Data Integrity and Data Consistency are for reproducing the correct information. Data integrity is the overall accuracy and consistency of data. A database can be said to be data consistent when the content under question does not give us the chance to infer a contradiction directly or indirectly.
Data can be entirely consistent but entirely wrong. So, the phrase data integrity is about the quality of data. Database management systems provide data consistency tools which can help around data integrity. Isolated execution of transaction preserves the consistency of the data. Here, the question of satisfying certain consistency constraints arises. The database systems check constraints whenever the database is updated.
The integrity of data is the whole data expected to be at one place. The data in the database usually remains in the form of tables. Each of the tables has some attributes. Modification in the database modification may cause violation of integrity. When we enter incorrect data into a database (garbage in), the database will produce incorrect information (garbage out). Garbage in, garbage out (GIGO) is a phrase to point the accuracy part.
Data consistency may become an important matter while planning data backup and recovery. You have to make sure that data which will be used as backup is usable. Rest of the basic theoretical matters around Data Integrity and Data Consistency already has been discussed in the earlier two articles.
There are other parameters which are used to indicate the condition of data such as data quality. Data quality is a measurement of the condition of data considering factors such as accuracy, completeness, consistency. Data integrity is not of data quality. Data quality answers some questions such as meeting the defined standards of an organization. Data quality is a part of data integrity. Data integrity includes all the aspect of data quality and also forces rules.