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We have recently sat down with Gary Lee, a SAP Master Data Manager, to get his take on some of the key topics to consider with SAP Master Data.

Gary gives some great insights not only for Consultants wishing to upskill into this space but also to Customers who wish to invest in a Master Data Strategy and why this is a good idea. 

We have separated this into a two - parts, with one focusing on skillset and what a position in Master Data involves whilst the other is focused on what customers may consider when developing Master Data Strategies.

We recommend reading both regardless of where your specific interest in Master Data falls. 

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In this article, Gary talks about what a Master Data Strategy can offer customers. 

Stay tuned for next weeks blog to see what Gary has to say on his journey into this space and advice for consultants wishing to get into this area.

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Why would you recommend a company to invest in its master data strategy?

Implementing a master data strategy enables the company to enhance the efficiency of operations and leads to better decision making.

As data gets even more scrutiny under the area of privacy, a sound master data strategy places the company in good stead in terms of internal and external audits and compliance. 

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What are the 3benefits you have seen from effective master data management tool?

1. Supports data governance framework and achieve better data accuracy

2. Improved business process through data insight (reporting on master data creation and related transactions)

3. Mechanised methods to offer easier ways to create, change and cleanse master data. 

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What are the biggest mistakes that a business could make while using Master Data?  

Often businesses implement master data processes as part of a large-scale ERP rollout project. The implementation is typically from the lens of the consultant with very limited business knowledge and input from the subject matter expert.

Moreover, some data are designed and implemented without the consideration of future maintenance for the business down the track. I have seen a series of custom field created for a bespoke function without provisions of a mass update resulting in extensive resources and time incurred by the business to manually update the data. 

Post green field implementation of S/4, Sales and Supply Chain typically get the focus for mid-cycle (1-2 years post rollout) business enhancement. The enhancement in other functional area’s without including master data in the development scope yields limited results and does not exploit the full benefit of the enhancement. 

Very little if at all in terms of master data enhancements are given budget approval to foster better efficiencies and data accuracy.

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What impacts do you see S/4HANA having on master data?

As part of the S/4HANArollout, there are more integrated bolt on systems such as Integrated Business Planning (IBP), EWM and workflow related collaboration tools that is typically rolled out in conjunction. 

These integrated programs constantly interact with S/4HANAusually with a dedicated server and are more data hungry to perform advanced functions. Therefore, it’s important to allocate time and resources for data cleansing and ideally starting on the legacy system well ahead of the S/4HANA rollout.

From a specific master data aspect, S/4HANA combines key Customer and Vendor data into a common record called Business Partners. The importance of cleansing and de-duplication in the Business Partner data is paramount as it will affect the business buying and sales. 

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What should companies do before implementing a new master data strategy?

Prior to implementing a new master data strategy, there needs to be top-down initiative in terms understanding master data fundamentals and the importance of having Data Governance. 

Business leaders, IT and data stewards must have clearly defined roles when developing an MDM solution. Establishing vision, guiding principles, scope and goals is also important.

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This will not only unify the decision-making process, but also help to ensure everyone is complying with ongoing governance regulations.

Well-considered policies and procedures are required to effectively manage data as an asset.

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Where do you see this technology going in the next year/5 years/10 years? 

Many of the Master Data Management (MDM) packages on offer are pushing AI in terms of predictive master data cleansing area. Data sets can be profiled via generic built in rules with a dashboard report break down of cleansed and un-cleansed records.

Currently, most of the MDM packages operate on an on prem model. In the next 5 years, we could see more solutions operate in the cloud for provisions for scalability. Artificial intelligence and machine learning will mature to interpret and potentially automate data governance processes. 

Looking in the horizon in the next 10 years, the concept of data fabric will potentially take form. The theory is to connect data from different sources, applications and systems (can be outside of the organisation) to create a unified view of data across the organisation.

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Example of this is taking a sample supplier: XYZ Air Filters Pty Ltd. The record would exist in many companies. Comparing data of multiple XYZ Air Filter Pty Ltd supplier records from different companies, the system can make a prediction of the accuracy of key data comparing records of the same company via a common code (ABN number as an example). 

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Thanks Gary for some great insights in to Master Data.

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Investing in a well-structured master data strategy is no longer just an operational advantage—it’s a necessity in today’s data-driven landscape. From improving data accuracy and governance to unlocking the full potential of advanced tools like S/4HANA and emerging technologies like AI, businesses can transform their operations and decision-making capabilities.

However, the key lies in thorough planning, alignment of stakeholders, and ongoing governance to ensure master data management continues to deliver value long after initial implementation. A proactive approach today will position companies to adapt, scale, and thrive in the increasingly interconnected and data-intensive world of tomorrow.