data quality & governance - dbguide.net2007/10/29 · • helzberg’s online personal...

© 2007 IBM Corporation

Data Quality & Governance:Leveraging the Business Value of Information for Financial Services

Andrew WarzechaVice President, StrategyIBM Information Management [email protected]

2 © 2007 IBM Corporation

The Innovation ImperativeSeizing the most promising opportunities

´´Over 60% of CEO’s believe their organizations need to do a better job leveraging information´´ IBM Global CEO Study

IBM Global CEO Study found:•Globalization & technology driving commoditization at increasing rate

•Operational efficiency and cost reduction providing diminishing returns

•The way to prosper is through top-line growth (new revenue streams, new clients, new markets, new channels)

•The way to growth is through differentiation (create new value for customers, employees, partners, shareholders)

•The path to differentiation is innovation (seizing the most promising growth opportunities


Reducing application processing time from weeks to two days. Transforms information sharing

Delivering real-time information across the enterprise

Identifying cross-sell opportunities and improving negotiating position at the point of saleTransforms sales effectiveness

Knowing the customer for improving sales & service

Reducing the time to retrieve bank clients’ stored documents from five days to five seconds Transforms bank service offerings

Reducing process cycle times

Identifying potentially fraudulent claims prior to approval and payment Transforms fraud management

Detecting fraudulent claims

Dramatically reducing time required to write individualand corporate policiesTransforms customer service

Providing innovative insurance solutions

Identifying related incidents and potential suspects prior to arriving at the crime sceneTransforms crime fighting

Delivering real-time crime statistics & reports

Information as a Strategic AssetInformation Challenge

Innovators are Establishing a Competitive Advantage by Using Information


BusinessValue

Maturity ofInformation Use

Crossing the chasm

The Road to Information Innovation

Information to Manage the

Business

Data to Run the Business

“Focus on DataAnd Reporting”

“Basic Information Interaction”

Information as a Competitive Differentiator

Information to Enable Innovation

“Information in Context”

“Adaptive BusinessPerformance”

Flexible Information Architecture

“Real-time SingleView of the Truth”

Experience with over 500

Clients

5X More Value creation by organizations effective at using

information as an asset

•Architecture•Industry know-how•Data Governance


How do you Deal with your Existing Data & Architecture?

LostTime

ReducedService

IncreasedCosts

LostRevenue


• Scalable System Platforms and Operating Environments

• Storage Systems and Management Software• System Optimization and Virtualization• Network, System and Application-level Security• IT Service Management• Development Services and Tools

Providing a resilient foundation

Capabilities NeededIt takes know-how, proven technology & industry expertise…

Guiding plans and execution• Information Strategy• IT Strategy and Architecture• Governance• Design, Planning and Implementation• Managed Operations• Financing

Delivering information in context• Data Management• Enterprise Content Management• Information Integration• Master Data Management• Search, Discovery & Business Intelligence• Content-centric Business Process

Management

Creating new business value & innovation• Customer Centricity• Operational Intelligence• Product and Service Optimization• Risk and Compliance• Threat and Fraud Intelligence• Industry Models


Definition of Data Quality

Data quality is defined by how effectively the data supports the transactions and decisions needed to meet an organization’s strategic objectives, as embodied in its ability to manage its assets and conduct its core operations

The level of data quality required to effectively support operations will vary by information system or business unit, depending upon the information needs to conduct that business unit’s operations


What is the Impact of Poor Data Quality?

Lost Sales Opportunity

SKU misplaced or hard to findOut of stocks attributed to the store

“Hard” Losses

Lost potential for cross-sell and up-sell (staff not trained or available)Reduced store visit frequencyAbandoned carts (poor service or excessive queues)

“Soft” Losses

1.5% 1.7%

2-4% 1-3%

1-2%

Total 7.2%- 12%

Source: GMA/FMI/CIES 2003 (US grocery), ECR Europe 2003, Lineraires.com, California Management Review, IBM case studies, interviews and IBM Institute for Business Value analysis


Data Quality Facts and FiguresThe “DataWarehouse Institute” estimates that poor quality customer data costs U.S. companies $611 billion in posting, printing and staff overhead.

