Enterprise R&D organizations replace Excel with a LIMS successfully when they choose a vendor that can match their existing spreadsheet workflows, appoint executive and scientific champions, build shared templates before training, and keep improving after go-live. Most failed LIMS projects fail on adoption rather than software: scientists go back to spreadsheets when the new workflows feel harder than the old ones.
Most R&D organizations want to digitize their processes to improve collaboration, reduce data errors, and increase productivity with more modern software, such as a laboratory information management system (LIMS). However, the most challenging part of this journey is often switching out Microsoft Excel, the most common tool for most scientists.
Excel is an excellent general-purpose tool and works very well for a single scientist, but is not designed for large organizations with disparate groups that need to understand and replicate each other's work. But its power leads to scientists having workflows they’re comfortable with in the program that they find difficult to move to a LIMS or electronic lab notebook (ELN). When we talk to organizations about why a LIMS implementation failed in the past, it’s often because they could not get users comfortable with the new workflows and reverted back to spreadsheets. This guide will help you avoid those mistakes and guide your team through migration.
Why switch from Excel to a LIMS?
But why switch to a LIMS in the first place? This guide is too short to go into all of the details of why you would implement a LIMS, but there are a few key things to look out for.
- Do you have scientists looking to communicate results with each other or with other users such as technicians, salespeople, or external customers? Excel is a great tool for a single scientist, but each scientist operating out of Excel templates is a recipe for failure as you scale your organization
- Are you looking to search for and replicate results from prior experiments reliably? A good LIMS will help you search through old data and maintain it in a consistent format - with Excel, there is no guarantee that any two users enter data the same way
- Do you need to enforce data security rules? Ad hoc shared folders are no replacements for a LIMS-specific data security plan.
- Do you want to take advantage of lab management tools - LIMS systems will help manage inventory, equipment, scheduling, requests, and more. Read our LIMS guide to learn the key capabilities of LIMS systems.
How to switch from Excel to a LIMS
Ok, you have decided to replace Excel with a LIMS. But where do you go from here? Switching from Excel to a LIMS works best as a sequence: select the right vendor, plan for change, appoint champions, build templates, train users, decide what data to migrate, and keep improving.
1. Select a LIMS vendor that can match your existing workflows
The most important step comes before the migration process even begins. There are many different LIMS vendors out there, but most will not be a fit for your organization. A structured LIMS selection guide can help you compare them on the same criteria. To ensure any vendor you select is the right fit, you should map out critical workflows with different teams within your organization. It’s likely they have different Excel templates set up for these workflows. You should evaluate your vendors to make sure that scientists can continue to work the way that they work out of these templates.
To evaluate this, sharing data with the vendors you’re interested in may be necessary. We recommend signing NDAs and sharing small data samples, anonymized if necessary, and asking the vendor to show how this would look in the platform. This should not come at any cost to you while evaluating systems.
But you’re not selecting a vendor just because they can match your existing workflows, you’re picking them because of what else they offer. Make sure you also see benefits that will entice scientists out of Excel. While retaining knowledge is an essential organizational goal, it is something scientists may not have a vested interest in. Instead, they will probably be more interested in searching capabilities, visualizations, and other tasks that might be cumbersome with their current workflows.
2. Create a change management plan for LIMS adoption
You have decided you need a LIMS and have picked a preferred vendor, but the work is not done here. This is the phase where the project is most likely to fail - without driving scientists towards your new tool, and they will revert to existing workflows they are comfortable with. There are a few critical aspects to this plan:
The steps that follow (champions, templates, training, and data migration) form the core of the plan. Around them, a good change management plan answers a few practical questions before anyone logs in:
- Why are we changing? Explain the problem the LIMS solves in terms scientists recognize, such as finding a past result without asking a colleague, or not re-entering the same data into three spreadsheets.
- Who moves first? Roll out to one team or workflow at a time, starting with a group that has a clear pain point and a willing champion, rather than switching the whole organization on one date.
- What happens to the old templates? Agree when a spreadsheet template is retired for new work, so the LIMS and Excel do not run in parallel indefinitely.
- How will we know it is working? Decide in advance what adoption looks like, for example which experiments should be recorded in the system and how often champions will review usage with each team.
- Where do questions go? Give users one route for questions and requests, usually through their scientific champion, so issues reach the vendor quickly instead of becoming reasons to return to Excel.
A common mistake is treating the plan as a one-off launch announcement. Adoption is built over months, and a plan that covers the period after go-live is far more likely to keep scientists in the new system. For more on structuring a rollout, see our guide to planning your implementation for success.
3. Find executive and scientific champion(s)
To implement a LIMS, you will need proponents of the tool within your organization. This will include executive stakeholders and scientific stakeholders.
- Executive Champions: The executive champion's role is to ensure that the organization understands that the new tool is a priority. Scientists will need to switch to the tool for the good of the research team, even if there are little pieces of their old workflow that they preferred in Excel. Managers will need to allocate time for training users and configuring the system. This will come at a short-term cost but provide long-term benefits for everyone involved.
- Scientific Champions: The scientific champion's role is to ensure that the processes in the system match your existing processes as closely as possible. For example, they may gather templates from Excel and work with the vendor to ensure the system matches them. They may also coordinate across teams to ensure that all teams use the same terminology in the new system, which is not enforced in Excel but is very important in structured systems like LIMS.
Scientific Champions may also be responsible for training. A “train the trainer” model is often used, where a vendor will train the champions in a research area, who will then train users in a group or individual setting. They may be responsible for gathering and answering questions from the team or relaying them to the vendor.
The Scientific Champions & Executive Champions are the most important pieces of any LIMS implementation. Do not expect to have a successful implementation without these individuals in place. Time-wise, the executive champion will not need to spend much time with the system, as they are mainly there to convey its importance. However, the scientific champion should expect to spend a lot of time - this will depend on the number of users, the complexity of the research process, and how many champions you have.
4. Create data templates & workflows
Once scientific champions have been identified, they should work with the vendor to create templates and workflows within the system. These should ideally match your existing processes as closely as possible; however, teams sometimes need to adapt. You may have a team in North America and Europe that makes the same product but works slightly differently. It’s likely they’ll need to figure out how to record data in a similar manner. This process will need to be figured out before onboarding these teams onto the system - you don’t want to onboard one team and have the other team feel left out and stay in Excel workbooks.
5. Train the users
A training plan should be discussed with your vendor. Typically, the vendor will be responsible for training all scientific champions and may also do group training with all users in the organization. This training plan will rely on the data templates & workflows being in place and should wait to kick off until they are.
6. Migrate Excel data (optional)
This is an optional step in the process commonly asked about. If you have data in Excel, it’s probably not a good idea to migrate this data. It will be a cumbersome process, and the data will have many flaws - you are switching off Excel for a reason. However, it’s plausible there are pockets of good data - exports from other structured systems attachments corresponding to ingredients, for example. Work with your vendor to identify them and discuss a migration plan in this case. This should rarely come before the first items here, especially when switching out of spreadsheets.
7. Continuously improve after go-live
Templates have been made, users are trained and using the system - what’s next? Your vendor should offer regular check-ins to see how your team is doing. Likely, there will still be pockets of work being done in Excel - perhaps certain types of experiments or analyses. Work with your scientists to identify why they feel this can’t be done in your LIMS, and then alert your LIMS provider. They should work with you to configure their system to meet your needs. Excel is a powerful tool and will likely be used by your organization for years to come - but there is always progress to be made in standardizing your experimental workflows so that your group can innovate faster.
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