How to Eliminate Data Silos in Your Lab

Picture this: It's 3:47 PM on a Friday when your biggest client calls.

They need to know the results for a test run earlier today – one that determines whether they can ship $2M worth of product the following Monday morning. You know the test was completed. But you have no idea where the results are, and the technician who ran the test left early for a long weekend.

This is an all-too-common issue that plagues labs across industries: data silos. 

When you only have to keep track of a few spinning plates, it’s easy to manage everything with spreadsheets and notebooks. But as your lab and throughput scale, suddenly those manual systems break down – sometimes with catastrophic results.

In this article, we’ll show you why data silos are dangerous and how modern labs overcome them.

What are Data Silos?

Data silos occur when data becomes fragmented, whether it’s sample data spread across multiple spreadsheets, differing software platforms for ingesting data, or version history issues for a spreadsheet, leaving you wondering what the source of truth is.  

No lab sets out to create data silos. But still, they happen. People come and go and carry knowledge with them, systems don’t play well together, and most of your staff will be more concerned with shipping tests over data infrastructure.

Once in place, though, data silos compound over time, causing the following:

• Delays: Analysts waste hours hunting down sample lineage or re-running tests due to missing data. Whether it’s a client or audit prep, you’re in for a long afternoon tracking down data across systems.
• Compliance gaps: Audit trails become fractured or incomplete, especially across legacy systems. This leads to poor data integrity and a lack of chain of custody in your lab.
• Scientific risk: Version mismatches or missed annotations can corrupt results or compromise reproducibility.
• Operational drag: Every workaround—manual entry, copy/pasting, email chains—is time stolen from real work.

Let’s look at a real-world example to show just how bad this can get.

A cosmetics lab we spoke with relied on manual methods for logging data. They used a slew of physical notebooks, index cards, and paper-based workflows, resulting in inventory and QA/QC being tracked by hand in a stack of paper no auditor wanted to touch.

Even more concerning? This lab worked with over-the-counter drugs and cosmetics, giving them even more scrutiny from the FDA and a higher risk of compliance failures due to their outdated systems. 

Is this an extreme case? Yes, we’ll give you that. But the second you start transcribing data in multiple places, you are susceptible to the very same risks as this lab.

Why Data Silos Occur

It’s not that your staff doesn’t want to share data, it’s that they are hamstrung by their systems.

As your lab scales, the risk increases exponentially for information to split. Soon, gaps begin to emerge. New spreadsheets are created without being shared. One department begins using a software tool that formats data just a bit differently than the other department is used to. Eventually, the system fragments so much that your lab is at a near-constant risk of crashing down.

The most common causes of data silos we see are:

• Software mismatch: If different departments purchase different software platforms that do not “speak” to each other, it can lead to data fragmentation and multiple sources of truth or double-data entry. 
• Communication breakdowns: As your lab expands or adopts a hybrid approach, there’s a greater risk of staff logging data without sharing, whether it’s in a physical notebook or a spreadsheet that was never uploaded to a shared drive.
• Incompatible file formats: Incompatible file formats or data structures can cause data silos, too. For instance, suppose your LIMS exports CSVs, but your analytics platform only accepts XML – this forces your team to reformat files manually (and increases the chance of errors).
• Security-driven silos: Sometimes, data is intentionally segmented due to security restrictions. While these are well-intentioned siloes, they do pose a risk to your lab’s data integrity.

Whatever it is, entropy in your lab’s systems results in fragmented data that slows you down and puts you at risk of non-compliance. 

Why Data Silos Matter – The Impact of Data Silos on Your Lab

While they may seem innocuous, data silos can have a serious impact on the efficiency and organization of your lab. 

This leads to operational challenges, as well as data integrity issues that slow your lab down in the following ways:

• Operational challenges: Data silos lead to increased time spent tracking down data or combining it from different sources, slowing down your staff.
• Scientific impacts: Data silos lead to missed opportunities due to ignored or lost data or slowdowns due to time spent tracking down test results or sample histories.
• Business impacts: Redundant systems increase costs and slow down response times.
• Risk of failed audits: Fragmented data leads to reconciliation issues and incomplete audit trails, threatening its integrity and leading to compliance failures.
• Compliance risks: Siloed systems, fragmented data, and incomplete audit trails put you at risk of failing to pass numerous compliance standards.

Data silos don’t just slow down your lab; they threaten the reliability and integrity of your lab’s data. 

The Math Behind Data Silos and the Integration Problem

Can’t it just be a simple matter of better practices and good SOPs? Maybe at a small scale, but as you adopt more systems and processes, the math does not work in your favor.

Most labs connect their systems point-to-point: each database talks directly to every other database it needs data from. This creates a "spaghetti" web of custom connections that gets exponentially more complex as you add systems.

The mathematical formula behind this, n(n-1)/2, scales quadratically, not linearly, so with 5 systems you need 10 integrations, but with 10 systems you need 45.

Each connection requires custom code, ongoing maintenance, troubleshooting when systems update, and fixes when data doesn't sync. IT teams end up spending more time maintaining integrations than supporting actual science. Scientists wait for data to appear or manually bridge the gaps themselves.

By the time you have a dozen systems, the integration overhead often outweighs the benefits of the individual tools.

What this looks like in practice.

Imagine a contract testing lab that's grown from a startup to mid-size over five years.

They started simple: just their LIMS and a few instruments. But success meant growth, and growth meant adding systems piecemeal – all the way to 9 systems requiring 36 potential integrations.

In practice, their lone IT hire spends months building a custom integration between all their software but two weeks after going live, one platform pushes an update to its API, requiring all customers to integrate with a new version in the next 90 days.

