Duplicate Data: This data quality issue arises when the same data is entered more than once in different ways. Duplicate data is created in the process of extracting  data from the multiple siloed systems and consolidated together in a data warehouse, creating copies of the existing and the same record. Duplication produces incorrect insights when undetected.

Incomplete Information: This data quality issue happens when critical pieces of information are missing due to ETL processes failure to input at the source systems. Fields which are not filled or left blank are the major constraints for the enterprises when using the data for any critical purpose.

Data Inconsistency: Data inconsistency issue arises when storing data in the same field that is either in different units and in different languages. When information such as address and names are duplicated that leads to a compromise in data integrity. Data Inconsistency is caused by redundancy as somebody may change the value in one file and not change in another file.

Inaccurate Data: This is one of the biggest data quality issues that occurs when every value is complete and format is correct but there are mis-spellings  or inaccurate data. Example of data quality related issue–  It is difficult to conduct forecast analysis when sales data entered into your system is incorrect.

Invalid Data: On simple logic or rules, data invalidity scenario arises when your data cant be exact and precise. Invalid data are the values which are generated incorrectly and can be challenging to identify visually but can be explored in analysis and have to be removed from the data set.

Data Imprecision: Lack of precision or data imprecision is when the data has been stored at an outline which is due to ETL process which does not help users to get to the detail which is needed for analysis. Lack of coordination and teamwork between different departments is one of the reasons for Data Imprecision.