Data owners and users clash on data quality priorities. How do you ensure a harmonious resolution?

When data owners and users clash over data quality, start by having an open discussion to understand each side's concerns. Find common goals, prioritize issues based on business impact, and propose balanced solutions. Set clear standards everyone agrees on, and regularly check in to ensure both sides are satisfied. This approach keeps everyone on the same page and resolves conflicts smoothly.

In my experience, clearly defining the roles of data owners and users is crucial for resolving conflicts over data quality priorities. In one organization, we identified data owners as senior members responsible for overseeing data quality, security, and governance. They were accountable for ensuring that data policies were adhered to and that data integrity was maintained. Meanwhile, users, such as data analysts and business intelligence teams, relied on this data to perform their job functions and make informed decisions.

Here it’s key to have a common framework to agree about. DAMA offers a great framework to start with in order to prioritise what’s important. When you agreed on the framework the priorities and ambition level needs to be set together with both business and technical people. Then create a unified roadmap and start the execution.

These two components are required for successful data quality prioritization: 1?? Metadata Management: understanding data lineage, data usage and data ownership provides insights into which data elements are critical. By analysing data usage patterns data stewards and owners will know which data quality dimensions have the highest impact 2?? Data Architecture: this is required to identify data owners and data consumers from clearly defined data flows. It also provides tools to map data assets to business processes so that your team has a single view on the importance of a particular dataset for particular business processes. ? Ultimately, use a data strategy framework like DAMA DMBoK to align data quality priorities with business goals

In my experience, implementing robust governance frameworks is essential for mitigating conflicts over data quality issues. In one organization, we developed a comprehensive framework that outlined clear procedures for data quality management. This included detailed processes for handling discrepancies, such as data inaccuracies or inconsistencies, and clearly defined roles for who was responsible for resolving them. By having these systems in place, both data owners and users understood what to expect and could work within established boundaries. This approach reduced potential conflicts and ensured that any disagreements were resolved systematically and efficiently, enhancing overall data quality management.

In my experience, empowering users by involving them in the data governance process is crucial for maintaining high data quality standards. In a recent project, we provided training sessions to help users understand the importance of data quality and governance. We also equipped them with tools, such as data validation software, to improve data quality within their workflows. By involving users in the governance process, they became more invested in maintaining high standards, understanding how their actions impacted the overall data ecosystem. Additionally, giving users some level of control over their data needs led to a more cooperative relationship with data owners, reducing conflicts and fostering collaboration.

In my experience, leveraging technology solutions can effectively satisfy the needs of both data owners and users. In a recent project, we implemented modern data governance tools that provided features like role-based access control, audit trails, and automated quality checks. These tools streamlined governance processes by ensuring that data was secure and accessible. For example, role-based access control allowed us to tailor data access permissions based on user roles, ensuring that sensitive data was protected while users still had the access they needed to perform their tasks. Additionally, automated quality checks helped maintain data accuracy and integrity without manual intervention