How do you prioritize data cleansing tasks for ERP implementation?

In my opinion, every ERP implantation or upgrade will have opportunity on data cleansing. The best way to commence this is to do a stock take. -identify what is available - identify what is needed - identify what needs to be collected to get there. Also identify why this data cleansing requirement is identified, this will inform who needs to be engaged and how it can be done. Once the details are available, it would be best to sit back and identify what can be done with the information available. This will give insights on the patterns needed to fix what needs to be fixed, Where possible identify opportunities to cleanse the data using mechanical or automated means . This will save considerable time, effort and man power.

Identify which data elements are critical for key business processes. Clean the data that directly affects operations or decision-making first. Consult with end-users to understand their needs and the data they rely on most. Prioritizing tasks based on user requirements can enhance system adoption. Determine if certain data sets are prerequisites for other tasks. For example, if sales data is needed for financial reporting, it should be cleaned before other related tasks.

To prioritize data cleansing for ERP implementation, focus on critical data entities (e.g., customer, vendor, product data) that impact core business operations. Start by assessing data quality—accuracy, completeness, and consistency. High-impact areas that influence essential processes or compliance should come first. Address dependencies by cleansing data needed for other processes early in the sequence. Additionally, consider stakeholder input to identify key data for decision-making. This approach ensures that data foundational to ERP functionality is accurate, reducing disruptions and supporting a smooth transition.

As an EA, I'd say data assessment is like taking inventory of your digital pantry. You gotta know what ingredients you've got before cookin' up that ERP feast! Use data profiling tools to map out your data landscape - it'll save ya headaches down the road. And don't forget to prioritize based on biz impact - not all data is created equal, ya know?

Before starting data cleansing for an ERP implementation, it’s crucial to define clear data standards and rules. This means setting guidelines on what data should look like - formats, accuracy, consistency, and completeness. Having well-established rules makes it easier to identify and prioritise cleansing tasks. Focus on aligning these standards with your business processes to ensure the data supports your ERP system effectively. This will help prevent future issues and make the implementation process smoother by ensuring you’re working with high-quality, reliable data from the start.

Implement change management practices to facilitate the adoption of new data standards and rules. This includes training sessions, regular updates, and feedback loops to ensure all stakeholders understand and commit to the new processes.

Before starting data cleansing for an ERP implementation, prioritize tasks by focusing first on critical data that directly impacts key business processes, such as financial, customer, and inventory data. This involves assessing the data's accuracy, completeness, and relevance, starting with high-volume, frequently used data sets. Aligning these efforts with compliance, reporting, and decision-making needs helps ensure that redundant, outdated, or inconsistent information is addressed. Working closely with key stakeholders throughout the process guarantees that the cleansed data aligns with business operations and supports the ERP system effectively.

To ensure effective data management within an ERP system, it’s essential that data standards and rules align with business requirements, the ERP’s data model, and industry best practices. These standards should be carefully documented and clearly communicated to all stakeholders engaged in data entry, cleansing, and maintenance. Well-defined data rules help maintain consistency and accuracy; they can cover essential elements such as date formats, naming conventions, and data field structures to streamline data handling. By establishing and following these guidelines, organizations can enhance data quality, reduce errors, and ensure that all teams work from a single, reliable source of truth within the ERP.

- Establish clear data standards for formats, consistency, and accuracy based on business requirements. - Set rules for missing or duplicate data, ensuring uniformity across systems. - Align standards with ERP system capabilities to avoid integration issues. - Involve key stakeholders to agree on and validate these data standards.

- Use automated tools where possible to clean and correct data, reducing manual errors. - Resolve duplicates, standardize formats, and remove outdated or irrelevant data. - Assign a team responsible for cleansing tasks, ensuring accountability and quality control. - Prioritize high-impact areas such as customer data or financial records for immediate cleansing.

Implement automated data cleansing solutions that leverage artificial intelligence and machine learning. These tools can streamline the process by identifying patterns, detecting anomalies, and executing cleansing tasks, significantly reducing manual effort and increasing accuracy.

This involves systematically identifying and correcting inaccuracies, inconsistencies, duplications, and outdated records to align with the newly defined standards. A variety of tools and methods are available to support this process, including automated data cleansing software, manual data validation techniques, and built-in ERP functionalities designed for data quality management. By focusing on high-impact areas first—such as customer information, financial records, or product data—organizations can significantly improve data reliability and usability across the ERP. Regular monitoring and ongoing cleansing efforts will ensure that the data remains accurate, ultimately supporting better business insights and smoother operations.

