Data Governance. David Loshin Knowledge Integrity, inc. (301)
|
|
- Emil Harrington
- 8 years ago
- Views:
Transcription
1 Data Governance David Loshin Knowledge Integrity, inc. (301)
2 Risk and Governance Objectives of Governance: Identify explicit and hidden risks associated with data expectations Actualize implementation of business policy Provide framework for auditing compliance Oversee definition of critical data elements Manage enterprise data ownership and stewardship Provide management oversight for organizational observance of different kinds of information policies
3 Aligning Information Objectives and Business Strategy Clarify and understand the existing Information Architecture Create an inventory of data assets Applications, data assets, documentation, metadata, usage Inventory of data elements and owning application Sales Human Resources Marketing Customer Service Finance Compliance Legal
4 Map Information Functions to Business Objectives Document the activities that support a business activity Example: a website privacy policy specifies age limits for data sharing based on parent s permission Implies the existence of child birth date and parent permission data elements Function is to verify compliance with privacy constraints by checking those data elements Standardize mapping from business activity to application function Associate all data elements associated with each application function Bottom-up assessment describes how information policy is implemented across application silos Objective: Correlate application functionality, business policy, and data life cycle
5 Areas of Information Risks Business/Financial Consistency across internal reports Regulatory Reporting Sarbanes Oxley, Basel II, 21 CFR 11, FAS 133 Customer Knowledge GLB, USA PATRIOT Act, BSA, Anti-Kickback Statute Protection of Private Information HIPAA, GLB Collaboration Delays in straight-through processing, delayed settlement Limitation of Use Digital Millennium Copyright Act Consensus and Collaboration Data Ownership Semantics
6 Data Governance, Information, and Risks Missing or Replicated Data Nonstandard or complex data transformations Failed identity management processes Undocumented, incorrect, or misleading metadata
7 Missing or Replicated Data Absent or unfindable data leads to Incomplete reporting Inability to accurately calculate risk Many distributed databases feeding many financial applications leads to Variant approaches to report generation Untracked copying of reports into desktop applications Examples: Basel II: Inaccurate or missing credit assessment data will impact correct calculation of credit risk DoD Guidelines on Data Quality: the inability to match payroll records to the official employment record can cost millions in payroll overpayments to deserters, prisoners, and ghost soldiers. the inability to correlate purchase orders to invoices is a major problem in unmatched disbursements.
8 Nonstandard or Complex Data Transformations Original data definition and intent may reflect application dependencies and semantics Integration across multiple applications across organizational boundaries introduce numerous opportunities for transformation inconsistencies Complex data (e.g. semi-structured and unstructured documents) must be transformed into usable formats before processing
9 Failed Identity Management Processes Inability to uniquely identify entities (people, organizations, products, etc. Inability to link multiple records representing the same entity Example: In 2004, Senator Ted Kennedy was subjected to extra screening when boarding a plane in Boston A DHS spokesman said that Kennedy was misidentified as someone who was mistakenly identified as someone on a watch list
10 Undocumented, Incorrect, or Misleading Metadata Laxity in enterprise metadata management leads to: Assumptions about meanings of commonly used business terms Implied qualification of data element meanings Inconsistency across application and enterprise information architectures Reduced trust in the correctness of the data Limitations in resolving trade settlement and counterparty transactions Consolidation, integration, migration are all impacted when variant definitions are assumed to mean the same thing Example: PWC estimates that 90% of the top 100 world banks are deficient in credit risk data management in maintenance of clean counterparty static data repositories, common counterparty identifiers,, staff dedicated to data quality, consistent data standards.
