Business Challenge

• Poor Data Quality – Inefficient and ineffective operational and financial planning due to poor data quality
• Excessive Manual Effort – Significant time and effort spent collecting, manipulating, correcting, and validating data in support of planning, reporting, and analytics
• Diffused Ownership & Accountability – Lack of clear, explicit ownership and accountability regarding most aspects of enterprise data governance (e.g. standards, data stewards, creation, updates, distribution)

Solution

• Discovery & Process Decomposition – Documented current data governance processes, highlighted issues and challenges, and identified highest priority areas of improvement
• Maturity Capability Assessment – Assessed existing data governance processes against industry leading practices and desired future state capabilities
• Data Governance Conceptual Vision – Developed future state conceptual vision around organizational structure, business operating model, and data management processes

Impact

• Catalyst for Change – Created awareness and understanding of the most important challenges and business needs, stakeholder alignment on the top business priorities and future state vision, and a compelling business justification for taking action
• Immediate Quick Wins – Defined a series of short duration quick win initiatives requiring limited investment but delivering significant process efficiency and effectiveness benefits
• Foundational Service Placement Architecture – Established optimized organization service placement framework incorporating (1) Centralized quality assurance activities in a Shared Service Center, and (2) Leveraging a Center of Excellence for reporting & analytics