Data extraction is the process of removing data from unstructured source types and further refining it. Humans can complete data extraction through manual entry into any kind of system. This often leads to errors, as humans are not perfect. And in an increasingly data driven world, correct, indexed data is becoming incredibly necessary. The best solution is to go through software. This software uses OCR to covert to usable data to any document type needed.

This is the first step in ELT, extraction-loading-transformation or ETL, extracting transforming-loading cycle. In layman terms that’s the process that creates scanned data into usable, converted data. The extraction step is exactly as it sounds. The important and good data is taken out of a scanned document to be converted into a new document type. For example, the data may be scanned in from a receipt, converted to computer understood language, and then extracted from the document by field for later conversion and use.

Some Benefits

Data Extractions biggest use comes from the reduction of errors and the saving of time and money. By having data extraction run through machine, as above human error is minimized. This error could potentially cause havoc in an accounting system or making customer service’s job much more complicated. Software data extraction also saves time and money. By having a employee input data it takes much more time to read the data, insert the data into the computer, and then transfer what part of the data is wanted to each system. This is time that could be spent on other tasks such as using the data to increase customer satisfaction or profit. Not paying an employee for those hours or hiring an employee simply to create these tasks also saves time in the future.


These steps are necessary to complete data extraction. The software will uses algorithms to recognize and group together data elements that are similar to one another, such as money value with money value.

  1. Paper is prepped to scan into the system. This may involve cleaning up unnecessary data or making sure it is easy to read.
  2. Paper is scanned on a document scanner
  3. OCR is utilized to recognize document imaging
  4. Algorithms locate data elements within text
  5. Fields that fail validation are manually reviewed. Once errors are corrected data is sent to final destination and is considered extraction.

If this process sounds beneficial to you, please contact Biel’s to learn more.