How Modern Data Cleansing Tools Are Transforming Master Data Management

Approximately 85% of businesses believe data quality issues directly impact their bottom line. This widespread concern is driving a surge in smarter ways to cleanse and manage enterprise data across industries. 

For asset-intensive industries such as manufacturing and energy, the reliability of systems including Oracle EAM depends on the accuracy of their master data. In environments where preventive maintenance, functional locations, and assets are managed at scale, data cleansing tools are critical for maintaining efficiency. They are no longer optional.

Understanding Master Data Management (MDM) in EAM

Master data refers to core business information about assets, equipment, vendors, and locations. Within EAM systems, this includes asset hierarchies, location identifiers, and spare parts catalogs. As organizations migrate or consolidate systems, inconsistencies quickly emerge. That is where the master data cleanse process becomes essential.

Master data management is not just about storing data. It is about ensuring that the data feeding into your CMMS or digital twin initiative is standardized and usable. According to Forrester, a significant portion of a company’s strategic decisions are based on flawed or duplicate data if master data isn’t properly managed.

How Data Cleansing Tools Automate the Transformation

Traditionally, data cleansing was manual. It involved spreadsheets and long stakeholder interviews. Today, modern data cleansing tools leverage AI, machine learning, and rule-based engines. This is applied in order to detect duplicates, fill in missing fields, and even recommend fixes based on historical patterns.

Imagine a utility company migrating its operations into an EAM platform. It uses an automated cleansing tool capable of processing over 2 million asset records in just a few days. The software identifies naming conventions, cross-references data between systems, and flags mismatches in PM schedules. What would typically take months using manual methods could be completed in under two weeks.

How Shell Used Data Cleansing to Power the Oren Digital Marketplace

As part of its broader digital transformation, Shell partnered with IBM to launch Oren, a global B2B digital mining services marketplace. The platform was designed to accelerate digital innovation across the mining sector. During this initiative, Shell prioritized the integrity and usability of its asset and operational data. This data was originally distributed across multiple legacy systems.

To address this challenge, Shell used data cleansing tools with AI-driven validation. These tools helped consolidate and standardize asset master data before the company deployed advanced digital solutions. As a result, Shell prepared its systems, including those using IBM Maximo, for integration with predictive analytics and reliability tracking. Eventually, these efforts also supported digital twin technologies.

This cleansing process allowed Shell to create a scalable model for clean and unified asset data. In turn, this supported internal operations and enabled broader ecosystem collaboration through the Oren platform.

Data Cleansing as a Strategic Enabler

Clean master data is not just about better reports. It also enables predictive maintenance and accelerates onboarding in new systems. Most importantly, it supports digitalization by providing a trustworthy data foundation. Whether migrating to an EAM platform or integrating IoT into a smart plant, data must be accurate.

Conclusion

Modern enterprises rely on interconnected systems and real-time analytics. As a result, data quality is more important than ever. A master data cleanse, powered by advanced data cleansing tools, transforms disorganized information into strategic assets. Companies that invest in data quality build a solid foundation for smarter decisions and better performance.