MDM AS A METHODOLOGY
|
|
- Trevor Reed
- 8 years ago
- Views:
Transcription
1 MDM AS A METHODOLOGY Written by Janet Wetter Principal MDM/BI Architect Submitted by: Wetterlands Ranch POB 247 Owenton, KY Tel: MDM AS A METHODOLOGY Page 1
2 The Reason Small tweaks and incremental investments in data accessibility and intelligence can have big returns in revenues, growth and innovation, according to findings from a study of Fortune 1000 companies released today. Touting millions of dollars in added revenue possibilities and avenues of business, the study entitled Measuring the Business Impacts of Effective Data was conducted by Sybase, an SAP company, and the University of Texas, in conjunction with the Indian School of Business. It reviewed large, global corporations and surveyed their positions and plans for data management systems. When data accessibility and intelligence are increased by 10 percent, revenue generated from new customers grew by 0.7 percent, or, as applied to the median organization in the study sample, $14.7 million. As far as revenue from new products and services a common measure of business innovation a 10-percent increase in development of data accessibility and intelligence registered 0.81 percent increase in revenue, which, for the median organization in the study - $17 billion in annual revenues with $2.1 billion coming from new products in services meant an additional $17 million for the year. Anitesh Barua, lead researcher with the University of Texas, says the summary of additional dedication to data should spark a dollars-and-cents interest with top level managers and executives as well as IT officers and CIOs. We re not suggesting it s low-hanging fruit. It s almost a mindset change, Barua says. Companies finding gains from investment in data or not taking advantage of the possibilities cross all business sectors. In particular, the petroleum market has the most to gain from enhancing its data quality, with a 14-fold increase in revenues from even a slight jump in company-wide, usable data, according to the study. Referencing fiscal advantages from changes to data use and understanding by employees made by Charles Schwab following an economic downturn in 2001, Barua said that corporations stand to make even greater gains with improvements and investment in data at all levels. We re saying at the end of the day, it s still a lot of data and we re drowning in transaction-level data. But, if you can make that meaningful not just accurate and timely you can reap a lot of benefits, he says. Still, the importance of usable, clear data is largely relegated to IT departments, the researchers concluded. For example, only a lowly 3.7 percent of large companies reported that investing in data accessibility and intelligence was part of their development of revenue streams and method of acquiring new customers, Barua says, alluding to a resistance or ignorance of data quality or advancing technology with many successful businesses. MDM AS A METHODOLOGY Page 2
3 These findings were the second release in a three-part installment quantifying the relationship between effective data and a company s performance. The first installment, release in July, reviewed the connections between incremental investments in data and the key financial indicators of an enterprise s health and profitability. Later this fall, researchers will release the final portion of findings on the operational impacts of effective data with a focus on improving the accuracy of planning and forecasting. The above article was written by Justin Kern who is an associate editor at Information Management Magazine. The History MDM is a lot of things to a lot of people and vendors and currently has no single definition. MDM stands for Master Data Management and has its roots in creating global reference tables of master data used within a company. Database Administrators are credited with developing the idea because each application they backed up had repeated reference tables that spanned the company, hence global reference tables. And, since the DBA backup window kept shrinking, the need to consolidate these identical application reference tables into a single instance for backing up became popular. Creating a database of global reference tables for common usage became known as Master Data Management MDM. The foundations for Data Governance also sprang from the development of MDM due to the combining of the many application reference tables of the same origin. Identical reference tables with common data values were often tweaked by the application so that the set of data values differed but the common master reference table needed a single set of data values everyone agreed on. Deciding on the common set of data values were all of the owners of the applications these values were consolidated from. Hence the beginning of Data Stewardship to derive a common set of data values for a given master reference table. Since this idea worked so well with common master reference tables and, since a process was in place to resolve conflicts for determining common data values, people began applying it to other conflicting data values like common customer information, then common product information and so on. The data stewards soon needed a repository to contain the flow of ever increasing metadata from these consolidations so the Metadata Repository (Data Dictionary) was utilized. And, soon after, the MDM Methodology for standardizing the process of capturing, resolving and recording the metadata developed by the data stewards was created and the process of Data Harmonization was born. The above described methodology was in its infancy when Sarbanes-Oxley was implemented and soon grew popular as a way to find the TRUTH in the multitudes of MDM AS A METHODOLOGY Page 3
