登录查看更多内容

Last updated on 2024年4月29日

How do you design and implement a data quality assurance plan for your experiment?

由人工智能和领英社区提供技术支持

Data quality is essential for any experiment, as it affects the validity and reliability of your results and conclusions. However, ensuring data quality is not a one-time task, but a continuous process that requires planning, implementation, and evaluation. In this article, you will learn how to design and implement a data quality assurance plan for your experiment, following six steps.

此文章中的业界达人

由社区从 5 条内容中精选。了解更多

Miriam Marling

Data Collection
Muhammad Waseem

Data Quality Engineer | Data Engineering | Data Analytics|

1 Define your data quality criteria

Before you collect any data, you need to define what data quality means for your experiment. Data quality criteria are the standards and expectations that you set for your data, based on your research question, hypothesis, and design. For example, some common data quality criteria are accuracy, completeness, consistency, timeliness, and relevance. You should also specify how you will measure and monitor each criterion, using appropriate indicators and tools.

添加您的观点

Miriam Marling

Data Collection
举报内容
It should also involve the tone of voice of anyone reading questions if the data is being collected via a telephone or internet interview. The way a person reads questions and elaborates will affect answers.

已翻译

赞
Muhammad Waseem

Data Quality Engineer | Data Engineering | Data Analytics|
举报内容
You know we are living in the age of data but we need to verify the correctness, accuracy, and relevance of the data. Also we need to check expected and unexpected data values in the data.

已翻译

赞

2 Identify and mitigate potential data quality risks

Once you have defined your data quality criteria, you need to identify and mitigate any potential data quality risks that could compromise your data. Data quality risks are the factors that could introduce errors, biases, or gaps in your data, such as faulty instruments, unclear instructions, missing values, or human errors. You should conduct a risk assessment to evaluate the likelihood and impact of each risk, and then implement mitigation strategies to prevent or minimize them. For example, you could use validation rules, pilot testing, training, or backup systems.

添加您的观点

Miriam Marling

Data Collection
(已编辑)
举报内容
You can simply have an experienced collector shadowing interviewers or have pilot participants flag poorly worded questions and check for basic grammar. You should place value on everyone in the collection process and allow for real-time feedback.

已翻译

赞

3 Implement data quality checks and controls

After you have mitigated the data quality risks, you need to implement data quality checks and controls to ensure that your data meets your criteria. Data quality checks and controls are the methods and procedures that you use to verify, correct, or improve your data, such as data entry validation, data cleaning, data transformation, or data auditing. You should apply data quality checks and controls at different stages of your data collection and management process, such as before, during, and after data entry, analysis, or reporting.

添加您的观点

加载更多内容

4 Document your data quality processes and results

As you implement your data quality checks and controls, you need to document your data quality processes and results. Data quality documentation is the record of how you planned, executed, and evaluated your data quality assurance plan, including the data quality criteria, risks, checks, controls, and indicators that you used. You should also document any data quality issues that you encountered, how you resolved them, and what impact they had on your data. Data quality documentation helps you to track your data quality performance, communicate your data quality status, and justify your data quality decisions.

添加您的观点

5 Review and improve your data quality plan

Once you have documented your data quality processes and results, you need to review and improve your data quality plan. Data quality review and improvement are the activities that you perform to assess the effectiveness and efficiency of your data quality assurance plan, and to identify and implement any necessary changes or enhancements. You should review your data quality plan regularly, using feedback from stakeholders, data quality indicators, and best practices. You should also improve your data quality plan based on the review findings, using continuous improvement methods, such as Plan-Do-Check-Act (PDCA) cycle.

添加您的观点

Rickie H.

Analytical Leader Specialized in Data-Driven Strategic Planning and Customer Experience Management | BSC, MBA, MSC
举报内容
Ensure that the data quality assurance plan adequately covers these 3 key components: the experiment definition, standard criteria, and the process. Review the data quality plan with the defined experiment objectives, scope, research questions, metrics, variables, and validated data sources. Align the established quality criteria such as match rates, timeliness, completeness, and consistency - review them before experimentation to ensure measurability and mitigate validity risks. Confirm that the data quality plan meets the set standards. Improve the plan by embedding audit points in project plans, workflows, or regular reporting. Lastly, practice data monitoring, especially in major data integration steps, to detect anomalies.

已翻译

赞

6 Share and reuse your data quality plan

Finally, you should share and reuse your data quality plan for your experiment. Data quality sharing and reuse are the practices that you adopt to make your data quality plan available and accessible to others, such as your research team, collaborators, reviewers, or future researchers. You should share and reuse your data quality plan to increase the transparency, reproducibility, and credibility of your experiment, and to facilitate the reuse and integration of your data with other data sources. You should also follow the FAIR principles (Findable, Accessible, Interoperable, Reusable) to make your data quality plan FAIR.

添加您的观点

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?

添加您的观点

Experimental Design

+ 关注

给文章评分

我们借助人工智能创建了此文章。您认为这篇文章怎么样？

很棒不太好

举报此文章

查看全部

How do you design and implement a data quality assurance plan for your experiment?

1

2

3

4

5

6

7

1 Define your data quality criteria

2 Identify and mitigate potential data quality risks

3 Implement data quality checks and controls

4 Document your data quality processes and results

5 Review and improve your data quality plan

6 Share and reuse your data quality plan

7 Here’s what else to consider

Experimental Design

给文章评分

感谢您的反馈

更多Experimental Design相关文章

更多相关阅读内容