Master data lays the foundation for your supplier and customer relationships. It identifies who you are doing business with, how you will do business with them, and how you will pay them or vice versa – not to mention it can prevent fraud, fines, and errors. However, teams often fail to reap the full benefits of their master data because the data is in disparate systems, not accurate, or simply not present. This poor data management comes at a price— bad master data costs the US economy $3.1 trillion each year and results in a 25% reduction in potential revenue gains. 

Enterprises must improve master data management to transform operations, drive revenue growth, and prevent fraud.

Why Master Data Is Important

Operating with bad master data creates circumstances for fraud to occur and compliance failures, leaving businesses with not only losses but also fines and reputational damage for not having properly vetted their business partners. The master data management process, especially at the time of onboarding, is the perfect opportunity to ensure master data quality and enroll trading partners in all your strategic initiatives and communication channels. There is no time in a business relationship when the supplier will be more motivated to adapt to an organization’s policies and business requirements than at onboarding.

Enterprises working in an environment with multiple systems capturing data associated with their suppliers, vendors, or customers will have different pieces of data stored in each of these systems. The problem with this is that sometimes the data one system captures would be valuable to another system. Getting a holistic view of the data across the entire enterprise is critical for developing usable and actionable insights, so you need a system that brings all the information together. 

Successful master data management (MDM) is key to a thriving business, as it aids in automating compliance, stopping fraud, and driving strategic initiatives. Thankfully, there are now automated approaches to initiate and enhance the return on investment (ROI) of an MDM program. By bringing the data together, enterprises can get a 360-degree view that enables them to create order out of the data, develop insights, and gain real value. Even better is a single, central point of entry for master data to ensure completeness and consistent data quality across all your internal systems. 

The Six Phases of a Failing MDM Project 

As many as 85% of all MDM projects fail. There are many ways an MDM project can be derailed, but many failed projects follow this pattern:

1. Fighting over what master data is: Different stakeholders have completely different definitions of master data. There is a narrow academic definition, often held by IT, that limits “master data” to fields that are consistent across all of the enterprise’s internal systems. For the management of supplier (vendor) and customer relationships and associated business processes, this narrow definition is not appropriate. Business users define master data as every field stored in the master data tables of the systems they support. And to be most effective in the management of supplier and customer relationships, they need additional data to meet the demands of global compliance, and advance corporate strategies. This often turns into strong disagreement and conflict between IT and the business.  

2. Disagreement on governance rules for all master data: Once an enterprise has aligned on what data is and isn’t going to be mastered, it must then define the rules for governing master data. Some look for governance rules that simply test for the presence of data in any required fields and confirm that the data is in the right format. But, for master data to be as effective as possible, business users want the data to be verified against authoritative external data sources to ensure the data is not only present and in the right format but also accurate.

3. Determining the scope, argue about the budget, and remove application data from the scope: Most MDM solutions require a lot of custom, hands-on work, which is tedious and can end up overwhelming the team. When enterprises realize their enabling technology requires them to code the governance rules for every field that’s in their master data, they conclude that the scope is too big.

4. Hiring a lot of data stewards: After descoping functionality to make the project more manageable, enterprises turn to a team of data stewards to manage and manually fix any gaps and errors within their master data. Aside from being an overwhelming task to handle, hiring data stewards is one of the biggest challenges associated with these projects, because many enterprises don’t have the budget to cover them.

5. Hitting the panic button: After all the descoping and budget limitations, enterprises – typically leaders in the business – realize the project has gone sideways, and the solution being built will not meet the business’s master data management needs. 

6. Shutting the project down: Difficult conversations are had with upper management and the project is shut down or dramatically scaled back with less value than intended. 

Best Practices for Revamping Your MDM Program to Gain Real Value

Data quality doesn’t improve on its own. Teams can avoid a failed MDM project by relying on these three best practices: 

Master the Existing Master

The most immediate chance to make a difference lies within your existing dataset. Take the initiative to compare your supplier and customer master data with reliable external sources, such as government databases, regulatory lists, and other trusted entities, to pinpoint discrepancies and omissions. Consider this approach as a form of “data governance as a service,” as a shortcut to data quality where you can rely on comparison with the authoritative data sources to make sure fields are the right length, in the right format, and, even more important, accurate.

This task may require significant effort (unless automated master data validation and enrichment is employed), but it can provide an immediate ROI. Each corrected error and updated entry contributes to greater compliance, lower risk and enhanced operational efficiency within the organization. However, many companies lack a consistent process for cleaning data, and even among those with a process in place, the scope and frequency of data cleansing is often insufficient. The best data quality comes from continuous automated cleansing and enrichment.  

Establish Clean Data Processes

While organizing your current data, it’s crucial to concurrently refine your approach to incorporating new master data records. Take proactive steps to establish procedures that guarantee the completeness and accuracy of fresh data before its inclusion.

However, as is often the case with various master data management challenges, executing this strategy is frequently easier said than done. Failure by suppliers to furnish accurate data or adhere to established policies, procedures, and regulatory standards during the data collection process raises compliance issues. This, in turn, jeopardizes both product safety and quality.  

By clearly defining data standards, setting up validation rules, and robust change controls into your master data management strategy, enterprises can create and maintain clean master data, ensuring its reliability and integrity for effective business operations.

Remain Vigilant

Master data management is an ongoing process. Data degradation begins immediately, which makes regular maintenance and monitoring a necessity. It’s crucial to establish systems that consistently monitor external data sources for signs of fraudulent entries, company mergers; bankruptcies, and closures; regulatory risks; or any other factors that may necessitate updates.

Failure to incorporate this ongoing monitoring not only undermines the efforts invested in the initial two steps but also exposes your organization to risks. Outdated vendor contacts and inadequate master data change controls, for instance, can be exploited by fraudsters and bad actors to carry out sophisticated attacks, such as sending fraudulent bank account changes through techniques like spoofing and other forms of business email compromise (BEC) scams. A future-proofed MDM strategy always includes vigilance to ensure you have the most up-to-date master data.

Enhancing master data management requires an investment of time, effort, and domain expertise. Unfortunately, many companies find that the challenges lead to corners being cut or, in some cases, the abandonment of their MDM initiatives.  While it may seem elusive, an effective vendor MDM strategy is essential for optimizing vendor-related processes, ensuring compliance, minimizing risks, and supporting the overall success of an organization’s procurement and supply chain activities.