2009-10 Key Trends & Best Practices Enterprise-Strength Data Governance & MDM Oracle OpenWorld 2009 Aaron Zornes Chief Research Officer The MDM Institute [email_address] +1 650.743.2278 David Butler Sr. Director MDM Marketing Oracle [email_address] +1 858.391.2709

“ Best Practices” Session Topics What are the business drivers for enterprise-strength DG? What are the technology challenges in implementing DG for enterprise magnitude business problems? Why is “active” DG superior to “passive” or “passive-aggressive” DG? How are large enterprises justifying and catalyzing their DG processes? Which are the “desirable” vs. “essential” DG solution criteria – e.g. GUI sizzle for hierarchy management vs. end-to-end team-based governance for the data steward function How does an organization organize and execute through the four stages of DG maturity: anarchy (basic), IT feudalism (foundational), business monarchy (advanced), and federalism (acme)

What are the business drivers for enterprise-strength DG?

What are the technology challenges in implementing DG for enterprise magnitude business problems?

Why is “active” DG superior to “passive” or “passive-aggressive” DG?

How are large enterprises justifying and catalyzing their DG processes?

Which are the “desirable” vs. “essential” DG solution criteria – e.g. GUI sizzle for hierarchy management vs. end-to-end team-based governance for the data steward function

How does an organization organize and execute through the four stages of DG maturity: anarchy (basic), IT feudalism (foundational), business monarchy (advanced), and federalism (acme)

Prologue Enterprise-level DG that includes entire master data lifecycle (creation, promotion, archiving, …) is extremely difficult to execute for a number of reasons – organizationally & technically. Yet increasingly this is being mandated as a core deliverable of large-scale MDM projects. Through 2009-10, both major systems integrators & MDM boutique consultancies will focus on productizing their DG frameworks/methodologies while MDM software providers struggle to link upstream DG processes with downstream MDM hubs. By 2011-12, all mega vendor MDM solutions will evolve from “passive aggressive DG” mode to “active DG” wherein they provide the capabilities to capture business rules which in turn are propagated into an MDM.

Enterprise-level DG that includes entire master data lifecycle (creation, promotion, archiving, …) is extremely difficult to execute for a number of reasons – organizationally & technically. Yet increasingly this is being mandated as a core deliverable of large-scale MDM projects.

Through 2009-10, both major systems integrators & MDM boutique consultancies will focus on productizing their DG frameworks/methodologies while MDM software providers struggle to link upstream DG processes with downstream MDM hubs.

By 2011-12, all mega vendor MDM solutions will evolve from “passive aggressive DG” mode to “active DG” wherein they provide the capabilities to capture business rules which in turn are propagated into an MDM.

Key MDM Issues for 2009-2010 Provisioning substantive amount of “MDM-specific data governance” Partnering with a faithful service provider Betting on odds-on favorite MDM solution (brand/architecture/platform) MDM 2.0 scenario: convergence of MDM & data governance

Provisioning substantive amount of “MDM-specific data governance”

Partnering with a faithful service provider

Betting on odds-on favorite MDM solution (brand/architecture/platform)

MDM Milestones www.tcdii.com/mdmresearch/assumptions.html Architecture & models Identity resolution Party data quality Analytics Policy hubs Enterprise search Strategic planning assumptions to assist IT organizations & vendors in coping with flux & churn of evolving MDM vendor landscape Market maturation Market momentum Market consolidation Budgets/skills Data governance MDM convergence

Architecture & models

Identity resolution

Party data quality

Analytics

Policy hubs

Enterprise search

Market maturation

Market momentum

Market consolidation

Budgets/skills

Data governance

MDM convergence

Key MDM Issues for 2009-2010 Provisioning substantive “MDM-specific data governance” Partnering with a faithful service provider Betting on odds-on favorite MDM solution (brand/architecture/platform)

Provisioning substantive “MDM-specific data governance”

