How to Manage Data Quality in an ERP Migration

Data migration was a top priority for over half of our customers this year, making it one of the biggest challenges we’ve tackled. Whether they were moving from legacy systems to the cloud or consolidating siloed platforms into an ERP system, our clients faced significant data quality challenges – especially those that were implementing an ERP migration. 

In this guide, we’ll walk you through the most common data quality challenges we’ve helped our clients overcome and also share effective solutions, and outline strategies to ensure a smooth and successful data migration process. 

At the end of the post, we also have a migration guide for managers interested in using WinPure to manage their data quality for an ERP migration. 

Let’s roll. 

Why Data Quality Matters in ERP Migration

ERP systems rely on vast amounts of data, integrating information from sales, finance, human resources, and more. Unfortunately, as our customers discovered at the time of a data audit, most of their datasets had missing critical fields, outdated customer records, mismatched identifiers, or duplicate entries. These errors not only created blind spots, leading to inaccurate financial reports, operational bottlenecks, and poor decision-making – but also affected the migration process, leading to missed deadlines and increasing tensions between teams.

Ensuring data quality through proper governance, cleansing, and validation processes is essential before you even start an ERP implementation. You don’t want your new system to be choked with errors carried forward from the old system! 

The Migration Process Explained

A successful ERP migration depends on data quality as much as it does on technology and infrastructure. Most companies would spend a fortune on purchasing a new storage system or even hiring an expensive migration consultant but not a dime on their data quality. When handling an ERP implementation, you must always look beyond the tech and the infrastructure – you must look at the source – which is your data. If the data is not profiled, cleansed, or deduplicated, it will lead to a migration failure. 

For example, one of our customers tried migrating their old data to an ERP system with only using Excel to standardize the data. During the migration, more than 100K records were duplicated into the new system! 

On top of cleaning old data, the team now had to also deal with the new duplicates. It was a nightmare. 

Want to avoid the same fate? Here’s a structured approach to ERP migration, keeping data quality as your core focus. 

Data Collection

The first step in any ERP migration is gathering all relevant data. This includes both soft data, such as customer preferences and behavioral insights, and hard data, such as contact records, sales records, financial statements, and inventory levels. Once collected, you have to make a decision on which records to import into the new system. That decision is subjective to a company’s objectives with the migration as well as the new ERP environment. 

There is no one-size-fits-all here. For example, one company may prefer to drop inactive contact data of the past five years, importing only active accounts into the new system. Some may prefer to keep legacy data for the sake of record-keeping however, they don’t want that into the new system but perhaps in a backup system. All this analysis needs to be done before initiating any kind of data treatment. Rushing through the data collection phase  can lead to gaps in reporting and business intelligence down the line, impacting decision-making and operational efficiency.

Data Profiling

Post data collection is the  data profiling step. Your team would need to audit and analyze the data to identify hidden errors, inconsistencies, and duplicates. Profiling helps reveal the extent of data quality issues that you would be dealing with and the corrective actions that would be required. . For example, missing values, inconsistent formatting, or outdated information are some key issues that are highlighted during an intensive data profiling session.

Data Processing (Cleansing & Deduplication)

Data processing is where the real cleanup begins. This step involves cleansing the data by removing inaccuracies, correcting errors, and ensuring consistency across datasets. Along with data cleaning, you will also do a data deduplication to either merge or purge  duplicate records, ensuring that only one accurate version of each entry is migrated. Without thorough cleansing and deduplication, businesses risk transferring bad data into their new ERP system.

Record Linkage

A crucial step is record linkage. This involves using entity resolution techniques to identify relationships between different data sources. For instance, if the same customer appears in multiple datasets under slightly different names, record linkage helps unify these entries into a single, accurate profile. This step is essential for businesses that rely on customer data to drive decision-making, as it ensures that all relevant information is connected and readily accessible. While it may seem complex, modern AI-powered data match tools like WinPure simplify this process, making it more efficient.

Data Evaluation and Storage

The final step is data evaluation and storage. At this stage, the cleaned and linked data is thoroughly evaluated to ensure it meets the quality standards required for the ERP system. This includes testing for accuracy, completeness, and consistency. Once the data has been validated, it is moved to the new ERP system, where it is ready for use in everyday operations. Ensuring that only high-quality data is migrated prevents future disruptions, enhances reporting accuracy, and supports better decision-making.

Each of these steps builds on the previous one, creating a seamless migration process that protects the integrity of your data from start to finish. By maintaining high data quality throughout, businesses can avoid operational breakdowns and ensure the new ERP system functions as intended.