11 Review: Challenges for Critical Data Elements Absence of clarity makes it difficult to determine semantics Ambiguity in definition introduces conflict into the process Lack of Precision leads to inconsistency in representation and reporting Variant source systems and frameworks encourage turf-oriented biases Flexibility of data motion mechanisms leads to multitude of approaches for data movement
12 Governance Commonalities Information policies differ depending on related business risks, but share commonalities: Federation Defined Policy Transparency Auditability
13 Objectives Identify critical data elements Define/Refine information policies Describe metrics and measurements Create process for monitoring and evaluation
14 Critical Data Elements Identify enterprise metadata in use across the organization and: Clarify unambiguous definitions, formats, and semantics Facilitate agreement to those definitions and semantics from all stakeholders Absorb replicated reference sets into a single managed repository
15 Define/Refine Information Policies Embody the specification of management objectives associated with data governance Relate assertions to related data sets Articulate how business policy is integrated with information asset Example: Anti-money laundering Establishing policies and procedures to detect and report suspicious transactions Ensuring compliance with the Bank Secrecy Act Providing for independent testing for compliance to be conducted by outside parties.
16 Metrics and Measurement Decompose information policies into specific measurable data rules Apply tools and techniques for measuring conformance to data rules (think: data profiling) Metrics can be rolled up from data rules defined as a byproduct of analyzing the information policy
17 Monitoring and Evaluation One business policy can encompass multiple information policies Each information policy may encompass multiple data rules Each data rule, therefore, contributes to monitoring compliance with business policy! Business Policy Information Policy Information Policy Information Policy Data rule Data rule Data rule Data rule Data rule Data rule Data rule Data rule Data rule Data rule Data rule Data rule
18 A Repeatable Data Quality Process Identify actual problems with the data as they relate to business client expectations Identify specific business impacts attributable to those problems Quantify the size of those impacts for prioritization Evaluate the costs to reconcile the data quality problems Once these details have been identified, the value of improved data quality can be quantified Prioritize and select projects for improvement
19 DQ Management Goals Evaluate business impact of poor data quality and develop ROI models for Data Quality activities Document the information architecture showing data models, metadata, information usage, and information flow throughout enterprise Identify, document, and validate Data Quality expectations Educate your staff in ways to integrate Data Quality as an integral component of system development lifecycle Governance framework for Data Quality event tracking and ongoing Data Quality measurement, monitoring, and reporting of compliance with customer expectations Consolidate current and planned Data Quality guidelines, policies, and activities
20 Technical Data Governance Framework Policies and Procedures Roles & Responsibilities Ongoing Monitoring Audit & Compliance Standards Oversight Performance Metrics Data Definitions Master Reference Data Taxonomies Enterprise Architecture Exchange Standards Data Quality Data Profiling Data Cleansing Auditing & Monitoring Parsing & Standardization Record Linkage Data Integration Data Access Transformation Delivery Discovery & Assessment Metadata Management
21 Roles and Responsibilities Executive Sponsorship Data Governance Oversight Provide senior management support at the C-level, warrants the enterprise adoption of measurably high quality data, and negotiates quality SLAs with external data suppliers. Strategic committee composed of business clients to oversee the governance program, ensure that governance priorities are set and abided by, delineates data accountability. Data Steering Committee LOB Data Governance LOB Data Governance LOB Data Governance LOB Data Governance Tactical team tasked with ensuring that data activities have defined metrics and acceptance thresholds for quality meeting business client expectations, manages governance across lines of business, sets priorities for LOBs and communicates opportunities to the Governance Oversight committee. Data governance structure at the line of business level, defines data quality criteria for LOB applications, delineates stewardship roles, reports activities and issues to Data Coordination Council
22 Metadata Consensus: Embedded in the Program Step One: Initial Request Submitted Review by Metadata Coordinator Step Two: Workgroup Formed Submission Development Review by Steering Committee Approved? yes Form Workgroup Review by Metadata Coordinator Step Three: Completed Candidate Proposed no Returned with explanation Review by Technical Committee Approved? no Returned with explanation yes Step Four: Public Comment Workflow incorporates both Consensus Governance Step Five: Steering Committee Approval Approved? no Returned with explanation yes Step Six: Data Governance Oversight Board Endorsement
