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What Is Unstructured Data?

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The term “unstructured data” refers to digital information that doesn’t have a predefined data model or schema. Also referred to as “qualitative data,” it cannot be processed and analyzed using conventional tools and methods. Because it lacks a consistent internal structure, such as a tabular format, it is managed and stored in non-relational (NoSQL) databases and data lakes to preserve its raw form.

Unstructured data can be textual or non-textual and human- or machine-generated. Its examples include:

  • Text files and documents
  • Emails
  • Social media posts
  • Customer feedback and open-ended survey responses
  • Images
  • Audio files
  • Videos
  • Internet of Things (IoT) sensor data
  • Server, website, and application logs

Because it is disorganized, indecipherable, and non-formatted, unstructured data was previously underutilized for analytics. However, with the availability of specialized tools and large amounts of storage, many businesses have begun to use unstructured data to make strategic decisions.


Use Cases of Unstructured Data

The significance of unstructured data is undeniable, accounting for the majority of data collected by enterprises. But why continue to gather and store huge volumes of it? Below are some use cases:

  • Customer Service: Analyze text and voice inputs from customers to provide the information needed or direct their concerns to appropriate channels.
  • Data Mining: Keep track of product sentiment, consumer behavior, and purchasing patterns to understand their target market’s wants and cater to these.  
  • Predictive Data Analytics: Anticipate and prevent malicious security attacks by analyzing suspicious activity and potential breaches.


Structured vs. Unstructured Data

There are many key differences between structured and unstructured data. For one, structured data is categorized as quantitative because it gives an overview of customers, whereas unstructured data provides a deeper understanding of their behavior and intent.

The main differences include:

  • Form: Structured data consists of alphanumeric characters, while unstructured data may be non-character-oriented digital representations, such as videos, images, and sensors.
  • Schema Creation: Structured data is formatted to a set data structure before its storage, known as schema-on-write. Unstructured data is only processed when used, or schema-on-read.
  • Storage: Structured data requires less storage space and can be stored in data warehouses for scalability. Unstructured data needs more storage as some file formats take up more space, making data lakes the better option for safekeeping.


Manage Unstructured Data Easier with Pachyderm

Managing unstructured data doesn’t have to slow down your machine learning projects. Let Pachyderm do all the heavy lifting in data management. With its top-notch versioning control, automated pipelines, and data lineage features, it scales your machine learning life cycle. Try Pachyderm for free today.

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