Data cleaning: the journey from dirty data to clean data

Real data is often full of missing values, outliers, duplicate values, and inconsistent formats. These problems are like hidden "time bombs" that may destroy the accuracy of data analysis at any time. This article will take you to a deeper understanding of the necessity of data cleaning, common problems and solutions, from "dirty data" to "clean data", unlocking the journey of data transformation and laying a solid foundation for data analysis and decision-making.

In daily life, we often clean the house, sweep the floor, mop the floor, and wipe the furniture to make the home comfortable and tidy again; we often wash clothes, wash, dry, and iron them to make our appearance elegant again.

In the data world, dirty data also exists, which will hinder subsequent data analysis, mining, and application, which requires [data cleaning].

What is data cleaning?

Data cleaning refers to the processing of raw data to correct or delete missing, abnormal, erroneous, and irregular parts, thereby improving data quality and availability.

Dirty data types are rich and varied, and common ones include null values, abnormal values, duplicate values, wrong data, and irregular formats. For example, in the employee information table, the contact information of some employees is empty, resulting in null values; in the user statistics table, the age is greater than 150, resulting in abnormal values; in the case of multiple people collecting sales leads from the same person, resulting in duplicate data; in the case of sales order unit price, the original price should be used instead of the discounted price, resulting in wrong data; in the case of the date, the normal format should be [YYYY – MM – DD], but it is recorded as [MM/DD/YYYY]….

If these dirty data are not processed, they will be like a time bomb hidden in the dark, which will suddenly explode when conducting data analysis and mining potential value, causing deviations in the analysis results and failing to provide a reliable basis for decision-making.

The core of data cleaning is to discover data problems and fix them in a targeted manner. The ultimate goal is to make the data meet the standards of [accuracy, completeness, consistency, and reliability]. When discovering data problems and fixing them in a targeted manner, you need to flexibly choose methods based on business scenarios.

For example: Financial risk control data: outliers and missing values ​​need to be strictly processed to avoid model misjudgment; social media text data: special symbols, stop words and spelling errors need to be cleaned.

How to clean data?

The goal of data cleaning is to make the data reach high quality standards, and targeted repairs need to be made to data problems.

Handling missing values

• Missing value problem: Some fields in the data are empty or not recorded, affecting the accuracy and completeness of data analysis.
• Solution: Delete missing records, fill in default values ​​(mean, median, mode, etc.), and use algorithms to predict missing values.
• Missing value example: In an e-commerce sales data, the purchase price of some orders is missing. The reason may be that the product has multiple price systems such as quotation, reserve price, discount, promotion price, etc., and the unit price cannot be obtained due to abnormal value acquisition strategy.

Effective solution to the case: Re-obtain normal prices based on order, activity, and product information and handle missing values.

Correcting outliers

• Outlier problem: The data deviates significantly from the normal range, affecting the accuracy of data analysis.
• Solution: Use statistical methods (Z-score, IQR) to identify outliers and correct or delete them based on the scenario.
• Outlier example: A patient's temperature is recorded as 50°C (clearly outside the human range). This may be a unit error (e.g. Fahrenheit is mistakenly marked as Celsius), corrected to 10°C (corresponding to 50°F);

Effective solution for this case: Randomly screen the data and compare the units. If the units are wrong, unify them. If they cannot be corrected, mark them as abnormal and eliminate them.

Remove or merge duplicate data

• Duplicate data problem: Duplicate records exist in the data set, which may lead to biased analysis results.
• Solution: Identify duplicate records (such as those with the same ID or timestamp) and delete or merge them.
• Duplicate data example: In a very short period of time, orders with the same customer, product, unit price, and total amount are submitted. The possible reason is that the anti-quick click function fails, and multiple clicks to submit result in duplicate orders.

Effective solution to the case: Delete duplicate order data, and be sure to retain data related to subsequent operations such as payment records.

Unified data format

• Data format issues: Inconsistent formats of the same field make data processing and analysis difficult.
• Solution: Standardize dates, times, units, text case, etc.
• Data format example: In a statistical table, there are multiple date formats, such as [2021-01-01], [01/02/2021], and [March 1, 2021].

Case effective solution: Convert all dates to [YYYY-MM-DD] format.

Resolving data inconsistencies

• Data inconsistency problem: The description of the same entity is inconsistent, common situations include nationality, province, city, district, address, month, day of the week, etc.
• Solution: Create a mapping table or unified expression of rules.
• Data inconsistency example: There are different ways of writing it, such as [北京], [北京], and [北京].

Effective solution for the case: Create a mapping table and replace all the abbreviations with [北京]; use regular expressions to match abbreviations (such as [京] is replaced with [北京]).

Why do we need data cleaning?

Through the above cleaning methods, the data quality can be effectively improved, providing a reliable basis for subsequent data analysis and decision-making.

Accurate data is the foundation of all decision-making. Data cleaning ensures that every data point is true and reliable by identifying and correcting erroneous data, thus providing a solid foundation for corporate decision-making and enabling decisions to be based on correct facts.

However, if the data contains a large number of outliers, duplicate values, or missing values, the analysis results will be extremely unreliable.

If companies formulate inventory management, marketing promotion and other strategies based on such analysis results, it may lead to adverse consequences such as inventory backlogs and waste of marketing resources.

By cleaning the data and removing these interference factors, the reliability of data analysis can be significantly improved, so that the analysis results can truly reflect the actual situation of the business and provide enterprises with accurate decision-making basis.

Different departments within an enterprise generally conduct their own business analysis and decision-making based on the same data. If the data quality is uneven, different departments may have different understandings and interpretations of the data, which will affect the efficiency of collaboration between departments.

By cleaning data, unifying data formats, and standardizing data standards, we can enhance data availability, enable each department to work based on consistent and accurate data, promote cross-departmental collaboration and communication, and improve the overall operational efficiency of the enterprise.

In fields such as machine learning and deep learning, data is the "fuel" for training models. The performance of the model depends largely on the quality of the input data.

Dirty data will interfere with the model's learning process, causing the model to be unable to accurately capture patterns and relationships in the data. After data cleaning, filling missing values ​​and correcting erroneous data, better data can be provided to the model, enabling the model to better learn data features, thereby optimizing model performance and improving prediction accuracy and stability.

Data cleaning is an indispensable and key link in the data processing process. It plays a vital role in ensuring data accuracy, improving the reliability of analysis, optimizing model performance, and promoting internal collaboration within the enterprise. In this data-driven era, only by paying attention to data cleaning can data truly become a powerful driving force for enterprise development.