How do you automate data validation and quality checks in your system?

1 Define your data quality rules

The first step in automating data validation and quality checks is to define your data quality rules. These are the criteria that your data must meet to be considered valid and high-quality. For example, you might have rules for data format, completeness, uniqueness, accuracy, timeliness, or consistency. You can use a data quality framework, such as the DAMA-DMBOK or the ISO 8000, to guide you in defining your data quality rules. You should also document your rules clearly and communicate them to your stakeholders.

2 Choose your data validation and quality tools

The next step is to choose the tools that you will use to automate data validation and quality checks. Depending on your data sources, formats, and needs, there are a variety of tools available. Examples of data validation tools include SQL queries, Python scripts, Excel formulas, or specialized software such as Talend Data Quality or Informatica Data Quality. To measure, monitor, and improve the quality of your data, you can use Data Quality Services, Data Quality Analyzer, or Data Quality Monitor. These tools can provide reports, dashboards, or alerts on data quality issues.

3 Implement your data validation and quality workflows

The third step is to implement your data validation and quality workflows. These are the sequences of steps that automate the execution of your data validation and quality tools. You can use a workflow management system, such as Airflow, Luigi, or Prefect, to orchestrate your workflows. You can also use a scheduling tool, such as Cron, to trigger your workflows at regular intervals or events. You should also test and debug your workflows before deploying them to production.

4 Review your data validation and quality results

The fourth step is to review your data validation and quality results. You should check if your data validation and quality tools are working correctly and producing the expected outcomes. You should also analyze the results and identify any data quality issues, such as missing values, duplicates, outliers, or inconsistencies. You can use data visualization tools, such as Tableau, Power BI, or Matplotlib, to create charts, graphs, or tables that show your data quality metrics and trends.

5 Resolve your data quality issues

The fifth step is to resolve your data quality issues. You should take action to fix any data quality problems that you find in your data validation and quality results. You can use data cleansing tools, such as Trifacta, OpenRefine, or Data Ladder, to clean, transform, or enrich your data. You can also use data governance tools, such as Collibra, Alation, or Data Catalog, to manage, document, or share your data assets and policies.

7 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?