Data Migration in an ERP Implementation

Table of Contents

By any parameter, an ERP implementation, whether a brand-new installation, an upgrade, or a consolidation, is a complicated and time taking initiative. With the huge amount of time, money, and other resources being utilized in such an implementation, any organization would like to ensure that the returns they get from their ERP are impressive. However, any ERP system runs on data that goes into it, and the data migration project is how your new ERP will get its fill of the fuel it needs.

The discussions around any ERP implementation is, at its core, a discussion of the data contained in it. How that data relates to the business, its environment, and the users who make the business work. This means that data migration is not just an important part of an ERP implementation, but a critical one.

With the 2027 end-of-support deadline for SAP ECC looming, organizations are actively migrating to the next-generation platform – SAP S/4HANA.

Migrating to a new system can be daunting, especially for organizations with complex MRO processes in SAP. However, with a well-defined SAP migration strategy that addresses MRO data migration challenges, you can ensure a smooth transition and unlock the full potential of your new system.

Here are some things that need to be considered during the ERP data migration

  1. Identifying the data – The data that needs to be migrated should be clearly identified before the project gets underway, ensuring that efforts are focused on data that matters to the business and redundant/outdated data pointers are identified and eliminated. This ensures that delays do not creep in the timeline in the later stages.
  2. Timing the migration – Talking of delays, it is also essential to know beforehand when the migration would be carried out. As real time data might be involved, the timing of the move becomes critical so as not to disrupt the business.
  3. Improving data quality – This is the part where master data management enters the picture. When you are spending time and money on a new system, it is essential that the data loaded into it is of an acceptable enough quality. To ensure this a data quality initiative should be a part of the data migration.

An organization has many different types of data and all of it cannot (and should not) be migrated at one go. The most common accepted upon order followed by companies is –

  1. Configuration Data – Initial data that needs to be entered once to setup the system.
  2. Master Data – The data that forms the backbone of most organizations. Should be treated very carefully.
  3. Transaction Data – This set of data contains most of the real time data and thus should be loaded as the last step.

ERP, just like all other systems in this world, works on the GIGO principle – Garbage In, Garbage Out. Corrupt data going in means that the results you get, the analytics you perform on them, and the decisions you take on their basis, would always be suspect.

Data quality improvement is a non-recurring process (in most cases). This means that performing it after data has been migrated to the new ERP will be time taking, cumbersome, expensive, and very challenging. Tackling it during migration makes much more sense.

It might not be obvious, but data quality issues can magnify when data migrates from a legacy system to a new, more advanced ERP system. This is because the new system is bound to have newer applications with a far richer feature set and a stricter data model. A clean data set going in will ensure that you are able to utilize your new ERP system in the best possible way.

Key Data Attributes to be defined

One of the most important steps to take while preparing for a ERP data migration project is to define the key attributes of data that need to be validated. This helps everyone involved in the project to understand exactly how the data is to be handled and what is expected of it. Also, it helps in maintaining and even improving the ultimate quality of the data.

Regardless of structure, type, or format, source data intended for migration should be validated in terms of the following key attributes:

  • Relevance – Is it relevant to its intended purpose?
  • Accuracy – Is it correct and objective, and can it be validated?
  • Integrity – Does it have a coherent, logical structure?
  • Consistency – Is it consistent and easily to understand?
  • Completeness – Does it provide all the information required?
  • Validity – Is it within acceptable parameters for the business?
  • Timeliness – Is it up to date and available whenever required?
  • Accessibility – Can it be easily accessed and exported to the target application?
  • Compliance – Does it comply with regulatory standards?

Most companies these days are swimming in data of all sorts, and even though they realize it is important, they are not always sure how to utilize it efficiently. On the other hand, the use of ERP, which itself thrives on data, is increasing across the business world, and companies are looking to derive greater returns from the solution. Proper data migration, with the help of an efficient data quality initiative, can ensure that the return on investment in your ERP is boosted. This also ensures higher operational efficiency during the data migration phase, as well as after it.

By taking a proactive approach to MRO data migration and incorporating it into your overall SAP migration strategy, you can pave the way for a successful transition. This ensures your MRO processes continue to run smoothly, leveraging the advanced functionalities of the new SAP system to optimize operations and maximize efficiency.

Verdantis offers, comprehensive data cleansing and enrichment services. Our team of seasoned professionals will guide you through every step of the process, ensuring a smooth and successful transition.

Get In Touch: [email protected] / www.verdantis.com/contact

About the Author

Picture of Verdantis

Verdantis

Related Posts

Book A Demo

Download The File

Your data is 100% protected with us via our non-disclosure agreement.

MRO Master Data Management for a leading $12Bn Oil and Gas company

Oil and Gas organizations face huge unwanted costs in the form of losses, penalties and compensation due to industrial accidents, taking a sizable chunk of their revenues. One of the important reasons for failures and accidents is the breakdown of key industrial infrastructure arising from ineffective management of the material base and inefficiencies in the MRO supply chain that are highly critical for the production and maintenance cycles.

More and more organizations are now looking to establish Master Data Management (MDM) implementations in order to manage their assets through informed decision-making based on reliable data and ensure optimal industrial safety across the enterprise.

About the company:

This US based company is a global leader in providing essential mechanical components for both land and offshore drilling rigs, complete drilling and well servicing rigs, as well as a comprehensive range of tubular inspection, internal coatings, drill string equipment, lifting and handling gear, and downhole drilling tools. It also offers supply chain services through distribution centers strategically located near major drilling and production sites worldwide. With over 800 manufacturing, sales, and service centers globally and annual revenues exceeding $12 billion, the company delivers customer-focused solutions tailored to meet the energy industry’s quality, productivity, and environmental needs.

Material Master Data Challenges

The company faced significant challenges with their master data.
• Lack of consistency and standards for the master data
• Incorrect, incomplete, unclassified and outdated attribute fields of master
data High volumes of data in multiple languages
• Multiple ERP and other legacy instances across the company
• Poorly structured material descriptions
• Lack of data governance and collaboration across cross-functional divisions and locations

Solution & Results Achieved

Solution: Material Master Data Harmonization & Ongoing Maintenance

The company implemented Verdantis Material Harmonize to cleanse and enrich its historical material master data. Using AI-powered tools and the Verdantis Content Repository, the solution classified, standardized, and eliminated duplicate materials.

Once the data was harmonized, Verdantis Material Integrity was deployed for ongoing data governance. It ensured continuous data accuracy through real-time validation, AI-driven classification, and duplication prevention, maintaining high data quality across enterprise systems.

Key Actions Taken:

  • Classified 150,000 materials into 1,210 UNSPSC and 500+ HTS codes
  • Parsed and normalized unstructured material attributes
  • Eliminated duplicates and enriched material descriptions
  • Established real-time data governance and approval workflows
  • Created a centralized repository for cross-functional data access

Results Achieved:

  • Improved Data Accuracy – Eliminated duplicate and inconsistent material records
  • Enhanced Classification – Standardized materials with UNSPSC & HTS codes
    Better Inventory Management – Optimized stock visibility and usage
  • Faster Procurement & Compliance – Enabled seamless material search & regulatory adherence
  • Sustainable Data Governance – Ensured ongoing accuracy with AI-driven maintenance
  • Informed Decision-Making – Centralized data repository for improved operational efficiency

By integrating Material Harmonize & Material Integrity, the company achieved a scalable, AI-powered Master Data Management framework, ensuring long-term data consistency, efficiency, and compliance.

Your data is secure and used solely for intended purposes. We prioritize your privacy and protect your information.