What is data extraction? Techniques and best tools of 2023

Portrait of Neha Gunnoo
by Neha Gunnoo
7 mins read
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what is data extraction

Data extraction is a critical process for businesses and individuals who need to collect, analyze, and use data from multiple sources. But what is data extraction, and why is it vital in today's economy?

By using a data extraction process in your organization, you can speed up many manual tasks, increase productivity, and automate workflows.

In this article, we will dive into the fundamentals of data extraction, the data extraction process, the best data extraction tools of 2023 and how they can benefit your business.

What is data extraction?

Data extraction refers to retrieving information from unstructured data sources. With data extraction, data the data can be refined, stored, and further analyzed.

Data extraction is used throughout industries like healthcare, financial services, the tech industry, among others. Businesses can optimize their efficiency using data extraction to automate their manual processes.

ETL infographic

ETL infographic

Data extraction and ETL

Data extraction is the first step in the ETL process. ETL stands for Extract, Transform, and Load, and it involves the 3 processes. The primary objective of ETL is to prepare data so that it can be loaded into a data warehouse, database, or directly into a business application. ETL is adaptable to any industry, including healthcare, SaaS, and retailers.

ETL processes

ETL processes

Difference between structured and unstructured data

Unstructured data includes data that lacks a defined structure whereas structured data is data that's already transformed into a well-defined data model.

Examples of unstructured data are e-commerce emails, confirmation orders, PDF invoices, and flight booking emails. CSV files, XML files, and JSON documents are considered structured data.

Read more about structured data vs. unstructured data

Data extraction vs. data mining

Source: Zapier - Data extraction vs. data mining

Source: Zapier - Data extraction vs. data mining

Data extraction and data mining are vital processes in analyzing a high volume of data, but they are not related.

Data extraction involves obtaining and collecting data, whereas data mining is the process of analyzing that data to uncover insights and patterns. Data extraction is a necessary step for data mining, but data mining involves more complex analysis and modeling techniques to derive value from the data.

Types of data extraction methods

There exist several methods of data extraction; here are some of them:

Text extraction

Text extraction refers to scanning and retrieving specific words, phrases, keywords from different types of documents such as surveys, purchase orders, and leads' emails. You only need to specify the data to extract, and the text extraction tool will do the job automatically.

Extract text from PDFs

Optical character recognition (OCR)

OCR extracts and reads data from images or scanned documents by identifying text inside the images, character by character, using Computer Vision. OCR is a complex process that requires many computations to identify text accurately. Today, the best OCR algorithms can even identify manually written text pretty reliably.

Automatic image annotation

This data labeling method known as automatic image tagging is a process through which metadata is assigned to various entities in an image using Computer Vision, as we have described for OCR. An example of image annotation would be to identify the name of an animal or a flower in a picture.

How is data extracted?

Data extraction process

Data extraction process

The extraction process depends on the type of data: unstructured and structured data.

1. Identify type of document

During this step, we identify the kind of document that is received: is it an email, an image, or a scanned PDF, for example.

2. Choose the data extraction method

Once the type of document has been identified, it's time to choose which data extraction technique (as described above) you will use. For example, text-based documents such as emails will use the Text extraction method, whereas scanned invoices (images) will use the OCR method.

In some cases, you can use several methods for the same document. For example, many PDFs contain both text encoded in the file on top of the image. You can then decide to directly access the text and figure out its position in the document or apply OCR and identify the text with computer vision in the image.

3. Extract the data

The raw data is then extracted and structured according to a specific schema.

Why is data extraction important?

At some point, any business would need to extract data automatically if they want to streamline their processes. Some data extraction tools are even powered by machine learning and artificial intelligence to better understand document processes. Parseur.com | @parseur

Did you know that AT&T had a lot of invoicing errors that cost the company millions of dollars?

Having an automated data extraction system in place will help diminish those mistakes and improve the accuracy and precision of your data.

45% of work activities can be automated using demonstrated technologies. McKinsey, 2015
  • Cost and time savings

According to an article by Harvard Business Review published in 2019, professionals have to check their mailbox 15 times a day and waste time reading irrelevant emails.

SaneBox claimed that this was around 650 hours spent in unproductive work.

A data extraction tool will not only automate this process and save you time, but it also allows your employees to focus their creativity elsewhere.

Imagine having a million documents to go through on a monthly basis? Hiring additional staff for this type of work will cost you more than investing in an automated system.

Organizations are losing $140 billion each year in wasted time and resources, duplication of effort, and missed opportunities as a result of disconnected data. ThinkAutomation, Global Market Statistics.
  • Increase in business efficiency

Data comes in different formats and layouts, and as your business grows, it can become difficult to sort and collect data quickly, if done manually. Data extraction can help you access that data faster and process it, leading to better decision-making as well.

An example is PDF data extraction which can be quite tedious to extract data from. A PDF data extractor software will automate this process and increase business efficiency.

Top data extraction tools for 2023

When selecting a tool, it's important to consider factors such as the complexity of the data you need to extract, the volume of data, the level of technical expertise required, and the output formats supported. Here are some top data extraction tools to consider for 2023:


Parseur is a powerful and no-code data extraction software to automatically extract data from documents such as emails and PDFs. The extracted data can be downloaded, exported to Google Sheets, or sent to any application of your choice.


Octoparse is a web scraping tool that can extract data from dynamic websites in various formats, such as CSV, Excel, or JSON.


Parsehub extracts data from JavaScript and AJAX pages to automate the data collection process.

Parseur as an email and PDF data extraction tool

Parseur operates on a Point & Click basis where zero technical knowledge is required. All you have to do is teach Parseur which specific data you want to extract by highlighting the data fields. The data extraction solution uses machine learning (ML), natural language processing (NLP) and optical character recognition (OCR) algorithms to capture data accurately.

Parseur also offers automatic layout detection where you can create as many templates as you want, and the email parser tool will always pick up the right template.

You can also use the built-in templates feature whereby data is extracted automatically, with no manual intervention for industries such as food ordering, Google alerts, real estate, and job search.

Try the best data extraction software
Having a powerful data extraction tool can help you automate your business processes, saving you countless hours of work.

Examples of data extraction

Whether you are in the real estate, food delivery, or other industries, data extraction will definitely be a competitive advantage.

How Barberitos sales increased to 30% with Parseur

Barberitos is a Fast Casual Burrito chain headquartered in Athens, GA, having restaurants in the Southeast US.

With the integration of Parseur as a document extraction tool, Barberitos has been able to:

  • Increase their sales revenue
  • Capture error-free data
  • Export extracted data to their POS automatically

Read its success story here: Customer success interview: Barberitos

How BuildYourBNB improved their data accuracy

BuildYourBNB is a management consulting company where they manage properties in short-term real estate rentals with over 10,000 guests.

With Parseur by their side, they have been able to:

  • Organize and control data more effectively
  • See fewer inconsistencies in data capture
  • Export extracted data to Airtable and Slack

Learn more about its success story here: Customer success interview: BuildYourBNB

There are other examples where Parseur has automated and extracted data efficiently, such as for Google Alerts and job search.

The future of data extraction

The global data extraction market is projected to reach $4.90 billion by 2027.

The future of data extraction is likely to be characterized by greater automation, better integration with other data technologies, more focus on unstructured data, increased use of APIs, and better data quality.

Without a doubt, data extraction is a solid solution to automate manual processes and help businesses to scale. The word “data extraction” may sound technical, but rest assured that data extraction tools work on their own.

All-in-one data extraction software. Start using Parseur today.

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