Data Cleansing Process is a part of Data Migration Phase of ERP Implementation Projects. During the process decision on Data Retension Policy, Migration Strategy and Data Cutover policy plays vital role in the cleansing process. Cleansing process became important since the entry of Cloud ERP Systems in to the market as the On Premise ERPs enjoyed the liberty of maintaining huge data without any recurring cost. Identifying the orphan data which will have no value in the ERP and will result in data corruption. Hence cleansing of data should prioritize identification and cleansing with business agreement is important. Functionalities such as Fusion - Archiving and Purging of Accounting Data introduced in 2024 can be helpful for entities

Data cleansing isn’t just a one-time chore, it’s what makes or breaks your ERP implementation. Start by tackling the biggest pain points first. Got duplicate customer records? Inconsistent product codes? Outdated supplier details? Fix those before they cause system-wide chaos. Automate where you can, but don’t rely on automation alone, some things need a human eye. Bring in the people who actually use the data daily; they’ll spot issues that an algorithm won’t. And once it’s clean, keep it that way! Set clear rules for data entry moving forward so you’re not back in this mess six months down the line. A solid cleansing process isn’t about perfection, it’s about making your ERP work smoothly from day one.

In the data migration process, reviewing and approving the cleaned data is crucial because the entire project’s outcomes depend on good-quality data. Therefore, it should be planned very carefully. From my perspective, the approval process should involve multiple levels of business and technical SMEs to review and approve the data. It is also recommended to use data migration tools (like BODS or other back-office tools) for a more efficient setup of the cleansed data approval process, as these tools can support data cleaning and approvals more efficiently

Cutover signoff in the ERP Implementation Projects is an important phase before the actual go-live is planned. The decision on the cleansed data to be migrated to the new ERP or upgraded ERP System lies with the core team of the entitity. The role of implementation partner is limited to providing the data to be migrated. The process is so flexible in the Cloud ERP System like Oracle Fusion as compared to the lengthy process in practice where involvement of technical person to extract data running various queries and logics. The new era ERP system provides export capability to extract the data from a non production or production equivalent system and review before they are considered for final data for golive of the ERP System.

Get the right people; finance, sales, procurement, IT—to double-check the data they’ll rely on daily. Cross-check reports, test sample entries, and make sure everything actually makes sense in context. If something feels off, dig deeper now, not after go-live when fixing it will be a nightmare. Once everyone’s given the thumbs-up, lock it in and document the process. A solid review means you’re not just hoping for the best - you’re making sure your ERP starts off on the right foot.

- After cleansing, review the data with key stakeholders for accuracy and completeness. - Set up validation checkpoints to ensure that all cleansed data meets predefined standards. - Ensure that cleansed data is approved by relevant teams before integration into the ERP system. - Continuously monitor data quality throughout the implementation process.

This critical step involves verifying the accuracy, completeness, and consistency of the data to confirm that all necessary corrections have been made. Involving key stakeholders—such as data owners, department leads, and IT personnel—in this review process ensures that the data aligns with both operational requirements and strategic goals. Once approved, the data can be safely migrated to the ERP, where it serves as a reliable foundation for reporting, analysis, and day-to-day decision-making. This careful final review mitigates risks associated with poor data quality and sets the stage for a successful ERP implementation.

When cleaning up system data in an ERP, it’s essential to look beyond basic accuracy and consistency checks. Consider the relevance of each data set: outdated or redundant information can clutter the system and reduce efficiency, so it’s often best to archive or remove unnecessary records. Additionally, pay attention to data relationships—ensuring that linked records (such as customer and order data) remain correctly associated is critical to maintaining data integrity. Security and compliance are also key; data privacy regulations require careful handling of sensitive information, which may need anonymization or strict access controls.

- Plan for ongoing data governance to maintain cleanliness post-implementation. - Establish a data stewardship team to oversee future data integrity. - Implement data cleansing as an ongoing practice, not a one-time task, to maintain ERP efficiency. - Consider training for employees on maintaining data accuracy going forward.

O processo de tratamento e limpeza de dados para migra??es ou implementa??es de ERP está mais desafiador com a LGPD. Ainda que se tenha registro de quem terá acesso e o que será acessado, é muito raro encontrar organiza??es que mantém organizado esse ledger. Frequentemente os ERPs com módulos de folha e ponto possuem dados sensíveis e o consentimento ao processamento dessas informa??es deve ser obtido e mantido.

It’s vital to document any issues or adjustments made during the cleansing process. This record not only provides transparency but also helps in addressing future data quality concerns.

If data cleansing is defined as the activities taking place prior to implementation where formal data conversion will take place, try to focus on data that cannot be programmatically changed as part of the conversion process, or can more easily be updated within the current solution. Paramount to this effort is managing the amount of data that will come into the new system, so less is more and reduces implementation risk.