1 October 2007 The IBM Data Governance Council Maturity Model: Building a roadmap for effective data governance
2 Page 2 Introduction It s been said that IT is the engine for growth and business innovation in the 21st century, and data is the gasoline that fuels it. And while data is undeniably one of the greatest assets an organization has, it is increasingly difficult to manage and control. From structured to unstructured data including customer and employee data, metadata, trade secrets, , video and audio organizations must find a way to govern data in alignment with business requirements without obstructing the free flow of information and innovation. For many organizations today, data is spread across multiple, complex silos that are isolated from each other. There are scores of redundant copies of data, and the business processes that use the data are just as redundant and tangled. There is little cross-organizational collaboration, with few defined governance and stewardship structures, roles and responsibilities. Businesses want to leverage information for maximum performance and profit. They want to assess the value of data as a balance sheet asset, and they want to calculate risk in all aspects of their operations as a competitive advantage in decision-making. It is for these reasons that data governance has emerged as a strategic priority for companies of all sizes.
3 Page Data governance is a quality control discipline for adding new rigor and discipline to the process of managing, using, improving and protecting organizational information. Effective data governance can enhance the quality, availability and integrity of a company s data by fostering cross-organizational collaboration and structured policy-making. It balances factional silos with organizational interest, directly impacting the four factors any organization cares about most: Increasing revenue Lowering costs Reducing risks Increasing data confidence Many companies are just learning to examine their data governance practices, and searching for industry benchmarks and common frameworks to ground their approach. The IBM Data Governance Council Maturity Model is a breakthrough initiative designed with input from a council of 55 organizations to build consistency and quality control in governance through proven business technologies, collaborative methods and best practices. This white paper will describe the Data Governance Council Maturity Model and explain how organizations can leverage it to help them govern the use of data more effectively.
4 Page Data Governance Council Members Organizations Abbott Key Bank ABN Amro Lumigent AirMagnet MasterCard Alltel Merrill Lynch American Express Monaris Application Security Novartis Axentis Nordea Bank Bank of America Northwestern Mutual Bank of Montreal OpenPages Bank of Tokyo/ Mitsubishi Organizational Policy Inst. Bell Canada Paisley BITS Financial Services Roundtable Principal Financial Group Cadence Design Regions Financial Corp. Citigroup RiskWatch City of New York, FISA SecNap Continuity Software Semantic Arts Danske Bank SPS Security Deutsche Bank TIAA-CREF Discover Financial Tizor Equifax TeliaSonera Fannie Mae Valid Technologies Freddie Mac VP Securities Services Guardium Washington Mutual Huntington Bank Wachovia IBM CIO Office The World Bank Intellinx ZANTAZ A forum for data governance With high-profile data breaches and incidents skyrocketing, the challenge to protect and manage data has become a universal concern for organizations. To help better understand the emerging space, IBM created a leadership forum in November 2004 for chief data, security, risk, compliance and privacy officers concerned with data governance issues. Since then the IBM Data Governance Council has steadily grown to comprise nearly 55 leading companies, universities and Business Partners, including large financial institutions, telecommunications organizations, retailers and even public sector governments. With a common forum for data governance practitioners to explore challenges and solutions, the Council has been instrumental in developing benchmarks, best practices and guides to successful data governance. Working together, the members of the Council have identified the top governance challenges facing organizations, including: A lack of cross-organizational data governance structures, policy-making, risk calculation or data asset appreciation, causing a disconnect between business goals and IT programs. Governance policies are not linked to structured requirements gathering, forecasting and reporting. Risks are not addressed from a lifecycle perspective with common data repositories, policies, standards and calculation processes. Metadata and business glossaries are not used to track data quality, bridge semantic differences and demonstrate the business value of data. Few technologies exist today to assess data values, calculate risk and support the human process of governing data usage in an enterprise. Controls, compliance and architecture are deployed before long-term consequences are modeled. Academia North Carolina State University Nova Southeastern University Bucerius Law School
5 Page In addition, the Council members collaborated to define a common benchmark of observable and desired behaviors that every organization can use to evaluate and design their own data governance programs. What emerged was the Data Governance Council Maturity Model. Based on insights and benchmarks from their own practices, the Data Governance Council Maturity Model helps define the scope of who needs to be involved in governing and measure the way businesses govern data uses, such as sensitive customer information or financial details. It enables organizations to: Assess where they currently are in terms of governance, where they want to be and the steps they need to take to get there. Gain an informed, objective, documented assessment of the maturity of their organization. Objectively identify, uncover, highlight and detail the strengths and weaknesses of their data management capabilities. Gain knowledge of existing capabilities and levels of understanding around these elements. Challenge internal assumptions and normalize methods for continuously examining business processes and IT practices. Benchmark future levels of organizational performance and develop a roadmap to get there. Document and centralize reference information that should reside across the organization to govern more effectively.
