Knowledgent White Paper Series. Developing an MDM Strategy WHITE PAPER. Key Components for Success

Size: px
Start display at page:

Download "Knowledgent White Paper Series. Developing an MDM Strategy WHITE PAPER. Key Components for Success"

Transcription

1 Developing an MDM Strategy Key Components for Success WHITE PAPER

2 Table of Contents Introduction... 2 Process Considerations... 3 Architecture Considerations... 5 Conclusion... 9 About Knowledgent , Knowledgent 1

3 Introduction More organizations are leveraging applications that require shared, synchronized information, thus driving the need for a single view of key data entities commonly used across the organization. At the technical view, the drivers and fundamentals of master data management (MDM) can be summarized as processes for consolidating variant versions of instances of core data objects, distributed across the enterprise into a unique representation. In turn, that unique representation is continually synchronized across the enterprise application architecture to allow master data to be available as a shared resource. The result is a master data asset of uniquely identified key data entities that can be integrated through a service layer with applications across the enterprise. This paper will explore some of the key components of any MDM solution and considerations that should be factored into an organization s overall MDM strategy. 2014, Knowledgent 2

4 Process Considerations The conceptual framework for the Master Data Management (MDM) service should include critical capabilities, such as master/reference data source identification, master data acquisition, metadata master hub management, integration, and access. Data Sources Organizations will require processes to identify and validate one or more sources of data associated to one or many subject areas. Business applications may contribute core data for the selected subject areas. External Data Providers also may be a source of reference data (from external agencies). Data Acquisition Data acquisition should include real-time, near real-time, and batch processes built on standard message formats, including ETL, EAI, EII, and SOA, to acquire and aggregate data from one or more sources. The data profiling and discovery capability provides supporting entity and attribute information to the data acquisition process. Metadata Metadata provides an array of functionality supporting core MDM hub functions, namely: Data Models: The MDM Hub supports user-defined data models for each subject area, such as Customer, Product, Reference Data, etc. Models contain attributes that identify the business structures of the master data record. The source enterprise master data attributes will span source systems. Schemas: Schemas support the localization of the physical data for every subject area. Standards and Rules Repository: The Master Data Hub is the repository for data standardization, match, and merge rules that are configured and stored as part of the hub metadata. Metadata Monitoring: Hub metadata can be logged, monitored, and/or versioned for changes to the metadata and the underlying model. 2014, Knowledgent 3

5 Data Hub The data hub provides core services for data management and for entity identification of gold copy reference data that will be deemed master data. Key components include: Cleanse Engine: Process to execute user-define cleanse functions on the data acquired. Plug-in capabilities enable a call-out to third party routines for extensive cleansing and standardization. Match/Merge Engine: Process to execute configured business rules to match data from multiple sources based on pre-defined attributes and parameters. Data Stewardship: Data stewardship processes support overriding match and merge rules at the record and field level and overall master data management. Hierarchy Management: Integrated hub capability to establish and track data relationships within the hub (such as customer and product hierarchies). This capability is supported using visualization and an automatic refresh based on underlying data changes. Data Management Data management supports the management of historical data (for example, records merged are saved to support an unmerge capability), audit tracking, and access/security (who can update records, attributes, models, hierarchies) within the master data hub. Integration Integration supports standard messaging formats across multiple protocols as well, as workflow management, cross-referencing, and data sharing. The integration layer is a key component for the different data integration processes, like EAI/EII/ETL, workflow management, and messaging. Access and Security Managing access and security requires a workbench of tools that enables the creation and delivery of reports, provides a GUI for Data Stewards to perform manual exception handling of master data record merging, and allows for the ability to monitor data quality in the hub. 2014, Knowledgent 4

6 Architecture Considerations The MDM reference architecture must be resilient and adaptive to ensure high performance and sustained value. Some of the key characteristics of the MDM reference architecture include: Processes to manage and maintain master data as an authoritative source to and securely deliver accurate, up-to-date master data across the business enterprise to authorized users and systems. Support for coordinating and managing the lifecycle of master data. Availability of accurate, critical business information as a service to be used in the context of a business process at the right time by any authorized user, application, and/or process. Ability to cleanse data and improve the quality and consistency for use in operational environments. Support for making master data active by detecting and generating operations to manage master data, implement data governance policies, and create business value. Attributes Master data in a subject area is made up of a collection of attributes that describe it. 1 Since there are a large number of attributes that describe sophisticated subjects, attributes are classified into the following categories: Identifier Attributes Identifier attributes are used to uniquely define an instance. These important attributes are further classified into the following subsets of attributes: Global Identifier: The unique non-intelligent and often system-generated identifier for an instance. Identifying Attributes: Minimal set of attributes, most often human legible, used to define a unique instance. Alternate Identifiers: Attributes that store cross-reference identification information of instances stored throughout the enterprise in other applications, systems, and processes. Core Attributes Core attributes are the most commonly reused attributes throughout the enterprise. For example, core attributes of customer master data could include attributes like name, address, contact info, etc. 1 Attributes are often called characteristics or fields. In this document, attributes and fields will be used interchangeably, while dimensions will be avoided due its alternate meaning derived from data warehousing literature. 2014, Knowledgent 5

