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We helped a health-tech startup transform their near real-time analytics, enabling
actionable insights and interactive reporting—driving better medical outcomes and operational success.
BIG QUERY
A leading health-tech startup partnered with Codenatives to overhaul their application’s near real-time analytics and reporting processes. We turned to Google’s BigQuery—a fully-managed, serverless data warehouse known for its ability to process vast datasets quickly and efficiently.
BigQuery’s robust SQL capabilities and automatic scaling make it ideal for handling complex queries, giving businesses the flexibility to achieve real-time, data-driven insights at scale.
BigQuery comes with a wide range of out-of-the-box support for machine learning models, allowing for advanced analytics capabilities and seamless integration with Google’s Vertex AI platform.
Our client faced a significant data challenge: vast amounts of information in raw, nested formats from multiple sources. Each dataset contained multiple levels of complexity, including hierarchically nested fields and numerous data types, which required meticulous parsing and datatype conversion.
Additionally, all data was desensitized to protect personally identifiable information (PII), ensuring compliance and ethical use solely for analytical purposes.
The client’s existing infrastructure struggled to process this volume and complexity at the needed speed, limiting their ability to deliver timely insights for healthcare professionals.
Codenatives leveraged BigQuery’s advanced SQL and data warehousing capabilities to develop a streamlined, highly efficient analytics workflow. First, we designed and implemented scheduled queries that periodically extracted the latest medical data and transformed it into semi-normalized tables.
This approach not only simplified downstream processing but also minimized data redundancies, ensuring swift and clean data retrieval for analytics.
To tackle the complexity of the data, we used BigQuery’s SQL functions to parse nested fields, convert various data types, and set default values for missing data. This transformed the raw, complex JSON into well-organized, reliable data tables optimized for real-time analysis. With these semi-normalized tables in place, the healthcare team could now run advanced cohort comparisons, identify emerging health trends, and monitor critical conditions with unprecedented accuracy and speed.
Additionally, we took advantage of BigQuery's support for machine learning models, employing clustering techniques like K-means to analyze symptom data. This enabled the identification of potential future symptoms and anomalies, further enhancing predictive analytics capabilities.
Looker Studio allowed us to build customized visualizations that translated BigQuery’s processed data into clear, intuitive reports. Healthcare professionals could now interact with the data in real-time, drilling down into specific metrics, monitoring changes instantly, and making data-driven decisions with ease. This integration also allowed us to automate reporting, so new insights were always available without manual updates.
Through this BigQuery-powered transformation, Codenatives empowered the healthcare provider with real-time, data-rich insights, turning raw data into actionable intelligence.
Our deep expertise in managing and processing complex datasets ensured the client’s analytics needs were met with precision, speed, and scalability—ultimately driving operational success and enhanced medical care outcomes.