4 company data for reporting. The Business Intelligence practices tried to use this methodology with mixed results because the underlying consolidated data foundation was in various stages of development across companies. The BI group needed the data foundation to find the truth. As MDM became popular the software vendors were not far behind including various versions of MDM in their products. So the concept of MDM grew from its humble origins to the buzz word of today with many flavors, ideas and concepts to fit the vendor tool it is implemented within. Some have a metadata repository with associated tools, some use just the physical definition of profiling data and integration and a few understand that a Data Management Life Cycle is involved containing all of the aspects of MDM. A Metadata Repository, a Data Governance Organization and a Data Harmonization Process that spans both the application and enterprise levels of a company at the conceptual, logical and physical layers of structured, semi-structured and unstructured data became known as the MDM Framework MDMF. The concept of a completely integrated agile framework for integrating data across an enterprise is popular because it utilizes the company s existing tool environment saving expensive software acquisitions. UST utilizes the MDM framework product with their clients. Below is a diagram showing the MDM Framework components. MDM AS A METHODOLOGY Page 4
5 The Mission At the core of the MDMF is the Enterprise Data Model which represents the six layers of data within a company. A Data Management Strategy for linking the Enterprise Data Model to both the Project Layer and the Global Enterprise Layer using standardized Templates that drive the Data Management Life Cycles for both layers. The Templates are the project artifacts (deliverables) utilized as collection devices for interfacing with the business community, requirements conformance for IT personnel, project gate reviews and for loading the evaluated and approved data into a Metadata Repository. Using a pre-defined yet flexible MDM Framework standardizes the data integration for a client with a custom environment by quickly incorporating an existing System Development Life Cycle (SDLC), tool set and Metadata Repository of any combination present in the client s current environment whereby avoiding new tool expenditures. The MDMF is an intranet based framework providing company wide access for all to view while providing a single place of reference for information management including Data Governance Decisions, Business Metadata and Project Data Gate Reviews. UST has a well defined Methodology to quickly implement the MDMF in a client environment. Beginning with the function of Road Mapping which documents the client s Vision and creates a prioritized Data Integration List covering the scope of the engagement. The Data Integration List is then used as the input for the Project Data Management Life Cycle to execute the Template Artifacts associated with the engagement. The Template Artifacts are the input for the Data Harmonization process where the Master Reference Tables and Base Data Elements and Entities are derived. Existing Master Reference Tables and Base Data Elements are mapped to the current Data Governance baseline of data using Source to Target Mapping techniques. Newly derived Master Reference Tables and Base Data Elements and Entities are then inputted into the Data Governance Process, as well as any metadata changes made to existing published metadata in the Enterprise Metadata Repository. As the Template Artifacts are approved thru Gate Reviews the information is loaded into the Metadata Repository and reports published to the MDMF. Project Management is enhanced by the MDMF published reports which detail the metadata captured and processed to date by any project. Additional reports from the Data Governance Group are also published for easy access to the most current information on approved Master Reference Tables and their data values and up to date information on the Base Data Elements and Entities for use by the projects in Source to Target Mapping and in the Data Harmonization Process. MDM AS A METHODOLOGY Page 5
6 The Core The core of the MDMF is the Enterprise Data Model detailed below with the six levels of company data identified. The Enterprise Data Model is a well known industry standard model of 5 layers but is enhanced by adding one additional layer to accommodate the requirements of MDM artifacts in the MDM Framework. Below is a diagram depicting the six layers of the Enterprise Data Model. The Enterprise Data Model represents all of the company information formatted into the Conceptual, Logical and Physical data that a company acquires. The three types of data are structured, semi-structured and unstructured information that is used to operate the company business. The top three layers are the conceptual artifacts. The fourth and fifth layers are the logical artifacts and the last, or sixth layer, is the physical layer where the three types of data exists. MDM AS A METHODOLOGY Page 6
7 The first layer is the Enterprise Subject Area Model ESAM which is a high level business view of the company data. It contains the major and sub-components of the business domains and it reflect the company structure or organization of data. Typical business domains of data would be People, Organizations, Product, Facility, ERP Functions (Ordering, Shipping and Billing of Products), Work Effort, HR and Accounting. Industry specific Business Domains are added and customized for the individual business type. MDM AS A METHODOLOGY Page 7
8 The second layer is the Enterprise Conceptual Data Model ECM which represents a high level view of the data in the company. It is similar to the Enterprise Subject Area Model but it contains the relationships between the Data Domains showing how the business functions. This model is an entity relationship view only and does not contain data elements. MDM AS A METHODOLOGY Page 8
9 The Enterprise Data Object Model EDOM is the third layer of the Enterprise Data Model and is an additional layer added to the traditional model for the MDM functions of a company. The EDOM is the MDM area which includes the Base Data Elements and Base Entities; Global Reference Tables, The Enterprise Metadata Repository Model and the Data Governance Organization structures and processes. MDM AS A METHODOLOGY Page 9
