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DATA INTEGRITY Vs. DATA VALIDITY
== In order to be accurate in our judgment about the quite difference between "Data Validation" and "Data Integrity", we have to emphasize the comparison between those terms can be emphasized in the following parameters:
1- Definitions:
- Data Integrity is the assurance that information is unchanged from its source, and has not been accidentally (e.g. through programming errors), or maliciously (e.g. through breaches or hacks) modified, altered or destroyed. In another words, it concerns with the completeness, soundness, and wholeness of the data that complies with the intention of data creators.
- Data Validation is the tests and evaluations used to determine compliance with security specifications and requirements, in order to ensure correctness and reasonableness of data
2- But, they have the same purpose :
- Data Integrity and Validation reduce the risk to agency system from malicious software and intrusions, or from accidental alteration or destruction.
- Data Integrity is a measure of the validity and fidelity of a data object.
- In spite of that, Data validity errors are more common than data integrity errors
3- Benefits:
- Data integrity addresses:
(i)- Protection information from accidental or malicious alteration or destruction.
(ii)- Providing assurance that the information meets expectations about its quality, i.e. the enforcement of data integrity ensures the quality of the data in the database.
(iii)- For example, if an employee is entered with an employee ID value of123, the database should not allow another employee to have an ID with the same value. Or, if the table has a department ID column that stores the department number for the employee, the database should allow only values that are valid for the department numbers in the company.
(iv)-As a function related to security, a data integrity service maintains information exactly as it was inputted, and is auditable to affirm its reliability. Data undergoes any number of operations in support of decision-making, such as capture, storage, retrieval, update and transfer. Data integrity can also be a performance measure during these operations based on the detected error rate.
- Data Validation:
a)-Establishes compliance with security specifications and requirements.
b)-Guarantees to your application that every data value is correct and accurate.
4- Types and categories:
- Data Integrity falls into these categories:
· Entity integrity- It defines a row as a unique entity for a particular table, through indexes, UNIQUE constraints, PRIMARY KEY constraints, or IDENTITY properties.
· Domain integrity– It is the validity of entries for a given column; by restricting the type (through data types), the format (through CHECK constraints and rules), or the range of possible values (through FOREIGN KEY constraints, DEFAULT definitions, NOT NULL definitions, and rules).
· Referential integrity– It ensures that key values are consistent across tables, in order to preserve the defined relationships between tables when records are entered or deleted. In Microsoft® SQL Server™2000, referential integrity is based on relationships between foreign keys and primary keys or between foreign keys and unique keys (through FOREIGN KEY and CHECK constraints).
· User-defined integrity– It allows you to define specific business rules that do not fall into one of the other integrity categories. All of the integrity categories support user-defined integrity (all column- and table-level constraints in CREATE TABLE, stored procedures, and triggers).
- Data Validity has several types:
· Data type validation– to verify the data type with your application's user interface, so as to find out if "the string is alphabetic?" or "the number is numeric?"
· Range checking- As an extension of simple type validation, to ensure that the provided value is within allowable minimums and maximums. As with data type validation, your application's interface can typically provide the necessary range validation, although as a design alternative you could create a business rule to handle range validations.
· Code checking – which typically require a lookup table as a validation table to hold the authorized codes, and this table could be part of a business rule, or it could be implemented directly in the database for query lookup.
· Complex validation– It is applicable when sometimes simple field and lookup validation is not enough, and data validation extends beyond the immediate data entry screen to complex multi-file data validation to be best handled with procedure-based business rules, in order to limit both year-to-date and lifetime accruals.
5- Design Pattern:
- Data validation design into your application can be done by using several differing approaches: user interface code, application code, or database constraints.
- Data integrity tables design can be planned by identifying valid values for a column and to deciding how to enforce the integrity of the data in the column.
6- State of guidance, standard or legislation:
- It is not always possible for humans to scan information to determine if data has been erased, added, or modified. Even if scanning were possible, the individual may have no way of knowing the validity of the data. Therefore, it is desirable to have an automated means of detecting both international and unintentional modifications of data.
- Data integrity – Reconciliation routine (e.g. checksums, hash totals, record counts) shall be used to ensure software or data has not been modified. This process is very essential because data integrity errors are caused by bugs in computer programs that, for example, cause the overwriting of some of the data in the database, when somebody attempts to retrieve a blank value from the database.
- Data Validation- Integrity verification programs (e.g. consistency reasonableness checks, validation during data entry and processing) shall be used to look for evidence of data tampering, errors, and omissions, in which data validity errors are usually caused by human beings - usually data entry personnel - who mistakenly enter that data.
- Data Validity Errors caused due to incorrect data entry called data validity errors are probably the most common data-related errors. These errors are also the most difficult to detect in the system, and are typically caused when a large volume of data is entered in a short time frame.
The difference between data validity and data integrity is simply this: Data validity deals with data that is input into a system (ex. a database) while data integrity deals with the maintenance of that data once it has been entered into the system. For example, a program that validates data to ensure it is in a proper format to be useful to the program is an aspect of data validity. Measures to prevent data corruption is an aspect of data integrity.
Difference No.1: Data validity is about the correctness and reasonableness of data, while data integrity is about the completeness, soundness, and wholeness of the data that also complies with the intention of the creators of the data.
Difference No.2: Data validity errors are more common, and data integrity errors are less common.
Difference No.3: Errors in data validity are caused by human beings - usually data entry personnel - who enter, for example,13/25/2010, by mistake, while errors in data integrity are caused by bugs in computer programs that, for example, cause the overwriting of some of the data in the database, when somebody attempts to retrieve a blank value from the database.
Data Integrity refers to the property which determines that data, once stored, has not been altered in an unauthorised way -- either by a person, or by the malfunctioning of hardware, while Data Validity has to do with determining if the data itself is valid -- context here plays a critical role, means data should be accurate.
Data Integrity is the assurance that information is unchanged from its source, and has not been accidentally (e.g. through programming errors), or maliciously (e.g. through breaches or hacks) modified, altered or destroyed. In another words, it concerns with the completeness, soundness, and wholeness of the data that complies with the intention of data creators.
Data Validation is the tests and evaluations used to determine compliance with security specifications and requirements, in order to ensure correctness and reasonableness of data.
But, they have the same purpose :
- Data Integrity and Validation reduce the risk to agency system from malicious software and intrusions, or from accidental alteration or destruction.
Date Integrity means completeness of information or wholeness of accomolated data. While Data Validity refers to unexpired or good condition, effective, usable or legal information or things.
Data validity deals with data that is input into a system (ex. a database) while data integrity deals with the maintenance of that data once it has been entered into the system.Difference number one: Data validity is about the correctness and reasonableness of data, while data integrity is about the completeness, soundness, and wholeness of the data that also complies with the intention of the creators of the data.
