Did you know that companies lose up to $15M every year due to poor data management? And this number will grow steadily as the amount of data generated worldwide increases too. By 2025, the total volume of data created globally is estimated to reach 175 zettabytes, that’s one sextillion bytes or a lot of zeros.
The life sciences sector too is affected by the steady growth of data. Different types of biomedical data, such as preclinical, laboratory, clinical, sample, and clinical subject data are driving drug discovery and development. At the heart of it all is biomarker data, which comprises information on target identification, disease understanding, a compound’s mechanism of action, and patient stratification.
Want to learn more about data management? Check out the white paper “From Data Chaos to Order & Insight”
Don’t collect valuable information in data silos!
Unfortunately, many companies collect this extremely valuable data in data silos, which only allow a fixed part of the organization to access a data set or data source. This either means that several groups are storing the same information ‒ wasting valuable space and money ‒ or that they are storing complementary data, which can lead to miscommunication and the loss of valuable information.
Let’s use an example. Say biomarker data has been generated by multiple labs during a clinical trial. All of the data, including gene expression, immune profiling, and cytokine assays, has to be linked to enable a proper insight into the clinical trial. If this doesn’t happen, critical decisions related to dose escalation and patient selection cannot be made properly leading to missed opportunities.
Worse still, is information that is completely unstructured and stored in untagged documents, presentations, or researchers’ notebooks. This type of data is inaccessible to other researchers and might get lost over time. These types of data silos prevent the dissemination of valuable scientific information, which could help drug developers and, in the long-run, patients.
Creating a data ecosystem for the collection, harmonization, and integration of real-time biomedical data
As more and more biomedical data is generated by drug discoverers and developers and public data sets are growing, the need for a harmonized organization framework for complex data sets is more urgent than ever before. What if biomedical data could be stored in a data ecosystem that allowed for a proactive and continuous data analysis? One where data is collected, harmonized, and integrated in real-time and made accessible to everybody?
An organized data ecosystem has many benefits. It can contain evidence from preclinical models; it can provide information on the background of a disease or an investigative drug, which can be useful for clinical trial organization; it can explain why a drug might work in one group of patients, while it doesn’t in another; and include knowledge on potential opportunities for combination treatments.
In its new white paper, QuartzBio talks about the challenges of organizing complex biomedical data sets and provides valuable recommendations for creating a data ecosystem that can generate actionable insights for efficient drug development.
DOWNLOAD the white paper here and become a data management expert!