10 The fourth layer is the Logical Data Model LDM which is a specific business functional view of a subset of the company data within the Enterprise Conceptual Model. It contains a low level normalized Entity Relationship diagram attributed with the Base Data Elements or their alias equivalent. An Alias Data Element is a data element that has been mapped to a Base Data Element thru the Data Harmonization Process. The Logical Transformation Model LTM, the fifth layer, is created from the completed Logical Data Model after deciding which DBMS is to be used in the physical environment. This is the physicalization of the logical design to determine whether it will remain in a normalized form or be transcribed into a dimensional model or other structure. The intention of the Logical Transformation Model is to maximize performance in the physical environment based on the best design for that environment and the usage of the data contained within that design. The final sixth layer is the Physical Data Model PDM which is the Data Definition Language (DDL) of the selected Database Management System (DBMS) that contains the code to implement the Logical Transformation design in the company environment. This layer also contains the semi-structured and unstructured data within the company which can be mapped to a data structure in the LDM. MDM AS A METHODOLOGY Page 10
11 The Templates The Templates are EXCEL spreadsheets designed to map the reviewed data into the Metadata Repository. If designed correctly they can be uploaded directly into the repository. They are used as both collection devices within the IT community and as an interface with the Business users. Vendor data tools are not wide spread throughout a company but everyone usually has access to Excel so they provide a common foundation to share information. They are also provide a standardized format for a System Development Life Cycle (SDLC) review process across all applications being incorporated into the Enterprise Data Model. There are several Templates possible but the four major templates common to all projects are: Conceptual Data Element Template is a standardized format to collect data and the metadata associated with it at the individual data element level for inclusion in Enterprise Metadata Repository. The conceptual data element is the foundation and the integration point for data design at the higher levels. The template is customized for each client based on the standardized metadata requirements across all projects and the Enterprise Metadata Repository being used by the client. Logical Data Element Template collects the outcome of the Entity Relationship Diagram process in a standardized format for inclusion in Enterprise Metadata Repository after the logical design process is completed. Physical Data Element Template describes the table and column attributes of the physical database design after the Logical Transformation Model is completed. Database Structure Template includes the physical attributes at a table level to complete the database design after the Logical Transformation Model. The templates require a design review using the Physical and Database Structure Templates as input. Additional Templates like Domain, Domain Values and Content media, ETC. are used based on the Metadata Repository being used by the client. MDM AS A METHODOLOGY Page 11
12 The Strategy The MDM Strategy ties together the Enterprise Data Model and the two levels of Methodology (Project & Enterprise Data Management Life Cycles, discussed in the next section) thru the Templates. Below is a diagram of that relationship. Information is usually collected thru the project level where a new application or COTS product is being incorporated into the Enterprise layer. The Conceptual Data Element Template can also be used to collect new data elements to be considered by the Data Governance process for inclusion as Base Data Elements. Most projects have an existing database which needs to be reverse engineered into a logical format and added to a Conceptual Data Element Template to be processed thru the Project Data Management Life Cycle Methodology. Proceeding thru the steps of the methodology will define a new Logical Data Model that is mapped to the existing Enterprise Data Model using the Data Harmonization process. Changes or enhancements to the Logical Data Model can be made at this point in time in a project. The Conceptual Data Element Template is the bridge between the Project Data Management Life Cycle and the Enterprise Data Management Life Cycle. Collected project data elements that were not mapped as an alias into the existing Base Data Elements in the Metadata Repository are then sent to the Data Governance process for consideration as new Base Data Elements. Updates to existing Base Data Element metadata can also be forwarded to the Data Governance process this way. MDM AS A METHODOLOGY Page 12
13 The Data Harmonization Process Data Harmonization is a reoccurring process within the Data Management Life Cycle at the project level which is managed by the Data Architect. Data Harmonization is also the bridge between the project layer and the enterprise layer of the Data Management Life Cycle which is managed by the Data Governance organization. The collection, cleansing and comparing of Data Element between both layers is recorded in an Enterprise Metadata Repository maintaining the original data source to target mappings and resulting transformation rules so impact analyst can be performed across the enterprise. Example of a Base Data Elements where all business rules are removed: Entity Person: Person First Name Text Person Last Name Text Person Identifier Two examples of the logical data elements derived from the Base Data Elements when a business rule is associated with it: Entity Customer: Business Rule Person is Customer Customer First Name Text Customer Last Name Text Customer Identifier OR Entity Employee: Business Rule Person is Employee Employee First Name Text Employee Last Name Text Employee identifier MDM AS A METHODOLOGY Page 13
