Remember that meme of the little dog in a room on fire saying 'This is fine'? That's you when trying to manually extract data from the documents. But don't sweat it! 2024 is the year of the Super Extractors—AI tools that can scoop out document data.
So, let’s step into the future as we unveil “the cream of the crop” (La creme de la creme) of data extraction tools this year. Are you ready to upgrade your data game from potato quality to 4K?
Types of data extraction methods
There exist several methods of data extraction; here are some of them:
Text extraction refers to scanning and retrieving specific words, phrases, and 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.
AI data extraction
In simple terms, it’s conducting data extraction with the help of artificial intelligence tools. Some AI tools have the possibility to extract data from any documents instantly, without the need for human intervention.
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?
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.
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 will also allow 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 2024
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 2024.
Parseur is a powerful, AI data extraction software that automatically extracts data from any document such as emails and PDFs. The extracted data can be downloaded, exported to Google Sheets, or sent to any application of your choice.
Nanonets is an AI platform that makes it easier for businesses to build and deploy custom image and document recognition models. However, training the custom model is time-consuming since a minimum of 10 annotated documents are required for the training. On top of that, on the free plan, you cannot create more than 5 labels (i.e. fields).
Email parser is a Windows standalone application and works well for those who want to keep all their data locally or connect to applications on their local network. The email parsing tool uses parsing rules to work, which can sometimes be a bit complex to manage.
PDF.ai is a cool tool where you can upload a PDF and “chat” with the AI tool to find specific information within that document. However, its features are limited; for example, you can't send this data to any other apps.
Tesseract is a free open source OCR that extract text from images and can support more than a 100 languages.
Parseur as an AI data extraction tool
Parseur’s main strength lies in its AI parser that can automate 98% of manual data entry work. What’s awesome is that you don’t need to train the AI model or build complex AI tools. The AI data extraction tool is already knowledgeable and knows its job.
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
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.