Almost half of companies do not have a plan for managing or improving data quality

Gartner Group reports that Data Quality is the #1 reason for failure in data related projects

In their January 2003 survey of Business Analytics Implementation Challenges, the IDC states that the second greatest implementation challenge after budget constraints is data quality

According to the Standish Group, 88% of major IT projects fail to deliver on time and within budget. Two primary reasons are related to Data Integration and Data Quality

"If you look at...any business function in your company, you're going to find some direct cost there attributed to poor data quality."

- Gartner 2006


Do You Trust Your Information?


Data Quality Architecture

External Mrkt. Data

External Mrkt. Data

Corporate Data/Involved Parties

Customer Data

Data IntegrationData IntegrationOperational DataOperational DataReporting LayerReporting Layer

Assessment and Remediation

Data Quality Trend Analysis

Data Quality Reporting

Data Quality Auditing

GeneralLedger

LOBData

LOB Data

Data WarehouseData Warehouse

Risk Calculation & Assessment

Risk Calculation & Assessment

Data Reengineering Transformation, Alignment, Consolidation

Standardization and Matching

Deduplication

Ongoing Data Quality Monitoring

Exception Resolution

Establish and Capture Data Lineage

Data Lineage and Audit Trail

Preventative Data Quality

Detective Data Quality


What Do You Need to Establish a Data Quality Program?

A foundation platform that centralizes quality rules and provides auditable data quality

Business-driven, data-centric design environment for data quality rules

An ongoing process for data quality

A way to measure quality over time

Universal deployment of quality rules across all points of entry

Data quality ownership and data governance

Management sponsorship and a corporate mandate for data quality improvement


Those elements that are relevant in affecting the structure and understanding of information

Key Data Quality Elements

Key Stakeholders:

Business SME

Data Stewards

Technology Defined Data Quality

Measured across multiple data quality components, used to measure a data elements quality compared to corp. goal

Key Stakeholders:

Technology SME

Data Stewards

Business Process Defined Data Quality

Define the business definition for a data quality element business rules associated with that element

Key Stakeholders:

Business SME

Data Stewards

Plan Define Implement

Plan a customized approach for data quality program

Identify requirements and outline the data quality solution

Develop and implement the components of the data quality program

Assess/ measure effectiveness of data quality program; adjust where appropriate

A Basic Data Quality Approach

Evolve


IBM’s Data Quality Framework

Data Quality Framework

Data Quality Policy

Data Quality Organization

Data Quality Data Administration

Data Quality Architecture

Data Quality ProceduresReporting

Measures

FrameworkCheck-ups

Each of the eight components highlights a framework element that should be fully implemented to achieve Data Quality


IBM Data Quality Framework

Data Quality Policy – Provides strategic and operational direction to an enterprise’s data quality program initiative. It highlights the high level enterprise-wide data quality goals

Data Quality Organization – The administrative and functional structure required to achieve the objectives of the data quality program (data stewardship community and governance group,performance measurement, training and communication)

Data Quality Architecture – The data, application, and technology structure that contributes to the overall improvement of data quality across the enterprise

Data Quality Procedures – Processes through which systems and personnel generate and assure high quality business outputs

Measures – The assessment of quality performance information to support the generation of metrics and reporting of results

Reporting – The creation and distribution of meaningful data quality performance information to various stakeholders

Framework Check-Ups – The assessment of the data quality program within the organization to identify implementation weaknesses and take corrective action

Each of the eight components highlights a framework element that should be fully implemented to achieve Data Quality

Data Quality Administration – The maintenance of consistent data definitions (meta-data) and business rules across the enterprise. Emphasis is on the process for maintaining metadata, not the tools, which are viewed as enablers


Lessons Learned & Best Practice:Involve the Business Early

Recruit an executive sponsor– Signals that the initiative is important– Assures that funds continue to be available– Discourages other business units from implementing conflicting projects

Convene a data quality working group– Assess and report on quality early in the process– May coincide with implementation teams or data warehousing teams– Business leads, but IT coordinates and facilitates– Strive for consensus

Have the business appoint a data quality steward for each business unit– For business units with large user populations, several stewards are appropriate


Lessons Learned & Best Practice:Control Scope Ruthlessly / Focus on Benefits

Business must own scope– Business should be owners, not renters– IT maintains its independence by not taking sides– Controlling scope encourages project discipline

Iterate– Projects which try to do it all in one pass generally fail

Measure, Report, and Deliver benefits regularly– Initial projects must provide some benefit within 6 - 9 months at the

minimum (even if a small benefit)– Subsequent phases should provide benefits every 3-6 months


Business Benefits

• Automated 80% of the manual research process for underwriting

• Enabled ROOM to offer this as a service to the entire insurance industry

• Initial piece built in 3 days and capable of processing 1,000 addresses in under a minute

Room SolutionsAutomating process and data standardization

• Unable to reliably share information with clients and channel partners on properties for underwriting purposes.

