How can you maintain data quality across different sources and systems in a data architecture project?

1 Define data quality criteria and metrics

Before you can measure and improve data quality, you need to define what data quality means for your project and how you will assess it. Data quality criteria and metrics are the standards and indicators that you use to evaluate the data against your expectations and requirements. For example, some common data quality criteria are completeness, consistency, validity, accuracy, timeliness, and relevance. Data quality metrics are the quantitative or qualitative measures that you use to calculate the data quality scores or ratings for each criterion. For example, some common data quality metrics are error rate, missing values, duplicates, outliers, and freshness.

2 Implement data quality checks and controls

Once you have defined your data quality criteria and metrics, you need to implement data quality checks and controls throughout your data architecture project. Data quality checks and controls are the processes and procedures that you use to monitor, verify, and correct the data quality issues that may arise from different sources and systems. For example, some common data quality checks and controls are data profiling, data cleansing, data validation, data standardization, and data enrichment. You can use various tools and techniques to automate or semi-automate these tasks, such as data quality software, data pipelines, data catalogs, and data rules.

3 Establish data quality roles and responsibilities

Another important aspect of maintaining data quality across different sources and systems is to establish data quality roles and responsibilities within your project team and stakeholders. Data quality roles and responsibilities are the functions and duties that different people or groups have to perform or oversee to ensure data quality throughout the project lifecycle. For example, some common data quality roles and responsibilities are data owners, data stewards, data architects, data engineers, data analysts, and data consumers. You can use data governance frameworks and policies to define and communicate these roles and responsibilities, as well as to assign data quality tasks, accountabilities, and permissions.