While these steps are essential, there are barriers that can make the process harder than it seems. Let’s look at these challenges and how to overcome them for a smooth migration.

Barriers in the Data Migration Process

While the steps to migrate data might seem straightforward, numerous barriers can complicate the process. Understanding and addressing these barriers is crucial to ensure a smooth transition, maintain data integrity, and avoid costly disruptions.

Small Mistakes Lead to Big Failures

Data migration often involves intricate scripts with hundreds or thousands of lines of code. Unlike long-running applications, where small mistakes can be patched in future updates, mistakes in data migration can lead to either a migration failure or a corrupted database, causing significant damage to your revenue & company reputation. 

Modern applications handle much of the migration process, but they still need precise configuration and validation. A misconfigured tool or error in data mapping can result in corrupt transfers. If a bank loses even 0.1% of its customer data during migration, the business impact can be devastating. The margin for error is slim, making accuracy non-negotiable.

AI-powered data matching tools, using advanced algorithms and global name recognition, help prevent these issues by identifying hidden identities and resolving duplicate records quickly. 

Neglecting Data Quality

Multiple studies have shown that poor data quality is a leading reason for ERP implementation failuresYet, every day companies are neglecting data quality challenges, hoping that a migration to a new system with automated cleansing or deduplication options will solve the problem for them. 9/10 times, the deduplication options available in these tools are either too complicated, requiring additional talent, or too basic, necessitating the use of Excel sheets. 

Master Data Capture and Handling Errors

Master data (such as customer, product, or vendor data) is prone to errors during capture and handling. These mistakes can propagate through the ERP system and compromise both the master data and transactional data. The challenges are amplified by differences between the data models of the legacy system and the new ERP system, which often require extensive modification to make the data compatible.

Lack of Data Quality Awareness in the Organization 

One of the most persistent barriers to high-quality data migration is the lack of awareness or willingness to address data quality issues. Often, managers refuse to acknowledge the severity of poor data or simply don’t know how to resolve it. In the same way, data creators within the organization may have little understanding of how their input impacts downstream processes. Overcoming this challenge requires cross-functional collaboration, where both the creators and users of data work together to ensure accuracy and consistency.

Organizational and Technical Barriers

Data migration is not just a technical but a business challenge that requires top-down support and organization-wide involvement. A lack of clear roles, responsibilities, and training can severely hinder a data migration project. Not only that, technical issues such as large data volumes, lack of adequate testing, and distributed teams can further complicate the migration process. To overcome these, organizations need proper planning, clear communication, and collaboration across departments.

Cultural and Process Changes

When implementing a new ERP system like SAP, businesses often face cultural resistance and process changes. Migrating data from one system to another frequently requires not just technical adjustments but also changes in business processes. Without buy-in from employees and adequate training, these changes can be a major barrier to successful migration.

Inadequate Planning and Lack of Expertise

Many companies struggle with planning their data migration, leading to delays, cost overruns, and poor outcomes. Furthermore, the lack of experienced personnel—both internally and from external consultants—can derail the migration process. Having a dedicated team with the right expertise and a solid project plan is key to overcoming this barrier.

These barriers make data migration far more complex than it might initially appear. The consequences of ignoring these challenges can be severe, affecting everything from operational efficiency to customer satisfaction. In the next section, we’ll dive into the specific consequences of poor data quality in ERP migration and how businesses can avoid them.

Consequences of Poor Data Quality in ERP Migration

Poor data quality in ERP migration can lead to serious consequences. Let’s break down the most common and critical impacts of poor data quality in ERP migration:

• Imprecise Reporting and Decision-Making: Poor data quality leads to inaccurate financial statements, incorrect inventory counts, and flawed business reports. For example, if inconsistent or incomplete data is entered into an ERP system, it results in unreliable information that can guide decision-makers down the wrong path. This not only weakens the company’s ability to respond to market changes but also threatens financial stability, making data accuracy a critical aspect of ERP success.
• Process Inefficiencies: ERP systems are designed to streamline business operations, but poor data can undermine these efforts. When incorrect data infiltrates the supply chain or inventory management, it often results in delayed orders, stock shortages, or even overstocking. This inefficiency not only raises operational costs but also creates a ripple effect of dissatisfaction across internal teams and customers alike.
• Increased Time and Effort: Inaccurate data leads to lengthy and complex data migration processes. Teams spend additional time cleansing and deduplicating bad data, causing significant project delays and rising costs. Every step of the migration is impacted as more effort is needed to manage poor data quality, ultimately straining resources and prolonging the implementation.
• User Resistance Challenges: Employees may resist using the new ERP system if they encounter ongoing data quality issues. Past experiences with flawed data can reduce their trust in the system, leading to reluctance in fully adopting the new processes. This resistance not only delays full implementation but also risks derailing the success of the entire ERP project.
• Customer Dissatisfaction: Bad data can directly impact customer experience. Incorrect billing information, shipping errors, or delays in service due to flawed customer records can result in dissatisfied clients. Over time, this leads to a loss of trust and can damage the company’s reputation in the marketplace.
• Regulatory Compliance Risks: Poor data can lead to non-compliance with regulations, especially when ERP systems fail to provide accurate data for legal reporting. Mistakes in data handling can lead to fines, penalties, or legal action. In regulated industries, such as finance or healthcare, the stakes are even higher, with poor data quality potentially jeopardizing the company’s standing.