• Dependence on manual, time-consuming research process

• Erroneous and incomplete data, inconsistent formatting, & miscalculation of insurance risk

• Implemented a real-time matching and enrichment service accessible from a Web portal, using IBM WebSphere QualityStage for standardization and matching

• IBM WebSphere DataStage SOA Edition used for on demand transformation and publishing of Web services to access data

Challenge

Solution


Business Benefits

• Improved customer service, resulting in an estimated 7-12% increase in annual sales (i.e. $36-$60M new sales per year).

• Better job satisfaction, employee retention and training among sales associates.

• Improved tools to measure, improve and track customer service and sales; likewise, better tools for employee coaching and development.

Helzberg DiamondsRetail jeweler beats competition with improved customer service

• Increased competition from mega retailers in the jewelry market was lowering retail transaction counts and threatening profits.

• To combat Wal*Mart and others advantage in terms of price and retail locations, Helzberg needed to build customer loyalty and offer better service.

• Helzberg’s Online Personal Assistant allows each sales associate to proactively service and sell with thank you notes, service reminders or birthday offers.

• IBM WebSphere DataStage and IBM WebSphere QualityStage are deployed in a service-oriented architecture (SOA) to synchronize central and store level customer data warehouses.

Challenge

Solution


The IBM Data Governance Council was formed in 2004 to explore enterprise challenges and develop solutions

Data Governance Council Members

Customers Business Partners AcademiaAbbott Freddie Mac AirMagnetABN Amro Huntington Bank Application SecurityAlltel IBM CIO Office Axentis Bucerius Law School

Continuity SoftwareBank of America MasterCard Guardium

Merrill Lynch IntellinxBank of Tokyo/Mitsubishi LumigentBell Canada Novartis OpenPages

Nordea Bank Organizational Policy Inst.Cadence Design Northwestern Mutual Paisley

PNC RiskWatchRegions Financial Corp. SecNap

Semantic Arts

Discover Financial VP Securities Services TizorEquifax Washington Mutual Valid Technologies

ZANTAZ

North Carolina State U i itNova Southeastern U i it

American Express Key Bank

Bank of Montreal Monaris

BITS Financial Services R dt bl

Citigroup City of New York, FISA Danske Bank TIAA-CREF Deutsche Bank TeliaSonera SPS Security

Fannie Mae Wachovia

21© 2007 IBM Corporation

What is Data Governance?Data Governance is the orchestration of people, process and technology to enable an organization to leverage data as an enterprise asset

DATA GOVERNANCE

Executive-LevelData Governance

Bodies

Line of Business

Stewardship Community

Data Quality Reporting Team

Project Teams

Virtual Teams

Executive Sponsorship

Risk Data Council

Data Governance

Program Manager

Technical Liaisons

(4)

Business Liaisons

(4)

Metadata Liaison

(1)

Data Governance PMA

Risk Data Governance Office (DGO)

Data Quality ReportingLiaison (1)

Data Definition

StewardshipFunction

DataProduction

Stewardship Function

DataUsage


Quality Measurement Stewardship

Function

Lead Steward


Bodies

Line of Business



Project Teams

Virtual Teams


Risk Data Council

Data Governance

Program Manager

Technical Liaisons

(4)

Business Liaisons

(4)

Metadata Liaison

(1)

Data Governance PMA



Data Definition

StewardshipFunction

DataProduction


DataUsage



Function

Lead Steward

People Process Technology

The core objectives of a governance program are:Guide information management decision-making

Ensure information is consistently defined and well understood

Increase the use and trust of data as an enterprise asset

Extract

Extract

Extract

Extract

Extract

Extract


Without Data Governance…

People make mistakes…Those mistakes more commonly result in losses than hackers…Those losses effect every aspect of IT and businessBut data is still an abstract concept and governance needs technology to be improved…


Data Governance DimensionsSuccessful data governance programs will have organization, process and

business rules around most of these dimensions

“Data governance frameworks are furthering strategic alignment and consolidation across businesses by spanning multiple policy, organizational, customer, product, service, people, process, and technology dimensions.” Guillermo Kopp, The Tower Group, 2006

