Requirements for Data Quality Management Projects

June 3, 2016

Requirements for Data quality management projects. Requirements gathering for data quality management Data quality management entails the roles, responsibilities, policies and procedures concerning the acquisition and maintenance and distribution of data across the enterprise. Organizations are constantly making decisions based on the data and hence the quality of data and its management becomes vital to the success of the organization.

 

Requirements gathering for data quality management

 

Data quality management entails the roles, responsibilities, policies and procedures concerning the acquisition and maintenance and distribution of data across the enterprise. Organizations are constantly making decisions based on the data and hence the quality of data and its management becomes vital to the success of the organization. These are some of the key questions that need to be addressed when trying to identify and scope a data quality project.

 

Sources: What are the different sources for the data? Is clear documentation available for all the data sources - this is important to establish at the beginning of the project and also to monitor and continuously review the documentation during the entire project life cycle. 


Meaning: are all internal stakeholders speaking the same data language and terminology. This is important as some of the terms could get very technical. A common understanding among all parties concerned can eliminate a lot of potential confusion down the road.

 

Provenance: what’s the chain of custody for the data and the project definition should provide clarification and also address changes to the data along the way and procedures to deal with them.

 

Quality: Is the data quality established? Are their duplicates? Is the data clean? 

  

Permissible use:  Is the data legal and ethical? Do the different entities with the organization have the appropriate permissions to use it?  

 

Obligations: Does the data include personally identifiable data? Is it necessary to encrypt the data? 

      

Data quality management involves facilitating companies and clients understanding of how data responsibility belongs to the company.

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