How do you align data validation processes and standards with business requirements in PDM systems?

1 Understand the business needs

Before you start validating your product data, you need to understand what the business needs are, and how they translate into data requirements. For example, what are the key attributes, categories, and values that define your products? How do they relate to your customers, markets, and channels? What are the data sources, formats, and systems that you need to integrate? How often do you need to update and refresh your product data? By answering these questions, you can define the scope, objectives, and criteria of your data validation process.

3 Communicate and collaborate with stakeholders

Data validation is not a one-person or one-department job. It involves multiple stakeholders, such as product managers, data analysts, data engineers, data quality specialists, and end-users. Therefore, you need to communicate and collaborate with them throughout the data validation process. For example, you can share the data standards and rules with them, solicit their feedback and input, assign roles and responsibilities, coordinate tasks and schedules, monitor progress and performance, and resolve issues and conflicts. By communicating and collaborating with stakeholders, you can align their expectations, needs, and perspectives, and enhance the efficiency and effectiveness of your data validation process.

4 Automate and streamline data validation tasks

Data validation can be a time-consuming and tedious task, especially if you have a large volume and variety of product data. Therefore, you need to automate and streamline data validation tasks as much as possible, using appropriate tools and technologies. For example, you can use data validation software, scripts, or workflows to perform data checks, transformations, and corrections automatically, based on predefined rules and logic. You can also use data quality dashboards, reports, or alerts to monitor and measure data quality indicators, such as accuracy, completeness, consistency, timeliness, and relevancy. By automating and streamlining data validation tasks, you can reduce human errors, save time and resources, and improve data quality.

5 Review and refine data validation processes and standards

Data validation is not a one-time or static activity. It is a continuous and dynamic process that needs to adapt to changing business requirements, data sources, and user needs. Therefore, you need to review and refine your data validation processes and standards regularly, based on feedback, results, and best practices. For example, you can conduct data audits, surveys, or tests to evaluate the effectiveness and efficiency of your data validation process. You can also identify gaps, issues, or opportunities for improvement, and implement changes or enhancements accordingly. By reviewing and refining your data validation processes and standards, you can ensure their alignment with business requirements, and maintain data quality.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?