93% Enterprise Data Quality Achieved and Customer Churn Reduced
Through governed data foundations for a semi-government authority.
Client Overview
A large semi-government organization operating across multiple business units and customer-facing services. The authority manages high volumes of operational, customer, and regulatory data across licensing, finance, and service delivery functions.
Business Challenge
As data volumes and regulatory expectations grew, the organization faced increasing challenges around data trust and accountability rather than availability. While core systems such as ERP and CRM were in place, leadership lacked confidence in:
- The consistency and accuracy of data across departments
- Ownership of critical data elements
- The ability to monitor and improve data quality in a measurable way
- Using data reliably for decision-making and advanced analytics
Customer attrition was also addressed reactively, with no data-driven mechanism to identify early warning signals.
Solution Delivered by DataPhi:
Rather than starting with dashboards, DataPhi partnered with the organization to establish enterprise-grade data governance and quality foundations using the Microsoft Azure data platform, aligned to the DAMA-DMBOK framework.
Data Quality Assessment
Performed a structured Data Quality Assessment across departments and key data domains
Defined critical data elements and quality dimensions (accuracy, completeness, consistency, timeliness)
Established baseline data quality scores by department and domain
Data Quality Monitoring
Implemented automated data quality rules and checks on Azure Data Lake data sourced from Dynamics 365 (ERP) and Salesforce (CRM)
Designed a Data Quality Monitoring framework with scorecards and trends visualized through Power BI, enabling departments to track and improve their own data quality over time
Data Governance Enablement
Implemented data governance using Microsoft Purview, covering data cataloging, lineage, ownership, and business glossary
Aligned governance roles, responsibilities, and processes to the DAMA framework, ensuring sustainability beyond the initial implementation
Advanced Analytics
Introduced a Machine Learning–based customer churn prediction model to identify at-risk customers using historical interactions and service usage patterns
Introduced a Machine Learning–based customer churn prediction model to identify at-risk customers using historical interactions and service usage patterns
Business Impact & ROI
Improved enterprise data quality scores from 78% to 93%, significantly increasing confidence in operational and regulatory reporting
Reduced executive decision-making time by ~33%, by eliminating cross-system reconciliations and manual data consolidation
Why it Matters
For a semi-government organization, trusted data is the foundation for transparency, accountability, and effective service delivery. This engagement established data governance and quality as core enterprise capabilities, enabling the authority to confidently scale analytics and AI initiatives on a governed data platform.