How do you design and implement data quality checks and audits in your data projects?

Before deciding on quality dimensions, we should first reach business to get requirement on what are their pain points and critical data elements used in business processes and are getting impacted due to poor data quality.

Data quality dimensions are the criteria that you use to evaluate the quality of data. They typically include: Accuracy: The extent to which the data is correct and free from errors. Completeness: The degree to which the data includes all the required information. Consistency: The extent to which the data is consistent across different sources or over time. Timeliness: The degree to which the data is up-to-date and relevant. Validity: The degree to which the data conforms to defined rules and constraints. Integrity: The degree to which the data is complete and accurate, without duplication or redundancy.

In my experience, we should be identifying interfaces of applications (Upstream and downstream). Enabling the interfaces, technical datasets involved in the interface which in turn carry data elements. This entire process helps to know the data movements across the applications, i.e. data lineage. Next step would be to identify the trusted source or authorized distributor of any data. Develop a scoring model based on number of downstream applications, branches and final consumption. If data is not consumed or left with no consumer then no DQ. Otherwise, based on the score, prioritize the criticality of DQ. Criticality of data at an application be defined on score and treated as authorized distributor of that data based on threshold.

Ensuring data integrity and accuracy demands a holistic. Three key types of checks: preventive, detective, and corrective. Preventive checks focus on preemptive measures, including validating data formats, enforcing foreign key constraints, and ensuring alignment with business rules. Detective checks aim to uncover any data quality issues within the pipeline. This involves data profiling for anomaly detection, automated testing for continuous quality assessment. If any data quality issues appear, they can be resolved with techniques such as data cleansing to address missing values and duplicates, data enrichment through additional sources, and data reconciliation across different datasets will be employed.

Data quality governance is the exercise of authority, control, and shared decision-making over the management of data assets1. It is a process that delineates owners who have rights to view and utilize information. It standardizes how this information is collected, stored, and ultimately analyzed or disseminated for a specific use. Data governance is essential in data projects to ensure that the data is accurate, complete, and consistent. It helps organizations to establish policies, procedures, and standards for data management, and to ensure that data is used appropriately and effectively.