680 words, 2.5 minutes read. By Gerard Blokdyk

Data Governance 1 big thing: Engage with various internal and external data providers and (internal) customers of those providers.

The big picture: Proactively develops and builds relationships with functional and business leaders to ensure data as a capability and as an asset is appropriately considered in your organizations strategies.

Why it matters: Organize and lead engagements including Regional Data Committee meetings in addressing data issues, finding solutions, ensure alignment on priorities and focus on roadmap to support delivery of those objectives.

Under the hood: Research/troubleshoot data issues identified by internal and external stakeholders, determining whether issues are process/logic based or vendor related, and following the appropriate processes to mitigate.


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Yes, but: Support the establishment and coordination of an enterprise data governance oversight forums including setting the agenda for periodic meetings and preparation of necessary documents.

The backdrop: Secure that your strategy ensures IT delivery methods and processes contain relevant data management activities and deliverables supporting the overall data governance and architecture strategy.

Be smart: Ensure that the account has a functional data dictionary and supporting documented policies/processes associated with maintaining data integrity/confidence and a data governance program.

On the flip side: Develop and implement data governance policies that ensure the highest quality throughout the lifecycle of the data, and specifically mitigate risk around data privacy.

What we’re hearing: “Make sure your operation defines the system of record as a single source of truth for each data element; resolves multiple sources of truth issues with key stakeholders via clear definitions of attributes., Sr Consultant, Data Governance

Between the lines: Make sure your group represents the DM program and department to executives, managers and at Data Governance to further the endeavors of your organization.

What to watch: Leverage industry best practices to manage and maintain data privacy and security practices in the processing of data and personally identifiable information across information technology and records management systems.

State of play: Be certain that your strategy is involved in relational database structures, theories, principles, and practices, Data Governance, Data Quality Management, Metadata Management, and Conceptual and Logical Data Design.

How it works: Partner with vendor teams on MDM solution implementation and provide oversight to ensure data and solution quality are maintained as per best practices.

Go deeper: Manage a flexible, extensible and open data model to hold the master data and all needed attributes (both structured and unstructured) based on business driven view of attributes, validation rules, security, and associated complex master data relationships.

The bottom line: Develop and implement robust data governance practices, including routine management review of key data integrity metrics and risk model indicators, establishing data ownership and enhancing data analytics for the key risk area of Data Integrity.

What’s next: Interface so that your design leads the evolution of the Data Warehouse Architecture, including the coordination of an appropriately balanced pursuit of enterprise analytics, business, information, technical and solution architecture viewpoints.

ICYMI: Conserve collaborate as a data governance expert with key partners and the business to understand requirements and find creative and flexible solutions to resolve problems.

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