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Pierre-Louis Usselmann, Ben Watt SOGETI Switzerland pierrelouis.usselmann@sogeti.com, ben.watt@sogeti.com Master Data Services
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Sogeti 2‘ The problems with Data 2‘ What is Master Data ?3‘ Master Data Management5‘ Master Data Services10‘ SS2008 R2 MDS Demo 30‘
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Capgemini Group Sogeti and Business Intelligence
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4 disciplines and 2 brands € 8’710 Million 92’000 employees Business Transformation Economic Performance Improvement “Big deals” IS Business Process Outsourcing Design & Integration of Enterprise Solutions Technological Transformation Program Answer to all IT needs that require Proximity: * Technical Assistance * Small/Medium Projects * Maintenance 20’000 employees
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Architecture Best Practices FrameworkRoadmap Maturity Assessment BI Assessment Data Quality Assessment Enterprise Information Management Data Governance Master Data Management Information Quality Management Metadata Management Information Management Data Modeling Data Warehousing Data Integration Information Delivery Portal Collaboration ReportsDashboards Scorecards KPI Analysis Data mining Performance Management MonitoringAnalyzing Planning Forecasting Migration / Support BI Testing Tools Upgrade Data Migration User Training
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The Problems with Data
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It takes us too long to introduce products. Product information is in too many places, and we don’t have a system or process for managing all of the information that we need to offer our products. It takes us too long to introduce products. Product information is in too many places, and we don’t have a system or process for managing all of the information that we need to offer our products. We have customer data spread across many systems. We don’t know which data is right, and we can’t see our complete relationship with our customers. We have customer data spread across many systems. We don’t know which data is right, and we can’t see our complete relationship with our customers.
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What is it ?
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e-mail, magazine articles corporate intranet portals, marketing Unstructured sales, deliveries, invoices trouble tickets, claims Transactional data about other data reside in a formal repository Metadata the relationships between other data the relationships between master data Hierarchical critical nouns of a business Master
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Master data, which may include reference data, is information that is key to the operation of business People Customers Vendors Sales Employees Partners Patients Things Products Org. Units Recipes Parts Storage bins Documents Places Stores Wells Power Lines Geographical areas Abstracts Contracts Warrantees Time Metrics
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What is MDM ? Core Capabilities Benefits
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Decouples master information from individual applications Becomes a central, application independent resource Ensures consistent master information across transactional and analytical systems Simplifies ongoing integration tasks and new application development Addresses key issues such as data quality and consistency proactively rather than “after the fact” in the data warehouse
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Master Data Existing Applications Data Existing Applications Data Existing Applications Data New Applications New Applications Data Analytical Systems Historical Data
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MDM is the discipline of providing an authoritative source for referential data in your organization. MDM is a continuous process, involving Technology & Tools People Processes MDM objective is creating and maintaining consistent and accurate lists of Master Data
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Hierarchy Management Governance and Stewardship Workflow Management Flexible and business user centric data modeling Source and IT system synchronization Matching, Cleansing & Standardization Authoring Master Data Versioning
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Creating and maintaining a high quality, consistent set of Master Data leads to Coherent and correct reporting & regulatory compliance An authoritative source of information used throughout your organization The ability to evolve Master Data to accommodate changing business needs MDM can also facilitate the merging of multisource environments e.g. as a result of mergers and acquisitions
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Concepts and Features Core Capabilities
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Models Entities Attributes Members Hierarchies Derived Explicit Collections Versions Database
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Container of logical entities Unit of data versioning Boundary for entites relationships Typically named for their „primary entity“ 1-N models per MDS instance RDBMS metaphor : Database OLAP metaphor : Cube
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Container for master data („members“) Identifies the attributes for entity members Can be related to each other (1:1, 1:N, N:M) Examples : Products, Customers, Country 1-N entities per Model RDBMS metaphor : Table OLAP metaphor : Dimension
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An instance of an entity Define the attribute values for the instance Can be related to each other (1:1, 1:N, N:M) Examples : „BK-M18B-40“, „Mountain 500 Black, 40“ 1-N members per entity RDBMS metaphor : Record OLAP metaphor : Dimension member
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A data value associated with an entity member Text, numeric, datetime, link,... Examples : Name, Size, Color, Width, Height Can be sourced from another entity Domain based attribute 1-N attributes per entity RDBMS metaphor : Column OLAP metaphor : Attribute
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Relational arrangement of entity members Types Derived : Based on attributes Explicit : each member can have one parent assigned 1-N hierarchies per model RDBMS metaphor : Parent/Child relationship OLAP metaphor : Hierarchy
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Master Data Hub SQL Server Data Platform Authoritative Source Versioning Stewardship Portal GovernanceModelingDocumentationImport & Export Stewardship Process WorkflowNotificationsBusiness RulesValidation Master Data Platform Multi domain Model Deployment Source and IT system synchronization
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Demo
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Model:Geography Entities:Region, Country, Status Members:France, Germany, Hungary, … Attributes: Population, Square km, … Hierarchy: Country → Region(derived) Country → Sales Volumes (explicit) Workflow:Business Rules Security: User Access Import/Export
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Master Data Excel Source Excel Source SQL Server Analysis Services SQL Server Analysis Services Versioning Governance Workflow Notification
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