Qvios vs Excel for managing laboratory work
Most labs run on Excel. It’s used for everything from designing plate maps to tracking samples to managing inventory. While Excel is convenient for simple tasks, it has some drawbacks when it’s the primary tool for running the lab.
It usually starts with a simple spreadsheet. A few samples, some measurement data, nothing too complicated. Over time, you add more conditions and branch into variations. Then someone overwrites a formula. At some point, you lose track of which sample came from where. Eventually, you notice that you have created a monstrosity of tables and formulas that run your entire lab, and no one is sure how to update or maintain it.
Over time, that handy spreadsheet quietly turns into the weakest link in your lab. Still, Excel isn’t the villain here — it’s just the wrong backbone for a growing lab.
When should you use Excel in the lab?
Excel works well for the tasks it is meant to be used for:
Quick calculations
Simple data tables
One-off data analysis
Managing small, linear experiments
For these tasks, Excel is hard to beat. Almost everyone knows the basics, so it naturally becomes the default.
When Excel falls short
While Excel is a great tool for some tasks, the problems start when you try to fit full experimental workflows into spreadsheets.
The problem with modeling workflows in Excel
With limited dimensions (rows and columns), Excel can represent only certain data types. It’s challenging to organize protocol steps, samples, and results into a logical, multi-step branching experiment. There’s no standard way to model these elements, making editing and reuse difficult and often reducing the process to copy-pasting or rebuilding each time.
This can lead to inconsistent execution and results that are hard to reproduce. It also increases cognitive load, as managing spreadsheets becomes a major part of lab work. Where did this sample come from, and which parameters were applied? Excel is no longer helping you run the experiment; it’s just storing fragments of it.
Workflow management tools do more than store data; they organize the process itself. That’s where tools like Qvios come in. Protocols in Qvios mimic real-world lab workflows, where relationships between steps and samples are built in.
The problem with traceability in spreadsheets
When workflows aren’t modeled explicitly, it becomes hard to describe what’s supposed to happen — or to trace what actually did.
Imagine an experiment where primary samples arrive in tubes, are transferred to a 24-well deep-well block for processing, and are then aliquoted into a 96-well plate for final measurement. How do you link the final measurement to the original primary tube in an Excel sheet? Tracking sample lineage is not trivial in a spreadsheet.
Another traceability issue with Excel is version control of the spreadsheets themselves. How do you know which is the latest version? What if someone changed a formula? Protocol version control can be improved by storing the spreadsheets in a separate document management system. However, these systems are usually inconvenient and add another layer of administration to already complex lab work.
Document management systems are good for highly regulated labs, where every change must be tracked and signed for quality and audit purposes, but most labs don’t need all that red tape when version control is built into the workflow management system.
The problem with manual data handling
And even if you manage to track sample lineage in spreadsheets, every manual step introduces another source of error.
“We sometimes need to rerun a batch of samples if someone copied values to the wrong column.” That’s a real comment from a lab manager I spoke with, and I’m pretty sure he’s not alone in this problem. In a busy lab, manual copy‑paste or file transfers between systems are not just tedious; they’re a predictable source of errors. Those errors can lead to wasted resources, reproducibility issues, or even incorrect diagnoses.
If you rely on a spreadsheet to manage your lab work, you need to be sure that a wrong value or wrong data type can’t quietly lead to mistakes at the bench.
The problem with scaling on spreadsheets
Every complex system evolves from simple systems. When the experiments evolve, the spreadsheets cannot keep up. It’s better to start with a system that can scale with your experiments rather than suffer the headache of migrating when you realize that Excel is not sufficient.
The same goes with your team. If you're just getting started or there are a handful of people working in the lab, it’s easy to think you can run things with a file in a shared folder. Once the lab starts growing, your tools need to be ready to scale for real collaboration and teamwork.
In Qvios, each team has a shared workspace by default. Everything is built with teamwork in mind. There’s also role-based access control for the labs that want to fine-tune visibility and access.
The problem with visibility in Excel
As your lab grows, spreadsheets don’t just strain under volume — they also make it harder to see what’s going on at a glance.
Close your eyes and try to imagine an ELISA assay. Do you see rows and columns? It is easier to understand what an experiment is supposed to do when you see the steps, branches, and sample flows rather than just cell references. It is also much easier to explain it to others when you can show what it looks like.
Good workflow tools get this right: a visual canvas with nodes and connecting lines makes it easy to quickly see and understand complex, multistep processes. This is the starting point for every protocol in Qvios.
As an added visibility bonus, all manager-level users in Qvios can see all the experiments in their lab on a single page. They can always keep track of who is doing what and when, without having to browse report files or ask around.
The problem isn’t Excel — it’s the lack of structure
Taken together, these issues can look like a long list of Excel’s flaws. But none of them come from Excel being a “bad tool.”
Spreadsheets do exactly what they’re designed to do:
• Store data in tables
• Let you calculate and manipulate values
• Stay flexible for almost any use case
That flexibility is also the limitation.
Experimental workflows aren’t just collections of data. They have structure:
• Steps happen in a specific order
• Samples move through transformations
• Results depend on earlier conditions
In Excel, all of that structure is implicit. You recreate it manually — through sheet layouts, naming conventions, and careful habits.
That works for a while. But as experiments become more complex, the gap becomes obvious: you’re no longer running experiments. You’re managing spreadsheets that try to represent them.
A different approach: structuring the workflow itself
So instead of asking:
“How do we organize this in Excel?”
A more useful question is:
“What does the experiment actually look like?”
• What are the steps?
• How do samples move between them?
• What depends on what?
Tools like Qvios start from there.
Rather than storing experiments as tables, they represent them as structured workflows:
• Steps are explicitly defined
• Relationships between samples are built in
• The process itself becomes visible, not just the results
This doesn’t remove flexibility; it just moves it to the right level. Instead of rebuilding the structure every time in a spreadsheet, you’re working with a system that already understands how experiments behave.
And that’s the point where the problems described earlier tend to disappear — not because they’re patched over, but because the underlying model changes.
Example: running a multi-step experiment
Once you treat experiments as structured workflows rather than tables, the way you set them up changes completely.
In Excel, a typical setup might look like this:
• One sheet for sample inputs
• Another for intermediate steps
• A third for results
• Manual references connecting them
• Notes scattered across cells or separate documents
It works, but only as long as you remember how everything fits together.
In Qvios, the same experiment is structured directly:
• Each step is defined as part of a workflow
• Samples move through those steps explicitly
• Relationships between inputs and outputs are tracked automatically
• The process is visible as a whole, not reconstructed afterward
The difference isn’t just convenience — it’s that the experiment exists as a system, not as a collection of spreadsheets.
Signs you’ve outgrown spreadsheets
If any of the problems listed above sound familiar, your workflows may already have outgrown spreadsheets.
At that point, you don’t need a better spreadsheet — you need a system that understands how experiments actually run.
Try Qvios with one of our example protocols to see the benefits instantly.
If you prefer a walkthrough first, you can also book a short demo.