Data Extraction Tool

Most online and offline data sources (e.g. documents, web pages) are not immediately processable by machines. Data extraction software enables companies to extract data out of these sources.

To be categorized as a data extraction software, a product must be able to automatically extract data from various types of unstructured and semi structured data sources.

Innovators Specialists Leaders Challengers Market Presence Momentum
Popularity
Satisfaction
Maturity
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Compare Data Extraction Tools
Results: 72

AIMultiple is data driven. Evaluate 72 products based on comprehensive, transparent and objective AIMultiple scores. For any of our scores, click the icon to learn how it is calculated based on objective data.

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72.65120472147996
94.03662804037204
1.189188647321625
100
0.031746031746031744
51.26578140258789
top10
4star
Datawatch Monarch
4.40
100%
100%
100%
= 1 review
= 10 employees
= 10,000 visitors

Monarch is desktop-based, self-service data preparation, offering the easiest way to access, clean, prepare and blend any data - including PDFs and semi-structured text files. Accelerate your reporting and analytics with easy, powerful data prep.

51.570653179367454
66.95282354505373
0.013513187219140197
71.22596409008007
0.0003968253968253968
36.18848281368118
5star
Parseur.com
4.90
100%
4%
23%
= 1 review
= 10 employees
= 10,000 visitors

The #1 email parser software. Automatically extract text from emails and documents.

51.49837135015333
62.85696240957729
0.09459465820375536
66.8651907784425
0.02817460317460317
40.13978029072938
5star
Ephesoft Transact
4.60
100%
100%
100%
= 1 review
= 10 employees
= 10,000 visitors

Leave manual data entry & sorting behind with Ephesoft Transact, our intelligent enterprise data classification & document capture software.

50.43033181128338
64.54623503153343
0.5270272137302247
68.63632388640082
0.4093253968253968
36.31442859103334
top10
4star
Kofax Capture
4.20
100%
100%
100%
= 1 review
= 10 employees
= 10,000 visitors

Accelerate business processes with advanced capture that transforms all types of documents into actionable information that's delivered into core systems.

47.43735228162674
61.52259125827107
1.189188647321625
65.41159967456332
0.0003968253968253968
33.3521133049824
top10
5star
Docparser
4.50
100%
4%
100%
= 1 review
= 10 employees
= 10,000 visitors

Extract data from PDF files & automate your workflow with our reliable document parsing software.

Popularity

Searches with brand name

These are the number of queries on search engines which include the brand name of the product. Compared to other product based solutions, data extraction tool is more concentrated in terms of top 3 companies' share of search queries. Top 3 companies receive 84% (6% more than average) of search queries in this area.

Web Traffic

Data extraction tool is a highly concentrated solution category in terms of web traffic. Top 3 companies receive 89% (12% more than average solution category) of the online visitors on data extraction tool company websites.

Satisfaction

Data extraction tool is less concentrated than average in terms of user reviews. Top 3 companies receive 43% (14% less than average solution category) of the reviews on data extraction tool company websites. Product satisfaction tends to be higher for more popular data extraction tool products. Average rating for top 3 products is 4.5 vs 4.3 for average data extraction tool product review.

Leaders Average Review Score Number of Reviews

Maturity

Number of Employees

Median number of employees that provide data extraction tool is 41 which is 23 less than the median number of employees for the average solution category.

In most cases, companies need at least 10 employees to serve other businesses with a proven tech product or service. 48 companies (0 less than average solution category) with >10 employees are offering data extraction tool. Top 3 products are developed by companies with a total of 101-500 employees. However, all of these top 3 companies have multiple products so only a portion of this workforce is actually working on these top 3 products.

IBM
Amazon Web Services (AWS)
OpenText
Nuance

Learn More About Data Extraction Tool

How is document capture software different than OCR?

While Optical Character recognition (OCR) technology captures all text in images and files, document capture goes one step further and converts text into structured data. Examples of structured data in images and documents include key value pairs (e.g. bank account numbers, customer names in invoices) and tables

What is document capture software?

Document capture software specialize in extracting data out of unstructured data.

There are 3 types of data: Structured, semi-structured and unstructured:

  • Structured data forms 5-10% of all data. It is in tabular form and is processable without errors by machines. Structured data include most excel tables, data in SQL databases, XML or JSON files that follow strict structure requirements
  • Semi-structured data forms 5-10% of all data. It is not in tabular form but still has a structure though this structure is not explicitly declared and not followed 100% of the time. Semi-structured data can be processed with low error rates but achieving zero errors is challenging. Semi-structured data include invoice slips, most PDF forms, XML or JSON files which do not follow strict structure requirements
  • Unstructured data forms ~80% of all data. It includes free text and images that do not follow any explicit structure. It is challenging to extract structured data out of these documents with low error rates. If unstructured data is actually found to follow a structure and that structure is identified, it can be correctly categorized as semi/structured data based on the strictness by which the identified structure is followed throughout the document.

What is the error rate?

Error rate in data extraction can be measured in a few ways but not every error has the same cost. Imagine making an incorrect payment because your data extractor made an incorrect character reading with high confidence. This is a costly error. However, failing to read a character and flagging it as unreadable is a less costly issue. Therefore it is important to focus on cases where data extraction tools make extraction errors while claiming a high level of confidence. These should be minimized.