6 Page Maturity models:an overview Developed by the Software Engineering Institute (SEI) in 1984, the Capability Maturity Model (CMM) is a methodology used to develop and refine an organization s software development process. The CMM describes a five-level graduated path that provides a framework for prioritizing actions, a starting point, a common language and a method to measure progress. Ultimately, this structured collection of elements offers a steady, measurable progression to the final desired state of fully mature processes. At Maturity level 1 (initial), processes are usually ad hoc, and the environment is not stable. Success reflects the competence of individuals within the organization, rather than the use of proven processes. While Maturity level 1 organizations often produce products and services that work, they frequently exceed the budget and schedule of their projects. At Maturity level 2 (managed), successes are repeatable, but the processes may not repeat for all the projects in the organization. Basic project management helps track costs and schedules, while process discipline helps ensure that existing practices are retained. When these practices are in place, projects are performed and managed according to their documented plans, yet there is still a risk for exceeding cost and time estimates. At Maturity level 3 (defined), the organization s set of standard processes are used to establish consistency across the organization. The standards, process descriptions and procedures for a project are tailored from the organization s set of standard processes to suit a particular project or organizational unit.
7 Page 7 At Maturity level 4 (quantitatively managed), organizations set quantitative quality goals for both process and maintenance. Selected subprocesses significantly contribute to overall process performance and are controlled using statistical and other quantitative techniques. And finally, at Maturity Level 5 (optimizing), quantitative process-improvement objectives for the organization are firmly established and continually revised to reflect changing business objectives, and used as criteria in managing process improvement. Maturity model Level 5 Optimizing Focus on CONTINUOUS process improvement Level 4 Quantitatively Managed Process QUANTITATIVELY measured and controlled Level 3 Defined Process characterized for the ORGANIZATION and is PROACTIVE Level 2 Managed Process characterized for PROJECTS and is MANAGEABLE Level 1 Initial Process unpredictable, poorly controlled and REACTIVE
8 Page The elements of data governance The Data Governance Council Maturity Model measures data governance competencies of organizations based on the 11 crucial domains of data governance maturity, such as organizational awareness and risk lifecycle management (see Table 1). An initial high-level grouping of DG domains demonstrates primary relationships between these groupings. Outcomes Enablers Core Disciplines Supporting Disciplines
9 Page As an example, consider that quality and security/privacy requirements for data need to be assessed and managed throughout the information lifecycle. Executive-level endorsement and sponsorship is an enabler for stewardship of information that requires standardization across processes and functional boundaries. Consistency in practice can be enabled through stewardship when there are enterprise-level policies and standards in place for data governance disciplines. The 11 categories of the Maturity Model are described below. Each category contains many subcategories grouped into five levels of maturity, with transition layers between maturity levels to denote concrete steps and milestones organizations can track as they advance. Through these multiple levels, organizations can not only assess where they currently are, but can set concrete goals about where they want to be.
10 Page 10 Table 1: IBM Data Governance Council Maturity Model Category 1 2 Organizational Structures & Awareness Stewardship Description Describes the level of mutual responsibility between business and IT, and recognition of the fiduciary responsibility to govern data at different levels of management. Stewardship is a quality control discipline designed to ensure custodial care of data for asset enhancement, risk mitigation, and organizational control. 3 Policy Policy is the written articulation of desired organizational behavior. 4 Value Creation The process by which data assets are qualified and quantified to enable the business to maximize the value created by data assets. 5 Data Risk Management & Compliance The methodology by which risks are identified, qualified, quantified, avoided, accepted, mitigated, or transferred out Information Security & Privacy Data Architecture Data Quality Management Classification & Metadata Describes the policies, practices and controls used by an organization to mitigate risk and protect data assets. The architectural design of structured and unstructured data systems and applications that enable data availability and distribution to appropriate users. Methods to measure, improve, and certify the quality and integrity of production, test, and archival data. The methods and tools used to create common semantic definitions for business and IT terms, data models, types, and repositories. Metadata that bridge human and computer understanding. 10 Information Lifecycle Management A systematic policy-based approach to information collection, use, retention, and deletion. 11 Audit Information, Logging & Reporting The organizational processes for monitoring and measuring the data value, risks, and efficacy of governance.
