How A Strong Data Foundation, Analytics & AI Fuelled Growth For A Health Tech

Accelerating growth potential by addressing data gaps

At the centre of the customer’s strategy is the use of cutting-edge technology to make swift business decisions. With increasing data volumes and multiple sources of data, the team felt it would benefit from some of the new solutions available from AWS and Snowflake to increase the speed of data retrieval while remaining cost efficient. 

This would help them continue to provide incisive Decision Support and Analytics, Network Solutions, Compliant Pricing Solutions, and Comprehensive Claims Management on their existing dynamic pricing and analytics platform, with even greater efficiency. 

There was potential to make the data work harder by breaking away data silos between departments. The Relational Database Service (RDS) used by the company was primarily designed for transactional databases, not for data warehousing tasks like complex queries and historical data analysis. A centralized datastore and a single source of truth were the need of the hour. 

KPI processing was managed with 100 QVD QlikView Data (QVD) files. The customer felt there was scope to simplify the system and use automation to make data retrieval for reports faster. They wanted a solution that would also continue to meet enterprise-level requirements around security, monitoring and auditing with comprehensive change log and audit trails for data modifications. 

Recognizing the need for a well-governed self-service data analytics platform and automation even as volumes of data being handled continued to rise, they partnered with Cloud Kinetics to build a Snowflake-powered solution on AWS.

An end-to-end data solution to power business goals 

Cloud Kinetics helped the customer implement a powerful data solution, leveraging AWS DMS, dbt and Snowflake’s Data Cloud. The Extract, Load and Transform (ELT) pipeline ingested, creating a single source of truth with transparent data lineage.

The customer is committed to building on the latest technology to maximize growth potential. This new adaptable, high-performance data architecture will support evolving analytical workloads and increasing data volume, while navigating security and governance needs, supporting their commitment to provide cutting-edge solutions and superior service to their customers.

• With CI/CD pipelines, orchestration, scheduling and monitoring, Cloud Kinetics delivered the robust data solution that the customer was looking for.
• Snowflake’s Data Cloud DWH formed the core of this adaptable, high-performance data architecture, enabling analysis of growing data volumes with evolving needs, eliminating silos, and facilitating deeper insights. 
• Managed services help auto-scale storage and compute for cost efficiency.
• Dbt Cloud’s model versioning, collaboration, testing, and documentation features would ensure data quality and maintainability.
• The customer could now leverage Snowflake’s Data Sharing and Snowpark’s Python APIs for secure data sharing, analysis, and in-Snowflake AI/ML workloads, while the Data Exchange would facilitate secure data exchange with external partners and platforms.

The customer’s analysts could now use a self-service platform with easy access to reliable data, reducing IT dependence.

Robust data-sharing features would also facilitate secure collaboration among internal and external stakeholders, while role-based access control (RBAC) and encryption ensure data security, compliance and adherence to HIPAA regulations. Comprehensive audit logging and data lineage capabilities enhanced data discoverability, allowing them to track data access, usage, and modifications, ensuring accountability and regulatory compliance.

Data-driven decisions for healthy growth and business efficiency

With a unified data platform built on Snowflake, the customer gets a holistic view of its data. This makes it more efficient for the team to identify trends, refine cost-containment strategies, and support data-driven decisions for their customers.  

Analysts can now access data via a clean, reliable user-friendly interface. Self-service capabilities support swift data-driven decisions, improving overall business efficiency. Automating with Snowflake’s ELT pipeline, eliminating data silos and streamlining data management processes have helped reduce infrastructure costs associated with maintaining separate systems and manual data processing.

Automation, advanced analytics and unified data platform, underpinned by robust governance and aligned with HIPAA regulations, has reduced the customer’s  IT dependence and is in line with their data-first, customer-first approach.

Early success metrics already show the impact: 

• Scalability: The flexible architecture can accommodate a 200% increase in data volume.
• IT dependence: A 70% decrease in reliance on IT leads to greater analyst efficiency.
• Depth of analysis: 30% deeper insights enable more informed choices.
• Data quality and maintainability: Versioning, collaboration, testing, and documentation could potentially halve data-related issues.

The customer business teams required easy access to accurate, reliable data, which meant eliminating complex dependencies that hindered data-driven decisions. They pulled this off with a robust data foundation – a single source of truth that broke down silos and made data access secure and convenient.