Authors: – Shawani Shome (Scientific Systems Analyst, Scientific Informatics)
Nowadays in laboratories, there is an increasing amount of available technology. There are promises of Electronic Laboratory Notebooks (ELN), Laboratory Information Management Systems (LIMS), and Scientific Data Management (SDM) platforms with improved data integrity, streamlined workflows and faster research. However, even with all this digitalization of the field, many scientists are still using spreadsheets, handwritten documentation, and even disconnected applications for the completion of their experiments.
This issue isn’t a lack of technology but rather lack of usability.
Scientists are not rejecting digitalization because they do not like technology. They are rejecting workflows that hinder their scientific work. The difference between the capabilities of a platform and the ease of use for scientists is known as the usability gap. It could be one of the greatest opportunities in the digitalization of laboratories today.
The usability gap is not simply a UX problem — it is a data quality problem and an R&D productivity problem. When scientists resort to spreadsheet workarounds and disconnected notes outside of ELN and LIMS systems, the data those systems were meant to capture becomes fragmented and unreliable. Excelra’s blog on The Hidden Cost of Bad Lab Data: Why Data Quality Is Now the Biggest Bottleneck in Life Sciences R&D examines the downstream consequences of exactly this pattern — showing how lab informatics adoption failure and data fragmentation translate directly into slower research timelines and compromised data integrity.
The real challenge: Technology vs. Workflow
Scientific research does not normally follow a straightforward path. The protocols change, the experiments evolve and sometimes new observations shape the next step. Scientists thus require a digital system that will adapt to this dynamic behaviour rather than forcing each activity to fit into a predefined rigid workflow.
Unfortunately, in most cases, the design process does not always take into account the ease-of-use aspect, and the scientist has to navigate through multiple tabs to infer one observation, enter the same information repeatedly, or use multiple fragmented applications to complete one experiment.
Slowly but surely all these minor inconveniences accumulate, and the scientists come up with their workarounds to overcome these troubles — maintaining personal spreadsheets, recording notes outside ELN and generating the reports manually. Ironically, the very systems designed to manage data better, end up contributing to its fragmentation.
The tension between rigid digital workflows and the non-linear reality of scientific experimentation is one of the most consistently reported reasons why ELN and LIMS implementations underperform expectations. Excelra’s blog on Overcoming Laboratory Data Chaos (Part 1): The Case for ELN-LIMS Integration explores this challenge from an integration architecture perspective — examining why disconnected systems create data chaos even when each individual platform is technically well-designed, and what a connected informatics approach looks like in practice.
Lab informatics platforms are usually evaluated based on features, integration, and technical capabilities. Although those aspects are important, they do not guarantee adoption.
In the 2024 survey of laboratory software, 48% of labs mentioned “disconnected systems” among their major barriers to adoption, even though 61% expected digital tools to automate manual processes. Expectations were high, but without workflows that align with day-to-day lab practices, even the feature-rich systems struggle to deliver value.
This explains why successful digitalization requires much more than just selecting a good platform. A LIMS needs to simplify sample management, an ELN needs to record scientific reasoning without disrupting experiments, and the data needs to flow freely between devices and software solutions. Technology that blends seamlessly into the research process enables scientists to spend less time in software management and more time for data analysis.
The 48% disconnected systems figure reflects a structural failure of implementation strategy rather than platform capability. Many organisations select feature-rich lab informatics platforms but invest insufficient effort in configuring those platforms to match their actual scientific workflows. Excelra’s blog on Lab Informatics Implementation and Delivery addresses this gap directly — explaining why implementation methodology, workflow alignment, and user engagement are as important to lab informatics success as technology selection.
A major reason why usability continues to be a problem is that laboratories differ from each other. Each laboratory — a discovery biology lab, an analytical chemistry team, and a quality control laboratory, for instance — runs experiments, generates data, and operates using its unique workflow. In other words, the workflow at a lab is different from the workflow of another organization.
Digital implementations tend to assume a standardized workflow. Rather than forcing scientists to fit into software requirements, it would make sense to configure software according to the actual scientific workflow. This includes intuitive templates, automation of routine tasks, instrumentation and data integration, and reduction of superfluous manual data entry.
However, such an approach is critically important because, in many cases, digital transformation initiatives do not deliver on their promises. In fact, the majority of transformation initiatives fail to deliver on their intended benefits due to the lack of attention to people, processes, and change management despite all efforts made in the technological realm.
