Product Data Cleansing: How to Fix Legacy Data After an Acquisition or ERP Migration
Merging systems often reveals hidden product data issues. This guide outlines how to clean legacy data after acquisitions or ERP migrations to create a reliable single source of truth.
- Sean Purdy
- January 16, 2026
- 11:11 am
Table of Contents
What You'll Learn:
Why data quality erodes during acquisitions and ERP migrations, and how it undermines operational efficiency
The real costs of dirty data, including the $9.7-$15 million annual losses manufacturers face from poor data quality
Five critical data issues that emerge when merging legacy systems or migrating platforms
How Best PIM for Manufacturers centralizes and cleanses product information to create a single source of truth
Proven data cleaning techniques to maintain accuracy during major system transitions and prevent future degradation
When a manufacturing company acquires another business or moves to a new ERP system, the challenge goes far beyond combining operations. The company also takes on years of inconsistent, incomplete, and duplicate data that already exists across systems.
This inherited data can quickly create problems in daily operations. Production planning becomes harder, inventory data is less reliable, and customer orders are more likely to be delayed or filled incorrectly. Product data cleansing is often the first and most important step in turning scattered information into reliable data that supports growth.
The need for data cleansing is even clearer when you look at outcomes! Businesses that clean their data before migration experience about 30 percent fewer issues during implementation. Still, many manufacturers delay or underestimate this step, only to discover that inaccurate data weakens their new systems from the very beginning and leads to higher costs and ongoing disruption.
1. Why Legacy Data Creates Operational Chaos
Bottom line: when companies are acquired or legacy ERP systems are involved, each system brings its own data standards, and those differences make integration far more difficult than it first appears.
As your teams begin merging product data from multiple sources, they’ll quickly learn that each of your systems seems to follow its own logic. One system may use inches to record measurement while another uses centimeters. This seems minor, but it can quickly escalate into mass confusion.
Product descriptions add another layer of complexity. They frequently vary in their format or level of detail and that makes comparisons difficult for the customer. Parts numbers might follow completely different conventions, too. Together, inconsistencies work to make your life harder – you can’t rely on your product data for everyday decisions.
Why it matters: Data quality erodes over time, so legacy ERP systems harbor years of accumulated data errors, information gaps, and duplicate records. These issues lead to inaccurate reporting and operational inefficiencies in your new system.
Manufacturing operations depend on precise product specifications to run smoothly. Even a small discrepancy in raw material counts can delay production lines, and it can absolutely create compliance risks tied to data protection requirements. Furthermore, it can put delivery commitments at risk. When your warehouse management system, ERP, and manufacturing execution system aren’t aligned through clean, reliable data flows, teams end up building processes on outdated or incorrect information.
The data fragmentation problem often starts inside the acquired organization itself. Over time, different departments may have maintained their own systems, such as standalone accounting software or spreadsheet-based inventory tracking, without shared standards across the business.
Each team followed its own data entry habits and processes, usually without shared standards across the business. As a result, the same product information could be stored in multiple places, formatted differently and updated inconsistently. As you could imagine, this made it difficult to create a single, reliable source of truth.
This creates what industry experts call “data silos” – these are isolated information pools that prevent comprehensive data analysis. According to NetSuite, manufacturers generate enormous volumes of information across operations, but most valuable intelligence remains trapped in disconnected systems, creating blind spots that lead to missed opportunities and costly inefficiencies.
The transformation challenge: Data transformation becomes exponentially more complex when handling multiple data sets that don’t share the same format. Your team must reconcile date formats, address syntax errors, and standardize numeric values across systems… all while ensuring the data remains accurate throughout transformation processes.
2. The Financial Impact of Dirty Product Data
Bottom line: Poor product data quality costs manufacturers between $9.7 million and $15 million annually, directly impacting your ability to compete and grow.
Research shows that dirty data can cost businesses up to 25 percent of their revenue, with an estimated impact on the U.S. economy exceeding $3 trillion annually. For manufacturing organizations, these losses manifest through operational disruptions, compromised customer experiences, and wasted resources spent correcting errors after the fact.
Where the money goes becomes clear when inaccurate data starts affecting your daily operations. Production delays happen when scheduling is based on incorrect information, which can frustrate customers and damage your reputation. Inventory errors caused by duplicate records or inaccurate product data can lead to overstocking costly materials or, just as damaging, running out of critical components in the middle of production.
Data errors can also be disruptive to your supply chain in ways that will add up – fast! Procurement teams may order the wrong quantities, sales teams may commit to delivery dates that cannot be met, and quality issues can surface when product specifications do not match actual requirements. Together, these breakdowns turn data problems into real financial losses.
The human cost: Research indicates that approximately 27 percent of operational time is spent remediating data. That represents time your team could redirect toward productive output instead of tracking down discrepancies, reconciling conflicts, and implementing data cleaning steps to fix preventable problems.