7 Extended Attributes Extended attributes are remaining attributes used in specialized business processes. Examples include description attributes. There are many extended attributes in number compared to the number of core, alternate identifiers, and identification attributes. Further, extended attributes are most often subdivided among categories grouped by business process. Authoritative Sources and Data Fragmentation Master data is fragmented (or distributed) in two dimensions. Attribute fragmentation is the distribution of attributes along the classification described in the previous section. Instance fragmentation is the distribution of master data records. Though both attribute and instance fragmentation occur, fragmentation does not directly impact data quality and complexity. It is the fragmentation of data in conjunction with the number of disparate authoritative sources of data that add to the complexity of maintaining high-quality master data. Master Data Management Services Master data quality is managed through architecture and manual processes governed by a stewardship model. The MDM services fall into the following groupings: Managing Metadata: Services for setting up metadata and managing changes. Managing Master Data Quality: Master data services that cleanse, view, edit, author, merge, etc. Master Data Applications: Services that allow applications to use master data through publishing, auditing, reporting, etc. Stewardship and Governance Master data stewardship enforces the policies and accountabilities for maintaining master data. It is critical to recognize that the data stewardship process and the master data management services intersect. The two can of course be handled independently of each other; however, for truly breakthrough business value, the two efforts must be carefully coordinated. 2014, Knowledgent 6

8 Hub Architecture Areas The two main areas of a Hub Architecture are the metadata management layer and the master data management layer. All of these capabilities must be accounted for in an organization s workplan. The Hub Metadata Management layer supporting functions include: Hub Data Modeling: This function is used to design the target master data record and input records that will be used to source data for the target master data record. Rules Management: Rules dictate how attributes from source records are mapped into a target master record. Trust factors are assigned and can be used to resolve conflicting attributes. User Management: Users of the hub, including hub administrators, data modelers, rules designers, and data stewards, are managed by the user management function. Security and Access: This function provides the administrator interfaces required to control access to subject areas, source and target records, source and target attributes, rules, design tools, etc. It is common to limit the data steward s ability to review, merge, unmerge, and make updates at the record and attribute level. Similarly, data modelers and rules designers will be limited to subject areas (e.g., customer and product) and may be further restricted at the record and attribute level. Performance and Scalability: The architecture provides horizontal and vertical scalability to support large volumes of data. The Hub Master Data Management layer supporting functions include: Data Upload: This function supports data loading from multiple sources using batch, near real-time, and/or real-time interfaces into the hub. Data Standardization and Cleansing: The hub provides basic cleanse/standardization capabilities; however, interfaces are provided to enable third-party tools and optionally custom routines, such as real-time data validation lookups. Match and Merge: Match and Merge engines use rules to identify matching source records and enable either automatic or manual merging into a golden master data record. Hierarchy Management: The Hierarchy Manager provides the ability to manage relationship structures across master data records with the goal of viewing those records in a hierarchical presentation (e.g., customers by territory, vertical, size). Stewardship and Reporting: User-friendly interfaces provide access to rules and data management functions, supporting both the stewards and administrators. Specific capabilities include: Information steward functions: o Identify and manage candidate master data sources and trusted sources o Manage data standardization and cleanse rules o Match and merge data o Manage and monitor data quality 2014, Knowledgent 7

9 Administrator functions: o Manage hub schemas and metadata and data models o Manage user access o Manage database resources o Monitor and manage performance and scalability o Manage operational tools and services Architectural Approaches to Master Data Management Architecture patterns capture reusable design templates to common problems. The designs are based on collective experience of proven techniques used by internal and external sources. This section presents the logical groupings of frequently used MDM architecture patterns. Since MDM is concerned with the creation of an enterprise-wide "system of record" for core business entities, it would seem natural to limit architecture patterns to only those that have a single system of record. Unfortunately, such a design is unrealistic in some companies due to scalability and reliability considerations, physical distribution of business processes, regulatory restrictions, and distribution of centers of expertise. Typical patterns to consider and their tradeoffs, which vary based on how the master data is distributed and shared across the enterprise, include those listed below. Point-to-Point In this approach, applications communicate with each other using a point-to-point interface. This approach may work very well for a small number of applications, but as the number grows, the interfaces will become complicated and redundant, affecting quality and reliability. Enterprise Service Bus This approach refers to application integration to multiple downstream applications via a common data bus. This approach reduces the need for redundant interfaces that repeatedly send the same data updates to multiple applications. It also provides master data access using publish/subscribe or request/reply techniques. However, this approach relies on source systems to manage data and does not provide capabilities to identify and resolve conflicts across source systems. Master Hub as a Channel In this approach, master data from multiple sources is aggregated into one application/system/database and distributed to downstream applications using a data bus. This approach adds value by centralizing master data and can be used to identify and resolve data redundancy. However, this approach does not centralize master data management processes, which remain at the local source systems. Master Data Hub Persistent Hybrid This improves on Master Hub as a Channel by adding centralized data management services to the hub. This is a self-contained master data hub (for key and core attributes) with integrated data services, such as data quality management, information stewardship, data enhancement, integration, quality monitoring, and harmonization. 2014, Knowledgent 8

10 Conclusion Organizations need to consider a number of factors, including processes and architecture attributes, when developing an MDM strategy. On the process side, the conceptual framework should include master data source identification, acquisition, hub management, integration, and access. In tandem with these processes, the MDM architecture should be capable of long-term high performance and responsiveness to continuous change. Although a comprehensive MDM strategy is essential for organizations to maximize the value of their data, knowing where to start and how to execute this strategy can be daunting. With our extensive experience and expertise deploying MDM strategies and implementations, Knowledgent can help organizations navigate the complexities of developing the right MDM processes and architectures for their environments. For more information on our MDM capabilities, please visit 2014, Knowledgent 9

11 About Knowledgent Knowledgent is a leading industry information consultancy. It combines advanced information management and analysis capabilities with deep industry domain expertise to maximize the value of information to empower clients with actionable business insights. Knowledgent leverages big data analytics, unstructured data mining, semantic enrichment and master information management to help clients optimize business operations. Knowledgent has offices in Boston, Massachusetts, New York City, New York, and Warren, New Jersey. 2014, Knowledgent 10