Closing the gap of usability calls not only for technical skills but also for an understanding of laboratory science. For a successful implementation, it is critical to involve scientists from the beginning of the project.
One reason usability remains a challenge is that no two laboratories work the same way. A discovery biology lab, an analytical chemistry team, and a quality control laboratory all follow different workflows, generate different data, and operate under different regulatory expectations.
Yet digital implementations often assume a standard process.
Instead of expecting scientists to adapt to software, organizations should configure software around the way scientists work. That means designing intuitive templates, automating repetitive tasks, integrating laboratory instruments, and reducing unnecessary data entry. Technology should support scientific workflows — not redefine them.
This human-centred approach is especially important because digital transformation projects frequently struggle to achieve their intended outcomes. Across industries, only a small proportion of transformation initiatives fully meet their objectives, often because organizations focus on technology while underestimating the importance of people, processes, and change management.
The instrument integration challenge deserves particular emphasis. Many lab informatics usability complaints stem not from the ELN or LIMS interface itself but from the friction of manually transferring data from laboratory instruments into those systems. Excelra’s case study on LIMS Integration with AI/ML Cheminformatics Pipelines illustrates what seamless instrument-to-platform data flow looks like in practice — eliminating one of the most common manual bottlenecks that drives scientists back to spreadsheets.
Closing the usability gap requires more than technical expertise. It requires an understanding of laboratory science.
Successful implementations begin by engaging scientists early, understanding how experiments are actually performed, and designing workflows that reflect real laboratory practices. As research evolves, digital systems should evolve alongside it through continuous feedback and refinement.
This is where implementation partners can make a meaningful difference. Their role isn’t simply to deploy software, but to bridge the gap between scientific workflows and digital technology.
At Excelra, this philosophy guides our approach to lab informatics. By combining scientific domain expertise with experience across ELN, LIMS, and integrated informatics platforms, we help organizations configure technology around laboratory workflows rather than expecting laboratories to adapt to technology. The result is greater adoption, improved data quality, and digital solutions that genuinely support scientific discovery.
For organisations evaluating how to approach this kind of scientist-centred implementation, Excelra’s ELN/LIMS Master Data Preparation: Building the Foundation for Data Excellence blog provides a practical framework for the often-overlooked data preparation work that determines whether a lab informatics deployment succeeds or struggles with adoption — including how to structure master data to reflect actual laboratory workflows rather than generic system defaults.
The success of a lab informatics platform isn’t measured by the number of features it offers. It’s measured by whether scientists trust it enough to make it part of their everyday work.
Digital transformation should reduce friction, not create it. When laboratory systems are designed around the people who use them, they become more than repositories of data — they become enablers of better science.
Ultimately, bridging the usability gap requires a simple shift in perspective: instead of asking “What can this software do?”, organizations should ask “How can this software help scientists do better science?” The answer to that question is what transforms a good system into a truly successful one.
Excelra’s lab informatics consulting and implementation services span the full journey from system selection and workflow design through deployment, configuration, user training, and managed operations. To explore how Excelra’s scientist-centred approach to ELN, LIMS, and scientific data management can help your organisation close the usability gap, visit our Lab Informatics service page.
What is the usability gap in lab informatics?
The usability gap in lab informatics is the difference between what a laboratory software platform is technically capable of doing and how easily scientists can actually use it to support their real experimental work. It explains why many laboratories invest in Electronic Laboratory Notebooks (ELN) and Laboratory Information Management Systems (LIMS) but find that scientists continue using spreadsheets, paper notes, and informal documentation alongside — or instead of — these systems. The usability gap emerges when platforms are designed around system requirements and feature specifications rather than the actual workflows of scientists, who work non-linearly, adapt protocols dynamically, and need to capture reasoning as well as results. It is not a technology problem but a design and implementation problem — and closing it requires engaging scientists in the design process, configuring workflows to match laboratory practice, and treating adoption as an ongoing process rather than a go-live milestone.
Why do ELN and LIMS implementations fail to achieve expected adoption?
