登录查看更多内容

How do you explain data cleaning's impact on your analysis to stakeholders?

由人工智能和领英社区提供技术支持

Data cleaning is a crucial step in any data analysis project, but it can be hard to communicate its importance and impact to stakeholders who may not be familiar with the process or the challenges involved. In this article, you will learn some tips and techniques on how to explain data cleaning's impact on your analysis to stakeholders, and how to present your data cleaning decisions and results in a clear and convincing way.

此文章中的业界达人

由社区从 3 条内容中精选。了解更多

Shubhanjali Sharma

Sr. Consultant | Director - Women Who Code, Chennai Chapter

1 Why data cleaning matters

Data cleaning is the process of identifying and correcting errors, inconsistencies, outliers, missing values, duplicates, and other issues in your data set that may affect the quality and validity of your analysis. Data cleaning matters because it can improve the accuracy, reliability, and efficiency of your analysis, and help you avoid misleading or erroneous conclusions. Data cleaning can also help you uncover hidden patterns, insights, and opportunities in your data that may otherwise go unnoticed or ignored.

添加您的观点

Shubhanjali Sharma

Sr. Consultant | Director - Women Who Code, Chennai Chapter
举报内容
In my experience, a model is as good as the data it is trained on. We do not want an overfitted or an under-fitted model. The training data should be a good representation of the future scenarios the model is expected to predict on. The training data should not be biased. We should also create new features, do feature crosses, for better explainability and predictive ability of the model.

已翻译

赞

2 How to assess data quality

Before you start cleaning your data, you need to assess its quality and identify the main problems and sources of error. You can use various methods and tools to evaluate your data quality, such as descriptive statistics, data profiling, data visualization, data auditing, and data validation. These methods can help you measure and monitor different aspects of data quality, such as completeness, consistency, accuracy, timeliness, relevance, and integrity. You should document your data quality assessment and share it with your stakeholders, so they can understand the current state of your data and the potential risks and benefits of cleaning it.

添加您的观点

Shubhanjali Sharma

Sr. Consultant | Director - Women Who Code, Chennai Chapter
举报内容
Even with good data, a link needs to be established. Clean raw data is the primary need. Adding a dash of perspective and domain knowledge to that data makes a world of difference. For instance, you need to determine the satisfaction level of an employee when receiving a bonus. Based on the data at hand, and common sense, we can say the employee will be happy with a bonus with, say, 95% accuracy. However if you merge that data point (bonus) with the last day of the employee in the organisation, you will get a deeper insight that the bonus in case of termination is different and might not make the said employee happy. This added insight can shoot the model accuracy for stray and corner cases upto, say, 99%.

已翻译

赞

3 How to choose data cleaning techniques

Once you have assessed your data quality, you need to choose the appropriate data cleaning techniques to address the specific issues and goals of your analysis. There are many data cleaning techniques available, such as data transformation, data normalization, data imputation, data deduplication, data filtering, data standardization, and data enrichment. The choice of data cleaning techniques depends on several factors, such as the type, size, and structure of your data, the complexity and purpose of your analysis, the availability and quality of external data sources, and the trade-offs between speed and accuracy. You should explain to your stakeholders why you chose certain data cleaning techniques over others, and how they can affect the outcome and performance of your analysis.

添加您的观点

Shubhanjali Sharma

Sr. Consultant | Director - Women Who Code, Chennai Chapter
举报内容
The data needs to be well-represented with minimal to no bias. Proper feature engineering needs to be done by creating feature vectors. For categorical values, we need to create a vocabulary of popular values as well as OOV (out-of-vocabulary) bucket. Sparse data also needs to be handled without compromising on the model quality.

已翻译

赞

4 How to implement data cleaning techniques

After you have chosen your data cleaning techniques, you need to implement them in a systematic and transparent way. You can use various tools and frameworks to perform data cleaning tasks, such as programming languages, libraries, packages, scripts, functions, and algorithms. You should follow the best practices and standards of data cleaning, such as defining clear objectives and criteria, documenting your steps and assumptions, testing your results and assumptions, and verifying your results and assumptions. You should also keep track of your data cleaning process and output, and store them in a separate and accessible location. You should show your stakeholders how you implemented your data cleaning techniques, and how they improved the quality and usability of your data.

添加您的观点

5 How to report data cleaning results

The final step in data cleaning is to report your results and findings to your stakeholders, and demonstrate how they impact your analysis. You can use various formats and methods to report your data cleaning results, such as tables, charts, graphs, dashboards, reports, and presentations. You should highlight the main changes and improvements in your data set, such as the number and percentage of errors, outliers, missing values, duplicates, and other issues that you fixed, the amount and quality of data that you added, deleted, modified, or transformed, and the impact of your data cleaning techniques on the descriptive and inferential statistics of your data. You should also explain the limitations and challenges of your data cleaning process, such as the assumptions, trade-offs, uncertainties, and biases that you faced, and how they may affect the interpretation and generalization of your analysis. You should aim to report your data cleaning results in a clear, concise, and compelling way, and use visual aids, examples, and stories to illustrate your points and engage your stakeholders.

添加您的观点

6 How to get feedback and improve data cleaning

Data cleaning is not a one-time or static process, but a continuous and dynamic one that requires constant monitoring, evaluation, and improvement. You should seek feedback and input from your stakeholders on your data cleaning process and results, and use them to refine and optimize your data cleaning techniques and strategies. You should also update and maintain your data set regularly, and check for new or emerging issues or opportunities that may require further data cleaning or analysis. You should communicate with your stakeholders frequently and openly, and establish a feedback loop that allows you to learn from your data cleaning experience and improve your data analysis skills and outcomes.

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 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 do you explain data cleaning's impact on your analysis to stakeholders?

1

2

3

4

5

6

7

1 Why data cleaning matters

2 How to assess data quality

3 How to choose data cleaning techniques

4 How to implement data cleaning techniques

5 How to report data cleaning results

6 How to get feedback and improve data cleaning

7 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 do you explain data cleaning's impact on your analysis to stakeholders?

1

2

3

4

5

6

7

1 Why data cleaning matters

2 How to assess data quality

3 How to choose data cleaning techniques

4 How to implement data cleaning techniques

5 How to report data cleaning results

6 How to get feedback and improve data cleaning

7 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