Establish the foundational data infrastructure required to support scalable analytics, data science, and business intelligence for Lucky Parking. This initiative focuses on creating reliable data ingestion pipelines, centralized storage, standardized transformations, and data quality frameworks, setting the groundwork for data-driven product and operational decisions.
Description
As the MVP launches and generates real-world usage data, Lucky Parking needs a modern, scalable, and governed data platform. The goal is to move from siloed or raw data to a trusted, analytics-ready environment. This includes building data pipelines from various sources, modeling data in a warehouse, ensuring reliability, and enabling analytics access.
KPIs
- Reliable, centralized, and scalable data infrastructure that supports all future product, analytics, and machine learning needs.
- 100% of key MVP data sources ingested automatically (daily or real-time)
- Data pipeline uptime ≥ 95%
- <1-hour lag between source and warehouse for critical data
- All Tier-1 datasets have data quality checks and documentation
- Core business metrics tracked via dashboard
Goal
Establish the foundational data infrastructure required to support scalable analytics, data science, and business intelligence for Lucky Parking. This initiative focuses on creating reliable data ingestion pipelines, centralized storage, standardized transformations, and data quality frameworks, setting the groundwork for data-driven product and operational decisions.
Description
As the MVP launches and generates real-world usage data, Lucky Parking needs a modern, scalable, and governed data platform. The goal is to move from siloed or raw data to a trusted, analytics-ready environment. This includes building data pipelines from various sources, modeling data in a warehouse, ensuring reliability, and enabling analytics access.
KPIs