How do you coordinate with other teams to validate data?

1 Identify data sources and owners

The first step in coordinating with other teams to validate data is to identify where the data comes from and who is responsible for it. Data sources can include internal systems, such as CRM, web analytics, email marketing, social media, or external sources, such as third-party vendors, partners, or public data. Data owners are the teams or individuals that have the authority and accountability for the data quality and integrity. You need to communicate with the data owners to understand how they collect, store, and update the data, what are the data definitions and formats, and what are the data access and security policies.

2 Establish data validation rules and methods

The next step in coordinating with other teams to validate data is to establish the data validation rules and methods that everyone should follow. Data validation rules are the criteria that determine whether the data is correct, complete, and consistent. For example, you can check if the data values are within a certain range, if the data types are consistent, if the data fields are populated, or if the data matches a reference source. Data validation methods are the techniques that you use to apply the data validation rules and detect any errors or anomalies. For example, you can use manual checks, automated scripts, data profiling tools, or data quality dashboards.

3 Implement data validation processes and workflows

The third step in coordinating with other teams to validate data is to implement the data validation processes and workflows that define when, how, and by whom the data validation should be performed. Data validation processes are the sequences of actions and tasks that are required to validate the data at different stages of the data lifecycle, such as data collection, data integration, data analysis, or data reporting. Data validation workflows are the systems and tools that enable the data validation processes to run smoothly and efficiently, such as data pipelines, data catalogs, data governance platforms, or collaboration tools.

4 Monitor data validation results and feedback

The fourth step in coordinating with other teams to validate data is to monitor the data validation results and feedback that indicate the status and quality of the data. Data validation results are the outputs and outcomes of the data validation processes and workflows, such as error reports, quality scores, or validation logs. Data validation feedback is the input and communication from the data owners and users that provide insights and suggestions on how to improve the data quality and validation. You need to track and review the data validation results and feedback regularly and share them with the relevant teams and stakeholders.

5 Resolve data validation issues and gaps

The fifth step in coordinating with other teams to validate data is to resolve the data validation issues and gaps that arise from the data validation results and feedback. Data validation issues are the problems and errors that affect the data quality and validity, such as missing data, duplicate data, incorrect data, or inconsistent data. Data validation gaps are the opportunities and areas that can improve the data quality and validation, such as new data sources, new data rules, or new data methods. You need to work with the data owners and users to fix the data validation issues and fill the data validation gaps as soon as possible.

6 Review and update data validation practices and policies

The sixth and final step in coordinating with other teams to validate data is to review and update the data validation practices and policies that guide and govern the data validation activities and standards. Data validation practices are the best practices and guidelines that help you perform the data validation effectively and efficiently, such as data documentation, data testing, or data auditing. Data validation policies are the rules and regulations that ensure the data validation complies with the organizational and legal requirements, such as data quality policy, data security policy, or data privacy policy. You need to evaluate and revise the data validation practices and policies periodically and communicate them with the other teams.

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?