How Can Pharma Use Data-Driven Frameworks to Ensure Commercial Success?
Biotech and pharma companies have a vast range of capabilities and data sophistication, from their data acquisition strategy down to the way they consume, curate and present data internally.
In the last 10 years of my career, I’ve been involved in hundreds of data assessments on the side of the data supplier. Regardless of an organization’s data proficiency level, there are discrete steps all manufacturers can take to improve their partnerships with data vendors—and ultimately, to ensure commercial success.
Articulate your use case
A plainly articulated use case allows vendors to recommend the best solutions from a data acquisition standpoint. In addition, organizations must understand that those recommendations may be quite different depending on the therapeutic area. Data on an oncology brand, for instance, may look quite different than data for a rare disease drug based on the source capturing.
The more vocal manufacturers are about what is driving the need for data, who is asking, and how the outcome will be presented, the better positioned all will be in ensuring that what is scoped and delivered is of the utmost relevance.
Seek data mapping transparency
Crucially, manufacturers must understand that there will never be one single data set that can support every use case. Instead, use cases will require multiple data suppliers and a tokenization strategy.
Manufacturers should also be aware that some data may fall out during the process of transforming the data and mapping disparate data sources to a common data model. Because of different data intake and cleansing methodologies, records can be lost if they are not aligned to the mapping methodology, especially when data sources are changing.
While certain mechanisms should be able to identify these gaps, sometimes the mapping process is not communicated well to stakeholders, who then mistakenly assume that there is an error within the data source. When the process is properly explained, organizations can understand that rather than a mistake, it is an expectation of the data.
Refreshing the bridging methodologies is key to preventing this data loss. At MMIT, we see many clients who fail to refresh their data integration strategies at a fast enough pace to realize the full value of the data sets they’re purchasing. By investing in some of the newer AI capabilities, the impact of this can be measured, monitored, and conveyed to all parties.
It’s important for data suppliers to be transparent with clients about the latency in the data, the frequency by which it changes, and any existing gaps in the data—as all these factors impact the way insights can be derived. While some suppliers may position themselves as possessing real-time data, nothing actually occurs in real time. Level-setting is an important conversation for suppliers and their clients to have.
Don’t make assumptions about suppliers
Don’t assume you know everything about a data vendor’s skills and expertise based on past experience. Manufacturers need to stay current on marketplace capabilities, as innovation is moving faster than any one person can keep up with.
Although you might believe you know all that a company is capable of, you may be surprised. For example, MMIT is constantly bringing in new patient-level data sources to enrich our core payer competencies and provide solutions to help our clients find the right HCP and patient at the right time.
Educate data suppliers about your organization
Finally, regardless of whether you’re a long-time industry veteran or a small start-up with a promising pipeline, make sure to explain how your organization is structured when you’re onboarding a new data set. How are you organized? What functions will be interacting with the new data source? What will they be looking out for? What’s important to them?
Setting your vendor up for success makes the partnership function more smoothly, because there are certain ways your vendor can recommend configuring the data based on your organization’s internal preferences. Educating data suppliers about what’s most important to your organization will make the partnership more successful because they can provide the quality insights you’re looking for.
Building close relationships with your data suppliers will positively impact your data strategy and results over the long term.
This blog was adapted from my remarks as moderator for the “Fuel Your Commercial Frameworks with a Data-Driven Decision-Making Engine” panel at Reuters’s Pharma USA 2023, held in late March in Philadelphia, PA.
