637 words, 2.4 minutes read. By Gerard Blokdyk
Data Governance 1 big thing: Ensure that the data is valid, reliable, traceable, timely, available, secure and consistent.
The big picture: Lead and direct staff team to collaborate with data owners and users throughout the organization to modify or implement new repeatable and reliable business processes in order to implement truly effective data governance.
Why it matters: Launch apply data governance for master data through data coordination and integration to ensure efficient processes and consistent data flows to business and stakeholders.
Be smart: Interface so that your strategy is supporting reviews of new data assets, applying governance principles to determine how best to implement into data environment.
How it works: Check that your group is involved in governance with modern environments, Big Data and traditional DW environments supporting Data Governance and master data management principles and techniques.
Yes, but: Be confident that your design is measuring the outcome of Enterprise Data Governance and associated implementations including cost avoidance/reductions, revenue opportunities, platform/product efficiencies.
The backdrop: Develop and sustain data strategy for Commercial in collaboration with appropriate stakeholders including Data Governance, Data Stewardship, Data Lake Build team(s), Reporting and Analytics Platform team(s), IT Business Partners, and Business Unit stakeholders.
What they’re saying: “Make sure the data assets created by your team reliably and easily support generating insights about your business in support of key initiatives.“, Associate Director, Global Commercial Data Analytics & Insights
Go deeper: Maintain data quality dashboards and scorecards for various master data elements and monitor compliance with master data governance policies and procedures, and report status.
On the flip side: Make sure the Data Governance specialization is responsible for defining and implementing information governance policies, standards, and processes to create consistent and proper handling of the organizations data.
Between the lines: Make sure your team is creating and maintaining budgeting tools including budget to actual/variance reporting, financial planning and analysis, cash flow analysis, and cost savings analysis.
State of play: Interface so that your team is ensuring creators, users and administrators of the data are educated on data governance standards, policies, processes, procedures and tools.
What we’re hearing: “Aid in the creation of data policies, procedures, and guidelines around the consumer data space and help drive awareness of such across your organization.“, Manager, Data Governance
The bottom line: Be certain that your strategy is confirming that new analytics adhere to data and platform guidelines created in partnership with platform architects, data governance, data engineering, and other relevant teams.
What’s next: Be confident that your company is involved in data analytics, BI, data reporting and visualization, all aspects of data management: data governance, data mastering, data warehousing.
ICYMI: Partner with the Enterprise Architecture and other technology teams to define a robust, scalable data architecture that aligns with the business objectives of the organization and adequately supports the organizational needs.
Look inside the Data Governance Kanban:
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