23 Data Governance Roles Data Governance Oversight Board Metadata Coordinator Data Steering Committee Technical Advisory Group Workgroup Member Data Quality Representative (Data Steward) Data Registrar
24 Data Governance Oversight Board Guides data quality management activities Oversees compliance with information policies and governance directives Approves governance policies Reviews and Endorses/Approves standards Institutes organizational data quality scorecard
25 Workgroups Cross-group collection of relevant stakeholders Involve representation from both the technical and business sides Act as interface to general user community Tasked with Developing proposed definitions and standards Ensuring community collaboration Ongoing maintenance of definitions and standards
26 The Steering Committee Provides direction to those tasked with data quality and metadata management Authorize workgroup activities Provide direction for development of semantics, taxonomies, and ontologies Recommend standards to the Data Governance Oversight Board Ensure that data quality controls are in place Ensure that key data quality indicators are communicated to stakeholders and data owners
27 Technical Advisors Tasked with: Providing technical input to workgroup definitions and standards development Identifying technical and infrastructure issues with standard definitions and expected uses Assess business needs for tools and technology Updating & maintaining technical specs Providing guidance on implementation Identifying and documenting existence of source of truth data sets
28 Metadata Developers Encapsulate data element definitions, format specification, and semantics in a formal representation Facilitate development of: Enterprise data definitions Exchange/sharing schemas (e.g., fixed-format, XML) Exchange application support (e.g., class definitions, code development, application objects) Functional support for shared application capabilities for information life cycle
29 Metadata Registrar Provides support and configuration management for standards within the Metadata Registry Manages access to the Metadata Registry Facilitates and manages data standards activity workflows Helps develop procedures Promote reuse across applications
30 Data Steward Tasked with: Determining the relevant data sets to be subjected to data quality management Managing data quality Documenting, communicating, and tracking issues and concerns to relevant stakeholders Verifying the metadata Assuming accountability for managing the quality of data Establishing data quality service level agreements
31 Coordinating the Data Governance Processes Manages the various data quality activities of data owners and workgroups Compiles, maintains, and monitors data quality performance indicators in process Supports the metadata and data quality rules definition, registration, and development processes Develops policies and procedures Provides training and knowledge transfer
32 Engineering Data Quality into the System Flat File RDBMS Analyze/profile data Assess data quality dimensions Data quality, Validity, & Transformation rules Create monitoring system Recommend data transformations IMS VSAM Improved enterprise data quality Application Generate data quality reports Send data quality reports to data owners
33 Data Quality Life Cycle Initially, many new issues will be exposed Over time, identifying root causes and eliminating the source of problems will significantly reduce failure load Change from an organization that is fighting fires to one that is building data quality firewalls Transition from a reactive environment to a proactive one facilitates change management among data quality clients Errors Time
34 Data Quality and the SDLC How can data quality become part of the system development lifecycle? Emphasize value of high quality information in business context Develop metrics and processes for measurement Extract implementation of validation from embedded sources and expose as business knowledge Integrate automated, business rule-based data quality testing and validation as part of system design
35 Stewardship: Remediation and Manual Intervention Issues with addressing data quality events: Immediate remediation of flawed data does this imply data correction? Not all data flaws can be captured via automated processes this implies manual reviews Accuracy may only be measured by comparing values directly Carefully integrate manual intervention when necessary in a controlled manner
36 Data Quality and Data Governance Develop high level data quality management framework incorporating: Methods to evaluate business impact of poor data quality Technical requirements of data quality as part of SDLC Operational guidelines for ongoing monitoring, reporting, tracking, and management Knowledge capture, including the coordination of data modeling, data standards, metadata, and information usage modeling efforts
