But for most MA organizations, the data sets we have focused on are purpose generated - either our own data or data from similarly-scaled studies conducted by others. Big Data refers to something different. I like the differentiation that SAS uses when comparing Big Data to the past data sets. They break it down to four “V”s and a C:
- Volume: Hugely increased data volume from the past
- Variety: Since the data is produced in many different ways, it has many different formats and structures
- Velocity: Both how fast the data is being produced and how fast it must be processed
- Variability: Inconsistent data flows, with peaks and valleys
- Complexity: Driving value out of these data sets is highly complex and difficult
This is not your grandfather’s data sets. What are some examples of Big Data as relevant to biopharma and MA:
- Electronic Health Records data from a variety of sources
- Search engine data (see an example of analyzing search data to find safety signals here)
- Sunshine Act Physician Spend Data (when it becomes available)
- Social media data
- Competitors clinical trial data as it is released
Contained within these and many Big Data sources are key tools for MA:
- Valuable therapeutic information
- Unique customer insights
- KOL identification and information
- Visibility of competitors drug development and support efforts
- Important drug safety signals
But, none of these benefits can be achieved unless the question is asked and the data is analyzed. I would suggest that effective MA organizations of the future will need to have the capacity to ask and answer these types of questions.
In order to do so, MA organizations will either need to build or have access to increased levels of biostatistical and epidemiological resources. And these resources need to have skills directly related to Big Data. The characteristics that differentiate Big Data from existing data sets also means that many existing biostats and epi staff do not have the expertise or confidence working with these large, external data sets. MA organizations need to ensure that people with exactly these skills sets are available within their organizations or from outside vendors and that these resources have the capacity to support MA.
Then, MA needs to improve its overall level of confidence defining Big Data questions, conducting Big Data analysis, and discussing the results with others. Given the difference in the source of this type of data, the way this data is presented and discussed must be different too. Everyone in MA, but especially the MSLs, must become more comfortable understanding the nuance of this type of data analysis and discussing both the strengths and weaknesses of working with Big Data.
The era of Big Data is here. MA has a long history of effectively using data and explaining data in support of its organization. MA leaders must investigate and embrace Big Data to take advantage of all the tools available today. The questions unasked are always the questions unanswered.
What is your experience with Big Data? Please leave a comment.