登录查看更多内容

How do you know if your data is reliable?

由人工智能和领英社区提供技术支持

Data is the foundation of any analytical project, but how can you trust that your data is accurate, consistent, and relevant? Data reliability is the degree to which your data measures what it is supposed to measure, and the extent to which you can rely on it for making decisions. In this article, you will learn some tips and techniques to evaluate and improve the reliability of your data.

此文章中的业界达人

由社区从 7 条内容中精选。了解更多

Supreet Kaur

LinkedIn Top Voice 2024 | Data & AI Solutions Architect | International Speaker | Patent Holder | Building Gen AI…
Leon Gordon

CEO of Onyx Data | Forbes Tech Council | Microsoft MVP | International Keynote Speaker | Gartner Ambassador
Vasileios (Vas) Gkionakis

Global Macro Strategy ? Monetary & Fiscal Policy ? Econometrics ? Quantitative Research ? Asset Management ? Portfolio…

1 Define your data needs

The first step to ensure data reliability is to define what kind of data you need, why you need it, and how you will use it. This will help you set clear criteria and expectations for your data sources, methods, and quality. For example, if you are analyzing customer satisfaction, you might want to specify the target population, the sample size, the survey design, the response rate, and the margin of error. By defining your data needs, you can also avoid collecting unnecessary or irrelevant data that might compromise your analysis.

添加您的观点

Leon Gordon

CEO of Onyx Data | Forbes Tech Council | Microsoft MVP | International Keynote Speaker | Gartner Ambassador
举报内容
In my professional experience, data reliability is crucial. I use the Microsoft Data Platform suite of products including Fabric, Azure Synapse and Azure Data Factory to ensure that data is reliable. These provide accurate data processing and analytics. Moreover, my guiding principle is to understand the source of the data, ensure data consistency over time, and validity check for outcomes that align with expectations. Robust AI and Data solutions assist in data security and process efficiency, certifying its reliability.

已翻译

赞
Tatiane Soares

Audit| Accountant| Risk management| Internal Controls| AICPA| CRC| Sarbanes-Oxley Act (SOX)
举报内容
I believe that in order to know whether data is reliable or not, it is necessary to perform a reasonable integrity test, such as making sure that the information being used originated from an official document.

已翻译

赞
Vasileios (Vas) Gkionakis

Global Macro Strategy ? Monetary & Fiscal Policy ? Econometrics ? Quantitative Research ? Asset Management ? Portfolio Management
举报内容
To define your data needs you need to know exactly what question you are trying to answer. Logic and previous reputable research should then guide the choice of data but you should be open to new ideas. In econometrics, a robust model will be able to spell out which variables are then necessary and which not. And importantly, dont forget that interaction of variables is also important. The bigger the sample size the better

已翻译

赞

2 Check your data sources

The next step is to check the credibility and validity of your data sources. Data sources can be primary or secondary, meaning that they are either collected by yourself or by someone else. Primary data sources are usually more reliable, as you have more control over the data collection process and the quality assurance. Secondary data sources, such as online databases, reports, or publications, can be useful, but you need to verify their origin, purpose, methodology, and timeliness. You should also check for any biases, conflicts of interest, or ethical issues that might affect the data quality.

添加您的观点

3 Clean your data

Once you have collected your data, it's essential to clean it before you can analyze it. Cleaning your data involves removing or correcting any errors, inconsistencies, or outliers that may skew your results. This includes checking for missing or incomplete data, duplicate or redundant data, inaccurate or incorrect data, inconsistent or incompatible data, and outliers or extreme values. You can use spreadsheets, databases, programming languages, and other tools and techniques to clean your data. It's important to document your data cleaning steps and decisions for future reference and reproducibility.

添加您的观点

Vasileios (Vas) Gkionakis

Global Macro Strategy ? Monetary & Fiscal Policy ? Econometrics ? Quantitative Research ? Asset Management ? Portfolio Management
举报内容
Again, the question you are trying to answer is crticical. For example, if you are trying to find the impact of higher Fed rates on exchnage rates it is important to control for the phase of the buisness cycle/global growth. But take note, cleaning the data too much can result in the data confessing to anything....

已翻译

赞

4 Analyze your data

After cleaning your data, you can proceed to analyze it using appropriate methods and techniques. Depending on your research question and data type, you might use descriptive, inferential, or predictive statistics, or other forms of data analysis. However, no matter what method you use, it's important to follow some basic principles of data analysis. This includes exploring your data before applying any models or tests, choosing the right level of measurement and scale for your data, selecting the right statistical tests or models for your data and hypothesis, checking the assumptions and limitations of your tests or models, and interpreting your results with caution and context. Additionally, you should use appropriate tools and software to perform your data analysis, such as calculators, spreadsheets, statistical packages, or programming languages. Lastly, document your data analysis steps and results for future reference and communication.

添加您的观点

Vasileios (Vas) Gkionakis

Global Macro Strategy ? Monetary & Fiscal Policy ? Econometrics ? Quantitative Research ? Asset Management ? Portfolio Management
举报内容
You should have a clear grasp of each individual series/data you are using. In econometrics, you cannot for example mix non-stationary with stationary variables; in addition, you should take a practical approach in what makes sense and be mindful that correlation does not neccessarily imply causation

已翻译

赞

5 Communicate your data

The final step to ensure data reliability is to communicate your data effectively to your audience. Data communication can take various forms, such as reports, presentations, dashboards, or visualizations. However, no matter the form you choose, some basic principles should be followed. These include defining the purpose and message of your data communication, knowing your audience and their needs and expectations, choosing the right format and medium for your data communication, choosing the right charts and graphs for your data and message, using clear and concise language and labels for your data communication, providing context and explanation for your data communication, and citing your data sources and references. Additionally, it is important to use appropriate tools and software to create and deliver your data communication, such as word processors, presentation software, dashboard software, or visualization software. Lastly, you should solicit feedback and evaluation from your audience and peers to improve your data communication skills.

添加您的观点

Vasileios (Vas) Gkionakis

Global Macro Strategy ? Monetary & Fiscal Policy ? Econometrics ? Quantitative Research ? Asset Management ? Portfolio Management
举报内容
Once satisfied with your results, this is the most difficult task: do not assume readers are aware of fancy statistical terms and methods. Be simple and concise; and if you need to go into more detail make sure you explain clealrly the issues/methods involved as if you are talking to someone who has never heard of these methods

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

Supreet Kaur

LinkedIn Top Voice 2024 | Data & AI Solutions Architect | International Speaker | Patent Holder | Building Gen AI Solutions for Financial Services
举报内容
These are excellent starting points for assessing data reliability. In addition, I highly recommend seeking a review of the final insights by a Data Subject Matter Expert (SME). Drawing from my experience, Data SMEs possess deep knowledge and hands-on expertise with specific data types, enabling them to effectively identify if any numbers are significantly inaccurate. Their insights can be invaluable in ensuring the overall quality and validity of the data analysis. Including their input as a final checkpoint adds an extra layer of confidence to the reliability of the findings.

已翻译

赞

Analytical Skills

+ 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 know if your data is reliable?

1

2

3

4

5

6

1 Define your data needs

2 Check your data sources

3 Clean your data

4 Analyze your data

5 Communicate your data

6 Here’s what else to consider

Analytical Skills

给文章评分

感谢您的反馈

更多Analytical Skills相关文章

更多相关阅读内容

How do you know if your data is reliable?

1

2

3

4

5

6

1 Define your data needs

2 Check your data sources

3 Clean your data

4 Analyze your data

5 Communicate your data

6 Here’s what else to consider

Analytical Skills

给文章评分

感谢您的反馈

查看其他技能