37 Pulling it All Together Review baseline of current business and information policies Develop a business case process for evaluating value of data quality improvement and risk mitigation Build an inventory of enterprise metadata Manage critical data elements Define/refine information polices and data rules Establish processes for measurements and monitoring Make accountability actionable
38 Questions? If you have questions, comments, or suggestions, please contact me David Loshin
Business Performance & Data Quality Metrics. David Loshin Knowledge Integrity, Inc. loshin@knowledge-integrity.com (301) 754-6350
Business Performance & Data Quality Metrics David Loshin Knowledge Integrity, Inc. loshin@knowledge-integrity.com (301) 754-6350 1 Does Data Integrity Imply Business Value? Assumption: improved data quality,
More informationData Governance for Master Data Management and Beyond
Data Governance for Master Data Management and Beyond A White Paper by David Loshin WHITE PAPER Table of Contents Aligning Information Objectives with the Business Strategy.... 1 Clarifying the Information
More informationDATA QUALITY MATURITY
3 DATA QUALITY MATURITY CHAPTER OUTLINE 3.1 The Data Quality Strategy 35 3.2 A Data Quality Framework 38 3.3 A Data Quality Capability/Maturity Model 42 3.4 Mapping Framework Components to the Maturity
More informationPrincipal 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 informationFive Fundamental Data Quality Practices
Five Fundamental Data Quality Practices W H I T E PA P E R : DATA QUALITY & DATA INTEGRATION David Loshin WHITE PAPER: DATA QUALITY & DATA INTEGRATION Five Fundamental Data Quality Practices 2 INTRODUCTION
More informationOperationalizing Data Governance through Data Policy Management
Operationalizing Data Governance through Data Policy Management Prepared for alido by: David Loshin nowledge Integrity, Inc. June, 2010 2010 nowledge Integrity, Inc. Page 1 Introduction The increasing
More informationBuilding a Data Quality Scorecard for Operational Data Governance
Building a Data Quality Scorecard for Operational Data Governance A White Paper by David Loshin WHITE PAPER Table of Contents Introduction.... 1 Establishing Business Objectives.... 1 Business Drivers...
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 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 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 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 informationMaster Data Management
Master Data Management David Loshin AMSTERDAM BOSTON HEIDELBERG LONDON NEW YORK OXFORD PARIS SAN DIEGO Ик^И V^ SAN FRANCISCO SINGAPORE SYDNEY TOKYO W*m k^ MORGAN KAUFMANN PUBLISHERS IS AN IMPRINT OF ELSEVIER
More informationGovernance through Data Controls and Data Quality Service Level Agreements
Governance through Data Controls and Data Quality Service Level Agreements David Loshin Knowledge Integrity, Inc. loshin@knowledge-integrity.com (301) 754-6350 2006 Knowledge Integrity, Inc. 1 Agenda Actualizing
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 informationMonitoring Data Quality Performance Using Data Quality Metrics
WHITE PAPER Monitoring Data Quality Performance Using Data Quality Metrics with David Loshin This document contains Confidential, Proprietary and Trade Secret Information ( Confidential Information ) of
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 informationEvaluating the Business Impacts of Poor Data Quality
Evaluating the Business Impacts of Poor Data Quality Submitted by: David Loshin President, Knowledge Integrity, Inc. (301) 754-6350 loshin@knowledge-integrity.com Knowledge Integrity, Inc. Page 1 www.knowledge-integrity.com
More informationData Governance Overview
Data Governance Overview Anthony Chalker Managing Director August 12, 2014 2:05 2:55 Session What is Data Governance? Data Governance is the specification of decision rights and an accountability framework
More informationEnabling Data Quality
Enabling Data Quality Establishing Master Data Management (MDM) using Business Architecture supported by Information Architecture & Application Architecture (SOA) to enable Data Quality. 1 Background &
More informationIntegrating Data Governance into Your Operational Processes
TDWI rese a rch TDWI Checklist Report Integrating Data Governance into Your Operational Processes By David Loshin Sponsored by tdwi.org August 2011 TDWI Checklist Report Integrating Data Governance into
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 informationData Quality Management and Financial Services
Data Quality Management and Financial Services Loretta O Connor Data Quality Sales Manager Data Quality Divion May 2007 1 PG 961 Content Introduction Defining the Data Quality Problem Solutions for Data
More informationData Quality Fundamentals
Data Quality Fundamentals David Loshin Knowledge Integrity, Inc. 1 Agenda The Data Quality Program Data Quality Assessment Using Data Quality Tools Data Quality Inspection, Monitoring, and Control 2 1
More informationPopulating a Data Quality Scorecard with Relevant Metrics WHITE PAPER
Populating a Data Quality Scorecard with Relevant Metrics WHITE PAPER SAS White Paper Table of Contents Introduction.... 1 Useful vs. So-What Metrics... 2 The So-What Metric.... 2 Defining Relevant Metrics...
