Data quality management

Typically, the primary responsibility and operation for data quality management will still be with the CDO’s data management team and not the cloud software solution provider. To assist the data quality efforts, the cloud software solution providers may provide data quality management services or add-ons.

A data quality checklist includes the following tasks.

Define

Define the accountable roles, responsibilities, and metrics for ongoing data quality maintenance between the cloud software solution provider and the company’s data management team.

Establish

Establish data archive schedules and rules. While cloud storage is economical, accumulating too much data is risky. Therefore, establish an archive-and-delete schedule to be performed automatically by the cloud provider.

Define or reuse

Define or reuse other data governance rules, standards, and policies for the master data fields that reside in the cloud. 

Evaluate

Evaluate the data quality services provided by the cloud software solution provider.

Integrate

Integrate the results in the overall CDO data quality dashboard if you use cloud software data quality monitoring or reporting services. 

Data processes and lifecycle management

When deciding which cloud software or Master Data Management (MDM) solution and repository to use as the leading system and trusted source, you will need to consider which data lifecycle processes will be executed in the cloud. Most cloud software solutions can execute these tasks as part of their standard functionality, but it will be up to the CDO team to decide if that capability is used. If you do not use the out-of-the-box cloud capability, then a central create-and-update data process and software application (such as an MDM system) must be developed by the CDO.

Important steps to this end are to:  

Decide

Decide which master data lifecycle tasks will be executed in the cloud software solution, such as create, update, delete, and archive.

Automate

Automate the field entry process to speed data entry and maintain consistent data quality.  Use known, trusted sources or look-ups where possible.