Data cleansing isn’t just background work; inaccurate records quietly erode your operations. A recent TDWI report finds U.S. workers waste an average of 540 hours per year dealing with bad data, costing about $24,000 per employee annually.
This means every incorrect supplier entry, missing field, or duplicate record forces your team to spend hours chasing down fixes instead of focusing on value-added work. It reduces the time available for innovation and increases the time lost in fixing avoidable problems.
That’s precisely why mastering the right cleansing steps matters. If you follow a structured cleansing process, you’ll stop errors from spreading and keep your core data clean, consistent, and ready for reliable operations.
This article explains the master data cleansing process step by step and shows how you can use it to maintain a clean core in your systems.
Get the full picture of the Clean Core Approach in our article “What is a Clean Core Approach?”.
Data cleansing is the process of checking your data and fixing anything that doesn’t belong. You’ll correct wrong values, remove duplicates, and fill in missing details so every record is accurate.
It focuses on master data like customers, suppliers, employees, and products, since these records connect to most of your business processes. By cleansing, you make sure your system runs on consistent and reliable information.
If cleansing tells you how to fix bad records, master data management shows you how to keep them organized. Read our article on Master Data Management System: Everything You Need to Know.
When your master data feeds multiple applications, a single mistake can multiply quickly.
For example, if an incorrect supplier bank account is stored in your ERP, the same error flows into procurement and finance systems, creating failed transactions.
Cleansing stops this chain reaction by correcting errors at the source, so downstream systems inherit only accurate records.
System integration doesn’t fail because of technology alone; it often fails because records don’t align.
If product codes differ between ERP and CRM, you’ll end up with mismatched transactions and reporting gaps.
Cleansing resolves these mismatches before integration, which saves you the cost of reconciliation later.
Compliance frameworks demand traceable and standardized data.
Cleansing ensures records like tax IDs, addresses, and payment terms meet regulatory formats, so audits don’t expose gaps.
Without cleansing, you risk non-compliance fines and lengthy investigations when records can’t be reconciled.
Processes such as order-to-cash or procure-to-pay depend on shared master data.
If a customer exists under multiple IDs, collections take longer, and invoices may be rejected. Cleansing harmonizes these records, allowing each department to work from the same trusted set and shortening cycle times.
When you migrate to a new ERP or cloud system, dirty data becomes technical debt.
If duplicates or inconsistent formats move into the new environment, you’ll spend more time fixing errors after the migration.
Cleansing before upgrades lowers rework costs and prevents failures during transformation projects.
Master data is the beating heart of your organization. Without it, you’re dead in the water. When master data is accurate, everything runs more smoothly.” — Hans‑Georg Emrich, SAP MDG via McCoy Partners
Defining quality requirements is about setting rules that every piece of master data must meet. You’ll need to agree on what counts as valid, complete, and consistent data for your organization.
For example, if you’re handling supplier data, the minimum requirements may include a legal entity name, a unique tax ID, and a payment term that matches finance policies.
This step goes beyond making a checklist. You’ll also need to decide how strict the rules should be.
If requirements are too relaxed, you’ll still carry errors into your core systems. If they’re too rigid, you may block useful records from entering.
Many organizations solve this by creating data quality scorecards, where records are graded instead of judged as only “clean” or “dirty.” That way, you can track progress and set thresholds for what enters critical systems like ERP.
Profiling data is the process of scanning your existing records to see how they compare against the rules you’ve set.
You’ll use tools or queries to measure things like how many customer records have missing tax IDs, how often phone numbers are in the wrong format, or how many duplicate names exist across different systems.
The value of profiling is that it shows you not just where problems exist but how they spread. For example, you might discover that 70 percent of missing supplier addresses come from a single entry system used by procurement. Knowing the source lets you fix the process, not just the data. Without profiling, you’d keep cleansing the same errors over and over.
Profiling also highlights data outliers. If one product catalog carries codes in a different structure than the others, you’ll know that harmonization rules are needed before integration.
Standardizing and normalizing means making records uniform so they can be trusted across all systems.
Standardization focuses on formats, such as using YYYY-MM-DD for every date or keeping currency codes in ISO format. Normalization goes further by mapping values to agreed standards. For instance, mapping “UK,” “U.K.,” and “United Kingdom” into one consistent entry.
This step often requires building lookup tables or using reference data sets. You’ll need to decide which source becomes the authority when conflicts occur.
For example, should product codes be taken from the ERP or from the product catalog? Standardization also requires governance.
If users can keep entering free-text values, you’ll lose consistency within weeks. Many organizations apply validation at the point of entry, so that incorrect formats are rejected immediately.
The trade-off is between speed and control. Allowing flexible entry makes processes faster, but weakens your clean core.
Enforcing strict normalization improves reporting and integration but may slow down operations if exceptions aren’t managed properly. Balancing these two is what makes the standardization step challenging.
Duplicates occur when the same entity is entered more than once, often in slightly different ways.
For example, one team may create a supplier record as “ABC Ltd” while another adds “ABC Limited.”
Both look valid, but they represent the same supplier. If you don’t handle duplicates, you’ll face problems in billing, reporting, and compliance.
To detect duplicates, you’ll need both automated checks and business rules. Automated checks can use fuzzy matching or similarity scoring to flag possible matches.
Business rules confirm which attributes must align before merging records, such as tax ID or registration number.