To mitigate these consequences, addressing data quality issues early in the migration process is critical. Ensuring data is accurate, standardized, and validated before migration helps avoid operational breakdowns, financial losses and customer dissatisfaction.

What Happens When You Ignore Data Quality? Two Popular Data Migration Gone Bad Examples. 

Here are two real-world examples of how data quality issues have impacted the success of ERP implementations in significant ways:

Hershey’s ERP Implementation Failure (1999)

In 1999, Hershey Foods Corporation faced one of the most well-known ERP implementation failures, which was exacerbated by poor data quality. Hershey attempted to migrate to a new ERP system as part of an ambitious technology overhaul. However, the company did not adequately address data quality issues in its existing systems before the migration. There were significant data inconsistencies and integration errors during the data transfer process.

As a result, Hershey was unable to fulfill orders properly during one of the most critical sales periods of the year, Halloween. This resulted in $100 million in lost sales and a severe blow to the company’s reputation. The root cause was traced back to the failure to clean and standardize data prior to migration, leading to problems with inventory management and order fulfillment. This example underscores the critical importance of high-quality data when migrating to an ERP system.

Vodafone’s ERP Data Migration (2017)

In 2017, Vodafone experienced major issues during its ERP data migration when moving to a new global ERP system. The company aimed to integrate and standardize its operations across multiple countries. However, data quality problems arose due to inconsistent formats, duplicated records, and missing values in the legacy data systems.

Vodafone’s inability to clean and verify its data before migration led to billing errors and customer service disruptions. This resulted in customer complaints and significant regulatory fines from the UK’s communication regulator, Ofcom. The company was forced to pay £4.6 million in fines due to the failure of accurate billing systems caused by poor data quality during the ERP migration. The incident highlighted the risk of regulatory penalties and financial loss when data integrity is compromised during migration.

Avoid revenue loss and heavy fines with a strategic data migration plan with data quality at its core. Your end goal isn’t a new cloud or ERP solution – it’s better-managed data. 

Best Practices for Managing Data Quality During ERP Migration

Here are some key best practices for an ERP migration recommended by our data specialists. 

1. Develop a Data Strategy: The first step in managing data quality during ERP migration is to establish a clear data strategy. This strategy should define the standards, processes, and metrics for ensuring data quality throughout the project. Key elements of this plan include data cleansing, validation procedures, and assigning responsibilities to manage data quality. This proactive approach sets the foundation for a successful migration.
2. Engage Data Stewards and Data Governance Teams: Assigning dedicated data stewards or a data governance team ensures that there are clear roles focused on overseeing data quality. These teams are responsible for resolving any data-related issues and making sure that data management policies are followed consistently throughout the migration process.
3. Foster Collaboration Among Stakeholders: Collaboration is key to a smooth migration. Ensuring regular and open communication between business users, IT teams, and data experts can help identify and resolve issues early. All stakeholders must stay aligned on data quality metrics, project goals, and timelines to avoid any missteps during the migration.
4. Perform Data Profiling and Analysis: Before migration begins, it’s critical to thoroughly profile and analyze the existing data. This step involves identifying inconsistencies, duplicate records, and other issues that could complicate the migration process. Addressing these problems upfront reduces the risk of transferring bad data into the new system.
5. Define Data Migration Requirements: Clearly outline the data that will be migrated, including data mapping, transformation rules, and validation criteria. By defining these requirements early and in detail, you ensure that all necessary data elements are properly captured and accurately migrated, reducing the chance of errors later in the process.
6. Implement Data Cleansing and Transformation: Once the data migration requirements are defined, the data needs to be cleansed, standardized, and deduplicated. This step ensures that only high-quality, accurate data is transferred into the ERP system, preventing operational disruptions and maintaining system integrity post-migration.
7. Ensure Data Integrity and Validation: After data cleansing, implement integrity checks and validation mechanisms to verify the accuracy of the migrated data. These checks help ensure that the data in the new ERP system is both reliable and fit for use, preventing potential issues that could arise from incorrect data.
8. Conduct Thorough Testing: Once the data is migrated, it’s essential to test the ERP system thoroughly. This testing should cover functionality, performance, and data accuracy. By simulating various business scenarios, you can ensure the system works as intended and that the data is accurately processed and displayed.
9. Train Users and Provide Documentation: User training is an important aspect of maintaining data quality in the new ERP system. Employees should be educated on the importance of data quality, how to input and manage data accurately, and the best practices for using the system. Providing detailed documentation ensures users have a resource to refer to after training.
10. Monitor and Maintain Data Quality Post-Migration: Data quality doesn’t end after the migration is complete. It’s crucial to establish ongoing data audits and governance processes to ensure continuous data integrity. Regular monitoring, combined with scheduled data quality checks, helps maintain the accuracy and reliability of the data over time.