Data Architecture Data Quality

Meta Data/Business Glossary

Audit and Reporting

Value Creation

StewardshipSecurity / Privacy / Compliance

Information Lifecycle Management

Risk Management

Policy

Organizational Awareness


Common Landscape in Financial Services Hinders Data Governance

Business Units

Wealth Management

Core Systems

Channels

Relationship Managers/

Agents

ATMs

Call Centers

Internet

Branches

DB App

DB

App

Core Systems

DW

DBCRM

App

Retail Banking

Corporate Banking

DB

CRM

App

AppDB

DW

Core SystemsCRM DB App

Capital Markets

Insurance

CIF

PartnersDB

CRM AppDB

App DW

DBCore

Systems

Data Systems

Core Systems

ERP

Customer

Inconsistent View of CustomerSilos of Information

Information Locked in RepositoriesInconsistent Data

No Single Version of the TruthPoor Channel Communication

DB App

DB

DB

AppCRM

ODS


Why is Data Governance Important?Effectively managing all dimensions of enterprise information enables:

Timely integrated information delivered to support business opportunities.

The most relevant information shared across channels and LOB.

Trusted accurate information supporting regulatory compliance.

Reliable information for business intelligence and executive dashboards.

Aggregate view of total customer relationship across the organization.

Faster time to market for new products and services.

“Data integration and data quality are no longer simply technology tools, they must enable an organizational competency responsible for delivering Trusted Data” - Forrester


The Need for Data Governance

0 20 40 60 80 100

Don't know

No

Yes

Yet -•Only 27% have centralized data ownership

•66% have not documented or communicated their data governance program

•73% have no KPI’s or measurements of success

84% Recognize the Business Impact of Data Governance

IBM Data Governance Survey 2006Sample Size- 50 FSI and Non-FSI Executives

“…in 2006 banks will continue to invest heavily in strategies and technologies to support a more integrated, enterprise-wide customer data repository for both consumer and corporate banking businesses.”

Jim Eckenrode, Tower Group - 2006


Survey: Data Governance Maturity Level

0%

10%

20%

30%

40%

50%

Basic Foundational Advanced Distinctive

FSP

Non-FSP

BASIC (“anarchy”) – App-centric approach; meets business needs only on project-specific basisFOUNDATIONAL (“IT monarchy”) – Policy-driven standardization on technology & methods; common usage of tools & procedures across projectsADVANCED (“business monarchy”) – Rationalized data with data & metadata actively shared in production across sourcesDISTINCTIVE – SOA (modular components), integrated view of compliance requirements, formalized organization with defined roles & responsibilities, clearly defined metrics, iterative learning cycle


55% respondents have only basic or foundational data governance programs

75% DO NOT have an enterprise wide data governance policy

54% have only conceptual support or less from senior management for data governance initiatives


Survey: Leading Drivers for Data Governance Programs

40%

66%

60%

53%

46%

88%

38%

75%

62%

56%

0% 20% 40% 60% 80% 100%

“Single version of the truth”

Reduce regulatory risk

Enable consistent usage of data acrossthe enterprise

Increase marketing effectiveness

Create value from integrated, high-qualityinformation

Non FSPFSP

67%

47%

60%

46%

47%

81%

63%

56%

56%

31%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90%

Defining processes & business rules forongoing governance

Developing standard metadata mgmt

Building governance infrastructure, technology& supporting organization

Monitoring to improve data quality

Developing architecture best practices &standards

Non-FSPFSP

IT Drivers

Business Drivers

‘Single version of the truth’(MDM) and enabling information across the enterprise are the top two business drivers.


Defining data processes and managing meta data are the top two IT drivers.


Survey: Data Type Focus

3rd party reference

data10%

Financial16%

Product23%

Customer51%


“Although customer data is the top priority for organizations on a global basis, financial and product data are receiving major attention, likely as a result of compliance pressures and broader master data management activity becoming prominent…”

Ted Friedman, Gartner - 2006


16%

38%

41%

6%

0% 5% 10% 15% 20% 25% 30% 35% 40% 45%

No

Yes - somewhat

Yes - to a great extent

Don't know

n/a

Survey: Redundant and Multiple Instances of Data

“We continue to hear from the LOBs that we provide too much data and not the kind of data they really need .”