14 The Methodology The Project Data Management Life Cycle PDMLC drives the Enterprise Data Management Life Cycle EDMLC thru the Data Harmonization process. And the Conceptual Data Element Template is the link between the two methodologies. The PDMLC is a standard data management process for a project creating a new database or enhancing an existing one. The difference is the addition of the Data Harmonization process which utilizes the Templates to communicate information to the EDMLC in a standardized fashion using the Data Governance process. The PDMLC is open source and the System Development Life Cycle SDLC can be of the client s choosing. Likewise, the Enterprise Metadata Repository is chosen by the client and as well as incorporating the existing tool set at the client site into the PDMLC tasks to product the project artifacts. The flexibility of the MDMF helps the client contain cost by avoiding new software expenditures while providing the advantages of integrating the company data. Below is the diagram for the PDMLC: The number of PDM Phases is adjusted by the client s choice of SDLC. The EDMLC is driven by the PDMLC s Data Harmonization process that maps the project data elements collected on the Conceptual Data Element Template to the existing Base Data Elements approved by the Data Stewards that exist in the Metadata Repository. Project data elements that are not mapable are then forwarded to the Data Stewards to be processed thru the Data Governance process for new Base Data MDM AS A METHODOLOGY Page 14
15 Elements. Below is the PDMLC diagram depicting the enterprise tasks for MDM data integration: These tasks align the Enterprise Object Data Model, the Enterprise Conceptual Data Model and the Enterprise Metadata Repository using the predefined methodology. The official version of the results from the Data Governance process can be posted for employee knowledge thru out the company via the company intranet MDMF interface providing an up to date version of the currently approved metadata. Other projects can immediately take advantage of these results and build on them. The Case Studies Two case studies are documented below. These case studies are both for a large data integration effort but this methodology can be used for any size of data integration effort. The first involves a project for US Customs (Department of Homeland Security) to integrate 24 diverse government agencies across the trade domain to create a web based application (ACE) that allows a single user interface for the import and export of all trade goods in the USA. A data dictionary was created for each of the 24 agencies using Data Harmonization for all of the agency applications that involved trade data. As Each data dictionary was completed for an agency it was the integrated into the Metadata Repository using Data Harmonization and a Data Governance process. After all data was compiled for the 24 agencies the total of initial data elements was over twenty two thousand. The Data Harmonization process reduced that number to less than twelve hundred Base Data MDM AS A METHODOLOGY Page 15
16 Elements which were added to the IBM Infosphere Data Architect Metadata Repository to provide the basis for the SAP Trade Data Module. The second case study involves the US Air Force Research Labs (HQ WPAFB in Dayton OH). Over time many common applications became customized at the 10 directorates under the Research Labs umbrella. HQ needed a common version and a single data model for all of the directorates to input their data for reporting. A common data dictionary was created using Data Harmonization and a Data governance process which provided the data elements for the unified data model and the original source to target mappings for each directorate. The initial effort was so successful the MDMF became the common procedure for the Research Labs for all home grown and vendor applications providing a common data framework across all Research Lab units and external interfaces. The Advantage The MDMF provides a low cost solution to integrating company data in a standardized repeatable fashion. The MDMF Methodology allows you to quickly integrate existing projects into a unified platform providing an Enterprise view of the data. The Enterprise layer can be built one application at a time while integrating the common data in any business domain. The pace of data integration is easily controlled and managed with the MDMF reusable Project Data Management Life Cycle eliminating a company-wide initial effort. The input for the Data Governance process is built into the MDMF so only the actual Data Governance structure, which is right for the organization of the company, needs to be implemented. The MDMF use of predefined Templates allows an automatic load of data into the Metadata Repository after design reviews and SDLC phase approval is completed. Metadata Repository reporting is unified and available to all of the employees of the company thru the intranet interface while the actual reports or files can be stored in any media. The MDMF is cost effective because it does not require the purchase of new software products but incorporates the existing company tool set into the complete methodology. The primary costs are the initial consulting fees for setup of the MDMF then each project can be funded separately as funds allow providing large savings over time. Information from COTS or vendor products can be assimilated into the Metadata Repository using the MDMF while the original data remains in the tool. The unique ability to be customized for the current environment without the restrictions of a predefined vendor process or a box approach provides easy acceptance by the company employees. The consolidation of data into to an easy to access information MDM AS A METHODOLOGY Page 16
17 reporting structure available to all employees cuts cost of locating and utilizing information and provides a foundation for Business Intelligence Reporting. This low cost open source solution provides a substantial cost savings over time while providing complete data integration at a pace determined by the client. Additional focused white papers produced by Wetterlands Ranch are available as part of the MDM Framework product. Enjoy! MDM AS A METHODOLOGY Page 17
Bringing agility to Business Intelligence Metadata as key to Agile Data Warehousing. 1 P a g e. www.analytixds.com
Bringing agility to Business Intelligence Metadata as key to Agile Data Warehousing 1 P a g e Table of Contents What is the key to agility in Data Warehousing?... 3 The need to address requirements completely....
More informationAn RCG White Paper The Data Governance Maturity Model
The Dataa Governance Maturity Model This document is the copyrighted and intellectual property of RCG Global Services (RCG). All rights of use and reproduction are reserved by RCG and any use in full requires
More informationUS 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 informationWhitepaper Data Governance Roadmap for IT Executives Valeh Nazemoff