Enable Business Agility and Speed Empower your business with proven multidomain master data management (MDM)

Enable Business Agility and Speed Empower your business with proven multidomain master data management (MDM) Enable Business Agility and Speed Empower your business with proven multidomain master data management (MDM) Customer Viewpoint By leveraging a well-thoughtout MDM strategy, we have been able to strengthen

More information

MDM and Data Warehousing Complement Each Other

MDM and Data Warehousing Complement Each Other Master Management MDM and Warehousing Complement Each Other Greater business value from both 2011 IBM Corporation Executive Summary Master Management (MDM) and Warehousing (DW) complement each other There

More information

SOA REFERENCE ARCHITECTURE: SERVICE TIER

SOA REFERENCE ARCHITECTURE: SERVICE TIER SOA REFERENCE ARCHITECTURE: SERVICE TIER SOA Blueprint A structured blog by Yogish Pai Service Tier The service tier is the primary enabler of the SOA and includes the components described in this section.

More information

JOURNAL OF OBJECT TECHNOLOGY

JOURNAL OF OBJECT TECHNOLOGY JOURNAL OF OBJECT TECHNOLOGY Online at www.jot.fm. Published by ETH Zurich, Chair of Software Engineering JOT, 2008 Vol. 7, No. 8, November-December 2008 What s Your Information Agenda? Mahesh H. Dodani,

More information

Master Data Management

<Insert Picture Here> Master Data Management Master Data Management 김대준, 상무 Master Data Management Team MDM Issues Lack of Enterprise-level data code standard Region / Business Function Lack of data integrity/accuracy Difficulty

More information

Three Fundamental Techniques To Maximize the Value of Your Enterprise Data

Three Fundamental Techniques To Maximize the Value of Your Enterprise Data Three Fundamental Techniques To Maximize the Value of Your Enterprise Data Prepared for Talend by: David Loshin Knowledge Integrity, Inc. October, 2010 2010 Knowledge Integrity, Inc. 1 Introduction Organizations

More information

DECOMMISSIONING CASE : SIEBEL UCM TO INFORMATICA MDM HUB

DECOMMISSIONING CASE : SIEBEL UCM TO INFORMATICA MDM HUB DECOMMISSIONING CASE : SIEBEL UCM TO INFORMATICA MDM HUB People Arhis Decommission Factory Team provides comprehensive end to end services to decommission Siebel Universal Customer Master application (UCM)

More information

Master data value, delivered.

Master data value, delivered. Master data value, delivered. Master Data Management making the most of information assets Master data consists of the information that is key to the core operations of a business. Master data may include

More information

Master Your Data and Your Business Using Informatica MDM. Ravi Shankar Sr. Director, MDM Product Marketing

Master Your Data and Your Business Using Informatica MDM. Ravi Shankar Sr. Director, MDM Product Marketing Master Your and Your Business Using Informatica MDM Ravi Shankar Sr. Director, MDM Product Marketing 1 Driven Enterprise Timely Trusted Relevant 2 Agenda Critical Business Imperatives Addressed by MDM

More information

IPL Service Definition - Master Data Management Service

IPL Service Definition - Master Data Management Service IPL Proposal IPL Service Definition - Master Data Management Service Project: Date: 16th Dec 2014 Issue Number: Issue 1 Customer: Crown Commercial Service Page 1 of 7 IPL Information Processing Limited

More information

The Influence of Master Data Management on the Enterprise Data Model

The Influence of Master Data Management on the Enterprise Data Model The Influence of Master Data Management on the Enterprise Data Model For DAMA_NY Tom Haughey InfoModel LLC 868 Woodfield Road Franklin Lakes, NJ 07417 201 755-3350 tom.haughey@infomodelusa.com Feb 19,

More information

Master Data Management Components. Zahra Mansoori

Master Data Management Components. Zahra Mansoori Master Data Management Components Zahra Mansoori 1 Master Data Abbreviation: MD Referring to core business entities an organization uses repeatedly across many business processes and systems Captures the

More information

DataFlux Data Management Studio

DataFlux Data Management Studio DataFlux Data Management Studio DataFlux Data Management Studio provides the key for true business and IT collaboration a single interface for data management tasks. A Single Point of Control for Enterprise

More information

Why is Master Data Management getting both Business and IT Attention in Today s Challenging Economic Environment?

Why is Master Data Management getting both Business and IT Attention in Today s Challenging Economic Environment? Why is Master Data Management getting both Business and IT Attention in Today s Challenging Economic Environment? How Can You Gear-up For Your MDM initiative? Tamer Chavusholu, Enterprise Solutions Practice

More information

Salesforce Certified Data Architecture and Management Designer. Study Guide. Summer 16 TRAINING & CERTIFICATION

Salesforce Certified Data Architecture and Management Designer. Study Guide. Summer 16 TRAINING & CERTIFICATION Salesforce Certified Data Architecture and Management Designer Study Guide Summer 16 Contents SECTION 1. PURPOSE OF THIS STUDY GUIDE... 2 SECTION 2. ABOUT THE SALESFORCE CERTIFIED DATA ARCHITECTURE AND

More information

Master Data Services Environment

Master Data Services Environment Master Data Services Training Guide Master Data Services Environment Portions developed by Profisee Group, Inc. 2010 Microsoft Master Data Services Overview Master Data Services Implementation Master Data

More information

Semarchy Convergence for Data Integration The Data Integration Platform for Evolutionary MDM

Semarchy Convergence for Data Integration The Data Integration Platform for Evolutionary MDM Semarchy Convergence for Data Integration The Data Integration Platform for Evolutionary MDM PRODUCT DATASHEET BENEFITS Deliver Successfully on Time and Budget Provide the Right Data at the Right Time

