In brief: How Codenatives rebuilt a health-tech startup's near real-time analytics on Google BigQuery, turning nested data into automated medical dashboards.
Overview
Codenatives assisted a health-tech startup in overhauling their near real-time analytics and reporting infrastructure using Google BigQuery. The solution transformed complex, nested data into actionable medical insights through automated workflows and interactive dashboards.
The Challenge
The client struggled with massive volumes of data in raw, nested formats from multiple sources. Critical issues included:
- Hierarchically nested fields requiring meticulous parsing
- Multiple data types needing conversion
- Null values and inconsistencies across datasets
- Data desensitization requirements for PII protection
- Infrastructure limitations preventing timely insights delivery
Our Solution
Data Processing
We designed scheduled queries to extract and transform medical data into semi-normalized tables, using BigQuery SQL functions to parse nested fields and handle missing data systematically.
Advanced Analytics
We implemented K-means clustering for symptom pattern analysis and enabled predictive analytics to identify potential future anomalies in patient health data.
Visualization & Reporting
We integrated Looker Studio for interactive, real-time dashboards. Customized visualizations translated processed data into actionable reports, while automated reporting workflows ensured stakeholders received timely updates.
Security & Compliance
The Google Cloud infrastructure was subjected to comprehensive penetration testing. We achieved compliance certifications aligned with healthcare industry standards, ensuring patient data remained protected throughout the pipeline.
The transformation enabled healthcare professionals to access real-time insights, monitor critical conditions with improved accuracy, and make data-driven decisions efficiently.
Outcomes
- Real-time access to processed medical insights for healthcare professionals
- Improved accuracy in monitoring critical patient conditions
- Efficient decision-making through automated data pipelines and dashboards
- Compliance achieved with healthcare data protection standards
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