Partnering with a faithful service provider

Betting on odds-on favorite MDM solution (brand/architecture/platform)

Enterprise Data Governance Challenges Break down functional & organizational stovepipes Integrate processes across the enterprise – including corporate technology, all LOBs, functional areas & geographic regions Engage all levels of management & adjudicate between centralized vs. decentralized data stewardship Evolve key stakeholders from “data ownership” to “data stewardship” Overcome lack of process integration in current “DG for MDM” offerings Based on recognition of issues at hand, an improving economy, & increasing regulatory requirements, businesses are now recognizing oppty to take more strategic view of enterprise data governance

Break down functional & organizational stovepipes

Integrate processes across the enterprise – including corporate technology, all LOBs, functional areas & geographic regions

Engage all levels of management & adjudicate between centralized vs. decentralized data stewardship

Evolve key stakeholders from “data ownership” to “data stewardship”

Overcome lack of process integration in current “DG for MDM” offerings

Data Governance Strategic Planning Assumption During 2009, most enterprises will struggle with cross-enterprise DG scope as they initially focus on customer, vendor, or product; enterprise-level DG that includes entire master data lifecycle will be mandated as core phase 0/1 deliverable of large-scale MDM projects Through 2010, major SIs & MDM boutiques will focus on productizing DG frameworks while MDM software providers struggle to link governance process with process hub technologies; concurrently G5000 enterprises struggle to evolve enterprise DG in cost-effective & practical way from “passive” to “active” DG modes By 2011-12, vendor MDM solutions will finally move from “passive-aggressive DG” mode to “active DG” MDM MILESTONE Data governance will remain problematic during 2009-10

During 2009, most enterprises will struggle with cross-enterprise DG scope as they initially focus on customer, vendor, or product; enterprise-level DG that includes entire master data lifecycle will be mandated as core phase 0/1 deliverable of large-scale MDM projects

Through 2010, major SIs & MDM boutiques will focus on productizing DG frameworks while MDM software providers struggle to link governance process with process hub technologies; concurrently G5000 enterprises struggle to evolve enterprise DG in cost-effective & practical way from “passive” to “active” DG modes

By 2011-12, vendor MDM solutions will finally move from “passive-aggressive DG” mode to “active DG”

“ Data Governance for MDM” Market – Chaos or Confusion? Data governance (DG) is vital to success of MDM projects – both initially & ongoing During 2009-10, Global 5000 enterprises will increasingly mandate that 'no MDM program be funded without pre-requisite DG framework’ Moreover, market-leading vendors will come to market in late with own active DG frameworks to take back the lucrative DG business currently defaulting to SIs Corollary is few MDM vendors will be able to market their solutions without integrated active DG capability – one that embeds a workflow engine with metadata support for both structured & unstructured info Where will that leave the SIs – as partners, competitors or both? Given lack of DG solutions from MDM platform vendors, SIs have had market largely to themselves; 2010 operative word = “coop-etition”

Data governance (DG) is vital to success of MDM projects – both initially & ongoing

During 2009-10, Global 5000 enterprises will increasingly mandate that 'no MDM program be funded without pre-requisite DG framework’

Moreover, market-leading vendors will come to market in late with own active DG frameworks to take back the lucrative DG business currently defaulting to SIs

Corollary is few MDM vendors will be able to market their solutions without integrated active DG capability – one that embeds a workflow engine with metadata support for both structured & unstructured info

Where will that leave the SIs – as partners, competitors or both?

Predictions of IBM Data Governance Council In some countries, DG will become regulatory requirement & companies will have to demonstrate DG practices to regulators as part of regular audits. This will likely affect Financial Services industries first, & will emerge as a growing trend worldwide. Value of data will be treated as an asset on balance sheet & reported by the CFO while quality of data will become technical reporting metric & key IT performance indicator. New accounting & reporting practices will emerge for measuring & assessing value of data to help organizations demonstrate how DQ fuels business performance. Calculating risk will become an IT function. Today in most organizations, risk calculation is done by a select group of individuals using complicated processes. In future, risk calculation will be automated allowing companies to more easily examine past exposure, forecast risk they face in future, & set aside capital to self-insure to cover risk.

