登录查看更多内容

How can you improve data quality metrics in your policies?

由人工智能和领英社区提供技术支持

Data quality metrics are quantitative measures that evaluate the accuracy, completeness, consistency, timeliness, and validity of your data. They help you monitor and improve the performance of your data-driven processes, such as analytics, reporting, and decision making. However, data quality metrics are not static or universal. They depend on your business goals, data sources, and policies. In this article, you will learn how to improve data quality metrics in your policies by following these steps:

在这篇协作文章中查找专家回答

由社区从 3 条内容中精选。了解更多

1 Define your data quality dimensions

The first step is to identify the dimensions of data quality that are relevant for your specific use case and context. For example, if you are using data for customer segmentation, you might care more about the completeness and validity of your data than the timeliness or consistency. On the other hand, if you are using data for real-time monitoring, you might prioritize the timeliness and accuracy of your data over the completeness or validity. You can use frameworks such as the ISO 8000 or the DAMA Data Quality Dimensions to guide you in defining your data quality dimensions.

添加您的观点

Bashir Daramola

Data Analytics Tutor @ CareerFoundry | Data Analyst | Database Admin
举报内容
Defining the dimensions of data quality relevant to your use case is crucial. For instance, in customer segmentation, completeness and validity might be paramount, while for real-time monitoring, timeliness and accuracy take precedence. Frameworks like ISO 8000 or DAMA Data Quality Dimensions can provide valuable guidance in determining the specific dimensions that align with your data quality requirements. This initial step sets the foundation for effective data quality management.

已翻译

赞

2 Establish your data quality indicators

The next step is to translate your data quality dimensions into measurable indicators that can be calculated and tracked. For example, if you have defined completeness as a data quality dimension, you might use indicators such as the percentage of missing values, the number of records with all fields populated, or the ratio of filled to empty fields. You can use tools such as data profiling, data auditing, or data quality dashboards to help you generate and visualize your data quality indicators.

添加您的观点

Bashir Daramola

Data Analytics Tutor @ CareerFoundry | Data Analyst | Database Admin
举报内容
Translating data quality dimensions into measurable indicators is a critical step. When completeness is a data quality dimension, you can use indicators like the percentage of missing values, the count of records with all fields populated, or the ratio of filled to empty fields. Tools like data profiling, data auditing, or data quality dashboards are valuable for generating and visualizing these indicators. This approach enables you to quantitatively track and manage data quality effectively.

已翻译

赞

3 Set your data quality targets

The third step is to set realistic and achievable targets for your data quality indicators that align with your business objectives and expectations. For example, if you have established the percentage of missing values as a data quality indicator, you might set a target of less than 5% for your customer data, based on your industry standards and customer satisfaction goals. You can use benchmarks, best practices, or historical data to help you define your data quality targets.

添加您的观点

4 Implement your data quality rules

The fourth step is to implement rules and policies that enforce your data quality targets and prevent or correct data quality issues. For example, if you have set a target of less than 5% of missing values for your customer data, you might implement rules such as mandatory fields, validation checks, default values, or data cleansing procedures to ensure that your data meets your target. You can use tools such as data governance, data stewardship, or data quality management to help you implement your data quality rules.

添加您的观点

5 Monitor and review your data quality metrics

The final step is to monitor and review your data quality metrics regularly and adjust your policies accordingly. For example, if you have monitored your data quality indicators and found that your customer data has more than 5% of missing values, you might review your data quality rules and identify the root causes and solutions for the data quality issues. You can use tools such as data quality reports, alerts, or feedback loops to help you monitor and review your data quality metrics.

添加您的观点

6 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

Sreekanth M.

Leader - Data and Analytics | Mentor | Career Coach
举报内容
There are two ways to implement Data Quality 1. Hand code all the different data quality issues and track them 2. Find a toolset that has out-of-the-box functionality and implement data quality rules I'd prefer option 2, 90% of data quality rules are the same across all industries and you'd get a head start in your process most tools like DQM(Informatica Data Quality Manager) have profiling and rules embedded in them and all you have to do is push a button most of the times,

已翻译

赞

Data Science

+ 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 can you improve data quality metrics in your policies?

1

2

3

4

5

6

1 Define your data quality dimensions

2 Establish your data quality indicators

3 Set your data quality targets

4 Implement your data quality rules

5 Monitor and review your data quality metrics

6 Here’s what else to consider

Data Science

给文章评分

感谢您的反馈

更多Data Science相关文章

更多相关阅读内容

How can you improve data quality metrics in your policies?

1

2

3

4

5

6

1 Define your data quality dimensions

2 Establish your data quality indicators

3 Set your data quality targets

4 Implement your data quality rules

5 Monitor and review your data quality metrics

6 Here’s what else to consider

Data Science

给文章评分

感谢您的反馈

查看其他技能