Data Optimization: Cloud-based healthcare data optimization delivering real-time sales insights and smarter decisions
Project Overview
A leading healthcare organization sought a scalable, cost-effective solution to track sales, monitor reps, and analyze top-performing products. The goal was to provide a 360-degree view of field force, prescription trends, and key stakeholders, enabling real-time, data-driven decisions to improve operational efficiency and market strategy.
Problem Statement
The client faced performance bottlenecks due to their OLTP system being used for both operations and analytics. The database structure wasn’t optimized for complex queries, hindering real-time insights into sales and prescriptions. They needed a scalable, cloud-based solution for secure access to healthcare data.
Key Findings
- Performance Bottlenecks: The client’s OLTP database was used simultaneously for operational transactions and analytical queries, leading to significant slowdowns in reporting and delayed decision-making.
- Inefficient Analytical Processes: The analytical workloads ran on a highly normalised Informix database, which slowed query performance and made it difficult to extract timely insights into prescription patterns and sales impact.
- Cloud Scalability: As healthcare data volumes grew, cloud migration became essential to support real-time analytics, scale infrastructure, and deliver consistent performance under heavy data loads.
Implemented Solution
We optimized healthcare data by separating OLTP and OLAP systems, streamlining data with an SSIS ETL pipeline, migrating to Snowflake for better scalability, and implementing row-level security for compliance.
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Separation of OLTP and OLAP:
We created a dedicated OLAP warehouse to handle analytical workloads separately from core healthcare operations, eliminating contention and ensuring smooth transactional performance.
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ETL Process (SSIS):
An SSIS-based ETL pipeline was developed to extract, clean, and transform data from multiple systems—consolidating patient engagement, prescription trends, and sales metrics into a unified analytics environment.
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Data Transformation & Business Logic:
Custom business rules were applied to measure KPIs such as sales performance, goal achievement, and prescription volume. The results were stored in a denormalised format to support fast, flexible reporting.
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Cloud Migration (Snowflake):
Migrating analytics to Snowflake significantly improved performance and scalability, allowing the organisation to generate real-time reports and explore large datasets with ease.
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Row-Level Security:
Row-Level Security (RLS) was implemented to enforce role-based access to sensitive healthcare and sales data, ensuring compliance with privacy standards and data governance policies.
Results
The healthcare data optimisation initiative delivered substantial operational and analytical gains. By migrating to Snowflake and automating ETL pipelines, the team reduced infrastructure costs, eliminated reliance on inefficient legacy systems, and enabled in-house data ownership. The platform now supports real-time dashboards and deep-dive analytics into prescription trends and rep performance. This transformation empowered the organisation to make faster, evidence-based decisions—enhancing sales operations, strategic planning, and overall agility in a rapidly evolving healthcare landscape.