In some countries, DG will become regulatory requirement & companies will have to demonstrate DG practices to regulators as part of regular audits. This will likely affect Financial Services industries first, & will emerge as a growing trend worldwide.

Value of data will be treated as an asset on balance sheet & reported by the CFO while quality of data will become technical reporting metric & key IT performance indicator. New accounting & reporting practices will emerge for measuring & assessing value of data to help organizations demonstrate how DQ fuels business performance.

Calculating risk will become an IT function. Today in most organizations, risk calculation is done by a select group of individuals using complicated processes. In future, risk calculation will be automated allowing companies to more easily examine past exposure, forecast risk they face in future, & set aside capital to self-insure to cover risk.

Predictions of IBM Data Governance Council - continued Role of CIO will change making this corporate officer responsible for reporting on DQ & risk to Board of Directors. CIO will have mandate to govern use of info & report on quality of info provided to shareholders. Individuals will be required to take more responsibility for recognizing problems & participating in governance process to facilitate greater operational transparency & identification of risk. They will be aided by new categories of operational software that will demonstrate common DG problems & allow employees to self-govern; sponsor & vote on new policies; provide feedback on existing ones & participate in dynamic DG. IBM DG Council established right approach – assessing DG from a maturity perspective across 11 categories with "Entry Points" to enable organization to embrace more pressing needs while being able to tackle other aspects when ready

Role of CIO will change making this corporate officer responsible for reporting on DQ & risk to Board of Directors. CIO will have mandate to govern use of info & report on quality of info provided to shareholders.

Individuals will be required to take more responsibility for recognizing problems & participating in governance process to facilitate greater operational transparency & identification of risk. They will be aided by new categories of operational software that will demonstrate common DG problems & allow employees to self-govern; sponsor & vote on new policies; provide feedback on existing ones & participate in dynamic DG.

MDM Institute’s Data Governance Maturity Model “ Anarchy” (basic) – Application-centric approach; meets business needs only on project-specific basis “ Feudalism” (foundational) – IT policy-driven standardization on technology & methods; common usage of tools & procedures across projects “ Monarchy” (advanced) – business-driven, rationalized data with data & metadata actively shared in production across sources “ Federalism” (distinctive) – SOA (modular components), integrated view of compliance requirements, formalized organization with defined roles & responsibilities, clearly defined metrics, iterative learning cycle Source: MDM Institute survey of 100+ Global 5000 IT organizations Fin Svc providers are leading the way – in spend & discipline; technology can only achieve so much as organization must be prepared to continually adapt & treat data as enterprise asset above project level; data needs to be an asset not a liability.

“ Anarchy” (basic) – Application-centric approach; meets business needs only on project-specific basis

“ Feudalism” (foundational) – IT policy-driven standardization on technology & methods; common usage of tools & procedures across projects

“ Monarchy” (advanced) – business-driven, rationalized data with data & metadata actively shared in production across sources

“ Federalism” (distinctive) – SOA (modular components), integrated view of compliance requirements, formalized organization with defined roles & responsibilities, clearly defined metrics, iterative learning cycle

Rosetta Stone of DG Maturity Models The MDM Institute Common inquiry is “How do I get from Level 2 to Level 4 or 5?” IBM Data Governance Council DataFlux/SAS Gartner Research Federalism IV Monarchy III Feudalism II Anarchy I Name Stage Optimizing V Quantitatively Managed IV Defined III Managed II Initial I Name Stage Governed 4 Proactive 3 Reactive 2 Undisciplined 1 Name Stage Effective 5 Managed 4 Proactive 3 Reactive 2 Aware 1 Unaware 0 Name Stage