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 Integrity and Integration: How it can compliment your WebFOCUS project. Vincent Deeney Solutions Architect
Data Integrity and Integration: How it can compliment your WebFOCUS project Vincent Deeney Solutions Architect 1 After Lunch Brain Teaser This is a Data Quality Problem! 2 Problem defining a Member How
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 Executive Summary Successful deployment of ERP solutions can revolutionize
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 Governance, Data Architecture, and Metadata Essentials Enabling Data Reuse Across the Enterprise
Data Governance Data Governance, Data Architecture, and Metadata Essentials Enabling Data Reuse Across the Enterprise 2 Table of Contents 4 Why Business Success Requires Data Governance Data Repurposing
More informationThe Role of Metadata in a Data Governance Strategy
The Role of Metadata in a Data Governance Strategy Prepared by: David Loshin President, Knowledge Integrity, Inc. (301) 754-6350 loshin@knowledge- integrity.com Sponsored by: Knowledge Integrity, Inc.
More informationPoint of View: FINANCIAL SERVICES DELIVERING BUSINESS VALUE THROUGH ENTERPRISE DATA MANAGEMENT
Point of View: FINANCIAL SERVICES DELIVERING BUSINESS VALUE THROUGH ENTERPRISE DATA MANAGEMENT THROUGH ENTERPRISE DATA MANAGEMENT IN THIS POINT OF VIEW: PAGE INTRODUCTION: A NEW PATH TO DATA ACCURACY AND
More informationMDM 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 informationRealizing business flexibility through integrated SOA policy management.
SOA policy management White paper April 2009 Realizing business flexibility through integrated How integrated management supports business flexibility, consistency and accountability John Falkl, distinguished
More informationThe Data Quality Business Case: Projecting Return on Investment
WHITE PAPER The Data Quality Business Case: Projecting Return on Investment with David Loshin This document contains Confidential, Proprietary and Trade Secret Information ( Confidential Information )
More informationData Governance in a Siloed Organization
The First Step in Master Data Management Data Governance in a Siloed Organization Kelle O Neal Managing Partner kelle@firstsanfranciscopartners.com Gurinder Bahl Principal Product Manager, Oracle gurinder.bahl@oracle.com
More informationData Governance Primer. A PPDM Workshop. March 2015
Data Governance Primer A PPDM Workshop March 2015 Agenda - SETTING THE STAGE - DATA GOVERNANCE BASICS - METHODOLOGY - KEYS TO SUCCESS Copyright 2015 Noah Consulting LLC. All Rights Reserved. Industry Drivers
More informationIMPROVEMENT THE PRACTITIONER'S GUIDE TO DATA QUALITY DAVID LOSHIN
i I I I THE PRACTITIONER'S GUIDE TO DATA QUALITY IMPROVEMENT DAVID LOSHIN ELSEVIER AMSTERDAM BOSTON HEIDELBERG LONDON NEW YORK OXFORD PARIS SAN DIEGO SAN FRANCISCO SINGAPORE SYDNEY TOKYO Morgan Kaufmann
More informationData Governance. Data Governance, Data Architecture, and Metadata Essentials Enabling Data Reuse Across the Enterprise
Data Governance Data Governance, Data Architecture, and Metadata Essentials Enabling Data Reuse Across the Enterprise 2 Table of Contents 4 Why Business Success Requires Data Governance Data Repurposing
More informationGetting Started with Data Governance. Philip Russom TDWI Research Director, Data Management June 14, 2012
Getting Started with Data Governance Philip Russom TDWI Research Director, Data Management June 14, 2012 Speakers Philip Russom Director, TDWI Research Daniel Teachey Senior Director of Marketing, DataFlux
More informationTen Steps to Quality Data and Trusted Information
Ten Steps to Quality Data and Trusted Information ABSTRACT Do these situations sound familiar? Your company is involved in a data integration project such as building a data warehouse or migrating several
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 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 informationInformation Governance Workshop. David Zanotta, Ph.D. Vice President, Global Data Management & Governance - PMO
