1. Accuracy:
Data can be correct or wrong and accuracy refers to the correctness of the data.
2. Completeness:
Data entries can be completely or partially entered in the database and Completeness evaluates the extent to which data is recorded without missing values.
3. Consistency:
Example#1 of data consistency:
Order_ID | Order_Date | Product |
---|---|---|
1 | 15-Jan-2024 | Laptop |
2 | 16-Jan-2024 | Smartphone |
3 | 17-Jan-2024 | Tablet |
4 | 07-Apr-2024 | Television |
5 | 03-May-2024 | Headphones |
Table 1: Dataset with consistent Date Formatting
Order_ID | Order_Date | Product |
---|---|---|
1 | 15-Jan-2024 | Laptop |
2 | 16-Jan-2024 | Smartphone |
3 | 17-Jan-2024 | Tablet |
4 | 07-04-2024 | Television |
5 | 03-05-2024 | Headphones |
Table 2: Dataset with Inconsistent Date Formatting
Example#2 of data consistency:
Student_ID | Name | Gender |
---|---|---|
1 | F.R.Shamil | Male |
2 | Jane Smith | Female |
3 | Talha | Male |
4 | Emily Davis | Female |
5 | Sam Brown | Male |
Table 1: Original Dataset with Consistent Gender Representation
Student_ID | Name | Gender |
---|---|---|
1 | F.R.Shamil | Male |
2 | Jane Smith | Female |
3 | Talha | Male |
4 | Emily Davis | F |
5 | Sam Brown | M |
Table 2: Inconsistent Gender Representation
4. Timeliness:
Timeliness assesses whether the data is up-to-date. It is essential for data that change over time to reflect the changes.
5. Believability:
How trustworthy the data are correct?
6. interpretability:
how easily the data can be understood?