Why Enterprise Data Governance Overly complex IT infrastructure Silo-driven, application area-centric solutions Slow-to-market delivery of new or enhanced application solutions Inconsistent definitions of key corporate data assets such as customer, supplier, & pricing masters Poor data accuracy within & across business areas LOB-focused data with inefficient or nonexistent ability to leverage information assets across LOBs Redundant IT initiatives to re-solve data accuracy problems for each individual LOB Uniform communications with customers, suppliers, & channels due to veracity & accuracy of key master data Common understanding of business policies & processes across LOBs & with business partners/channels Rapid cross-LOB implementation of new apps requiring shared access to master data Singular definition & location of master data & related policies to enable transparency & auditability essential to regulatory compliance Continuous DQ improvement as DQ processes are embedded upstream rather than downstream Increased synergy for cross-sell & upsell Pre-Governance Post-Governance

Overly complex IT infrastructure

Silo-driven, application area-centric solutions

Slow-to-market delivery of new or enhanced application solutions

Inconsistent definitions of key corporate data assets such as customer, supplier, & pricing masters

Poor data accuracy within & across business areas

LOB-focused data with inefficient or nonexistent ability to leverage information assets across LOBs

Redundant IT initiatives to re-solve data accuracy problems for each individual LOB

Uniform communications with customers, suppliers, & channels due to veracity & accuracy of key master data

Common understanding of business policies & processes across LOBs & with business partners/channels

Rapid cross-LOB implementation of new apps requiring shared access to master data

Singular definition & location of master data & related policies to enable transparency & auditability essential to regulatory compliance

Continuous DQ improvement as DQ processes are embedded upstream rather than downstream

Increased synergy for cross-sell & upsell

Enterprise Data Governance Objectives Understand & manage strategic & tactical data, project ownership from data perspective, & priority setting for data projects Define day-to-day activities of creating, using & retiring data Describe how, when & by whom data was received, created, accessed, modified &/or formatted Determine whether data is fit for its intended use, including completeness & business-rule compliance Implement processes to cleanse, transform, integrate & enrich fresh data across subject areas Address security & privacy compliance across integrated subjects Manage master data by examining data assets & relationships that define enterprise operations Source: Lance Miller/ Teradata Teradata Magazine-March 2008

Understand & manage strategic & tactical data, project ownership from data perspective, & priority setting for data projects

Define day-to-day activities of creating, using & retiring data

Describe how, when & by whom data was received, created, accessed, modified &/or formatted

Determine whether data is fit for its intended use, including completeness & business-rule compliance

Implement processes to cleanse, transform, integrate & enrich fresh data across subject areas

Address security & privacy compliance across integrated subjects

Manage master data by examining data assets & relationships that define enterprise operations

MDM Institute MarketPulse™ Survey Overview Objectives Determine top business justifications for DG programs Understand key technology challenges (failings) of current DG offerings Provide evaluation framework for both current & soon-to-market DG offerings Methodology = online surveys & interviews Pre-qualified, pre-existing relationship C-level or next level below Survey pool of 100+ Global 5000 size enterprises Oracle Data Governance Advisory Board IBM Data Governance Council MDM Institute Advisory Council

Objectives

Determine top business justifications for DG programs

Understand key technology challenges (failings) of current DG offerings

Provide evaluation framework for both current & soon-to-market DG offerings

Methodology = online surveys & interviews

Pre-qualified, pre-existing relationship

C-level or next level below

Survey pool of 100+ Global 5000 size enterprises

Oracle Data Governance Advisory Board

IBM Data Governance Council

MDM Institute Advisory Council

MDM Institute’s Enterprise Data Governance Survey 50% enterprise-level perspective; 25% LOB/divisional; Survey Participants – CISOs, CIOs, CTOs, Enterprise Architects, VPs & Directors of IT = pre-qualified senior IT executives – not random IT personnel or “Starbucks card collectors”