Hidden compliance risks: Thesecan surface quickly, especially for organizations operating in regulated industries. Poor data quality increases the risk of non-compliance, which can lead to fines and long-term reputational damage. When audit trails don’t line up or product specifications fail to meet industry standards, the consequences can be far-reaching and difficult to undo.
The impact is just as real on the e-commerce side of the business. For manufacturers selling through digital channels, product data cleansing is essential to maintaining visibility and trust. Incomplete descriptions, missing attributes, and simple typos can hurt SEO performance and make customers question the reliability of your brand.
Manufacturing businesses can’t afford to ignore data quality during critical transitions like acquisitions or ERP migrations. Fixing data issues after a system goes live almost always costs more than addressing them upfront, which is why proactive product data cleansing is a far smarter investment.
3. Common Product Data Quality Issues After Acquisitions and Migrations
Bottom line: There are five specific data problems that consistently emerge during major system transitions, and each requires targeted remediation through data scrubbing.
Duplicate product records: When you’re merging catalogs from multiple companies, the same entity often appears under different SKUs, part numbers, or descriptions. These duplicate listings skew inventory reports and create confusion across departments. In short, they undermine analytics. Your sales team might offer customers different prices for identical items simply because they’re pulling from different database entries.
Advanced duplicate detection requires more than matching exact values. Your system must identify records that represent the same product, despite variations in naming, formatting errors, or missing information. This challenge gets bigger when you’re dealing with thousands of data points across legacy systems.
Inconsistent attribute formatting: One system stores product dimensions as “10x5x3” while another uses “10 inches length, 5 inches width, 3 inches height.” Specs might list materials as “SS304” in one database and “Stainless Steel 304” in another. According to TechTarget, when data is formatted differently by each database, it must be reformatted before being used by other systems, severely limiting utility and making integration far more difficult.
Why standardization matters: Your new ERP system expects source data in specific formats. Without standardization through proper data cleaning techniques, automated processes break down. Reports generate errors. Integration between modules fails. Customer-facing systems display inconsistent data that erodes customer trust.
Missing critical information: It’s rare for your legacy systems to capture all of the attributes that modern operations require. You may have basic product descriptions but no detailed tech specs. maybe your material composition data is incomplete. Or perhaps your compliance certifications aren’t documented.
Your data cleaning process should identify all of these issues and determine whether the info exists elsewhere. Maybe it’s in your source system, or could be obtained from your suppliers. In any case, handling missing values involves deciding whether to use default values or remove records based on analysis.
Outdated product information: Acquired companies may have discontinued products still listed as active. Pricing hasn’t been updated to reflect current costs… the data isn’t up to date. Supplier relationships have changed but old vendor codes remain. Product lifecycles aren’t tracked, leaving obsolete items cluttering your catalog without proper data validation to flag them.
Conflicting hierarchical relationships: Bill of Materials structures don’t align your between systems. One company’s “parent-child” component relationships might contradict another’s product taxonomy. Manufacturing data integration challenges include maintaining these complex relationships during migration, which is crucial for accurate production planning.
Structural errors in raw data: Beyond content issues, the underlying data architecture typically contains structural errors: tables with inconsistent schemas, relationships that don’t maintain referential integrity, and fields that combine multiple data points where they should be separate. By analyzing source data, you can reveal architectural problems that your processes should address.
4. How PIM Software Solves Product Data Cleansing Challenges
Bottom line: Product Information Management systems provide the centralized infrastructure manufacturers need to cleanse data, standardize quality, and maintain accuracy across all operations using automated tools.
Creating a single source of truth: The best PIM for manufacturers consolidates all of your product information from multiple legacy data sources into just one unified platform. Rather than maintaining separate data warehouses that will eventually drift out of sync, PIM gives you and your teams centralized control. Each and every department accesses clean data from the same authoritative system.
This architectural approach addresses one of the biggest challenges in acquisitions and system migrations: multiple systems operating with different, incompatible logic. Instead of forcing every platform to manage product data on its own, PIM serves as the central place where high-quality, reliable data is maintained.
From there, all of your data is carefully synchronized to your ERP, MES, e-commerce platforms, and other connected systems through controlled data flows. This ensures that each system receives consistent information… without reintroducing the errors and inconsistencies that caused problems in the first place!
Why centralization matters for manufacturers: When engineering updates a product specification, that change automatically propagates to production planning, quality control, and customer-facing documentation. No more manual updates across multiple systems. No more version conflicts. No more wondering which database contains accurate data.
Automated validation and cleansing: Modern PIM platforms include intelligent validation rules that identify and flag data quality issues. The system automatically implements duplicate detection by analyzing similarities across multiple attributes, not just exact matches. It spots formatting errors and suggests standardization. Missing required fields trigger alerts before incomplete data causes operational problems.
