One of the critical task in MDM project is to identify and define owners for various data elements. A sample spreadsheet as shown below can be used as a starting point to collect this information and which can be further used to define goverance around those elements like who updates what , who can view what ?
Each cell can have values like C(Create), U(Update),R(Read Only),D(Delete). The cell which intersects function and data elements and having values C or U are the natural owner for that element. Such spreadsheet will also help to identify any conflicts in ownsership which is possible in a large enterprise.
Apr 23, 2010
data quality dimensions
This is a very well written article on Data quality .
http://www.information-management.com/issues/2007_58/master_data_management_mdm_quality-10015358-1.html?ET=informationmgmt:e963:2046487a:&st=email
The most challenging aspect in any MDM initiative is to bring awareness among business and IT community about data qualty. In my current assignment , I interviewed few Data stewards and realized that data collection process is still not streamlined in many industries. Data is extracted from spreadsheet, various design documents before entering it into the system. In a engineering organization, a proper SDLC cycle is followed to design,develop,test and release the actual product. And during this process product related information is captured from various departmetns like Product Managers, Marketing, Finance, Release , Legal etc and most of the information is either in human head or documented in some unstructured formats. And extracting such information from human head and putting it into system consumes considerable amount of time and effort. Also, non availability of information leads to inconsistent and incomplete data. Data collection process is a multi step and iterative process. And having SLAs defined for each step of the process will yield timely and accurate data.
Also data definition,dimension, domain values changes over period and not having a well defined roadmap or process to manage such changes is also cause of inconsistent information.
http://www.information-management.com/issues/2007_58/master_data_management_mdm_quality-10015358-1.html?ET=informationmgmt:e963:2046487a:&st=email
The most challenging aspect in any MDM initiative is to bring awareness among business and IT community about data qualty. In my current assignment , I interviewed few Data stewards and realized that data collection process is still not streamlined in many industries. Data is extracted from spreadsheet, various design documents before entering it into the system. In a engineering organization, a proper SDLC cycle is followed to design,develop,test and release the actual product. And during this process product related information is captured from various departmetns like Product Managers, Marketing, Finance, Release , Legal etc and most of the information is either in human head or documented in some unstructured formats. And extracting such information from human head and putting it into system consumes considerable amount of time and effort. Also, non availability of information leads to inconsistent and incomplete data. Data collection process is a multi step and iterative process. And having SLAs defined for each step of the process will yield timely and accurate data.
Also data definition,dimension, domain values changes over period and not having a well defined roadmap or process to manage such changes is also cause of inconsistent information.
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