Managing data effectively can be challenging in any industry. But it’s especially difficult in pharmaceuticals, which faces especially stringent data management, privacy and security needs.

In pharma, much of the data that businesses work with – such as personally identifiable information (PII) in patient records and information from the supply chain involved in the production of medications – is highly sensitive. It’s also subject to data privacy regulations in many cases.

On top of this, the consequences of making mistakes when working with pharma data are especially serious. In most other industries, the worst thing that could happen from mismanagement of data is ineffective business decisions or information security risks – which are bad, to be sure, but which are not matters of life and death. In contrast, data management errors in pharma could lead to issues like the inaccurate tracking of medication expiration dates, with severe consequences for human health and safety.

For these reasons, pharma companies must develop especially sophisticated data management strategies. Those strategies should start with basic data management best practices, like validating data quality and implementing processes to track data throughout its life cycle. But they must also include extra steps that address the unique challenges of pharmaceutical data management.

Effective data management in pharma can be tough for two main reasons. The first is that, again, pharma data is often highly sensitive. In cases where data managed by a pharmaceutical company includes PII, the information may be regulated by data protection laws like the GDPR, which restrict how pharmaceutical companies can collect, analyse and store data associated with consumers.

In addition, pharma data may include sensitive business information, like the status of a drug currently in development. This information isn’t typically regulated by compliance laws, but it’s nonetheless highly sensitive data that businesses don’t want to expose to competitors – which means that this type of data, too, must be managed in ways that maximise data security and privacy.

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. Likewise, businesses involved in the pharma supply chain must also ensure that they can accurately trace the origins of drug ingredients and products so that they can recall tainted medications when necessary.

These two challenges – the highly sensitive nature of pharmaceutical data and the high stakes surrounding accurate and reliable data management – would be tough enough to manage if a single company managed the data, or if all data resided in a central location. But that’s not typically how things work in the pharma industry.

Instead, pharma companies tend to share data frequently with external parties. For example, logistics operations may require a pharmaceutical business to coordinate with factories, regional distribution centres, local distribution centres and pharmacies to bring its products to market. This means that the business must ensure that its data remains secure and accurate not only within its own systems, but also within those of the various partners and suppliers it works with.

Differences between the data systems used by a pharma company and those that exist elsewhere in the supply chain add complexity to this process. For instance, the identifiers that a drug manufacturer uses to trace product categories, stock keep units (SKUs) and other codes within its own systems are typically different from those used by retail pharmacies that actually sell the drugs. As a result, the data becomes inconsistent across different stages of the supply chain, making it even harder to monitor the data, identify accuracy issues and so on.

Unfortunately there are no simple solutions for streamlining the way businesses in the pharmaceutical industry work with data, but there are, however, a variety of practices that pharma companies can implement to address the unique challenges they face in this domain. When used collectively, these strategies help ensure that information remains secure and accurate across all stages of the pharma supply chain.

Adopt a granular approach to data privacy management
While much of the data that pharmaceutical companies work with is sensitive, some data is more sensitive than other data. For example, sales-related data may be sensitive from the perspective of business competitiveness, but it’s not as sensitive as personal patient data.

The steps that pharma companies adopt to protect sensitive data should reflect the level of sensitivity of the specific types of data they’re dealing with. For instance, it may be necessary to use techniques like data anonymisation (which strips or obscures PII within data sets) when working with patient records. But this isn’t necessary when managing sales data.

Preserve business context
When working with highly sensitive data, pharma companies should strive to ensure that their data privacy practices don’t degrade the value of the data from a business perspective. If they do, the data may as well not exist in the first place.

To strike the right balance, it’s important to adopt data privacy strategies that ensure the usability of data for key business needs while still keeping it private. For example, imagine that you have a data set that includes information about clinical trials for a drug you’re developing. The data includes PII from the patients who participated in the trial. If you were to anonymise the data by simply erasing the PII entirely, it would become impossible to check in with patients in the future to determine how they are faring in months or years after the trial.

A better strategy in this case might be to replace the PII with encoded patient identifiers that link to names and contact information stored in a separate database. That way, you decouple the PII from the clinical trial data, but you can still track individual patients if necessary.

Implement comprehensive 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 unauthorised access.

Going a step further, organisations should consider centralising 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.

Harmonise data
Data harmonisation means standardising data types and structures. In the pharma industry, harmonisation is especially valuable because it 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 standardised 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.

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 standardised way, businesses in this industry should consider using a data platform that makes it possible to store data in a centralised 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 anonymised patient information to enable better inventory management for distribution.

In the pharma industry in particular, 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 data systems, but also – most critically – the data that flows through pharma supply chains.

Daniel Avancini is the Chief Data Officer at Indicium, an AI and data consultancy