657 words, 2.4 minutes read. By Gerard Blokdyk

Data Governance 1 big thing: Collaborate cross functionally across internal teams marketing, product, technology, research, etc.

The big picture: Manage and maintain the product backlog of features, engineering and quality aspects for engineering teams focused on various areas of the service such as security incidents, compliance reporting, and integrations with third party alerting and ticketing systems.

Why it matters: Safeguard that your operation investigates and understands current system behavior through system interaction and data analysis definition and consumption of reports, SQL queries, website usage analytics tools, etc.

Between the lines: Implement data governance in large scale data ecosystems for data protection, privacy, for structured and unstructured data in on premise and cloud environment.

How it works: Warrant that your design works with (internal) clients and (internal) customers to assess the needs, provide information or assistance, resolve the problems, or satisfy the expectations.

Yes, but: Make sure the Data Warehouse and Governance Architect is responsible for undertaking multiple engagements, working with different teams and business stakeholders, focusing on multiple aspects of data architecture, data initiatives, data projects, data governance and analytics.

What they’re saying: “Liaison so that your company is developing and supporting a data governance operating model for the client, including engaging with members of the (internal) clients IT and data governance committee., Enterprise Data Officer

What we’re hearing: “Maintain for managing responsibility of the administration of data governance and quality software, maintaining a corporate data dictionary, ensuring data quality standards are met., Solutions Architect, DevOps – Sr Consultant

Go deeper: Help evolve and refine Applied Materials data governance and protection strategy, including records management, policies, and procedures while ensuring alignment with privacy and security policies.

On the flip side: Lead and champion Analytics Working Group for knowledge sharing, best practices and to ensure compliance with other IT Policies and Procedures including data governance.

What to watch: Operationalize GRC Governance Backlog management by participating in aggregation and prioritization of enhancements and ensuring alignment with data governance principles and enterprise data governance requirements.

Be smart: Be sure your organization is accountable for driving organizational acceptance of enterprise data governance established data standards, policies, definitions, and process standards for critical/related enterprise data.

Under the hood: Identify, define, and manage Data Domains and Elements, including the business and technical definitions, metadata and source of truth, and ensuring its full inclusion in the Data Governance tools and workflows.


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The bottom line: Ensure periodic certification or business objects and critical data elements and develop and manage data governance awareness and training for all data owners and stewards.

What’s next: Partner with Data Governance and engineering teams to define the data elements and data structure that the team should use to enable analytical capabilities.

ICYMI: Complete serve as a liaison between Business and Functional areas and technology to ensure that data related business requirements for protecting sensitive data are clearly defined, communicated and well understood and considered in the context of operational prioritization and planning.

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