Information Governance Workshop David Zanotta, Ph.D. Vice President, Global Data Management & Governance - PMO Recognition of Information Governance in Industry Research firms have begun to recognize 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 informationData Governance Demystified - Lessons From The Trenches
Introduction Data Governance Demystified - Lessons From The Trenches Jay Zaidi, PMP December 11, 2011 Data Governance is gaining importance lately, due to a renewed focus on regulatory compliance and risk
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 informationORACLE ENTERPRISE DATA QUALITY PRODUCT FAMILY
ORACLE ENTERPRISE DATA QUALITY PRODUCT FAMILY The Oracle Enterprise Data Quality family of products helps organizations achieve maximum value from their business critical applications by delivering fit
More informationEnterprise Data Quality Dashboards and Alerts: Holistic Data Quality
Enterprise Data Quality Dashboards and Alerts: Holistic Data Quality Jay Zaidi Bonnie O Neil (Fannie Mae) Data Governance Winter Conference Ft. Lauderdale, Florida November 16-18, 2011 Agenda 1 Introduction
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 informationCohasset Associates, Inc. NOTES. 2014 Managing Electronic Records Conference 1.1. The discipline of analyzing the. Value Costs and Risks
Understanding Today s Economics of Information Get Your Act Together Now! Sylvan Sibito H Morley III IBM Worldwide Director Information Lifecycle Governance Information Economics: The discipline of analyzing
More information10426: Large Scale Project Accounting Data Migration in E-Business Suite
10426: Large Scale Project Accounting Data Migration in E-Business Suite Objective of this Paper Large engineering, procurement and construction firms leveraging Oracle Project Accounting cannot withstand
More informationBest Practices in Enterprise Data Governance
Best Practices in Enterprise Data Governance Scott Gidley and Nancy Rausch, SAS WHITE PAPER SAS White Paper Table of Contents Introduction.... 1 Data Governance Use Case and Challenges.... 1 Collaboration
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 informationMaster Data Management
Master Data Management Managing Data as an Asset By Bandish Gupta Consultant CIBER Global Enterprise Integration Practice Abstract: Organizations used to depend on business practices to differentiate them
More informationMaster 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 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 informationGovernance Is an Essential Building Block for Enterprise Information Management
Research Publication Date: 18 May 2006 ID Number: G00139707 Governance Is an Essential Building Block for Enterprise Information Management David Newman, Debra Logan Organizations are seeking new ways
More informationNCOE whitepaper Master Data Deployment and Management in a Global ERP Implementation
NCOE whitepaper Master Data Deployment and Management in a Global ERP Implementation Market Offering: Package(s): Oracle Authors: Rick Olson, Luke Tay Date: January 13, 2012 Contents Executive summary
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 informationImplementing a Data Governance Initiative
Implementing a Data Governance Initiative Presented by: Linda A. Montemayor, Technical Director AT&T Agenda AT&T Business Alliance Data Governance Framework Data Governance Solutions: o Metadata Management
More informationInformation Governance
Information Governance The Why? The Who? The How? Summary Next steps Wikipedia defines Information governance as: an emerging term used to encompass the set of multi-disciplinary structures, policies,
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 informationHOW TO USE THE DGI DATA GOVERNANCE FRAMEWORK TO CONFIGURE YOUR PROGRAM
HOW TO USE THE DGI DATA GOVERNANCE FRAMEWORK TO CONFIGURE YOUR PROGRAM Prepared by Gwen Thomas of the Data Governance Institute Contents Why Data Governance?... 3 Why the DGI Data Governance Framework
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 informationMaking Business Intelligence Easy. Whitepaper Measuring data quality for successful Master Data Management
Making Business Intelligence Easy Whitepaper Measuring data quality for successful Master Data Management Contents Overview... 3 What is Master Data Management?... 3 Master Data Modeling Approaches...
