Optimizing Data Architecture with AWS to Improve Patient Care for Lifescan

Customer Overview

Lifescan is a US-based medical device company that specializes in diabetic treatment with a vision of creating a world without limits for people with diabetes.

More than 20 million people around the world depend on its blood glucose monitoring devices to help them manage their condition. For over 35 years Lifescan has had an unwavering commitment to improving the quality of life for people with diabetes by developing products defined by simplicity, accuracy, and trust.

Lifescan is a US-based medical device company

World Leader

in blood glucose monitoring


people depend
on their products


years industry

Expertise in Digital Services & Industry Leading Tools

The Challenge

One of Lifescan’s biggest challenges was obtaining long-term clinical data accuracy and consistent system performance for blood glucose monitoring of diabetic patients.

It was critical that the company met and sustained the minimum requirements of the International Organization of Standardization (ISO) and the European harmonized version, demonstrating both product accuracy and consistency.

They take data quality very seriously – and for good reason. Gartner estimates that the average financial impact of poor data quality on organizations is $9.7 million per year. But bad data is much more than a cost center. It is a serious impediment to digital transformation.

The stakes were even higher for Lifescan. Since it partnered with a number of HIPAA (Health Insurance Portability and Accountability Act) compliant healthcare organizations, Lifescan was required to be HiTRUST certified. Ensuring compliance in a SaaS context was a complex and specialized area, encompassing a variety of technical challenges covering intrusion detection, encryption of data, sophisticated auditing capacity and OS level security establishments.

More notable technical requirements include:

  • Infrastructure setup to handle terabytes of data processing
  • Design of standardized coding practices for complex ETL logic blocks
  • Security design
  • Data retention policies
  • Provisioning for auditing needs

The company’s goal was to create the ability to build and deploy vetted, reusable coding procedures that would save the team significant time and money while meeting mission critical objectives.

Amazon Web Services (AWS) and Infostretch

It was imperative to identify an ETL tool that could process the glucose monitoring datasets with 100% accuracy, enable in-memory iteration on complex data models at the field level, provide serverless interactive query services, and provide flexibility around query modelling. With the right tool, the company could reduce the cost of on-boarding new diabetic patients by more than half compared to its existing approach.

Amazon Web Services’ (AWS) Glue was the right choice because of its ability to provide a fully managed ETL tool employing an in-memory Spark Engine with a dedicated data frame to handle field-level logics. Its underlying in-memory engine was fully managed Apache Spark, a distributed processing system primarily utilized for big data workloads. Spark enabled in-memory caching, and optimized execution for fast performance. It also supported general batch processing, streaming analytics, machine learning, graph databases, and ad hoc queries.

Infostretch was chosen as the service and consulting partner to architect,
build, and deploy the end-to-end solution.

This included building a data lake in which Amazon Athena and AWS Glue were employed to meet various corporate reporting needs with high accuracy.

By moving to a standard configuration that could be self-provisioned, the team was able to deploy new code changes, enhancements, and updates to the transformation logic. This shift allowed for the in-time data provisioning needed to bring visibility to some of the key business KPIs and scalable systems to support sudden data growth.

As a part of this project, Infostretch also created compliant infrastructures, error-proof data flow mechanisms, data reconciliation procedures, and reports using an AWS Athena reporting tool.

Testing strategy, planning, and methodology were critical items for ensuring a high level of accuracy in the reports. AWS Enterprise Support also ensured the client’s ability to gain long term access to the product developers behind the services.

ETL tools like AWC Glue bring much needed functionality enabling new approaches to pulling, processing and pushing data from source to target, and introducing concepts such as data transformation tasks using SparkSQL scripts in Apache Spark environments.

However, these migrations are not without their challenges, leading to a number of questions related to:

Necessary coding

Design aspects to
enable incremental
load design and
data conversion

Accurate reporting when the
data processing step truncates
decimals points which can
create huge data discrepancies

Infostretch was able to provide solutions to each of these challenges and enable the company
to transform its data analytics and intelligence platform.

AWS Services Used

Amazon RDS

Amazon Glue

Amazon S3

Amazon Athena

Amazon Spark

Enterprise Support

Achievements & Outcomes

IT Infrastructure

45% Reduction in IT
Infrastructure Costs

End-to-End Process

Saved 2,200 hours of
operational maintenance and over $120K per year


60% less effort via
automation of monthly
analytics reporting scripts

Reusable Architecture

Accelerates release of data
pipelines in new regions;
as fast as 2 weeks

About Infostretch

Infostretch leverages a proven combination of technology, processes, and expertise to help enterprises accelerate the execution of their digital strategy. We deliver faster and more effectively by unifying expert professional services and best practices with pre-built software frameworks and products. Our 1,000+ development, testing and integration specialists have deep capabilities in DevOps, QE, app development, Cloud, AI, IoT and mobility.