Data quality is a continuous process, an underpinning of operations. Data quality approaches or strategy involves implementing programs that support continuous improvement within systems. Because data quality impacts and touches all parts of a business, data quality strategies involves all stakeholders at varying levels.

In my current workplace, I proposed and designed a scalable data quality monitoring framework that integrates seamlessly with our existing engineering tools like Airflow, Python, and SQL. This framework monitors data stored on AWS (S3, Athena) and a lot more other checks relevant to key concern. Using Python and a behavioral design pattern, I ensured the framework is modular and reusable, with custom Airflow operators enabling engineers to easily configure checks across different tables in just a configuration yaml file. This approach ensures faster detection of anomalies, allowing to take action promptly. The framework is flexible to accommodate new check rules in, and detailed documentation and examples for easy adoption.

When your team is divided on data quality approaches, it's imperative to do the below: - Understand the quality metrics defined and agreed as in scope of work and align the team to wards it. - Choose the appropriate framework that achieves the quality objectives optimally for your master data. This can be as simple as added logic to the pipeline, MDM tools like Profisee, SAP MDM - Measure the effectiveness continuously and update approach as needed.

What are the different data quality approaches that people are divided on? Is this the difference between right and wrong? If your goal is good quality, it shouldn't matter if one approach gets you there in 10 steps and another in 12 steps. However, there is a difference if one approach brings in all the data regardless of quality and then you hunt it down later, or another approach where you try to prevent all bad data from getting into the databases in the first place. If you choose to let bad data in, you have to know where it goes, you have to have a plan to resolve the issues, and you have to communicate the risk to the users. If you don't let bad data in, you have to implement controls at the point of entry.

Focus on collaborative problem-solving, by organising workshops where team members can share their perspectives and concerns regarding data quality approaches. Use brainstorming sessions to visualize ideas and identify common themes. Encourage experimentation by piloting various strategies on a small scale, gathering data on their effectiveness for collective review. Establish a data quality task force with representatives from different functions to oversee the implementation of the chosen strategy, fostering ownership and accountability. Regularly revisit the strategy, adapting it based on feedback and evolving needs to maintain alignment and commitment.