Biotech - Data: The True Currency

Decoding Data's Role in Biotech: A Currency Beyond Measure

Common data issues that the biotech sector faces...

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Security Struggles

Biotechs fight to secure sensitive data, like genes and trials, needing tight defenses against breaches.

Complex Integration

Biotech wrestles with merging diverse data types, from genomics to clinical records, seeking ways to simplify for meaningful insights.

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Complex Integration

Biotech wrestles with merging diverse data types, from genomics to clinical records, seeking ways to simplify for meaningful insights.

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Ethics & Compliance

Biotech's use of extensive data brings ethical debates and the constant challenge of staying compliant with ever-changing standards.

Reliability Worries

In precision medicine, accuracy is everything. Biotechs face the constant battle of ensuring trustworthy data, eliminating biases, and navigating the variability.

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Reliability Worries

In precision medicine, accuracy is everything. Biotechs face the constant battle of ensuring trustworthy data, eliminating biases, and navigating the variability.

Embark on the Data Journey: Explore the intricate world of data in biotech, and the type of leadership that can maximise it. Contact us today!

Data is no doubt one of the most significant factors across the biotech industry, particularly in areas such as drug discovery, precision medicine, and personalised healthcare. 

With the explosion of new technologies such as genomics, proteomics, and metabolomics, researchers now have access to unprecedented amounts of data on the human body and disease that they did not before. Analysing and making sense of this data is crucial to developing new treatments and understanding disease mechanisms, but it goes much deeper.

Come with us into the world of data, the many ways it is being used across the life sciences, how AI and data are working together, and how data recently changed the world. 

Precision medicine & drug discovery

One of the key areas where data is having an impact in biotech is in the development of precision medicine. Precision medicine is an approach that takes into account individual variations in genes, environment, and lifestyle to tailor treatment to the specific needs of each patient. 

‘Precision medicine requires detailed data on each patient,’ says Roop Chandwani, CEO of Mazards, a life sciences executive search firm. ‘Including genomic and other biomarker data, as well as data on their medical history and other factors.

Data is also playing an important role in drug discovery, allowing researchers to identify potential drug targets more quickly, efficiently, and cost-effectively. These elements mean reduced cost of drugs once they hit the store shelves.  

AI & Data

Machine learning and other AI techniques are being used to analyse large datasets and identify patterns that may not be visible to the human eye. Medical professionals and other positions involved in life science strategy consulting are already using AI tech to improve their quality of care through streamlined workflows and new insights into patient data. 

Piers Morgan, Chief Corporate Development Officer of Pangea Botanica, a research company at the forefront of nature-inspired biotech, also believes that AI will relieve the cost of innovation. ‘AI is the most exciting field to watch, specifically, the technologies that can convert data and demonstrate value.’ 

AI & data threaten jobs 

Yet, AI is seeing a great deal of resistance from many medical professionals and other life science jobs as fears of losing positions become increasingly real. Current medical professionals are trained to diagnose and treat in a way in which they can trace their professional decisions to a source, this allows them to know exactly why they are giving a certain diagnosis. However, Artificial Intelligence works differently. 

‘AI algorithms are like a black box, they have been trained on a particular data set, so it is not clear why AI classifies the way it does,’ says Aamir Butt, Chairman and CTO of Mazards, a life science executive search firm in London. ‘This means that as a clinician, you have no idea what the underlying reasons were for a particular conclusion; this makes medical professionals highly uncomfortable.’  

Market gaps

Butt also sees several market gaps that leave space for AI and data to accomplish more: 

‘Do we know the underlying reasons for what AI predicts? Mostly we do, but not always. AI looks for patterns in the data that it is trained on, and sometimes we may not know the reasons for these patterns. This, right here, could imply a significant new space in medical R&D as AI signposts the gap in human knowledge.’ 

If this new space of R&D could be capitalised upon, it would imply a change in the relationship we have with AI, and mend the trust issues that medical pros and other life science analyst jobs face with AI data processing today.  

How data recently changed the world

In 2020, the biotech market saw a rapid growth in valuations made by biotech VC companies. This caused a bubble which has since burst in the same year. Since then, many strategic investors (experienced investors) have retargeted their investment funds towards evaluating the right opportunities more accurately. In doing so, they discovered that various biotechs that had dropped in value due to poor phase trial results were proving valuable in unexpected ways. Through their data. 

Data analysts found that many of these ‘failed’ biotechs had data that could be repurposed, similar to how Moderna’s mRNA research turned out to be COVID’s $80 billion vaccine darling. 

The Moderna unicorn seemed to have done more than solve the COVID crisis, it inspired how excess research data could be used three-dimensionally. But in a world where data protection is controversial, where will that data repurposing begin and where might it end? And, will it remain ethical?   

‘Stocks will continue to struggle,’ Morgan says about what the biotech sector needs to move forward. ‘But the companies with quality design behind them who are producing good data will not. Data is the key factor.’ 

Conclusion

Overall, while data is an important factor in the biotech industry, it is not the only factor. Biotech companies still need to invest in R&D, clinical trials, and manufacturing to bring new products to market. 

However, the ability to collect and analyse data is becoming increasingly important in many areas of biotech, and companies that are able to do this effectively may have a competitive advantage.
 

Mazards, a retained executive search firm in London, commissioned this piece from Dastrum, a digital marketing company. 

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