What are our data sources?
We use the data sources on the side for ranking solutions and awarding badges in data extraction tool category. Our data sources in data extraction tool category include;
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.
If you’d like to learn about the ecosystem consisting of Data Extraction Tool and others, feel free to check AIMultiple Automation.
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We use the data sources on the side for ranking solutions and awarding badges in data extraction tool category. Our data sources in data extraction tool category include;
review websites
social media websites
search engine data for branded queries
According to the weighted combination of 7 data sources
ABBYY Recognition Server
Docparser
Altair Monarch
Datamatics TruCap+
IBM Datacap
Taking into account the latest metrics outlined below, these are the current data extraction tool market leaders. Market leaders are not the overall leaders since market leadership doesn’t take into account growth rate.
ABBYY Recognition Server
Docparser
Altair Monarch
Rossum
Datamatics TruCap+
These are the number of queries on search engines which include the brand name of the solution. Compared to other Automation categories, Data Extraction Tool is more concentrated in terms of top 3 companies’ share of search queries. Top 3 companies receive 61%, 6% more than the average of search queries in this area.
9 employees work for a typical company in this solution category which is 12 less than the number of employees for a typical company in the average solution category.
In most cases, companies need at least 10 employees to serve other businesses with a proven tech product or service. 44 companies with >10 employees are offering data extraction tool. Top 3 products are developed by companies with a total of 400k employees. The largest company building data extraction tool is IBM with more than 300,000 employees.
Taking into account the latest metrics outlined below, these are the fastest growing solutions:
ABBYY Recognition Server
Docparser
Altair Monarch
Datamatics TruCap+
IBM Datacap
We have analyzed reviews published in the last months. These were published in 4 review platforms as well as vendor websites where the vendor had provided a testimonial from a client whom we could connect to a real person.
These solutions have the best combination of high ratings from reviews and number of reviews when we take into account all their recent reviews.
This data is collected from customer reviews for all Data Extraction Tool companies. The most positive word describing Data Extraction Tool is “Easy to use” that is used in 8% of the reviews. The most negative one is “Difficult” with which is used in 2.00% of all the Data Extraction Tool reviews.
According to customer reviews, most common company size for data extraction tool customers is 1-50 Employees. Customers with 1-50 Employees make up 40% of data extraction tool customers. For an average Automation solution, customers with 1-50 Employees make up 21% of total customers.
These scores are the average scores collected from customer reviews for all Data Extraction Tools. Data Extraction Tools is most positively evaluated in terms of "Overall" but falls behind in "Ease of Use".
This category was searched on average for 1.8k times per month on search engines in 2022. This number has increased to 2.1k in 2023. If we compare with other automation solutions, a typical solution was searched 1.4k times in 2022 and this decreased to 1.3k in 2023.
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
There are 3 types of data: Structured, semi-structured and unstructured:
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.