CTO, British Tier 1 Bank



Survey: Data Stewardship

67%

47%

60%

46%

47%

81%

63%

56%

56%

31%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90%

Defining processes & business rules forongoing governance

Developing standard metadata mgmt

Building governance infrastructure, technology& supporting organization

Monitoring to improve data quality

Developing architecture best practices &standards

Non-FSPFSP

Data stewardship is a business-oriented function


“Each steward has a mandate to change the process and structure of any business, person, or IT system, if that’s what it takes to improve data.”

Phillip Russom, The Data Warehouse Institute - 2006

56% have clearly defined and communicated Data Steward roles and responsibilities

59% of Data Stewards reside outside of business


Data Quality is Fundamental to Data Governance

Data quality is recognized as a key enabler for data governance, yet only 35% of companies have data quality initiatives well under way.


33 2007-09-17

There are Six Questions Every Organization Should ask Themselves about Data Governance Today

1. Do we have a Government?o Who is responsible for governing?o How do we share accountability across the enterprise?

2. How do we assess our situation?o Are benchmarks available?o How do we measure our Maturity?

3. What is our Strategy?o How do we get from here to there?o What does our CEO and Board want?

4. What is our data worth?o How much revenue is it producing?o How much does low quality data cost?

5. What are our vulnerabilities?o How do we calculate risk?o Which risks do we accept, mitigate, transfer?

6. How do we measure progress?o What do audits tell us?o How do we report results that matter?


IBM Data Governance Solution - OverviewLeveraging our experience assisting clients implement and sustain Data Governance Programs, IBM has assembled a “bundle of accelerators”, driven by a proven methodology

• Metadata Repository, populated with the industry standard business definitions

• Profiling technology• Data Quality remediation technology• Industry data, process and integration

models• Master Data Management applications

Technology

• Organizational model and role definitions• Operational models• Executive sponsors and leaders• Data Governance experts• Data Quality experts• Change Management/Training experts

People


Bodies

Line of Business



Project Teams

Virtual Teams


Risk Data Council

Data Governance

Program Manager

Technical Liaisons

(4)

Business Liaisons

(4)

Metadata Liaison

(1)

Data Governance PMA



Data Definition

StewardshipFunction

DataProduction


DataUsage



Function

Lead Steward


Bodies

Line of Business



Project Teams

Virtual Teams


Risk Data Council

Data Governance

Program Manager

Technical Liaisons

(4)

Business Liaisons

(4)

Metadata Liaison

(1)

Data Governance PMA



Data Definition

StewardshipFunction

DataProduction


DataUsage



Function

Lead Steward

• Data Governance Implementation Methodology

• Pre-defined Data Governance Policies and Procedures

• Pre-defined Data Quality Standards• Industry-Standard Business Definitions

Process


Data Governance Critical Success Factors

Executive support & commitment Direction and involvementRecognition of data as a corporate asset

Commitment to resolve data issuesMeasurable resultsManage expectationsCommitted resources

Clear accountabilityEnterprise stewardship roles and responsibilitiesEffective and standardized toolsetSupport processes and technology

ExecutiveLeadership

Data Governors

Data Stewards

Policy DecisionsRequirements Definition

Decision Making Input

End Users, Customers, etc.

User Acceptance Testing


Information On Demand Supports Key Banking InitiativesCreating new business value in banking with information innovation

•Basel II•Sarbanes Oxley•MiFID•E-mail archiving•Credit Risk Mgt•Operational Risk Mgt•Market Risk Mgt•Records Mgt

Manage Risk and Compliance

•Data Quality•Data Architecture•Organizational Structures•Data Stewardship•Data Security•Audit and reporting•Meta Data Management•Policy

Improve Data Governance

•Master Data Management•Channel Integration•Customer Data Hub•System of Record•Customer Information File•Cross Sell/Up Sell•CRM•Reverence Data

Enterprise Customer View

•Data Marts•Business Performance•Profiling/Segmentation•Customer Profitability•Channel Usage•Householding•Reporting and Dashboards •Cross sell/up sell

Operational Intelligence

•Enterprise Risk Mgmt•AML/KYC•Insider Threats •Global Name Recognition•Identity & Relationship Resolution•Anonymization / Privacy

Threat and Fraud Intelligence

•Transform core systems •Payment Systems•Loan Systems•Trading Systems•Enterprise integration

Optimize Back Office

•Business Process Mgt•Workflow•STP•Content Centric Processes•Enterprise Content Management

Optimize Business Processes

Integrated business intelligence embedded within applications

Alphablox

Threat and Fraud Intelligence – Who’s who and who knows who

IBM Entity Analytics Solutions

Manage unstructured content and optimize business processes

IBM Enterprise Content Management including FileNet

Database, Data WarehouseData Servers – DB2 IMS Informix

Understand, cleanse, transform and access enterprise information.

