The pharmaceuticals industry collects massive amounts of data. Estimates are measured in petabytes – tens of billions of records of prescriptions and other transactions, hundreds of millions of patient records, hundreds of thousands of data sources.
How the pharmaceuticals industry uses big data is unlike other industries’ approach, however. Pharma companies use data to fail as soon as they can – that is, through clinical research trials, pharmas aim to figure out which drugs don’t work as soon possible, so they can focus on developing and marketing those that do.
Big Data Use Case: The Pharmaceuticals Industry
For these companies, data holds the key to improving…well, everything. In the near term, data determines which drugs move through clinical trials and which populations may be most receptive to new medicines. Longer term, data is critical to fulfilling the promise of personalized medicine based on the genetic profiles of individual patients.
The field of bioinformatics largely reflects the emergence of big data within a biological and scientific context. In addition to bioinformatics, there is a huge opportunity to positively reinforce healthy behaviors through tracking patient outcomes.
Imagine what kind of brand loyalty pharmas could achieve if patients could easily access a critical health reading over time such as blood pressure. Seeing those numbers declining over time with the help of a medication could be a powerful message to share.
Interestingly, many stakeholders within the industry – ranging from marketing and innovation teams, to clinical and research staff – now categorize big data broadly in terms of “real-world” and “research” (or clinical) data, as highlighted in this report from Health Affairs.
Research data is collected during the trial process:
Clinical trial data is often collected for the specific purpose of obtaining regulatory approval for a new medicine, or a new indication for a medicine. Clinical trials are rigorously designed, often focused on a highly specific patient population, take significant time to complete, and can be very expensive to complete.
Real-world data, on the other hand is “any data that is not captured within the context of a clinical trial and is not explicitly intended for research purposes.”
Real-world data includes social media posts, which can be incredibly valuable for pharmaceutical companies. Drug companies can supplement clinical findings with the actual experiences of people taking the drugs.
Such “crowdsourced” information about medications can provide insights into surprising side effects or new uses of treatment. These new indications can often become billion-dollar businesses in their own right – as shown by the long history of “happy accidents” in drug development.
Of course, if you have good customer intelligence and know how to capture, manage and analyze a wide range of data, then maybe that’s not an accident. This is similar to what companies in many other industries face – from media and entertainment and telecommunications to financial services.
Pharmas must aggregate data – structured and unstructured – from multiple sources to get a clear view of who their consumers are and what they want. They can also test certain offerings and may discover new uses of existing products.
Undoubtedly, pharmaceuticals face very intense regulatory scrutiny – so matters of data ownership, security and confidentiality are tricky. But it’s interesting that an industry that succeeds largely by failing faster is increasing its customer intelligence through more channels than ever.