Whitepaper Data Governance Roadmap for IT Executives Valeh Nazemoff The Challenge IT Executives are challenged with issues around data, compliancy, regulation and making confident decisions on their business
More informationA 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 informationIPL 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 informationNew York Health Benefit Exchange
New York Health Benefit Exchange Blueprint Summary for 9.7.4 Data Management Plan October 26, 2012 Item Number Topic 9.7.4 Data Management Plan Version Number Modified By Revision Date Description of Change
More informationEnterprise Data Governance
DATA GOVERNANCE Enterprise Data Governance Strategies and Approaches for Implementing a Multi-Domain Data Governance Model Mark Allen Sr. Consultant, Enterprise Data Governance WellPoint, Inc. 1 Introduction:
More informationUS 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 informationEnabling Better Business Intelligence and Information Architecture With SAP Sybase PowerDesigner Software
SAP Technology Enabling Better Business Intelligence and Information Architecture With SAP Sybase PowerDesigner Software Table of Contents 4 Seeing the Big Picture with a 360-Degree View Gaining Efficiencies
More informationThe Business in Business Intelligence. Bryan Eargle Database Development and Administration IT Services Division
The Business in Business Intelligence Bryan Eargle Database Development and Administration IT Services Division Defining Business Intelligence (BI) Agenda Goals Identify data assets Transform data and
More informationData Modeling in the Age of Big Data
Data Modeling in the Age of Big Data Pete Stiglich Pete Stiglich is a principal at Clarity Solution Group. pstiglich@clarity-us.com Abstract With big data adoption accelerating and strong interest in NoSQL
More informationMergers and Acquisitions: The Data Dimension
Global Excellence Mergers and Acquisitions: The Dimension A White Paper by Dr Walid el Abed CEO Trusted Intelligence Contents Preamble...............................................................3 The
More informationDATA 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 informationKnowledgent White Paper Series. Developing an MDM Strategy WHITE PAPER. Key Components for Success
Developing an MDM Strategy Key Components for Success WHITE PAPER Table of Contents Introduction... 2 Process Considerations... 3 Architecture Considerations... 5 Conclusion... 9 About Knowledgent... 10
More informationEffecting 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 informationInformation Management & Data Governance
Data governance is a means to define the policies, standards, and data management services to be employed by the organization. Information Management & Data Governance OVERVIEW A thorough Data Governance
More informationData Governance Best Practice
Data Governance Best Practice Business Connexion Michelle Grimley Senior Manager EIM +27 (0)11 266 6499 Michelle.Grimley@bcx.co.za Inri Möller Master Data Manager +27 (0)11 266 5146 Inri.Möller@bcx.co.za
More informationTurning Data into Knowledge: Creating and Implementing a Meta Data Strategy
EWSolutions Turning Data into Knowledge: Creating and Implementing a Meta Data Strategy Anne Marie Smith, Ph.D. Director of Education, Principal Consultant amsmith@ewsolutions.com PG 392 2004 Enterprise
More informationNASCIO EA Development Tool-Kit Solution Architecture. Version 3.0
NASCIO EA Development Tool-Kit Solution Architecture Version 3.0 October 2004 TABLE OF CONTENTS SOLUTION ARCHITECTURE...1 Introduction...1 Benefits...3 Link to Implementation Planning...4 Definitions...5
More informationThe following is intended to outline our general product direction. It is intended for informational purposes only, and may not be incorporated into
The following is intended to outline our general product direction. It is intended for informational purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any
More informationData Management Emerging Trends. Sourabh Mukherjee Data Management Practice Head, India Accenture
Data Management Emerging Trends Sourabh Mukherjee Data Management Practice Head, India Accenture Data has always been an important asset for companies as it is the basis for making business decisions.
More informationSupporting Your Data Management Strategy with a Phased Approach to Master Data Management WHITE PAPER
Supporting Your Data Strategy with a Phased Approach to Master Data WHITE PAPER SAS White Paper Table of Contents Changing the Way We Think About Master Data.... 1 Master Data Consumers, the Information
More informationData Governance 8 Steps to Success
Data Governance 8 Steps to Success Anne Marie Smith, Ph.D. Principal Consultant Asmith @ alabamayankeesystems.com http://www.alabamayankeesystems.com 1 Instructor Background Internationally recognized
More informationBusting 7 Myths about Master Data Management
Knowledge Integrity Incorporated Busting 7 Myths about Master Data Management Prepared by: David Loshin Knowledge Integrity, Inc. August, 2011 Sponsored by: 2011 Knowledge Integrity, Inc. 1 (301) 754-6350
More informationMaster Data Management Architecture
Master Data Management Architecture Version Draft 1.0 TRIM file number - Short description Relevant to Authority Responsible officer Responsible office Date introduced April 2012 Date(s) modified Describes
More informationData 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 informationWHITEPAPER BIG DATA GOVERNANCE. How To Avoid The Pitfalls of Big Data Governance? www.analytixds.com
BIG DATA GOVERNANCE How To Avoid The Pitfalls of Big Data Governance? of The need to provide answers quickly... 3 You can t measure what you don t manage... 3 Aligning the overall architecture with the
More informationBuilding the Bullet-Proof MDM Program
Building the Bullet-Proof MDM Program Evan Levy Partner, Baseline Consulting www.baseline-consulting.com Copyright 2007, Baseline Consulting. All rights reserved. 1 Agenda Understanding the critical components
More informationSOA: The missing link between Enterprise Architecture and Solution Architecture
SOA: The missing link between Enterprise Architecture and Solution Architecture Jaidip Banerjee and Sohel Aziz Enterprise Architecture (EA) is increasingly being acknowledged as the way to maximize existing
