Data Governance Apply Processes Operationalize Human and Machine Roles
As mentioned in my post describing the major business processes that comprise a data governance function, the Apply processes aim to operationalize and ensure compliance with all the data governance policies, business rules, stewardship processes, workflows, and cross-functional roles and responsibilities captured through the Discover and Define process stages.
The most relevant processes that comprise the Apply stage include:
Implementation of Automated Rules. Process that implements data quality, data privacy and other business rules and policies captured during the Define process stage into automated systems, processes and rules engines.
- These rules – once implemented – are the key to ensuring data is trusted, secure, and ultimately fit for business usage.
Application of Manual Rules. Process that implements data quality, data privacy and other business rules and policies captured during the Define process stage into human-centric workflows and processes to manage exceptions and make decisions requiring high levels of confidence that automated rules are unable or untrusted to make.
- These rules – once implemented – are the best way to minimize the risk that a data exception will negatively impact the efficiency of your business processes, the productivity of your workforce, the quality of your decisions or the satisfaction of your customers.
Employment of End to End Workflows. Apply and maintain workflow designs to support all four stages of data governance and stewardship processes. These workflows include all relevant system à system, human à system, and human à human data governance and stewardship-specific interactions.
- These workflows enable the necessary handoffs and collaboration required across business and IT stakeholders to enable truly holistic data governance.
Enactment of Business/IT Collaboration. Enable and facilitate collaboration across business and IT roles, including Business à Business, Business à IT and IT à IT working relationships.
- Effective data governance requires coordinated discipline across many roles throughout the organization. Leveraging collaboration best practices and enabling technologies facilitate this discipline.