Introduction
Master data is the core of an organization’s data architecture. It consists of important information that informs decision making, operations, and business strategy. Over time, this data is often splintered, duplicated, incorrect, or stale.
Master Data Cleanup is the key process for achieving data quality, trustworthiness, and consistency. Recently, organizations are increasingly making use of AI, machine learning, and cloud technologies to empower their data cleanup functions to support reliable operations and compliance with international standards.
Master Data Cleansing is essential for ensuring that an organization’s phenomenal data—such as supplier, customer, product, material, and employee information—is consistent, and unified across all processes.
What is Master Data Cleansing and Why is It Needed?
Definitions &
Differentiations
- Master Data Cleansing: The process of detecting and correcting corrupt, inaccurate, or incomplete records.
- Data Cleansing vs. Data Enrichment: Cleansing removes errors, while enrichment enhances data with additional attributes.
- Data Cleansing vs. Data Governance: Governance sets policies, while cleansing ensures data quality.
Operational Efficiencies Across Functions
- Procurement – Eliminates duplicate vendors, prevents maverick spending.
- IT – Improves data standardization and system integration.
- Supply Chain – Ensures data accuracy for forecasting and inventory management.
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Operational Efficiencies Across Functions:
Procurement
In procurement, data cleansing is crucial for keeping vendor and supplier data current and accurate. Having unclean data in the procurement system can create duplicate vendor records, which can lead to uncontrolled spending, mistakes in sourcing, and inefficiencies in overall operations.
Maintaining only the most valid and accurate vendor information can help procurement teams be more efficient in their work, negotiate better pricing, and prevent unauthorized or unnecessary spending.
Key Benefits of Data Cleansing in Procurement:
- Duplicate Vendor Records: A company may have two records in their procurement system for the same vendor, one listed as “ABC Supplies” and the other as “ABC Supply Co.” If procurement creates orders for both vendors, they may unknowingly pay two different prices for the same item. Data cleansing provides the information needed to combine these records into one accurate record to avoid paying more than necessary.
- Inconsistent Vendor Information: Another scenario is if a vendor has discrepant contact information, or payment and shipping terms from one record to another (for example, different addresses for the same vendor). In this scenario, the procurement team may receive the order late, or not at all, because the vendor did not know which address to ship to, or parceled the payment to an incorrect mailing address.
Cost Savings: By eliminating redundant supplier records and ensuring correct pricing and supplier terms, procurement can avoid overpaying, negotiate better deals, and prevent maverick spending (unauthorized or unplanned purchases).
Streamlined Processes: Cleansed data ensures that procurement teams are working with consistent information, reducing time spent searching for accurate vendor data and eliminating the need for manual data correction.
Information Technology
For IT departments, data cleansing is a critical part of ensuring data consistency and standardization across various systems and platforms. IT is responsible for managing the integration of data from multiple sources—whether from different departments, external systems, or cloud applications.
Cleansing ensures that data across systems (e.g., Enterprise Resource Planning (ERP) systems, Customer Relationship Management (CRM), or Master Data Management (MDM) systems) remains accurate and compatible, facilitating seamless integration and operational efficiency.
Key Benefits of Data Cleansing in IT:
Data Standardization Across Systems: When different departments or systems use varying naming conventions or formats for similar data (e.g., product names, vendor names, or inventory codes), it can create problems during data consolidation. For example, one system may list “Product A” while another lists it as “Product A-123,” which can cause discrepancies when integrating data into an ERP system. Data cleansing standardizes the product names across all systems, ensuring accurate synchronization.
System Integration Challenges: If there are inconsistencies in how data is structured (e.g., fields for customer information differ between CRM and ERP systems), it can lead to errors when trying to integrate data between systems. Cleansing ensures that data from different systems can be aligned and integrated effectively into a unified platform, minimizing integration issues and improving system performance.
Data Consistency: When migrating data from legacy systems to newer platforms, data cleansing helps eliminate inconsistencies and errors. For example, if customer data is being moved from a legacy system that has multiple formats for phone numbers (e.g., some have country codes, others do not), cleansing corrects these discrepancies to ensure smooth transfer and integration into the new system.
- Reduced Data Errors: Cleansing data ensures there are no discrepancies or errors in critical business data, reducing the risk of incorrect reporting, business insights, or system malfunctions.
