1. Implementing MDM for BI & Data Integration by Kabir Makhija

2. What’s the holdup?

3. Typical understanding of “Master Data” Holistic view of “Master Data”
What is Master Data?
Any enterprise has 6 mutually exclusive, collectively exhaustive (MECE) types of organizational data, which are:
Type 1) Transaction Structure Data – Dimensional context to business transactions. Eg: Products, Customers, Departments etc.
Type 2) Enterprise Structure Data - Inter-relationships between organization elements. Eg: Chart of Accounts, Org Structure, Bill of materials, etc.
Type 3) Reference Data - Set of codes, typically name-value pairs that drives business rules. Eg: Region Codes, Customer Types etc.
Type 4) Transaction Activity Data - These are the transactions themselves. Eg: Purchase Order data, Sales Invoice data etc.
Type 5) Metadata – Data about Data
Type 6) Audit Data – For Compliance
Typical understanding of “Master Data”
Holistic view of “Master Data”
Comprehensive view of “Master Data” encompasses Transaction Structure Data, Enterprise Structure Data and Reference Data.

4. Master Data Management
Master data management (MDM) enables dependable cross-system, enterprise-wide business processes and analytics – ensuring that everyone involved in the process has access to the same information and knowledge
MDM is the opportunity to:
Implement a data integration platform that can access the facts about core business entities from anywhere in the enterprise
Automate the creation of a single logically correct view, based on business rules that agrees with the facts in the real world
Deliver that high quality master data to the current suite of business applications in real time

5. Customer FAQ What is MDM ? How to get started ? Who are the vendors ?
How do the products compare ?
What is the ROI ?

6. System Integrator FAQ
Does the organization consider data governance as a nice to have or must have ?
How does the client rate the current Data Quality ?
What is the current solution in place ?
Is an enterprise data model available ?
Operational and / or Analytical MDM ?
Is there a service oriented architecture ?

7. Business Drivers Runaway Costs Missed Revenue Opportunity
M & A Integration
Support existing initiatives
Regulatory Pressures

8. MDM – Critical Leverage Points1
MDM is not just Technology – Process Institutionalization is critical 2
Keeps Evolving over time - MDM systems are dynamic in nature 3
MDM is at the heart of business decisioning – Needs “Total Alignment” with corporate vision 4
Data Management & Governance is crucial for Business Buy-in 5
Organization should be geared for “Change” – Cultural issue

9. Challenges Vs Solutions
MDM Vendor Offerings
CDI / PIM DQ
ETL
Enterprise Challenges
Scoping
Data Governance
Organization Culture
Prioritization

10. Domain Specific Solutions
Technology Solutions
Enterprise Solutions
Enterprise Data Warehouse (EDW)
Data Federation
Customer Relationship Management (CRM)
Enterprise Resource Planning (ERP)
Domain Specific Solutions
Customer Data Integration (CDI)
Product Information Management (PIM)

11. Implementation Styles
Single Physical Data Store Approach
Single consolidated master data store that contains master data from multiple source systems
Latency depends on whether batch or on-line data consolidation is used, and update frequency
Federated Approach
Virtual business view of the reference data in source systems is defined. Used by business applications to access current master information
May employ a metadata reference file to connect related master information together based on a common key
Hybrid Approach
Combines data consolidation and data federation approach
Common master data (name, address, etc) could be consolidated in a single store, but master data unique to a specific source application (customer orders, for example) could be federated.
This hybrid approach can be extended further using data propagation

12. Customer Data Integration
Multiple & Federated Data Sources
Standardization of different sources that store data in different formats
Integration of data from multiple data sources
Consolidating diverse data integration tools
Global time synchronization in multi-geography systems
Identification of common batch windows for extraction and processing
Data Cleanliness & De-Duplication
Conversion from free form text of Source systems
Cross-organization data standardization
Geo-coding and cleansing
Consumer data de-duplication
- Identify a customer uniquely across organization Identify the parameters for house-holding Defining survivor and merge rules

13. Product Information Management
Data Source / Domain
Data Completeness & Validity
Standardization of data source format and layout across the multiple regional databases
Consistency of data type and allowable range of values
Seamless handling of changes to data attributes
Reusable framework for implementing new data sources and regional databases
Master data completely updated with all regional data
Checks and balances to ensure that source - regional data and regional- master data match
Master database is maintained with integral data
Setup and Maintenance of Validated meta data
Data Lifecycle Management
Data Management
Optimal storage mechanisms and capacity planning for regional and master databases
Efficient data roll-up decisions for SKU realignment- SKU to department or brand can be automatically realigned & SKU orients under the brand
Reduced Data mismatch with respect to SKU realignment
Maintenance of obsolete data from source system in the master
Tracking and handling of bulk movement of data between departments
Efficient historic treatment of changing data

14. BI Solution Architecture

15. Deployment Framework

16. Steps in a MDM Implementation
Identify sources of master data
Identify the producers and consumers of the master data
Collect and analyze metadata about for your master data
Appoint data stewards
Implement a data-governance program and data-governance council
Develop the master-data model
Choose a toolset
Design the infrastructure
Generate and test the master data
Modify the producing and consuming systems
Implement the maintenance processes

17. MDM Maturity Levels Level 1 Level 2 Level 3 Level 4 Level 5
Data Integration with minimal focus on DQ
Level 2
Managing basic Data Quality
Level 3
Master Data within Silos
Level 4
Enterprise Master Data for a single domain
Level 5
Cross-Enterprise Master Data for multiple domains

18. Marketplace Mega Vendors Pure Play Data Quality IBM Siperian DataFlux
Oracle
Kalido
Trillium
SAP
Initiate
Informatica
Microsoft
Purisma
Netrics
Teradata, TIBCO, etc
OneData,
Acxiom, etc
Pitney Bowes, Zoomix, etc

19. Research Credits : Hexaware BI & A team
Thank You
Research Credits : Hexaware BI & A team