CASE STUDY
Geospatial Data Quality Analysis at Scale
Validation and quality control service for complex multi-source geospatial datasets, enabling reliable sourcing decisions and reducing production data risks.
Problem Solved
The client needed a reliable way to validate large volumes of diverse geospatial datasets delivered by multiple global vendors. Inconsistent formats, schemas, and documentation created significant operational complexity and increased the risk of poor-quality data entering the production environment.
Problem Solving Approach
The project focused on establishing a scalable and repeatable framework for validating complex geospatial datasets across multiple vendor sources and delivery formats. The approach combined automated analysis, custom preprocessing workflows, and rigorous quality control procedures to support accurate and efficient data sourcing decisions.
Using a combination of Bash/Shell, Python, R, QGIS, SQL, Spark, Scala, and client-developed tools, the team standardized incoming datasets, executed automated quality checks, and delivered actionable reporting tailored to different vendor and dataset requirements. The service was designed to be highly adaptable, enabling the team to respond to evolving business rules, data structures, and operational priorities across multiple client projects.
Outcome
The solution established a standardized and well-documented analysis framework that became a trusted quality gate for the client’s data sourcing operations.
By combining automated validation with expert analytical oversight, the service significantly reduced the risk of production data corruption while delivering faster and more reliable insights needed to support vendor management and operational decision-making.
Trusted quality gate for vendor data sourcing
Reduced risk of production data corruption
Standardized large-scale geospatial data validation
Key Features Implemented
Multi-Format Data Preprocessing
Review vendor documentation and preprocess data into a standard format using Bash/Shell, Python, R, QGIS, OGR, SQL (PostgreSQL), Spark and client-developed tools.
Automated Quality Analysis
Executed automated checks measuring quality, accuracy, freshness, consistency, and coverage against client-defined business rules.
Actionable Reporting & Scorecards
Delivered detailed reports and quality scorecards highlighting critical issues and ingestion recommendations.
Multi-Layer Quality Control
Implemented peer review and automated verification checks to ensure consistency, integrity, and accuracy of analysis outputs.
Technologies
Development
Project Timeline and Team Structure
The project began in 2018 and expanded continuously over the years, growing to 15 team members working across 4 data projects by 2023. The team included Data Analysts, Data Engineers, Machine Learning Engineers, and a Data Analyst Manager delivering Data Science and Analytics services.
Data Analyst
Data Engineer
Product Owner
AI/ML Software Engineer
Methodology
The project is delivered using the Scrum methodology, supporting continuous collaboration, scalable delivery, and iterative process improvements across long-term data operations.
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