More informationData Governance And Modeling Best Practices. 2007-2008 Axis Software Designs, Inc. All Rights Reserved
Data Governance And Modeling Best Practices All Rights Reserved Welcome! Let Me Introduce Myself Marcie Barkin Goodwin President & CEO Axis Software Designs mbg@axisboulder.com www.axisboulder.com All
More informationFortune 500 Medical Devices Company Addresses Unique Device Identification
Fortune 500 Medical Devices Company Addresses Unique Device Identification New FDA regulation was driver for new data governance and technology strategies that could be leveraged for enterprise-wide benefit
More information5 Best Practices for SAP Master Data Governance
5 Best Practices for SAP Master Data Governance By David Loshin President, Knowledge Integrity, Inc. Sponsored by Winshuttle, LLC 2012 Winshuttle, LLC. All rights reserved. 4/12 www.winshuttle.com Introduction
More informationAnd Modeling Best Practices. 2007-2008 Axis Software Designs, Inc. All Rights Reserved
Data Governance And Modeling Best Practices All Rights Reserved Welcome! Let Me Introduce Myself Marcie Barkin Goodwin President & CEO Axis Software Designs mbg@axisboulder.com www.axisboulder.com All
More informationIPL Service Definition - Master Data Management for Cloud Related Services
IPL Proposal April 2014 IPL Service Definition - Master Data Management for Cloud Related Services Project: Date: 10 April 2014 Issue Number: Customer: Crown Commercial Service Page 1 of 11 IPL Information
More informationThree 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 information17 th Petroleum Network Education Conferences
1.1.1. 17 th Petroleum Network Education Conferences Making Data Governance Work At All Levels of the Organization Prepared by: Joseph Seila Devon Energy and James Soos Noah Consulting March 21, 2013 Table
More informationEnabling Better Business Intelligence and Information Architecture With SAP PowerDesigner Software
SAP Technology Enabling Better Business Intelligence and Information Architecture With SAP PowerDesigner Software Table of Contents 4 Seeing the Big Picture with a 360-Degree View Gaining Efficiencies
More informationEnterprise Information Management
Enterprise Information Management A Key Business Enabler July 2012 The Vision Auckland Council s vision is for Auckland to become the worlds most liveable city. In order to achieve this vision, it needs
More informationWhy Data Governance - 1 -
Data Governance Why Data Governance - 1 - Industry: Lack of Data Governance is a Key Issue Faced During Projects As projects address process improvements, they encounter unidentified data processes that
More informationData Discovery & Documentation PROCEDURE
Data Discovery & Documentation PROCEDURE Document Version: 1.0 Date of Issue: June 28, 2013 Table of Contents 1. Introduction... 3 1.1 Purpose... 3 1.2 Scope... 3 2. Option 1: Current Process No metadata
More informationUniversity of Michigan Medical School Data Governance Council Charter
University of Michigan Medical School Data Governance Council Charter 1 Table of Contents 1.0 SIGNATURE PAGE 2.0 REVISION HISTORY 3.0 PURPOSE OF DOCUMENT 4.0 DATA GOVERNANCE PROGRAM FOUNDATIONAL ELEMENTS
More informationData Governance: A Business Value-Driven Approach
Data Governance: A Business Value-Driven Approach A White Paper by Dr. Walid el Abed CEO January 2011 Copyright Global Data Excellence 2011 Contents Executive Summary......................................................3
More informationIT Operations Managed Services A Perspective
IT Operations Managed Services A Perspective 1 Introduction This paper examines the concept of Managed Services for IT Operations, the real business drivers, the key factors to be considered, the types
More informationVermont Enterprise Architecture Framework (VEAF) Master Data Management (MDM) Abridged Strategy Level 0
Vermont Enterprise Architecture Framework (VEAF) Master Data Management (MDM) Abridged Strategy Level 0 EA APPROVALS EA Approving Authority: Revision
More informationA WHITE PAPER By Silwood Technology Limited
A WHITE PAPER By Silwood Technology Limited Using Safyr to facilitate metadata transparency and communication in major Enterprise Applications Executive Summary Enterprise systems packages such as SAP,
More informationTHOMAS RAVN PRACTICE DIRECTOR TRA@PLATON.NET. An Effective Approach to Master Data Management. March 4 th 2010, Reykjavik WWW.PLATON.
An Effective Approach to Master Management THOMAS RAVN PRACTICE DIRECTOR TRA@PLATON.NET March 4 th 2010, Reykjavik WWW.PLATON.NET Agenda Introduction to MDM The aspects of an effective MDM program How
More informationMaster Data Management
InfoSphere Powered by Software Master Data Management Data at the Core of the Enterprise Most major financial services firms are pursuing strategies to better manage the data that flows throughout these
More informationAPPLYING FUNCTION POINTS WITHIN A SOA ENVIRONMENT
APPLYING FUNCTION POINTS WITHIN A SOA ENVIRONMENT Jeff Lindskoog EDS, An HP Company 1401 E. Hoffer St Kokomo, IN 46902 USA 1 / 16 SEPTEMBER 2009 / EDS INTERNAL So, Ah, How Big is it? 2 / 16 SEPTEMBER 2009
More informationData Governance, Data Architecture, and Metadata Essentials
WHITE PAPER Data Governance, Data Architecture, and Metadata Essentials www.sybase.com TABLE OF CONTENTS 1 The Absence of Data Governance Threatens Business Success 1 Data Repurposing and Data Integration
More informationData Governance Best Practices
Data Governance Best Practices Rebecca Bolnick Chief Data Officer Maya Vidhyadharan Data Governance Manager Arizona Department of Education Key Issues 1. What is Data Governance and why is it important?
More informationMDM 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 informationSolution Architecture Framework Toolkit
Solution Architecture Framework Toolkit Health and Human Services Agency, Revision History REVISION HISTORY REVISION/WORKSITE # DATE OF RELEASE OWNER SUMMARY OF CHANGES Initial Release (v1.0) December
More informationData Governance and CA ERwin Active Model Templates
Data Governance and CA ERwin Active Model Templates Vani Mishra TechXtend March 19, 2015 ER07 Presenter Bio About the Speaker: Vani is a TechXtend Data Modeling practice manager who has over 10+ years
More informationData Governance on Well Header. Not Only is it Possible, Where Else Would you Start!
