Article
How to take a data-first approach to your ERP implementation
Dec. 2, 2025 · Authored by Brian Wilson, Nathan Olson, Robby Burmeister
Organizations undertaking an ERP transformation often concentrate on functionality, such as modules, workflows, integrations and design decisions that shape the future-state system. But the true determinant of ERP success sits beneath these features: data. Without a trusted, well-governed and properly migrated data foundation, even a best-in-class system cannot deliver on its promise.
A data-first approach reframes ERP implementation by bringing data strategy, governance and quality to the forefront. Rather than treating data as a late-stage task, this method positions it as an early investment that reduces project risk, increases user confidence and enables long-term value through analytics, automation and AI.
Why data needs to come first
ERP programs are inherently complex, often involving multiple business units, legacy systems and process shifts. Research consistently highlights that data issues sit among the primary drivers of overruns, rework and post–go-live disruptions. When organizations postpone data activities, whether until functional design, testing or near go-live, they often find themselves resolving structural issues under pressure.
A data-first mindset mitigates these risks by embedding data considerations into the earliest phases of the project. It ensures organizations launch with cleaner, more consistent information and establish governance disciplines that protect the integrity of the system long after go-live.
Building the foundation: Data governance, MDM and quality
A strong ERP implementation mirrors a strong construction project: success depends on the quality of what lies beneath the surface. A data-first approach establishes this foundation through three core components.
Purpose: Defines ownership, roles and decision-making structures that guide how data is managed throughout the project and beyond.
Key roles:
- Data owners: Set direction for how data should be structured, validated and used within their domain.
- Data stewards: Manage data at the granular level, performing validation and ensuring alignment with standards.
- Governance committee: Provides oversight, resolves cross-functional conflicts and maintains accountability.
Why it matters:
Without clear decision-making structures, ERP teams often encounter inconsistent definitions, misaligned expectations and delays during migration and testing. Governance creates transparency, establishes consistency and ensures the right stakeholders are engaged at the right time.
Purpose: Creates unified, standardized, accurate master records for critical data domains.
Core domains include:
- Customer
- Vendor/Supplier
- Material
- Product
- Asset
- Employee
- Chart of Accounts
What MDM provides:
- Golden records: Single, validated sources of truth across locations and legacy systems.
- Standardized formats and business rules: Ensures consistent data interpretation.
- Repeatable processes: Sustains quality beyond go-live and prevents the system from degrading over time.
Why it matters:
MDM reduces duplication, eliminates conflicting definitions and ensures all downstream processes — from financial consolidation to supply chain planning — operate on trusted information.
Purpose: Ensures that data loaded into the ERP is complete, consistent, accurate and usable.
Critical quality dimensions:
- Accuracy: Data reflects real-world facts.
- Completeness: All required fields are populated.
- Consistency: Data is uniform across systems and time.
- Timeliness: Information is current and relevant.
- Validity: Values follow defined rules, formats and standards.
- Uniqueness: Records are free from duplication.
- Integrity: Relationships between data elements make sense.
Why it matters:
Once ERP data “sets,” correcting it becomes costly, time-consuming and disruptive. Addressing quality before migration prevents failures during testing and avoids post–go-live operational bottlenecks.
