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A CIO's guide to ERP data governance: How to increase trust
CIOs play a critical role in making sure that the organization's critical ERP data is accurate, safe and accessible. One key to success: Assign the right ownership.
ERP data governance has gained significant traction in recent years. Businesses face a growing array of privacy and security regulations, including GDPR, CCPA and a myriad of data sovereignty laws. Data quality is a concern as well. Organizations rely more than ever on data analytics and AI/machine learning (ML) to inform decisions and optimize results. Today's competitive market demands that CIOs orchestrate a strategy that helps their organizations master ERP data governance.
For many companies, ERP systems comprise the single source of truth for customers, inventory, financial records and more. That vital information is used to close the books, ship products, pay vendors and forecast demand. Sound ERP data governance ensures that the business runs smoothly, that it complies with regulatory requirements and that AI and analytics investments deliver optimal results. The CIO plays an all-important role that eliminates the gap between the organization's technical infrastructure and the information that underpins its ERP deployment.
Ownership and ERP data governance
Ownership is the foundation of ERP data governance. CIOs and other leaders should designate an accountable person for each key domain, such as customers and receivables, vendors and payables and inventory/supply chain. This group includes functional leaders in finance, procurement, sales or operations. These executives and managers are accountable for definitions and quality standards.
Understand who uses the data, how it's used and which data integrity issues are most common. Data quality issues should not be regarded as "system problems" to be handled by IT. Instead, they must be the responsibility of subject matter experts who understand the fundamentals of their respective domains and can tend to the accuracy, completeness and currency of the data they use every day.
IT, in contrast, owns the infrastructure: storage, security, access workflows, integrations and the tools that enforce rules. This model works exceptionally well as long as employees outside the IT department fully take ownership of ERP data governance for their respective areas.
The importance of consistent categories
Inconsistent categories create problems by classifying real-world entities unpredictably, assigning a variety of different names, codes and hierarchies to data that should otherwise be similar. When, for example, units of measure vary across different SKUs that are otherwise closely related, generating intelligible reports becomes more challenging. Likewise, if inventory items are categorized differently across various business units, that can make it difficult to perform apples-to-apples comparisons on sales metrics.
This disconnect impacts virtually every domain within ERP data governance. Categories define how the organization thinks about its vital information. If product families, customer segments, cost centers and account structures are not proactively governed, then every downstream process potentially suffers from confusion. Inconsistent nomenclature can lead to operational friction, misunderstandings and poor decisions. Buyers can't see total spend for a vendor. Planners can't compare inventory quantities across multiple locations. Finance spends time reconciling definitions instead of analyzing results.
ERP data governance establishes and enforces definitions for the data elements that matter most to the business, such as customers, vendors, inventory general ledger accounts and so on.
Why the guidelines for ERP data quality must be reviewed
Data quality rules are not a "set it and forget it" proposition. Businesses inevitably add SKUs, expand sales channels or open new locations. Regulations and functional requirements change. An unused field is assigned a new purpose. Codes and categories change. Information is required that was once optional.
As such, CIOs should continuously review ERP data governance standards as part of their regular operating cadence and not as a mere follow-up to the initial project implementation.
Quality guidelines should cover the standard dimensions such as accuracy, completeness, consistency, validity, uniqueness and timeliness. Assess these dimensions for specific records and processes; they should not simply exist as abstract principles. Schedule reviews quarterly for high-change domains such as customers, vendors and inventory items, and at least annually for slower-moving elements such as the chart of accounts.
Domain owners should confirm their documented rules still conform to the way work is done on the front lines. When guidelines remain static, teams often create their own set of exceptions, fueling inconsistencies and problems downstream. Confirm that validations and reconciliations still catch the errors that matter.
How data governance affects reporting and AI
ERP data governance underpins both credible reporting and safe AI. Weak governance extends far beyond the ERP system itself. It undercuts reporting and analytics. As organizations expand their use of AI and ML, inconsistent results and errors can occur.
Imagine this scenario: Two managers run revenue reports and come up with different top-line numbers. This can happen because of inconsistent category definitions, variations in timing or different approaches to exception handling. When the numbers are unclear, stakeholders often waste time arguing over the accuracy of the numbers rather than working together on solutions. Leaders may lose faith in the official numbers, piecing together their own "modified" version of the truth and making it even more difficult to agree on a path forward.
These differences affect AI. Models and agents trained on inconsistent data, for example, often amplify the inconsistencies. Duplicate vendor master records hide payment anomalies. Missing context lead AI agents to make inconsistent or untrustworthy decisions. Many finance and generative AI efforts stall because the underlying data is simply not fit for use.
How organizations can get better at ERP data governance
CIOs, in combination with other C-suite executives, must engineer a proactive approach to ERP data governance.
Data is a strategic asset on par with capital and talent. To that end, regularly review domain ownership coverage and ERP data quality metrics. The chief risk and compliance officer is responsible for making sure that data governance gets the attention it needs, while the CFO owns the economic case for the integrity of financial master data and transactional definitions. That includes consistency in the chart of accounts, intercompany rules and quality standards for numbers posted to the general ledger.
The CIO and CFO, meantime, should partner on ERP data governance, relying on shared KPIs such as reconciliation time, duplicate rates and close-cycle variance. COOs should tie data governance to their own performance metrics such as order cycle time, fulfillment accuracy and inventory reliability. CIOs provide the governance platform, access workflows and enforcement mechanisms, but they should strongly resist any notion that they are solely responsible for data governance.
Practically, executives should insist on several "must-have" policies. First, owners should be assigned based on business domain, not based on software systems. Second, fund data governance as a critical function, not an afterthought. Third, data quality and compliance metrics should get strategic visibility, alongside financial and operational results. ERP data governance improves when leaders measure it, staff it and refuse to accept finger-pointing.
Trust in ERP data is not solely a function of the software. It requires clear ownership, consistent categories, meaningful business rules and CIOs who understand the strategic value of data governance. Organizations that treat ERP data governance as a shared responsibility will benefit from cleaner closes, more accurate and consistent reports and a stronger foundation for nascent AI initiatives.
James Kofalt spent 16 years at SAP working with SME business applications and was a product manager for integration technology at Microsoft's Business Solutions division. He is currently the president of DX4 Research, a technology advisory practice specializing in ERP and digital transformation.
