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Detailed Data Extraction Process

Data extraction has become the cornerstone of many processes in today’s technological world. Most industries heavily depend on the information gotten from data extraction as a basis for decision-making. Therefore, the extraction of data exactly how it appears is critical for drawing economically sound conclusions.

Definition of Data Extraction

Data extraction refers to the scheme of collection of data (structured and unstructured) from a wide range of sources into a central location. It makes the extraction of meaningful information from raw data possible. Data extraction techniques are able to collect data from cloud systems and local storage systems.

In ETL, – extract transform load – the extraction process is the first step. Therefore, the extraction process is mostly conducted together with ETL. Nonetheless, data extraction can also be conducted without ETL.

ETL Data Extraction

When data extraction is combined with ETL, then the data collected is going to be transformed before being stored. The processes involved in ETL – extract transform load – are highlighted below.


In the extraction phase, data is collected from several sources. In most cases, data extraction tools are deployed with a set of instructions that allow for the collection of specific types of data or all types of related data. In this stage of ETL, the tools are only concerned with getting the required data. Therefore, the quality of data is of no concern.


After the extraction process, the data is then transformed from multiple data formats into a single data format. Therefore, in this stage, data is categorized, organized, and cleaned to remove unwanted data points. In addition, unintelligent data points, duplicates, corrupted, and missing data are removed when required. As a result, only high-quality data makes it after the transforming process.


The transformed data is now collected into a single location and stored for the purpose of future use. The storage of transformed data can be via the cloud or local storage system.

None ETL Data Extraction

Data extraction without ETL is possible. However, it is mostly inadequate because freshly extracted data, which has neither been transformed nor stored can be challenging to work with. Therefore, data extraction without ETL is mostly done for documentation. As a result, using the complete ETL data extraction scheme is more beneficial for a user.

Commonly Extracted Types of Data

Several types of data are extracted for transformation and loading. Yet, there are some types of data that are commonly extracted. These are discussed below.

Customer Data

All businesses that provide products or services to clients are interested in customer data. Customer data points include customer name, customer contact address, customer phone number, and much more. Customer information can be collected from reviews of products, social media platforms, and other forms of feedback systems.

Financial Data

Financial data consists of data points such as marginal costs, competitor’s product prices, sales number, and more. Companies use this type of data to evaluate the financial condition of a business and provide ways of improving productivity.

Benefits of Data Extraction Process

Data extraction is not just done for the purpose of doing it. It has several benefits for the users. As a result, many people are willing to pay thousands of dollars to convert unstructured data into a structured database. To give you a deeper understanding of the process, below are the benefits of the data extraction process.

Minimize Errors Within a Database

Having errors within a database is inevitable. However, you want the percentage of mistakes to be so insignificant that it does not affect the output of analysis and conclusions. The data extraction process is one tool that helps reduce the number of errors in any given data warehouse. Problems within data can be due to duplicates, incomplete information, or typing errors. With the help of data extraction, you can eliminate most of these problems. You can significantly improve the quality of a database by using artificial intelligence and machine learning with data extraction.

Reduces Cost

Manually entering data is not only challenging and predisposed to error. It can be quite expensive. You will have to spend a lot of money finding people who will do the job. With data extraction, you only make an initial investment for buying or developing the extraction tool. With time, you will make up for the money spent as you get to extract data quickly, consistently, and accurately.

Makes Data Ready for Analysis

Most data points are not suitable for analysis. You have to perform some operations to make it ideal for drawing conclusions. With the aid of data extraction, you can have a database ready for analysis.

Improves Work Speed

In today’s world, are larger and more repetitive actions are being automated. Doing this allows workers to concentrate on more essential aspects of a business. Similarly, data extraction enables individuals and companies to speed up their work processes.

Improves Decision Making

Data-based decision-making is the best form of decision-making. It lets you reach conclusions why considering facts about a particular issue. Before any verdict is reached, information must be gathered from various sources. Such sources may include customer reviews, social media platforms, or forums.

Enhances Staff Morale

These types of repetitive tasks are frustrating to do. If you want your workers to hate you, give them manual data entry tasks to perform. With an automated data extraction process, these types of work are done by applications. In turn, your workers will have the necessary push to engage in other activities.

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