More information

whitepaper The Evolutionary Steps to Master Data Management

whitepaper The Evolutionary Steps to Master Data Management The Evolutionary Steps to Master Data Management Table of Contents 3 Introduction 4 Step 1: Implement a Foundational Service Layer 6 Step 2: Choose a style 11 Summary The Evolutionary Steps to Master Data

More information

Cordys Master Data Management

Cordys Master Data Management PRODUCT PAPER Cordys Master Data Management Understanding MDM in the SOA-BPM Context Copyright 2013 Cordys Software B.V. All rights reserved. EXECUTIVE SUMMARY Rolling-out new Service-Oriented Architecture

More information

Master Data Management. Zahra Mansoori

Master Data Management. Zahra Mansoori Master Data Management Zahra Mansoori 1 1. Preference 2 A critical question arises How do you get from a thousand points of data entry to a single view of the business? We are going to answer this question

More information

Talend Metadata Manager. Reduce Risk and Friction in your Information Supply Chain

Talend Metadata Manager. Reduce Risk and Friction in your Information Supply Chain Talend Metadata Manager Reduce Risk and Friction in your Information Supply Chain Talend Metadata Manager Talend Metadata Manager provides a comprehensive set of capabilities for all facets of metadata

More information

Enterprise Data Quality

Enterprise Data Quality Enterprise Data Quality An Approach to Improve the Trust Factor of Operational Data Sivaprakasam S.R. Given the poor quality of data, Communication Service Providers (CSPs) face challenges of order fallout,

More information

ORACLE HYPERION DATA RELATIONSHIP MANAGEMENT

ORACLE HYPERION DATA RELATIONSHIP MANAGEMENT Oracle Fusion editions of Oracle's Hyperion performance management products are currently available only on Microsoft Windows server platforms. The following is intended to outline our general product

More information

Delivering information you can trust June 2007. IBM Multiform Master Data Management: The evolution of MDM applications

Delivering information you can trust June 2007. IBM Multiform Master Data Management: The evolution of MDM applications June 2007 IBM Multiform Master Data Management: The evolution of MDM applications Page 2 Contents 2 Traditional approaches to master data management 2 The enterprise application 4 The data warehouse 5

More information

California Enterprise Architecture Framework Master Data Management (MDM) Reference Architecture (RA)

California Enterprise Architecture Framework Master Data Management (MDM) Reference Architecture (RA) ` California Enterprise Architecture Framework Master Management (MDM) Reference Architecture (RA) Version 1.0 Final January 2, 2014 This Page is Intentionally Left Blank Version 1.0 Final ii January 2,

More information

Choosing the Right Master Data Management Solution for Your Organization

Choosing the Right Master Data Management Solution for Your Organization Choosing the Right Master Data Management Solution for Your Organization Buyer s Guide for IT Professionals BUYER S GUIDE This document contains Confidential, Proprietary and Trade Secret Information (

More information

A discussion of information integration solutions November 2005. Deploying a Center of Excellence for data integration.

A discussion of information integration solutions November 2005. Deploying a Center of Excellence for data integration. A discussion of information integration solutions November 2005 Deploying a Center of Excellence for data integration. Page 1 Contents Summary This paper describes: 1 Summary 1 Introduction 2 Mastering

More information

Disclosure of Drug Promotion Expenses: The Importance of Master Data Management and Considerations for Choosing a Reporting Solution

Disclosure of Drug Promotion Expenses: The Importance of Master Data Management and Considerations for Choosing a Reporting Solution Disclosure of Drug Promotion Expenses: The Importance of Master Data Management and Considerations for Choosing a Reporting Solution April 2010 This document contains information specific to Cegedim Dendrite

More information

The Informatica Solution for Improper Payments

The Informatica Solution for Improper Payments The Informatica Solution for Improper Payments Reducing Improper Payments and Improving Fiscal Accountability for Government Agencies WHITE PAPER This document contains Confidential, Proprietary and Trade

More information

Effecting Data Quality Improvement through Data Virtualization

Effecting Data Quality Improvement through Data Virtualization Effecting Data Quality Improvement through Data Virtualization Prepared for Composite Software by: David Loshin Knowledge Integrity, Inc. June, 2010 2010 Knowledge Integrity, Inc. Page 1 Introduction The

More information

Enterprise MDM: Complementing & Extending the Active Data Warehouse. Mark Shainman Global Program Director, Teradata MDM

Enterprise MDM: Complementing & Extending the Active Data Warehouse. Mark Shainman Global Program Director, Teradata MDM Enterprise MDM: Complementing & Extending the Active Data Warehouse Mark Shainman Global Program Director, Teradata MDM Agenda MDM and its Importance MDM, The Enterprise Data Warehouse and Data Mart Consolidation.

More information

Informatica Master Data Management

Informatica Master Data Management Informatica Master Data Management Improve Operations and Decision Making with Consolidated and Reliable Business-Critical Data brochure The Costs of Inconsistency Today, businesses are handling more data,

More information

MDM for the Enterprise: Complementing and extending your Active Data Warehousing strategy. Satish Krishnaswamy VP MDM Solutions - Teradata

MDM for the Enterprise: Complementing and extending your Active Data Warehousing strategy. Satish Krishnaswamy VP MDM Solutions - Teradata MDM for the Enterprise: Complementing and extending your Active Data Warehousing strategy Satish Krishnaswamy VP MDM Solutions - Teradata 2 Agenda MDM and its importance Linking to the Active Data Warehousing

More information

Data Vault and The Truth about the Enterprise Data Warehouse

Data Vault and The Truth about the Enterprise Data Warehouse Data Vault and The Truth about the Enterprise Data Warehouse Roelant Vos 04-05-2012 Brisbane, Australia Introduction More often than not, when discussion about data modeling and information architecture

