Industry 4.0 is revolutionizing manufacturing – from production on the factory floor to operations in corporate offices. While conversations continue to focus on how robotics and automation tools are presenting new levels of productivity and safety to the industry, the data that manufacturers hold is truly the engine behind making these possibilities a reality. As a result, the market for big data analytics in manufacturing is projected to reach 4.55B by 2026 as the sector continues to adopt “smart industry” initiatives.

COVID-19 has proved to be a forcing function for life science companies to improve processes to expedite the development and distribution of vaccines and other therapeutics. As a result, the life sciences industry is increasing its interest in solutions that can enhance drug discovery capabilities. For example, Deloitte found that 81% of large pharmaceutical companies are prioritizing investments in AI to address these needs.

Organizations are facing a data problem with critical insights stored across various silos and hidden in a number of different file formats. As a result, nearly 80% of organizational data is siloed and difficult to access. For industries like healthcare and life sciences, this lack of available insight can impact overall paths of care, research around emerging diseases, R&D strategies for future drug development and more.

Citation (title and abstract) screening for a systematic literature review may be accelerated by 25% through the application of artificial intelligence (AI) in a novel method developed by Oxford PharmaGenesis in partnership with Vyasa.

The development of a new drug takes an average of ten years from discovery to approval, with the discovery and pre-clinical stages taking three to four of those years. Critical to the development process, successful drug research and discovery requires analyzing numerous data sources such as clinical trials, pharmacology reports, small compounds and more. These data sources are rich with valuable insight but largely unstructured making them increasingly challenging to analyze via traditional methods. 

Our increasingly digital world combined with health tech innovations such as wearable devices, has presented new opportunities for how the healthcare industry monitors and understands the patient experience. As a result, healthcare providers are evaluating how to assess even greater amounts of data in an effort to gain a better understanding of the patient experience – which could revolutionize patient care, if only there were fewer challenges associated with data analysis in healthcare.