Introduction to MDM Part 4 - Enterprise Data Architecture Master Data Management



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Transcription:

Introduction to MDM Part 4 - Enterprise Data Architecture Master Data Management Education Pierre Bonnet, IS Consultant, March 2012 Last update: March 21, 2012 pierre.bonnet@orchestranetworks.com skype: pierre.orchestra www.orchestranetworks.com

Objective Understanding how to build a sustainable Enterprise Data Architecture Definition and goals Concepts Examples

Definition & concept

Definition Enterprise Data Architecture Enforces a politics encouraging a unified data modeling and data knowledge management at the whole scale of the system Prevents from establishing siloed data models and data implementations Four types of data must be distinguished Reference and master data Transactional data Decisional data Data flow

Enterprise Data Architecture avoids Duplication of data models When duplication is introduced in data modeling it impacts heavily the MDM quality it generates siloed data management and siloed MDM systems Isolation of reference and master data from the rest of the IS When reference and master data rely on concepts not shared by other types of data (transactional, decisional, data flow) then it generates huge costs and concerns discrepancies appear between semantics of a same piece of information depending on types of data. Eg. Is the semantic of Customer for a MDM system aligned with other semantics used in the transaction, decisional and data flow domains?

Building blocks used The Enterprise Data Architecture relies on three normative levels of data construction Business object Data category Domain of business object

Example A pre-built Enterprise Data Architecture

First level domains of business objects Second level data categories

Each business object includes both master data and transactional data Other classes are used to collect simple reference data Eg. Party relationship type, Type of party, Party role, Gender

The domain of business objects 'Catalog' includes four data categories Product, Service, Contract, Marketing

Other domains of business objects with their data categories Portfolio, Service delivery, Accounting

The rest of domains of business objects with their data categories Human resources, Supervision

Rules applied to the Enterprise Data Architecture Data category Building block fully isolated from the rest of the architecture A data category is a reusable scope It means that an information in a data category cannot be typed by an information located outside its scope: no nested data type spanning data categories In other words a data category interacts with other data categories through identifiers only -> loosely coupled data Domain of business objects First level of the Enterprise Data Architecture Usually less than fifteen domains is sufficient to manage the whole IS scope Each data category is located within a domain Business Object A business concept based on one to many classes Associated with a state machine describing its business life-cycle Do not confuse with simple reference data

How to drive your Enterprise Data Architecture? Follow this rating

Calculate your mark in Reference and master data architecture

Calculate your mark in Transactional data architecture

Calculate your mark in Decisional data architecture

Calculate your mark in Data flow architecture

Calculate your mark in unified Enterprise Data Architecture

How to start? Take a few weeks to stabilize your own Enterprise Data Architecture List of domains of business objects Definition of your key business objects Dictionary First overview of your data categories Appoint an Enterprise Data Architect Supporting data projects and collecting all information to improve and maintain your Enterprise Data Architecture Ensuring that data projects contribute to the shared Enterprise Data Architecture

Findings To succeed in a MDM program you cannot avoid To tackle the Enterprise Data Architecture issue To appoint an Enterprise Data Architect

To get further information Please attend the fifth part of this MDM introduction training course This part is dedicated to the data governance issue

Stay tuned pierre.bonnet@orchestranetworks.com skype: pierre.orchestra www.orchestranetworks.com