Thoughts on Global Medical Affairs
Topic 32: Big Data in MA – Revisited
March 13, 2015Posted by on
A couple of years ago I wrote a post (check it out here) on the emergence of big data for Medical Affairs. Given the rapid evolution of big data, two years is a long time ago so it’s worth revisiting this topic.
Let’s recap what we mean by “big data.” It is a broad concept, but for our discussion today we will be using big data to refer to the new capability to pull together huge quantities of data that were not directly generated for the purpose they are now being applied. Biopharma has excelled at generating proprietary data sets for a specific purpose, but big data take advantage of non-proprietary data that was generated for a different purpose by applying it in a new way.
These external data sources range in structure, format and value. The real trick to big data is pulling the data from disparate sources, efficiently cleaning it and standardizing it to allow it to be cross-referenced, then finding novel ways to use it.
Example of Big Data in MA
In the last couple of years we have seen examples of companies set up to provide big data services to MA. I will single out one here as an example, but this is not intended as an endorsement. I have no relationship with this company or practical experience with their products.
The company, Med’meme, is a case study of big data in MA. Based on their website, Med’meme takes large, public data sets – in this case lists of scientific presentations from medical meetings and peer-reviewed journals and clinical trial information at least – and in their backroom they apparently standardize it to make all those data cross referenceable. How well they do this, how complete and how accurate the data is, I can’t say. But, when you think about that data source as an MA professional I am sure you are jumping to a bunch of potential uses – like the ability to rank KOLs, to identify new KOLs, to track TA trends in publishing, to identify potential investigators, to be alerted to new publication identification, etc.
And that is the beauty of big data – there does not appear to be anything in their data set that has not been available (with some costs) to biopharma for years. Their service is finding a way to scrape it all together, standardize it and allow it to be searched effectively.
Buy v Build in Big Data
When I first published the article about big data I had a number of “buy vs. build” questions. The reality of big data in its current form is about re-using publically available data in novel ways, so building it internally is unlikely to produce proprietary value. However, combining these data sets with proprietary data, or asking interesting and unique questions of the data is something that can remain proprietary – so some hybrid solutions may be valuable.
If big data is not a part of the MA information technology planning it should be. This capability represents an opportunity for strategic advantage in the short-term until it is widely adopted.
Big data is a new reality. A huge new data set, the Sunshine Act database, has just come on-line, and other data sources are increasingly making their data available for these types of analysis. Expect to see major development in this area in the coming couple of years.
What has been your experience with big data in MA? Leave a comment.