More information

White paper Interstage Business Operations Platform: Master Data Management

White paper Interstage Business Operations Platform: Master Data Management White paper Interstage Business Operations Platform: Master Data Management Document version 1.0 Date: Aug. 29, 2012 Page 1 of 10 This page intentionally left blank Page 2 of 10 Table of Contents Table

More information

Oracle Role Manager. An Oracle White Paper Updated June 2009

Oracle Role Manager. An Oracle White Paper Updated June 2009 Oracle Role Manager An Oracle White Paper Updated June 2009 Oracle Role Manager Introduction... 3 Key Benefits... 3 Features... 5 Enterprise Role Lifecycle Management... 5 Organization and Relationship

More information

IBM Master Data Management and data governance November 2007. IBM Master Data Management: Effective data governance

IBM Master Data Management and data governance November 2007. IBM Master Data Management: Effective data governance November 2007 IBM Master Data Management: Effective data governance Page 2 Introduction Gone are the days when doing business meant doing so only within the borders of the organization. What used to be

More information

A Service-oriented Architecture for Business Intelligence

A Service-oriented Architecture for Business Intelligence A Service-oriented Architecture for Business Intelligence Liya Wu 1, Gilad Barash 1, Claudio Bartolini 2 1 HP Software 2 HP Laboratories {name.surname@hp.com} Abstract Business intelligence is a business

More information

Data Ownership and Enterprise Data Management: Implementing a Data Management Strategy (Part 3)

Data Ownership and Enterprise Data Management: Implementing a Data Management Strategy (Part 3) A DataFlux White Paper Prepared by: Mike Ferguson Data Ownership and Enterprise Data Management: Implementing a Data Management Strategy (Part 3) Leader in Data Quality and Data Integration www.flux.com

More information

ORACLE SUPPLIER MANAGEMENT: SUPPLIER HUB & SUPPLIER LIFECYCLE MANAGEMENT

ORACLE SUPPLIER MANAGEMENT: SUPPLIER HUB & SUPPLIER LIFECYCLE MANAGEMENT ORACLE SUPPLIER : SUPPLIER HUB & SUPPLIER LIFECYCLE A SINGLE SOURCE OF TRUTH FOR SUPPLIER- SPECIFIC DATA KEY FEATURES ORACLE SUPPLIER LIFECYCLE 360 o Supplier View Extensible Supplier Profile Registration

More information

Integrating SAP and non-sap data for comprehensive Business Intelligence

Integrating SAP and non-sap data for comprehensive Business Intelligence WHITE PAPER Integrating SAP and non-sap data for comprehensive Business Intelligence www.barc.de/en Business Application Research Center 2 Integrating SAP and non-sap data Authors Timm Grosser Senior Analyst

More information

www.ducenit.com Analance Data Integration Technical Whitepaper

www.ducenit.com Analance Data Integration Technical Whitepaper Analance Data Integration Technical Whitepaper Executive Summary Business Intelligence is a thriving discipline in the marvelous era of computing in which we live. It s the process of analyzing and exploring

More information

Informatica PowerCenter Data Virtualization Edition

Informatica PowerCenter Data Virtualization Edition Data Sheet Informatica PowerCenter Data Virtualization Edition Benefits Rapidly deliver new critical data and reports across applications and warehouses Access, merge, profile, transform, cleanse data

More information

Creating the Golden Record

Creating the Golden Record Creating the Golden Record Better Data through Chemistry Donald J. Soulsby metawright.com Agenda The Golden Record Master Data Discovery Integration Quality Master Data Strategy DAMA LinkedIn Group C.

More information

Evolutionary Multi-Domain MDM and Governance in an Oracle Ecosystem

Evolutionary Multi-Domain MDM and Governance in an Oracle Ecosystem Evolutionary Multi-Domain MDM and Governance in an Oracle Ecosystem FX Nicolas Semarchy Keywords: Master Data Management, MDM, Data Governance, Data Integration Introduction Enterprise ecosystems have

More information

Using SAP Master Data Technologies to Enable Key Business Capabilities in Johnson & Johnson Consumer

Using SAP Master Data Technologies to Enable Key Business Capabilities in Johnson & Johnson Consumer Using SAP Master Data Technologies to Enable Key Business Capabilities in Johnson & Johnson Consumer Terry Bouziotis: Director, IT Enterprise Master Data Management JJHCS Bob Delp: Sr. MDM Program Manager

More information

DATA GOVERNANCE AND DATA QUALITY

DATA GOVERNANCE AND DATA QUALITY DATA GOVERNANCE AND DATA QUALITY Kevin Lewis Partner Enterprise Management COE Barb Swartz Account Manager Teradata Government Systems Objectives of the Presentation Show that Governance and Quality are

More information

Paper DM10 SAS & Clinical Data Repository Karthikeyan Chidambaram

Paper DM10 SAS & Clinical Data Repository Karthikeyan Chidambaram Paper DM10 SAS & Clinical Data Repository Karthikeyan Chidambaram Cognizant Technology Solutions, Newbury Park, CA Clinical Data Repository (CDR) Drug development lifecycle consumes a lot of time, money

More information

Data Governance Maturity Model Guiding Questions for each Component-Dimension

Data Governance Maturity Model Guiding Questions for each Component-Dimension Data Governance Maturity Model Guiding Questions for each Component-Dimension Foundational Awareness What awareness do people have about the their role within the data governance program? What awareness

