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How can you document and communicate data quality standards and policies in your data engineering projects?

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Data quality is a crucial aspect of any data engineering project, as it affects the reliability, usability, and value of the data products and services you deliver. However, data quality is not a static or universal concept, but rather a dynamic and contextual one, depending on the needs and expectations of your stakeholders, the characteristics and sources of your data, and the goals and requirements of your project. Therefore, it is essential to document and communicate your data quality standards and policies clearly and consistently throughout your data engineering lifecycle. In this article, we will discuss how you can do that using some best practices and tools.

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Joost Kuckartz

Senior Analytics Engineer, Product Owner & Generative AI Champion at BearingPoint

1 Define your data quality dimensions and metrics

The first step to document and communicate your data quality standards and policies is to define what data quality means for your project and how you will measure it. Data quality dimensions are the attributes or characteristics of data that determine its fitness for use, such as accuracy, completeness, consistency, timeliness, validity, and so on. Data quality metrics are the quantitative or qualitative indicators that evaluate how well your data meets the desired dimensions, such as error rate, completeness ratio, freshness, validity rate, and so on. You should define your data quality dimensions and metrics based on the expectations and requirements of your stakeholders, the nature and purpose of your data, and the standards and regulations of your industry or domain.

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Joost Kuckartz

Senior Analytics Engineer, Product Owner & Generative AI Champion at BearingPoint
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Especially when dealing with stakeholders, it is important to document not only data quality through objective dimensions, but also communicate usage and reasons for creation of tables. Users, customers and individuals might overlap but are different concepts, and those conceptual definitions should also be communicated so that in usage one will keep creating quality results.

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2 Document your data quality standards and policies

When defining your data quality dimensions and metrics, it is essential to document them in a standard format and language that is easily understood by all stakeholders. Additionally, you should also document your data quality policies, which are the rules or guidelines that specify how you will ensure, monitor, and improve your data quality throughout the data engineering lifecycle. This documentation should include the scope and objectives of the project, the roles and responsibilities of the data quality team and other stakeholders, the data sources, types, and formats involved, the data quality dimensions and metrics that apply, the thresholds or targets to be achieved or maintained, the tests or checks to be performed at different stages of the project, any anticipated or encountered data quality issues or risks and how they will be resolved or mitigated, as well as the reports or dashboards that will be used to monitor and communicate your data quality performance.

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3 Communicate your data quality standards and policies

The final step to document and communicate your data quality standards and policies is to share them with your data engineering team and other stakeholders. It's important to do this in a timely and transparent manner, using the appropriate channels and formats for your audience, while also seeking feedback and input from your stakeholders. There are various ways you can communicate your data quality standards and policies, such as publishing documentation on a central repository, creating data quality reports/dashboards, conducting data quality reviews/audits, sending data quality alerts/notifications, and hosting data quality workshops/webinars. These can be done using tools such as Data Quality Services, Data Studio, Power BI, Great Expectations, dbt, SQL, Airflow, Slack, Email, Zoom, Google Meet, or Teams.

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Joost Kuckartz

Senior Analytics Engineer, Product Owner & Generative AI Champion at BearingPoint
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The tools listed in here currently range from specific data quality tools to dashboarding tools to chat-based tools. Standards and policies are often specific documents that might not change too often, so use documentation tools for this that are generally available in your organization. Examples to add are SharePoint, Notion, Google Docs. I would not consider SQL or Airflow for documentation. Email, Zoom, Google Meet and Teams are too individual. The interesting aspect of dbt however is that while it is used for data transformation, it does have yml documentation capabilities, which brings the policy and standards close to the data. This would only work if the data transformation teams also decide on standards and policies.

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4 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?

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How can you document and communicate data quality standards and policies in your data engineering projects?

1

2

3

4

1 Define your data quality dimensions and metrics

2 Document your data quality standards and policies

3 Communicate your data quality standards and policies

4 Here’s what else to consider

Data Engineering

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How can you document and communicate data quality standards and policies in your data engineering projects?

1

2

3

4

1 Define your data quality dimensions and metrics

2 Document your data quality standards and policies

3 Communicate your data quality standards and policies

4 Here’s what else to consider

Data Engineering

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