Scientific Data Management Becomes a Core Life Sciences Priority
Scientific organizations involved in life sciences are increasing their attention to scientific data management due to increasing amounts of data created through their research activities. However, the problem is not only related to the storage of data. Companies require data management systems that will ensure data preservation, collaboration, data protection and decision-making based on accurate data.
This problem is present in all research-intensive companies. For example, laboratory data might be stored in one place, whereas clinical data, genomic data and manufacturing data are stored in other places. In case of the need to obtain an integrated picture of the research process, researchers may spend valuable time on integrating data instead of analyzing it.
Scientific data management solves this problem by creating a structure for the collection and integration of the information. A good system is more than a repository for data; it connects data to the experiments, samples, methods, instruments and decision-making that gave rise to it. This provides the context needed to understand the significance of the results gained.
Life sciences work now demands an extended evidence chain as findings generated during an early stage of a project could affect the way trials or manufacturing processes are conducted further down the line. An observation of high quality could determine regulatory actions. Data generated at one site may be needed for other sites around the globe. Data without persistence loses its significance.
This is shifting the focus of investments. The life sciences organizations are moving from standalone software solutions to experimenting with digital environments to check if they can withstand the lengthy research process. The strongest systems should be able to link research teams and business operations and at the same time be flexible enough to work with legacy systems.
Another significant aspect is the commercial impact. Organizations that effectively organize their scientific data will be able to avoid redundancy and improve knowledge exchange. Structured data can be compared, analyzed and reused with greater ease. Poorly handled data may lead to delays in project completion, despite sound science.
However, this does not necessarily imply that organizations need a common platform for every activity. The priority is coherence. Data should flow through organizations with enough structure and context to stay meaningful. It is not necessary to recreate the story of the result each time information changes hands.
Scientific data management is becoming part of the research discipline. Data management enhances scientific confidence through ensuring that the evidence used in carrying out research is preserved. The message for leaders in life sciences is clear: discoveries cannot be achieved merely through generation of data, but also preservation of the same.
