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?
Interested in learning more about Data quality
management?
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.
