Managing data effectively can be challenging in any context. But it’s especially difficult in an industry like pharmaceuticals, due both to the sensitivity of the data and the severity of consequences that result from data management mistakes.
What can pharma companies do to implement an effective data management strategy? There are no simple answers, but as I explain below, there are best practices that can help optimize the way pharmaceutical businesses collect, integrate, analyze and store data.
Key Data Management Challenges in the Pharma Industry
Effective data management in pharma can be tough for two main reasons.
The first is that pharma data is often highly sensitive. It may be regulated by data protection laws like the GDPR, which restrict how pharmaceutical companies can collect, analyze and store data associated with consumers. In addition, pharma data may include sensitive business information, like the status of a drug currently in development, that businesses don’t want to expose to competitors.
The second fundamental data management challenge for pharma companies is that mistakes can have dire consequences. In addition to regulatory fines triggered by compliance violations, failing to manage data accurately could lead to issues like the sale of expired medications, causing harm to patients.
Complicating both of these challenges is the fact that pharma companies often need to share data with multiple parties. For example, logistics operations may require a pharmaceutical business to coordinate with factories, regional distribution centers, local distribution centers and pharmacies to bring its products to market. Ensuring that data is shared accurately and securely across this supply chain is no simple feat
Differences between the data systems a pharma company uses and those elsewhere in the supply chain also add complexity. For example, internal sell-in data is not likely to be identical to product categories, SKUs and other codes used externally. Inconsistencies make it more challenging to ensure data accuracy and integrity across the pharma supply chain.
Best practices for managing pharmaceutical data
Again, there are no simple solutions for streamlining data management in the pharmaceutical industry. However, there are a variety of practices that pharma companies can implement to address the unique challenges they face in this domain.
Manage Data Sensitivity
Pharmaceutical companies deal with highly sensitive data, but the risks that it poses can vary. There is a spectrum of sensitivity from less critical data (such as supply chain or sales-related data) to very sensitive patient data. Businesses should manage each type of data differently based on the access control and governance practices best suited to it.
By applying data management best practices such as anonymization and filtering, even highly sensitive data such as patient or diagnostics data can be consumed or shared with a broader audience without problems. For instance, patient data can be anonymized or grouped into larger buckets, which removes links between the data and individual patients but maintains the business value necessary to support a variety of use cases.
Implement Data Governance
Data governance is another key practice for protecting sensitive data. By implementing data governance policies and procedures, pharma companies can define standards surrounding how data is processed and secured in order to mitigate privacy risks. For instance, they could require consumer data to be encrypted to reduce the risk of unauthorized access.
Going a step further, organizations should consider centralizing their data platform and data governance teams. Business areas should work inside the boundaries of a central data platform to avoid data leakage and reduce risks.
Harmonize data
Data harmonization means standardizing data types and structures. In the pharma industry, harmonization can mitigate the risk of introducing inaccurate or incomplete data to the supply chain due to differences in the way various stakeholders label and structure data.
For instance, by ensuring that SKUs are standardized across the supply chain, businesses can lower the risk of failing to identify expired products due to SKU inconsistencies. This also helps pharma companies to work with multiple market data providers that use varying product categories and data models.
Data federation
Many of the best practices described above require collaboration between pharma companies and other stakeholders in the pharma supply chain. To share data in a standardized way, businesses in this industry should consider using a data platform that makes it possible to store data in a centralized repository while allowing different groups to access it in a secure, federated way.
Each group should have unique access rights that reflect what it needs to do with the data. A manufacturer might require the ability to write data so that it can record manufacturing data, for example, while pharmacies can share anonymized patient information to enable better inventory management for distribution
Conclusion: A better approach to pharmaceutical data management
In the pharma industry, a haphazard or ad hoc approach to data management just doesn’t work. It exposes pharma companies to too many risks and liabilities. Instead, pharma businesses should establish a data foundation that allows them to implement a comprehensive set of controls and processes to protect data not just within their own IT estate, but also – most critically – the data that flows through pharma supply chains.
About Daniel Avancini
Daniel Avancini is the Chief Data Officer and co-founder of Indicium, an AI and data consultancy that helps companies gain an analytical edge through data. He specializes in helping companies build their modern analytics stack using cutting-edge tools and processes for data lake, data warehousing, data governance and advanced analytics.