11 Page 11 Each category is both a starting place for change and a component in a larger plan. Grouping the 11 domains of data governance can help organizations gain insight into how to establish the larger plan. Each of the 11 categories has five levels of maturity, which include: Level 1 Initial Level 2 Managing Level 3 Defined Level 4 Quantitatively Managed Level 5 Optimizing
12 Page 12 In turn, each category has subcategories that further break down the path to maturity. Organizations can start with any of the 11 domains, based on their own business needs. For example, an organization could assess the level of governance maturity within its organizational structures by identifying the following milestones and benchmarks: Level 1: Policies around regulatory and legal controls are put into place. Data considered critical to those policies is identified. Risk assessments may also be done around the protection of critical data. Level 2: More data-related regulatory controls are documented and published to the whole organization. There is a more proactive approach to problem resolution with team-based approach and repeatable processes. Metadata becomes an important part of documenting critical data elements. Level 3: Data-related policies become more unambiguous and clear and reflect the organization s data principles. Data integration opportunities are better recognized and leveraged. Risk assessment for data integrity, quality and a single version of the truth becomes part of the organizations project methodology. Level 4: The organization further defines the value of data for more and more data elements and sets value-based policies around those decisions. Data governance structures are enterprise-wide. Data Governance methodology is introduced during the planning stages of new projects. Enterprise data models are documented and published. Level 5: Data Governance is second nature. ROI for data-related projects is consistently tracked. Innovations are encouraged. Business value of data management is recognized and cost of data management is easier to manage. Costs are reduced as processes become more automated and streamlined.
13 Page 13 Similarly, an organization could assess its Data Risk Management Framework maturity through the following benchmarks: Level 1: There is no formal high-level risk assessment process in place. Risk assessments are done on an as-needed basis, but not yet systematically integrated into strategic planning. Level 2: Some lines of business have processes and standards for performing risk assessments. Risk assessment criteria are defined and documented for specific items (such as credit risk) and the process is repeatable. There is limited context to validate that the risks identified are significant to the organization as a whole. Level 3: Enterprise-wide adoption of risk assessments for specific items. Example: The privacy risk of a third-party vendor relationship uses a common scoring methodology. Risk assessment criteria are defined and documented for specific items and the process is repeatable. Data on risk assessments is aggregated for senior management. Risk assessments outside of norm are reviewed. Level 4: Enterprise-wide adoption of high-level risk assessments for all components of the organization such as new projects, products, technologies, vendor relationships and/or applications and systems exist. Risk assessment criteria are defined and documented for all items and processes are repeatable. Data on risk assessments is aggregated. Level 5: A consistent controls framework exists and is customized to the specific profile of the firm. Output of the controls assessment process is integrated into incident, reporting, and customer notification processes. A formal, ongoing high-level risk assessment process exists.
14 Page 14 The Data Governance Maturity Model is a tool to assess your organization s current state of Data Governance awareness and effectiveness. Through this concrete and objective set of benchmarks, organizations can evaluate current gaps in their data governance practices and define new opportunities quickly for improving the governance based upon the observable behaviors and insights of the Data Governance Council. However, it is important to recognize that the Maturity Model acts as a framework for addressing data governance rather than a prescription. The Model is presented both as a Maturity Framework and as a set of assessment questions with answers constructed to indicate maturity levels. But how an organization chooses to use this tool will depend on many factors. Just as every organization is unique, each organization will set its own goals and methods based on individual business scenarios, plans and needs. In addition, the Maturity Model is not intended to be static and continues to evolve along with the data governance space. The IBM Data Governance Council welcomes new members who would like to contribute and provide feedback.
