Article
Building stronger ERP outcomes with a data first approach
Feb. 26, 2026 · Authored by Brian Wilson, Robby Burneister and Nathan Olson
Enterprise resource planning (ERP) system implementations are one of the most complex and transformative initiatives an organization can undertake. These systems touch nearly every aspect of business operations, from finance and supply chain management to human resources and customer service. Yet despite their scope and complexity, one critical success factor is often overlooked: data. The statistics paint a sobering picture. According to Gartner, 80% of digital organizations will fail because they don’t take a modern approach to data governance.
At a recent conference, Baker Tilly’s Tom Walton, Director and Joe DeVroy, Senior Manager, discussed the critical role of data first approach in ERP implementations. Their insights below highlighted why prioritizing data governance from day one can mean the difference between ERP success and costly failure.
Q: What is a “data first” approach?
A: ERP projects have many possible starting points. Organizations can begin by focusing on process redesign, technology selection, change management or various other priorities. However, delaying a focus on data governance can be a costly mistake. A data first approach means prioritizing data readiness and governance from the very beginning of the ERP journey. Rather than treating data quality as a technical afterthought or a problem to solve later, this approach establishes frameworks, roles and tools needed to ensure clean, consistent and trusted data flows into the new system.
Q: Why is understanding the data foundation essential to achieving long-term success?
A: Clean, well-structured data is not just “nice to have”, it’s the foundation upon which an ERP project’s success is built. Think of your ERP implementation like constructing a building. You can have the most sophisticated architectural plans, the best construction materials and highly skilled workers, but if the foundation is weak or unstable, the entire structure is at risk. Taking a data first approach lays the foundation for success while also developing long-term analytics value for any organization.
Effective data governance requires clear roles and accountability. A framework typically includes these components:
- Data owners are responsible for the business use and accuracy of specific data domains. They understand the data context and make decisions about data standards and access.
- Data stewards serve as the day-to-day custodians of data quality. They implement the standards set by data owners, monitor data quality and coordinate data related activities within their domains. These individuals bridge the gap between business requirements and technical implementation.
- Data governance committee provides strategic oversight and coordination across the organization. Typically led by an executive sponsor or Chief Data Officer (CDO), this committee includes product owners, architects and security, legal and training officers. This committee establishes enterprise-wide data policies, resolves conflicts and ensures alignment with business objectives. Supporting this structure is a data analytics team of business analysts and developers who implement technical solutions and provide analytical capabilities.
Quality data exhibits seven essential characteristics:
- Accuracy ensures that data reflects real-world facts without errors or misinterpretations.
- Completeness means all required data elements are present and accounted for.
- Consistency requires that data remain uniform across systems, time periods and departments.
- Timeliness ensure data is current and available when needed for decisions.
- Validity confirms data follows defined formats, rules and accepted values.
- Uniqueness means each entity appears only once to avoid duplication.
- Integrity ensures relationships between data elements are logically and structurally sound.