IBM Information Server

Process, Integration and Data ModelsIBM Industry Models for Banking, Insurance and Financial Markets

Master Information Management, Operational Customer Information Hubs

WebSphere Customer Center

WebSphere Product Center


• Getting Started Offerings

–Data Governance Maturity Assessment

–Information On Demand Workshop

• Center of Excellence Goals

–Develop information management best practices based on customer experiences

–Help clients optimize the business valueof their information

Take the Data Governance Maturity Assessment now to check your company’s data governance maturity level and receive recommendations for improvement……

Data Governance: Getting Started


IBM Data Governance Maturity Model and Assessment

IBM has developed an assessment tool and maturity model to measure DG maturity

• Continuous Improvement• Innovation / Leadership• Collective / Shared Efforts• Consistent & Rigorous• Significant Automation

• Initial Process Definition• Basic Infrastructure

Modeling• Project Discipline• Automation Opportunities

• Measured and Managed Efforts• Understood / Shared Practices• Consistent Application• Improving Performance• Advancing Technology

• Consistent Performance Measurement against Stated Goals

• Objectivity and Trust• Advanced Tools / Usage

• Lack of Processes• Stand-alone Structures• No Tracking / Management• Heroic Efforts• Ad Hoc Attempts

Business Transformation

Key contributors to maturity:RigorComprehensivenessConsistency


Benchmark where you are and where you want to go


GoalsImprove operational efficiency/save moneyImprove customer intimacy

SolutionReorganized bank around customerOne group responsible for enterprise-wide Data and Information ManagementMade enterprise decision to consolidate data Implemented data governance program utilizing IBM Information Server platform

Lessons LearnedObtain executive business and IT buy-in and commitment earlyBuild solutions aligned with strategic goalsCreate data governance organizational structure to empower data stewardsStandardize on toolsets and enforceThink enterprise

ResultFaster time to market for initiativesImprovement in operational efficiency through data consolidation

Case Study: Royal Bank of CanadaBusiness problemsOperational efficiency was trending downTremendous amount of duplication – silos, data,

databases and information toolsets


GoalsComply with regulators and improve information quality, flow and availability.

SolutionImplemented DG program for governance methodologies, polices & procedures, roles & responsibilities. Established a DG Organization including executive, middle, and operational level structures, processes, and participants.Created process and infrastructure for identifying and supporting data quality initiatives and implemented data quality remediation program including a data quality monitoring & reporting dashboard. Established a forum and processes and accelerated definition and adoption of standard data definitions, particularly “customer.”Develop business rules around critical / core Credit Risk Management data elements in support of Data Quality dashboards.

Lessons LearnedObtain executive business and IT buy-in and cooperation earlyCreate data governance organizational structure and empower data stewardsStandardize data definitions.Think enterprise

Business ValueSuccessfully passed an extensive Federal Reserve audit focused on data and established foundational DG elements which can be leveraged as the bank continues their march toward Basel II complianceDemonstrable improvement in data quality and trust in it’s accuracy.

Data Governance Case Study: Top 10 US BankBusiness problemsRegulatory Compliance - The bank was cited by the US Federal Reserve for not having adequate controls in place to ensure quality and integrity of information reported to regulators.


RecommendationsRecognize information as an enterprise asset that can deliver business value

Enterprise data governance is difficult –organizations need to embrace a data governance blueprint for success

Executive sponsorship is key for data governance success. A mandate is necessary to drive compliance

IT and business management must align and understand impact of data governance on their information reliant initiatives

Successful companies drive stewardship & business metadata definitions early

Master Data Management goes hand in hand with data governance

IT is still perceived as responsible for data quality and data stewardship, but business users must take responsibility

Be prepared for political and territorial battles


Visit the IBM website for materials, news, and best practices on Data Governance:

http://www.ibm.com/software/tivoli/governance/servicemanagement/data-governance.htmlhttp://www.ibm.com/software/data/information-on-demand/

Leverage your Information

data quality & governance - dbguide.net2007/10/29 · • helzberg’s online personal...

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