Successful ERP implementations often lead to the assumption that data in the system is accurate and reliable, but this is frequently not the case. Organizations need a Data Governance Model to ensure the integrity of their ERP data.
Training end users on accurate data entry is not sticking long-term, leading to shortcuts. Additionally, the absence of strict data input requirements frequently results in invalid number ranges being entered and mandatory fields being left blank. The lack of clear data definitions has made it difficult to understand data relationships. Lastly, the organization’s controller has raised concerns over the lack of audit log reports. Below, we define what a Data Governance Framework is and outline some of its key subsets.
A Data Governance Framework outlines the policies and procedures encompassing an organization’s management of ERP data assets. Organizations may lack the time or internal resources to formally document and enforce a data governance framework, potentially compromising data integrity.
Implementing a Data Governance Framework that clearly defines how data is entered, stored, and maintained across the organization establishes essential controls to protect data. This framework also addresses the need for audit trails to ensure accountability and transparency. Additionally, appointing data owners across the organization can ensure continuous review of data for accuracy and completeness. By adhering to the framework, organizations can ensure the long-term success of their ERP system.
Data integrity ensures the accuracy and reliability of data. Inaccurate or incomplete data assets can lead to misleading reports, significantly impacting business decisions. Data integrity is frequently compromised due to user errors during data entry. Organizations often provide training only during initial onboarding, neglecting ongoing education for their user base. Additionally, the system allows users to input invalid data ranges and leave mandatory fields blank.
To address these issues, organizations should prioritize continuous training and designate key users to oversee data accuracy. Establishing comprehensive policies and procedures for data entry, storage, and maintenance ensures consistency across all departments. Additionally, implementing input requirements ensures the organization’s predefined criteria for ERP data is met. This includes setting valid ranges for numerical data, enforcing mandatory fields, and incorporating automated checks to identify anomalies. By adopting these practices, organizations can maintain data accuracy and reliability.
Data definition refers to the clear specifications of data elements, including their formats, relationship, and rules for usage. The vast amount of data stored in an ERP system can be difficult to maintain, especially as the organization and system grow. Data definitions often become unclear as new data types are introduced to the ERP system, especially when integrating to an external supporting system. Clear data definitions can be achieved by creating a data model, which visually represents data elements and their relationships. These models must be updated to reflect any changes in data structure. This relational clarity enhances the ability to perform accurate data analysis and generate informative reports.
Audit trails provide a chronological log of who accessed or modified data and when these actions occurred. Organizations may encounter issues when a single user sign-in is shared among multiple users. This practice can compromise audit visibility, making it difficult to accurately attribute actions to individual users. Additionally, failing to review audit trail reports can allow modifications to go unnoticed.
Implementing proper audit trails is essential for detecting and preventing unauthorized access, ensuring data integrity remains intact. Adding credential reminders to employee badges and the ERP login screen can help mitigate the issue of sign-in sharing. Additionally, limiting permissions to key users reduces the risk of unauthorized or unintended access.
By adopting key aspects of the framework, such as data integrity, data definition, and audit trails, organizations can protect their ERP data. If your organization is seeking advice regarding an ERP assessment, our team can help guide you in the right direction. Reach out to us at info@trenegy.com.
Our Post-ERP Assessment Guide also provides more information and is available here.
