登录查看更多内容

How do you evaluate and improve your data quality skills and competencies as a DMAIC practitioner?

由人工智能和领英社区提供技术支持

Data quality is a critical factor for the success of any DMAIC project, as it affects the accuracy, reliability, and validity of your analysis and decisions. As a DMAIC practitioner, you need to constantly evaluate and improve your data quality skills and competencies, to ensure that you can deliver optimal results and value to your stakeholders. In this article, we will share some tips and best practices on how to do that, based on the five phases of DMAIC: Define, Measure, Analyze, Improve, and Control.

在这篇协作文章中查找专家回答

由社区从 2 条内容中精选。了解更多

1 Define the data quality requirements

The first step is to define the data quality requirements for your project, based on the voice of the customer, the business objectives, and the scope and boundaries of the problem. You need to identify the key data sources, metrics, and dimensions that are relevant and necessary for your analysis, and specify the criteria and standards for data quality, such as completeness, accuracy, timeliness, consistency, and relevance. You also need to document the data definitions, formats, and structures, and establish a common data language among the project team and the stakeholders.

添加您的观点

Reed Seaver
举报内容
It is essential that the people analyzing the data understand the people describing the problem (i.e. entering the data). Written data definitions are necessary but not sufficient; people need to talk. Analysts need to hear what's going on at the Gemba and frontline workers need to believe their efforts will lead to improvements. Conversation. Relationship. Trust.

已翻译

赞

2 Measure the current data quality level

The second step is to measure the current data quality level of your data sources, using various tools and techniques, such as data profiling, data auditing, data cleansing, and data validation. You need to collect, inspect, and verify your data, and identify any errors, gaps, duplicates, outliers, or anomalies that may affect your analysis. You also need to quantify the impact and severity of the data quality issues, and prioritize them according to their potential risk and benefit. You should report and communicate your findings and recommendations to the stakeholders, and seek their feedback and approval.

添加您的观点

3 Analyze the root causes of data quality issues

The third step is to analyze the root causes of the data quality issues, using various methods and tools, such as fishbone diagrams, Pareto charts, 5 whys, and hypothesis testing. You need to dig deeper into the data sources, processes, systems, and people that are involved in the data generation, collection, transformation, and storage, and identify the factors and variables that contribute to the data quality problems. You should also explore the relationships and correlations among the data elements, and test your assumptions and hypotheses.

添加您的观点

4 Improve the data quality processes and systems

The fourth step is to improve the data quality processes and systems, by implementing solutions and actions that address the root causes of the data quality issues. You need to design, test, and validate your solutions, using various tools and techniques, such as brainstorming, benchmarking, pilot testing, and experimentation. You should also monitor and measure the results and outcomes of your solutions, and compare them with the baseline and the target data quality level. You should document and communicate your changes and improvements to the stakeholders, and seek their feedback and approval.

添加您的观点

Reed Seaver
(已编辑)
举报内容
In high-mix, low-volume production, it is sometimes necessary to describe issues with unstructured text notes. Whenever I use such data to solve a problem, I thank the person that wrote them. Positive reinforcement gets you more of what you want.

已翻译

赞

5 Control the data quality performance and sustainability

The fifth and final step is to control the data quality performance and sustainability, by establishing and maintaining controls and standards that ensure the data quality level is consistent and reliable over time. You need to develop and implement data quality policies, procedures, rules, and roles, and assign responsibilities and accountabilities for data quality management. You should also use various tools and techniques, such as dashboards, scorecards, audits, and reviews, to track and evaluate the data quality performance and metrics, and identify any deviations or variations. You should also document and communicate your results and lessons learned to the stakeholders, and celebrate your achievements.

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

DMAIC

+ 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 evaluate and improve your data quality skills and competencies as a DMAIC practitioner?

1

2

3

4

5

6

1 Define the data quality requirements

2 Measure the current data quality level

3 Analyze the root causes of data quality issues

4 Improve the data quality processes and systems

5 Control the data quality performance and sustainability

6 Here’s what else to consider

DMAIC

给文章评分

感谢您的反馈

更多DMAIC相关文章

更多相关阅读内容