Analytics And Insight: Digging Into Data

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While there is cautious optimism, the pandemic continues to impose pressure points and challenges requiring us to change the way we do things. This period in time calls for us to significantly broaden our thinking.

The need for collaboration in organizations has never been more apparent, and the challenges of collaboration never greater. Eliminating departmental working silos is only the beginning. The entire ecosystem needs to be strung together in a secure yet frictionless communication framework, allowing a connected data flow and integrated view of data to achieve the ultimate strategic plan, speed to market and overall success. This need was greatly amplified throughout the pandemic and specifically across life sciences industries. One spotlighted area is pharmaceutical supply chain practices, business continuity and drug/supply shortage prevention.

But many leaders are wondering: How can my organization be future-ready?

The biotech and pharma spaces are often lagging in adopting new technology for a variety of reasons, often due to regulatory and validation requirements. While not specifically focused on life sciences, a recent McKinsey study indicates one silver lining of the Covid-19 crisis is that companies appear to be digitizing many activities 20-25 times faster than previously observed.


Data is being generated rapidly all around us. But, proper use, interpretation and understanding of this data is required for efficiency and successful outcomes. Industry 4.0 deepens the need for companies to ask the right questions regarding their data. In the biopharma world, technical changes are a crucial component of prosperity. Edge computing and smart manufacturing allow for more equipment to be digitally enhanced and cloud-enabled. Temperature monitoring of a shipment from factory to pharmacy is a great example of this.

Additional investments in technology are key to the education and implementation of systems capable of handling high data volumes through experiments that produce large data sets (i.e., metabolomics). After all, data can be meaningless if not interpreted correctly. Success can be achieved by following the lead of other industries that have shifted away from a “this is how we have always done it” mindset.

Digital Transformation

But why all this talk about digital transformation now? Executives are being pressed daily that they need to “go digital.” It’s transform or be left behind.

This is not new; ERP systems began this process in the 1990s. But what does digital transformation really mean? It is not just about technology; it’s about rethinking the current, often longstanding processes with a new approach where technology is not the driver but rather the enabler — new ways through evolution and innovation to change customer, vendor, employee experience….

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