This takes a month or so to fix. But what if another one of their systems just so happens to make the same decision? The result could be a catastrophic slowdown if the changes cannot be made in time.

The Real Costs of Fragmented Data in Your Lab

There are real costs to these failures behind a little wasted time sifting through spreadsheets.

In 2024 alone, 61% of FDA warning letters cited data integrity issues, with three major pharmaceutical manufacturers receiving citations for microbiology and environmental monitoring failures.  In 2022, one lab failed to maintain legible laboratory notebook records and couldn't attribute test results to specific personnel. To meet FDA standards, they were then required to meet a lengthy and stringent set of requirements, including the following:

• A comprehensive, independent assessment and CAPA plan for all computer system security and integrity.
• A list of all hardware.
• Identification of all hardware and software vulnerabilities.
• A list of all software configurations and integrations.
• A breakdown of user roles and privileges for all staff with access to computer systems.

And much more. The risks here aren’t a lost afternoon or a slowdown for one week. Data integrity failures caused by fragmentation could put a complete stop to work in your lab for weeks, delaying revenue and severing customer relationships in the process.

4 Tactics to Eliminate Data Silos in Your Lab

As dangerous as data siloes can be, they are fortunately simple to rectify – even in a large lab. 

Try the following to reduce/eliminate silos in your lab:

1. Adopt a FAIR data approach
2. Implement lab data management software
3. Integrate instruments with your software
4. Improve communication and collaboration 

Adopt a FAIR Data Approach

Start with a better approach to data management in your lab. The FAIR data approach is an acronym for:

• Findable: Every sample has a unique ID that lets you search across all systems. Instead of hunting through folders named "July_tests_v2_final," you can search "Sample ID: MD-2024-0847" and instantly pull up everything you need.
• Accessible: Your west coast team can access the same data as your east coast team without calling someone to unlock a system or dig through a locked filing cabinet.
• Interoperable: Your HPLC software exports data in a format your LIMS can actually read, eliminating the copy-paste routine that introduces errors.
• Reusable: When a client asks about a similar test from six months ago, you can find and reference that methodology instead of starting from scratch."

This framework ensures that data not only flows freely within the organization but also maintains its utility and value over time. Together, these approaches create a comprehensive strategy for breaking down data silos and creating a more efficient, collaborative laboratory environment.

Eliminate Manual Data Entry and Implement Lab Data Management Software

One of the best things you can do to reduce or eliminate data silos in your lab is to leverage software to eliminate any need for paper/spreadsheet data entry while consolidating data from your systems and software.

Platforms like a LIMS (lab information management software) provide a centralized platform for data storage and management. These platforms can integrate with a variety of lab instruments and software while providing API capabilities, allowing you to build custom integrations as needed. This gives your lab a single source of truth for its data and ensures that every sample, test result, or inventory order is accessible to your entire team.

The key is selecting a system that can handle your specific data types and workflows while remaining scalable and integration-friendly with other systems.

Integrate Instruments With Your Software

A LIMS alone does not guarantee that you have a single source of truth in your lab. 

You will need to integrate your systems and software with it to consolidate your lab’s data in one place. Through instrument, software, and API integrations, you can connect every piece of equipment in your lab to automatically input data. 

This virtually eliminates manual transcription and associated errors while providing standardized data formats and structures. Thanks to automatic data capture with timestamps and user tracking, you can reduce the risk of data becoming trapped in instrument-specific formats or local drives.

Improve Communication and Collaboration 

Apart from software, there are the “human aspects” of your lab that you can try to transform.

By reworking SOPs and staff training to support data management best practices, you can reduce the risk of future data silos. This often involves cultural changes and cannot be overlooked when creating a truly integrated laboratory environment. That said, you are fully reliant on your staff without software to consolidate and capture data.

4 Ways a LIMS Eliminates Data Silos

As we said above, manual processes and staff changes can help, but nothing compares to a LIMS when it comes to organizing and maintaining the data in your lab. A LIMS like QBench can help eliminate data silos in the following ways:

1. Centralize data: This is a given. Rather than store inventory, test results, calibration schedules, and other data across spreadsheets, you can centralize every piece of data in one place with a LIMS. QBench supports robust inventory management and sample management features, giving you a complete source of truth for your lab.
2. Integrate your systems: This is where a LIMS truly excels. By integrating your software and instruments with your LIMS, you can automate data capture, speeding up your lab while reducing data entry errors. QBench supports 50+ popular software platform integrations along with a RESTful API to connect pretty much anything to your LIMS.
3. Standardize data collection: Through data validation, barcode scanning, and more, you can standardize the data you collect and ensure your LIMS remains a source of truth for your lab.
4. Audit data and activity: QBench supports robust security features like audit trails, role-based access, and regular data backups. This ensures your data is secure and recoverable – both necessary to pass compliance checks and protect your data in the event of an emergency.

For modern labs looking to scale compliantly and get more done, a LIMS is a necessity. While a LIMS can be an invaluable tool for your lab, it’s critical that you select the right LIMS.

Looking For a LIMS? Download the LIMS Buyer’s Guide to Make the Right Choice

Audit failures. Lost test data. Delayed shipments. These are the risks that data silos pave the way to.

When it comes to mitigating or preventing them, you need a single source of truth that connects each instrument and data point in your lab. That source of truth should be a LIMS.

A LIMS centralizes your operations, eliminates manual errors, and gives you the audit-ready visibility you need to stay compliant and competitive.

But not all LIMS are created equal. The wrong system will leave you stuck with clunky workflows, poor integrations, and more headaches.

Our free LIMS Buyer’s Guide breaks down exactly what to look for – and what to avoid. If you're serious about eliminating data silos, click the button below to learn how to select the right LIMS for your lab.

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