According to Control Engineering, the data cleansing process can be time-consuming, but it’s a critical step in preparing information for effective use. PIM software accelerates this process through automated workflows while maintaining accuracy that manual methods cannot achieve at scale.
Key benefits of automation: Using automated tools for data cleaning gets rid of much of the repetitive manual work that can consume as much as 27 percent of your operational time. Rather than fixing errors after the fact, rule-based validation identifies problems at the point of entry. You can stop bad data in its tracks. Machine learning models add another layer of protection; they’re good at spotting unusual patterns in product data that human reviewers may overlook.
The platform applies data cleansing rules consistently across the entire product catalog, no matter which legacy system the data came from. By enforcing the same standards everywhere, it creates reliable product data that teams can trust when making day-to-day and strategic decisions.
Handling complex manufacturing data: PIM systems excel at managing hierarchical product relationships essential for manufacturers. Bill of Materials structures, component dependencies, and assembly hierarchies all maintain integrity within the platform. When you update a sub-component, the system automatically reflects that change across all products using that component through intelligent data transformation.
Product variants and configurations, a major challenge in manufacturing, become manageable. Instead of creating separate records for every color, size, or specification combination (generating massive duplicate data), PIM will use attribute-based logic to generate variants… meanwhile maintaining data consistency.
Data enrichment capabilities: Beyond cleaning up existing information, PIM also supports data enrichment at scale. Teams can efficiently add missing details, improve product descriptions, attach compliance documents, and link images or other media, all within structured workflows that maintain consistency across the catalog.
The system also handles missing values in a smart, deliberate way. It can tell the difference between information that is truly missing and needs further research and fields that are empty simply because they were never filled in correctly. This approach helps prevent both incomplete records and incorrect assumptions from entering the system.
Integration with data science workflows: For manufacturers who use advanced analytics, PIM delivers clean, well-structured data that is ready to go! Machine learning models depend on consistent and accurate data, and that’s exactly what data cleanup results in. When statistical methods are applied to clean data, the insights are much more actionable.
Because of this, product data shifts from being a liability during acquisitions and system migrations to becoming a strategic asset. Instead of slowing the business down, clean data supports data science initiatives, improves operational efficiency, and enables customer experiences that truly set your business apart.
5. Building a Sustainable Product Data Cleansing Process
Bottom line: Successful product data cleansing requires methodical planning, clear ownership, and commitment to ongoing data governance, not just a one-time cleanup effort.
Start with comprehensive data auditing: Before you try to cleanse your data, you’ll need to know what you’re working with. Conduct a thorough profiling of your product data in each and every legacy system you’ll involve. Document the structure and completeness of that data, and analyze the quality level while you’re at it.
Identify any specific pain points you suffer. Which product attributes have the most inconsistent data? Where are the critical gaps representing missing data? What duplicate patterns exist? This diagnostic phase informs your cleansing priorities and resource allocation for maximum impact.
Establish clear data standards: Next, you’ll develop comprehensive guidelines for your data across your entire organization. Define standard formats for measurements, date formats, and naming conventions. Create data dictionaries that specify the allowed values for categorical attributes. Document how product hierarchies and relationships should be structured to ensure consistency.
According to Panorama Consulting, discussion of your requirements with each of your departments helps everyone understand each other a little better. It also helps you understand how your teams will be using the system. Align every component of your project around data quality, and involve your stakeholders.
Why cross-functional involvement matters: Your product data is sent to every single part of your operations. Your engineering crew needs accurate tech specs. Production needs accurate BOMs. Sales needs current pricing. And quality control should be able to access compliance documentation.
Form a data governance team, recruiting reps from each department. This will help you make sure that your cleansing priorities reflect your actual business needs. Standardization decisions gain cross-functional support across your business.
Phased implementation approach: Don’t attempt to cleanse data across everything all at once! Prioritize the steps based on business impact. Start with products that are actively in production or with your high-revenue items. Address the most critical data attributes first, like those directly affecting operations or customer experiences.
This phased approach delivers quick wins that build momentum while making the overall project manageable. It also allows you to refine the data cleaning process based on early results before scaling to the complete catalog.
Leverage the right tools: While some organizations attempt data scrubbing using spreadsheets and manual processes, this approach doesn’t scale for manufacturers with thousands of SKUs. The best PIM for manufacturers should provide your with purpose-built capabilities that can accelerate product data cleansing, all while maintaining the accuracy your business needs.
Ongoing maintenance practices: Product data cleansing isn’t just a one-time project. It’s an ongoing discipline. Plan to establish regular review cycles, and to implement automated monitoring that flags new data errors as they pop up. And be sure that you create processes that prevent dirty data from entering your systems in the first place through validation at the point of entry.
