What you need to understand today about the state of analytical data management
Author: Sanji Bhal, Director, Marketing & Communications
From new instruments and software to the rise of the digital laboratory, the landscape of analytical chemistry data is evolving. To keep our finger on the pulse of analytical data management, we conduct a survey every few years to uncover the latest trends and preferences related to 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. To answer these questions, it is often necessary to perform several different analytical experiments (e.g. LC/MS and NMR). Analytical labs are equipped with a variety of instruments, allowing analysts to choose the best instrument for the answers they are looking for. Additionally, many research teams use instruments from multiple vendors, leading to file compatibility issues.
Not surprisingly, our survey found that over 92% of respondents collect data across multiple instruments, use multiple techniques, and rely on different software to process analytical data.
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Analytical data is maintained in multiple applications and shared arbitrarily
The diversity of analytical data means that it is stored and managed in many different applications and systems. Microsoft applications remain the most popular way to manage and share analytics results, chosen by 80% of respondents. Whether it’s Excel spreadsheets, PowerPoint presentations, or email, the ubiquitous access to these applications makes them an easy choice, although they’re neither designed nor best suited for sharing and managing scientific data. Instrument software was the second most popular choice at 70%. Although the instrument software is limited to only processing and analyzing the data collected on that instrument, it is designed to do so. It was surprising to learn that many organizations still use internally developed software to manage and share analytical data, even with the development and maintenance effort required.
Dispersed data makes reporting time-consuming
Reports are an important way to share information within an organization or with external partners. 40% of respondents indicated that they compile analytical reports on a weekly or daily basis using data from various tools and techniques. Today that means collecting data from multiple systems to compile results and make decisions.
Nine out of ten respondents say they need NMR, LC/MS, GC/MS or other analytical data every day to make decisions. However, 68% say an element critical to their work is difficult to access and share with others. Scientific research and development is on the brink of a cloud revolution. In addition to reducing IT maintenance overhead, cloud-based storage offers rapid scalability and additional data security. More direct access to data means higher ROI and reduced expenses. Although there is a lot of potential for artificial intelligence (AI) and machine learning (ML) technologies in the life sciences, our results show that the industry is still years away from full implementation. Only 6% of the organizations surveyed 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, normalized data, which poses a challenge for analytical data. This can be an important reason for the differences in the implementation of AI and ML. Many analytical data management solutions in use today do not prepare this data for use in data science projects. Automating the collection of data without burdening scientists and agreeing internally on how that data will be normalized are important first steps. Most of our survey respondents (70%) agreed that their organizations need to invest in newer/better data management technologies. As individuals tasked with meeting these needs, it’s important that you understand the needs of scientists, data scientists, and their workflows. You need to stop expecting one or two systems to “do it all”. Implement systems that meet the specific needs of analytical data.
Cloud-based data management is increasingly tempting to optimize storage and access
Advanced technologies – like AI and ML – are attractive, but few have implemented them for analytical data
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