15 Page 15 Summary In the past decade, data governance has emerged as one of the top strategic priorities for organizations everywhere. As a result, there is a growing industrywide need for services to help companies become more proactive to gain insight on where important information resides within the organization, governing its use appropriately. Based on collaborative methods and practices from Council members, the IBM Data Governance Council Maturity Model provides a set of benchmarks and milestones to help organizations of all sizes measure their data governance maturity. Beyond maturity levels, business practices and organizational behavior, IBM provides a number of tools and solutions to help organizations take advantage of the Maturity Model to reach new levels of maturity. By leveraging the Maturity Model, organizations can take the first step to determine where they are today and where they want to go tomorrow. For more information To learn how the IBM Data Governance Council Maturity Model can help you get started on the path to data governance, visit ibm.com/ software/data/information/trust-governance.html or call a representative at
16 Copyright IBM Corporation 2007 IBM Software Group Route 100 Somers, NY Produced in the United States of America All Rights Reserved IBM and the IBM logo are trademarks of International Business Machines Corporation in the United States, other countries, or both. Other company, product or service names may be trademarks or service marks of others. Each IBM customer is responsible for ensuring its own compliance with legal requirements. It is the customer s sole responsibility to obtain advice of competent legal counsel as to the identification and interpretation of any relevant laws and regulatory requirements that may affect the customer s business and any actions the customer may need to take to comply with such laws. IBM does not provide legal advice or represent or warrant that its services or products will ensure that the customer is in compliance with any law. LO11960-USEN-00
IBM Data Governance Council The Six Questions every Organization should ask about Data Governance Steven B. Adler IBM Data Governance Solutions email@example.com http://www.ibm.com/itsolutions/datagovernance
November 2007 IBM Master Data Management: Effective data governance Page 2 Introduction Gone are the days when doing business meant doing so only within the borders of the organization. What used to be
IBM Data Software Governance Group Council Toxic Content and Data Governance Steven Adler IBM Data Governance Solutions firstname.lastname@example.org http://www.ibm.com/itsolutions/datagovernance 2007 IBM Corporation
1 September 2013 Industry Models and Information Server Data Models, Metadata Management and Data Governance Gary Thompson (email@example.com ) Information Management Disclaimer. All rights reserved.
MDM and Data Governance T-86.5161 Janne J. Korhonen Helsinki University of Technology Lecture Contents Master Data Management, lecture (40 min) SOA Characteristics and MDM, group work (60 min) Break (5
Service assurance for communications service providers White paper Improve service quality and enhance the customer experience. December 2007 2 Contents 2 Overview 2 Move to a competitive business model
GOVERNANCE DEFINED Governance is the practice of making enterprise-wide decisions regarding an organization s informational assets and artifacts Governance over the use of technology assets can be seen
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
IBM Sales and Distribution Chemicals and Petroleum White Paper Tapping the benefits of business analytics and optimization A rich source of intelligence for the chemicals and petroleum industries 2 Tapping
Data Management Maturity Model Overview SEPG Tysons Corner May 6, 2014 Who Needs Better Data Management? Every organization in business. Why? Collection of data assets developed over time legacy application
IBM Unstructured Data Identification and Management Discover, recognize, and act on unstructured data in-place Highlights Identify data in place that is relevant for legal collections or regulatory retention.
IBM Analytical Decision Management Deliver better outcomes in real time, every time Highlights Organizations of all types can maximize outcomes with IBM Analytical Decision Management, which enables you
Enterprise Content Management Optimizing government and insurance claims management with IBM Case Manager Apply advanced case management capabilities from IBM to help ensure successful outcomes Highlights
Service quality management solutions To support your business objectives Implement a unified approach to service quality management. Highlights Deliver high-quality software applications that meet functional
IBM Global Technology Services Thought Leadership White Paper November 2010 Strategies for assessing cloud security 2 Securing the cloud: from strategy development to ongoing assessment Executive summary
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:
Security solutions To support your business objectives Implement security solutions that help protect your IT systems and facilitate your On Demand Business initiatives. For an On Demand Business, security
Information Governance as a Holistic Approach to Managing and Leveraging Information BeyeNetwork Custom Research Report Prepared for IBM Corporation By Judith R. Davis Brought to you compliments of Copyright
