578 words, 2.1 minutes read. By Gerard Blokdyk
Data Governance 1 big thing: Manage requirements traceability information and track requirements status throughout the project.
The big picture: Invest in the creation of data governance processes, policies, governance KPIs and coordinate the execution and roll out of assurance activities with data owners.
Why it matters: Make headway so that your staff ensures alignment of the data governance processes and standards with applicable enterprise, business segment, and local data support models.
What they’re saying: “Ensure your operation educates business and technical communities on the purpose of data governance, and drives acceptance and adoption of data governance practices across all Heartland Alliance companies.“, Director – Data Management & Governance
How it works: Secure that your organization conducts regular reviews of each team members plan and ensures the team member is progressing and addressing skill development needs.
Between the lines: Facilitate the development and implementation of data quality standards, data protection standards and adoption requirements across the enterprise partnering with technology and operations.
The backdrop: Check that your operation is developing and promoting data governance models supporting the consistent use of cloud technologies aligned to institutional data and analytics strategies, and associated policies procedure.
Go deeper: Ensure your personnel reviews processes for governing the identification, collection, and use of corporate metadata; creates methods for tracking data for quality, completeness, redundancy, and improvement.
What we’re hearing: “Interface so that your team is working with other functions, develops governance control programs including information security, business resiliency, data governance, data privacy and other various risk areas.“, Staff Data Scientist/Statistician (Entry Level)
What to watch: Ensure you take on the most challenging problems your (internal) customers face and provide innovative solutions that can involve data strategy, use cases and analytical modeling, data governance, AI/ML platforms, and enterprise architecture strategy.
Yes, but: Make sure your design has involvement with data governance and data management practices as metadata management, data quality, data stewardship, and master data management.
The bottom line: Define and implement strategies, practices and technologies for the integration staging of data, development and maintenance of data lakes warehouses and data marts, for use across the organization.
What’s next: Be a critical member of the Data Governance Office and is responsible for establishing, operationalizing and sustaining a program that identifies, defines, manages and publishes data standards and policies for enterprise data assets.
ICYMI: Make sure your design understands the power inherent in applying those techniques to transform data and information into simple and clear visualizations that improve business insights.
Look inside the Data Governance Kanban:
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