Making Enterprise Data Work as One
Industry
Manufacturing
About the Client
The client is one of the world's largest and most diversified industrial manufacturing and technology conglomerates, with a global footprint spanning automation, digitalization, and infrastructure solutions. Operating at the intersection of physical and digital industries, the company delivers software, cloud platforms, and lifecycle management solutions for large-scale capital projects, including a widely used construction program management platform acquired as part of a strategic expansion.
Challenges
As data needs scaled across teams, the cracks became harder to ignore. Outputs diverged, ownership blurred, and what teams once relied on became a source of doubt rather than confidence.
Growth simply made visible what patchwork fixes had long concealed, inconsistent definitions across business sources, manual reconciliation, and no clear path to root-cause analysis when discrepancies arose. The organization had a foundation problem, not a reporting problem. Key objectives included:
Key objectives included:
- Standardizing data definitions across all business sources
- Eliminating manual effort in validation and reconciliation
- Building traceability to isolate and resolve data issues faster
- Aligning reporting outputs tightly with business logic
- Creating a repeatable, scalable process for reporting and validation
- Ensuring long-term maintainability as reporting needs continued to evolve
The Solution
Sigma Solve reframed the engagement from the outset, this wasn't a reporting fix, it was a data foundation rebuild. The focus shifted to how data flows, transforms, and gets validated before it ever reaches a report.
A layered architecture was introduced to connect source data, transformation logic, reporting outputs, and business validation into one coherent, auditable system, built for scale, not just the immediate need. Key solution components included:
- Standardized data transformation and reporting layers across all business units
- Clear separation between source data issues and reporting logic discrepancies
- Structured validation cycles embedded across reporting workflows
- Root-cause classification framework for faster, more accurate issue resolution
- Business logic consolidated into documented, reusable data models
- Continuous feedback loops established between business and technical teams
- Reporting governance strengthened with defined ownership and accountability tracking
Outcome
What the organization gained wasn't just cleaner reports, it was a fundamental shift in how data is trusted. Teams stopped questioning outputs and started acting on them. Validation cycles that once consumed hours of manual effort now run with structure and speed. Root causes surface faster, ownership is clear, and the foundation is built to scale with the business, not against it. Reporting went from a liability to a competitive asset.
Technology Stack
- Snowflake - Cloud Data Platform
- dbt - Data Transformation