THE DATA DOWNLOAD

Topics: Informatica Data Quality, Data Quality, Blog

CONSISTENT NAMING AND CODING STANDARDS

When designing rules in Informatica Data Quality, the developers and data stewards will see the same rules. Therefore, it is important to develop consistent naming and coding standards. For example, both the data steward and the developer will understand what “rule_” means while not everyone will understand what “mplt_” means. Therefore, mapplets should be named rule_ if they are used in both the Analyst and Developer tools.

Topics: Data Profiling, Informatica Data Quality, Blog

One of the first steps in solving a data quality problem is to perform data profiling. As seen in Jason Hover’s article, Data Profiling: What, Why and How?, data profiling allows you to analyze your data to determine what it looks like and what problems exist in the data. Manual data profiling can be performed; however, using software such as Informatica Data Quality allows both data stewards and developers to collaboratively profile the data in a common repository more quickly, often yielding a more thorough analysis.

Topics: Informatica Data Quality, Data Quality, Blog

CONSISTENT NAMING AND CODING STANDARDS

When designing rules in Informatica Data Quality, the developers and data stewards will see the same rules. Therefore, it is important to develop consistent naming and coding standards. For example, both the data steward and the developer will understand what “rule_” means while not everyone will understand what “mplt_” means. Therefore, mapplets should be named rule_ if they are used in both the Analyst and Developer tools.