In every organisation, the promise of an ERP system rests on a simple principle: to centralise, automate and secure management processes. But for this promise to become reality, the data entered must be accurate, complete, and consistent. And this is precisely where things often go wrong.
Despite modernisation projects, training campaigns or internal procedures, data quality remains a recurring and structuring issue, at the heart of concerns for business units and IT departments alike.
1. ERP as the reference system for critical processes
ERP systems are the backbone of the company’s most sensitive processes: financial management, procurement, accounting, logistics, inventory management, even payroll and project management. Every data entered into the system can directly impact invoicing operations, supplier orders, budget reporting or financial closing.
Therefore, a single input error, such as in a purchasing category, order date or analytical code, can trigger a cascade of consequences: incorrect budget allocation, errors in reminders, misalignment between departments, etc. The more integrated the ERP, the more rapidly these consequences can spread.
💡 Tip: Map out the ERP’s risk areas like high-impact business-critical fields. This analysis will help you prioritise control and data validation points, and mobilise the right departments around these critical zones.
2. Users: the primary actors responsible for data quality
Contrary to popular belief, data errors are not caused by technical faults in the system but by incorrect input by users.
76% of data quality issues originate from user input (The Warehousing Institute).
This may stem from a lack of understanding of fields, inadequate training on the rules to apply, or a desire to move quickly without checking the consequences.
In an ERP system, each user operates at their own level: creating orders, linking projects, recording expenses… Every action feeds into the overall database. That’s why data quality always starts with the quality of input, right from the first point of entry.
💡 Tip: Run regular awareness campaigns to explain the real consequences of input errors. By linking each data item to its business use, you give meaning to quality requirements.
3. The tangible impacts of incorrect or incomplete data
Poorly entered, incomplete or inconsistent data is never trivial. It leads to extra manual processing, delays, additional costs, and dissatisfaction from customers or suppliers. But it can go even further, it can distort management indicators and undermine strategic decision-making.
Take, for example, an incorrectly assigned budget code: it may cast doubt on the validity of a monthly report, require accounting rework, and skew the calculation of a commitment rate. At scale, these anomalies become systemic and damage the overall reliability of the system.
💡 Tip: Implement a quality control process for critical data, with regular audits. These checks can be shared between business and IT teams to ensure collaborative oversight.
4. The challenge of establishing consistent and sustainable rules
Another major obstacle to ERP data quality is the inability to establish and enforce consistent rules over time. ERP projects often involve multiple entities, departments or subsidiaries, each with potentially different local practices.
The absence of governance over data standards, the proliferation of exceptions, and workforce turnover all hinder consistency. This lack of alignment makes it difficult to embed sustainable best practices and results in inconsistent usage that weakens the system’s reliability.
💡 Tip: Set up ERP data governance, including business data stewards by domain. Their role is to define, maintain and enforce applicable data management rules, in coordination with operational departments.
Conclusion
Data quality in an ERP system is not optional. It is a strategic lever, essential for smooth process execution, reliable reporting and resource optimisation. It does not depend solely on the system but also on the users, the shared rules, and daily support.
Rethinking data entry as a core business activity, giving meaning to management rules, and establishing robust governance are key to evolving from a merely “functional” ERP to a truly reliable and high-performing one.
👉 Our next article will focus on what data entry errors are really hiding
