2008Communications of the Association for Information SystemsOpen access

Developments in Practice XXX: Master Data Management: Salvation Or Snake Oil?

Heather A. Smith, James D. McKeen

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Abstract

The newest hot topic in IT is Master Data Management (MDM). MDM is either “the most overused IT buzzword,” the exact meaning of which vendors have yet to agree on, OR it describes a discipline and technologies for developing a consistent set of identifiers and attributes that describe the core data entities of an organization (e.g., customer, product) OR possibly, something in between. While the definition of MDM is vague, the problem it purports to solve certainly is not. Here, there is general agreement that the data in most organizations is a mess, resulting from years of developing in silos. Most organizations therefore face a multitude of inconsistencies in data definitions, data formats and data values which make it next to impossible for an organization to understand and use its key data. This paper first outlines the business needs which MDM purports to address and defines what MDM is and is not. Next it looks at the “data ecosystem” and where MDM fits within it. Then, it outlines the value proposition for MDM and some of the challenges organizations face in making a business case for it. The last two sections look at some of the pre-requisites which must be put in place before embarking on an MDM program and describe “the data journey” of which MDM is only a part.

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The newest hot topic in IT is Master Data Management (MDM). MDM is either “the most overused IT buzzword,” the exact meaning of which vendors have yet to agree on, OR it describes a discipline and technologies for developing a consistent set of identifiers and attributes that describe the core data entities of an organization (e.g., customer, product) OR possibly, something in between. While the definition of MDM is vague, the problem it purports to solve certainly is not. Here, there is general agreement that the data in most organizations is a mess, resulting from years of developing in silos. Most organizations therefore face a multitude of inconsistencies in data definitions, data formats and data values which make it next to impossible for an organization to understand and use its key data. This paper first outlines the business needs which MDM purports to address and defines what MDM is and is not. Next it looks at the “data ecosystem” and where MDM fits within it. Then, it outlines the value proposition for MDM and some of the challenges organizations face in making a business case for it. The last two sections look at some of the pre-requisites which must be put in place before embarking on an MDM program and describe “the data journey” of which MDM is only a part.

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Available abstract

The newest hot topic in IT is Master Data Management (MDM). MDM is either “the most overused IT buzzword,” the exact meaning of which vendors have yet to agree on, OR it describes a discipline and technologies for developing a consistent set of identifiers and attributes that describe the core data entities of an organization (e.g., customer, product) OR possibly, something in between. While the definition of MDM is vague, the problem it purports to solve certainly is not. Here, there is general agreement that the data in most organizations is a mess, resulting from years of developing in silos. Most organizations therefore face a multitude of inconsistencies in data definitions, data formats and data values which make it next to impossible for an organization to understand and use its key data. This paper first outlines the business needs which MDM purports to address and defines what MDM is and is not. Next it looks at the “data ecosystem” and where MDM fits within it. Then, it outlines the value proposition for MDM and some of the challenges organizations face in making a business case for it. The last two sections look at some of the pre-requisites which must be put in place before embarking on an MDM program and describe “the data journey” of which MDM is only a part.

Key concepts: Multitude, Master data, Data science, Key (lock), Identifier, Computer science, Set (abstract data type), Product (mathematics)

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