Blog
Creating a Quality-First Approach to Regulatory Data
May 20, 2025 | Kim Brownrigg
May 20, 2025 | Kim Brownrigg
High confidence in data quality is a key attribute of high-performing global regulatory information management (RIM) programs, according to the 2024 Gens & Associates Report, “World Class RIM: Extending the Power of RIM.” These organizations average 63% high data quality confidence compared to 38% for other organizations. High performers also lead their peers across key data quality practices, including formal regulatory data governance structures, dedicated data quality roles, and regulatory executives who advocate for a culture of quality.
As regulatory operations mature, a strong data quality foundation enables operational excellence by driving efficiency, reducing risk, and accelerating speed-to-market. Data-centric organizations also have better insight into operational issues, optimize processes more quickly, and enable innovation through automation and AI.
Building a data quality foundation requires the right mix of technology, processes, and organizational culture. “Data quality doesn’t fix itself,” says a product director at a large global biopharma. “There needs to be a lot of change management and communication to help people understand why it’s a pain point, and why we need to solve it.”
In this blog, we’ll discuss the six principles of data quality and show how a data-centric approach is creating better insight and visibility to bring products to patients faster.
Six principles of data quality
Establishing a data-centric culture requires optimizing and accurately reporting on the six principles of data quality: timeliness, uniqueness, completeness, validity, accuracy, and consistency. Ensuring good data quality metrics and practices serves as the foundation for measuring operational effectiveness and tracking progress toward specific key performance indicators (KPIs), such as meeting compliance requirements and improving speed-to-market.
Achieving data quality in RIM systems requires a strategic blend of automated and manual review methods, as well as systems that are easily flexible, support transparency, and actively leverage the data to drive improvements and support regulatory decision-making. For each data quality principle, we recommend certain quality measures to optimize your RIM data quality:
- Timeliness: speed of data entry, so it is accessible when it’s needed
- Uniqueness: remove data and document duplication
- Validity: ensure data and documents are in an acceptable format and structure
- Consistency: maintain uniform and reliable data entry across all users
- Accuracy: enter the right information for the right submission or registration
- Completeness: ensure all information has been populated
Establish target lead times to define the maximum duration allowed from the receipt of data or documents to their entry or archiving in the system. Monitor compliance with these timelines by implementing automated reporting and dashboards to notify users of pending actions.
Leverage Veeva RIM’s uniqueness constraints to prevent creation of duplicate records and utilize a duplicate document report to identify and take actions on duplicate documents to maintain a clean, organized system.
Build validity checks by creating predefined rules that dictate the acceptable format and structure of data or documents. Configure and apply these rules in real time to prevent invalid data entry or execute periodically on existing data for retrospective validation. Maintaining data integrity consistently upholds important criteria, like ensuring a health authority (HA) approval date does not precede an HA submission date.
Establish clear, well-defined data policies and standards, implement them effectively, and monitor adoption through periodic reviews. If there are inconsistencies, retrain and iterate based on learnings to enhance standardization.
Leverage the use of relationships in Veeva RIM to connect associated data and mitigate against inaccuracies. Advancements in AI can also allow for some automated checks by utilizing machine reading technology to assist with data accuracy. The autoclassification feature in RIM bot can streamline the process and improve overall data integrity. Periodic manual reviews that ’audit’ the accuracy of data can also be considered.
Optimized customer configurations allow for the definition of mandatory data requirements based on specific use cases. Ensuring all necessary information is captured improves data completeness and leads to better insights.
Building end-to-end value with data quality
One of the world’s top biopharmas has been driving a multi-year regulatory transformation. Now with the full Veeva RIM platform in place – replacing 10 heavily customized legacy systems – its regulatory team has turned its focus to improving data quality. With a unified platform, the goal is to unlock end-to-end transparency and insight across regulatory operations.
“What we really want is transparency of the end-to-end value chain. We want to know what we are doing, how we are performing, how our processes are performing, and how we are delivering on the outcomes that we want,” says the product director. “And we also want to understand quality: are we doing it right? Do we do things the way they should be done?”
The global biopharma focused on five key outcomes:
- Improving data quality
- Ensuring transparency
- Enabling insight-driven decision-making
- Planning and prioritization
- Driving behavior and action
To achieve this, the biopharma uses quality dashboards that provide real-time insights into regulatory operations across five core areas: events, submissions, registrations, safety and commitments, and miscellaneous reporting. The dashboards give teams real-time visibility into usage metrics, allowing them to identify potential areas of risk or non-compliance with organizational procedures.
For example, the company defined a specified timeline for archiving submissions post-filing. As deadlines approach, users receive notifications prompting them to take action on overdue submissions. If a deadline is missed, additional alerts are issued and then noted in monthly reports to leadership.
This data-driven approach helps identify problems related to knowledge or performance gaps or other factors, like a regional issue. By measuring system engagement and identifying areas of improvement, leaders can deliver targeted communications, enforce accountability, and ensure consistent delivery on goals.
Building a culture of data quality
Good data quality builds trust in the system. As regulatory operations continue to grow in strategic importance, so does the need for a data-centric approach. End-to-end RIM on a unified platform ensures teams have access to trusted, consistent data to track and improve regulatory operations and identify potential improvements to processes or training. Once the system and processes are in place, organizations should consider investments in three core areas to drive success:
Mindset and transparency: Provide data quality reports to leaders and teams, reinforcing the value of high-quality data and its impact on goals and submissions in regular communications. Identify and showcase the impact of high performers.
Roles and responsibilities: Good data quality requires effort and prioritization. Review and adjust work allocation where necessary for optimal task assignments. Consider interim central support as the organization adopts a change in mindset.
Process and training: Document clearly defined processes and accountable data owners in training materials. Establish repeat training sessions to address data quality concerns observed through process and compliance reporting.
Data quality isn’t a one-and-done initiative. Teams should plan to iterate and refine data quality initiatives to better master data governance and ensure the right people are in the right roles. This will help strengthen accountability, optimize performance, and support faster, more informed regulatory decisions.
By prioritizing the establishment of a solid data foundation, organizations can streamline processes, empower teams to make data-driven decisions, and bring products faster to market and to patients.
To learn more, join us at the Veeva R&D and Quality Summit in Madrid, June 4-5. For support services on RIM Data Quality, contact Kim Brownrigg.