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
SOA policy management White paper April 2009 Realizing business flexibility through integrated How integrated management supports business flexibility, consistency and accountability John Falkl, distinguished
IBM ediscovery Identification and Collection Turning unstructured data into relevant data for intelligent ediscovery Highlights Analyze data in-place with detailed data explorers to gain insight into data
STATS-DC 2012 Data Conference July 12, 2012 Washington State s Use of the IBM Data Governance Unified Process Best Practices Bill Huennekens Washington State Office of Superintendent of Public Instruction,
IBM Software White Paper Information Management Getting started with a data quality program 2 Getting started with a data quality program The data quality challenge Organizations depend on quality data
Implementing an Information Governance Program CIGP Installment 2: Building Your IG Roadmap by Rick Wilson, Sherpa Software www.sherpasoftware.com 1.800.255.5155 @sherpasoftware firstname.lastname@example.org
DATA GOVERNANCE AT UPMC A Summary of UPMC s Data Governance Program Foundation, Roles, and Services THE CHALLENGE Data Governance is not new work to UPMC. Employees throughout our organization manage data
Transforming your content into a trusted, strategic asset Agile enterprise content management and the IBM Information Agenda. Delivering a common information framework for uncommon business agility Highlights
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,
Master Data Management Defining & Measuring MDM Maturity, A Continuous Improvement Approach DEFINE IMPROVE MEASURE Presentation by Mark Allen 1 About the Author Mark Allen has over 25 years of data management
IBM SPSS Modeler Three proven methods to achieve a higher ROI from data mining Take your business results to the next level Highlights: Incorporate additional types of data in your predictive models By
-oriented architecture White paper March 2009 Introduction to SOA governance and Best practices for development and deployment Bill Brown, executive IT architect, worldwide SOA governance SGMM lead, SOA
IBM Software Industry Solutions IBM Tealeaf solutions Empowering e-business with pioneering Customer Experience Management solutions IBM Tealeaf solutions IBM Tealeaf solutions are designed to help companies
IBM InfoSphere Guardium Data Activity Monitor for Hadoop-based systems Proactively address regulatory compliance requirements and protect sensitive data in real time Highlights Monitor and audit data activity
Enterprise Data Management - The Why/How/Who - The business leader s role in data management Maria Villar, Managing Partner Business Data Leadership Introduction Good Data is necessary for all business
Data Security and Privacy Principles for IBM SaaS How IBM Software as a Service is protected by IBM s security-driven culture 2 Data Security and Privacy Principles for IBM SaaS Contents 2 Introduction
IBM Software Integrated Service Management: Visibility. Control. Automation. Enabling service innovation 2 Integrated Service Management: Visibility. Control. Automation. Every day, the world is becoming
Asset solutions To support your business objectives Achieve greater efficiency in asset by managing all your asset types on a single platform. Obtain an entirely new level of asset awareness Every company
Data Sheet Cisco Optimization s Optimize Your Solution using Cisco Expertise and Leading Practices Optimizing Your Business Architecture Today, enabling business innovation and agility is about being able
ITIL-aligned solutions White paper Use product solutions from IBM Tivoli software to align with the best practices of the Information Technology Infrastructure Library (ITIL). January 2005 2 Contents 2
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
IBM Forum: Risk, Governance & Regulatory Compliance Solutions October 31 - November 2, 2005 Mohonk Mountain House, New Paltz, New York October 31 Keynotes & Solutions Expo 1:00 p.m. - 6:00 p.m. November
IBM Software IBM Business Process Management Suite Increase business agility with the IBM Business Process Management Suite 2 Increase business agility with the IBM Business Process Management Suite We
Technology Building Your Cloud Strategy with Accenture 2 Cloud computing, in its simplest form, allows companies to procure technology as services, including infrastructure, applications, platforms and
IBM Policy Assessment and Compliance Powerful data governance based on deep data intelligence Highlights Manage data in-place according to information governance policy. Data topology map provides a clear
IBM Software IBM TRIRIGA: The power of true integration in workplace management solutions 2 IBM TRIRIGA: The power of true integration in workplace management solutions Introduction In today s increasingly
Making critical connections: predictive analytics in government Improve strategic and tactical decision-making Highlights: Support data-driven decisions using IBM SPSS Modeler Reduce fraud, waste and abuse
Technology Building Your Cloud Strategy with Accenture 2 Cloud computing, in its simplest form, allows companies to procure technology as services, including infrastructure, applications, platforms and
Data Governance Unlocking Value and Controlling Risk 1 White Paper Data Governance Table of contents Introduction... 3 Data Governance Program Goals in light of Privacy... 4 Data Governance Program Pillars...
