Explore the difference between unstructured data like social media posts and structured data such as ingredient lists. Learn which type of data is harder to analyze.
Table of Contents
Question
Which of the following is most likely to be unstructured data?
A. Ingredients for baking bread
B. Prescriptions for patients in a large hospital
C. Social media posts
D. The breeds of entrants in a dog show
Answer
C. Social media posts
Explanation
The content of social media posts is an example of unstructured data. Unstructured data, also known as dark data, is typically categorized as qualitative data. It can’t be processed and analyzed with conventional data tools and methods.
Of the options provided, social media posts are the most likely to be unstructured data. Unstructured data refers to information that either does not have a pre-defined data model or is not organized in a pre-defined manner. Unstructured data is typically text-heavy, but may contain data such as dates, numbers, and facts as well.
Social media posts often contain free-form text that does not follow a standardized format or structure. They may include abbreviations, misspellings, emojis, links, images and other content that is difficult to parse and analyze.
In contrast, the other options are more likely to be structured data:
- Ingredients for baking bread would typically be listed in a consistent format, like an array or table with quantities and units.
- Prescriptions for patients would usually be captured in structured fields in a database, with dosages, medications, patient IDs, etc.
- The breeds of entrants in a dog show would normally be stored in a structured format listing each dog’s breed, owner, handler, etc.
So in summary, while social media posts are free-form and unstructured, the other data examples have a more rigid, organized structure making them easier to analyze and query using traditional databases and algorithms. The unstructured nature of social media data poses challenges for NLP and machine learning applications.
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