Scientific R&D Data Liquidity: What It Is and Why It Matters


A lot of the potential customers we meet are struggling with R&D infrastructures that are complicated, inefficient and siloed. And 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 talking to these companies about data flow in their R&D infrastructure, some of the biggest concerns we hear 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. Data liquidity requires a flexible, scalable, harmonized platform that can seamlessly handle an ever-increasing volume and variety of data and ensure it’s accessible to different users in a secure, clean, relevant, and timely matter.

Collaborative Scientific R&D Data Liquidity

Key data liquidity in scientific R&D is getting not just getting everything to function in unison, but also everyone. This means not just working to establish interconnectivity behind the scenes, but also 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 an incredible amount of work. Dotmatics helps take the pain out of that process.

The ways in which the Dotmatics Platform handles, processes, shares, and stores data all work together 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 need 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 amongst behind-the-scenes systems, but also amongst end users via research collaboration tools that help different teams talk to each other, 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 that are layered with data points from multiple (often cross-team) analyses, which provides 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, which make it possible for 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

Not only have we created the world’s largest enterprise data platform focused on enabling data-driven decisions, but we are also experts at assessing existing R&D infrastructure to see what specific changes will help users get the data they need and then put that data to work.

If you want to explore how Dotmatics can help improve your data liquidity, contact us today to get the conversation started.

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