(also data extraction)
Extraction refers to organizations obtaining relevant information from various sources, such as databases, web pages, documents, or other unstructured or structured data repositories. Organizations often transform or convert the data into a structured format for further analysis, processing, or storage.
See also: exploit, malicious code
- It is efficient. Automated extraction is far less time-consuming than manual data collection. It is also scalable and customizable so organizations can tailor, grow, and adapt it to their changing needs.
- Improves data quality. Extraction helps organizations filter out irrelevant or inconsistent information, reduce errors, and ensure they have only accurate and reliable information on hand.
- Aids in decision-making. Organizations can gather valuable insights and develop effective strategies by quickly collecting and processing large data volumes.
- Grants a competitive advantage. Proper decision-making and insights help organizations better identify and benefit from market trends, patterns, and opportunities than their competitors.
Data extraction vs. data acquisition
- Data extraction. Obtaining information from various sources and transforming the data into a structured format for further analysis, processing, or storage. But unlike data acquisition, data extraction primarily collects data that already exists somewhere.
- Data acquisition. Collecting data from different sources, often in real-time, and converting it into a format that organizations can quickly analyze or store for future use. Data acquisition primarily gathers new data, which might not be readily available in existing databases or repositories.