To better understand unstructured data, lets look at how it compares to structured data: Structured data Structured data is typically numerical information — dates, names, addresses, phone numbers, SSNs, credit card numbers, medical record numbers, product numbers, transaction information, etc. More importantly, structured data is organized in a pre-defined structured format, such as the columns and rows of an Excel spreadsheet or Google Sheet. Structured data usually lives within a relational database (RDBMS). Most importantly, the framework of structured data makes it easy for data entry, searching, comparison and data extraction functions. The structured format means it is eminently searchable, whether with human-generated queries using a structured query language (SQL) — or via algorithms that pull in specific types of data and field names, such as alphabetical or numeric, currency or date. Unstructured data By contrast, unstructured data is usually text-heavy, does not have a pre-defined format and does not live within an RBDMS — and as such is configured in such a way that makes it very difficult for traditional search and analytics tools to search and analyze. For example, while emails or social media posts can be searched based on hashtags or metadata, traditional search and analytics tools cannot parse the full text of these messages. Unstructured Data Examples Here are a few of the most common examples of unstructured data in the modern enterprise: Business Documents Everything from Word documents and PDFs, to PowerPoint presentations and many spreadsheets and XML files. Even though the text in many of these files may follow a common format, the data is not fully structured in a pre-defined schema that can be analyzed with traditional search and analytics tools. Intra-business Communications Text messages, chat and messaging apps like Slack and Microsoft Teams, as well as phone recordings are all examples of unstructured data generated by intra-business communications every day. Webpages and Apps Developers know that coding is a very consistent and methodical practice, but the code itself does not follow a pre-defined structure or schema, meaning that the code behind almost all web pages and applications is unstructured data. Customer Feedback Another extremely common and voluminous source of unstructured data in the enterprise is customer interaction data, whether from multichannel contact center interactions (phone, chat, text, etc.), online reviews or surveys, etc. Emails Business emails generate a tremendous amount of unstructured data every day. Emails may be considered semi-structured by metadata-driven categories, but the full text of the email is unstructured. Media (Images, Audio and Video) Media files can be tagged using metadata and saved in consistent formats, but the full content of those media files represents unstructured data. Social Media Incoming data from social media networks like Facebook, Twitter, YouTube, LinkedIn and the like are much like emails. Hashtags and metadata-driven categorization present some structure, but the full text/content of the posts represents unstructured data. Machine-Generated Unstructured Data One of the fastest-growing categories of unstructured data in the enterprise is machine-generated unstructured data. This includes monitoring data, such as photo and video surveillance, as well as scientific monitoring and measurement devices. But it also includes the exponentially growing Internet of Things (IoT) environment where nearly every machine, device or “thing” is monitoring something (its own condition, its usage, external factors, etc.) and reporting back. This machine-generated unstructured data is now essential to operations in manufacturing, tech, consumer electronics and more.