More information

SOA REFERENCE ARCHITECTURE: WEB TIER

SOA REFERENCE ARCHITECTURE: WEB TIER SOA REFERENCE ARCHITECTURE: WEB TIER SOA Blueprint A structured blog by Yogish Pai Web Application Tier The primary requirement for this tier is that all the business systems and solutions be accessible

More information

Introduction to TIBCO MDM

Introduction to TIBCO MDM Introduction to TIBCO MDM 1 Introduction to TIBCO MDM A COMPREHENSIVE AND UNIFIED SINGLE VERSION OF THE TRUTH TIBCO MDM provides the data governance process required to build and maintain a comprehensive

More information

Managing Data in Motion

Managing Data in Motion Managing Data in Motion Data Integration Best Practice Techniques and Technologies April Reeve ELSEVIER AMSTERDAM BOSTON HEIDELBERG LONDON NEW YORK OXFORD PARIS SAN DIEGO SAN FRANCISCO SINGAPORE SYDNEY

More information

University. Course Catalog. December 2009

University. Course Catalog. December 2009 University Course Catalog December 2009 Course Catalog December 2009 The Siperian Master Data Management (MDM) training courses address the needs of all core roles involved in implementing, developing,

More information

SQL Server Master Data Services A Point of View

SQL Server Master Data Services A Point of View SQL Server Master Data Services A Point of View SUBRAHMANYA V SENIOR CONSULTANT SUBRAHMANYA.VENKATAGIRI@WIPRO.COM Abstract Is Microsoft s Master Data Services an answer for low cost MDM solution? Will

More information

Corralling Data for Business Insights. The difference data relationship management can make. Part of the Rolta Managed Services Series

Corralling Data for Business Insights. The difference data relationship management can make. Part of the Rolta Managed Services Series Corralling Data for Business Insights The difference data relationship management can make Part of the Rolta Managed Services Series Data Relationship Management Data inconsistencies plague many organizations.

More information

Data Management Roadmap

Data Management Roadmap Data Management Roadmap A progressive approach towards building an Information Architecture strategy 1 Business and IT Drivers q Support for business agility and innovation q Faster time to market Improve

More information

DATA GOVERNANCE AT UPMC. A Summary of UPMC s Data Governance Program Foundation, Roles, and Services

DATA GOVERNANCE AT UPMC. A Summary of UPMC s Data Governance Program Foundation, Roles, and Services DATA GOVERNANCE AT UPMC A Summary of UPMC s Data Governance Program Foundation, Roles, and Services THE CHALLENGE Data Governance is not new work to UPMC. Employees throughout our organization manage data

More information

Semarchy Convergence for MDM The Next Generation Evolutionary MDM Platform

Semarchy Convergence for MDM The Next Generation Evolutionary MDM Platform PRODUCT DATASHEET Semarchy Convergence for MDM The Next Generation Evolutionary MDM Platform IT MANAGEMENT BENEFITS Get successful on time and budget Start with a tactical solution, build for tomorrow

More information

Continuing the MDM journey

Continuing the MDM journey IBM Software White paper Information Management Continuing the MDM journey Extending from a virtual style to a physical style for master data management 2 Continuing the MDM journey Organizations implement

More information

US Department of Education Federal Student Aid Integration Leadership Support Contractor June 1, 2007

US Department of Education Federal Student Aid Integration Leadership Support Contractor June 1, 2007 US Department of Education Federal Student Aid Integration Leadership Support Contractor June 1, 2007 Draft Enterprise Data Management Data Policies Final i Executive Summary This document defines data

More information

USING TAXONOMIES AS MASTER REFERENCE METADATA FOR THE ENTERPRISE

USING TAXONOMIES AS MASTER REFERENCE METADATA FOR THE ENTERPRISE USING TAXONOMIES AS MASTER REFERENCE METADATA FOR THE ENTERPRISE April 11, 2012 Jeannine Bartlett Chief Solutions Architect, Earley & Associates Data Management Association San Francisco Chapter (SF DAMA

More information

Master Data Management (MDM) in the Public Sector

Master Data Management (MDM) in the Public Sector Master Data Management (MDM) in the Public Sector Don Hoag Manager Agenda What is MDM? What does MDM attempt to accomplish? What are the approaches to MDM? Operational Analytical Questions 2 What is Master

More information

US Department of Education Federal Student Aid Integration Leadership Support Contractor January 25, 2007

US Department of Education Federal Student Aid Integration Leadership Support Contractor January 25, 2007 US Department of Education Federal Student Aid Integration Leadership Support Contractor January 25, 2007 Task 18 - Enterprise Data Management 18.002 Enterprise Data Management Concept of Operations i

More information

An Introduction to Master Data Management (MDM)

An Introduction to Master Data Management (MDM) An Introduction to Master Data Management (MDM) Presented by: Robert Quinn, Sr. Solutions Architect FYI Business Solutions Agenda Introduction MDM Definition MDM Terms Best Practices Data Challenges MDM

More information

Logical Modeling for an Enterprise MDM Initiative

Logical Modeling for an Enterprise MDM Initiative Logical Modeling for an Enterprise MDM Initiative Session Code TP01 Presented by: Ian Ahern CEO, Profisee Group Copyright Speaker Bio Started career in the City of London: Management accountant Finance,

More information

Data Ownership and Enterprise Data Management: Leveraging Technology to Get Control of Your Data (Part 2)

Data Ownership and Enterprise Data Management: Leveraging Technology to Get Control of Your Data (Part 2) A Flux White Paper Prepared by: Mike Ferguson Ownership and Enterprise Management: Leveraging Technology to Get Control of Your (Part 2) Leader in Quality and Integration www.dataflux.com 877 846 FLUX

More information

Master data done right.

