Accelerating the Pace of Science Discovery Through Digital Transformation

The future of the connected world is not just about the newest frontier technologies, such as high-band 5G. It will be defined by the expansion and evolution of existing advanced connectivity technologies – and the new architecture of connectivity will feature cloud and edge computing that is growing more powerful and more affordable. The connectivity ecosystem will be populated with more technologies, services, and providers than ever before.

Nowhere is this more visible than in the life sciences industry. The International Data Corporation (IDC) estimates that approx. 270 GB of healthcare and life science data will be created for every person in the world in 2020. However, the true potential value of these data goes largely untapped. A recent McKinsey report states that, “Technology has not delivered the kind of progress that many expected and compared to other domains healthcare has lagged in digitizing.”

Unlocking new value from data

Without universal data connectivity, the life sciences industry will never be ready to embrace digital transformation. Legacy systems, vendor-specific instruments that cannot be inter-connected, and an unwillingness to embark on introducing new processes into the highly regulated life sciences and pharma sectors have created a huge challenge for technology harmonization.

According to Forbes, “Between 60% and 73% of all data within an enterprise go unused for analytics,” – meaning that, essentially, the majority of data generated are just wasting away in a digital drawer.

Data complexity and volume have had a significant impact on lab integration. A typical lab now has multiple software and hardware platforms, as well as laboratory information management systems (LIMS) to handle the reams of data it generates. It is the end-users who are, effectively, creating the integration problem in the first place by pushing the boundaries of scientific research.

Overcoming the challenge of corralling data produced by different systems, particularly in an industry that still relies on USBs and manual keystrokes, and organizing those data into a usable format is stifling scientific progress. Because the data comes from a huge variety of sources, the time spent curating data, hunting down the correct data – particularly those from unconnected instruments – and solving data issues takes more time than actually using the data for science.