Structured data organizes values in a row-and-column format with consistent attributes. A retail transaction dataset, for example, can record each customer’s transaction date, amount, item count, payment mode, and city. This format makes the data easier to store, analyze, visualize, and use with many machine-learning algorithms. section 1.2.2
Table 1.1 An example of a structured dataset with attributes like amount, date, city, items, etc.
|
Customer ID
|
Transaction date
|
Amount ($)
|
No. of items
|
Payment mode
|
City
|
|---|---|---|---|---|---|
|
1001
|
01-June-2024
|
100
|
5
|
Cash
|
New Delhi
|
|
1002
|
02-June-2024
|
101
|
6
|
Card
|
New York
|
|
1003
|
03-June-2024
|
102
|
7
|
Card
|
London
|
|
1004
|
04-June-2024
|
103
|
8
|
Cash
|
Dublin
|
|
1005
|
05-June-2024
|
104
|
9
|
Cash
|
Tokyo
|
Inspect a concrete transaction table showing how customer records become rows and business attributes become columns. section 1.2.2
A structured dataset is useful when:
- Each record can be represented as a row with identifiable attributes stored in columns. section 1.2.2
- Business events such as transactions, employee records, call records, or banking activity can be organized into consistent fields. section 1.2.2
- Raw data is cleaned and organized before analysis so it can support reliable information, insights, and decisions. section 1.7
- The data is distinguished from unstructured material such as text, images, audio, and video, which is not easily sorted into rows and columns. section 1.2.2
