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How can you ensure complete and accurate data cleaning?

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Data cleaning is the process of identifying and correcting errors, inconsistencies, and anomalies in a data set. It is a crucial step for ensuring the quality, reliability, and validity of any data analysis or decision-making based on data. However, data cleaning can also be challenging, time-consuming, and prone to errors or biases. How can you ensure complete and accurate data cleaning? Here are some tips and best practices to follow.

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1 Define your data quality criteria

Before you start cleaning your data, you need to have a clear idea of what constitutes good data quality for your specific purpose and context. Data quality criteria can include aspects such as accuracy, completeness, consistency, timeliness, relevance, and integrity. You can use these criteria to set standards and benchmarks for your data, and to identify and prioritize the data quality issues that need to be addressed.

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Ash Tulsi

Recruitment Specialist for Interior Design & Architecture - Luxury Hospitality Sector | Middle East & Asia
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The benchmarks guide in the question is important to ensure before you use resources to clean the data you have, that it actually will be useful in your specific use case. Without ths critical stepm you will end up wasting a lot of time, as I did before you realize the quality is not quite right, in our case the data would not necessarily translate well into validation data, meaning no real-world utility.

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2 Use appropriate tools and methods

Depending on the size, complexity, and format of your data set, you may need to use different tools and methods to perform data cleaning tasks. For example, you can use spreadsheet software, databases, or programming languages to manipulate, filter, sort, and validate your data. You can also use specialized tools or libraries that offer data cleaning functions, such as pandas, OpenRefine, or Trifacta. You should choose the tools and methods that suit your data type, structure, and quality goals.

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Ash Tulsi

Recruitment Specialist for Interior Design & Architecture - Luxury Hospitality Sector | Middle East & Asia
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The biggest task we had was data organization, ensuring we had a clean pipeline to work with, with traceability, for when we needed to edit or make changes. Standardizing file labels, and annotation labels (all before we even started to pre-process for our task). our initial attempt was using spreadsheets before my Co-founder found smarter methods to index and use the data, using python and SQL databases

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3 Apply consistent rules and logic

One of the main challenges of data cleaning is to ensure that the data is consistent and follows a logical pattern. This means that you need to apply consistent rules and logic to your data cleaning process, such as defining how to handle missing values, outliers, duplicates, or conflicting records. You should also document your rules and logic, so that you can track, explain, and justify your data cleaning decisions.

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Ash Tulsi

Recruitment Specialist for Interior Design & Architecture - Luxury Hospitality Sector | Middle East & Asia
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Consistency stems from having you data well organised in a traceable pipeline. It is vital to deeply understand your data needs, and desired/required outcomes from you models. This allows you to effectively handle the data and importantly documenting missing values, outliers, and conflicts. Domain experts are an essential part of the team to help with this process. Documentation of this process will help with transparency, explainability, a future justification. Being a healthtech startup, we have medical domain experts and technical domain experts working in cross-functional teams, helping key knowledge translate and impact exactly where it is required.

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4 Verify and validate your results

After you have cleaned your data, you need to verify and validate that your results match your expectations and data quality criteria. You can use various techniques to check and confirm your data quality, such as calculating summary statistics, creating visualizations, performing quality audits, or comparing your data with external sources. You should also test your data for errors, inconsistencies, or anomalies that may have been introduced or overlooked during the data cleaning process.

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5 Seek feedback and collaboration

Data cleaning is not a one-time or isolated activity. It is an ongoing and collaborative process that requires feedback and input from different stakeholders, such as data owners, users, analysts, or decision-makers. You should seek feedback and collaboration from these stakeholders to ensure that your data cleaning process meets their needs, expectations, and standards. You should also communicate your data cleaning process and results clearly and transparently, so that they can understand and trust your data.

Data cleaning is a vital skill for decision-making based on data. By following these tips and best practices, you can ensure complete and accurate data cleaning that enhances your data quality and reliability.

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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?

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How can you ensure complete and accurate data cleaning?

1

2

3

4

5

6

1 Define your data quality criteria

2 Use appropriate tools and methods

3 Apply consistent rules and logic

4 Verify and validate your results

5 Seek feedback and collaboration

6 Here’s what else to consider

Decision-Making

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How can you ensure complete and accurate data cleaning?

1

2

3

4

5

6

1 Define your data quality criteria

2 Use appropriate tools and methods

3 Apply consistent rules and logic

4 Verify and validate your results

5 Seek feedback and collaboration

6 Here’s what else to consider

Decision-Making

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