The topic deals with two aspects of data or a valid state of information based on which a business either flourishes or perishes. It includes the quality of data that is to be used and its integrity or reliability during application. Unfortunately, many businesses unfortunately do not or cannot pay adequate attention to these factors on which their life depends. Even a commoner will realize that a faulty data or data that has no integrity would surely lead a business to its predictable death either today or tomorrow because really, data is crucial to business, no matter whether it is a large or a small one. Redman (Redman, 1996) reports that error rates of 1-5% are typical, with an estimated immediate cost of 10% revenue loss. Customers, distributors, suppliers and employees are depressingly impacted through billing errors, unintentional but poor quality of service and inconvenience.

Data warehouses prove useful to business houses for improving customer services but if quality data is not available, the whole purpose is lost. What really happens is something like this. Organizations will at first spend adequate time and energy ensuring the quality of data, but that initial importance on quality dies away as time passes, resulting in ‘dirty data’ flooding the data warehouse. And as a result, the business suffers.

Coming down to data integrity, which also means the state of its consistency and precision Message Authentication Code, Message Integrity Code or MAC implies some relationship with its quality. This integrity is often ensured by the using a number that is known as . With reference to security of the information in general, integrity indicates the very validity of the data which, however can be affected by malevolent altering as happens in the case of an attacker changing the account number in the bank transaction or forging of a document. Also, it can involve accidental altering through error in transmission or due to a hard disc crash.

This is why data security has today become such an important issue. To ensure that data quality is upheld, data security has to be enforced and so not all people in an organization has access to the data. There may also be cases where certain people have access to data for just a short time, and after their needs are fulfilled, the access is withdrawn.

Data integrity may be imposed in a system by a succession of rules or integrity constrains while three kinds of such constrains form a part of the rational data model namely, Domain Integrity, Referential Integrity and Entity Integrity. The first one is related to the idea of a Primary Key. The Entity Integrity clarifies that each table should have the Primary Key as also that the columns or column chosen as Primary Key must be exclusive and not a void.

The Referential Integrity relates to the idea of a Foreign Key while it clarifies that the value of any Foreign Key is only in one of the two states. The normal phenomenon suggests that the value of the Foreign Key will refer to the value of a Primary Key in the database. Often this depends on the business rules when the value of the Foreign Key would be invalid. In such cases it may be presumed the relationship does not exist between objects represented or that the relationship is of unknown quality.

So far as Domain Integrity is concerned, it needs all relational database columns to be declared to a defined domain and the primary data unit in a relational model will be the item data. Such data is atomic or Non-decomposable. A domain usually stands for a set of values that are the same and as such consists of groups of values from where the actual value appears in a column.

MD5 hash values are an example of data integrity in cryptography while the various byte blocks work as numerical summation of the data item’s content. When the data changes, MD5 hash does not give the same result.

Strange but nevertheless true, many business houses have failed and would perhaps continue to fail because the management and control of data quality is not given the priority it deserves. However most businesses today realize the value of data and thus maintaining data quality and integrity is given the importance it deserves. Of course, building an effective organization-wide data management strategy may prove to be a difficult job. However, most US trade and commerce are no more based on world-wide framework and unless adequate attention is given to maintaining data quality and integrity, even home market may collapse any day.