ERP Starts With Data: Why Governance Comes First

Enterprise resource planning (ERP) implementations are among the most transformative and challenging initiatives an organization can undertake. These projects touch nearly every aspect of business operations, from finance and supply chain to human resources and customer service. Yet, despite their scope and complexity, one critical success factor is often overlooked until it’s too late: data.

Clean, well-structured data is not just a nice-to-have, it’s the foundation upon which an ERP project’s success is built. Taking a “data first" approach lays the foundation for this success while also developing long-term analytics value for an organization.

What is a “data first” approach to an ERP project? 

ERP projects are inherently complex with many possible starting points, but regardless of the chosen path, delaying data governance until after go-live is a costly mistake. Rather than treating data as a downstream task, taking a data first approach has organizations prioritizing data readiness and governance from the very beginning of the ERP journey by proactively establishing frameworks, roles and tools needed to ensure clean, consistent and trusted data flows into the new system.

A data first approach begins with establishing a data governance program early in the ERP project timeline. A well-structured data governance program typically rests on three foundational components:

1. Defining a data governance charter: This charter outlines the guiding principles and desired outcomes of the governance effort. Whether the organization is prioritizing risk mitigation, data quality or improving data accessibility and literacy, the charter serves as a strategic compass for all data-related decisions.
2. Establishing a governance framework: This involves clearly defining the roles, responsibilities and timing of stakeholder involvement throughout the ERP lifecycle. By mapping out who needs to be involved at each state, organizations can avoid costly rework and ensure that decisions are informed, timely and aligned with broader data objectives.
3. Developing policies and best practices: Based on the organization's goals, this step includes setting standards for data security, data quality and master data management – core elements of any ERP project. These policies not only promote consistency, but also elevate the data literacy of ERP stakeholders, many of whom may be experts in their business functions but less familiar with the implications of their decisions on data integrity.

Parallel to these components, success hinges on organizations identifying and engaging key data stakeholders/subject matter experts across each data domain/business unit. These individuals bring essential operational knowledge and should be actively involved in shaping ERP workflows and defining system functionality.

Beyond identifying data owners and stewards, it’s important to establish a strategic advisory group with enterprise-level insight into the organization’s data landscape. This group plays a key role in coordinating across functional areas, resolving cross-domain issues and ensuring that ERP decisions are made with a clear understanding of their downstream implications.

Lastly, when transitioning from a legacy system to a modern ERP platform, one of the most critical tasks is determining which data to migrate. This typically includes essential historical data for analysis, open transactions and core master data such as customer, product, location and employee records.

While most ERP systems offer direct integration capabilities, many organizations still rely on manual, labor intensive processes, like exporting and re-importing data via Excel or CSV templates. Although functional, these traditional methods are prone to human error which can lead to inconsistencies, repeated work and compromised data integrity.

To manage these risks, organizations should utilize modern data management tools such as Microsoft Fabric, Amazon Web Services (AWS) or Snowflake, to automate and streamline data transformation and loading. These platforms enable teams to:

• Build ingestion pipelines that extract data from legacy systems
• Define and apply business rules for data cleansing, consolidation and mapping
• Automate the transformation process and load clean, structured data into the new ERP system

Automating these steps not only reduces the likelihood of errors but also accelerates the migration timeline. While there is some upfront work in configuring these tools, the long-term benefits, such as faster reloads, improved accuracy and reduced work, far outweigh the initial effort. This approach also creates a reusable data model that supports post-go-live analytics. Instead of having to discard the effort invested in manual migration, organizations retain a curated data warehouse that can ingest new ERP data and serve as the foundation for reporting, planning and artificial intelligence (AI) initiatives.

Overall, by taking a data first approach, organizations can avoid the post-implementation scramble to reconcile data inconsistencies or retroactively define data governance policies. Instead, they move forward with a unified strategic direction – one that ensures data integrity, supports analytics readiness and maximizes the value of the ERP investment.

Avoid common ERP challenges with a data first approach 

Taking a data first approach to an ERP implementation directly addresses and resolves many of the most common challenges organizations face during this complex transformation.

• Misaligned or siloed data ownership: Leads to inconsistent definitions, duplicated efforts and conflicting datasets

Implementing a robust data governance framework helps unify these efforts by clarifying how data is shared, used and impacts multiple areas of the organization. This not only reduces work and inefficiencies, but also ensures that data is accurate, non-redundant and aligned across all functional areas.

• Low data quality undermining trust in the new system: If users encounter errors or inconsistencies on day one, adoption suffers

A data first approach emphasizes data cleansing, validation and quality standards before migration. Paired with automating data preparation, this ensures users access accurate, reliable data, building trust in the new ERP system and supporting user adoption and change management efforts.

• Poor visibility into legacy data and mappings: Makes it difficult to trace decisions or support historical reporting

Utilizing cloud-based platforms allows organizations to document and automate data transformation logic. This retains visibility into how legacy data maps to the new ERP system, supporting both operational continuity and future analytics. While ERP systems are not designed to retain legacy data, a well-structured analytics platform can bridge the gap – integrating both old and new data-sources for a unified view.

• Bottlenecks during migration and testing: Resulting from manual migration processes as teams wait for individual contributors

A data first approach mitigates these delays by automating key steps and making data accessible to all relevant stakeholders. This fosters a more collaborative environment, accelerates testing cycles and reduces dependency on single points of failure.

• Missed opportunities for analytics post go-live: With valuable transformation logic lost in spreadsheets and ad hoc processes

Rather than needing to start from scratch, a data first approach ensures that the data infrastructure developed during the ERP implementation can be leveraged for ongoing reporting, planning and innovation. This maximizes the return on investment (ROI) and enables data-driven decision-making well beyond go-live.

The benefits of a data first approach are not just complementary to ERP challenges – they are purpose-built to overcome them. This alignment is what makes the approach so effective in driving both immediate implementation success and long-term data value.

Real-world example: Success with a data first approach 

This multi-site mining company was undergoing a major ERP transformation and needed to consolidate six legacy systems into a unified platform. Facing challenges such as fragmented data ownership, inconsistent data quality, limited visibility into historical data and manual migration processes, the company engaged Baker Tilly to deliver a data governance-driven approach.

Utilizing a data first approach, Baker Tilly established a robust data governance framework, defining master data management standards and empowering business stakeholders through data training and stewardship roles. Leveraging Microsoft Fabric, the team automated data ingestion, transformation and validation, enabling iterative data mapping and quality assurance.

This approach not only ensured a smooth ERP rollout with reduced rework and greater trust in ERP system outputs, but also laid the foundation for long-term analytics, reporting and artificial intelligence (AI) initiatives.

How we can help 

Implementing a new ERP system is more than just a technology upgrade, it’s a data transformation. By taking a data first approach and investing in data governance from the outset, organizations can mitigate the risks associated with an ERP project and unlock value faster.

The IFS Cloud platform is designed to be customizable and can integrate successfully with other data migration tools that meet a user’s needs. These robust in-solution tools allow data to be imported from various sources which can help users leverage familiar environments.

Baker Tilly’s digital solutions team can help turn your organizational data into a strategic asset – before, during and after an ERP implementation. Utilize our data first approach to get started turning your organizational data into a competitive advantage. Contact one of our specialists to learn more.