Chapter 16 16-1 © 2012 Pearson Education, Inc. Publishing as Prentice Hall.

Master Data Management

Chapter 16

16-1© 2012 Pearson Education, Inc. Publishing as Prentice Hall

© 2012 Pearson Education, Inc. Publishing as Prentice Hall

The Data Management Challenge

The IT landscape is littered with legacy, packaged and developed applications, coupled with unstructured data.

The uncontrolled silos of data make managing information very difficult and limit its strategic value.

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Factors Increasing the Data Management Challenge

Increased storage capabilities

Layers of “enterprise” solutions

Multiple groups managing data

Ownership issues

Short term workarounds16-3


What is Master Data Management (MDM)?

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MDM is an application-independent process that describes, owns, and manages core business data entities.

MDM ensures the consistency and accuracy of these data by providing a single set of guidelines for their management and thereby creates a common view of key data.


The MDM and the Data Ecosystem

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InformationDelivery

DataWarehouse

MasterDataStore

Data Quality

Data Integration

MetadataData Management

MDM

IM Strategy & Principles

Enterprise Architecture


The Data Ecosystem: IM Strategy and Principles

Information Management (IM) covers all forms of information needed and produced by the business.

The IM strategy and principles structure, secure, and improve information assets.

IM strategy and principles provide the context in which MDM is accomplished

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The Data Ecosystem: Enterprise Architecture

The IM strategy and principles should be important contributors to the enterprise architecture.

Information and architecture should be as separate as possible.

The establishment of a dialogue and discipline for core corporate data will provide the highest value.

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The Data Ecosystem: Data Management

Data management (DM) is the critical work of making decisions about data.

Information stewards are responsible for DM and check the accuracy, timelines, life cycle, and redundancy of the data.

MDM is a subset of DM that focuses on core data. 16-8


The Data Ecosystem: Data Quality

Data quality aims to ensure data are correct, complete, current, and consistent.

It is possible to have data quality without DM, but it is not possible to have data DM without data quality.

MDM efforts focus the costs and challenges of data quality on the core data. 16-9


The Data Ecosystem: Data Integration

The goal of data integration is to create a data warehouse as a credible source of integrated information.

Data integration serves two purposes: Enables data to be combined and collected in a

warehouse. Consolidates data that are not deemed to be core

but which are created and updated by several applications.

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The MDM Value Proposition

A single source of a company’s data provides:

Better information

Cost savings

Improved business capabilities

Improved technical capabilities16-11


The MDM Value Proposition Continued

Better information

Improves compliance reporting, generates operational efficiencies, and achieves competitive differentiation.

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Cost savings

Two main costs can be avoided by better DM:

-- Costs caused by poor data quality (e.g., need to verify data, poor decisions).

-- Costs caused by assuring data quality (e.g., prevent, detect, or repair poor data).

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Improved business capabilities

A single source of data can improve customer service (e.g., ensuring customer privacy) and support flexibility (e.g., supporting globalization, acquisitions).

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Improved technical capabilities

A single source of data can eliminate data redundancy and facilitate integration.

MDM is the prerequisite of a service-oriented architecture.

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Prerequisites for MDM Success

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1.

2.

3.

4.


Prerequisites for MDM Success Continued

Develop an enterprise information policy

It is essential to delineate the principles around issues such as corporate data objectives, data ownership and accountability, privacy, security, and risk management.

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Business ownership

All stakeholders must be involved in MDM or political problems will likely ensue (e.g., executive and business sponsorship, data stewards, change management specialists).

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Governance

A cross-functional and collaborative IT and business data governance process should be established.

“MDM can’t be sustained without governance”.

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The role of IT

DM is primarily a nontechnical problem; however technology and IT staff play important roles:

-- IT staff has the skills to develop a data strategy,

model the data, and assess applications.

-- Technology maintains data models and repositories. 16-20


The Data Journey

Stage 1. “I admit I’ve got data, so I’ll inventory it.”Stage 2. “Let’s identify what data is used by which application and processes. I’ll discuss the role of information and ownership.”Stage 3. “Let’s limit how much data we move around and maybe design some information exchange requirements.”

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Conclusion

MDM is wrapper for concepts and issues that have been afflicting IT for long time.

MDM initiative needs a thorough planning and an incremental approach: Identify some small, quick wins. Focus efforts on one type of data. Learn with the business how to manage process. Develop and continually revisit and information

roadmap and strategy.16-22

© 2012 Pearson Education, Inc. Publishing as Prentice Hall 16-23

Copyright © 2012 Pearson Education, Inc. Publishing as Prentice Hall

Chapter 16 16-1 © 2012 Pearson Education, Inc. Publishing as Prentice Hall.

Documents

core data

companys data

unstructured data

prentice hall data quality

prentice hall data management

challenges of data quality

core corporate data

prentice hall slide