Dotmatics

Improving Scientific R&D Data Liquidity

The article discusses how many scientific R&D organizations face challenges with complex, inefficient, and siloed infrastructures that hinder data liquidity—the secure, timely, and relevant flow of data to users—and explains that achieving improved data liquidity requires a flexible, scalable platform like Dotmatics, which facilitates efficient data acquisition, centralized scientifically-aware repositories, and interconnectivity at the application level to enable seamless data sharing, collaboration, and monetization across diverse users and systems.

A lot of potential customers are struggling with R&D infrastructures that are complicated, inefficient, and siloed. As a result, their data flow is unorganized, and users often struggle to access the data they need, when and how they need it.

When discussing data flow in R&D infrastructure, some of the biggest concerns relate to:

  • Data liquidity: Getting the right data to the right users, exactly how and when they need it.
  • Data monetization: How users derive value from data once they have it.

What is data liquidity?

Data liquidity is the ability for data to flow securely. Poorly interconnected systems are the enemy of data liquidity. Achieving data liquidity requires a flexible, scalable, harmonized platform that can seamlessly handle an ever-increasing volume and variety of data, ensuring it’s accessible to different users in a secure, clean, relevant, and timely manner.

Collaborative Scientific R&D Data Liquidity

Key data liquidity in scientific R&D is not just about getting everything to function in unison, but also everyone. This means establishing interconnectivity behind the scenes and facilitating data transfer and communication at the application level, where users work, share data, make discoveries, and gain insights. Achieving this level of interconnectivity can take significant effort.

The Dotmatics Platform handles, processes, shares, and stores data to deliver data liquidity. Notable features include:

  • Efficient data acquisition from all data producers: Clean and fast data collection, whether through automated instrument data capture, LIMS sample management, database integration, or error-proof data entry via electronic laboratory notebooks (ELNs).
  • Centralized data repository: A scientifically-aware master repository that eliminates disconnected (often discipline-specific) data silos and creates a single source of truth for all users and collaborators across an organization and its partners.
  • Common data model: Standardized data model that breaks away from proprietary data formats, automates QC and QA, and provisions the model quality data needed for in-depth scientific analyses.
  • Centralized biological and chemical entity registration: Centralized, formalized system for single-entity or batch registration, provenance tracking, and relationship tracking for biological, chemical, and mixed entities.
  • Interoperability and seamless data exchange: Secure data exchange not only among behind-the-scenes systems, but also among end users via research collaboration tools that help different teams communicate, share data and insights, collaborate, and move projects forward.
  • Accessible and traceable data for easy auditing: Fast and constant access to data, such as through scientific search, reports and dashboards, and browseable experiments.
  • Iterative data layering: Records layered with data points from multiple (often cross-team) analyses, providing a complete picture that accurately reflects context.
  • Secure data storage, transfer, and sharing: Safeguarded data, at rest and in transit, on the cloud or on-premises with granular authorization and access controls, enabling different users, collaborators, and CROs to work on and exchange data within the same platform without threatening privacy.

Improving Data-Driven Decisions with Scientific R&D Data

Dotmatics has created a large enterprise data platform focused on enabling data-driven decisions and is experienced in assessing existing R&D infrastructure to identify specific changes that will help users get the data they need and put that data to work.