Optimized Oral Health Device Production: Real-Time Monitoring for Quality and Efficiency

Client is an oral hygiene product manufacturer delivering an innovative and professional level at-home oral irrigator designed for easy, fast, and effective oral care. Zimetrics implemented Telemetry Data Pipeline for Class I Medical Device Test Data for accelerated GTM.

Challenges​

  • Telemetry Device Data: Managing, processing, manually tracking and analyzing telemetry data from various devices was time-consuming and prone to errors.​​
  • CAD Design Time Sheet Data: CAD team frequently updated designs and revised dental mouthpieces. Managing and tracking design time sheets for these iterative processes was cumbersome and often resulted in data discrepancies and version control issues.​
  • Securely capture and process large volumes of telemetry and CAD data
  • Move away from manual processes that limited scalability and historical analysis
  • Deliver real-time and historical insights to customers through intuitive dashboards

​

Solution​

  • Automated Snowflake data Pipeline ​to process telemetry and CAD data​
  • Scalable ​transitioning to Snowflake enabled generating insights for a larger date range (Previously done manually)​
  • Created tailored dashboards and reports using PowerBI. These reports provided customer with real-time and historical insights.
    • Key features include:
      • Interactive data visualizations, allowing users to drill down into specific metrics and timeframes.​​
      • Trend analysis for Telemetry Hydro Test Data, helping to identify long-term patterns and areas for improvement
  • Amazon S3 as the data lake for raw and processed data.
  • AWS Lambda for automated serverless data processing.
  • Amazon SQS to ensure reliable and decoupled data ingestion.
  • Amazon S3 Glacier for cost-efficient long-term archiving.
  • Amazon CloudWatch for monitoring system performance and Amazon SNS for proactive notifications.
  • Automated Snowflake Data Pipeline to process both telemetry and CAD data.
  • Snowflake Snowpipe for near-real-time streaming analytics.
  • Power BI dashboards and reports tailored for end-users, providing both real-time and historical insights
    • Tech Stack:
      • Pipelines: Snowflake – SnowPipe + SnowPark, AWS S3, Python/PySpark​
      • Reporting: Power BI​​
      • API: AWS API Gateway, AWS Lambda
      • Event trigger: Amazon EventBridge

Impact Generated

  • Scalability: Transitioning to Snowflake enabled analysis across larger date ranges (previously done manually).
  • Automation: Fully automated ingestion, processing, and analytics pipeline reduced manual effort.
  • Actionable Insights: Real-time dashboards and historical reporting improved decision-making.
  • Cost Optimization: Storage tiering with S3 and Glacier for efficient long-term retention.
  • Reliability: Continuous monitoring and alerts ensured system uptime and smooth operations.
  • Customer Value: Delivered tailored, data-driven insights directly to customers through Power BI.

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