Q.Differentiate between structured and unstructured data giving one example. The principal of a school wants to do following analysis on the basis of food items procured and sold in the canteen:
Create an appropriate dataset for these items (fruit juice, biscuits, samosa) by listing their purchase price and sale price. Apply basic statistical techniques to make the comparisons.
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Data Classification Types
Data comes from many sources, and its format depends entirely on where it originates. An image is a grid of pixels, a video is a sequence of frames, a fee slip mixes numbers and text, and a chat message can contain text, emojis, and media. Because of this variety, data is classified into two broad categories based on its format: structured and unstructured.
Structured Data — The Organised Kind
Data that is organised and can be recorded in a well-defined format is called structured data. It is typically stored in a computer in a tabular form — that is, in rows and columns.
- Each column represents a particular parameter, also called an attribute, characteristic, or variable.
- Each row represents a single observation — the data for one entity across all attributes.
For example, consider the inventory of kitchen items in a shop. The data can be arranged in a table where columns are Model Number, Product Name, Unit Price, Discount (%), and Items in Inventory. Each row gives the details for one product.
With structured data stored this way, calculations become easy. To find the total number of items in stock, you simply sum the values in the "Items_in_Inventory" column. To find the total value of all inventory, multiply each "Unit Price" by the corresponding "Items_in_Inventory" and add those products.
Other everyday examples of structured data:
| Entity / Activity | Data Fields / Parameters / Attributes |
|---|---|
| Books at a shop | BookTitle, Author, Price, YearofPublication |
| Depositing fees in a school | StudentName, Class, RollNo, FeesAmount, DepositDate |
| Amount withdrawal from ATM | AccHolderName, AccountNo, TypeofAcc, DateofWithdrawal, AmountWithdrawn, ATMid, TimeOfWithdrawal |
Because structured data follows a known, fixed schema, it can be stored directly in database tables and queried with SQL — this is exactly what makes the rest of this chapter (and the whole database unit) possible.
Unstructured Data — No Fixed Shape
Not all data fits neatly into rows and columns. Data that does not have a traditional row-and-column structure is called unstructured data.
Consider a newspaper. It contains news items, images, and advertisements, but there is no fixed pattern for how these are arranged — one day a page might have three images and five articles, another day one large image and three text articles. An email is the same: there's no fixed rule for how many lines, paragraphs, or attachments it must contain.
Common examples of unstructured data:
- Web pages (mixing text, images, graphics, audio, and video)
- Text documents and business reports
- Books
- Audio and video files
- Social media messages
Although methods exist to process unstructured data (natural language processing, computer vision, and so on), a Class-12 CS course typically focuses only on handling structured data — that's why nearly every table, database, and SQL example in this chapter uses structured data.
Metadata: Data about Unstructured Data
Unstructured data is sometimes described using metadata — literally "data about data." Metadata gives a structured way to describe an unstructured item, even though the item itself has no fixed structure. …
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