Rethinking ROI in ​​​​R&D: How modern teams measure success

The pace of scientific research and development continues to increase, alongside more collaboration and the collection, storage, and management of ever-increasing volumes of data. The adoption of digital solutions is necessary to meet these challenges, yet many R&D labs fail to use metrics that provide meaningful insights into the value of these digital transformations.

Traditionally, digital transformation ROI has relied on measuring cost savings and efficiency, but improvements to cost and efficiency are only part of the story.

To understand the impact of embracing modern scientific solutions, ROI must expand to include the measurement of the effects of today’s automated and data-rich research landscape. These metrics include time-to-insight, reproducibility, scalability, and collaboration across multiple teams and external partners. Whether you’re optimizing workflows or building a business case for your next project, these modern metrics speak both languages. 

For Your Next Leadership Meeting 

Scientific metrics beyond cost and efficiency are needed to gauge the success of modern digital transformations. These include:

• Time-to-insight: A global science and tech company automated its R&D lab instruments, improving data integrity, reducing errors, and saving 28 minutes per run.
• Scalability: AstraZeneca rapidly scaled its transition to a new ELN, onboarding 3,800 scientists in only 18 months.
• Collaboration: Merck MSD improved collaboration with CROs by deploying secure ELN workspaces.

Beyond Cost - Measuring Scientific Impact

Conventional ROI metrics, like cost and efficiency, have proven their value, as witnessed by companies that transitioned to digital workflows from manual and paper-based record-keeping and data collection. When Axxam transitioned from fragmented paper documentation to a unified ELN, they eliminated 3,000 paper notebooks of experimental data, improving quality and audit readiness. This saved the company thousands of hours in manual documentation and reduced material costs. And SK Life Science Labs minimized manual CRO data exchange delays, reducing turnaround from several days to minutes with Signals Notebook.

While examples like these demonstrate the importance of measuring cost and efficiency, these metrics have the limitation of ignoring scientific quality, collaboration, and scalability. Layering in additional metrics builds a richer, more holistic view. Here are some modern metrics for scientific R and D that go beyond cost: 
 

Time-to-insight

A global science and technology company sought to reduce time to market by undergoing digital transformation in an R&D testing laboratory. By integrating 8 lab instruments, Signals Notebook, and Snowflake data lake with the Signals DLX platform, the company automated data flows, reduced potential data entry errors (eliminating 1,568 errors/year), and saved 28 minutes per run (~188 days/year). 
 

Reproducibility

Manual record-keeping is notoriously cumbersome and hampers quick access to data, traceability, and regulatory compliance. Axxam, a drug discovery CRO, chose an ELN to improve data integrity and ISO quality compliance. The benefits of this digital transformation included better reproducibility, management of workflows, scalability, and improved quality. 

Collaboration

FrieslandCampina, one of the largest global dairy cooperatives, centralized, standardized, and secured its previously fragmented R&D lab data, which was spread across numerous files and spreadsheets, by deploying Signals Notebook for 600 employees across multiple sites. This has reduced their scientists’ data-handling workloads and enhanced productivity.

Merck MSD was able to unify internal and external research & development through a secure data infrastructure, seamlessly integrating CROs into secure digital workspaces for real-time data sharing, streamlined workflows, enhanced visibility, trust, and faster research decisions. 
 

Innovation & scalability 

Scientists at AstraZeneca were reluctant to use their existing ELN because they found it slow and clunky. By transitioning to Signals Notebook, the company efficiently onboarded 3,800 scientists in only 18 months, accelerating complex drug development timelines and maximizing the value of R&D data. The company is now extending this to outsourcing partners.

SK Life Science Labs is a biotech company seeking to discover and develop new central nervous system (CNS) medicines. Initially, manual data sharing with their CROs using spreadsheets through a Cloud platform was inefficient, cumbersome, and error-prone. By adopting an ELN, SK Life Science Labs was able to improve efficiency, traceability, collaboration, inventory management, and operational visibility. The result was accelerated biotech innovation, most notably with data-sharing turnaround times reduced from several days to minutes.

4 Practical Steps to Benchmark ROI and Measure Success

Digital transformation initiatives often struggle not from lack of value, but from inability to articulate and measure that value effectively. Demonstrating tangible impact requires a systematic approach that connects technology investments to business outcomes.

This framework provides a structured methodology to establish baselines, track meaningful metrics, quantify ROI, and create a continuous feedback loop that drives both adoption and optimization.  

1. Define KPIs

Use modern scientific metrics, like time-to-insight, reproducibility, collaboration frequency and reach, error reduction, and ability to scale, to assess the value of a digital solution. 

2. Capture baseline metrics 

This was done by Axxam, when they sought to replace paper notebooks with an ELN. They measured the extent of manual workflows and paper-based documentation, the limitations of reproducibility, the difficulty sharing raw data with clients and collaborators, and the extent of information loss and errors. 

3. Monitor KPIs

KPIs are monitored via dashboards native to digital solutions, such as Signals Notebook and Signals One. 

4. Iterate for continuous improvement 

Regularly review KPIs, identify gaps or inefficiencies, and refine processes to ensure the digital solution continues to deliver measurable value.

Accelerate Discovery by Measuring What Really Drives It—Scientific ROI 

ROI in scientific R&D can no longer be reduced to cost savings and efficiency alone. While these gains remain important, they only tell part of the story in a research landscape rich in data, collaborative opportunities, and an ever-increasing need for speed to market.

Modern scientific success depends on time-to-insight, reproducibility, seamless collaboration, and the ability to scale innovation across teams and CRO partners. As real-life examples show, digital solutions prove their worth when these multidimensional metrics track progress. By defining the right KPIs and monitoring them continuously, organizations can progress beyond incremental improvements and deliver meaningful scientific impact. 
 

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