Implementing MDM for BI & Data Integration by Kabir Makhija

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

2. What’s the holdup?

3. 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 Points 1 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. 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 • 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

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