Tag Archives: implementation

Top 10 Best Practices for Successful MDM Implementations

Recently Aaron Zornes and I hosted a webinar on the “Top 10 Best Practices for Successful MDM Implementations.” Almost 700 people registered for it – one of the highest number of registrations I’ve seen for a webinar, ever! This number tells me that many practitioners are eager to learn the best ways to ensure that their Master Data Management (MDM) implementations are successful.

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How To Avoid Common MDM False Starts

Companies wishing to start a Master Data Management (MDM) project may be unsure where and how to begin.  MDM is a journey and success or failure at the first step either defines or dooms the further evolution of the project.  Recently, industry analysts have been recommending a cautious approach to starting with MDM – suggesting that companies start with a single data type (such as customer), implement MDM using a small footprint (such as registry style) or deploy MDM solely with a data warehouse to improve reporting.  Inherently these technology focused approaches reduce project risk and relieve the data governance burden.  Companies may readily adopt these approaches as perfectly reasonable starting points and lean to a more risk-averse approach to their initial MDM implementation in hopes of mitigating risks.  However, these same approaches may limit the scope and potential return on investment (ROI) from MDM since they do not attempt to solve the most pressing and difficult business problems. 

Some MDM vendor solutions only support a single data set (customer, product, etc), architecture style such as registry or can only be deployed for a single usage – either operational or analytical.  These solutions simply cannot be extended to other architectural styles or another usage mode which can severely limit their usefulness in addressing the most challenging of business problems.  In addition, a technology-centric start will not fulfill the most important needs around enterprise master data governance.

MDM is more precisely about solving business problems by efficiently managing master data that is critical to a company’s business operations.  How to get started? A pragmatic place to begin is to answer these three questions:

1. Which business problems need to be tackled? 
2. What is the business use? 
3. What are the business requirements for master data governance? 

What becomes obvious from answering these questions is that MDM will almost always require a multi-entity deployment (such as customer and product) and an architectural style that is not restricted to registry alone.  In most instances, synchronization with both operational and analytical systems may also be essential to effectively address the specific business needs of your organization.

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