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Choosing a secure workspace for data analysis and collaboration

Executive summary

Organizations across healthcare and life sciences need secure, governed environments that allow teams to access data, run analyses, and collaborate without multiplying tools, risks, or operational overhead. This buyer’s guide outlines how to evaluate these environments and identify the criteria that matter most for regulated research.

Key takeaways:

How to recognize when your research requires a trusted research environment (TRE)
The main research use cases that drive the need for governed data access and collaboration
The core capabilities to compare when evaluating TRE solutions
How different approaches to collaboration and federation affect data access across organizations
How security, compliance, and operational effort influence cost, scalability, and sustainability
What to assess to ensure a solution can support multiple studies and long-term research programs

Evaluating your options

Data is at the heart of any research project: How to access it, work with it, and collaborate with the confidence that security and compliance won’t slow you down.

Whether your objective is generating regulatory-grade real-world evidence, assessing trial feasibility, developing AI models, or advancing precision medicine, the same challenge arises: which type of secure workspace can support access, analysis, and collaboration across clinical, real-world, multi-omics, or imaging data without relying on multiple platforms, prolonged timelines, or unnecessary risk?

Below are some tips as you evaluate secure data environments that will help you advance your research with confidence – not slow it down.

Step 1: Determining when you need a trusted research environment

A trusted research environment (TRE) becomes relevant when research requirements make traditional data access and analytics approaches impractical or non-compliant with governance standards.

You typically need a TRE when:

Sensitive health data cannot be freely copied, downloaded, or transferred
Data owners or partners must retain control over how data is accessed and used
Multiple datasets need to be linked and analyzed together under governance
Research involves collaboration across teams, institutions, or countries
Auditability, traceability, and data provenance are required for review or submission

A TRE provides a secure, governed workspace where approved users can access and analyze data without exporting raw records, while access rules, audit trails, and data partner controls are enforced by design.

Step 2: Clarify the research challenges driving the need

Common drivers include:

Generating real-world evidence from linked EHR, claims, and outcomes data
Assessing clinical trial feasibility, identifying cohorts, or building synthetic control arms
Supporting pharmacovigilance and long-term safety monitoring
Developing AI and machine learning models using sensitive health data
Integrating genomic, single-cell, and other multi-omics data with clinical data
Collaborating securely with biotechs, academic partners, or consortia

A TRE must support these use cases without fragmenting data, workflows, or governance.

Step 3: What to look for in a research-ready TRE

Not all TREs are made equal or are for the same types of research. When comparing solutions, teams should evaluate several distinct capability areas.

Governed data access and research planning

A TRE should allow researchers to:

Explore cohorts and feasibility using summary-level views
Request deeper access only when approved
Work with linked datasets without moving raw data

This supports early research design while maintaining control and compliance.

Support for complex, multimodal data

Life sciences research increasingly depends on combining:

Clinical and real-world data
Genomic, single-cell, and other multi-omics data
Imaging data

A TRE must support these data types together, within one single environment, rather than across disconnected tools.

Multi-tier, role-based governance

Different users need different levels of access. A TRE should:

Govern visibility from summary information to aggregated analyses and patient-level data
Apply permissions at the project and user level
Align access models with institutional and regulatory requirements

This reduces both risk and administrative friction, providing peace of mind that data is both secure and compliant with privacy standards.

Analytics and reproducibility

Beyond data access, a TRE should provide:

Integrated analytical environments
Support for reproducible workflows
The ability to run advanced bioinformatics and AI analyses without exporting data

This is critical for genomics, population-scale research, and model development.

Step 4: Collaboration and federation at scale

Many projects involve multiple data sources and partners. A TRE should support:

Data remaining under local control
Secure sharing of approved results across organizations
Cross-site and cross-border collaboration without centralizing raw data

Federated research models are essential when working with external data partners or international programs.

Step 5: Security, compliance, and operational effort

Rather than assuming one regulatory context, a TRE should be able to:

Prevent raw data export
Provide audit trails, chain of custody, and event logging
Align with applicable governance and data protection requirements, such as GDPR or EHDS where relevant

Equally important is operational effort. Long deployment cycles, heavy customization, and ongoing IT dependency slow research and increase cost. Think about whether you truly need to customize your TRE or if configurable options will meet your needs.

Step 6: Assess long-term fit

A TRE should not serve a one-off project environment. Teams should assess whether a solution can:

Support repeated studies and long-term programs
Onboard new datasets and partners efficiently
Adapt governance and access models as research evolves

The goal is to enable a consistent, secure research environment — not another platform that will need to be replaced for every new study.

BC Mosaic: Your collaborative research canvas

BC Mosaic is designed specifically for life sciences and healthcare research involving real-world data (RWD), clinical trial data, and complex multi-modal datasets such as genomic or imaging data. It combines governed data access, integrated analytics, and federated collaboration in a single environment – all while reducing the time and operational effort typically required to deploy and manage a compliant TRE.

Delivered via AWS Marketplace, BC Mosaic is available with standard capabilities ‘out of the box,’ configurable to project needs, and designed to move from set-up to active research use in hours rather than weeks or months.

Comparing your options

	BC Mosaic	Other TRE solutionS
Easy and intuitive to use
Built-in security and compliance
CPU / GPU scalable on demand
Multi-modal data analysis pipelines
Easy data import / export
Add-on features from BC Platforms or our partners via AWS Marketplace
Comprehensive audit trails for operational, user-event, data, and analysis tracing
Near-immediate deployment
Wide range of pricing options to suit buyers’ needs
Ready to support EHDS regulations
Toolkits for data science and machine learning (including NVIDIA tech)

BC Mosaic is developed with your needs in mind – cost-effective, fast and easy to deploy, and in compliance with data governance standards as well as emerging EHDS regulations.

Talk to us about your data access and analytics requirements

If your research involves sensitive data, external data partners, or multi-site collaboration, choosing the right TRE is critical.

Get in touch to schedule a demo.