This is a video about EpiCube, where the CEO of J+D Forecasting, David James, discusses the challenges and importance of data in response to our latest launch – EpiCube.
Find out more about EpiCube here.
The current challenges relating to data
There’s lots of data out there of different types and different qualities. I think there is a lot of data our clients have access to, but the challenge is that that data is still very siloed, still very static, still very two dimensional and really is not getting the value out of the data is becoming harder and harder for our clients.
Why is looking at data differently important?
Well, I think if you look at how pharmaceutical companies are investing in, they’re not really going down the disease-based approach to developing products. If you look at oncology, for example, historically, you’d look at breast cancer, lung cancer, looking at those individual diseases in the data would provide insight into those diseases. But now, more and more, they’re looking at it from a perspective of biomarkers. So the genetic coding behind patients.
So looking at it as an individual disease isn’t very useful. You need to look at it across different diseases, but driven by that particular biomarker. And I think the data makes that difficult and means they have spent a lot of time manipulating and analyzing data to get it into a format they need to be able to forecast.
Why do we need to consider a change in approach when it comes to data?
I think for clients to understand their disease is better, understand their investments better, they need to also spend less time on manipulating data. I think the more time you can spend thinking about the forecast, looking at the assumptions, testing your hypothesis, the better.
So if you’re spending all your time taking static data sets and trying to combine them in a way that brings you value, you’re not spending that time on the thought process of developing a good forecast.
EpiCube
EpiCube is an epidemiology database for multiple therapy areas (inc Oncology), multiple diseases, attributes, countries, age groups and genders.
Choose from a dataset of over 9,500 sub groups and over 200 diseases across 50 countries. Create your dataset by region, country, disease or a specific sub group attribute. Utilise characteristics that specifically affect your product. Created with pharmaceutical forecasting structures in mind.
