766 words, 2.8 minutes read. By Gerard Blokdyk
Data Governance 1 big thing: Analyze current tools, processes or data structures and make recommendations for improvement or simplification.
The big picture: Ensure your organization needs in depth knowledge and Create a team managing the design, implementation, tuning, and troubleshooting of a corporate data warehouse environment.
Why it matters: Check that your design is involved in supporting critical functions as IAM operations, Data governance, and Service delivery functions for the IAM Program.
Be smart: Confirm that your design provides leadership and direction and/or actively participates in ongoing development and evolution of an Enterprise Data Model (EDM) and/or major subject areas of the EDM.
The backdrop: Conduct bring into consideration 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.
On the flip side: Operate you are for managing responsibility of the development of enterprise data policies, standards, and data processes to enable business teams to leverage data as strategic asset, while ensuring its integrity and quality.
State of play: Confirm that your team works to ensure the manipulation and administration of data and systems are secure and in accordance with enterprise governance by staying compliant with industry best practices, enterprise standards, corporate policy and department procedures; handles the manipulation (extract, load, transform), data visualization, and administration of data and systems securely and in accordance with enterprise data governance standards.
Under the hood: Guarantee your process develops, plans and schedules the development, introduction, communication and maintenance of the corporate systems architecture including the applications, data, and infrastructure architectures and assures that this architecture is integrated with the business area architectures and data governance.
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Go deeper: Liaison so that your personnel defines roles and responsibilities related to the data organizations participation in various company initiatives and ensures clear accountability for execution of data responsibilities in these initiatives.
How it works: Lead BI key initiatives project delivery: including requirements analysis, charter (technical), managing project stakeholders, managing development lifecycle (development, test/QA, UAT, bug fix/retest cycles) through to training, go-live and phasing in of support & maintenance structures.
What they’re saying: “Make headway so that your company participates in and engages with Balls Project Management Office to represent data projects, ensure proper assessment of data for significant projects and drives alignment participation from key data stakeholders.“, Data Modeling Manager
Yes, but: Make sure the HR Data and Analytics Manager leads analytic design and execution efforts across a wide range of HR disciplines through problem solving, consultative support activities, and deep analytic, visualization and technical expertise.
Between the lines: Evaluate the Investment in Data and recommend to the leadership team strategies to improve competitive capabilities, increase predictive analytics and reduce or redeploy cost where necessary.
What to watch: Define and lead the Master Data Management (MDM) strategy, develop and deliver key MDM capabilities, and provide guidance to MDM operation and its integration with other systems.
The bottom line: Make sure the Data Governance Manager is an individual contributor and serves as the primary liaison for aligning the business units and technology teams for the purpose of coordinating governance of your information assets.
What’s next: Confirm that your staff acts as a trusted partner with the risk, compliance, and audit organizations to understand data opportunities and challenges impacting the organizations.
ICYMI: Manage the data steward is also for managing responsibility of establishing data-quality metrics and requirements, including defining values, ranges, and parameters, that are required for each data element.
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