Data integration is essential to ensure meaningful data usage; expert place emphasis on addressing data standards issues to achieve data integration in a laboratory.Substantial investments should be made in strategising and combining data from a set of instruments in order to turn a medley of free-floating data points into a coherent, integrated message.
With that said, failing to automate data collection and standardisation via instrument integration is the biggest bottleneck that causes substantial lag in laboratory operations.
In a recent article published in European Pharmaceutical Review, Samantha Kanza, a senior enterprise fellow at the University of Southampton, sheds light on the leading challenges of adopting digital technologies in the laboratory data collection process and how to overcome them.
What Is Data Integration?
Data integration means accumulating datasets from multiple sources and equipment and merging them to get a unified view.
Data integration across the value chain—from discovery all the way through production to commercialisation—is critical to strengthening decision-making.
Even though the massive troves of data a laboratory generates are a great asset, most of it remains unexploited due to failure to connect data and processes.
Leading Data Integration Challenges
The researcher underscores the lack of data standardisation and data standard saturation as two major roadblocks to laboratory data integration.
Inconsistent datasets, proprietary data formats, and a lack of compatible instruments are a few of the many barriers worth mentioning.
Let’s dive deeper into the issues with proprietary data formats:
One of the key aspects of successful data integration is to have all datasets from all sources in the same format (or at least convertible into a single format).
However, in many cases, data attributes in two different sources may conceptually convey the same information, but the datasets are in two different formats, which are sometimes difficult to convert into a desired format.
These lexical and structural disparities in datasets may cause unfixable errors or even data loss if data gets integrated without standardising or cleaning.
The Solution
Overcoming data standard issues is paramount to enabling a fully integrated and digitally interconnected laboratory.
The research fellow at the University of Southampton advises laboratories to carefully evaluate the software programs they would leverage while generating data and the file formats these systems support.
On the other hand, software providers should also ensure their programs don’t use intricate proprietary formats that could hinder data integration, sharing, and reuse.
According to Dr. Kanza, researchers must be provided with specific guidelines on the standard process of data structure to efficiently evade the risk of data inconsistencies.
On top of that, all datasets generated directly from the instruments should include metadata to ensure effective utilisation.
Electronic Systems Adoption Barriers
For laboratories looking to accelerate their R&D, using Laboratory Information Management Software (LIMS) or Electronic Lab Notebook (ELN) as the core part of their research activities is critical.
Besides enabling instrument integrations for automatic data capture, ELN or LIMS helps automate workflows and ensure efficient management of samples and associated data.
The result: streamlined operations and quick recognition of bottlenecks across experiments leading to substantially improved efficiency
Despite all the benefits, the adoption of electronic systems in labs has been slow.
Let’s flesh out the reasons:
First off, transitioning away from familiar methods to use a new system may seem challenging for some researchers.
“Whether rightly or wrongly, researchers don’t necessarily trust ELN systems, particularly if they are in the cloud…While trust is important, some of this comes down to a lack of education and understanding on data security coupled with some learned behaviour that needs to be addressed,” explained Dr. Kanza.
Secondly, the scepticism of lab operators about the security of the stored data in cloud-based LIMS or ELN is another leading reason behind the slow adoption rate of LIMS.
Thirdly, even though digital systems help labs streamline the data capture and management processes, the greatest concern arises when labs aim to leverage these digital systems in their daily operations. The full ELN/LIMS adoption requires buy-in from everyone involved. For some users, the learning curve may seem steeper.
Overcoming the Electronic System Adoption Challenges
For laboratories looking to address the above challenges and centralise data once siloed in multiple systems to turbocharge their R&D efforts, cashing in on a high-end digital lab notebook like Sapio Sciences is a sensible decision.
A class-leading ISO-certified system ensures top-notch data security while also offering easy training so that users can get the hang of it in a short time.
On top of that, some high-end integrated LIMS-ELN systems offer automated data capture from instruments via integration that helps minimise experimental redundancy.
Plus, these systems foster decision-making by enabling interactive visualisation of all data points.
However, digitally transforming a laboratory needs a complete overhaul of lab culture, a change of attitude, and a willingness to learn.
Besides, before investing in a system, laboratories should evaluate the disparities between the system and their laboratory requirements. Through careful vendor evaluation, they can choose the most profitable compromise between the features offered by a seller and a fully customizable solution.
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