An ERP system is only as good as the quality of data flowing through it. Data and analytics leaders should establish a metrics-based approach to understand the quality and status of their data before migration. Therefore, during a cloud ERP implementation, it is best to include a comprehensive plan for data cleansing and governance, and best practices for ongoing data management.
Attributes of ERP Data Quality
Ensuring data quality in ERP systems requires evaluating several key attributes that directly impact business processes, decision-making, and overall system performance. The primary attributes of ERP data quality include:
Accuracy
Data should correctly represent the real-world entities it refers to. Inaccurate data, such as incorrect pricing, quantities, or customer details, can lead to poor decision-making and operational inefficiencies.
Completeness
Complete data ensures that all required fields and records are filled, with no missing information. Missing data (e.g., incomplete customer profiles or product details) can disrupt workflows and cause errors in forecasting or reporting.
Relevance
The data in your ERP system must be relevant to your current business needs and operations. Outdated or irrelevant data, such as old vendor contracts or inactive customer accounts, can clutter the system and impact performance.
Consistency
Data consistency refers to ensuring that data is the same across all systems and modules. For example, customer details should match in CRM, sales, and finance modules. Inconsistencies can lead to reporting errors and miscommunication between departments.
Validity
Valid data meets the established rules and constraints of your system. For example, phone numbers should adhere to a specific format, and dates should follow the same structure. Invalid data can cause transactional errors or block processes.
Timeliness
Timely data is up-to-date and reflects the current state of operations. Outdated information can hinder decision-making, especially in areas like inventory management or customer service, where real-time data is critical.
Uniqueness
Uniqueness ensures that there are no duplicate records in the system, such as repeated customer profiles or supplier accounts. Duplicate data can lead to confusion, increased costs, and inefficiencies in processes like billing or customer service.
Integrity
Data integrity ensures that the relationships between different data elements are maintained correctly. For example, an invoice should be linked to the correct customer and corresponding sales order. Poor data integrity can cause critical issues in financial reporting and auditing.
Common Data Quality Issues in Implementing Cloud ERP
Several data quality challenges can arise during a cloud ERP implementation. Some of the most frequent include:
Duplicate Data: Duplicate records can stem from poorly maintained legacy systems or errors in data entry. This issue leads to redundant information, increases costs, and impacts reporting accuracy.
Incomplete or Missing Data: Incomplete data, such as missing customer contact details or product codes, hinders efficient operations and decision-making. Often, missing data can be traced back to the lack of standardized input fields in older systems.
Inconsistent Data Formats: Data that lacks standardization, such as inconsistent date formats or currency representations, can create difficulties in consolidating and migrating data to the cloud ERP system.
Data Obsolescence: Over time, data can become outdated (e.g., inactive customer records, old supplier contracts). Incorporating outdated data into your ERP system can negatively impact operations, especially in processes like procurement and financial forecasting.
Poorly Defined Data Governance Policies: Without clear guidelines on data ownership, responsibilities, and lifecycle management, data quality can quickly deteriorate in a cloud ERP environment.
Importance of ERP Data Quality
ERP data is integral to daily business operations, driving decisions across finance, procurement, HR, and more. As businesses increasingly rely on data-driven insights, the quality of data directly affects ERP functionality and operational efficiency. Low data quality can hinder reporting, forecasting, compliance, and customer service.
Furthermore, regulatory compliance around data handling and security has become stricter. Inaccurate or incomplete data can lead to breaches in compliance and, consequently, regulatory penalties, damaged reputations, and customer trust.
Steps to Resolve Data Quality Issues During Cloud ERP Implementation
Develop a Data Quality Plan
Identify the key data critical for your business processes. Start by focusing on high-priority data, such as customer, supplier, and financial records. Create validation rules to standardize data formats before they are migrated to the cloud ERP system. Automating this process will make it easier to manage in the future.
Conduct a Data Audit Before Migration
Perform a full audit of your data to assess its quality. This step helps to identify any gaps or inconsistencies early on. Data audits can uncover obsolete records, inconsistencies, or errors that need addressing before migration.
Validate Your Data
Data validation is essential to ensure that only accurate, high-quality data enters the ERP system. Automated tools, including AI-driven solutions, can clean and validate data in real-time, reducing manual intervention and ensuring long-term data accuracy.
Remove Duplicates and Fill Gaps
Use deduplication tools to eliminate redundant records and fill in missing data. Missing or incorrect fields such as emails, phone numbers, or product codes should be addressed before they enter the system. Third-party data sources can help augment incomplete data where necessary.
Analyze Your Data Regularly
Post-migration, data quality needs to be continuously monitored. Automated monitoring tools can flag new inconsistencies or emerging trends that may indicate larger data governance issues. Regular data analysis ensures that your system remains accurate and reliable for business intelligence.
Implement Automation Tools
Leverage automation to maintain high data quality. Automated workflows, scheduled in real-time or in batches, should clean new data as it enters the system. These tools ensure consistency and accuracy both for new and existing records, reducing the need for ongoing manual interventions.
Communicate With the Team
After implementing data cleansing and automation processes, communicate the changes with your team. Clear guidelines should be provided for data entry and management to prevent future quality issues. Set up periodic reviews to address any ongoing data quality challenges and adjust processes accordingly.
Conclusion
Ensuring data quality is a critical success factor in cloud ERP implementations. By addressing common issues such as duplicate records, inconsistent formats, and missing data, companies can avoid the pitfalls that lead to operational inefficiencies and compliance risks. Establishing a robust data governance framework and leveraging automation are key to maintaining high-quality, reliable ERP data.
Note: This blog was originally published in October 2019 and modified in September 2024