Ensuring data quality throughout the ERP migration process is not a one-time task, but a continuous effort. By implementing these best practices, businesses can protect the integrity of their data, prevent operational disruptions, and set a solid foundation for long-term success.

Strategies for Data Migration

Data migration strategies vary based on the organization’s goals, data complexity, and the urgency of the migration. Selecting the right approach is crucial to ensuring a smooth transition. There are two primary strategies for data migration: Gradual Introduction and the Big Bang Strategy.

Gradual Introduction

This method involves migrating data in phases, giving users the opportunity to adapt to the new system over time. The old and new systems may run in parallel during the migration, ensuring that any issues with the new system can be addressed while the old system remains functional.

Pros:

• Lower risk, as users can test the new system incrementally.
• Easier to fix errors or bugs without major disruptions.
• Allows time for training and adaptation.

Cons:

• The process can be slow and resource-intensive.
• Data consistency issues may arise from running two systems simultaneously.
• Increased complexity in maintaining both systems.

Big Bang Strategy

In this approach, all data is migrated at once, often within a short, defined period. The old system is switched off, and the new system takes over entirely after the migration.

Pros:

• Faster migration process with less prolonged disruption.
• Cost-effective in the short term, as resources are focused on a single migration event.
• Provides a clear-cut switch, reducing potential data synchronization issues.

Cons:

• High risk, as any errors during migration can cause significant operational disruptions.
• Requires thorough planning and testing beforehand to ensure success.
• Can overwhelm users who have to adapt to the new system all at once.

Recommended Approach

The best strategy depends on the complexity of the migration and the risk tolerance of the organization. For large enterprises with complex data systems, a Gradual Introduction may be more appropriate to minimize disruption and ensure data consistency. On the other hand, for smaller, less complex systems or companies with limited migration windows, the Big Bang Strategy may offer a quicker, more efficient solution.

Learn How WinPure Makes ERP Migration Efficient

WinPure is  a powerful AI-Powered data quality solution that simplifies the data cleansing and deduplication process during ERP migration. Its no-code platform allows users to clean vast amounts of data effortlessly, ensuring that duplicate records are removed and inconsistencies are resolved. This accelerates the migration process, reducing the time spent on manual data corrections and freeing up valuable resources. Whether it’s identifying duplicated customer profiles or cleaning up inconsistent address data, WinPure makes data migration seamless and error-free.

A critical component of data migration is ensuring that relationships between records are correctly identified. WinPure’s advanced entity resolution capabilities allow users to link related records across different data sources. Its AI-powered data match engine can identify hidden identities and resolve complex duplicate records within seconds. Whether handling variations in names or addresses, WinPure guarantees that data is migrated without losing its relational integrity. This reduces the risk of operational issues caused by missing or mismatched records.

WinPure is designed to work across various platforms, ensuring compatibility with major systems like Microsoft SQL, IBM, SAP, and Salesforce. Its flexibility allows businesses to seamlessly integrate it into their existing infrastructure without requiring extensive technical knowledge. This easy integration minimizes disruptions during migration, making the process more efficient and reliable.

Curious to learn more about how AI-Powered WinPure can transform your ERP migration project? Download our detailed whitepaper for in-depth insights into how WinPure can accelerate your data migration, reduce costs and ensure flawless execution.

Get the technical guide and see how you can use WinPure’s no-code technology to clean, dedupe, merge and purge data in just minutes before migrating the data. Click Here.​

Share this Post