Case Study: Enhancing Data Scalability for a Growing Health Tech Company

Health Tech Data Infrastructure

TL;DR

I partnered with a fast-growing health tech company to build an entirely new data platform from the ground up. The old platform lacked scalability and reliability, so I designed and implemented a modern solution that streamlined data ingestion, accelerated processing times, and created a strong foundation for advanced analytics, machine learning, and future growth.

Project Overview

My client, a rapidly growing company in the health tech sector, offers a comprehensive workforce management solution specifically tailored for healthcare professionals, addressing the unique challenges of healthcare staffing.

As the company experienced significant growth, their reliance on a legacy reporting tool was no longer sufficient for their expanding data needs. Originally adopted for reporting, the tool was stretched beyond its intended use, leading to inefficiencies in data management. It became evident that they needed a more robust and scalable data platform capable of handling the growing complexity and volume of data.

My objective was to thoroughly reevaluate their data infrastructure and implement a new, scalable data platform, designed to facilitate advanced analytics and machine learning capabilities. This overhaul aimed to transform the company’s approach to data, turning it into an integral part of decision-making and business intelligence in the fast-growing health tech market.

Challenges

  • Lack of Scalability: The existing data platform struggled to meet the company’s growing needs, resulting in frequent bottlenecks and performance limitations.
  • Slow Data Refresh Rates: The outdated system resulted in slow data refreshes, limiting the company’s ability to make timely, data-driven decisions.
  • Inconsistent Data Logic: Redundant data logic across different sources increased the risk of errors and made data management more complex.
  • Limited Advanced Capabilities: The platform restricted the company's ability to implement advanced analytics and machine learning, limiting their potential for innovation.

Approach

To address these challenges, I began with a comprehensive review of the existing data infrastructure, identifying the most critical areas for improvement. I worked closely with stakeholders across the organization to understand both current and future data needs.

After evaluating a range of tools and solutions, I recommended a scalable cloud-based architecture. A Proof of Concept (POC) phase was initiated to validate the proposed solutions, ensuring that they would meet the company's needs in real-world scenarios. The POC helped identify the most cost-effective and scalable solution.

I collaborated closely with the internal team and DevOps to ensure seamless integration with existing systems, gradually migrating off the legacy tools and consolidating data logic into the new platform. My hands-on approach included regular meetings, pair programming, and knowledge sharing to empower the internal team to manage and build upon the new system independently.

Results

The new data platform significantly improved the company’s data management capabilities. Data refresh rates increased, processes were streamlined, and the system scaled effectively to handle the company's growing data volumes.

The internal team was able to take the lead on managing the platform, with minimal external support. They implemented new features faster, reduced bugs and incidents, and improved overall system reliability.

With a robust data foundation in place, the company is now exploring advanced analytics, machine learning, and AI initiatives to further enhance their service offering.

Testimonial

"Dan has been an all-round data superhero whilst working with us. He has spearheaded the migration to a new data platform, which has involved complex requirements gathering and market appraisal, tool and data environment set-up and building, and lots of complex logic clarification to build and test data pipelines and automation tasks. He's done all this whilst passing on his experience and knowledge to help upskill the rest of the team (and myself). Throughout he's just been an utter calm and stable presence in the team, no matter what was thrown at him. He's added so much value to the massive transformational journey we've been on with our data architecture project. But more than anything, we all agree that he is just a genuine pleasure to work with and an all-round good guy!"