ELN and LIMS implementations fail to achieve expected adoption for predictable reasons that have little to do with the quality of the software itself. The most common cause is workflow misalignment: the platform is configured to match a generic or idealised process rather than how experiments are actually performed in that specific laboratory. When scientists find the system slower than their current workaround, harder to use than a spreadsheet, or disconnected from the instruments and applications they already rely on, they route around it. A 2024 survey found that 48% of labs cited disconnected systems as a major barrier to adoption — even though 61% expected digital tools to automate manual processes. The gap between expectation and reality is where adoption fails. Successful implementations address this by involving scientists early in design, configuring templates and workflows to reflect real practices, integrating with existing instruments and systems, and investing in change management as a project deliverable, not an afterthought.
What does scientist-centred design mean for ELN and LIMS platforms?
Scientist-centred design in ELN and LIMS means configuring the platform around the way scientists actually work rather than expecting scientists to adapt their work to fit the platform’s default structure. In practice, this means several things: designing templates that reflect the actual experiment types and data fields used in that specific laboratory, rather than generic templates from the vendor; automating the most repetitive data entry tasks so scientists spend time on scientific thinking rather than data transcription; integrating with laboratory instruments so data flows automatically from instrument to platform without manual transfer; building workflows flexible enough to accommodate protocol changes and unexpected observations; and involving scientists throughout the design process, not just at go-live. The result is not a softer version of lab informatics — it is more rigorous, because when scientists trust the system and use it consistently, the data it captures is more complete, more accurate, and more valuable for downstream analysis and compliance reporting.
How does poor lab informatics usability affect data quality?
Poor lab informatics usability affects data quality through a specific and predictable failure chain. When scientists find an ELN or LIMS difficult to use, they develop workarounds — maintaining personal spreadsheets, recording observations in notebooks outside the system, or delaying data entry until they have time to navigate a cumbersome interface. Each workaround creates a data silo: information that exists somewhere but not in the system where it was supposed to be captured. When this data is eventually transferred, it may be incomplete, formatted inconsistently, or stripped of the experimental context that makes it interpretable. Over time, the laboratory ends up with a fragmented data landscape: some records in the LIMS, some in spreadsheets, some in email threads, and some only in the scientist’s memory. This fragmentation directly undermines data integrity, reproducibility, and the laboratory’s ability to leverage its historical data for AI and machine learning applications — making the usability gap a strategic data risk as well as a productivity issue.
What is the role of change management in a lab informatics implementation?
Change management in a lab informatics implementation is the structured process of preparing the scientists and operations teams who will use the new system to understand, adopt, and sustain it. It is consistently identified as one of the most underinvested aspects of lab informatics projects — and its absence is one of the most common reasons implementations fail to deliver their intended benefits. Effective change management includes engaging scientists before the implementation begins to understand their workflows and concerns; communicating clearly about what will change, why, and when; designing training that teaches scientists how the new system supports their specific work rather than generic platform features; identifying and supporting early adopters who can model the new workflows; establishing feedback channels so usability issues are surfaced and addressed continuously; and treating go-live as the beginning of adoption, not the end. Digital transformation research across industries consistently shows that the people and process dimensions of a transformation programme determine its outcome more reliably than the technology selected.
How should organisations choose between ELN and LIMS for their laboratory?
ELN and LIMS serve different but complementary functions, and most modern research laboratories ultimately need both — though they may implement them at different stages. An Electronic Laboratory Notebook (ELN) is designed for scientific documentation: recording experimental design, protocols, observations, interpretations, and reasoning. It captures the scientific context around data. A Laboratory Information Management System (LIMS) is designed for operational workflow management: sample tracking, test assignment, workflow orchestration, results reporting, and compliance audit trails. It manages the operational process that generates data. The decision about where to start depends on the laboratory’s most pressing problem. If the primary pain is scientific documentation quality, reproducibility, and knowledge capture, an ELN is the higher-priority investment. If the primary pain is sample management, workflow bottlenecks, and compliance reporting, a LIMS addresses those more directly. The most powerful approach is to implement both with an integration strategy that allows data to flow between them — and to work with an implementation partner who can design that integration around the laboratory’s actual workflows.
Ready to Close the Usability Gap in Your Lab Informatics Platform?
Excelra's lab informatics team combines scientific domain knowledge with implementation expertise across ELN, LIMS, and scientific data management platforms. We configure technology around your laboratory workflows — not the other way around. The result is higher adoption, better data quality, and digital systems that scientists actually want to use.
