Data Quality Assessment, Monitoring and Issue Management services from our very own Labs.A standard set of repeatable, collaborative service operations that provide consistent outcomes to provide good quality data. Moreover, these service operations are discovered from your own landscape, standardized and operationallized.
Managing Metadata required capturing characteristics of data at the right stage of the change (like a project) by including the right stakeholders, consistently. All this can be enabled by a holistic operating model guaranteeing that metadata is collected by project stakeholders, in coordination with data owners enforced by data stewardship.
The challenges of efficiently managing data are significant in today’s in-organic data landscapes. There are many political and adoption barriers that an organization needs to overcome to simplify, appraise these landscapes, better govern data to bring value and reduce risk to the organization.
Risk Management needs to be an integral dimension of Data Governance for which policy needs to be defined in close coordination with the enterprise risk function. Accountability can be provided to a sustainable, standalone function within the risk office.
