Unstructured data

What is the Unstructured Data?

Unstructured data is the data that does not conform to a data model and has no easily identifiable structure such that it can not be used by a computer program easily. Unstructured data is not organized in a pre-defined manner or does not have a pre-defined data model, thus it is not a good fit for a mainstream relational database.

Unstructured data is information that is not arranged according to a pre-set data model or schema, and therefore cannot be stored in a traditional relational database or RDBMS. Text and multimedia are two common types of unstructured content. Many business documents are unstructured, as are email messages, videos, photos, webpages, and audio files.

From 80 to 90 percent of data generated and collected by organizations, is unstructured,, and its volumes are growing rapidly — many times faster than the rate of growth for structured databases.

Unstructured data stores contain a wealth of information that can be used to guide business decisions. However, unstructured data has historically been very difficult to analyze. With the help of AI and machine learning, new software tools are emerging that can search through vast quantities of it to uncover beneficial and actionable business intelligence.

Unstructured data vs. structured data

Let’s take structured data first: It’s usually stored in a relational database or RDBMS and is sometimes referred to as relational data. It can be easily mapped into designated fields — for example, for zip codes, phone numbers, and credit cards, respectively. Data that conforms to RDBMS structure is easy to search, both with human-defined queries and with software.

Unstructured data, in contrast, doesn’t fit into these sorts of pre-defined data models. It can’t be stored in an RDBMS. And because it comes in so many formats, it’s a real challenge for conventional software to ingest, process, and analyze. Simple content searches can be undertaken across textual unstructured data with the right tools.


Beyond that, the lack of consistent internal structure doesn’t conform to what typical data mining systems can work with. As a result, companies have largely been unable to tap into value-laden data like customer interactions, rich media, and social network conversations. Robust tools for doing so are only now being developed and commercialized.

What are some examples?

Unstructured data can be created by people or generated by machines.

Here are some examples of the human-generated variety:

  • Email: Email message fields are unstructured and cannot be parsed by traditional analytics tools. That said, email metadata affords it some structure, and explains why email is sometimes considered semi-structured data.
  • Text files: This category includes word processing documents, spreadsheets, presentations, email, and log files.
  • Social media and websites: Data from social networks like Twitter, LinkedIn, and Facebook, and websites such as Instagram, photo-sharing sites, and YouTube.
  • Mobile and communications data: Text messages, phone recordings, collaboration software, Chat, and Instant Messaging.
  • Media: Digital photos, audio, and video files.

Here are some examples of unstructured data generated by machines:

  • Scientific data: Oil and gas surveys, space exploration, seismic imagery, and atmospheric data.
  • Digital surveillance: Reconnaissance photos and videos.
  • Satellite imagery: Weather data, landforms, and military movements.

Characteristics :

  • Data neither conforms to a data model nor has any structure.
  • Data can not be stored in the form of rows and columns as in Databases
  • Data does not follow any semantic or rules
  • Data lacks any particular format or sequence
  • Data has no easily identifiable structure
  • Due to the lack of identifiable structure, it can not be used by computer programs easily

Sources :

  • Web pages
  • Images (JPEG, GIF, PNG, etc.)
  • Videos
  • Memos
  • Reports
  • Word documents and PowerPoint presentations
  • Surveys

Advantages :

  • Its supports the data which lacks a proper format or sequence
  • The data is not constrained by a fixed schema
  • Very flexible due to the absence of schema.
  • Data is portable
  • It is very scalable
  • It can deal easily with the heterogeneity of sources.
  • These types of data have a variety of business intelligence and analytics applications.

Disadvantages :

  • It is difficult to store and manage unstructured data due to the lack of schema and structure
  • Indexing the data is difficult and error-prone due to unclear structure and not having pre-defined attributes. Due to which search results are not very accurate.
  • Ensuring security to data is a difficult task.

Problems faced in storing unstructured data:

  • It requires a lot of storage space to store unstructured data.
  • It is difficult to store videos, images, audios, etc.
  • Due to unclear structure, operations like update, delete, and search is very difficult.
  • Storage cost is high as compared to structured data
  • Indexing the unstructured data is difficult

A possible solution for storing Unstructured data:

  • Unstructured data can be converted to easily manageable formats
  • using a Content addressable storage system (CAS) to store unstructured data.
    It stores data based on their metadata and a unique name is assigned to every object stored in it. The object is retrieved based on content, not its location.
  • Unstructured data can be stored in XML format.
  • Unstructured data can be stored in RDBMS which supports BLOBs

Extracting information from unstructured data:
unstructured data do not have any structure. So it can not easily be interpreted by conventional algorithms. It is also difficult to tag and index unstructured data. So extracting information from them is a tough job. Here are the possible solutions:

  • Taxonomies or classification of data helps in organizing data in a hierarchical structure. Which will make the search process easy.
  • Data can be stored in a virtual repository and be automatically tagged. For example Documentum.
  • Use of application platforms like XOLAP.
    XOLAP helps in extracting information from e-mails and XML based documents
  • Use of various data mining tools


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