Page 82 - CITS - Computer Software Application -TT

COMPUTER SOFTWARE APPLICATION - CITS

- Preventing update anomalies (problems that arise when changes to data aren’t properly reflected).
- Enhancing data integrity by maintaining consistent relationships between entities.

Benefits of Normalization
1 Reduces data duplication.
2 Enhances database organization.
3 Ensures uniform data across the database.

4 Enables increased flexibility in database design.
5 Upholds relational integrity principles.
Disadvantages of Normalization
1 Requires clear user requirements before database construction.
2 Performance deterioration occurs with higher-level normalization, like 4NF and 5NF.

3 Normalizing relations of higher degrees is time-consuming and complex.
4 Inadequate decomposition can lead to suboptimal database design and subsequent issues.

Various data types Data integrity, DDL DML and DCL
statements

Data integrity refers to the accuracy, consistency, and reliability of data in any given context. It is a fundamental
aspect of data management and is essential for ensuring that data remains intact and trustworthy throughout its
lifecycle. Data integrity is particularly important in various domains, including databases, information systems,
and data storage, as well as in compliance with regulations and data security.

1 Accuracy: Data should be free from errors and represent the true and correct values or information. Inaccurate
data can lead to flawed decision-making and operational problems.
2 Consistency: Data should remain uniform and coherent across various data sources, databases, or data
sets. Inconsistent data can lead to confusion and hinder data analysis.
3 Completeness: Data must be complete, meaning that all expected data elements or fields are present and
appropriately filled in. Incomplete data can result in incomplete analysis or reports.
4 Reliability: Data should be reliable and available when needed. Unreliable data or data that is frequently
unavailable can disrupt business processes.

5 Security: Data integrity also involves ensuring that data is protected from unauthorized access, tampering, or
corruption. Security measures like encryption and access controls are essential to maintaining data integrity.
Methods and practices for ensuring data integrity include:
1 Validation Rule: Implement validation rules and constraints to ensure that data entered into a system adheres
to predefined criteria, such as data types, ranges, and formats.
2 Data Validation: Employ data validation techniques to check data for accuracy and consistency. This may
involve data cleansing, deduplication, and normalization.
3 Backup and Recovery: Regularly back up data and have robust disaster recovery plans in place to ensure
data can be restored in case of corruption or loss.
4 Access Control: Implement access controls and authentication mechanisms to prevent unauthorized access
and modification of data.
5 Audit Trails: Maintain audit trails to record and track changes made to data, including who made the changes
and when they occurred. This helps identify and rectify unauthorized alterations.

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CITS : IT&ITES - Computer software application - Lesson 18 - 36