Data is one of our most valuable commodities – it gives us critical information about our world and holds the key to cures for diseases. It’s also very expensive to create.
Companies, universities and other institutions invest enormous amounts of time and money in research programs or clinical trials to produce important data with the hope that it will have an impact on health through the creation of new treatments or cures. Unfortunately, after having invested substantially in research and trials , these stakeholders are not incentivized to share this data with others.
If one institution shares data with another, which then uses it to develop a treatment, that second institution will reap most of the financial reward and acclaim.The great irony here is that presumably most researchers and scientists entered their fields to drive change and help humanity.
A combination of economic incentives and inter-institutional collaboration issues have, over time, resulted in the formation of central data silos around institutions who can afford to invest in large research programs. This is counterproductive.
Despite the amount of money invested in medical research, success rates for developing new treatments are low. Clinical Leader reports that only about one in 10 drugs entering clinical trials in the U.S. will be approved by the FDA. To make matters worse, a single clinical trial costs anywhere between $800 million and $1.4 billion. Healthcare research will always involve some trial and error, but under our current system, we’re putting in a lot of effort for very little return.
Data siloing exacerbates this inefficiency because researchers are unaware of or unable to access each other’s work and end up duplicating results or overlooking promising research avenues.
Essentially, clinical trials today involve a lot of guesswork, because scientists don’t have all of the puzzle pieces they need to see the full picture.
Siloed Data Means Less Data
When data is shared, there is more of it to go around. Keeping data in silos means that a given researcher or institution only has their own limited pool of data to pull from for ambitious research projects like finding a cure for a certain kind of cancer or developing a critical vaccine.
When barriers between datasets are broken down, the amount of research any scientist has access to grows exponentially. This not only equips scientists with more information to solve complex problems, but also allows data to unveil patterns and correlations with the help of machine learning to better guide scientists in the right direction.
Robert Metcalfe, the co-inventor of Ethernet, developed Metcalfe’s Law, which applies perfectly to this principle. As it’s summarized in the 2015 Forbes article titled, “Data Silos: Healthcare’s Silent Shame,” Metcalfe’s Law “is the idea that the value of a network is proportional to the square of the number of participants – i.e. adding more people to a network increases value not linearly, but exponentially.”
The more minds and data we can bring into a shared network, the more valuable it will become for all of us. Data may be a valuable commodity on its own, but until we decentralize it, we are missing out on its exponential value.