Importance of Data Extraction in Research

It is easy to understand how valuable raw or processed data is by looking at its colossal demand on the market, among businesses, and in independent research studies. Altogether, separating actionable bits of data contributes significantly to gaining new insights and access to high-quality research further outside of the domain of healthcare and academia.

What is Data Extraction in Research?

Data Extraction refers to taking significant pieces of information from multiple research studies and organizing them in a way that we can use to summarize those studies, draw conclusions, or assist in other research. Data extraction processes can be either automated or manual, or both.

Two examples of data extraction include:

1. Data Mining

Data mining involves extracting only useful information or fragments of data from massive datasets. Analyzing these data can help solve business problems by anticipating future developments through pattern identification and correlations.

2. Web Scrapping

Web scrapping involves extracting data and content from websites or databases based on their HTML code using artificial intelligence (AI) automation tools.

The extraction of data must follow a prearranged set of guidelines. And these guidelines also specify the type and proportion of data a person can extract for research purposes and who the compliance authorities are concerning any objection and consent.

Why Do We Need Data Extraction in Research?

Data extraction also plays many side roles than just being a tool for advancing further research. Extraction of data from lengthy texts primarily aims at shortening the amount of information by using only the most pertinent piece of information.

In a research process, however, data extraction has much more importance and additional benefits beyond this:

1. In Boosting Productivity

Having easily accessible and organized data reduces the time and effort spent searching for the same datasets again. We can further automate the extracted information by exporting it to a few spreadsheets and databases, thereby increasing productivity.

2. In Making Reasonable Decisions

With the extracted data, companies and organizations can gain a deeper understanding of the research process, thus enabling more informed and strategic decision-making. Some of these decisions may even guide the organization or the government policy.

3. For Advancing in Competition

In the same industry, high-quality and original data gives organizations an edge over their competitors. Data from the initial stage help create differential values in the production of goods or in the delivery of services, which makes businesses different from one another.

4. In Saving Future Expenses

In addition to saving the public money, removing only the relevant datasets will save organizations and governments from spending money on similar future work. Furthermore, a computer-aided data extraction process can reduce the time, money, and effort required to extract data manually.

Conclusion

It is a fact and a universal understanding that the data used in one study can become the foundation of another study. Ultimately, that is how any research progresses. Higher accessibility and transparency to information achieved by this route encourage high-quality, original research that profits businesses, researchers, their communities, and humanity.

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