Data Extraction vs Data Cleaning
Data has become central to the operations of most businesses and institutions. It gives people the ability to collect feedback, run experiments, and reach meaningful conclusions. However, you cannot do much with data without using technological processes. Therefore, data extraction and data cleaning tools were developed to meet data manipulation requirements.
Although the terms might sound similar, each tool is used for specific uses. As a result, this article will highlight the difference between data extraction and data cleaning.
Data extraction has gained a lot of traction in today’s world. This tool has become an integral aspect of science, business, and other fields. Also, it lets individuals gather information from several sources in a central location. These sources include websites, online databases, emails, phonebooks, and other information repositories in most cases. On the other hand, the central area can be remote, physical, or both. After data has been stored, it can be used for future use, such as analysis. At this point, it is essential to note that data extraction does not include storage. It stops at data storage.
Before and after data storage, extraction tools allow users to restructure data. The process of restructuring data is critical as information is collected in different formats. Therefore, a single data extraction tool can retrieve data from various locations, restructure it, and store it in a central storage system. In most cases, data extraction is deployed on the internet. Variables and parameters are primarily defined to ease the extraction process. Including these limiters help shape the quality of data you collect.
Data cleaning is also referred to as data cleansing. It is a fundamental aspect of data science. In basic terms, data cleaning is defined as a scheme that identifies and corrects problems with data points. Therefore, data cleaning is a process that may occur after data extraction. Data cleaning tools run through a database to find and correct duplicates, errors, and data corruption. It can also be used to harmonize incomplete and irrelevant data points. In either case, this process is conducted to improve data quality.
Data cleaning can be done in several ways. It is mainly done using batch processing or data wrangling. The type of process used depends on the form of data to be cleansed. Similarly, you will want to use a tool that cleans data points into a finished product of comparable characteristics within any system. As a result, such data cleaning tools ensure consistent forms of data throughout a database.
Data cleaning tools were created mainly due to the errors within a database. These errors occur due to entry mistakes, transmission issues, and processing interruptions. Therefore, these errors are addressed using a list of defined values or terms. These entities allow you to clean the data effectively. The list used for data cleansing is a type of validation system. But, the rules behind data cleaning are not as constrained as that of data validation.
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