What is Data Extraction? Why is it Important?

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Written By Haisam Abdel Malak
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Data extraction plays a vital role in this data-driven world where organizations need to constantly extract data from a variety of sources at the same time and transform it into a usable format for analysis and better decision making. As businesses and organizations increasingly rely on data to drive their operations, the significance of proficient data extraction tools cannot be overstated.

Data extraction is the process of systematically retrieving information from diverse sources, including databases and documents, and converting it into a usable format for analysis and decision-making in various fields such as business, research, and data science.

In this article, we will explore in depth data extraction, examining its importance, methods and techniques used, and how it is applied in real world scenarios to uncover insights.

Why data extraction is important?

The benefits of data extraction are:

#1- Easily access data

One of the most important benefits of data extraction is having accurate data delivered to the right audience at the right time and the right place.

When we deploy an automatic mechanism to retrieve data from unstructured or structured documents whether it is in paper format or digital and store in in a secure digital repository, we are allowing our users to easily have access to this data whenever they need.

This will help improve productivity (as we will discuss later) and improve the decision-making process.

#2- Improve data accuracy

The introduction of errors through manual data entry can have a significant effect on data accuracy, potentially leading to costly decisions made based on inaccurate information.

By automating the extraction process, it minimizes the risk of human errors associated with manual data entry. In addition, it can ensure that data is validated and adheres to different available business rules before being stored and used.

#3- Improve productivity

When your organization automate the act of extracting information from different available sources, employees will have more time to focus on more important tasks which will increase their productivity by eliminating repetitive manual tasks and allow them to help in other topics.

Also, it allows for real-time or scheduled data updates, ensuring that decision-makers have access to the most current information without waiting for manual updates.

#4- Reduction of manual errors

Humans are prone to errors like typos, incorrect entries, and data duplication, which can significantly compromise data accuracy. The software, on the other hand, adheres to predefined rules and consistency, ensuring that data is collected accurately and consistently. This not only reduces the potential for errors but also enhances the reliability and integrity of the data being extracted.

As per Zdnet, only %56 of available data was leveraged by organization leaving a whooping 47% of these data unused. The reason is simply because these businesses are not used all the methods and techniques available to effectively collect the available information.

#5- Help automate processes

Having your most important asset available to any audience whether an employee or a system will give your organization the ability to fully automate business processes. At any moment of time, these automated workflows need to integrate with other technologies available within your organization environment to automatically access data and deliver it to the next actor.

#6- Data-driven decisions

Perhaps the most obvious benefit is that it can help businesses to make better decisions. Data can provide insight into customer behavior, trends, and preferences. This information can be used to make strategic decisions about pricing, product development, and marketing.

#7- Improve competitive position

Finally, it can help businesses to improve their competitive position. By understanding the data that their competitors are collecting, businesses can develop strategies to gain a competitive edge.

Methods and techniques

Data extraction methods and techniques

Here are some of the top data extraction methods commonly used:

#1- Web Scraping

Using techniques to gather data from websites is a practice known as web scraping. This method allows us to collect types of information such, as product prices, reviews, news articles and more. It’s widely used in areas like e commerce, competitive analysis, and content aggregation.

#2- APIs (Application Programming Interfaces)

Numerous online platforms offer APIs that enable developers to access their data in an automated manner. These APIs are widely utilized for extracting information from social media platforms, financial markets, and diverse cloud-based applications.

#3- Database Queries

In case is stored in a relational database or ETL, you need to write SQL (Structured Query Language) queries to retrieve the needed dataset and extract insights.

#4- OCR (Optical Character Recognition)

OCR technology is used to extract text data from scanned documents, images, or PDF files. It’s often employed in data entry, research, and document management systems.

#5- Screen Scraping

This method is used to capture data displayed on computer screens. it is used in scenarios where there is no direct access to the underlying data source.

#6- Text Extraction

NLP methods have the potential to extract information from text sources like emails, social media posts, and customer reviews.

#7- Manual Data Entry

In case automated methods and techniques can’t be used to collect information, the old school way can be used.

What are the Challenges of Data Extraction?

The challenges of data extraction include the cost and time required to extract data, as well as the accuracy of the data. Effectively extracting data can be a costly and time-consuming process, and the accuracy of the data depends on the quality of the data source.

It is the first step in managing the full lifecycle of data and should be handled with care.

The following are some of the challenges that can be faced while extracting data:

1. Data quality

Data quality is one of the most important aspects in analytics. Many companies extract data from different sources to get a richer, more accurate picture of what is happening in their business, but this can come at a cost. The benefits of extracting data from multiple sources might not outweigh the risks that come with poor data quality.

This is considered one of the top challenges related to extracting data and information that organizations are facing in this digital age.

2. Lack of standardization

Information is everywhere, but it’s not always in the format you need. Most companies store their information in a way that only they can read, which means that you’ll need to use their software. This can be costly and time-consuming when you’re looking for information from different sources and they don’t conform to your needs or expectations.

3. Lack of access

Finding the right data can be a daunting and costly process. There are many reasons why you might not be able to easily extract data from a source. One reason could be that the sources don’t have the required data or it is hidden behind a high paywall.

4. Incomplete data

The extraction process is not always perfect. Some data may be missing due to errors or omissions during the extraction process.

Data extraction examples and use cases

Consider a company that wishes to keep track on its position in the industry. Data from a wide range of sources, such as online reviews on web sites, mentions on social media, and online purchases, may be needed for this.

Data from these many sources can be extracted using an ETL tool, which can then load it into a data warehouse where it can be evaluated and insights are analyzed.

Another example would be that if a retail business wants to stay competitive and make informed pricing decisions, it needs to automatically monitor the prices of the competitors on a regular basis. This can be easily done using the web scrapping method already described in the previous paragraph.

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