Entact Bio builds a Strong Data Foundation, Reducing Lookup Time by 90%

The Need

As an early-stage company, Entact Bio hadn’t yet established a systematic foundation for data management. The team relied on numerous internal and external data sources from research, public, and instruments to run experiments, and needed to establish proper data infrastructure to support key R&D milestones upstream.

The Challenge

Without foundational data management in place, the Entact Bio team faced several core challenges that impacted their computational and bench science teams:

1. Fragmented data storage: The team lacked a centralized database for their key data sources. Data was scattered over numerous locations – Excel files, SharePoint presentations, local laptops, and directly on lab or instrument computers. Additionally, teams lacked a consistent framework for labeling data, making it difficult to find, access, and connect information.
2. Difficulty accessing, sharing, and using data: With every team member using their own tools and systems for data management, teams struggled to share information and quickly find the data they needed. Leveraging public datasets was just as difficult. With this information spread across clunky, cumbersome Excel files, scientists lacked an effective way to integrate and analyze this important public data alongside their internal research.
3. Risk of data loss: Without proper systems to manage information and track versions of data and analyses, valuable research becomes vulnerable. This data fragmentation creates downstream risks for critical R&D milestones like IND filing.

The Solution

There isn’t a one-size-fits-all approach to data management. While some organizations consider robust data management and tracking noncritical until scale-up, Entact Bio took a different approach. They hired Ryan Abo, Director of Data Science at Entact Bio, to figure out the right strategy. Ryan understood the importance of implementing proper data management early, having witnessed the challenges other companies faced when scrambling to dig up key results to support critical regulatory filings. In some cases, data was lost forever, forcing scientists to re-run experiments to regenerate data. 

With extensive experience implementing Quilt at four previous biotech companies, he played a pivotal role in bringing Quilt to Entact Bio and helping set the team up for success. Under his guidance, the team selected Quilt to centralize their data management and unify various data sources in one powerful platform. 

This implementation has enabled the team to:

1. Handle large-scale data: Built on top of AWS, Quilt can store and manage large-scale data, a critical requirement as the Entact Team progresses towards IND and continues to develop their platform. Unlike traditional bioregistries which only handle small-scale data and don’t always integrate with every relevant source, Quilt scales as data volumes and types evolve.
2. Balance deep analysis with easy data access: combining complex analytical capabilities with an intuitive platform, Quilt accommodates the needs of both computational and bench scientists. Data scientists can leverage Quilt's programmatic interface to conduct sophisticated data analysis while the wet lab team can easily locate, organize, and examine data through simple search and dashboard tools.
3. Track, manage, and share data: Access to a single, easy-to-use source of truth democratizes valuable information and simplifies data sharing for better collaboration. And with version control and metadata tagging capabilities, teams can track data lineage and instantly retrieve precise results when key milestones – like IND filings – are approaching.
4. Leverage functionality purpose-built for scaling life sciences teams: Unlike horizontal data platforms, Quilt is tailored specifically for biotech workflows. The platform accommodates specialized research data types and evolving regulatory requirements, and can scale with teams from early-stage to larger enterprises without sacrificing performance.

"If you set up Quilt correctly, you can quickly find the data you need for regulatory filings. Teams can potentially save hundreds of hours of work instead of having to dig up all these experiments, or regenerating them because the data is lost."

The Results

With Quilt in place as Entact Bio’s central data infrastructure, the team can now access and leverage data from their three primary sources in one integrated workflow: 

1. Instruments: Instead of sitting on lab computers, data instantly flows from various lab instruments directly into Quilt – eliminating manual transfers and providing the entire team with instant access to run results. Now, 90%+ of Entact’s data is discoverable through automated packages.
2. Public Sources: The team has integrated over 30 ubiquitin-related datasets from public sources, making it simple to incorporate external knowledge into their research, experiments, and analyses.
3. Internal Research: Scientists across disciplines can now access, analyze, and build upon each other's work, enabling collaborative analysis and sharing throughout the organization. 

Since using Quilt, the team has achieved:

• 90%+ reduction in data lookup time
• 3x more reuse of internal datasets across teams
• Audit-ready and traceable data for future regulatory filings, with complete version history

This transformation not only supports critical research today but also positions Entact Bio for success as they advance towards IND filing. By eliminating data fragmentation with a single source of truth, Entact Bio accelerates research and transforms scattered information into strategic assets.