Data Governance on Well Header Not Only is it Possible, Where Else Would you Start! Agenda Intro (Noah) A (not so) Brief History Methodology Prioritization Why Well Header Attribute versus Process Oriented
More informationTurn Information into a Strategic Asset with SAP Solutions for Information Management. Jens Sauer, SAP Switzerland 11 th September 2013
Turn Information into a Strategic Asset with SAP Solutions for Information Management Jens Sauer, SAP Switzerland 11 th September 2013 Agenda The new Reality & Drivers for Information Management SAP Solution
More informationEnterprise Data Management Data Governance Plan. Version 1.0
Version 1.0 Table of Contents Table of Contents Executive Summary... 1 1. Understanding Data Governance... 2 1.1 What Data Governance Is... 2 1.1.1 What Data Governance Isn t... 2 1.2 Why Data Governance
More informationIntegrated Data Management: Discovering what you may not know
Integrated Data Management: Discovering what you may not know Eric Naiburg ericnaiburg@us.ibm.com Agenda Discovering existing data assets is hard What is Discovery Discovery and archiving Discovery, test
More informationSAP BusinessObjects Information Steward
SAP BusinessObjects Information Steward Michael Briles Senior Solution Manager Enterprise Information Management SAP Labs LLC June, 2011 Agenda Challenges with Data Quality and Collaboration Product Vision
More informationData Governance: A Business Value-Driven Approach
Global Excellence Governance: A Business Value-Driven Approach A White Paper by Dr Walid el Abed CEO Trusted Intelligence Contents Executive Summary......................................................3
More informationINTRODUCTION. Ryan White, Vice President of Business Development, DTI Integrated Business Solutions
DEVELOPING AN ENTERPRISE VISION FOR CONTENT MANAGEMENT Ryan White, Vice President of Business Development, DTI Integrated Business Solutions EXECUTIVE OVERVIEW The amount of content organizations create,
More informationImproving 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 informationThe Role of the BI Competency Center in Maximizing Organizational Performance
The Role of the BI Competency Center in Maximizing Organizational Performance Gloria J. Miller Dr. Andreas Eckert MaxMetrics GmbH October 16, 2008 Topics The Role of the BI Competency Center Responsibilites
More informationJOURNAL 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 informationCorporate Performance Management Framework
Version 1.0 Copyright 2004 Answerport, Inc. Table of Contents Table of Contents... 2 Conceptual Overview... 3 Conceptual Overview Diagram... 4 The Foundation... 4 Analytic Presentation Layer... 5 Reports...
More informationAutomated Business Intelligence
Automated Business Intelligence Delivering real business value,quickly, easily, and affordably 2 Executive Summary For years now, the greatest weakness of the Business Intelligence (BI) industry has been
More information<Insert Picture Here> Introducing Data Modeling and Design with Oracle SQL Developer Data Modeler
Introducing Data Modeling and Design with Oracle SQL Developer Data Modeler Sue Harper Senior Principle Product Manager 1 The following is intended to outline our general product
More informationMaster 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 informationMaking SAP Information Steward a Key Part of Your Data Governance Strategy
Making SAP Information Steward a Key Part of Your Data Governance Strategy Part 2 SAP Information Steward Overview and Data Insight Review Part 1 in our series on Data Governance defined the concept of
More informationThe Business Case for Information Management An Oracle Thought Leadership White Paper December 2008
The Business Case for Information Management An Oracle Thought Leadership White Paper December 2008 NOTE: The following is intended to outline our general product direction. It is intended for information
More informationASYST Intelligence South Africa A Decision Inc. Company
Business Intelligence - SAP BusinessObjects BI Platform 4.0... 2 SBO BI Platform 4.0: Admin and Security (2 days)... 2 SBO BI Platform 4.0: Administering Servers (3 days)... 3 SBO BI Platform 4.0: Designing
More informationBusiness User driven Scorecards to measure Data Quality using SAP BusinessObjects Information Steward
September 10-13, 2012 Orlando, Florida Business User driven Scorecards to measure Data Quality using SAP BusinessObjects Information Steward Asif Pradhan Learning Points SAP BusinessObjects Information
More informationConsiderations: 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 informationSOLUTION BRIEF CA ERWIN MODELING. How Can I Manage Data Complexity and Improve Business Agility?
SOLUTION BRIEF CA ERWIN MODELING How Can I Manage Data Complexity and Improve Business Agility? CA ERwin Modeling provides a centralized view of key data definitions to help create a better understanding
More informationFive Core Principles of Successful Business Architecture. STA Group, LLC Revised: May 2013
Five Core Principles of Successful Business Architecture STA Group, LLC Revised: May 2013 Executive Summary This whitepaper will provide readers with important principles and insights on business architecture
More informationEnterprise Data Governance
Enterprise Aligning Quality With Your Program Presented by: Mark Allen Sr. Consultant, Enterprise WellPoint, Inc. (mark.allen@wellpoint.com) 1 Introduction: Mark Allen is a senior consultant and enterprise
More informationMaster Data Management
Master Data Management Patrice Latinne ULB 30/3/2010 Agenda Master Data Management case study Who & services roadmap definition data How What Why technology styles business 29/03/2010 2 Why Master Data
More informationSystem Architecture Review Glossary
AAP Architect ASAI Availability Bulk Mail Business Case Review Change Management Chief Enterprise Architect Application Architecture Planning. Replaced by the SAR (System Architecture Review) process,
More informationTECHNOLOGY BRIEF: CA ERWIN SAPHIR OPTION. CA ERwin Saphir Option
TECHNOLOGY BRIEF: CA ERWIN SAPHIR OPTION CA ERwin Saphir Option Table of Contents Executive Summary SECTION 1 2 Introduction SECTION 2: OPPORTUNITY 2 Modeling ERP Systems ERP Systems and Data Warehouses
More informationTop 10 Trends In Business Intelligence for 2007
W H I T E P A P E R Top 10 Trends In Business Intelligence for 2007 HP s New Information Management Practice Table of contents Trend #1: BI Governance: Ensuring the Effectiveness of Programs and Investments
More informationHospital Performance Management: From Strategy to Operations
Hospital Performance Management: From Strategy to Operations Every hospital wants to be on top in terms of revenue and quality of care. It is tough enough to get to the top, but tougher still to stay there.
