Streamlining Real-World Data Extraction and Analysis by 70% Using AWS Data Exchange with Moderna

Moderna is a pharmaceutical and biotechnology company that researches and develops primarily mRNA therapeutics. As a biotechnology company, Moderna depends on fast, seamless access to a wide range of real-world evidence to generate insights and make informed business decisions that improve patient outcomes. Real-world evidence—data related to the delivery of healthcare—is the foundation for the development of pharmaceutical drugs and therapeutics.

Moderna collects this data in various formats from multiple sources, but it previously had to code and tailor a new data onboarding process for every public and private data source. That meant using different scripting languages and custom processes, which required significant manual time and effort for Moderna’s data engineers to set up. Critically, it delayed getting the data to where it was needed most. “A lot of extraction from all these customized solutions was a big burden on the transformation itself,” says Carlos Peralta, director of data engineering at Moderna. “That led us to not having a unified data strategy.”

Having all these processes scattered among different sources made it difficult to catalog the data that Moderna was acquiring and severely limited any traceability for the company and its stakeholders. It also resulted in siloed datasets that had poor usability and were inconvenient for users. Further, Moderna had to set up separate billing processes for each of its vendors, which added complexity because it managed individual invoices and risked purchasing duplicate datasets.

Scientists and researchers at Moderna needed a scalable, unified solution to collect and store data from lab experiments and equipment. To achieve this, the company implemented AWS Data Exchange, which makes it easy to find, subscribe to, and use third-party data in the cloud.

On AWS Data Exchange, Moderna now has one location to source and subscribe to the real-world data that it needs. This data is particularly valuable in helping inform enrollment strategies for new mRNA vaccine candidates for respiratory syncytial virus, influenza, and other diseases. The company no longer needs to set up custom integrations, negotiate contracts, or establish individual billing processes for each subscription because it is all consolidated on one AWS invoice. And because it’s on AWS, all data is encrypted in transit and at rest, which meets Moderna’s security requirements.

“On AWS Data Exchange, we can customize how we want to partition the data and how we want to organize it in our data lakes,” says Peralta. “We can make sure we get the data in the right format beforehand so we don’t have to think about transforming, loading, or extracting it. We get everything that we need in a single solution.”

By using AWS Data Exchange, Moderna made the process of securely getting third-party data directly into the services and applications where it’s needed faster and easier. Now, it spends less time searching for data, building infrastructure to get it into production, and complying with company and industry regulations. “We want to make use of the data where we focus the most,” says Peralta. “We’ve removed the viscosity of writing customized code, and now we get near-real-time data.”

Moderna extracts and stores the data in data lakes on Amazon Simple Storage Service (Amazon S3), an object storage service built to retrieve any amount of data from anywhere. It also uses Amazon Redshift, a cloud data warehouse that can analyze exabytes of data and provide near-real-time insights.

Data is directly accessible in Amazon Redshift and then updated in near real time for analysis in disease-surveillance dashboards. This helps Moderna to efficiently target the geographies and demographics that are most at risk to inform where to conduct enrollment trials. AWS Data Exchange helps Moderna save time by having data almost immediately available for analysis and is now a critical part of the company’s Real-World Data Strategy.

“The purpose of having the data in Amazon Redshift is to empower our business intelligence tools and teams across the company to unlock the power behind the data,” says Peralta. Now, the company can connect stakeholders directly to the data providers. “We have accelerated the data acquisition process from 8–10 days to only 3 days using AWS Data Exchange,” says Peralta. “We’re empowering users to make more informed, data-driven decisions.” Moderna also no longer needs to wait for vendors to deliver the data; the company accesses the data in near real time. “We make the data immediately available so internal stakeholders can analyze the data to drive business decisions 70 percent faster,” says Peralta.

With better data pipelines, Moderna is able to automate data flows and harmonize disparate data formats across hundreds—if not thousands—of laboratory instruments and software sources to accelerate scientific research. This can eliminate silos that stifle scientific breakthroughs and slow innovation. Additionally, scientists can conduct safer and more collaborative research by eliminating error-prone manual transcription practices and by using algorithms that reveal risks before any issues occur.

The rapid development of the COVID-19 vaccine is a powerful example of what’s possible with faster data workflows. Moderna was able to complete the sequence for its mRNA COVID-19 vaccine in just 2 days using machine learning (ML) on AWS, and the first clinical batch was released just 25 days later. In order to produce an effective drug, researchers need to see the structure of proteins inside cells so that they can design the drug to bind effectively to a specific protein type. This is particularly important for use cases at Moderna such as cryogenic electron microscopy, which uses special microscopes to produce detailed pictures of proteins. These microscopes can generate tens of terabytes of data per day. User data can add as much as 20 percent overhead, as well.

Moderna needed a solution to reduce the time and effort of manually extracting, validating, and transforming large and complex datasets that were stored in different locations. It used TetraScience Data Cloud, a software-as-a-solution (SaaS) solution on AWS Marketplace with artificial intelligence and ML integrations, to automate and centralize data ingestion from disparate sources. Now, Moderna’s scientists and researchers can immediately collect the data that’s uploaded to the TetraScience Data Cloud to verify, enrich, and harmonize it into a data model. The company can access and analyze the data across the value chain with ease.

By procuring both SaaS solutions through AWS Marketplace, Moderna quickly accessed the technology that it needed to help accelerate scientific outcomes. “AWS Marketplace has helped us procure and access the technology we need up to 60 percent faster,” says Peralta. The company improved its productivity by using AWS Marketplace solutions to decrease purchasing cycle times by a factor of 3, to streamline contracting and negotiation times, and to simplify onboarding practices for buyers. It has also benefitted from its consolidated billing feature, which centralizes its payments so that it can manage its software and data purchases with ease. “By using AWS Marketplace, we have significantly improved the overall procurement process for SaaS solutions—from vendor onboarding to payments and obtaining licenses,” says Peralta.