What Is Data Extraction? (With Benefits and Importance)
Organizations extract information from various sources when performing data retrieval. With the right method for extracting all types of data, businesses can maximize the value of information and make effective and informed decisions. Understanding how the data retrieval process works can help you determine whether it's the best option for the organization. In this article, we discover what data extraction is, discuss how you can extract data, explore the benefits of using extraction tools, examine the data retrieval importance, and discuss the CDC method.
What is data extraction?
Data extraction, also known as data retrieval, is the method of obtaining data and moving it to a data repository or conducting additional analysis. This process may also include data transformation. For instance, you may wish to perform calculations on the data like aggregation and then store the results in the data warehouse.
If you're extracting data for storage in a data warehouse, you can add additional metadata or augment the data with timestamps or geolocation information. A timestamp is a sequence of numbers and letters that specifies when an event occurred. You may combine the data with other records in the target data store. These processes, known as ETL, comprise the extraction, transformation, and loading of data.
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Extraction with ETL
Companies in most industries may use the ETL processes for various purposes. For instance, many retailers may collect customer data via mobile applications, websites, and in-store transactions. Without a mechanism for migrating and merging the data, companies may face limitations of inadequate information and statistics. The ETL process can improve the possibilities of gathering valuable information for the organization. You can divide the ETL process into three stages:
Extraction: Data collection occurs from several sources or systems. Extraction enables the combination of a variety of data and its usage for business intelligence.
Transformation: Once you have successfully extracted the data, you can refine it. For instance, you may delete duplicate entries, remove missing values, and conduct audits to ensure that the resulting data is reliable, consistent, and usable.
Loading: In this stage of ETL, you can deliver the transformed data to a centralized location for analysis and storage.
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Data extraction without ETL
Data retrieval can occur independently of ETL, but there are limitations in the absence of a more comprehensive data integration process. Extracted data that you haven't transformed or loaded properly may be difficult to organize and analyze, which can also be incompatible with newer programs and applications. The data may be beneficial for archival purposes only. If you're planning to migrate data from legacy databases to a more modern or cloud-native system, it's better to extract it using a comprehensive data integration tool.
Another disadvantage of extracting data as a stand-alone process is that it sacrifices efficiency, especially if you intend to perform the extraction manually. Manual coding can be a time-consuming and error-prone process that is difficult to replicate across multiple extractions. This means that each time there is an extraction, the code may require rebuilding from the beginning.
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How you can extract structured and unstructured data
Unstructured data comprises information you haven't sorted according to your data model and analytic methods. Here's how you can extract structured and unstructured data:
Structured data retrieval
Structured data refers to data configured based on systematic models, preparing it for analysis. You can extract this type of data using a relatively simple technique called logical data retrieval. Structured data retrieval includes two subtypes:
This method entails a one-way data retrieval from a given source. You can extract it from the system with no additions as additional logical information. This is a relatively straightforward process when using the appropriate data retrieval tools. If it's critical to determine which data changes are occurring continuously within the source system, you may require the second extraction method.
Incremental extraction is a more complex logical process with no limitation on initial retrieval. Repeated visits to the source system are essential to monitor and extract any recent changes made to the data by the source. The additional logic helps determine the occurrence of these changes without extracting the entire data set repeatedly. You can refer to this as change data capture (CDC) and you can use it as the preferred method.
Unstructured data retrieval
Extracting unstructured data is more complex than extracting structured data. This is because of the various data types that comprise this group. Web pages, emails, text documents, PDFs, scanned text, mainframe reports, and spool files are all examples of data sources. The data within this group is as valuable as those contained in structured forms.
Capacity for extracting and processing unstructured data is also critical, despite the process's difficulty. It requires more than just extracting the data to prepare it for analysis. This includes removing white space, symbols, duplicate results, and completing missing values.
Benefits of using an extraction tool
Businesses and organizations in every industry and sector may require data retrieval during their life cycle. For some, the requirement may arise during the process of upgrading legacy databases or migrating to cloud-native storage. Some organizations may wish to merge databases following a merger or acquisition. Businesses frequently seek to streamline internal processes by combining data sources from various divisions or departments.
Most organizations now rely on data retrieval tools to manage the extraction process from the beginning. Using an ETL tool streamlines and automates the extraction process, releasing resources for other tasks. Among the advantages of utilizing a data retrieval tool are the following:
Increased control: Data retrieval enables businesses to import data from external sources and store it in their databases. You can avoid separating the company's data due to out-of-date applications or software licenses.
Enhanced agility: As businesses expand, they frequently work with disparate types of data stored in similar systems. Data retrieval enables you to merge that information into a single location to unify multiple data sets.
Simplified sharing: For organizations that want to share some data with external partners but not all of it, data retrieval can be a simple way to provide beneficial but limited data access. Extraction enables you to help the company share data in a standardized and usable format.
Enhanced accuracy: Manual processes and hand-coding increase the likelihood of errors and the time required to enter, edit, and re-enter large volumes of data.
Why is data retrieval important?
Almost every organization in any industry may require data retrieval. For many businesses, the requirement arises as part of a larger shift to a cloud-based data storage and management platform. Other organizations require data retrieval to upgrade databases, consolidate systems following an acquisition, or merge data from multiple business units. Businesses implement automated data retrieval solutions to accomplish the following:
Concentrate personnel efforts on high-value activities: Manual processes are extremely time-consuming and resource-intensive in terms of human resources. Businesses can reduce the administrative burden on IT staff by automating data retrieval processes, allowing them to devote more time to higher-value tasks.
Increase accuracy and decrease human error: By automating repetitive data entry processes, extraction tools can help increase the accuracy of the data inputs by minimizing human error. This can help eliminate the probability of human errors and produce more accurate data.
Boost employee output: By eliminating the need for extensive manual data entry, team members can allocate more time to critical tasks that only a human can perform. These types of tasks typically add more value to a business as they're applying their skills to more meaningful tasks, which increases productivity.
Enhance visibility: Having the data in a shared digital storage platform can increase its visibility. When employees have access to the information they require, there are no delays in data entry.
Increase cost savings: Businesses can save money in the short and long term by automating lengthy and repetitive tasks. The company may avoid scaling and investing in a large team to handle their data needs daily or as they grow.
Data retrieval with change data capture
The best method for incremental extraction is to use change data capture or CDC. By using this method, the company can load into the warehouse only the data they modified during the previous data retrieval. By doing this, they avoid reloading all the data, which is extremely time-consuming and resource-intensive. CDC enables near-real-time data access and on-demand data warehousing. This method is essentially more efficient, as it requires extracting a much smaller volume of data.
Please note that none of the companies, institutions, or organizations mentioned in this article are affiliated with Indeed.
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