A. Data Governance Assessment Data Maturity Level: 2/5 (Developing) Governance Maturity Level: 1/5 (Ad Hoc) Information Risks Duplicate customer records Sensitive data shared through email No retention policy Multiple versions of reports Missing data ownership Business Priorities Single source of truth Better reporting Higher data quality Improved customer experience Enterprise Data Governance Summary The organization has valuable business data but lacks standardized governance. Data ownership is unclear, quality varies by department, and inconsistent processes reduce trust in reporting. Establishing governance foundations, assigning ownership, and improving data quality should be prioritized. B. Enterprise Data Inventory Data Domain Source Consumers Business Value Classification Customer CRM Sales, Marketing, Support Very High Critical Finance ERP Finance, CEO Very High Critical HR HR System HR High Important Product Product Database Engineering High Important Marketing Marketing Platform Marketing Medium Supporting Website Analytics Analytics Platform Marketing Medium Supporting Historical Logs Archive Storage Compliance Low Archive C. Data Ownership Matrix Data Domain Executive Owner Business Owner Data Steward Data Custodian Consumers Customer Chief Revenue Officer Sales Director CRM Manager IT Sales, Marketing Finance CFO Finance Manager Financial Analyst IT Finance HR HR Director HR Manager HR Administrator IT HR Product CTO Product Manager Product Analyst Engineering Product Team D. Data Classification Framework Classification Access Storage Sharing Monitoring Public Everyone Standard Storage Allowed Annual Review Internal Employees Company Systems Internal Only Quarterly Confidential Authorized Teams Encrypted Storage Need Approval Monthly Restricted Named Individuals Encrypted + MFA Strictly Prohibited Continuous Monitoring E. Information Lifecycle Management Stage Owner Controls Risks Review Create Business Owner Standard Templates Incorrect Data Monthly Capture Data Steward Validation Rules Missing Fields Weekly Store IT Encryption & Backup Data Loss Daily Use Department Managers Access Controls Misuse Monthly Share Business Owner Approval Workflow Data Leakage Monthly Archive IT Archive Policy Retrieval Issues Quarterly Retain Compliance Retention Schedule Legal Risk Annual Dispose IT Secure Deletion Unauthorized Recovery Annual F. Data Quality Intelligence Dimension Score Findings Accuracy 6/10 Customer addresses outdated Completeness 7/10 Missing phone numbers Consistency 5/10 Different naming conventions Timeliness 6/10 Sales updates delayed Validity 8/10 Good validation rules Uniqueness 4/10 Many duplicate customer records Recommended Improvements Implement duplicate detection. Standardize customer naming conventions. Enforce mandatory fields. Schedule monthly data quality reviews. Automate validation at data entry. G. Information Risk Analysis Risk Probability Impact Mitigation Duplicate Customers High High MDM implementation Missing Ownership High High Assign Data Owners Data Silos Medium High Central Data Platform Poor Quality High High Data Quality Program Unauthorized Access Medium Critical Role-Based Access Control Retention Failures Medium High Retention Policy Obsolete Data High Medium Annual Data Cleanup H. Data Governance Scorecard Category Score Ownership 5/10 Data Quality 6/10 Information Security 8/10 Governance 4/10 Lifecycle Management 5/10 Accessibility 7/10 Business Alignment 8/10 Overall Data Governance Score 43 / 70 (≈61/100) I. 12-Month Data Governance Roadmap Quarter 1 – Governance Foundation Deliverables Governance committee Data policies Data catalog Governance charter KPIs 100% critical data domains identified Governance roles assigned Success Metrics Approved governance framework Risks Limited executive engagement Quarter 2 – Ownership & Quality Deliverables Data ownership matrix Data quality dashboard Duplicate detection process KPIs 30% reduction in duplicate records 95% mandatory field completion Success Metrics Improved reporting accuracy Risks Resistance to new processes Quarter 3 – Lifecycle Management Deliverables Retention policy Archiving process Secure disposal procedures KPIs 100% critical datasets with lifecycle rules Reduced storage of obsolete data Success Metrics Better compliance and lower storage costs Risks Legacy system limitations Quarter 4 – Optimization & Continuous Improvement Deliverables Governance dashboard Automated quality monitoring Quarterly governance reviews KPIs Governance score above 80/100 Fewer data incidents Success Metrics High trust in enterprise data Risks Maintaining long-term governance discipline J. Executive Data Governance Report Enterprise Data Governance Summary The organization should prioritize governance foundations, establish clear ownership, improve customer data quality, and implement lifecycle management. These actions will increase confidence in reporting, reduce operational risk, and support scalable growth. Top 10 Data Insights Customer data is the most valuable business asset. Duplicate customer records affect sales efficiency. Data ownership is not consistently defined. Finance data has the highest reliability. Marketing data lacks standardization. Multiple reporting versions reduce trust. Archived data continues to grow without review. Manual data entry introduces errors. Departments use disconnected systems. Strong governance can improve cross-functional decision-making. Top 5 Data Quality Issues Duplicate customer records Missing mandatory fields Inconsistent naming standards Outdated contact information Delayed updates Top 5 Governance Improvements Assign Data Owners for every critical domain. Implement Master Data Management (MDM). Establish enterprise data quality rules. Create a centralized data catalog. Define lifecycle and retention policies. Top 5 Information Risks Duplicate data Missing ownership Data silos Unauthorized access Retention policy gaps Data Governance Score 61/100 Most Important Data Asset to Govern Customer Master Data, because it directly supports revenue generation, customer service, marketing effectiveness, and executive reporting. One Rule for All Future Data Governance Decisions Every critical business data asset must have a clearly assigned owner, defined quality standards, controlled access, and a documented lifecycle before it is used for business decisions or automation.