Digging Into Data

While cautious optimism prevails, the pandemic continues to bring pressure points and challenges that are forcing us to change the way we operate. This period requires that we broaden our thinking considerably.

The need for collaboration in organizations has never been more evident, and the challenges of collaboration have never been greater. Eliminating department work silos is just the beginning. The entire ecosystem must be connected in a secure but smooth communication framework that enables networked data flow and an integrated data view to achieve the ultimate strategic plan, rapid time to market, and overall success. This need has been greatly heightened throughout the pandemic and particularly in the life sciences industries.One focus is a pharmaceutical supply chain practitioners, business continuity and prevention of drugs / supply shortage prevention.

But many managers ask themselves: How can my organization be prepared for the future?

Biotech and pharmaceutical industries often lag behind in adopting new technologies for a variety of reasons, often due to regulatory and validation requirements. A recent McKinsey study, while not specifically targeting the life sciences, shows that one silver lining of the Covid19 crisis is that companies are digitizing many activities apparently 20-25 times faster than previously observed.


Data is being generated rapidly around us, but proper use, interpretation, and understanding of that data is necessary for efficiency and successful outcomes. Industry 4.0 increases the need for companies to ask the right questions about their data. Change is an essential part of prosperity. Edge computing and smart manufacturing enable digital advancement and cloud support for more devices.A good example of this is controlling the temperature of a shipment from the factory to the pharmacy.

Additional investment in technology is key to the education and implementation of systems that can process large amounts of data through experiments that produce large data sets (e.g., metabolomics) because data may not make sense if not interpreted correctly. Executives from other industries who have turned their backs on the “this is how we always did it” mindset.

Digital Transformation

But why is there so much talk about digital transformation now? Managers are under the daily pressure to “go digital”. It is to transform or to lag behind.

This is not new; ERP systems started this process in the 1990s, but what does digital transformation really mean? It’s not just about technology; It’s about rethinking existing, often long-term processes with a new approach in which technology is not the driver but the enabler: new ways through evolution and innovation to change the customer, supplier and customer experience. Add more data, faster, from more devices (e.g. wearables, sensors, watches, machines, etc.) to reduce risk and increase information by turning data into smart decisions. These include Artificial Intelligence (AI), Machine Learning (ML), Robotic Process Automation (RPA), Blockchain and the Industrial Internet of Things (IIoT).

Bottom Line

The key to success is understanding where and what technology is relevant and what should be invested in. A lack of clarity can prove costly, waste effort and resources, and potentially hinder business success.

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