Data extraction is a critical process for businesses and individuals who need to collect, analyze, and use data from various sources. But what is data extraction, and why is it so important?
By using a data extraction process in your organization, you can speed up a lot of manual work, increase your productivity and automate workflows as well.
In this article, we will explore the basics of data extraction, the data extraction process and how they can benefit your business.
What is data extraction?
Data extraction refers to the process of retrieving information from unstructured data sources. With data extraction, the data can be refined so that it can be stored and further analyzed.
Data extraction is used throughout industries such as healthcare, financial services and the tech industry among others. With data extraction, businesses can automate their manual processes and thus, increase efficiency.
Did you know that Domino uses a data extraction tool to capture and extract data throughout its different channels?
ETL infographic
Data extraction and ETL
Data extraction is the first step in the ETL process. ETL stands for Extract, Transform and Load and includes the 3 processes. The main goal of ETL is to prepare the data to be loaded into a data warehouse, database or directly into a business application. ETL can be used in any type of industry such as healthcare, SaaS, and even retailers.
ETL processes
Difference between structured and unstructured data
Unstructured data include data which does not have a defined structure whereas structured data is data which has already been transformed into a well-defined data model.
Examples of unstructured data are e-commerce emails, confirmation orders, PDF invoices and flight booking emails. CSV file, XML file and JSON documents are structured data.
Read more about structured data vs unstructured data
Data extraction vs data mining
Source: Zapier - Data extraction vs data mining
Data extraction and data mining are important processes in analyzing large volume of data but they are not related.
Data extraction is the process of obtaining and collecting data, while 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
Data extraction can be done using several different methods. We have outlined some of them below:
Text extraction
Text extraction refers to scanning and retrieving specific words, phrases, keywords from different types of documents such as surveys, purchase orders, leads' emails. You just have to specify which data you want to extract and the text extraction tool will do the work automatically.
Optical character recognition (OCR)
OCR extracts and read 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 a lot of computations to correctly identify text. Today, best OCR algorithms can even identify manually written text fairly reliably.
Automatic image annotation
Also known as automatic image tagging, this data labelling method is a process through which metadata are assigned to various entities in an image using Computer Vision, like 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
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 methods, 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 both contain 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.
Here are the 3 top reasons why any organization should use include automatic data extraction in their workflows:
- Less manual and human errors
It’s inevitable that errors will occur especially if your staff is going through hundreds of documents on a daily basis. Those errors can include missing, incomplete, or duplicate information.
Did you know that A&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.
- 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.
- Increase in business efficiency
Data come 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 to access those data faster and process them 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
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
Octoparse is a web scraping tool that can extracts data from dynamic websites in various formats, such as CSV, Excel, or JSON.
Parsehub
Parsehub extracts data from JavaScript and AJAX pages to automate the data collection process.
Parseur as a document 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 zero manual intervention for industries such as food ordering, Google alerts, real estate, and job search.
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 to automate 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.
With no 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.