Supply Chain
In the Supply Chain, data accuracy is paramount for successful inventory management, forecasting, and logistics operations. Master Data Cleansing in this domain ensures that information related to suppliers, inventory levels, and product specifications is accurate, reducing the risk of stockouts, excess inventory, and delivery delays.
Key Benefits of Data Cleansing in Supply Chain:
Order Management: Suppose the data for a specific order is incorrect (e.g., an item listed as “Shipped” when it is still in production). This can cause confusion and lead to delayed deliveries. Data cleansing ensures that all order data is updated in real time and correctly reflects the actual status of goods, enhancing order accuracy and timely deliveries.
Accurate Forecasting: By removing outdated or inconsistent data, supply chain teams can more accurately predict demand and plan future procurement needs, ensuring that they can meet customer demands without overstocking or understocking.
Enhanced Supplier Collaboration: With cleansed data, businesses can rely on accurate and up-to-date supplier records, leading to more efficient order processing and better collaboration with suppliers.
Obsolete Material Data: If a supplier is no longer providing certain materials or a part number is outdated (e.g., “Part 1244” is no longer valid but is still listed in the system), cleansing removes or updates these obsolete records, preventing unnecessary procurement or inventory costs.
Importance of Data Cleansing:
Data analytics cannot be overstated as it directly impacts the decision-making, and overall success of an organization.
Here are some key clues to explain why master data cleansing is critical:
Master Data Cleansing Processes for Different Data Domains
- Materials Master
Standardizing Part Names, Descriptions, and Classifications
Objective: To ensure that materials are consistently named, and described across all systems, minimizing errors in inventory management, procurement, and production processes.
Data Cleansing Process
Duplicate Identification: Use automated tools to identify and merge duplicate material records based on part numbers, descriptions, and classifications.
Standardize Naming Conventions: Establish a uniform format for part names (e.g., material type + size + grade).
Description Standardization: Automate the process to standardize descriptions (e.g., consistent use of terms like “mm”, “inch”).
- Item Master
Ensuring Unique Product Identification and Specifications
Objective: To guarantee each product is uniquely identifiable, with accurate and consistent specifications across all systems (e.g., inventory, and procurement).
Data Cleansing Process
Unique Identification:
Ensure each product has a unique identifier (e.g., SKU, UPC, or Item ID) to avoid confusion.
Review and resolve any conflicts or redundancies in identifiers.
Data Consistency:
Standardize product descriptions and attributes (e.g., size, colour, and material).
Set up rules for consistent formatting (e.g., units of measurement).
Attribute Verification:
Cross-check product data with external sources to verify accuracy
- Supplier Master
Eliminating Duplicate Vendors and Verifying Supplier Credentials
Objective: To maintain a clean supplier database, ensuring only verified suppliers are included.
Data Cleansing Process
Duplicate Vendor Removal: Use fuzzy matching and deduplication tools to identify duplicate supplier records.
Regularly audit: Audit supplier records to reflect any changes in supplier status
Standardize Supplier Information: Ensure consistent formatting for supplier names, phone numbers, payment terms, and other key data fields.
- Employee Master
Cleaning Outdated or Duplicate Employee Records
Objective: To ensure that employee records are complete, and free of duplication, helping with HR, payroll, and compliance.
Data Cleansing Process
Outdated Information Removal: Regularly verify and update employee records, removing outdated information
Data Standardization: Standardize the format of employee names, dates (e.g., date of birth, employment start date), and other key data fields.
Attribute Review: Regularly review job titles, departments, and other organizational data to ensure accuracy.
MRO Master
Standardizing Maintenance, Repair, and Operations (MRO) Data for Efficiency
Objective cleansing MRO data: To standardize and maintain accurate records for MRO parts, tools, and equipment, ensuring they are efficiently managed and available when needed.
Data Cleansing Process
Inventory Review: Regularly check inventory records to ensure all MRO items are properly tracked, including quantities, locations, and statuses.
Classification Alignment: Organize MRO items by category (e.g., maintenance supplies, repair parts) for easier access and management.
Supplier & Manufacturer Data Validation: Validate supplier and manufacturer information for MRO items, ensuring records are up-to-date and reliable.
- Customer Master
Customer Records with Sales and Marketing Databases
Objective: To maintain clean, accurate, and up-to-date customer records, enabling effective sales, marketing, and customer relationship management.
Data Cleansing Process
Duplicate Customer Identification: Use fuzzy matching and deduplication techniques to merge duplicate customer records across different systems (e.g., CRM, ERP).
