588 words, 2.2 minutes read. By Gerard Blokdyk
Data Governance 1 big thing: Invest in the integration of Data Governance controls in existing business and technical processes.
The big picture: Work closely with IT technical service providers to ensure outsourced work packages are delivered to specifications, meeting key parameters of quality, schedule, cost, security, performance, and scalability.
Why it matters: Develop experience working in Data Governance, Data Quality, and Enterprise Data Management, ideally in the Financial Services with focus on Master Data management.
The backdrop: Integrate change management across the organization to incorporate data governance principles into programs and practices, including through the communication of technical concepts to a variety of stakeholders at all organizational levels.
Between the lines: Safeguard that your process applies a strong knowledge base of relevant systems/applications as it relates to workflow, process, and problem resolution and recommends improvements.
What they’re saying: “Ensure your operation is submitting and tracking remedies to correct systems issues when other resources are involved, and determining root causes of problems.“, Infrastructure Engineering Program Manager, Wallet and Payments
On the flip side: Make headway so that your workforce collaborates with the Information Owners, Data Stewards and Business Analysts to roll out new capabilities and support data governance initiatives.
Be smart: Verify that your team is involved in data standards, data policies, metadata management, data governance strategies, data profiling, and source to target mapping documentation.
Yes, but: Verify that your design is working closely with UX researchers and product designers, conduct user research (discovery sessions), collect feedback and test prototypes with (internal) customers and prospects in an effort to truly understand their needs and pain points.
How it works: Support the development, maintenance and implementation of common language for enterprise data managed through a standardized data governance platform to organize and maintain data domains and data taxonomy.
Go deeper: Establish that your workforce is responsible for identifying and detailing data sources for each line of business, product and legal entity and classify them (Systems of Origin, System of Record and Authoritative Data Sources).
The bottom line: Customize lead efforts to maintain and revise the data access governance policy, inclusive of data access controls and processing standards, in partnership with the Data Privacy office and legal.
What’s next: Make headway so that your workforce is assisting in the strategy and implementation of Master Data Management, Data Governance, Enterprise Data Management, Enterprise Content Management and Knowledge Management.
ICYMI: Confirm that your workforce defines data flows, identifies the most suitable sources for required data that is fit for purpose, referring to external sources with priority.
Look inside the Data Governance Kanban:
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