Building a digital environment

Data harmonization can drive workflow efficiency and breakdown data silos to enhance your smart lab environments. The path to digital lab data management is full of critical challenges and the scale and complexity of the data coming in are growing in complexity as technology becomes more ingrained in how pharma companies do business.

With the pharma industry calling for more innovation and ingenuity to improve data management, Pharma IQ analyses how improved data management can enhance your data harmonization efforts and improve the reproducibility of your data to achieve digital excellence within the laboratory.The upstream trend toward digitalization in the lab has caused an explosion of data. The scale and complexity of data coming into the lab is limiting the ability of any individual to manually interpret it and draw conclusions on optimal courses of treatment. With technology becoming more ingrained in how pharma companies do business, specifically around improving patient care and personalized medicine, this overflow of data coming into the lab is only going to become more prevalent. Harmonizing data for meta-analysis brings about a number of clinical and statistical advantages for labs. Data harmonization ensures the data is standardized, labeled and comparable making it particularly useful for clinical decision-making. However, achieving data harmonization has been a huge challenge for the biopharma industry and it is a key obstacle for achieving efficient smart lab environments. Without data harmonization, it is impossible to truly implement technologies that perform to their potential due to the technologies’ dependence on the availability of trusted and timely data.

“Having a data-centric and standards-based approach that structures data in an agnostic way is key, so in my opinion new designs should always look to increase the degree of data harmonization to ensure research reproducibility, increase system visibility and overcome the natural variability that exists in data and enable scientists to have access to information at the point of experimentation.” “If you want a machine to learn from data, you need to give the machine good data... Achieving data harmonization is a big condition for AI and machine learning at scale.”