登录查看更多内容

Last updated on 2024年8月19日

What are the best practices for data quality assessment before and after conversion?

由人工智能和领英社区提供技术支持

Data conversion is the process of transforming data from one format, structure, or system to another. It is a crucial step in data migration, integration, or analysis projects. However, data conversion can also introduce errors, inconsistencies, or quality issues that affect the usability and reliability of the data. Therefore, data quality assessment is an essential practice before and after conversion to ensure that the data meets the requirements and expectations of the users and stakeholders. In this article, we will discuss some of the best practices for data quality assessment before and after conversion.

此文章中的业界达人

由社区从 7 条内容中精选。了解更多

Jill Perry Terho, MS

Technology Leader | Cyber | Identity Access Management SME

1 Define data quality dimensions and criteria

Before converting any data, you need to define what data quality means for your project and how you will measure it. Data quality dimensions are the aspects of data that affect its fitness for use, such as accuracy, completeness, consistency, timeliness, validity, and relevance. Data quality criteria are the specific standards or rules that you will use to evaluate the data against the dimensions. For example, you may define accuracy as the degree to which the data matches the source or the real-world entity, and use a percentage of error-free records as a criterion. You should also define the acceptable thresholds or ranges for each criterion, and the methods and tools for data quality assessment.

添加您的观点

Jill Perry Terho, MS

Technology Leader | Cyber | Identity Access Management SME
举报内容
A data dictionary that defines each data point os helpful to ensure consistent use of a field or to prevent multiple fields with same values but with different field names.

已翻译

赞
Zia Ibn Hasan

Digital Marketer | Data Analyst | R | Content Creator | #Opentowork
举报内容
In my overall journey, I'd like to say that: 1. Start by defining what data quality means for your project ?? 2. Identify dimensions like accuracy, completeness, consistency, timeliness, validity, and relevance ?? 3. Establish specific standards or rules for each dimension ?? 4. Determine acceptable thresholds or ranges for data quality criteria ?? 5. Utilize appropriate methods and tools for data quality assessment ??? 6. Regularly monitor and update your data quality measures to ensure ongoing accuracy and relevance ??

已翻译

赞
Dylan Pulver

Senior Software Developer | Specialist in Full-Cycle Development & Advanced Data Systems | Proven Success in System Architecture & Operational Efficiency | Exploring Opportunities in Tech Leadership & Startups
举报内容
1. Establish Quality Benchmarks: Define key data quality dimensions (accuracy, completeness, consistency) and set specific criteria to evaluate data against these benchmarks before and after conversion. 2. Set Thresholds: Determine acceptable error rates or ranges for each quality dimension, ensuring that data remains fit for its intended use post-conversion. 3. Utilize Assessment Tools: Implement tools and methods for ongoing data quality checks, allowing for pre- and post-conversion evaluations to detect and address issues.

已翻译

赞

2 Perform data profiling and analysis

Data profiling and analysis are the processes of examining the data to understand its structure, content, distribution, relationships, and patterns. Data profiling and analysis can help you identify the sources, formats, types, and volumes of data that you need to convert, as well as the potential issues or challenges that you may encounter during conversion. For example, you may discover data anomalies, outliers, missing values, duplicates, inconsistencies, or errors that affect the quality of the data. You can use various techniques and tools for data profiling and analysis, such as descriptive statistics, data visualization, metadata analysis, data cleansing, and data validation.

添加您的观点

Zia Ibn Hasan

Digital Marketer | Data Analyst | R | Content Creator | #Opentowork
举报内容
In my overall journey, I'd like to say that: 1. **Define Goals**: Start with clear objectives ??. 2. **Quality Check**: Ensure data quality through profiling ???. 3. **Choose Tools Wisely**: Pick tools that suit your needs ??. 4. **Visualize Insights**: Use visualization for clarity ??. 5. **Iterate**: Be open to refining insights ??. 6. **Communicate**: Share findings effectively ??. Stay curious and adaptable!

已翻译

赞

3 Design and test data conversion processes

Data conversion processes are the steps and procedures that you will use to transform the data from the source to the target format, structure, or system. Data conversion processes may involve data extraction, transformation, loading, mapping, normalization, standardization, enrichment, or verification. You should design and test your data conversion processes carefully to ensure that they are efficient, accurate, and secure. You should also document your data conversion processes and track their performance and results. You can use various techniques and tools for data conversion processes, such as data conversion software, scripts, workflows, or APIs.

添加您的观点

Zia Ibn Hasan

Digital Marketer | Data Analyst | R | Content Creator | #Opentowork
举报内容
In my journey, I've learned: 1. Efficiency, accuracy, and security are crucial. 2. Document processes for troubleshooting and compliance. 3. Monitor and adapt regularly. 4. Choose tools wisely and stay updated. ???

已翻译

赞

4 Conduct data quality audits and reviews

Data quality audits and reviews are the processes of verifying and validating the data after conversion to ensure that it meets the data quality dimensions and criteria that you defined earlier. Data quality audits and reviews can help you detect and correct any errors, inconsistencies, or quality issues that may have occurred during conversion. For example, you may check if the data is complete, accurate, consistent, timely, valid, and relevant for the target format, structure, or system. You can use various techniques and tools for data quality audits and reviews, such as data quality software, reports, dashboards, or feedback mechanisms.

添加您的观点

Joseph Yacura - M.B.A., M.S., M.Q.M

M.I.T. - Senior Advisor - Chief Data Officer Information Quality
举报内容
Please see our “Data Audit Guidelines” at www.IADQGA.com. This guideline is currently being utilized as a framework to assist organizations in understanding what a “data audit” is and how to conduct one.

已翻译

赞

5 Implement data quality improvement actions

Data quality improvement actions are the steps and measures that you will take to enhance the quality of the data after conversion based on the findings and recommendations of the data quality audits and reviews. Data quality improvement actions may involve data cleansing, correction, standardization, enrichment, or verification. You should also document your data quality improvement actions and track their impact and outcomes. You can use various techniques and tools for data quality improvement actions, such as data quality software, scripts, workflows, or APIs.

添加您的观点

Susan Coelius Keplinger

CEO at Force of Nature | Performance Marketing at Scale
举报内容
Based on audit findings, take corrective actions to improve data quality. This might include cleaning data, standardizing formats, or deduplicating records. For example, implementing a standardized format for phone numbers can enhance consistency.

已翻译

赞

6 Monitor and maintain data quality

Data quality is not a one-time event, but a continuous process that requires ongoing monitoring and maintenance. Data quality can change over time due to various factors, such as changes in the source or target systems, user behavior, business requirements, or external events. Therefore, you should monitor and maintain the quality of the data after conversion to ensure that it remains fit for use and meets the expectations and needs of the users and stakeholders. You can use various techniques and tools for data quality monitoring and maintenance, such as data quality software, reports, dashboards, alerts, or feedback mechanisms.

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

Data Conversion

+ 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

What are the best practices for data quality assessment before and after conversion?

1

2

3

4

5

6

7

1 Define data quality dimensions and criteria

2 Perform data profiling and analysis

3 Design and test data conversion processes

4 Conduct data quality audits and reviews

5 Implement data quality improvement actions

6 Monitor and maintain data quality

7 Here’s what else to consider

Data Conversion

给文章评分

感谢您的反馈

更多Data Conversion相关文章

更多相关阅读内容