Segmentation and Categorization: Segment customers based on key attributes (e.g purchase history) for more targeted sales and marketing efforts.
Data Enrichment: Enrich customer profiles by pulling in additional data (e.g., company size, or industry information) from third-party sources.
You can also read our client use cases below to understand how we have helped our clients to achieve enriched and standardized data.
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Best Practices for Data Cleansing
Establish Data Quality Rules: Develop and enforce naming conventions, classification structures, and data quality rules that all teams follow for consistency and clarity.
Align Cleansing Goals with Business Needs: Ensure that data cleansing efforts are aligned with business goals, whether for procurement, marketing, or financial purposes.
Centralized Data Repository: Centralizing data storage ensures accessibility, consistency, and transparency across departments.
Automated Data Validation & Cleansing: AI-driven tools can automate repetitive data cleansing tasks, reducing manual effort and improving accuracy.
Periodic Audits & Data Governance: Regular data audits and strong governance frameworks ensure long-term data accuracy and compliance with internal policies and industry regulations.
Data Governance and Cleansing Integration
Data governance and data cleansing work in tandem to ensure high data quality, consistency, and compliance across an organization.
How Data Governance Complements Data Cleansing
Data Governance defines the rules, policies, and standards for data management, ensuring compliance, consistency, and security. Data Cleansing ensures adherence to these standards by identifying and correcting errors, inconsistencies, and duplicates in the data.
Governance sets data entry rules (e.g., format, classification).
Cleansing ensures data complies with these rules and maintains accuracy over time.
Establishing Data Ownership & Responsibility
Data ownership assigns specific teams or individuals responsibility for the quality of specific data sets. Data stewards monitor, audit, and ensure data quality by identifying and correcting issues regularly. Clear ownership reduces ambiguity and maintains accountability for data quality across the organization.
Preventive vs. Corrective Data Cleansing
Preventive Cleansing
It involves implementing data validation rules at the point of entry, preventing data errors before they occur (e.g., format checks, mandatory fields).
Key Focus: Minimizing errors during data input (e.g., standardizing formats, validating mandatory fields).
Corrective Cleansing
It addresses issues in data that has already been entered, such as fixing duplicates, correcting misclassifications, or enriching missing values. This step ensures the data remains accurate and consistent after it has been ingested into the system.
Key Focus: Rectifying existing data errors and ensuring data integrity post-entry.
The Future of Master Data Cleansing
The future of master data cleansing in 2025 will likely see significant advancements, driven by emerging technologies, evolving business needs, and increasing data complexity. Here are some trends and directions that we can expect:
Artificial Intelligence (AI) and Machine Learning (ML) Integration
– AI and ML will play a central role in automating data cleansing tasks.
– Machine learning models will anticipate and correct data issues before they impact operations, learning from historical data and continuously improving their accuracy.
Data Quality as a Service (DQaas)
– Companies will increasingly rely on cloud-based platforms that offer data quality and cleansing as a service.
– These platforms will offer scalable solutions with built-in AI capabilities, making data management easier for organizations of all sizes.
Integration of IoT and Real- Time Data
– With the growing use of the Internet of Things (IoT), real-time data will increasingly become a factor in master data management.
– Companies will need to address the challenges of cleansing data that comes in real-time from various sensors and devices.
How Verdantis solves Master Data Cleansing?
- Process Automation: AI can automate the entire data cleansing pipeline, from error detection to data enrichment and validation, ensuring that the data is cleaned continuously and consistently.
- Processing Large Datasets: AI and ML can efficiently handle vast amounts of data, cleansing it in a fraction of the time that manual processes would take. This is particularly valuable for large enterprises with vast datasets, where traditional data cleansing methods are slow and cumbersome.
- Scalable Solutions: AI-powered data cleansing solutions scale with the organization’s data volume, allowing businesses to easily manage and clean data as it grows without needing additional manual intervention.
- AI-Powered Data Enhancement: Machine learning can enrich data by suggesting or pulling in additional information from external data sources.
Conclusion
Master Data Cleansing is a crucial process for organizations aiming to maintain data integrity, enhance operational efficiency, and make informed decisions. As businesses increasingly adopt AI-driven tools and cloud-based solutions, data cleansing is evolving to become more automated, real-time, and scalable, ensuring that organizations can meet the growing demands of the future.
By implementing best practices and leveraging the right technologies, businesses can achieve improved data quality, better decision-making, and streamlined operations across all functions.
