What you Need to Understand About the State of Analytical Data Management Today
From new instruments and software to the rise of the digital lab, the landscape of analytical chemistry data continues to evolve, writes Sanji Bhal, Director, Marketing & Communications at ACD/Labs.
To keep a finger on the pulse of analytical data management, every few years we run a survey to uncover the latest trends and preferences regarding analytical chemistry data and its management. Here’s what we found in our 2022 survey.
Data diversity is a real problem
Analytical data is primarily collected to ensure the identity, purity, and composition of materials and compounds. It is often necessary to run several different analytical experiments to answer these questions (e.g. LC/MS and NMR). Analytical labs are equipped with a variety of instruments so that analysts can choose the best instrument for the answers sought. In addition, many research teams use instruments made by multiple vendors, which leads to file compatibility issues.
Unsurprisingly, our survey found that over 92% of respondents collect data on numerous instruments, use multiple techniques, and rely on diverse software for processing analytical data.
Analytical data is managed in multiple applications and shared haphazardly
The diversity of analytical data means that it’s stored and managed in many different applications and systems. Microsoft applications are still the most popular way to manage and share analytical results, selected by 80% of respondents. Whether its Excel spreadsheets, PowerPoint presentations, or email, ubiquitous access to these applications makes them an easy choice even though they are neither designed nor best-suited for scientific data sharing and management. Instrument software was the second most popular choice at 70%. While instrument software is restrictive to only processing and analysis of the data collected on that instrument, it is designed for it. It was surprising to learn that many organisations are still using software developed internally to manage and share analytical data, even with the development and maintenance overhead required.
Scattered data makes assembly difficult. It forces scientists to search multiple locations for answers. When there are many possible locations for data, the path of least resistance is often to repeat the experiment or request the data from a colleague, which wastes time, materials, and causes frustration.
Scattered data makes reporting time-consuming
Reports are a key way to share information within an organisation, or with external partners. 40% of respondents said they collate analytical reports with data from different instruments and techniques weekly, or daily. Today, that means collecting data from multiple systems to compile results and make decisions.
Analytical data is mission-critical but difficult to access and share
Nine out of ten respondents note that they need NMR, LC/MS, GC/MS, or other analytical data daily to make decisions. However, for an element that is critical to their job, 68% say it’s hard to access and share with others.
Opportunities for improvement
Cloud-based data management is increasingly enticing to streamline storage and access
Scientific R&D is on the edge of a cloud revolution. In addition to reducing IT maintenance overhead, cloud-based storage provides fast scalability, and added data security. More immediate access to data means increased ROI and reduced expenditure.
Nearly half of respondents (47%) agreed that cloud-based data management solutions are important.
Advanced technologies—Like AI and ML—are appealing but few have implemented them for analytical data
While there is lots of potential for artificial intelligence (AI) and machine learning (ML) technologies in the life sciences, our results reveal that the industry is years away from full implementation.
Only 6% of respondents’ organisations have fully implemented the use of analytical data for data science projects, while 43% are in the process of doing so.
The cornerstone of data science projects is curated, normalised data, which is challenging for analytical data. This may be an important reason for the disparity around the implementation of AI and ML. Many analytical data management solutions in place today, do not prepare that data for use in data science projects. Automation to gather data without burdening scientists and internal agreement on how that data will be normalised, are critical first steps.
Most of our survey respondents (70 %) agreed their organisations need to investment in newer/better data management technologies. As the people charged with fulfilling these needs, it is important that you understand the demands of scientists, data scientists, and their workflows. You must stop expecting one or two systems to “do it all”. Implement systems that meet the specialised needs of analytical data.