Big Data

Unstructured Data In The Big Data World: What To Know

Unstructured Data

Unstructured data is data without a specific format. A majority of the data you deal with is unstructured. This information is everywhere and is the largest piece of data equation.

Structured Versus Unstructured Data

Structured data is clearly defined. For example, customer information presented in a spreadsheet program like Microsoft Excel is structured. The data is represented in rows and columns. Each column has a different attribute, while each row corresponds with the attribute of the column it is associated with.

Like one column may be titled “Customer Address,” while the corresponding row will have details of the address. These rows and columns create a table you can reference easily for specific information, in this case, a particular customer’s personal information.

On the other hand, unstructured data has a qualitative aspect you cannot capture in numbers. For example, in a YouTube video, the content inside the video title, video description, and video itself are unstructured.

Examples Of Unstructured Data

Unstructured data can either be generated by machines or humans.

Examples of machine-generated unstructured data are as follows:

  • Satellite images: This includes weather forecasts or any other information the government gathers from its satellite surveillance imagery
  • Photographs and videos: This includes surveillance, security, and traffic video footage
  • Scientific data: This includes atmospheric data and seismic imagery
  • Radar data: This includes oceanographic and meteorological reports

Examples of human-generated unstructured data are as follows:

  • Texts Information: This includes emails, documents, survey results, and logs
  • Social Media Data: This is information gathered from social media platforms like Facebook, Twitter, and YouTube
  • Mobile Data: This includes text messages
  • Website content: This includes content on websites such as YouTube, Instagram, or Flickr

These examples show several distinct characteristics of unstructured data. These are data that is:

  • Digital and unpredictable
  • Constantly being created
  • Blended, interoperable, and multimodal
  • Geo-distributed for improved protection

The use cases for unstructured data are on the rise. For example, text analytics helps analyze unstructured text, highlight relevant information, and turn it into structured data that can be applied in numerous ways.

Take the example of social media analytics and how it can help understand customer experiences. Managers can analyze customer behavior and reactions toward specific products and services by compiling information from comments in a survey, call center notes, emails, and social media platforms.

How CMSs Contribute To Big Data Management

Organizations store unstructured data in databases or in content management systems. CMSs facilitate the management of unstructured data, including document content, web content, and any forms of media.

The Association for Information and Image Management (AIIM) states that Enterprise Content Management (ECM) features the methods, tools, and strategies for capturing, managing, storing, preserving, and delivering data associated with the organizational process. Technologies adopted by an ECM system include records management, web content management, workflow management, document management, and collaboration.

Many content management vendors produce solutions that can handle bulk unstructured data. Additionally, there are advancements in technology that facilitate the analysis of unstructured data. Some of these technologies address both structured and unstructured data. Others even capture and analyze real-time streams. Examples of these technologies include MapReduce, Hadoop, and streaming.

Currently, content management systems aren’t stand-alone solutions but are combined with other technologies to store and analyze content. For example, your business may track Twitter feeds that programmatically activate a CMS search. The person who initiated the tweet receives a response that directs them to the location where they can find the product they are looking for.

One of the main benefits of this system is that the interaction takes place in real-time. This demonstrates the importance of combining real-time unstructured, structured, and semi-structured data. In this case, the real-time unstructured data is the tweet, while unstructured data is information about the person who tweeted. The semi-structured data is the content in the CMS.

Summing It Up

Unstructured data has the potential to provide significant insight into information. However, the setbacks involved in analyzing this information warrant an efficient storage solution. You should identify a storage solution that offers fast storage technology and makes it easier for managers to decipher unstructured information.

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