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Tackling the Problem of Unstructured Data with Big Data

March 30, 2018
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3 minutes read
Tackling the Problem of Unstructured Data with Big Data

Unstructured data can pose big risks for enterprises. Unstructured data is data that is not organized in a defined manner. It is typically comprised of text. Things like dates and numbers are often present in unstructured data. One of the biggest obstacles with processing unstructured data is that it often features irregularities that are incompatible with traditional database programs. Many enterprises have large volumes of unstructured data floating around their networks. Here are some common examples of unstructured data that enterprises commonly use:

  • Word documents
  • PowerPoint presentations
  • Survey responses
  • Transcripts
  • Emails
  • Images
  • Audio files
  • Video files
  • Social media posts

Mobile devices can contain a wide variety of unstructured data. Activity logs from servers, applications and networks create a large amount of unstructured data. This type of data is constantly being absorbed and stored on equipment that is linked to the Internet. Enterprises cannot effectively set parameters on who can access data if they don’t know what type of information that data contains. Unstructured data is both risky and costly. Enterprises spend millions of dollars each year storing data that they can’t actually use. Enterprises are often unaware that this type of data even exists.

How Big Data Can Fix the Problem of Unstructured Data

Poorly categorized data is a risk that all enterprises should be aware of. Big data can be effective at helping to integrate unstructured data into a network and turn it into a useful and secure asset. Big data stream processing can discover the presence of unstructured data on a network. It can then analyze the metadata that is tied to that data. This includes identifying the owners of the data and setting appropriate access controls. Big data also makes it possible for a system to take action on the data that it receives. This can include custom responses, moving data to specified areas and automatically archiving loose data. Data platforms can also integrate rogue data into larger ecosystems of data and make it usable. This can include creating a query interface and providing application support. Data that is typically under utilized can be retained, categorized, reported, analyzed, and presented via user-friendly visualization tools. 

Putting Unstructured Data to Use

Unstructured data leaves enterprises vulnerable to internal and external attacks. Big data platforms can help to ensure that all types of data are utilized in an efficient manner. Having the right platform in place can help to protect confidential information and ensure that data does not become exposed. Enterprises can mitigate risk and prevent issues by integrating unstructured data using a big data platform.