Master data done right. Master data done right. MDM for Business Master data value, delivered. Master data consists of the information that is key to the core operations of a business. Master data may include data about people

More information

Principles and Foundations of Web Services: An Holistic View (Technologies, Business Drivers, Models, Architectures and Standards)

Principles and Foundations of Web Services: An Holistic View (Technologies, Business Drivers, Models, Architectures and Standards) Principles and Foundations of Web Services: An Holistic View (Technologies, Business Drivers, Models, Architectures and Standards) Michael P. Papazoglou (INFOLAB/CRISM, Tilburg University, The Netherlands)

More information

Thank you for attending the MDM for the Enterprise Seminar Series!

Thank you for attending the MDM for the Enterprise Seminar Series! Thank you for attending the MDM for the Enterprise Seminar Series! Please do not distribute this presentations without permission from the speaker (see contact information within.) This is just intended

More information

Master Data Management and Data Warehousing. Zahra Mansoori

Master Data Management and Data Warehousing. Zahra Mansoori Master Data Management and Data Warehousing Zahra Mansoori 1 1. Preference 2 IT landscape growth IT landscapes have grown into complex arrays of different systems, applications, and technologies over the

More information

Enterprise Information Management Services Managing Your Company Data Along Its Lifecycle

Enterprise Information Management Services Managing Your Company Data Along Its Lifecycle SAP Solution in Detail SAP Services Enterprise Information Management Enterprise Information Management Services Managing Your Company Data Along Its Lifecycle Table of Contents 3 Quick Facts 4 Key Services

More information

PPDM Well Master and Enterprise MDM Tools

PPDM Well Master and Enterprise MDM Tools PPDM Well Master and Enterprise MDM Tools 2013 PPDM Data Management Symposium Calgary Kelly Guillory & Prasanna Balakrishnan Agenda About Us Paper Background What People Want Business Value of MDM MDM

More information

HANDLING MASTER DATA OBJECTS DURING AN ERP DATA MIGRATION

HANDLING MASTER DATA OBJECTS DURING AN ERP DATA MIGRATION HANDLING MASTER DATA OBJECTS DURING AN ERP DATA MIGRATION Table Of Contents 1. ERP initiatives, the importance of data migration & the emergence of Master Data Management (MDM)...3 2. 3. 4. 5. During Data

More information

Course 803401 DSS. Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization

Course 803401 DSS. Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization Oman College of Management and Technology Course 803401 DSS Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization CS/MIS Department Information Sharing

More information

TDWI Data Integration Techniques: ETL & Alternatives for Data Consolidation

TDWI Data Integration Techniques: ETL & Alternatives for Data Consolidation TDWI Data Integration Techniques: ETL & Alternatives for Data Consolidation Format : C3 Education Course Course Length : 9am to 5pm, 2 consecutive days Date : Sydney 22-23 Nov 2011, Melbourne 28-29 Nov

More information

Data Integration and ETL with Oracle Warehouse Builder

Data Integration and ETL with Oracle Warehouse Builder Oracle University Contact Us: 1.800.529.0165 Data Integration and ETL with Oracle Warehouse Builder Duration: 5 Days What you will learn This Data Integration and ETL with Oracle Warehouse Builder training

More information

Master Data Management Driving Industry Convergence

Master Data Management Driving Industry Convergence Driving Industry James Kobielus Principal Analyst, Data April 16, 2007 Summary Master data management (MDM) is critical to enterprise success. MDM refers to the infrastructure, tools and best practices

More information

Master Data Management Framework: Begin With an End in Mind

Master Data Management Framework: Begin With an End in Mind S e p t e m b e r 2 0 0 5 A M R R e s e a r c h R e p o r t Master Data Management Framework: Begin With an End in Mind by Bill Swanton and Dineli Samaraweera Most companies know they have a problem with

More information

ORACLE CUSTOMER HUB. Consolidate & govern a unique, complete and accurate set of Master Customer information from across the enterprise.

ORACLE CUSTOMER HUB. Consolidate & govern a unique, complete and accurate set of Master Customer information from across the enterprise. ORACLE CUSTOMER HUB KEY FEATURES TRUSTED MASTER CUSTOMER DATA Comprehensive Customer Data Model Roles and Hierarchical Relationships Vertical and Related Child Data Entities Industry Variants: Includes

More information

HANDLING MASTER DATA OBJECTS DURING AN ERP DATA MIGRATION

HANDLING MASTER DATA OBJECTS DURING AN ERP DATA MIGRATION HANDLING MASTER DATA OBJECTS DURING AN ERP DATA MIGRATION Table Of Contents 1. ERP initiatives, the importance of data migration & the emergence of Master Data Management (MDM)...3 2. During Data Migration,

More information

Data Warehouse Overview. Srini Rengarajan

Data Warehouse Overview. Srini Rengarajan Data Warehouse Overview Srini Rengarajan Please mute Your cell! Agenda Data Warehouse Architecture Approaches to build a Data Warehouse Top Down Approach Bottom Up Approach Best Practices Case Example

More information

OLAP and OLTP. AMIT KUMAR BINDAL Associate Professor M M U MULLANA

OLAP and OLTP. AMIT KUMAR BINDAL Associate Professor M M U MULLANA OLAP and OLTP AMIT KUMAR BINDAL Associate Professor Databases Databases are developed on the IDEA that DATA is one of the critical materials of the Information Age Information, which is created by data,

More information

Vermont Enterprise Architecture Framework (VEAF) Master Data Management Design

Vermont Enterprise Architecture Framework (VEAF) Master Data Management Design Vermont Enterprise Architecture Framework (VEAF) Master Data Management Design EA APPROVALS Approving Authority: REVISION HISTORY Version Date Organization/Point

