Data relevance and quality is key, and is often the issue that paralyzes executives from implementing sophisticated analytics.
Even the multitude of datasets available in the United States (public and commercial claims, public health data from the Centers for Disease Control and Prevention, research data from the National Institutes of Health, all payer/all claims datasets, electronic health record data) are often incomplete and may not contain the variables needed to answer the questions that are most critical to success.
In other markets, such as Japan and Europe for example, we have a lot less data. However, the good news is that we know where to find the key data we need – even if it is incomplete.
Incomplete or irrelevant data is not a roadblock. No dataset will be perfect, and you can still find data that does provide the variables required to answer the questions.
We use different types of data depending on the client’s data availability and needs.
Internal data
This is the most basic dataset. It is typically large and includes all IMS data available, conference attendees, doctor prescribing data and patient data. We see the same data from most companies and this is usually Big Data. The challenge is that it often misses critical elements needed to provide the insights we require. It typically doesn’t get to the “why” behind the findings. This is best for companies with limited budgets.
Collected data
This is data we collect ourselves. It is often useful for filling the gaps in internal datasets. The data collected is individualized to the outcomes for which the client needs answers. The advantage of this approach is the ability to provide resource allocation, including budget allocation, as well as obtain the “why” behind the findings and next steps.
Big data
The third approach is to analyze all the data a company has available at the time. This involves creating machine-learning algorithms to mine the data, formulate hypotheses and create insights. It leads to bespoke visualization platforms which allow Pharmaceutical teams to pull insights out of the complex. It also allows for the addition of data as more is collected. This, in turn, allows the data and insights to be embedded in the “business-as-usual” processes.
By matching the data to the right technique, marketers will reap far stronger results than randomly choosing just one.
Found this article interesting?
To learn more about how Eularis can help you find the best solutions to the challenges faced by healthcare teams, please drop us a note or email the author at abates@eularis.com.