What a Scientist Should Look for When Evaluating a Digital Lab Platform

A digital lab platform should make your work easier to run, easier to repeat, and easier to explain six months later.

But plenty of systems look good in a polished demo and miss the mark when you ask them to handle everyday lab work. This might include finding a sample, checking which protocol version was used, booking an instrument, tracking a reagent, or showing who changed a record and why. 

This gap between demo and reality is more common than you think. Broad research on enterprise software finds that roughly 66% of technology projects end in partial or total failure, and 70% of digital transformation initiatives fail to meet their original objectives, often because tools don't fit real day-to-day workflows. The right platform fits the way your lab actually works, even if it doesn't have the longest feature list.

So let's talk about how to choose the best software platform for your lab.

Start with the workflow, not the software category

"ELN," "LIMS," "inventory software," and "lab management platform" are useful labels, but they can hide a lot.

Before comparing vendors, write down one or two workflows your team runs every week. For example:

• A sample arrives, is labelled, stored, used in an experiment, and split into aliquots.
• A scientist follows a protocol, records observations, links instrument output, and gets a result reviewed.
• A reagent is received, assigned a location, monitored for expiry, and reordered before stock runs out.
• A shared instrument is booked, calibrated, used, serviced, and taken out of circulation when needed.

Then ask each vendor to show that complete workflow using your terminology and your sample types.

If the demo only works with a fictional "Sample A" in a perfect linear process, you have not properly evaluated the system yet. 

This matters because a large share of laboratory error still originates upstream of the analysis itself. One peer-reviewed audit of clinical labs found that 60%-70% of all reported errors were pre-analytical (sample handling, labeling, and identification), rather than problems with the analysis itself. 

Look for records that stay connected

McKinsey research indicates that data users, including scientists and researchers, can lose 30% to 40% of their working time simply searching for information when there's no clear system of record. A survey of 4000 workers found that they spend 3.6 hours on average daily looking for information relating to their jobs.

This is where a lab software platform earns its keep. You should be able to start with a sample and answer: What is it? Where is it? Who used it? Which experiment used it? Which protocol version applied? What happened next?

If those answers still need you to switch between spreadsheets, shared drives, email threads, and paper notes, the platform isn't carrying enough of the operational load.

For example, you should be able to connect:

• Samples and their parent materials, derivatives, locations, and lifecycle history.
• Experiments, protocols, observations, results, and attached files.
• Reagents, consumables, stock levels, expiry dates, etc.
• Equipment bookings and service history.
• The people, roles, approvals, and permissions involved in each workflow.

SciSure LIMS connects configurable sample records with storage, lineage, experiments, protocols, inventory, and equipment. Its sample-management capabilities are designed to help teams trace materials from creation through use, movement, and disposal.

Test traceability before you need it

Traceability is easy to underestimate until a result needs to be checked, a sample can’t be found, or an auditor asks how a record changed.

Look for clear, searchable audit history, role-based permissions, version-controlled protocols and experiment records, electronic signatures or witness steps where your workflow requires them, exportable records that are readable outside the system, and backup, recovery, and data-retention options that fit your lab.

For regulated work, software capabilities are only one part of the picture. FDA 21 CFR Part 11 applies to certain electronic records and signatures. Your organization is responsible for validation, procedures, training, and change control.

And the records themselves should support data integrity. The MHRA’s GxP data-integrity guidance sets out the ALCOA principles (attributable, legible, contemporaneous, original, and accurate) alongside the additional expectations that records are complete, consistent, enduring, and available.

In other words, ask what the platform can support, but do not accept "Part 11 compliant" as a substitute for understanding your own intended use.

Make sure it works at the bench

The best system is the one scientists will actually use. That means testing ordinary actions, not just administration screens:

Examples of what to check:

• Can someone find a sample in seconds?
• Can they update a record while standing at a freezer or bench?
• Can they use templates without losing the flexibility research requires?
• Can they scan labels, move materials, and log use without duplicate data entry?
• Can they handle an exception without inventing a workaround in Excel?

Also test search, navigation, bulk actions, and file handling. These sound minor until your team is repeating them hundreds of times a week, so a good rule is to give several future users a normal week's worth of tasks in a trial environment to test it out. 

Software adoption, not feature depth, is the most common reason enterprise tools underperform: broader implementation research finds that 70% of software rollouts struggle specifically because of poor user adoption, regardless of how capable the underlying platform is.

Check what connects… and what doesn’t

No platform exists in isolation. Your lab may already rely on instruments, barcode scanners, label printers, storage hardware, analytics tools, electronic medical records, purchasing systems, or safety software.

So ask specific questions:

• Which integrations are native, which use add-ons, and which require custom work?
• Is there an API or developer toolkit for systems you’ll need later?
• Are integrations available on the hosting model and subscription tier you’re considering?
• Can data move both ways, or is it only an export?
• Who owns the integration when an external system changes?

SciSure is worth evaluating when you want research documentation, sample and inventory management, equipment records, workflows, integrations, and laboratory health and safety capabilities under a connected platform strategy. It's especially relevant for multidisciplinary organizations and chemical-heavy labs that need research operations and safety oversight to work together.

But fit matters more than breadth. If your central need is biology-aware sequence design, manufacturing batch release, or a very simple low-cost notebook, another specialist may be the better choice.

For an independent look at the platform's strengths, limitations, and ideal fit, read this detailed SciSure review. 

Consider how your lab will change over time

The system you choose today should solve a real problem now, without boxing you in when your lab grows.

Look for a platform that can support new sample types, changing workflows, additional users, more sites, new instruments, and stronger governance without forcing a complete rebuild. 

But do not buy complexity simply because you might need it someday. It’s far cheaper to expand a well-fitted platform later than to walk back an oversized one now.

So start with the workflows causing the most friction. Test them properly. Include scientists, lab operations, IT, quality, and safety early if they’ll all depend on the system.

And if SciSure is on your shortlist, get a personalized demo. Bring your real sample workflows, organizational structure, integration requirements, and deployment needs to the conversation.