introduction to mdm part 4 - enterprise data architecture master
TRANSCRIPT
EducationPierre Bonnet, IS Consultant, March 2012Last update: March 21, 2012
[email protected]: pierre.orchestra
Introduction to MDMPart 4 - Enterprise Data ArchitectureMaster Data Management
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-builtEnterprise Data Architecture
● First level○ domains of business objects
● Second level○ data categories
● Each business object includesboth master data and transactional data
● Other classes are used to collectsimple 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 yourEnterprise 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
