Informatica Data Quality Architecture ...

Below diagram ... is the typical ... Informatica Data Quality Architecture ... where it can be used in stand-alone mode or in client-server mode ... depending upon the requirement ...

Let me know ... if you have any questions regarding this ...

Introduction: Informatica Data Quality tool 8.6.2

Once it is identified ... which are all the business key columns and what are all the corresponding data quality problems that needs to be addressed ... and what is the data quality solution approach ... needs to be applied to validate, standardize, cleanse and de-duplicate the data. The next job is to select the tool, which will do that job.

One such tool is ... Informatica Data Quality 8.6.2 ...

Informatica Data Quality is a suite of applications and components that deliver enterprise-strength data quality capability. It can be used in stand-alone mode or in client-server mode, and in integration with Informatica PowerCenter, to analyze and enhance the quality of the data in several following areas:

• Standardize data to agreed or formally-correct terms
• Cleanse your data of errors
• Validate your data against reference materials (like custom dictionaries, Postal databases ... )
• Parse and derive missing data values from existing data
• Identify and remove duplicate data records
• Generate Reports

I would say ... Informatica acquisition of Dublin-based data quality vendor Similarity Systems ... is a good move ... as it helped Informatica to expand it's functions in area of Data Quality domain ...

Data Quality Approach ...

What should be the Data Quality approach  ??? ... Sharing few points ... which I have implemented in my current project ...

• Source systems should be throughly analyzed and profiling should be done on parameters like completeness, conformance, consistency, accuracy and integrity.  Various tools can be used to achieve this ... like Informatica Data Explorer and Data Quality, OWB Profiling and Quality options (DWR), Business objects Data Insight and many more ...

• Next is to identify key business columns for which data rules needs to be derived and apply them during data quality standardizing and cleansing process ...

• After identifying key business columns, derive data quality rules for those columns and discuss with the client to finalize the data rules and to move next level of quality process ...

• Now create Data Quality Plans based on rules dervied to address the four dimensions of Data Quality namely Completeness, Conformity, Integrity and Consistency

• Implement and test the data quality plans before moving the data to target systems ...