What is PDQ (Product Data Quality)?
PDQ refers to the accuracy, consistency, and completeness of data used throughout the product lifecycle, from design and development to manufacturing and beyond.
PDQ is typically handled using PDM (product data management) tools, which ensure that all data related to a product – such as CAD models, specifications, and manufacturing instructions – meets high standards of quality. This is significant because poor-quality data can lead to costly errors, delays, and inefficiencies in product development.
The role of PDQ in digital engineering
Precision and compliance are paramount in industries like automotive, aerospace, and industrial production. For example, if a CAD model contains errors or inconsistencies, it could result in production delays or even product recalls. By maintaining high-quality product data throughout the lifecycle, companies can avoid these issues while improving collaboration between teams, reducing rework, and ensuring that products meet both customer expectations and regulatory standards.
What are the benefits of PDQ?
Improve decision-making
High-quality product data enables more informed decision-making across the product lifecycle. Engineers can trust that the data they are working with is accurate and up-to-date, which reduces uncertainty and allows for faster iterations during design and manufacturing phases.
Reduce costs
Errors in product data can lead to expensive rework or even product recalls. By ensuring that data is accurate from the start, companies can avoid these costly mistakes. Additionally, high-quality data reduces the need for manual checks and corrections, freeing up resources for more value-added activities.
Enhance collaboration
High-quality data fosters better collaboration between teams across different departments and even external partners, such as suppliers. When all stakeholders have access to accurate, consistent data, communication improves, and teams can work together more effectively.
Optimize production efficiency
Accurate and consistent product data ensures that manufacturing processes run smoothly, reducing the likelihood of production delays or errors. High PDQ enables manufacturers to optimize production schedules, reduce waste, and increase overall productivity by minimizing the need for rework or adjustments during production.
Ensure compliance
Meet regulatory requirements more easily by ensuring that product data is complete and accurate. This reduces the risk of non-compliance-related penalties and helps companies stay ahead of industry standards. Additionally, accurate data allows for better risk management by identifying potential issues early in the product lifecycle.
Futureproof scalability
Maintaining high PDQ ensures that data infrastructure can scale efficiently. High-quality data supports advanced manufacturing practices like predictive maintenance and digital simulations, allowing organizations to stay competitive.
What's the best way to get started with PDQ in your organization?
Implementing an effective PDQ process begins with understanding your current data quality challenges and setting clear quality standards for your product data. Keep reading to discover our recommended approach to PDQ, or reach out to us for a free consultation today.
Assess the current state of your data across all systems, including aspects such as geometry, metadata, and file formats. This step will provide you with a clear understanding of where improvements are needed.
Develop clear and actionable data quality standards to align with both industry regulations and internal requirements. These standards will serve as the benchmark for validating data quality moving forward.
With your standards in place, it’s time to automate the validation process using tools like Q-Checker. Q-Checker integrates directly into your CAD environment to automatically check models against predefined quality criteria. This ensures that errors are identified early in the design phase before they can propagate downstream.
Tools like Q-Monitor can be used to continuously track key data quality metrics across your PLM system. By setting up automated alerts and reports, you can quickly identify any deviations from your established quality standards and take corrective action before they impact production or compliance.
Data quality management is not a one-time activity but an ongoing process of improvement. Once issues are identified through monitoring, it’s crucial to implement corrective actions promptly. This might involve revising design processes, updating software configurations, or providing additional training for teams involved in data creation and management.
Establish clear communication channels between these teams helps ensure that everyone is aligned on data quality objectives and that any issues are addressed holistically.
Need help with PDQ product data quality?
Arnd Feye and team are on-hand to provide tailored guidance and support with a deep knowledge of the full Dassault Systèmes portfolio. Reach out for a free consultation today.
Essential software for ensuring PDQ
For complex engineering design and assembly modeling.
For improving quality assurance in virtual product development.
For end-to-end PLM (product lifecycle management) and collaborative innovation.
For robust product data management, business analytics, and decision support.
View our extensive range of software solutions