For those that work in healthcare strategy, marketing, business development, pre-commercialization, and product launch, getting fresh data to inform your work is a bit like opening up a wrapped gift. You’re excited and maybe nervous to discover what’s under the gift wrap, hoping you’ll like it. Healthcare data has the potential to transform strategies from intuition-based to evidence-based. But, the reality is: that purchasing data is often fraught with obstacles, including data visibility, turning what should be a clear decision into an inefficient, lengthy, and costly process. We’ve put together some fundamental questions about data to help improve the data purchase process - so you can get the results you need and make the most of your data purchasing investments.
Different types of data and data elements support different business questions and applications. By starting with the end goal or application, you’re more likely to get the data needed. Ask yourself and your team, “how will the data be used, and what kind of data do I need?”
Once you determine what the data will be used for, dig a little deeper into the data itself.
When you feel comfortable with your application/use case and the data elements needed, think about how you will use that data.
In addition to the data, you should ask other readiness questions about your technology and team.
The data landscape is changing. If you are a maverick looking to try new things, consider how you might execute your strategy differently if your data was available with these added benefits:
These suggestions are intended to help you make more informed data purchasing decisions - accelerating the time from data to insights to strategic impact. If you have any questions or suggestions about new ways of thinking about data, we’d love to hear from you.
Enhancing the accuracy and utility of pricing transparency data with claims data and a powerful data analysis platform.
Machine learning and AI can make the healthcare data explosion Findable, Accessible, Interoperable, and Reusable (FAIR).