More informationService Oriented Architecture and the DBA Kathy Komer Aetna Inc. New England DB2 Users Group. Tuesday June 12 1:00-2:15
Service Oriented Architecture and the DBA Kathy Komer Aetna Inc. New England DB2 Users Group Tuesday June 12 1:00-2:15 Service Oriented Architecture and the DBA What is Service Oriented Architecture (SOA)
More informationKalido Data Governance Maturity Model
White Paper Kalido Data Governance Maturity Model September 2010 Winston Chen Vice President, Strategy and Business Development Kalido Introduction Data management has gone through significant changes
More informationDisparate Data, Disparate Systems, Disparate User Groups (How to Architect The Enterprise Business Needs) Robert Schork, General Dynamics IT
Disparate Data, Disparate Systems, Disparate User Groups (How to Architect The Enterprise Business Needs) Robert Schork, General Dynamics IT April 27, 2011 2011 Waters North American Trading Architecture
More informationIBM Solution Framework for Lifecycle Management of Research Data. 2008 IBM Corporation
IBM Solution Framework for Lifecycle Management of Research Data Aspects of Lifecycle Management Research Utilization of research paper Usage history Metadata enrichment Usage Pattern / Citation Collaboration
More informationAMB-PDM Overview v6.0.5
Predictive Data Management (PDM) makes profiling and data testing more simple, powerful, and cost effective than ever before. Version 6.0.5 adds new SOA and in-stream capabilities while delivering a powerful
More informationAssessing and implementing a Data Governance program in an organization
Assessing and implementing a Data Governance program in an organization Executive Summary As companies realize the importance of data and the challenges they face in integrating the data from various sources,
More informationThe following is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into
The following is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material,
More informationBasic Unified Process: A Process for Small and Agile Projects
Basic Unified Process: A Process for Small and Agile Projects Ricardo Balduino - Rational Unified Process Content Developer, IBM Introduction Small projects have different process needs than larger projects.
More informationData Quality Assessment. Approach
Approach Prepared By: Sanjay Seth Data Quality Assessment Approach-Review.doc Page 1 of 15 Introduction Data quality is crucial to the success of Business Intelligence initiatives. Unless data in source
More informationOPTIMUS SBR. Optimizing Results with Business Intelligence Governance CHOICE TOOLS. PRECISION AIM. BOLD ATTITUDE.
OPTIMUS SBR CHOICE TOOLS. PRECISION AIM. BOLD ATTITUDE. Optimizing Results with Business Intelligence Governance This paper investigates the importance of establishing a robust Business Intelligence (BI)
More informationAdvanced Analytic Dashboards at Lands End. Brenda Olson and John Kruk April 2004
Advanced Analytic Dashboards at Lands End Brenda Olson and John Kruk April 2004 Presentation Information Presenter: Brenda Olson and John Kruk Company: Lands End Contributors: Lands End EDW/BI Teams Title:
More informationA Holistic Framework for Enterprise Data Management DAMA NCR
A Holistic Framework for Enterprise Data Management DAMA NCR Deborah L. Brooks March 13, 2007 Agenda What is Enterprise Data Management? Why an EDM Framework? EDM High-Level Framework EDM Framework Components
More informationA McKnight Associates, Inc. White Paper: Effective Data Warehouse Organizational Roles and Responsibilities
A McKnight Associates, Inc. White Paper: Effective Data Warehouse Organizational Roles and Responsibilities Numerous roles and responsibilities will need to be acceded to in order to make data warehouse
More informationUsing 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 informationExplore the Possibilities
Explore the Possibilities 2013 HR Service Delivery Forum Best Practices in Data Management: Creating a Sustainable and Robust Repository for Reporting and Insights 2013 Towers Watson. All rights reserved.
More informationData Warehouse. Project Process. Project Documentation. Revised Aril, 2013
Data Warehouse Project Process & Project Documentation Revised Aril, 2013 1 Contents Introduction Project Process Process Diagram Define Scope Inventory Analyze Design Prototype Prototype Validation Iterate/Refine
More informationChallenges in the Effective Use of Master Data Management Techniques WHITE PAPER
Challenges in the Effective Use of Master Management Techniques WHITE PAPER SAS White Paper Table of Contents Introduction.... 1 Consolidation: The Typical Approach to Master Management. 2 Why Consolidation
More informationPreferred Strategies: Business Intelligence for JD Edwards
Preferred Strategies: Business Intelligence for JD Edwards For the fourth year in a row, Business Intelligence software tops the list for IT investments according to Gartner Research. If you are not currently
More information