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Master Data Management
Master Data Management

Forward-looking companies are realizing the benefits of creating a unified and authoritative source of master data in response to a number of drivers. By moving to master data management, you can:

  • Develop and leverage customer-centric, rather than product-centric, views of customer and business interactions.
  • Create a unified definition of what constitutes a “product” across distributed lines of business and geographies.
  • Integrate organization, employee, and location data in response to merger and acquisition activity.

Integrating multiple data stores has proven difficult because of the disparate systems and inconsistent methods used to define data. Information is fragmented, and its sheer volume is sometimes overwhelming. Systems may not maintain documentation indicating the data structure, business definitions, or purposes for which data is being used.

Descriptive Info

EMC provides a range of master data management (MDM) services. The first steps in building a master data plan are to develop a strategy and create a roadmap. Then EMC consultants will select the tools you need for your MDM, create a design, and help you to implement a business-driven solution.

Once the master data repositories have been established, EMC consultants will help you standardize and convert data—and to establish data governance. Finally, it is important to establish metrics to maintain and enrich data quality over time.

EMC Consulting has helped clients implement master data repositories for reuse across enterprise activities. We have also helped establish the policies, process, and tools that sustain master data.

Descriptive Info

EMC consultants can help facilitate the design and implementation of a process to manage data. We help you define or develop roles, responsibilities, policies, processes, procedures, and the organizational structure for managing data.

Our Data Governance Enablement service includes four elements:

  • Envisioning and defining — We conduct an assessment and gap analy­sis, then generate a governance operating model and an initial set of data governance policies.
  • Building and deploying — Next we establish data quality, security, and privacy compliance rules.
  • Operat­ing and refining — The rules, policies, and standards are accepted and put into practice.
  • Measuring and managing


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