We’re living in the golden age of data. We have seen, and continue to see, exponential growth in the computational power and number of data-generating devices during the last ten years. While the dramatic reduction in the cost of data storage means that we’re able to access an ever-increasing volume of statistical data – one third of which is Health data. And it’s expected that this proportion will continue to increase.

Big Data and Artificial Intelligence

Why Apply Big Data & Artificial Intelligence in the Life Science Sector?

The expansion of the Healthcare sector is outpacing our ability to track, analyze and use this data. With the move toward a digitalized, value-based Healthcare system, we can begin to harness the power of big data and artificial intelligence. We can begin to restructure our Healthcare systems to be more economically and environmentally sustainable. We can begin to develop more accurate, more efficient and more comprehensive care – from detection and diagnoses to treatment and cure.

There are already several areas in which the Life Science industry is effectively using Artificial Intelligence today. These range from the advancement of new product research to ensuring clinical trial transparency. But, according to a 2017 Accenture report, “in just the next five years, the health AI market will grow more than 10x.” This same report quotes Bill Gates,

“We always overestimate the change that will occur in the next two years and underestimate the change that will occur in the next 10.”

The Healthcare sector is at a tipping point where digitalization is allowing Life Science organizations access to years of accumulated health data – “data lakes”. And these organizations must start preparing now to harness the full, positive disruptive power of this technological wave. But there are challenges to the application of big data and artificial intelligence in the healthcare sector.

Barriers to Change


Dependent on the context, technology and application of big data and artificial intelligence, there are various legal frameworks which apply. The major challenge to the application of big data and AI will be fitting these technologies into regulatory frameworks designed for an analogue working environment.

Data Fragmentation

Another significant obstacle to big data and AI is the considerable fragmentation of data within the Healthcare sector. With data stored in silos by various owners and for numerous purposes, a systematic approach to achieve an industry standard is essential to ensuring organizations can share data in an efficient and purposeful way.

Traditionally, the Healthcare sector has been slow to adopt a shared approach and this reluctance will need to be shed universally for the industry to enjoy the full benefit of these new technologies.

In Our Experience…

The potential benefits of applying artificial intelligence and big data in the Healthcare sector are dramatic. However, the challenges are numerous and significant. Beyond those discussed in this blog, there are also data security, digital literacy and more to be concerned with. Thankfully, the technologies and support needed for Life Science organizations to prepare for this promising future exist today.

Product Lifecycle Management platforms provide a robust structure to capture and store data from all sources and stakeholders of product development stages. This data is invaluable for drawing useful insights, predicting trends, improving processes and automating labor-intensive tasks. More than this, PLM platforms enable the sharing of data across resource programs such as ERP, PDM and more.

Onboarding your teams and migrating data to a PLM platform now will stand your organization in good stead to benefit from the inevitable full digitalization of the Healthcare sector.

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