Question

Exercises 28-55 are problems or shortanswer questions. Other than those presented in this chapter, give three examples of data integrity violations.

Short answer

Examples of data integrity violations include duplicate entries, incorrect data types, and data truncation.

Step-by-step solution

Understand Data Integrity Violations

Data integrity violations occur when the accuracy and consistency of data are compromised. This can happen through incorrect data entry, data corruption, unauthorized access, or system errors. In identifying integrity violations, think about scenarios where data could become invalid or unreliable.

Example 1 - Duplicate Entries

One common data integrity violation is duplicate entries in a database. If a customer's information is entered more than once, it can create inconsistencies and errors in processing data, which may result in wrong insights or billing errors.

Example 2 - Incorrect Data Types

Another example is the entry of data using the wrong data type. For instance, entering a textual value in a field meant for numerical data, like entering a name where only an age should be, could lead to processing errors and skewed results.

Example 3 - Data Truncation

Data truncation occurs when data is entered into a field that is not large enough to store it fully, leading to loss of information. For example, a text field that can only hold 10 characters would truncate a name longer than 10 characters, causing incomplete data storage.

Key concepts

Duplicate Entries

Duplicate entries occur when the same data is repeatedly entered into a database. This repetition can lead to numerous issues that undermine data integrity. Some potential problems include:

• Inconsistencies in data analysis
• Errors in data processing and reporting
• Misleading insights or information
• Incorrect billing or customer contact issues

For example, imagine a customer record is accidentally entered twice into a database. This might cause the company to send two emails instead of one, or even bill the customer twice. Such errors not only waste resources but can also damage customer trust. To combat duplicate entries, databases often utilize unique identifiers like primary keys, ensuring each record is unique.

Incorrect Data Types

Incorrect data types in a database refer to situations where the data entered does not match the expected format or type. Consider a database that anticipates a numerical value like age, but instead receives a text value such as a name. This mismatch can lead to processing errors and inaccurate results.
Here are some complications that arise from incorrect data types:

• Misleading analytical outcomes
• Software and algorithm errors
• Data storage inefficiencies

An effective way to prevent this issue is through the use of validation rules and constraints. These measures ensure that all inputs adhere to expected formats, such as validating age fields to only accept integer values.

Data Truncation

Data truncation takes place when a piece of data is cut off because it doesn't fit within the designated storage space. This results in incomplete data which can be misleading or entirely unusable.
This often occurs in situations like:

• Short character limits on text fields
• Insufficient storage allocation for numerical data

Imagine dedicating only 10 characters for a name field, but trying to input a name which exceeds this limit. The extra characters would be cut off, leaving you with only part of the name in storage. To avoid data truncation, implement field size validations and carefully plan database storage allocations to suit the expected length or size of data inputs.