Background
Growth through acquisitions created a complex data environment for this manufacturing organisation, with operational, financial, and reporting data distributed across multiple systems and business processes.
Before integrating more than 20 financial datasets into its ERP environment, leadership required greater confidence in how business performance was being measured, reconciled, and reported.
Despite a strong internal focus on data, reporting outputs varied across teams particularly where metrics relied on inherited processes, multiple source systems, or manual intervention.
With analytics and AI initiatives under consideration, leadership sought a clearer view of data quality, reporting integrity, and readiness for future investment.
The Challenge:
The core issue was not data availability, it was confidence in the data. Key challenges included:
- Inconsistent data definitions across acquired businesses, processes, and systems
- No common definition of core operational and manufacturing metrics across teams
- Fragmented data across multiple systems with limited traceability
- Heavy reliance on manual reconciliation and spreadsheet-based reporting
- Unclear ownership and governance of critical data assets
This created practical operational risk:
- Multiple versions of performance data across leadership and operational teams
- High effort required to produce recurring operational and executive reporting
- Reduced confidence in forecasting, planning, and management reporting
- Uncertainty around readiness for analytics and AI initiatives
Before scaling analytics, automation, or broader transformation initiatives, leadership needed an independent, evidence-based view of data quality and priority issues.
Approach:
Exco Partners delivered a structured Data Health Check to assess data maturity and identify practical improvement priorities. The assessment included:
- Review of key data sources and end-to-end data flows
- Evaluation of data quality, consistency, and reliability
- Mapping of data to target business and manufacturing processes
- Assessment of governance, ownership, and control structures
- Review of reporting outputs for duplication, inconsistency, and decision relevance.
Results:
The Data Health Check provided leadership with an independent, evidence-based view of the organisation’s data environment clarifying what could be trusted, what required remediation, and where the greatest risks to reporting integrity existed. Key outcomes included:
- Clear visibility of inconsistencies in core operational, financial, and manufacturing metrics
- Identification of manual reconciliation embedded within reporting processes
- Improved alignment around priority data issues and reporting definitions across stakeholders
- Clarified ownership and governance requirements for critical data assets
- A prioritised roadmap sequenced by business value and effort, providing a clear path forward
The organisation left the engagement with clarity on where to focus and why.
Moving Forward
The organisation is now using the roadmap to sequence targeted improvements - strengthening reporting confidence, improving governance, and building the data foundation required for analytics and AI.