How Operational Metadata Unlocks Mystery and Hastens Discovery

Though unlikely to populate headlines in scientific journals or conference keynotes, operational metadata plays a crucial role in the scientific method. And now, it’s more accessible than ever.

All too often, scientists are left pondering a laundry list of reasons why one of their experiments failed or spending countless hours attempting to pinpoint the cause of pesky outlier data points. The common scientific idiom “causation does not equal correlation” is all too familiar to those living in the scientific world. The confident assignment of cause and effect between experimental variables is the foundation of science that leads to the discovery and development of life-saving medicines, innovative technologies, and beyond. 

But how can one gain confidence in experimental results? It starts by controlling for confounding factors – those things that impact science but are not of interest to the scientific hypothesis at hand. If operating conditions are optimal and equipment is running to specification during experimentation, the impact of confounding factors can be significantly reduced and guesswork can be eliminated.  

The implications of this are felt throughout entire organizations. From a reduction in the number of times the same experiment has to be performed (e.g. cost of reagents and personnel hours) to the complexity of data analysis (hours spent on digging through experimental data and redoing analysis), collecting and leveraging operational metadata in the scientific process can deliver significant process efficiency. Read more in our white paper.