Autor: Daniel Camacho

Data cleaning is the process that allows us to correct or discard erroneous, inconsistent, incorrectly formatted, duplicated, or incomplete records within a dataset. In the everyday analysis of data, various data sources are used, increasing the probability of repetition and mislabeling. If the information is incorrect, the resulting algorithms and outcomes will be inaccurate, even if they appear to be valid. Although there is no single method to determine the precise steps in data cleaning that guarantee an optimal result, it is essential to design a scheme or template for the cleaning process to ensure the truthfulness and reliability of the results generated from the analyzed information.

It is important to mention that this cleaning process focuses on eliminating records that do not fit within the dataset, while data transformation refers to changing the format or structure of the information. This latter process may include data manipulation and mapping, even passing through intermediate storage prior to the evaluation of indicators and even Machine Learning models. In this article, we will focus on the procedures for cleaning such data.

Since there is no particular path to fulfilling this task of cleaning data, below are explained some basic steps that will allow validating a significant amount of characteristics in the generated information.

Duplicate Validation

Duplicate records often occur during data collection. When information is combined from various sources, such as extracting data from customers or several departments, there are opportunities to create duplicate data. These duplicates can distort analysis, resulting in erroneous data interpretations. By eliminating duplicates, the overall quality of the data is improved, ensuring that the analysis reflects the reality of the business.

Additionally, duplicate records consume storage space and computational resources, increasing operational costs. Their removal not only frees up these resources but also streamlines analysis processes, improving overall performance.

The presence of duplicates can lead to variations in analysis results, depending on how and when these duplicates are identified. Eliminating them beforehand ensures that the analysis is consistent and comparable over time.

Identification of Missing Values

It is a crucial step in the data treatment process. Missing values can arise for various reasons: from errors in data collection to intentional omissions during data storage. The precise identification of these values is crucial, as their presence can lead to biased analysis or incorrect conclusions. There are several techniques for handling missing values, each with its own advantages and limitations, depending on the context and nature of the data. Among these techniques, the elimination of such data stands out, which involves removing rows or columns containing missing values. Although it is the simplest solution, it can result in the loss of valuable information, especially if the amount of omitted data is significant. There is also statistical imputation, which involves replacing missing values with the mean, median, or mode of the column where these inconsistencies occur. Finally, there is the assignment of a constant value, which may be appropriate to replace missing values with a scalar that has meaning within the context of the analysis.

Outlier Detection

Los outliers son valores en un conjunto de datos que difieren drásticamente del resto de las observaciones, lo que puede indicar una variabilidad inusual, errores de medición o entrada de datos, o incluso novedades importantes dentro del dominio del problema. Dentro de las alternativas que existen para validar estos valores está inicialmente el análisis gráfico, donde herramientas visuales como histogramas, box plots y scatter plots pueden ayudar a identificar valores atípicos de manera intuitiva y rápida. Ahora bien, existen procesos estadísticos avanzados para la correcta interpretación de estos valores como lo son la desviación estándar, el rango intercuartil y diversas pruebas de coeficientes estadísticos.

Once these anomalies are detected, several paths can be taken depending on the business context and the relevance of the values found. A first option could be to eliminate the outliers if they are determined to be the result of measurement or data entry errors. Another option may be the imputation of such values. If the outliers are considered errors but their exclusion is not desirable, they can be replaced with median values, averages, or through more sophisticated imputation techniques. The last option that may be useful is to separate the outliers from the rest of the data and analyze them independently to identify possible anomalies or to better understand their impact on the overall dataset.

Inconsistencies and Input Errors

Sometimes inaccuracies or faults can be found in the data directly from its source, which can occur at the time of data collection, storage, or processing. Faults can take various forms, such as contradictory information, incorrect formats, typographical errors, among others. The presence of these errors can compromise the integrity of the data, leading to erroneous analysis and uninformed decisions.

During this cleaning phase, it is most convenient to eliminate these records, but identifying them will allow making decisions to prevent these cases, such as developing control implementation processes for validation during the data entry phase, so that it can help prevent errors, ensuring that only valid formats and values are accepted. Also, developing data entry and coding standards can significantly reduce inconsistencies, and even educational processes for personnel involved in data entry and handling about the importance of accuracy and best practices can improve data quality.

Each of these data cleaning processes

not only contributes to improving data consistency and reliability but also emphasizes the need for a continuous and proactive strategy towards data quality. This comprehensive approach to data quality fosters an organizational culture that values, protects, and effectively utilizes its data.

Educating and training staff in data management best practices, along with the implementation of advanced technologies for automation and analysis, are fundamental steps in maintaining data integrity over time.

From identifying incorrect or incomplete data to refined handling of outliers and correction of input errors, each step is crucial to ensure that data accurately and effectively reflects the reality of the business.

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