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.

Context & Challenges

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.

Data Governance & Quality Program Led 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

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.