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Top 13 master data management (MDM) buzzwords and definitions

MDM definitions

In its simplest form master data management (MDM) is a comprehensive method of letting companies leverage and re-use common and accurate business data. However, it's also a quickly evolving data management strategy that can do more than just help organizations "reach a single version of the truth." MDM systems are designed to maintain a master version of a company's data, freeing the organization to act on the data by mapping it to business rules and initiatives more easily than previous data management methods. One of the main reasons companies turn to MDM is the ability to reconcile data from different sources and applications.

Whether you're thinking about implementing a new MDM system or evaluating possible tools and software to help your current data management and data integration initiatives, it's vital to understand current trends in this market, including the latest MDM definitions and industry buzzwords.

 And to help you get started (or reacquainted) with MDM, we've compiled a list of 13 top terms in this market, including data governance, product information management (PIM), enterprise master patient index (EMPI), MDM hub and more. Each MDM definition is linked to useful MDM tutorials, training, advice and articles to help you optimize MDM initiatives at your organization.

 


Table of contents

Top 13 MDM definitions and buzzwords *

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1. Metadata
2. Product information management (PIM)
3. Enterprise master patient index (EMPI)
4. Data governance
5. Customer data integration (CDI)
6. MDM hub
7. MDM architecture
8. "Collaborative" vs. "analytical" MDM
9. MDM return on investment (ROI)
10. MDM stakeholders
11. Enterprise hierarchy management
12. MDM metrics
13. Data competency center

 


Top 13 MDM definitions and buzzwords

Without metadata there would be no MDM, according to Forrester's Rob Karel. He said everything that enables MDM, including definitions, business rules, policies, data relationships and quality metrics are all maintained through the use of metadata. "In other words, metadata is the only way that master data can truly be trusted," he said.

 Product information management (PIM) masters an organization's product data in a single location so that all product data is consistent, regardless of the system or application that needs it. Gartner defines PIM as "software products that support the global identification, linking and synchronization of product information across heterogeneous data sources through the semantic reconciliation of product master data; create and manage a central database system of record; enable the delivery of a single product view (for all stakeholders); and support data quality and compliance through monitoring and corrective-action techniques."

 Enterprise master patient index (EMPI), is a form of CDI specific to the healthcare industry. Some healthcare organizations are using EMPI to merge patient identities across systems and then use that data to feed stronger data to data warehouses for better analytics. EMPI directories typically include patient demographics and facilities a patient has visited and they can often determine if information from other facilities should be matched to already-existing directories, according to Gillogley Services.

 Data governance refers to the overall management of the availability, usability, integrity and security of the data employed in an enterprise. A strong data governance program will include a central body or council to create governance rules, a set of procedures and a plan to follow through those procedures. It differs from data management in that data management is a tactical execution of the policies created by the data governance body.

 Customer data integration (CDI) is the process of consolidating and managing customer information from different sources. This customer information may include contact details, customer valuation data and information gathered through interactions (i.e., direct marketing). When properly carried out, CDI ensures that all relevant departments in the organization have continuous access to the most current and complete customer information. Because of this CDI is a vital element of customer relationship management (CRM).

An MDM hub is a database with software that manages the master data in a database and keeps it in synch with transactional systems that use the master data. One of the most frequently-asked questions about MDM hubs is, Can one MDM hub handle both customer and product data?

Different types of MDM architecture have unique strengths and weaknesses, according to MDM expert Jill Dyché. MDM architecture styles include persistent, registry, hybrid, co-existence and transactional.

According to MDM experts, terms such as "collaborative" vs. "analytical" MDM do nothing more than add confusion to MDM functionality. What dictates the development of MDM is how the master data is stored, accessed and broadcasted to the enterprise. And while data usage adds complexity to MDM, it's secondary to how an organization uses and manages its master data.

After implementing your MDM system, don't worry too much about the MDM return on investment (ROI), experts say. Rather, what's important is how the implementation will solve the company's data management problems and if the MDM system helped save money for business initiatives.

MDM initiatives can be tricky waters to navigate, but having the right team of professionals can make the project go a lot smoother. Main MDM players include the following MDM stakeholders: senior management, business clients, application owners, information architects, data governance and data quality practitioners, metadata analysts, system developers and operations staff.

Enterprise hierarchy management provides a place for companies to update, model and maintain information hierarchies (i.e., customer and/or product, among others). Historically, when business changes such as acquisitions or restructuring occurred, the affected hierarchies had to be manually updated in different analytical systems for the reports to be accurate. New products, however, provide a solitary place to make all necessary changes which can then be published to multiple business intelligence (BI) or corporate performance management (CPM) software systems.

Developing a set of MDM metrics to help measure and demonstrate the impact of MDM to a company is vital to an MDM initiative's success. The metrics, however, should be focused on the business and not IT, according to John Radcliffe, an analyst with Gartner Inc. Metrics which show that MDM has helped increase the accuracy of customer data by 10%, for example, aren't likely to impress management; however, metrics which show that customer retention and/or cross-selling rates increased will make an impression because they are key to business success.

A data competency center, or data management competency center, is a group within an organization (typically a group of data specialists who are data-centric and data-skilled) that creates policies and procedures for MDM and helps to enforce those MDM rules within the company.

Have more MDM-related questions needing answering? Browse Q/As in our master data management expert section with Jill Dyché. Don't see the answer you're looking for? You can also ask Jill a personalized MDM question.

* Some of these definitions were excerpted from definitions on WhatIs.com.

View these top 13 tips of 2013 from SearchUnifiedCommunications.


This was first published in July 2009

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