691 words, 2.6 minutes read. By Gerard Blokdyk
Data Governance 1 big thing: Develop experience developing data governance frameworks, reference architecture, security principles, and standards.
The big picture: Ensure strong knowledge and involvement in data related activities (data quality, data governance, metadata, data management, data standards, data structures, data aggregation, and business requirements).
Why it matters: Interface with business units and various corporate and technology functions for activities such as obtaining buy-in for policies/standards, execution and enforcement of data governance requirements and driving associated KPIs.
What we’re hearing: “Assure your personnel collaborates with Data Science, Technology, Data Governance, and Business teams to identify data needs across the organization and ideate and develop best-in-class solutions/products using traditional and non-traditional sources of data and information.“, Data Modeling Manager
The backdrop: Ensure strong cloud data architecture knowledge with involvement developing architecture strategies and plans to enable cloud data transformation, MDM, data governance, and data science capabilities.
Meanwhile: Make headway so that your company coordinates with Data Governance teams to understand business rules and objectives to include in data audit reporting and suggests changes to business rules to improve data quality.
On the flip side: Verify that your personnel is identifying potential areas where existing policies, processes, and standards require change or where new ones need to be developed to ensure data governance objectives are met.
Under the hood: Be confident that your organization creates Conceptual data models and reviews Logical, Physical and Canonical data models working collaboratively with other architects and data engineers.
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Be smart: Lead/Support the creation and implementation of Target Operating Model across lines of business and functions with a key focus on establishing data stewardship representation.
What to watch: Correspond develop a common language for enterprise data managed through a standard process and a platform to organize and maintain metadata around data domains/data taxonomy.
State of play: Establish that your workforce supports (internal) client & Producer Domain Owner in the establishment of the following products and deliverables: (internal) customer master definition, data access policy, data quality processes, metadata requirements, roles and responsibilities matrix.
Go deeper: Make sure the work involves guiding an operational team to execute the full lifecycle of a project data: validation, testing, loading, maintenance, as well as extracts/reporting, and critically, conversion to and from your data specifications to external system specs.
Between the lines: Develop experience running or developing sustainability supplier management programs and leading data analysis projects using tools like problem solving and value stream mapping.
The bottom line: Partner with your external vendors, your business teams and internal stakeholders to define, capture, develop and deliver on core business metrics and KPIs that support key requirements for your companys initiatives and objectives, including related analysis and planning, reporting and ongoing project support across your organization.
What’s next: Provide Master Data Management MDM Leadership by working with the business and data governance groups to help the (internal) client develop the MDM program.
ICYMI: Work as a key member of the Enterprise Data Governance team with responsibility for preparing Data Governance framework and execution models for key reference/master data, data quality and the enterprise data catalog.
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