Current Data Governance Survey

Data Governance “Framework” “Top 10” Evaluation Criteria Methodology Data exploration/profiling Model management Rules management Decision rights management MDM hub integration Enterprise application integration E2E data lifecycle support Integrated metrics Vendor integrity/viability “ Data Governance for MDM” fracas will escalate as MDM vendors rush to usurp SIs; during 2009-10, MDM vendors increasingly unable to sell MDM w/out integrated DG

Methodology

Data exploration/profiling

Model management

Rules management

Decision rights management

MDM hub integration

Enterprise application integration

E2E data lifecycle support

Integrated metrics

Vendor integrity/viability

Overall Critique of Existing DG Capabilities Mismatch of applying project-oriented methodology rather than asset-focused methodologies Methodologies missing the asset aspect of data … cost, decaying value, ROI for cleansing data, etc. Frameworks not addressing “community” aspect of shared asset development – e.g. wikis for global corporate business vocabulary, etc. Current DG solutions do not provide systemic rigor nor E2E lifecycle support “ (Integrated) Data Governance for MDM” market is a vacuum … nature hates a vacuum

Mismatch of applying project-oriented methodology rather than asset-focused methodologies

Methodologies missing the asset aspect of data … cost, decaying value, ROI for cleansing data, etc.

Frameworks not addressing “community” aspect of shared asset development – e.g. wikis for global corporate business vocabulary, etc.

Current DG solutions do not provide systemic rigor nor E2E lifecycle support

SUMMARY – Data Governance for MDM Don’t settle for “passive” / downstream data governance Demand “active” / upstream enterprise data governance Don’t expect “data governance maturity assessments” to provide road map out of anarchy Realize that “data steward consoles” are more than demo-ware for headless apps … but substantially less than enterprise data governance Acknowledge that vendor viability matters Prepare to spend $250-$500K for initial DG solution Enterprise data governance & MDM are codependent/interdependent … invest upfront in data governance for sustainability & ROI of MDM programs

Don’t settle for “passive” / downstream data governance

Demand “active” / upstream enterprise data governance

Don’t expect “data governance maturity assessments” to provide road map out of anarchy

Realize that “data steward consoles” are more than demo-ware for headless apps … but substantially less than enterprise data governance

Acknowledge that vendor viability matters

Prepare to spend $250-$500K for initial DG solution

BOTTOM LINE Invest in DG for long-term sustainability & ROI of MDM Acknowledge currently “DG for MDM” does not exist as integrated solution Primarily processes with custom workflow One-way export to MDM hub (if any) Minimal support for “enterprise” decision rights Plan for most MDM vendors to deliver DG workflow engine during next 6-12 months with metadata engine for both structured & unstructured Recognize mega vendors (IBM, Oracle) focused to deliver capability in 2H2009 – with resultant SI partner chaos Manage DG provider To integration roadmap with MDM platform of choice To avoid “brain drain”

Invest in DG for long-term sustainability & ROI of MDM

Acknowledge currently “DG for MDM” does not exist as integrated solution

Primarily processes with custom workflow

One-way export to MDM hub (if any)

Minimal support for “enterprise” decision rights

Plan for most MDM vendors to deliver DG workflow engine during next 6-12 months with metadata engine for both structured & unstructured

Recognize mega vendors (IBM, Oracle) focused to deliver capability in 2H2009 – with resultant SI partner chaos

Manage DG provider

To integration roadmap with MDM platform of choice

To avoid “brain drain”

MDM SUMMIT™ Conference Series MDM SUMMIT Europe 2009 Park Plaza Victoria Hotel | April 20 – 22, 2009 MDM SUMMIT Asia-Pacific 2009 Four Points by Sheraton Sydney | April 28 – 29, 2009 MDM SUMMIT Canada 2009 Hotel Admiral Toronto-Harbourfront | June 25 – 26, 2009 MDM SUMMIT Americas 2009 San Francisco Hyatt Regency August 24 – 26, 2009 MDM SUMMIT New York 2009 New York Hilton | December 1 – 2, 2009

