Data Quality Issues Affecting the Pharmaceutical Industry: Finding a Solution

Pharmaceutical enterprises worldwide navigate a complex ecosystem where vast amounts of sensitive datasets are central to their operations. The data typically includes information about clinical trials, electronic health records (EHR), drug manufacturing, and internal workflows, making data management a cornerstone of patient safety and innovation.

The volume and variety of this data are rising due to rapid industry growth, increasing complexity in drug development, and distributed supply chains. Meanwhile, its management is challenging, given the disparate data sources and legacy systems most organizations use. Data governance strategies, too, are largely inconsistent and have failed to keep pace with technological advancements.

Besides, limited standardization and underinvestment in modern analytics exacerbate inaccuracies, delaying critical patient care and drug safety decisions. As reliance on data increases, the quality of information being processed in the sector is essentially marred by five top challenges:

1. Inaccurate or incomplete patient records leading to potential misdiagnoses
2. Inconsistent drug formulation data leading to errors in manufacturing and dosage
3. Delayed pharmacovigilance reporting slowing adverse drug reaction detection
4. Fragmented data silos hampering collaboration and real-time decision-making
5. Poor data standardization complicating regulatory compliance

Inefficient data management in the pharmaceutical industry does impact patient care and safety. For instance, a lack of reliable information on drug interactions or allergies might result in adverse reactions, potentially endangering lives. Similarly, errors in inventory data result in shortages of critical medications or the unintentional administration of expired drugs, jeopardizing patient health.

Another threat of poor data quality is distorted research findings that cause ineffective or harmful medications to reach the market. This mismanagement has a direct impact on treatment efficacy and patient outcomes.

Impact of Poor Data Quality Management: Real-World Instances

When the quality of data for pharmaceutical operations does not receive adequate attention, knock-on impacts will be on all stages of drug development. The pharmaceutical landscape is also strictly regulated by policies such as those enforced by the US Food & Drug Administration (FDA) and the European Medicines Agency (EMA) in the EU. Lapses in data governance, therefore, trigger legal repercussions and irreparably damage market trust and credibility. Here are some actual examples of how data management blunders have negatively impacted pharma enterprises:

1. In 2019, US-based pharma Zogenix faced an FDA application denial for its seizure-control drug Fintepla. The FDA asserted that datasets from clinical trials lacked certain nonclinical toxicology studies needed to assess the drug’s chronic administration. The company’s share value fell by 23% after the news was publicly disclosed.
2. As per its FY 2023 report, the FDA added 93 companies to its import alert list for drug quality issues, such as non-compliance with good manufacturing practices (CGMP). The action was taken to prevent substandard products from entering the US market, particularly those involving record-keeping lapses or refusal by suppliers to provide quality records.
3. In another case, the EMA issued warnings and imposed penalties after a manufacturing site inspection revealed inadequate documentation and discrepancies in quality control measures. These issues delayed drug approval processes and prompted further audits​.

Why is Data Validation Challenging?

Pharma companies analyze massive amounts of complex data while ensuring that drug R&D is fast-paced to deliver on the healthcare industry’s expectations. Labor-intensive data validation methods and legacy systems that verify data quality often fail to provide accurate results in stringent timelines.

Manual data checks can lead to oversights, such as recording incorrect units of measure, relying on irrelevant datasets, and limited supply chain transparency. These issues cascade into inaccurate sales reports, disrupting business operations and jeopardizing regulatory compliance. Conventional applications, designed to apply rules table by table, aggravate these challenges as they are difficult to scale and maintain. They typically need constant code tweaking to accommodate changes in file structures, formats, column details, and other attributes. Such modifications introduce delays in drug approvals and use cases requiring efficient data processing.

How to Address these Challenges?

Pharmaceutical companies must transform their data quality (DQ) check practices to enhance accuracy and adapt to changing data structures. By deploying customized digital tools that facilitate seamless validation of data and its integration into business processes, they can address their top data quality needs, including:

1. Trend checks
2. Unit of measure checks
3. Multiple environments DQ checks
4. DQ auto threshold setting
5. Month-to-month sales comparison
6. User review and approval process
7. Performance of large volume sets
8. Improvement in SLA time of operations
9. Building custom trends
10. Reduction in cost/time of onboarding

FirstEigen deploys machine learning-powered DataBuck to meet these requirements for effective data validation. It automatically recommends the baseline rules to validate datasets and can write tailored regulations to supplement the essential ones. Instead of moving data, the tool moves the rules to where data resides. DataBuck allows users to edit data validation checks in a few clicks and enables scaling DQ checks by 100X without additional resources.

DataBuck is a cloud-native solution that works smoothly with Amazon Redshift, Snowflake, SQL Server, Microsoft Azure, Google Cloud, and Teradata.

Best Practices to Optimize Data Quality in the Pharmaceutical Industry

In addition to leveraging a system that automatically validates large datasets and schemas, pharmaceutical organizations should adopt the best practices that help them improve data management outcomes. Some of these are:

• Robust data governance: A comprehensive data governance framework helps define ownership, accountability, and policies for data management. A company’s DQ team should consistently ensure compliance with regulatory standards such as 21 CFR Part 11 and GDPR.
• Standardized data formats and processes: Developing and enforcing standardized templates for data collection, storage, and reporting makes DQ management more efficient. Standardization minimizes the errors caused by inconsistencies in data formats, naming conventions, or units of measurement, reducing the risk of non-compliance.
• Regular audits: A pharma company must schedule periodic audits to review data integrity, accuracy, and compliance. The audits must involve checks for duplicate, incomplete, or outdated records, especially in critical areas such as clinical trials, manufacturing, and supply chain data.

Driving Data Excellence in a Transforming Pharmaceutical Landscape

The pharmaceutical industry rapidly evolves to keep up with the broader healthcare sector. With the increasing adoption of cell and gene therapies, its data complexity has also grown. These new treatments are backed by vast, intricate datasets that involve patient genomics, manufacturing details, and outcomes tracking. Streamlined data validation will be critical for the success of personalized therapies and associated compliance.

Another factor that pharmaceutical enterprises cannot overlook is the growth of decentralized clinical trials fueled by wearable devices, telemedicine, home-based care, and data streams from multiple decentralized sources. These trials require robust validation processes to ensure data quality and real-time consistency.

With strategically planned management, autonomous DQ validation by DataBuck, and modernized workflow protocols, pharmaceutical companies can transform their data pools into revenue-generating assets while maximizing operational efficiency, patient outcomes, and market agility.

To know more about DataBuck and schedule a demo, connect with a subject matter expert at FirstEigen.