Last Updated on September 4, 2025
Why pharma and biotech companies need to move away from manual interfaces quickly and how it effects your business goals.
I still see it a lot. Various systems are used to store and process HCP information (consent status, name , adresss, HCP reg number etc.) such as CRM, Excel sheets, marketing automation tools, etc.
Data stewards are appointed to monitor the quality of this. I see a lot of work done by doing manually matching and merging data.
But who really checks the incoming and outgoing data for accuracy, timeliness, and completeness across the different applications and channels?
An example from one of our clients shows that manually executing this process turned out to be insufficient
By deploying our data quality algorithm, we found various opportunities and risks for our client in a days.
- 35% of the total number of customer records appeared to be duplicated in several systems.
- It turned out there were over 1000 more opt-ins from doctors, allowing the client to engage with this target group digitally.
- There were hundreds of doctors with the most current opt-out status, that seems to be unnoticed.
- There were hardly any complete doctor profiles. It turned out that 5% has already a complete profile (golden record) after deploying the data quality algorithm
For a small biotech, poor data quality can make the difference between commercial launch success and failure, and for an established pharmaceutical company, it can amount to millions of lost opportunities or wasted costs.
Suppose you translate this and quantify it for your organization in terms of revenue, brand experience, risk, and time to market. What would the outcome be for your organization?
Take data quality seriously.