MDM SUMMIT Europe 2009 Park Plaza Victoria Hotel | April 20 – 22, 2009

MDM SUMMIT Asia-Pacific 2009 Four Points by Sheraton Sydney | April 28 – 29, 2009

MDM SUMMIT Canada 2009 Hotel Admiral Toronto-Harbourfront | June 25 – 26, 2009 MDM SUMMIT Americas 2009 San Francisco Hyatt Regency August 24 – 26, 2009

MDM SUMMIT New York 2009 New York Hilton | December 1 – 2, 2009

About the MDM Institute Founded 2004 to focus on MDM business drivers & technology challenges MDM Advisory Council™ of 100 Global 5000 IT organizations with unlimited advice to key individuals, e.g. CTOs, CIOs, data architects MDM Business Council™ website access & email support to 15,000+ members MDM Road Map & Milestones™ annual strategic planning assumptions MDM Alert™ bi-weekly newsletter MDM Market Pulse™ monthly surveys MDM Fast Track™ one-day public & onsite workshop rotating quarterly through major North American, European, & Asia-Pacific metro areas MDM SUMMIT™ annual conferences in NYC, San Francisco, London, Frankfurt, Madrid, Sydney, Toronto, & Tokyo “ Independent, Authoritative, & Relevant” About Aaron Zornes Most quoted industry analyst authority on topics of MDM & CDI Founder & Chief Research Officer of the MDM Institute Conference chairman for DM Review’s MDM SUMMIT conference series Founded & ran META Group’s largest research practice for 14 years M.S. in Management Information Systems from University of Arizona

Founded 2004 to focus on MDM business drivers & technology challenges

MDM Advisory Council™ of 100 Global 5000 IT organizations with unlimited advice to key individuals, e.g. CTOs, CIOs, data architects

MDM Business Council™ website access & email support to 15,000+ members

MDM Road Map & Milestones™ annual strategic planning assumptions

MDM Alert™ bi-weekly newsletter

MDM Market Pulse™ monthly surveys

MDM Fast Track™ one-day public & onsite workshop rotating quarterly through major North American, European, & Asia-Pacific metro areas

MDM SUMMIT™ annual conferences in NYC, San Francisco, London, Frankfurt, Madrid, Sydney, Toronto, & Tokyo

About Aaron Zornes

Most quoted industry analyst authority on topics of MDM & CDI

Founder & Chief Research Officer of the MDM Institute

Conference chairman for DM Review’s MDM SUMMIT conference series

Founded & ran META Group’s largest research practice for 14 years

M.S. in Management Information Systems from University of Arizona

MDM Institute Advisory Council 3M Bell Canada Caterpillar Cisco Systems Citizens Communications COUNTRY Financials Educational Testing Svcs EMC GE Healthcare Honeywell IHS Intuit Loblaw McKesson Medtronic 100 organizations who receive unlimited MDM advice to key individuals, e.g. CTOs, CIOs, & MDM project leads Representative Members Microsoft Motorola National Australia Bank Nationwide Insurance Norwegian Cruise Lines Novartis Polycom Roche Labs Rogers Communications Scholastic Stryker SunTrust Sutter Health Westpac Weyerhaeuser Woolworths

3M

Bell Canada

Caterpillar

Cisco Systems

Citizens Communications

COUNTRY Financials

Educational Testing Svcs

EMC

GE Healthcare

Honeywell

IHS

Intuit

Loblaw

McKesson

Medtronic

Microsoft

Motorola

National Australia Bank

Nationwide Insurance

Norwegian Cruise Lines

Novartis

Polycom

Roche Labs

Rogers Communications

Scholastic

Stryker

SunTrust

Sutter Health

Westpac

Weyerhaeuser

Woolworths

Authoritative Relevant Independent Aaron Zornes Founder & Chief Research Officer The MDM Institute The-MDM-Institute.com a.k.a. www.tcdii.com