Research shows that organizations that invest in data cleansing before migration experience 30 percent fewer issues. But the benefits extend far beyond implementation. Clean data is a competitive advantage because it enables faster decision-making and improves your overall customer satisfaction.
Measuring success: Start by defining clear metrics to track improvements in data quality over time. Common measures include duplicate entry rates, data completeness scores, and how consistently information follows established standards.
It’s also important to measure the business impact of cleaner data. Fewer errors, faster workflows, and better system performance help you demonstrate to leadership just how your data quality is impacting the bottom line. Together, these metrics support continued investment in data governance and make the return on investment easier to demonstrate. Since roughly 30 percent of company data becomes outdated each year, regular data quality assessments are super important to keeping product information accurate and reliable.
The transformation payoff: If manufacturers like you approach ERP migrations with a strong strategy, you set yourself up for long-term success. Rather than spending years dealing with inherited data problems, you can build a foundation of reliable data that supports growth from the very beginning.
Key Takeaways
Product data cleansing is mission-critical during acquisitions and ERP migrations – not an optional nice-to-have that can be deferred
Financial stakes are substantial, with manufacturers losing $9.7 to $15 million annually from poor data quality that undermines operations
Five common data issues consistently emerge: duplicate records, inconsistent formatting, missing information, outdated entries, and structural errors requiring data transformation
PIM software provides the enterprise infrastructure with automated tools, validation rules, and data enrichment capabilities needed to maintain quality data at scale
Successful strategies require cross-functional collaboration, phased implementation using proven data cleaning techniques, clear governance, and commitment to ongoing maintenance
Clean and accurate data drives competitive advantage through improved customer experiences, operational efficiency, and reliable analytics supporting data science initiatives
FAQs:
What is product data cleansing and why is it important for manufacturers?
Product data cleansing refers to the process of finding and fixing mistakes in your product information. The goal is data that’s accurate, complete, and consistent. This may include correcting errors, filling in missing details, and making sure all your information displays consistently across your systems.
For a manufacturer, clean data is of the utmost importance. It supports accurate product specs, inventory counts and more! When the info is incorrect or incomplete, it can lead to confusion and delay – and both cost you money.
Data cleansing becomes especially important during major changes such as acquisitions or ERP system upgrades, when data from multiple sources must be combined. Without proper cleansing, problems in the data can quickly multiply.
How long does the data cleansing process take after an acquisition or ERP migration?
The amount of time you should allocate to data cleansing will depend on how much data you have. You’ll also need to consider how consistent it is. Smaller manufacturers with about 5,000 SKUs can likely complete the process in around 8 to 12 weeks, if they’re using automation within a PIM.
Bigger organizations with 50,000 or more SKUs may need several months – sometimes between 6 and 9 – because of the added complexity of their data. Many manufacturers choose to clean the most critical data first so they can implement systems using just that. This allows them to see benefits sooner while they continue to clean the rest of the catalog over time.
What’s the difference between data cleansing and data migration?
Data migration refers to the process of moving info from a legacy system to a new one like a PIM platform. Data cleansing focuses on improving the quality of that information by fixing errors, removing duplicates, and standardizing the format of your data.
While migration moves data, cleansing makes sure it’s usable. The most successful projects combine both – they clean your data first, check it during transfer, and monitor it after the new system is live.
Can we cleanse data manually or do we need specialized software?
Manual data cleansing using spreadsheets can work for super-small product lists, but it becomes difficult to manage as data grows. If you have thousands of products, this manual work will take a lot of time – plus, it increases your costs and introduces human error.
Specialized PIM software helps you automate data cleansing. It identifies errors and catches duplicates before they’re shown to your customer. Formatting rules are enforced, and workflows are as well!
How do we prevent product data from becoming dirty again after cleansing?
To keep your product data clean over time, you’ll need to set clear rules and consistent processes. Companies also need the right tech, and should define how product information must be entered so that everyone’s following the same standards.
Approval workflows, automated validation, and regular reviews will help you catch issues before they spread. PIM supports this. It enforces rules across your systems and keeps your data consistent – no matter where your customers find it.
What happens if we skip data cleansing during an ERP migration?
When dirty data is moved into a new ERP system, your existing problems are just moved. Reports could be inaccurate from the start, your inventory data may be bad, and your product details may look different on each storefront. Obviously, this won’t work.
These issues can disrupt your operations and your customers. Many manufacturers will have to clean their data anyhow, but doing so before the system is live makes a lot more sense.
How does PIM software help manufacturers with product data cleansing?
PIM software brings product data from many sources into one central system. This makes it easier to review and correct. Built-in validation rules will help you catch errors early, and detected duplicates can be removed to avoid confusion.
Manufacturing-focused PIM systems are designed to handle complex product structures, variations, and Bills of Materials. By keeping data accurate and complete, PIM software supports daily operations and provides a strong foundation for long-term improvement.
