What is Data Extraction?

The process of collecting data and its different types from multiple sources, these sources can either be fully structured or completely unstructured. And then retrieving that data is called as Data Extraction.

Nowadays, a lot of data is present throughout the web but the main purpose for the companies is to extract that data. To extract this data different types of tools are used.

Data Extraction Process:

The process of Data Extraction usually involves three basic steps in spite of what sources are involved. Steps are as follows:

~ Check if there are any changes in your data.

~ Select which part or parts of the data you want to extract. ~ In the end, execute the extraction.

~ In the end, execute the extraction.

Importance Of Data Extraction:

These days, many companies in most of the fields need to extract data at any instant. Many businesses require data extraction when they want to transfer data between cloud platforms for storage and management of data. Data Extraction is also helpful when need for upgrading databases rises as well as combining data from different businesses.

Mostly Businesses and Companies use Data Extraction so they can have following advantages:

~ It improves and quickens their decision making process and extract data from sources at a faster rate.

~ As Data Extraction is an automated process, companies use it offload the burden on their staff. Therefore, staff is involved in more productive and creative tasks.

~ Use of data extraction minimizes the error to a greater extent. For instance, when company employees enter data into system manually errors like inaccuracy and duplicate information are unavoidable. Companies reduce these errors by using Data Extraction.

~ Most companies free their workers by using Data Extraction and allow them to work on their main tasks which are more strategic. As a result their overall productivity is increased.

Types of Data Extraction:

Data Extraction can be of two types:

Structured Data And Unstructured Data:

Structured Data:

The data which has a well defined structure and is in more standardized form is known as Structured Data. Structured Data is in continuous order and programs and humans can access this type of data easily. This type of data is commonly stored in a system database. Structured Data is also characterized as data which is quantitative as well as highly organized. For example, Names, Addresses, Dates and credit card numbers are included in Structured type of data.

Machine learning algorithms use structured data more easily. Organized design of structured data querying easy.

Business users can easily use structured data as it does not demands comprehensive study of types of data. If a user knows the topics which is related to data, it can easily access the data. There are large number of tools available to analyze and use structured data.

Downside of structured data is that it has a very limited usage and storage options and this is the reason why its flexibility and usability is limited.

Unstructured Data:

Unstructured data is the quite opposite of structured data.

In this type of data, data stored is not structured, database format or standardized. In todays world, the data which is most abundant and commonly found is unstructured type of data as it can be anything. For instance, it can be images, audio, text and much much more. Unstructured data can either be generated by humans or machines. It might be in a textual format or non textual format. As the usage of digital applications is increasing day by day, Unstructured Data is also growing at an exceptional rate due to this. If Unstructured Data is analyzed correctly businesses put more importance in this type of data than Structured Data.