The Path to Data-Driven Quality Management

Aug 20, 2025 | Jessica Navarro and Rob Weisz

By leveraging data for advanced automation, quality organizations are transitioning from reactive to proactive, and even predictive, quality strategies. The current priority for many companies is connecting data across systems and processes for better visibility, control, and decision-making. Most are advancing but would also agree that they are not there yet.

Digital maturity is a prerequisite for data-driven quality management. Yet limiting factors include siloed applications, custom processes (often varying by site), and ‘dead’ data that lives in multiple systems and on paper. Each company has its own unique mix to contend with.

Meanwhile, those further along the path are experiencing the benefits. Data integrity and connecting end-to-end processes were key components of Sanofi’s quality transformation, which has resulted in 60% of deviations being closed on a fast-track basis. A unified quality platform means Neuraxpharm can ensure compliance and improve collaboration with its 23 affiliates in Europe.

Manual data entry leaves room for errors and introduces variation and inconsistency. In contrast, data-driven quality means processes and data are standardized for the whole organization, increasing accuracy and reliability. For example, full visibility addresses bottlenecks during collaboration between quality and regulatory on change controls.

Five steps to data-driven quality management

Structured and standardized data contributes to better reporting capabilities and dashboards, as well as more reliable trend predictions. Quality leaders are taking the initiative and undergoing transformative data cleaning projects, so as to proactively understand risks and efficiently focus their resources.

1. Simpler, standardized processes across the organization
   Aligning with industry standard (rather than custom) processes reduces implementation time and increases efficiency. CDMO Recipharm, which serves a diverse customer base with distinct needs, found that harmonizing processes across 14 sites led to faster lead times, improved collaboration, and greater customer value.
2. Right data, right decisions
   In the past, teams would enter data into spreadsheets and aggregate information for analysis and reporting from disconnected applications. This undermined data integrity. Using a unified system instead means there is one source of accurate product data for cross-functional decision-making. For example, companies connecting quality and regulatory on the same platform benefit from data-driven change control, leading to better impact assessments and faster time to market.
3. More effective industry collaboration
   Many quality organizations are reassessing how to collaborate efficiently and compliantly with suppliers and partners, or customers. In addition to security concerns, email and attachments are falling out of favor when sharing information because they lack traceability. Regulated quality content management solutions provide a secure way for companies and partners to author, collaboratively review, and approve documents such as quality agreements and batch-related documents. Forge Biologics, a CDMO focused on rare diseases, has reduced document review and approval cycle times, improving the client experience.
4. Batch release moves into the fast lane
   Qualified persons and disposition owners find it easier to complete their reviews when all relevant batch information is centralized and available in real time — think deviations, change controls, registrations and testing data, batch documents, and ERP and third-party systems. Being able to trace decisions to source data removes a key driver of uncertainty and delays to market ship decisions, so batches can be released as soon as health authority (HA) approvals are visible in the system.
5. Robust business case for quality transformation
   A robust business case will clarify the objectives and business drivers for quality transformation, and link those to intended improvements. For instance, what are the opportunity costs of persevering with legacy vendors and systems in the quality control (QC) lab? How would modernizing now enhance lab capacity for product launches? Given effective change management is critical to adoption, your case will also need to specify who will champion change and new ways of working across the organization.

For pharma companies that invest in digital systems and aspire to data-driven quality, the goal isn’t just an IT transformation, it’s business transformation. Simplifying processes and standardizing data will improve efficiency across the value chain. These are the enablers of predictive quality — and better risk management on behalf of patients.

Watch this 30-minute webinar to learn how companies are benefiting from data-driven quality management.