Data Extraction vs Data Collection
Data science tools and words can sometimes be confusing to a non-technical person. This problem occurs because they are mostly similar. However, each data science tool means and does a different thing. In line with this, data extraction and data collection are two different terms.
Data extraction is also referred to as web scraping. This process allows you to collect data from different locations into a single central location. In addition, this location can be cloud-based or physical storage. Data extraction is mostly used for unstructured data. Examples of unstructured data origins include emails, phone logs, PDF documents, web pages, and much more. However, data extraction does not involve analyzing and processing collected data points. Data extraction is mainly done for two purposes, which are retrieval and format conversion. Data retrieval allows you to gather data in different formats into digital form. For example, you can use data retrieval to get physical copies of documents into digital records. Next, data are quickly transformed into a uniform format before being stored. Ultimately, the data gathered from data extraction is used for data analysis. However, this data analysis is not part of the data extraction process. In effect, people conduct data extraction to perform data analysis. A typical example of the data extraction process is extracting customer reviews of competitors to gain an advantage over rivals. Many individuals and organizations use data extraction because it can operate semi-autonomously. This ability gives the users several advantages. In addition, it has effectively made manual data extraction obsolete as it is demanding.
Data collection can be described as collecting information based on specific variables for a specific system. In turn, the information gathered is used to monitor a process and answer particular questions. Individuals in different fields use data collection for various purposes. Therefore, it is not only used in sciences and engineering. But it is equally relied on within other fields like humanities and other social science fields. Regardless of how data collection is used, the same process integrity level is required. The quality of each data collection process is crucial to ensure a fair outcome. The steps taken for data collection depend on the need for the process. Below are the stages of data collection.
Specify the Need for Data Collection
Before starting any data collection process, it is essential to identify the reason for the procedure. Doing this gives you a better idea of what you want to achieve.
From the needs of data collection, you can generate the objectives of the process. These objectives narrow the scope of the data collection process. You also decide on the data collection method to use.
This stage allows you to deploy the data collection process. These collection methods can include interviews and direct observations.
This stage is the final step of data collection. You get to make sense of the information collected from the collection process stage.
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