By Laércio Fernandes, Product Marketing Manager, Thermo Fisher Scientific
Hypothesize, test, and repeat. For centuries, the fundamentals of the scientific method have helped researchers stride toward greater understanding, and these principles are just as sound today. In recent years, leaps in technological innovation have brought us faster analysis and a greater depth of understanding — two qualities that underpin the modern laboratory. Yet, despite sensitive and efficient chromatography and mass-spectrometry equipment generating vast amounts of data at a rate that continues to accelerate, scientists are often frustrated by software barriers that prevent them from gaining deeper insights from this data. Much of this data continues to be siloed which creates a disconnect and slows analysis, knowledge transfer, and understanding.
To come closer to scientific truths and gain new insights, we must use technology more efficiently and foster a truer sense of collaboration. This route will enable scientists to easily access data and ensure that results are obtained as quickly and efficiently as possible.
For this, a connected and unified laboratory ecosystem is needed, one that harnesses efficiency, facilitates streamlining, and removes barriers through fast and effective knowledge transfer. Digital transformation is needed to facilitate and drive this ecosystem and bring scientists closer to the truth. And the good news? This process has already begun.
Identifying the barriers to progress
The latest chromatography and mass-spectrometry equipment offer unparalleled levels of separation and detection. Scientists can now accurately identify unknown compounds and trace elements even in complex matrices and, as equipment becomes even more sensitive, new levels of understanding are made possible.
However, technology is only part of the picture. It operates within a complex ecosystem of workflows, scientists, and software, each a necessary cog in the analytical machine, albeit not without their limitations. In particular, the opportunity to provide deeper insights from acquired datasets is generally missed, due to information being isolated. This restricts scientists by limiting knowledge transfer and the opportunity to better understand their own data.
Firstly, connectivity issues are starting to emerge as more instruments and software packages are added to the laboratory. This means that manual processes are needed to transfer data between packages, causing significant data security and integrity risks. Added to this, when the laboratory ecosystem is disconnected, siloed data restricts the insights that can be gained from analysis.
Secondly, as instruments have become more advanced, a greater burden is put on the operator to derive faster and deeper knowledge from the data. Some software requires scientists to complete comprehensive, time-consuming, and continuous training programs that take them away from their core purpose: progressing scientific understanding. Where software doesn’t quite meet requirements, workarounds are often required, particularly when different software packages don’t speak the same language. These, largely manual, processes are time-consuming and can introduce errors, negating the efficiencies that software is designed to deliver.
Finally, as we embrace the world of big data, we find bottlenecks developing in processing capacity as systems struggle to cope with the volume of information that can be generated from each separation and detection cycle.
When the proposed solution isn’t fit for purpose, the problem remains. This is what we see in the analytical space, with software, technology, and scientists unable to connect and coordinate activities in the way that brings the most powerful insights. To enable scientists to get closer to the truth, we must look towards fully cohesive and fit-for-purpose solutions that are no longer restricted by software barriers.
Entering a new era of digital transformation
Forward-thinking vendors are starting to tackle connection, complexity, and capacity issues by re-engineering software to combine capabilities and provide an integrated laboratory platform. These systems focus on providing a central storage and collaboration space with fit-for-purpose workflows and straightforward, intuitive interfaces.
Importantly, this is about reinventing software for full integration but doing so in a way that maintains known and respected models and interfaces. It embraces the idea that scientists shouldn’t carry an additional user burden but should be allowed to access readily available tools. This concept of full integration enables end-to-end workflows to be fully pre-configured or customized which embeds best practices and supports more automated processes.
It’s still early days, but we are already seeing providers start to rework their entire software suites, including CDS, spectral libraries, and even laboratory information management systems (LIMS), with future-proofed integration in mind.
What will these laboratory ecosystems look like?
A key component of these new ecosystems is the development of a single centralized source of truth. Typically, this is achieved through creating a data lake, where all data is stored centrally (ideally cloud-based), in a common digital language, and accessible from anywhere at any time. This approach amalgamates fragmented and siloed data and allows true collaboration across teams, regardless of geography or data source. Artificial intelligence (AI) and machine learning (ML) can then drive long-term data mining and deliver even greater insights by accessing data from a single repository.
Developers are already leveraging the increased processing power that accompanied the big data revolution. By doing so, they ensure that the plethora of data created from analytical instruments can be fully and quickly integrated and every insight explored.
Fundamentally, data must be processed and stored in a common, readable format and available to be passed securely, seamlessly, and automatically between laboratories, sites, and even companies, in a remotely accessible way. This means that scientists can access all instruments and data through one central platform, benefitting from full communication between all laboratory software and related instruments.
By establishing this integrated system, laboratories can create a seamless ‘data handshake’ and end-to-end workflow automation can be established. Data can then flow from acquisition to processing, analysis, and reporting with minimal manual intervention. The centralized digital environment can manage all workflows from one place.
Developers who design new platforms are maximizing their learnings from our app-based culture. In this new digital age, we expect software to be easy to use so developers must also reduce the user burden in the complex analytical space by introducing standard, intuitive interfaces. More scientists will soon be able to access and use systems more easily without undertaking comprehensive training.
However, this simplification and greater data access must not come at the detriment of security. Fortunately, a centralized data ecosystem, by its very nature, means better control. Embedded user management, audit trails, security features, and privacy settings adhere to the principles of least access and will deliver greater data security and integrity as data lakes continue to grow and evolve.
New systems that embed all of these much-needed features are unlikely to be released as ‘big bang’ launches, but gradually evolve to incorporate existing software as part of a wider strategic shift. This future-proofed approach ensures that systems can adapt and change with technology as it advances while maintaining the central focus on integration.
Digital transformation is already underway
Some providers are already working on plans to bring teams, software, and technology together within an integrated laboratory ecosystem to tackle the three Cs that act as a barrier to scientific discovery: connection, complexity, and capacity.
These systems continue to evolve, including a growing number of elements and functionality as they integrate and centralize data, increase processing power, automate workflows, and simplify interfaces.
The benefits are already trickling into the laboratory as systems start to:
- Facilitate collaboration – by providing a secure, connected, global data ecosystem that allows people, data, and instruments to interface more effectively.
- Increase productivity – by enabling easier set-up and productization of workflows, automated data flow, and faster data processing.
- Provide deeper insights – allowing AI and ML to analyze big data and create deeper and more accurate understanding.
- Enhance security – ensuring that data is collected and stored securely, yet easily accessed by those who need it.
Digital transformation protects the scientific method that has brought us some of the greatest discoveries of the modern age, while delivering the integration needed to foster greater collaboration and drive deeper insights, faster than ever before. Ultimately, this will get us all closer to the truth.
With a background rooted in analytical chemistry, sales and support, Laércio has over 15 years of experience in the analytical field and has worked in constant connection with Chromeleon CDS and chromatography instrumentation with a focus on Ion Chromatography and Cloud computing.