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 informationGOVERNANCE AND MANAGEMENT OF CITY COMPUTER SOFTWARE NEEDS IMPROVEMENT. January 7, 2011
APPENDIX 1 GOVERNANCE AND MANAGEMENT OF CITY COMPUTER SOFTWARE NEEDS IMPROVEMENT January 7, 2011 Auditor General s Office Jeffrey Griffiths, C.A., C.F.E. Auditor General City of Toronto TABLE OF CONTENTS
More informationIBM Software A Journey to Adaptive MDM
IBM Software A Journey to Adaptive MDM What is Master Data? Why is it Important? A Journey to Adaptive MDM Contents 2 MDM Business Drivers and Business Value 4 MDM is a Journey 7 IBM MDM Portfolio An Adaptive
More informationHow To Manage It Asset Management On Peoplesoft.Com
PEOPLESOFT IT ASSET MANAGEMENT KEY BENEFITS Streamline the IT Asset Lifecycle Ensure IT and Corporate Compliance Enterprise-Wide Integration Oracle s PeopleSoft IT Asset Management streamlines and automates
More informationReal World Strategies for Migrating and Decommissioning Legacy Applications
Real World Strategies for Migrating and Decommissioning Legacy Applications Final Draft 2014 Sponsored by: Copyright 2014 Contoural, Inc. Introduction Historically, companies have invested millions of
More informationFor more information about this proposal, contact: [David Greenbaum, Director IST Data Services, 2195 Hearst Avenue #250B, 510-642-7429]
FY 07-08 IT Budget Proposal Chief Information Officer: Data Management Governance ABBA Category One: Institutional Effectiveness ABBA Category Two: Information Technology For more information about this
More informationMeasure Your Data and Achieve Information Governance Excellence
SAP Brief SAP s for Enterprise Information Management SAP Information Steward Objectives Measure Your Data and Achieve Information Governance Excellence A single solution for managing enterprise data quality
More informationDataFlux 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 informationData Integration Alternatives Managing Value and Quality
Solutions for Customer Intelligence, Communications and Care. Data Integration Alternatives Managing Value and Quality Using a Governed Approach to Incorporating Data Quality Services Within the Data Integration
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 informationProactive DATA QUALITY MANAGEMENT. Reactive DISCIPLINE. Quality is not an act, it is a habit. Aristotle PLAN CONTROL IMPROVE
DATA QUALITY MANAGEMENT DISCIPLINE Quality is not an act, it is a habit. Aristotle PLAN CONTROL IMPROVE 1 DATA QUALITY MANAGEMENT Plan Strategy & Approach Needs Assessment Goals and Objectives Program
More informationIBM Analytics Make sense of your data
Using metadata to understand data in a hybrid environment Table of contents 3 The four pillars 4 7 Trusting your information: A business requirement 7 9 Helping business and IT talk the same language 10
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 informationBreaking Down the Silos: A 21st Century Approach to Information Governance. May 2015
Breaking Down the Silos: A 21st Century Approach to Information Governance May 2015 Introduction With the spotlight on data breaches and privacy, organizations are increasing their focus on information
More information4th Annual ISACA Kettle Moraine Spring Symposium
www.pwc.com 4th Annual ISACA Kettle Moraine Spring Symposium Session 2 Big Data May 14th, 2014 Session Objective Learn about governance, risks, and compliance considerations that become particularly important
More informationEXPLORING THE CAVERN OF DATA GOVERNANCE
EXPLORING THE CAVERN OF DATA GOVERNANCE AUGUST 2013 Darren Dadley Business Intelligence, Program Director Planning and Information Office SIBI Overview SIBI Program Methodology 2 Definitions: & Governance
More informationGlobal Headquarters: 5 Speen Street Framingham, MA 01701 USA P.508.872.8200 F.508.935.4015 www.idc.com
Global Headquarters: 5 Speen Street Framingham, MA 01701 USA P.508.872.8200 F.508.935.4015 www.idc.com W H I T E P A P E R L e v e r a g e R e c o r d s M a n a g e m e n t B e s t P r a c t i c e s t
More informationAnalytics Strategy Information Architecture Data Management Analytics Value and Governance Realization
1/22 As a part of Qlik Consulting, works with Customers to assist in shaping strategic elements related to analytics to ensure adoption and success throughout their analytics journey. Qlik Advisory 2/22
