Pragmatic Data Governance
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Starting a new data governance program? Resetting a program that's stalled? On a tight budget? Apply the "what and how" to deliver data governance. Pragmatic Data Governance is an approach that was able to deliver a functioning program in less than two years. Implement Business-Led Data Governance is the strategy and Synchronize Organizational Opinion Regarding Shared Data Assets is the goal. Six supporting tactical objectives and their activities support the goal and deliver business-led data governance. Pragmatic Data Governance distills on-the-job experience from assignments with banking an...
Starting a new data governance program? Resetting a program that's stalled? On a tight budget? Apply the "what and how" to deliver data governance. Pragmatic Data Governance is an approach that was able to deliver a functioning program in less than two years. Implement Business-Led Data Governance is the strategy and Synchronize Organizational Opinion Regarding Shared Data Assets is the goal. Six supporting tactical objectives and their activities support the goal and deliver business-led data governance. Pragmatic Data Governance distills on-the-job experience from assignments with banking and government clients over a 14-year period enhanced with books webinars and training modules. Special mention goes to the insights gained from the DAMA Data Management Body of Knowledge (DMBOK). A minimalist and hand-made metadata repository called the Data Governance Integrated Business Glossary (DG-IBG) supports the Pragmatic Data Governance approach. A data governance program catalogs and documents an organization's data asset inventory with business and technical metadata such as definitions data quality rules and table and column details. The DG-IBG is modeled using Oracle SQL Developer Data Modeler and implemented with 60+ tables and 320+ columns in Microsoft SQL Server Express both free downloads from Oracle and Microsoft. By capturing and documenting data issues in the DG-IBG we can support business-side stakeholders in prioritizing and resolving data issues. Reflecting on the data issues gives us insight into data management success factors that may be absent such as formal data quality or master data management programs. This insight supports cost/benefit analysis in business cases for these initiatives.
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