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Data cleansing, also known as data cleaning or data scrubbing, is an essential process in data engineering that involves identifying and correcting inaccuracies, inconsistencies, and errors in data. This involves various steps leading to data cleansing. The purpose of data cleansing is to ensure that data is accurate, complete, consistent, timely, and relevant to its intended purpose. Clean data is critical for effective data analysis, reporting, and decision-making, as it provides a reliable and trustworthy foundation for these activities. Data cleansing is a vital aspect of data engineering that helps organizations to derive maximum value from their data assets.
Clean data helps organizations to make informed decisions. When data is accurate, complete, and consistent, decision-makers can trust the insights derived from it. Clean data enables organizations to make data-driven decisions with confidence, which can ultimately lead to better organizational outcomes.
It also helps organizations to reduce the risk of errors or regulatory issues. Inaccurate or inconsistent data can lead to legal or regulatory issues, which can be costly and time-consuming to resolve. Clean data ensures that organizations comply with data regulations and avoid penalties associated with non-compliance. Clean data also reduces the risk of reputational damage caused by inaccurate data.
It helps organizations to improve operational efficiency. Clean data reduces the time and resources required for data processing, analysis, and reporting. This enables organizations to work more efficiently, reducing costs and improving productivity. Clean data also enables the identification of areas for improvement, optimization of processes, and streamlined workflows.
There are several steps involved in the data cleansing process in data engineering. These steps may vary depending on the specific requirements of the project, but generally, they include the following:
Data profiling - This involves analyzing the data to identify any inconsistencies, missing values, duplicates, or other anomalies that need to be addressed.
Data validation - Check if the data conforms to the expected format and meets the required quality standards.
Data transformation - Convert the data into a standardized format that can be easily analyzed and used for further processing.
Data enrichment - Add additional data or metadata to the existing data to improve its quality or to provide more context.
Data deduplication - Identify and remove any duplicate records in the data.
Data normalization - This step involves standardizing the data so that it is consistent and can be easily compared or analyzed.
Data verification - Verify the accuracy of the data by comparing it to external sources or by running it through validation rules.
Data cleansing - Clean the data by removing any errors, inconsistencies, or irrelevant data that may have been identified during the previous steps.
The goal of data cleansing is to ensure that the data is accurate, complete, and consistent so it can be used effectively for analysis, reporting, and decision-making.
Clean data has important characteristics that make it so successful.
Accuracy - Clean data is accurate, meaning that it reflects the actual values or facts that it represents. There are no errors, inconsistencies, or discrepancies in the data that could lead to incorrect conclusions or decisions.
Completeness - Clean data is complete, meaning that it contains all the necessary information required for its intended purpose. There are no missing values or fields that could limit the usability or effectiveness of the data.
Consistency - Clean data is consistent, meaning that it is uniform and standardized across all fields and records. There are no variations or discrepancies in the data that could lead to confusion or misinterpretation.
Timeliness - Clean data is timely, meaning that it is up-to-date and relevant to its intended purpose. There are no outdated or irrelevant data points that could distort the analysis or decision-making process.
Relevance - Clean data is relevant, meaning that it is applicable and useful for its intended purpose. There are no extraneous or unnecessary data points that could add confusion or noise to the analysis or decision-making process.
Overall, clean data is characterized by its quality, reliability, and usability. It is essential for effective data engineering and analytics, providing a solid foundation for accurate and informed decision-making.
There are great benefits that come with data cleaning from improving quality to saving time and effort.