The challenge is that not all duplicates are obvious, and not all close matches are duplicates. Merging too aggressively can delete valid entries, while being too cautious can leave duplicates in place.
The best practice is to create a review process where flagged records are validated by data stewards before final action.
Validation ensures that each record follows the rules you defined and, where possible, matches external reference data.
For example, you can validate tax IDs against government formats or check product codes against the official catalog. This step stops incomplete or false records from flowing into your clean core.
The key decision here is which references to trust. If you use multiple sources, conflicts may arise, and you’ll need to define which one becomes the system of record.
For instance, should customer addresses be validated against your CRM, against postal data, or both?
Another challenge is cost. Some external validations require subscription services, and you’ll need to balance the value of higher accuracy against the expense.
Without validation, errors will keep slipping into your system and multiplying downstream.
Enrichment fills the gaps in records so they’re more useful. For instance, a supplier record may be missing industry classification codes, or a customer record may not include demographic details.
By enriching this data, you’ll make it easier for teams to analyze, segment, and make decisions.
Enrichment can be done with internal data or external sources.
Internal enrichment might mean linking HR data with payroll data to fill in missing employee attributes.
External enrichment could involve adding credit ratings from a third-party provider. The risk is that enrichment adds cost and sometimes brings new errors. If external data isn’t updated regularly, you might enrich your system with outdated information.
To avoid this, you’ll need governance rules on when and how enrichment should be applied.
Governance defines who owns the data, who can change it, and what processes they must follow.
Monitoring checks data quality continuously, while auditing tracks the history of changes. Together, these practices keep cleansing from being a one-time project and turn it into a sustainable discipline.
In practice, governance may assign ownership of supplier data to procurement, while finance oversees payment terms. Monitoring then runs automated checks to flag anomalies, such as duplicate bank details or missing tax IDs.
Auditing makes sure every change is logged, so you’ll know who updated a record and when. Without governance, errors return quickly.
Without monitoring, you won’t notice problems until they spread. Without auditing, you’ll lack the traceability needed for compliance.
Once data is cleansed, you’ll need to push it across all connected systems so everyone works from the same source of truth.
Integration ensures that ERP, CRM, HR, and other applications use the same standardized records. Propagation keeps those records synchronized whenever updates occur.
The challenge here is two-way flow. If one system updates a supplier’s details but the change doesn’t reach the others, duplicates and mismatches return.
To avoid this, you’ll need synchronization rules and sometimes middleware that handles updates across applications.
Another factor is timing. Real-time synchronization reduces conflicts but requires more resources, while batch updates are cheaper but can create temporary mismatches.
Choosing the right model depends on how sensitive your processes are to timing.
eSystems supports your organization in cleaning, consolidating, harmonizing, and standardizing master data as part of its Master Data Management solution. The goal is to give you a single, accurate view of core records such as customers, suppliers, employees, and products.
The process begins with identifying and curating data sources across your systems. This step gives you visibility into how different applications contribute to fragmented records and prepares them for consolidation into a unified view.
Workflows are then applied to monitor how data pipelines run. If a job fails, notifications are sent to the right teams to prevent bottlenecks. This keeps cleansing embedded in your operations rather than leaving it as a one-off project.
Harmonization and two-way synchronization follow. With the Harmonization Orchestrator, eSystems aligns master data across all systems and ensures that updates flow both ways. When one system changes, the others are automatically updated.
To sustain cleansing, eSystems equips you with tools like the MD Catalogue, MD Repository, and MD API services. These allow you to enrich incomplete data, store and manage records, and give external applications controlled access. This means your system not only stays clean after the initial project but continues to support reporting and compliance.
With eSystems Master Data Management, you’ll move from one-time cleanups to continuous master data cleansing that sustains a clean core across your enterprise.
Master data cleansing isn’t a one-time activity but an ongoing process that protects the accuracy of your core systems.
By setting clear rules, profiling records, standardizing formats, removing duplicates, and monitoring changes, you’ll prevent errors from spreading across applications.
A structured cleansing process builds trust in your data, reduces rework in future upgrades, and keeps your organization ready for growth, compliance, and efficient decision-making.
eSystems is a Nordic partner that helps organizations simplify digital transformation and manage their core data with confidence. We focus on removing complexity from your systems so you can rely on consistent, accurate, and accessible information across every function.
Through our Master Data Management approach, we support you in cleaning, consolidating, and harmonizing records that flow across multiple applications. This ensures that your master data stays accurate and your systems operate on a clean and reliable foundation.
Get started with eSystems today to make master data management and cleansing a sustainable advantage for your organization.
Master data cleansing means checking your core records like customers, suppliers, or products, and fixing errors, removing duplicates, and filling missing details so all records are accurate and consistent.
You can remove duplicates by using matching rules or software that compares key fields like tax IDs or names, then merging or deleting extra records so only one correct version remains.
The main steps are defining quality rules, profiling data, standardizing formats, removing duplicates, validating against trusted references, enriching missing fields, and monitoring records to keep them clean.
You standardize by putting data in the same format, such as one date format or one currency code. You normalize by mapping values to one accepted standard, like “USA” instead of “US” or “United States.”
Without cleansing, reports pull data with errors, duplicates, or gaps. Clean data ensures every department uses the same trusted information, which makes reports correct and decisions reliable.