More information

What's New in SAS Data Management

What's New in SAS Data Management Paper SAS034-2014 What's New in SAS Data Management Nancy Rausch, SAS Institute Inc., Cary, NC; Mike Frost, SAS Institute Inc., Cary, NC, Mike Ames, SAS Institute Inc., Cary ABSTRACT The latest releases

More information

Introduction to Master Data Management

Introduction to Master Data Management Introduction to Master Data Management Mark Rittman, Director, Rittman Mead Consulting What is Master Data Management? Managing the reference data used to support applications and analysis Master data

More information

MANAGING USER DATA IN A DIGITAL WORLD

MANAGING USER DATA IN A DIGITAL WORLD MANAGING USER DATA IN A DIGITAL WORLD AIRLINE INDUSTRY CHALLENGES AND SOLUTIONS WHITE PAPER OVERVIEW AND DRIVERS In today's digital economy, enterprises are exploring ways to differentiate themselves from

More information

EII - ETL - EAI What, Why, and How!

EII - ETL - EAI What, Why, and How! IBM Software Group EII - ETL - EAI What, Why, and How! Tom Wu 巫 介 唐, wuct@tw.ibm.com Information Integrator Advocate Software Group IBM Taiwan 2005 IBM Corporation Agenda Data Integration Challenges and

More information

Technical Layer (Technical Interoperability) Information Layer (Information Interoperability. Business Layer (Business Process Interoperability)

Technical Layer (Technical Interoperability) Information Layer (Information Interoperability. Business Layer (Business Process Interoperability) Layers of Interoperability Technical Layer (Technical Interoperability) Information Layer (Information Interoperability Business Layer (Business Process Interoperability) Information Interoperability Identify

More information

Principal MDM Components and Capabilities

Principal MDM Components and Capabilities Principal MDM Components and Capabilities David Loshin Knowledge Integrity, Inc. 1 Agenda Introduction to master data management The MDM Component Layer Model MDM Maturity MDM Functional Services Summary

More information

Klarna Tech Talk: Mind the Data! Jeff Pollock InfoSphere Information Integration & Governance

Klarna Tech Talk: Mind the Data! Jeff Pollock InfoSphere Information Integration & Governance Klarna Tech Talk: Mind the Data! Jeff Pollock InfoSphere Information Integration & Governance IBM s statements regarding its plans, directions, and intent are subject to change or withdrawal without notice

More information

Introduction to Glossary Business

Introduction to Glossary Business Introduction to Glossary Business B T O Metadata Primer Business Metadata Business rules, Definitions, Terminology, Glossaries, Algorithms and Lineage using business language Audience: Business users Technical

More information

Data Integration for the Real Time Enterprise

Data Integration for the Real Time Enterprise Executive Brief Data Integration for the Real Time Enterprise Business Agility in a Constantly Changing World Overcoming the Challenges of Global Uncertainty Informatica gives Zyme the ability to maintain

More information

The Importance of a Single Platform for Data Integration and Quality Management

The Importance of a Single Platform for Data Integration and Quality Management helping build the smart and agile business The Importance of a Single Platform for Data Integration and Quality Management Colin White BI Research March 2008 Sponsored by Business Objects TABLE OF CONTENTS

More information

MDM Components and the Maturity Model

MDM Components and the Maturity Model A DataFlux White Paper Prepared by: David Loshin MDM Components and the Maturity Model Leader in Data Quality and Data Integration www.dataflux.com 877 846 FLUX International +44 (0) 1753 272 020 One common

More information

Next-Generation Data Virtualization Fast and Direct Data Access, More Reuse, and Better Agility and Data Governance for BI, MDM, and SOA

Next-Generation Data Virtualization Fast and Direct Data Access, More Reuse, and Better Agility and Data Governance for BI, MDM, and SOA white paper Next-Generation Data Virtualization Fast and Direct Data Access, More Reuse, and Better Agility and Data Governance for BI, MDM, and SOA Executive Summary It s 9:00 a.m. and the CEO of a leading

More information

Improving your Data Warehouse s IQ

Improving your Data Warehouse s IQ Improving your Data Warehouse s IQ Derek Strauss Gavroshe USA, Inc. Outline Data quality for second generation data warehouses DQ tool functionality categories and the data quality process Data model types

More information

SOLUTION BRIEF CA ERwin Modeling. How can I understand, manage and govern complex data assets and improve business agility?

SOLUTION BRIEF CA ERwin Modeling. How can I understand, manage and govern complex data assets and improve business agility? SOLUTION BRIEF CA ERwin Modeling How can I understand, manage and govern complex data assets and improve business agility? SOLUTION BRIEF CA DATABASE MANAGEMENT FOR DB2 FOR z/os DRAFT CA ERwin Modeling

More information

EAI vs. ETL: Drawing Boundaries for Data Integration

EAI vs. ETL: Drawing Boundaries for Data Integration A P P L I C A T I O N S A W h i t e P a p e r S e r i e s EAI and ETL technology have strengths and weaknesses alike. There are clear boundaries around the types of application integration projects most

More information

Considerations: Mastering Data Modeling for Master Data Domains

Considerations: Mastering Data Modeling for Master Data Domains Considerations: Mastering Data Modeling for Master Data Domains David Loshin President of Knowledge Integrity, Inc. June 2010 Americas Headquarters EMEA Headquarters Asia-Pacific Headquarters 100 California

More information

The Data Reservoir as an enabler of differentiating Analytics initiatives

The Data Reservoir as an enabler of differentiating Analytics initiatives Mandy Chessell CBE FREng CEng FBCS Distinguished Engineer, Master Inventor Chief Architect, Solutions The Reservoir as an enabler of differentiating Analytics initiatives 3 rd March 2015 Agenda Changing

More information