IBM Global Services September 2004 IBM and the IT Infrastructure Library. How IBM supports ITIL and provides ITIL-based capabilities and solutions Page No. 2 Contents ITIL Planning for Service 2 Executive
RSA ARCHER OPERATIONAL RISK MANAGEMENT 87% of organizations surveyed have seen the volume and complexity of risks increase over the past five years. Another 20% of these organizations have seen the volume
Whitepaper Bridging the IT Business Gap The Role of an Enterprise Architect Today s enterprises understand the value that Information Technology (IT) can bring to their business. IT supports day-to-day
IBM Netcool network management solutions for enterprise The big picture view that focuses on optimizing complex enterprise environments Highlights Enhance network functions in support of business goals
The Smart Archive strategy from IBM IBM s comprehensive, unified, integrated and information-aware archiving strategy Highlights: A smarter approach to archiving Today, almost all processes and information
Assessing Your Business Analytics Initiatives Eight Metrics That Matter WHITE PAPER SAS White Paper Table of Contents Introduction.... 1 The Metrics... 1 Business Analytics Benchmark Study.... 3 Overall
IBM InfoSphere Master Data Management Server Overview Master data management (MDM) allows organizations to generate business value from their most important information. Managing master data, or key business
IBM Software Healthcare Thought Leadership White Paper For healthcare, change is in the air and in the cloud Scalable and secure private cloud solutions can meet the challenges of healthcare transformation
IBM Software Oil and Gas IBM Maximo Asset solutions for the oil and gas industry Helping oil and gas companies achieve operational excellence 2 IBM Maximo Asset solutions for the oil and gas industry Highlights
Information Governance as a Holistic Approach to Managing and Leveraging Information BeyeNetwork Custom Research Report Prepared for IBM Corporation By Judith R. Davis TABLE OF CONTENTS Executive Summary
IMPROVING RISK VISIBILITY AND SECURITY POSTURE WITH IDENTITY INTELLIGENCE ABSTRACT Changing regulatory requirements, increased attack surfaces and a need to more efficiently deliver access to the business
IBM Learning Solutions Executive Brief Learning strategy an investment in the future. September 2005 Page 2 Contents 2 Introduction 3 Do you need a learning strategy? 4 Current learning strategy insights
IBM Global Technology Services Thought Leadership White Paper Cloud computingi IBM Global Technology Services Networking for cloud computing Optimize your network to make the most of your cloud 2 Networking
IBM Security QRadar Risk Manager Proactively manage vulnerabilities and network device configuration to reduce risk, improve compliance Highlights Visualize current and potential network traffic patterns
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
IBM Software Business Analytics Cognos software The IBM Cognos family Analytics in the hands of everyone who needs it The IBM Cognos family Overview Business intelligence (BI) and business analytics have
Position Paper Cross-Domain vs. Traditional IT for Providers Joseph Bondi Copyright-2013 All rights reserved. Ni², Ni² logo, other vendors or their logos are trademarks of Network Infrastructure Inventory
Solutions for Communications with IBM Netezza Analytics Accelerator The all-in-one network intelligence appliance for the telecommunications industry Highlights The Analytics Accelerator combines speed,
IBM Global Business Services Microsoft Dynamics CRM solutions from IBM Power your productivity 2 Microsoft Dynamics CRM solutions from IBM Highlights Win more deals by spending more time on selling and
PRODUCT BRIEF: CA REPOSITORY FOR DISTRIBUTED SYSTEMS r2.3 CA Repository for Distributed Systems r2.3 CA REPOSITORY FOR DISTRIBUTED SYSTEMS IS A POWERFUL METADATA MANAGEMENT TOOL THAT HELPS ORGANIZATIONS
SOLUTION BRIEF CA IT Asset Manager how can I manage my asset lifecycle, maximize the value of my IT investments, and get a portfolio view of all my assets? agility made possible helps reduce costs, automate
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
Debbie Schmidt FIS Consulting Services 1 800 822 6758 Table of Contents More Than an IT Project...2 One Size Does Not Fit All...2 Moving Toward an Effective CRM Strategy...3 The Process...4 The Technology...4
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)
Automating policy enforcement to prevent endpoint data loss IBM Data Security Services for endpoint data protection endpoint data loss prevention solution Highlights Protecting your business value from
Risk mitigation for business resilience White paper A comprehensive, best-practices approach to business resilience and risk mitigation. September 2007 2 Contents 2 Overview: Why traditional risk mitigation
IBM Algo Asset Liability Management Industry-leading asset and liability management solution for the enterprise Highlights The fast-paced world of global markets presents asset and liability professionals
SOA governance and organizational change strategy White paper November 2007 Enabling SOA through organizational change Sandy Poi, Global SOA Offerings Governance lead, associate partner, Financial Services
Identity and access management (IAM) is the most important discipline of the information security field. It is the foundation of any information security program and one of the information security management
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
IBM asset management solutions White paper Using IBM Maximo Asset Management to manage all assets for hospitals and healthcare organizations. September 2007 2 Contents 2 Executive summary 3 Introduction
August 2009 Lowering business costs: Mitigating risk in the software delivery Roberto Argento IBM Rational Business Development Executive Valerie Hamilton IBM Rational Solution Marketing Manager and Certified
Agile Master Data Management TM : Data Governance in Action A whitepaper by First San Francisco Partners First San Francisco Partners Whitepaper Executive Summary What do data management, master data management,