Introduction

Data cleaning is the process of identifying and correcting or removing errors, inconsistencies, and inaccuracies in datasets to improve data quality. Data cleaning is an essential step in the data analysis process, as the quality of the data can significantly impact the accuracy and reliability of the results.

A study by Gartner found that the average cost of poor data quality to an organization is $15 million per year. Also, another study by IBM found that bad data quality costs US businesses an estimated $3.1 trillion per year, due to issues such as inaccurate insights and missed opportunities.

Data cleaning is an essential step between data collection and data analysis. In order to prepare data for analysis after data collection, the data analyst ensures to get rid of all inconsistencies that are found in the data in a bid to obtain better results.

Central to this article, I will walk you through the outine below:

1. What is data cleaning?

2. Difference between data cleaning and data wrangling

3. Why you should clean data

4. Data cleaning tools

5. Data cleaning techniques

Let's get started...

What is data cleaning?

Data cleaning is the process of detecting and fixing inconsistencies and errors in data making sure it is fit for analysis. These errors could be in form of missing values, duplicates, incorrect formats, outliers, etc.

Data cleaning is the most difficult task in the chain of data analytics. Arguably, 70% of a data analyst’s time is spent preparing the data for exploration. There will be an improvement in the quality of analysis, visualization, modeling, etc. if the errors in the data are handled well.

It is rare to have real-life data that is free of errors. Incomplete data could be a result of respondents’ lack of knowledge about the questions being asked. Duplicates could result from copying and pasting an item at different points.

Differences between data cleaning and data wrangling

Data cleaning and data wrangling are two related but distinct processes in data preparation.

Data cleaning refers to the process of identifying and correcting errors, inconsistencies, and inaccuracies in datasets to improve data quality. The focus of data cleaning is on identifying and correcting issues that could lead to incorrect or biased results in data analysis. Data cleaning often involves tasks such as removing duplicates, handling missing data, standardizing data formats, and correcting inconsistencies.

Data wrangling, on the other hand, refers to the broader process of transforming and preparing data for analysis. Data wrangling encompasses data cleaning, but also includes tasks such as data integration, data aggregation, and data transformation. The focus of data wrangling is on preparing data for analysis by transforming it into a format that is suitable for modeling or visualization.

In essence, data cleaning is a subset of data wrangling. Data cleaning is focused on addressing data quality issues, while data wrangling encompasses all of the tasks involved in preparing data for analysis. Both data cleaning and data wrangling are essential steps in the data analysis process, as they ensure that the data is accurate, complete, and in a suitable format for analysis.

Why you should clean your data

Do you know you can go on analyzing your data without cleaning it? But I bet that you will not do so after understanding the reasons you should first clean it before carrying out any operations on it.

Below are some of the reasons you should clean your data.

A clean data:

• improves the quality of data analysis
• helps to reduce loss and increase sales in the organization
• makes data automation easier
• reduces the amount of time spent on the analysis and modeling.
• encourages more efficient business practices and faster decision-making
• enhances better reporting

Data cleaning tools

There are several tools available for data cleaning that can help streamline the process and improve data quality. Here are some examples of data cleaning tools:

• Microsoft Softwares: Microsoft provides several tools for data cleaning and preparation that can be used to improve the quality and accuracy of data. Examples include Microsoft Excel, Power Query, Azure Data Factory, and SQL Server Integration Services (SSIS)

• OpenRefine: OpenRefine is a free, open-source data cleaning tool that allows users to explore and transform large datasets. It provides features such as faceting, clustering, and data enrichment to help users clean and enrich their data.

• Trifacta: Trifacta is a commercial data wrangling tool that provides a visual interface for cleaning and transforming data. It includes features such as data profiling, data quality checks, and machine learning-powered suggestions for data cleaning.

• DataWrangler: DataWrangler is a free, web-based tool for data cleaning and transformation. It provides a visual interface for cleaning and transforming data, and includes features such as automated cleaning suggestions and real-time previewing.

• Talend: Talend is a commercial data integration and management platform that includes data cleaning and preparation features. It includes a visual interface for designing data pipelines, as well as a range of built-in data quality checks and transformation functions.

• Python libraries: There are several Python libraries that can be used for data cleaning, such as Pandas, Numpy, and Scipy. These libraries provide a range of functions for data cleaning and transformation, and can be used in conjunction with other Python libraries for data analysis and visualization.

• Tableau Prep

Data Cleaning Techniques

There is no one specific way to address data errors as the process differs from one data tool to another. However, it is necessary to design a template that you will follow to achieve your goals at all times.

1. Dealing with missing values

Vet your data carefully to find the missing cells, omitted survey responses, blank spaces, etc. You then decide to either remove the whole row or column or impute the missing values.

Imputation is one of the approaches to dealing with missing values. It is the process of replacing missing values with substituted values. Mean substitution, forward substitution, and backward substitution are types of imputation techniques.

Missing values at times can be informative which will require renaming. You can rename all missing values to 9999 or missing depending on how the case may be.

2. Deduplicating data

Duplicates occur at times when you combine data from multiple sources. It is essential to deduplicate data to avoid bias in results. Most especially when you are building a model, more weight is apportioned to duplicated records, as such, they have to be removed to avoid unbalanced results.

3. Removing irrelevant data

It is essential to study the data and remove the data that will not assist in answering your research questions nor is required for the analysis. This actually depends on the analysis you are carrying out.

Imagine that you are interested in analyzing the age of the transporters while your data contains data on doctors and mechanics, this will not be useful for the analysis. Checking out for irrelevant data before analyzing will help fast-track your analysis.

4. Fixing structural errors

Structural errors are errors that come from data transfer measurement. These may be in the form of typos, and inconsistent capitalization, e.g. US and USA. Since both connote the same thing, you correct the spellings and stick to one option.

This is an example of a chart with typographical errors.

You will observe that both US and USA are the same countries while Turkiye and Turkey also represent the same country.

This is how the chart looks after replacing the typographical errors

5. Handling outliers

Outliers are observations that differ significantly from others. Outliers are detected using boxplots, and cook’s distance. Two analyses are done in dealing with outliers, including outliers and the other without outliers. After which both results are compared to see if there is a significant difference. If there is, then the outlier has to be eliminated from the data otherwise leave it alone.

This is an example of data with an outlier

6. Validating your data

This is the concluding step of all where you have to confirm if your data is free of all inconsistencies and errors. You vet the data to ensure it is of high quality and properly formatted for further analytics. You should ensure that

• the data is complete and devoid of anomalies
• the data is enough for the intended analysis
• the data is in a format that the cleaning tools will work with.

Conclusion

Data cleaning is an essential process that has to be run in order to achieve the correct results. Doing analysis without cleaning the data will affect the quality of your results and reduce your productivity as an organization.

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