Data extraction and data collection are part of data management that still often get mixed up by the terms. The two processes are related and complement each other in organizing data effectively, yet actually two different things. There are several ways to distinguish data extraction vs data collection, including how both of them function and aim for different goals.
What is Data Collection?
Data collection is the process of compiling, gathering, and organizing information for specific purposes. This practice is typically driven by certain needs and interests to collect relevant data. Several methods can be used to conduct data collection, such as observation, survey, focus group discussion, interviews, questionnaire, and many others. Choosing the right method is essential for effective data collection, as it can influence the scope and depth of the information obtained.
What is Data Extraction?
Data extraction is the process of pulling, extracting, and retrieving a wide range of data from multiple sources. The extracted data sets come in more accessible formats that are easier for you to analyze and visualize, such as JSON, TXT, CSV, XLS, and others. This process can further enable you to move large amounts of data more seamlessly into another designated system. Moreover, the selection of data extraction tools is important to ensure accurate data extraction.
Let’s Compare the Process of Data Extraction and Data Collection!
Here, we summarize the differences based on the purpose, method, and output.
| Data Collection | Data Extraction |
| Purpose | Purpose |
| To collect a variety of data for a complete understanding | To extract pieces of data from existing material |
| Method | Method |
| Survey, questionnaire, observation, FGD, others | Manual data input with copy paste, automated data extraction |
| Output | Output |
| Raw data that still need further processing | Machine-editable dataset that is ready to use |
How Is the Correlation Between Data Extraction and Data Collection?
Despite their differences, data collection and extraction are actually correlated and can become one continuous process. In sequence, data collection will take place first and followed by data extraction.
Data collection plays a role in gathering raw data from various sources. After preparing the data, the extraction process then carried out by examining and processing the data into formats that are easier to manage or transfer into your current management system.
Now, let’s see the example of data collection and extraction in managing expense records.
The Latest Advancements of Data Collection and Extraction With AI
Technology continues to evolve, advancing various areas of operations, including data collection and extraction.
AI is a driving force behind modern data collection methods. It enables the gathering of data from a variety of sources, including social media, websites, and other platforms. By rapidly tracking and curating large volumes of information, AI facilitates an efficient data collection.
AI has also introduced the enhanced way of extracting data. AI-powered OCR is a powerful tool for automating data extraction from images, pdf, or scanned documents. Concerned about data accuracy? Meet OCR by Fintelite with a high-level accuracy rate you can count on. Just send your document, and our OCR will derive text contained into easily searchable data accurately in seconds. Fintelite OCR also supports documents with any templates, making it perfect for all applications.
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