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It’s after midnight on a Friday. A scientist is working late, frustrated because her project is stalled at step one. She is trying to use data from previous experiments that is hard to compile and that her computer system can’t read. The software and operating system that were used at the time the data was generated is obsolete and no longer used. Despite advances in technology that have improved the productivity, performance and predictability of scientific work in laboratories, data compatibility problems like this persist. Systems, applications and devices lack a standard way to share information with each other.

 

Steve Hayward
Product Marketing Manager, BIOVIA

So many data formats, so little time 

Most modern lab equipment—from balances and spectrometers to chromatographic systems—can be connected to a computer to supply data sets and results. But because these instruments typically originate from different manufacturers, they often convey information in unique or proprietary data formats. Even if an organization works with equipment from a single vendor, data formats can vary as the provider advances its product offerings over years.

Data incompatibility derails operational efficiency and contributes to the reputation of laboratories as a bottleneck in the enterprise. To optimize productivity, organizations need a unified lab that supports easy access to data and interoperability of all instruments and information systems. People who perform experiments and analysis in laboratories shouldn’t be required to change software systems depending on what data they are analyzing. Project teams should be able to view all data and experimental results in one place. Information should flow seamlessly throughout scientific processes, spanning the organization and partners.

When information was paper-based, data formats were not as important. Information from various sources was transcribed into a physical notebook. Although moving to electronic systems can solve the issue of data sharing and re-use, the plethora of different data formats is a barrier for efficiency and collaboration. Today, the goal of leading organizations is to create digital continuity and preserve data and its context wherever it goes throughout the extended enterprise. This can only happen if laboratory data is standardized. If data is easily converted into a consistent format, all subsequent steps are easier to perform. 

Companies in science- and process-driven industries benefit from unified data for many reasons. Standardized data is reusable. A project team can revisit it in later phases of their work, or other professionals may access it in order to avoid repeating experiments, tests and other activities. Standardized data supports collaboration that extends through all departments and to partners. Standardized data protects and preserves all data generated in a lab—the intellectual property (IP) of an organization. It is this knowledge—not products or services—that represents the real value of a company.

Ongoing efforts to standardize data within science-based industries offer encouraging results. But even among pre-competitive consortia where experts from various companies, vendors, publishers and academic organizations come together to create a standard for laboratory data and methods, the different groups are currently developing a variety of standards. There is some confusion as to which type of standardized data format people should be working toward. That won’t work. We either have a data standard for the laboratory, or we don’t. 

Daniela Jansen
Director of Product Marketing, BIOVIA

 

Data quality for good science

Not only is laboratory data generated in many formats, it is also often not automatically captured in a consistent manner. This introduces unnecessary steps and potential errors into the workflow with people manually recording results or entering information into spreadsheets and applications instead of recording them directly into digital systems. This has ramifications for both compliance and data quality. Many science-based industries are highly regulated. When compliance is a factor, maintaining high quality data is business-critical. Beyond that, it’s just good science. 

There is a drive among companies to operate more efficiently while also ensuring data integrity and data quality. The hurdle is getting everything connected to make that possible. Many organizations are reluctant to do this kind of integration. It is a lot of work. The solution is an informatics platform that offers software-automated, standardized instrument integration services. Only an open, flexible and science-aware platform can offer a holistic information flow that spans the enterprise. This makes laboratory data available to analysts, managers and other professionals so they can leverage IP for knowledge-based decision-making that is informed by both current and previous work.  

This approach not only delivers compliance, quality and efficiency but also user satisfaction. Scientists don’t want to spend time doing meaningless manual tasks. They want to spend their time on science. Company culture is changing as end-user expectations rise. Younger members of the workforce have grown up in the digital realm with apps, laptops and tablets. They expect a modern experience in their work environment, not the paper-based manual paradigm of yesterday. They want to make decisions instead of doing cumbersome data entry. This represents an opportunity for organizations to attract higher quality employees. Better workers will generally choose to work for a company that implements modern processes in its workflows.

Unlimited data flow

Scientists must be able to utilize all relevant lab data from all sources. This should include results from smaller instruments and older equipment that may not be easily connected. It can be difficult to get data out of them and into an enterprise system or knowledge base. Vendors such as Tetrascience and Cubuslab offer devices that connect to instruments and convert data into formats that are compatible with enterprise information systems. An open information technology platform makes it possible to integrate these types of third-party data capture and conversion devices so that no data source is omitted. 

Bidirectional communication further improves efficiency. With bidirectional information flow, laboratory equipment shares data with the information system and the system communicates back to the instruments. For example, a balance might receive relevant data about a sample and also specific information such as the target weight, tolerance or specifications for a particular process or sample relevant to the current task. This bidirectional exchange of data eliminates manual data transfer and brings essential information to the scientist’s fingertips, improving both efficiency and data quality. 

Connecting the dots

The goal for lab organizations should be to get to a point where all systems are integrated—an Internet of Laboratory Things. All data is captured automatically and consistently from the start of any project or process and converted into a standard format, or at least into a few compatible standard formats that can be brought together in a unified way. This data can then be easily re-used throughout the enterprise. The continuity of scientific data is preserved as a continuous digital thread throughout the organization. 

Through integration, non-value-adding tasks are reduced or removed so scientists and other skilled workers can focus on accomplishing tasks that reflect their expertise, rather than spending time recording information or flipping back and forth between various systems and software packages. They have easy access to previous data and work. The bonus is that organizations can attain a “big picture” view of activities. There is enterprise-wide visibility into what data is collected and what instruments are being used at various times, improving the overall efficiency of the organization. 

In practical terms, this can be a daunting task, especially for large organizations with many different systems. Whatever information management technology is adopted, it should be open and flexible to integrate with existing systems and expert tools. It should not be necessary to start from scratch. A promising approach is the implementation of a science-aware platform that understands scientific data and workflows, one that is scalable and can be implemented in a staged process. Such a platform represents a new vision for transforming the enterprise into a fully integrated operation that is optimized for both lab efficiency and scientific excellence. 

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