660 words, 2.4 minutes read. By Gerard Blokdyk

Data Governance 1 big thing: Develop and execute a comprehensive communications strategy around strategic initiatives, analytics, metrics and reporting.

The big picture: Advance cross-functional collaboration in driving (internal) customers commercial business innovation and advanced analytics while providing guidance on best practices and standards in information management, agile data governance, and modern data security and protection.

Why it matters: Make sure your personnel identifies and manages continuous process improvement initiatives to serve the needs of the business in an efficient and effective manner.

Under the hood: Be a voice and advocate for using data to improve the quality of your decision making including evangelizing the use of BI tools and other data sources.


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On the flip side: Provide a vision and process for lean data governance to ensure continuous improvement, access, timeliness, high alignment, visibility, and quality of data while allowing flexibility and autonomy to individual teams.

What they’re saying: “Make sure the team is responsible for data governance, metadata, and data quality, and the implementation of data management capabilities in large projects., Sr. Manager, Data Governance Project Management

Go deeper: Provide visibility to Data Quality issues and work with the business owners to fix the issues -Implement an Enterprise Data Governance model and actively promote the concept of data standardization, integration, fusion and quality.

The backdrop: Identify, analyze, and interpret trends and patterns in complex data in order to make data driven decisions to solve business questions as well as provide recommendations for action.

Be smart: Be confident that your workforce organizes and leads cross functional teams in planning, development, and execution of Data Governance and privacy oriented business processes.

Yes, but: Capture, interpret and present data and insights to support data enablement strategies, solve business challenges, and drive efficiencies in technical and business performance.

State of play: Check that your design is developing data analytics frameworks, policies, and processes that comply with your organizations Architecture and Data Governance best practices.

How it works: Make sure your workforce is creating and executing a robust prioritization methodology/framework that drives the scope for the initiative to bring data under governance or enhances existing data governance processes.

Between the lines: Ensure your personnel is conducting sustainability assessment and working with Data Stewards to ensure data governance capabilities are maturing and continuing after use cases have been completed.

The bottom line: Verify that your design leads the most complex enterprise-wide data management programs and may act as the operational domain manager overseeing execution of initiatives pertaining to data quality, meta data and/or data governance.

What’s next: Expand the organizations data and technology offerings, especially in emerging analytical approaches (robotic process automation, machine learning, artificial intelligence), focusing on digital innovation.

ICYMI: Roll out an enterprise wide data governance framework, with a focus on improvement of data quality, single source of truth and the protection of sensitive data through modifications to organization behavior policies and standards, principles, governance metrics, processes, related tools and data architecture.

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