More informationCordys 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 informationAddressing IT governance, risk and compliance (GRC) to meet regulatory requirements and reduce operational risk in financial services organizations
White Paper September 2009 Addressing IT governance, risk and compliance (GRC) to meet regulatory requirements and reduce operational risk in financial services organizations Page 2 Contents 2 Executive
More informationData Integration Alternatives Managing Value and Quality
Solutions for Enabling Lifetime Customer Relationships Data Integration Alternatives Managing Value and Quality Using a Governed Approach to Incorporating Data Quality Services Within the Data Integration
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 informationBetter Data is Everyone s Job! Using Data Governance to Accelerate the Data Driven Organization
Better Data is Everyone s Job! Using Data Governance to Accelerate the Data Driven Organization Intros - Name - Interest / Challenge - Role Data Governance is a Business Function Data governance should
More informationBIG DATA KICK START. Troy Christensen December 2013
BIG DATA KICK START Troy Christensen December 2013 Big Data Roadmap 1 Define the Target Operating Model 2 Develop Implementation Scope and Approach 3 Progress Key Data Management Capabilities 4 Transition
More informationConnecting data initiatives with business drivers
Connecting data initiatives with business drivers TABLE OF CONTENTS: Introduction...1 Understanding business drivers...2 Information requirements and data dependencies...3 Costs, benefits, and low-hanging
More informationSoftware Asset Management on System z
Software Asset Management on System z Mike Zelle Tivoli WW IT Asset Management Marketing SAM in SHARE Project Manager mzelle@us.ibm.com Agenda Why Software Asset Management (SAM) The Discipline of Software
More informationData Governance Good Practices and the role of Chief Information Officer
Data Governance Good Practices and the role of Chief Information Officer Deepjyoti Choudhury, Assistant Professor, Department of Business Administration, Assam University, Silchar, Assam : Introduction
More informationHow Global Data Management (GDM) within J&J Pharma is SAVE'ing its Data. Craig Pusczko & Chris Henderson
How Global Data Management (GDM) within J&J Pharma is SAVE'ing its Data Craig Pusczko & Chris Henderson Abstract See how J&J Pharma organizational alignment drove the evolution of Global Data Management
More informationPEOPLESOFT IT ASSET MANAGEMENT
PEOPLESOFT IT ASSET MANAGEMENT K E Y B E N E F I T S Streamline the IT Asset Lifecycle Ensure IT and Corporate Compliance Enterprise-Wide Integration P E O P L E S O F T F I N A N C I A L M A N A G E M
More informationIT Outsourcing s 15% Problem:
IT Outsourcing s 15% Problem: The Need for Outsourcing Governance ABSTRACT: IT outsourcing involves complex IT infrastructures that make it extremely difficult to get an accurate inventory of the IT assets
More informationENTERPRISE ASSET MANAGEMENT (EAM) The Devil is in the Details CASE STUDY
ENTERPRISE ASSET MANAGEMENT (EAM) The Devil is in the Details CASE STUDY 1 EXECUTIVE SUMMARY Enterprise Asset Management (EAM) is a strategy to provide an optimal approach for the management of the physical
More informationCapabilities, Sample Use Cases, Case Studies
Capabilities, Sample Use Cases, Case Studies Core capabilities of Diaku Axon Visibility & Understanding Analysis & Alignment Control Measurability Collaborate on a shared understanding of the organisation
More informationMake information work to your advantage. Help reduce operating costs, respond to competitive pressures, and improve collaboration.
Make information work to your advantage. Help reduce operating costs, respond to competitive pressures, and improve collaboration. May 2011 Advisory Consulting Table of contents Transform data from a hindrance
More informationImportance of Data Governance. Vincent Deeney Solutions Architect iway Software
Importance of Data Governance Vincent Deeney Solutions Architect iway Software Some Puzzles Which way is this guy looking? Copyright 2007, Information Builders. Slide 2 Some Puzzles Copyright 2007, Information
More informationBig Data for Higher Education and Research Growth
Big Data for Higher Education and Research Growth Hao Wang, Ph.D. Chief Information Officer The State University of New York 8/1/2013 What is Big Data? 8/1/2013 Draft for Discussion 2 Big Data 250 Years
More information