Mastering Healthcare Provider Data: AI-Native MDM for a Global Oncology Company
How a global oncology company consolidated fragmented, conflicting healthcare provider records with Tamr’s AI-native MDM platform while supporting a Google BigQuery data lake transition.
A global oncology company specializes in treating some of the rarest and most aggressive forms of cancer. As the company expanded its reach in the industry, it built a database of healthcare organization (HCO) and healthcare provider (HCP) records—spanning CRM systems, external data vendors, and internal reporting—to support its commercial and clinical teams worldwide.
Challenges
- Rapid business growth led to an increase in data volume, raising concerns about data quality and control
- Conflicting HCO/HCP data from multiple external vendors and internal CRM systems made it difficult to establish a single, clear picture of providers
- Managing HCO/HCP data required significant manual effort, limiting scalability
- A data mastering solution would need to integrate natively with Google BigQuery on a tight implementation timeline
Outcomes
- Replaced manual HCO/HCP data management with an AI-native master data management (MDM) platform
- Native integration with the company’s BigQuery environment, meeting the implementation timeline and security requirements
- Improved speed, accuracy, and confidence in HCO/HCP data across the organization, reducing the need for internal manual upkeep
- Better analytics and enhanced trust in data-driven decisions
For this global oncology company that treats some of the rarest forms of cancer in the world, having trustworthy and complete healthcare organization and healthcare provider data is of the utmost importance. As they expanded their reach in the industry, the company’s volume of HCO and HCP data increased exponentially, leading to concerns about data quality and control. The company sought a solution that would give them higher confidence in the quality and security of their data, while being quick to implement and produce value.
The company had already decided to transition to Google BigQuery to establish a cloud-based foundation for their data and be able to run advanced analytics. With this foundation in place, the next step was to populate it with data they could trust.
Then the question became: ‘What data are we going to start with, and how are we going to ensure that it's really good data?’”
The company already had multiple systems in place—including SAP and Veeva—that held significant volumes of essential data. SAP held core business and operational data, and Veeva held CRM data for field teams working with doctors, nurses, and other practitioners. The priority was to take the data in these systems and ensure it was clean and trustworthy, since it would need to be migrated into BigQuery via Fivetran.
However, once data started flowing into BigQuery, the company realized that with provider data being pulled from multiple sources, there were many duplicative and conflicting HCO and HCP records. The company had no clear, single view of providers. While they could move the data from different sources into BigQuery, that wouldn’t solve the problem of conflicting and overlapping data. They realized that they needed a data mastering solution to unify and clean their data first.
When evaluating MDM solutions, the company had a few non-negotiables. The solution needed to easily integrate with Google BigQuery and be implemented quickly, providing fast time-to-value.
What we were really looking for was something that would be very complementary to Google BigQuery. And we wanted something that would be very lightweight and efficient that would allow us to load this data, master it, and then start leveraging it incredibly quickly.”
The combination of Tamr’s AI-native MDM, Google BigQuery, and an upskilled internal team allowed this oncology company to establish a top-notch data mastering program. With Tamr in place, the company replaced manual management of HCO and HCP data with AI/ML-driven mastering, cleansing, and curation. The result was a single, trusted view of their data that was more accurate, supported better decisions, and required minimal manual intervention from internal teams.
See for yourself
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