Typical unstructured data sources include web pages, emails, documents, PDFs, social media, scanned text, mainframe reports, spool files, multimedia files, etc. Extracting data from these unstructured sources has grown into a considerable technical challenge, where as historically data extraction has had to deal with changes in physical hardware formats, the majority of current data extraction deals with extracting data from these unstructured data sources, and from different software formats. This growing process of data extraction from the web is referred to as "Web data extraction" or "Web scraping".
The act of adding structure to unstructured data takes a number of forms
Using text pattern matching such as regular expressions to identify small or large-scale structure e.g. records in a report and their associated data from headers and footers;
Using a table-based approach to identify common sections within a limited domain e.g. in emailed resumes, identifying skills, previous work experience, qualifications etc. using a standard set of commonly used headings (these would differ from language to language), e.g. Education might be found under Education/Qualification/Courses;
Using text analytics to attempt to understand the text and link it to other information
Data mining, discovery of patterns in large data sets using statistics, database knowledge or machine learning
Data retrieval, obtaining data from a database management system, often using a query with a set of criteria
Extract, transform, load (ETL), procedure for copying data from one or more sources, transforming the data at the source system, and copying into a destination system
Information extraction, automated extraction of structured information from unstructured or semi-structured machine-readable data, like for example using natural language processing to extract content from images, audio or documents
