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January 13, 2009

An Evolutionary Implementation of Master Data Management

A holistic master data management (MDM) initiative requires attention to four key areas. It is not sufficient merely to identify and load data for master entities like customer, product, etc. from the sources to the MDM Hub.

Streamlined data processes, workflows, and data governance rules must be established to ensure that data is managed effectively. The master data repository needs to enforce data quality rules and ensure data is updated through internal data synchronization with other enterprise systems. Equally importantly, the data should be enriched through electronic data synchronization with partners, suppliers, and reference data pools such as those provided by Dun & Bradstreet and Silver Creek Systems. Finally, uniform global hierarchies need to be established for customers, vendors, items, etc. to ensure the business definitions are used consistently across the enterprise.

These activities cannot be performed all at once. An MDM program that attempts to include all systems and user groups in the first release will find it difficult to realize its business objectives and achieve desired levels of user participation, given the often conflicting perceptions of data between business units and their need to fit enterprise IT programs into their own business roadmaps. An evolutionary implementation of MDM is the key to success. This lets people opt in to the enterprise vision in a measured pace and also demonstrates the realization of business benefits (along with the initial pain) by the early adopters.

In the first Enablement phase, the objective is to establish the MDM repository for core master data. Data governance rules and processes are defined through workshops with data owners, architects, and business users. Importantly, metrics are defined using the MDM Value Realization Framework. The latter half of the enablement phase focuses on data acquisition and consolidation from primary source systems, and the pilot rollout of MDM-enabled processes. MDM tool selection and data modeling is also done in this phase. Proof of concept scenarios are defined and executed to test product capabilities as well as to define an implementation plan for the program roadmap.  

Once pilot MDM processes have been deployed and feedback incorporated, the Growth phase of the MDM program can kick in. This is typically an iterative phase, with each wave expanding to cover additional systems and user groups. The end state of the Growth phase is typically a rationalized system landscape with over 60% of the master data integrated into the hub and streamlined lifecycle processes. Importantly, data quality and compliance adherence should exceed 80%, which is easier said than done, given the challenge in creating an enterprise-wide business glossary and enforcing data quality rules. Business benefit realization should be evident by this stage for a well-performing MDM implementation.

The final stage can be termed the Profitability phase. This is a state of continuous improvement, with the ability to easily integrate additional systems and data elements without significant rework to the existing hub. Over 90% of master data should be managed through the hub, conforming to the data governance processes and rules. Redundant systems and data storage can be minimized and some systems retired. MDM and BI reporting should be against master data sourced from the MDM repository, and critically, the MDM Value Realization Framework benchmarks should be continuously validated.

Infosys MDM Implementation Journey

This multi-phase journey requires attention to budget and scope, and active participation of all involved business units with centralized governance a good fit for a heterogenous IT organization.