Written by:
Purandar Das Big data and advanced data analytics techniques, including AI and machine learning, are capable of driving innovation across the pharmaceutical and healthcare industries. The potential impact can be seen in everything from the discovery of new drugs and improvement in patient outcomes to the optimization of business performance.
There’s just one small problem. Although data – experimental data, clinical trial data, patient data – has long played a central role in pharma and healthcare, working with this data has gotten more challenging. For one thing, it can be hard to get all the data you need in one place. As PwC wrote in their whitepaper on advanced analytics in pharma, “Siloed data continues to prevent pharmaceutical and life sciences companies from extracting the maximum amount of value from data.”
Related to this challenge is the fact that these “silos” may actually be other companies. In a May 2020 white paper on applications for AI and ML in pharm research put out by Syneos, a biopharmaceutical solutions company, the authors point out that, “Access to large datasets of millions of data points has facilitated a leap in machine learning for other applications in images, text and video analysis. For this to happen in drug discovery and development, pharma needs to open up and share some of its proprietary datasets.”
When Caution Undercuts Innovation
It’s not surprising that pharma and healthcare companies have been hesitant to share data, even if it would both benefit patients and drive revenue. Aside from concerns about providing competitors with valuable insights, sharing data exposes these companies to risks. It could put them out of compliance with a broad array of regulations focused on data privacy, for example, and it could likewise expose the data itself to loss or theft.
Data breaches are costly. IBM Security’s Cost of a Data Breach Report 2020 shows that the average cost of a data breach in the pharmaceutical industry is $5.04 million, a figure that rises to $7.13 million in healthcare. Add to these costs those that are more difficult to calculate, such as the damage to a company’s reputation, and it’s easy to understand why these companies want to keep their data locked up tight.
While leaders in these companies are right to be cautious, this caution imposes a cost of its own. Sharing data, as we just mentioned, is critical to unlocking the power of AI and ML in these industries. When a hesitation to share data means companies cannot take full advantage of these technologies, it places an unnecessary drag on innovation.
We see this drag extending beyond cutting edge technology like AI. Concerns about data security can mean that pharmaceutical companies and others also hesitate to use third party platforms for data analytics. In fact, this hesitancy has also been named as a barrier to adoption of technologies that are now ubiquitous such as the cloud.
Secure Data, Unlock Innovation
The question becomes: How might we secure data so that these companies can safely share it and make the most of transformative technologies?
Some have suggested that blockchain technology could be the answer. As Alison McCauley wrote in the Harvard Business Review, using blockchain, “…companies can safely work together in a shared, permanent ledger. They can do this without giving up control of or even revealing their data, as mathematical proof of data can stand in as a trustworthy proxy for actual data.”
Challenges associated with implementing this approach aside, and there are many, it would only cover certain use cases. That is, it might work for tracking the supply chain, but it would not work if the goal were to provide data to a third party for analysis or to an AI for purposes of mining or training.
The key, we believe, is end-to-end encryption executed in a way that allows data to be used without it being exposed. If data can remain encrypted – down to the field level – at rest, in transit and in use, then the concerns that get in the way of data sharing, or even simply storing data in the cloud, go away.
To be truly usable, the encryption technology here must be scalable, database-agnostic and deployable either in the cloud or on-prem. It must also function without any slow down for the user, a kind of “performance tax” that has made traditional efforts to encrypt data in use impractical. (As you may have guessed, we built Sotero with exactly these principles in mind.)
In the middle of a global pandemic, it would be difficult to overemphasize the importance of innovation in pharma and healthcare. Concerns around data security constitute a kind of friction in the innovation process. Usable, end-to-end encryption removes that friction. In doing so, it helps these companies create life-saving treatments that truly benefit everyone.
If you would like to learn more about how Sotero can spur innovation, in pharma and elsewhere, by making it easy to share data securely, let’s talk.