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1. Structure of Data: Defines how data is stored. Data needs to have a clear structure. It should be organised
in a way that makes sense so that it can be used effectively.
Like when your mother starts cooking your favourite food she ensures before cooking that all ingredients
are available and are put in order for smooth and organised cooking. For example,
Marks of a students arranged in a spreadsheet.
Rohit Rawat a student with ID 10187
of Class 12 Section D has scored
72%.
Spreadsheet – Good structure Text document – Poor structure
Data is stored in a sheet with the details of each Data is stored in a text document
individual stored according to a set of rules. with no set of organising rules.
2. Cleanliness: Clean data should not have duplicates, missing values, outliers, and other anomalies so that
its reliability and usefulness for analysis is not affected. In the given example, cleaning of data removes the
duplicate values.
3. Accuracy: Accuracy is same as reliability so it indicates how well the data matches real-world values. Accurate
data closely reflects actual values without errors, enhancing the quality and trustworthiness of the dataset.
When your measurement is accurate, it makes your data really good. It’s like having a gold star on your
homework—it shows you did a great job!
In the example given below, we are comparing data gathered for measuring the weight of 12 eggs in a box in
grams.
166 Artificial Intelligence Play (Ver 1.0)-IX

