登录查看更多内容

How do you analyze data for quality management?

由人工智能和领英社区提供技术支持

Quality management is a crucial aspect of any project, as it ensures that the deliverables meet the expectations and requirements of the stakeholders. To achieve quality management, you need to analyze data that reflects the performance, processes, and outcomes of your project. Data analysis helps you identify problems, find root causes, implement solutions, and monitor improvements. In this article, we will discuss how you can analyze data for quality management using some common tools and techniques.

此文章中的业界达人

由社区从 4 条内容中精选。了解更多

Giovanni Sisinna

??Portfolio-Program-Project Management, Technological Innovation, Management Consulting, Generative AI, Artificial…
Aftab Raza

???????????? | ?????????????? ???????????????????? | ?????????????? & ?????????????????? | Project Management

1 Define quality metrics

Before you can collect and analyze data, you need to define what quality means for your project and how you will measure it. Quality metrics are quantitative indicators that reflect the quality standards and objectives of your project. They can be related to the scope, schedule, cost, customer satisfaction, or any other aspect of your project that is important for quality. You should define your quality metrics in the planning phase of your project, and align them with the project charter, scope statement, and stakeholder expectations. You should also specify how and when you will collect, store, and report the data related to your quality metrics.

添加您的观点

Giovanni Sisinna

??Portfolio-Program-Project Management, Technological Innovation, Management Consulting, Generative AI, Artificial Intelligence??AI Advisor | Director Program Management @ISA | Partner @YOURgroup
举报内容
?? In my opinion, effectively delineating quality metrics requires precision and adaptability. ?? Always adopt the SMART framework for metrics which enhances their reliability in monitoring quality. ?? Regularly review and update your metrics, ensuring they remain relevant and reflective of evolving project objectives. ?? Incorporate both leading (predictive) and lagging (historical) indicators within your metrics to ensure comprehensive data analysis. ?? Embrace visual representations, like control charts, for intuitive and easy data interpretation. ?? Remember, fostering a culture of ongoing improvement ensures metrics consistently drive the desired quality outcomes.

已翻译

赞
Aftab Raza

???????????? | ?????????????? ???????????????????? | ?????????????? & ?????????????????? | Project Management
举报内容
In this step, in addition to the quality metrics, we should also define the quality criteria - for example, the acceptable range, accuracy, consistency and completeness of data. This is part of the "plan quality management" process as prescribed by PMBOK. We can also specify the data sources, methods and tools that will be used to collect and analyze the data.

已翻译

赞

2 Collect data

Once you have defined your quality metrics, you need to collect the data that will help you evaluate your project's quality performance. You can use various sources and methods to collect data, such as surveys, interviews, observations, inspections, tests, audits, or records. You should collect data regularly and consistently throughout the project lifecycle, and ensure that the data is accurate, reliable, and relevant. You should also follow the data management plan that you created in the planning phase, which outlines how you will organize, document, and protect the data.

添加您的观点

3 Analyze data

After collecting the data, you need to analyze it to identify any gaps, issues, or trends that affect the project's quality. You can use different tools and techniques to analyze data depending on the type, amount, and purpose. Pareto charts are bar graphs showing frequency or impact of different factors or causes of a problem. Histograms are frequency distributions showing the variation of a set of data. Control charts are line graphs illustrating how a process or variable changes over time. Cause-and-effect diagrams are diagrams demonstrating the relationship between a problem and its possible causes. Scatter diagrams are graphs displaying the correlation between two variables. All these tools and techniques help you prioritize the most significant factors, understand the shape, spread, and central tendency of data, monitor stability and performance of a process or variable, brainstorm and organize potential causes of a problem into categories, and determine if there is a relationship between two variables.

添加您的观点

Aftab Raza

???????????? | ?????????????? ???????????????????? | ?????????????? & ?????????????????? | Project Management
(已编辑)
举报内容
Three additional points to consider here: 1- Assess the quality of data itself and resolve any data quality issues - before we analyze the data to identify areas of improvement in the project. We can use various techniques such as data profiling, data validation, data cleansing, data auditing, and data testing, to identify and quantify any data quality issues or errors. 2 - Assess the data or metrics against the quality thresholds and rules that are defined at the outset of the project to identify any deviations from the required quality. 3 - The objective of the data analysis should be to identify the root causes of the quality issues and agree the method or steps to address those issues.

已翻译

赞

4 Implement improvements

Based on the results of your data analysis, you need to implement improvements to address any quality problems or gaps that you identified. You can use the Plan-do-check-act (PDCA) cycle, Root cause analysis (RCA), and quality audits to help you achieve this. The PDCA cycle is a four-step method that assists with planning, executing, evaluating, and adjusting improvement actions. It also helps you test solutions in a controlled way and monitor their effectiveness and impact. RCA is a technique that helps you find the underlying cause of a problem instead of treating the symptoms. Quality audits are systematic reviews of quality processes and activities conducted by internal or external auditors to verify if standards and requirements are being met, as well as identify any best practices or opportunities for improvement.

添加您的观点

5 Communicate results

Finally, you need to communicate the results of your data analysis and improvement actions to your stakeholders. Dashboards can be used to provide a quick and clear overview of your quality performance, while reports can give detailed information and analysis. Meetings are also useful for engaging stakeholders in your quality management, and soliciting their feedback and input. All of these tools and techniques help you explain the rationale, methods, findings, and recommendations of your quality management to your stakeholders.

添加您的观点

Aftab Raza

???????????? | ?????????????? ???????????????????? | ?????????????? & ?????????????????? | Project Management
举报内容
According to PMBOK, the main output of this process is quality reports that provide information on project performance and recommendations for improvement.

已翻译

赞

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

Project Management

+ 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 analyze data for quality management?

1

2

3

4

5

6

1 Define quality metrics

2 Collect data

3 Analyze data

4 Implement improvements

5 Communicate results

6 Here’s what else to consider

Project Management

给文章评分

感谢您的反馈

更多Project Management相关文章

更多相关阅读内容

How do you analyze data for quality management?

1

2

3

4

5

6

1 Define quality metrics

2 Collect data

3 Analyze data

4 Implement improvements

5 Communicate results

6 Here’s what else to consider

Project Management

给文章评分

感谢您的反馈

查看其他技能