Master Data Management

Compiled by Sung Kuan

Kuansung@gmail.com

Disclaimers

What is MDM?

• It is a systematic approach to clean up customer data so that business can managed efficiently and grow effectively.

• It help business to move towards “single version of truth about the customers”

• The strategy, architecture and technology deals with customer data constitute known as Customer Data Integration or CDI.

Why MDM?

• Regulatory compliance

• Privacy and data protection

• Safety and security

• Meaningful data mining

• You can’t control what you don’t know

• You can’t measure what you don’t control

Courting Strategy• Start small and demonstrate value• Look for business drivers that give the most

benefits and ‘quick win’• Explore the ideas and gather feedbacks• Identify the service, its level, its costs, its

limitations, its potential, its stakeholders• Craft a story aka draft business proposal• Propose to do a pilot before full implementation.

If pilot fails, the project is better to be terminated as the organization is not ready.

How to propose MDM? (high level)

• CEO and senior management commitment

• Understand the business drivers

• Understand the resource requirements

• Understand the strategic risks

• Provide estimate of the benefits e.g. reduce workload, more sales, reduce costs

• Output: Board Approved Business Plan

How to propose MDM? (middle level)

• Provide training to project team so as to establish common lingo and working styles

• Define goals and objectives of project

• Set boundaries and rules of engagement

• Plan and keep agreement sacred

• Output: Business Approved Project Plan

How to propose MDM? (low level)

• Conduct risk assessment or ‘operationally what can go wrong’?

• Reduce risk, if not apply compensatory control

• Optimize work breakdown structure

• Plan regular and consistent project reviews

• Output: Team Agreed Implementation Plan

How to do MDM

• Architectural considerations

• Data control considerations

• Customer integration considerations

• Measure the benefits / values / savings

Architectural Considerations

• Meta-data driven Service Oriented Architecture – Reference Architecture Viewpoint

• ER diagram

• Data Governance

Data Control Considerations

• Risk Taxonomy (transaction, reputation, strategic & compliance)

• End to End Security

• Access (XACML, User provisioning & entitlements)

• Nonrepudiation

• Visible Security Architecture

Customer Identification Considerations• Use unique attributes to minimize matching

errors along with discrimination attributes (e.g. match first_name, last_name, birthday, social security number to identify an individual)

• Define matching rules

• Understand Effect of Chaining

Measure & Evaluate• The objective is to provide assurance that

benefits are realized, if not what went wrong and what went right.

• Review the financial figures presented in business case, project plan and determine if it exceeded expectations or not.

• Identify soft benefits e.g. improve morale, less stress, increase compliments, reduce complaints, less sick leave etc

Summary

Disclaimers

• This slides is for my personal understanding only and will not be 100% accurate but please feel to use it for your work.

• Your feedback is most welcome to improve on it.

Reference

• Berson, A. & Dubov, L. (2007). Master Data Management and Customer Data Integration for a Global Enterprise. United States of America: McGraw Hill

• ITGI. (2007). IT Governance Implementation Guide 2nd Edition. http://www.isaca.org/Template.cfm?Section=Home&Template=/ContentManagement/ContentDisplay.cfm&ContentID=32290

Reference Architecture Viewpoint

Visible Security Architecture