The pharmaceutical sector is on the verge of a major shift in the way drugs are discovered, offering exciting opportunities for researchers and investors to get ahead of that transition. Information technology is accelerating rapidly and advances in ML and AI are being experimented with to see patterns in data that were invisible before.
A blog by Hans Constandt, Founder of ONTOFORCE
These advances are not restricted to self-driving vehicles and automated factories, but apply to a wide variety of sectors, including healthcare and life sciences. Enhanced early decision-making, facilitated by big data analytics in drug discovery, improves project timelines and reduces clinical attrition. Computational biologists with a Silicon Valley mindset are developing personalized drug treatments based on the patient’s individual genome. Data scientists look for previously unseen biomarkers using algorithmic models. The old way of approaching product development and market access in a silo-ed way, is being replaced by a new, agile, data science driven model that takes advantage of ongoing digital innovation and leverages linked data on a large scale.
Data increasingly describes our world
Using these data ecosystems, real-world evidence can be identified earlier, to match the right patient to the right trial, and engage customers proactively throughout the full drug development process. Democratizing access to data is a key aspect of the data economy, where data is not only made FAIR (Findable, Accessible, Interoperable and Reusable) but also easy to consume in a secure and regulatory compliant way.
In addition to data-driven evidence based on clinical trials, researchers also tap into data gathered via lab automation and the digitization of claims and medical records. The next data wave will be generated by wearables, ingestibles and other Internet of Things devices. Advances in analytical technologies and artificial intelligence will enable companies to translate massive amounts of data into actionable insights about disease populations and treatments on a large scale. The opportunities for healthcare and life sciences are immense.
Unlocking customized care, faster drug discovery and preventive medicine
Companies that control large quantities of data are often highly valued. Initially using data for advertising purposes, Google and Facebook have applied their user data to train voice-recognition, image-recognition using AI. Uber provides cheaper rides and holds the world’s biggest pool of supply and demand data related to personal transportation.
Data enables companies to better understand the world’s economic, social, consumer and business infrastructures and build data-driven decision-making into every aspect of their operations. In the healthcare and life sciences data economy, data has clear and measurable value – streamlining business processes, discovering new cures, and unlocking better care to help prevent diseases.
The urgent need for data valuation frameworks in healthcare and life sciences
Many businesses own massive undervalued and underutilized databases, resulting in untapped potential. Data driven revenue models are missing there are more and more lost opportunities in the flourishing data economy.
It is crucial for healthcare and life sciences companies to start treating data as an asset and not just as a back-office matter. We need to establish marketplaces to promote the correct (re-)use of data to demonstrate its value. On top of that, the utility of data doesn’t decline with its (re-) use, unlike other assets such as oil, iron and coal. Will we be able to define economic valuation and trading principles to demonstrate the value of data and maybe even bring it onto the balance sheet, so businesses can evaluate and treat data as an asset, as they are already doing with their brands?
Learning about data economies and data valuation models, our next blog will focus on the role of FAIR data, how to calculate ROI, examples showcasing faster market access, and improving the probabilities of success across the full drug discovery process.
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