How Modern Data Architecture Transformed a Dental SaaS Platform’s Analytics Operations?
A leading Dental SaaS company, focused on providing analytics solutions to dental clinics, engaged us to modernize their fragmented and outdated data infrastructure. Their existing system, built on a legacy data stack, was no longer scalable and had become increasingly difficult to maintain as their customer base and data volumes grew.
Key operational challenges were slowing down growth: onboarding a new client took up to a week, and each enterprise customer required a separate set of reports due to large data volumes and complex security requirements. These inefficiencies hindered scalability, delayed decision-making, and limited the company’s ability to deliver consistent insights across their client base.
We were brought in to design a modern, robust analytics architecture focused on streamlining workflows, migrating to a scalable data platform, and optimizing pipelines for performance, security, and maintainability.
Key Results
The Challenge
The client’s data infrastructure was disjointed and outdated, leading to inefficiencies in reporting and data onboarding:
- Onboarding new clients took up to a week due to manual data integration processes
- Multiple separate reports needed per enterprise client due to data volume and security segmentation
- Inconsistent data definitions and reporting metrics across departments
- Data sourced from a variety of tools such as Salesforce, Zuora, Pendo, Intercom, and multiple storage platforms created maintenance overhead
- Legacy architecture limited scalability and performance
- Lack of centralized governance, making compliance and reporting more difficult
Our Solution
We delivered a fully modernized data solution using cutting-edge tools and architectural patterns to address the client’s scalability, integration, and efficiency challenges:
- Implemented custom data extractors using PySpark on Databricks for flexibility and scalability
- Migrated data processing to Delta Lake within Databricks for reliability and performance
- Standardized transformation workflows using dbt, with CI/CD integration and testing
- Adopted Kimball dimensional modeling for scalable and maintainable data models
- Integrated Tableau and Power BI dashboards with Row-Level Security (RLS) for personalized, secure insights
- Embedded Angular-based analytics directly into the client’s platform using MS SQL Server backend
Implementation Process
Data Audit & Discovery
Reviewed all data sources, pain points, and documented technical requirements
Architecture Design
Defined the modern architecture using Databricks, Delta Lake, and dbt for transformation
Extraction & Ingestion
Built PySpark-based custom connectors and scheduled ingestion workflows
Modeling & Transformation
Created scalable dimensional models, implemented tests, and integrated CI/CD pipelines with dbt
Visualization & Integration
Deployed dashboards in Tableau and Power BI with embedded Angular components and RLS
Advanced Analytics
Built churn prediction models using SparkML and deployed calculated fields with RETL pipelines
Optimization & Handoff
Optimized pipelines for cost-efficiency and performance, documented processes, and trained internal teams
Technologies Used
"The overhaul of our data systems has been transformational. We now deliver insights to clients in hours, not days. The predictive models and streamlined dashboards have added immense value to both our internal teams and our customers."
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