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“Go Governance, Go Early”

A Practical Guide to Master Data Governance Solutions for the Large Enterprise

An MDM Institute MarketPulse™ Analysis of the Master Data Governance Market (2012-15)

November 2012

Data is an important asset to most companies & leveraging that data properly can result in operational & IT cost savings as well as drive business growth. Furthermore, managing strategic data assets is foundational to a service-oriented architecture (SOA), which in turn facilitates business process management (BPM). Clearly, Master Data Management (MDM) is an enticing proposition for many

enterprises but to achieve these results, a proper “Master Data Governance strategy” must be in place.

When deploying MDM, a proper Data Governance discipline should consider the business drivers, project scope, roles & people filling each role, policies & procedures, data quality, inheritability, social norms, &

the business operating model. “Master Data Governance” is more than a single product or process, rather, it is an ecosystem of products, processes, people, & information. Based on recognition of issues at hand, an improving economy, & increasing regulatory requirements, businesses are now recognizing

the opportunity to take a more strategic view of enterprise data governance.

MDM & Master Data Governance are codependent/interdependent. During 2012-15, market-leading enterprises must invest upfront in Master Data Governance to realize MDM sustainability and ROI.

Throughout 2012-13, skill shortages will greatly inflame project costs as demand for data stewards & enterprise data architects, & individuals with Data Governance experience outstrip market supply;

concurrently, systems integrators (SIs) will fill this void via project management/implementation & staff augmentation.

During 2012, the market will stabilize as enterprises react by training & protecting their own MDM staff with specific product & project expertise; until then, enterprises will struggle with re-skilling the same

resources multiple times as emerging/evolving data management technologies mature (e.g., Oracle Fusion, SAP NetWeaver, …). During 2012-13, major SIs & boutique MDM consultancies will focus on

productizing Data Governance (DG) frameworks while MDM software providers struggle to link governance processes with process hub technologies; concurrently, G5000 enterprises will struggle to

evolve enterprise DG in cost-effective & practical ways from “passive” to “active” DG modes.

Through 2013-14, most enterprises will struggle with cross-enterprise DG scope as they initially focus on customer, vendor, or product; enterprise-level DG that includes the entire master data lifecycle will be mandated as a core phase 0/1 deliverable of large-scale MDM projects.

“The market for Master Data Governance software (including MDM) reached US$1.3B during

2012 and will exceed US$2.2 billion by 2015. Clearly, Master Data Governance is essential to

the success and sustainability of the majority of MDM projects, yet prior to 2012 most MDM

software vendors did not recognize this market requirement. As a result, Master Data

Governance assistance from SIs was especially critical to the success of MDM programs as

organizations dealt with a shortage of tools, experience, and tool expertise. Given the

substantial investment enterprises undertake with MDM programs, Master Data Governance

must be given close scrutiny – not only to contain costs, but to insure success and

sustainability of this vital corporate initiative. ” Aaron Zornes, Chief Research Officer, The MDM Institute

“Go Governance, Go Early” A Practical Guide to Master Data Governance Solutions for the Large Enterprise



TABLE OF CONTENTS

Executive Summary ............................................................................................................. 2

Business Benefits of Master Data Governance ................................................................. 4

Understanding the “Top 10” Evaluation Criteria for MDG Solutions ............................... 7

Methodology ....................................................................................................................................................................... 7 Data Exploration/Profiling ................................................................................................................................................... 8 Data Model, Policy Model & Business Glossary Management ........................................................................................... 8 Rules/Policy Management .................................................................................................................................................. 8 Decision Rights Management ............................................................................................................................................ 9 MDM Hub Integration ......................................................................................................................................................... 9 Enterprise Application Integration....................................................................................................................................... 9 Multi-Level, Role-Based Security ....................................................................................................................................... 9 Integrated Metrics............................................................................................................................................................. 10 E2E Data Lifecycle Support ............................................................................................................................................. 10

Appraising the Market Leaders in Master Data Governance .......................................... 11

Market Overview .............................................................................................................................................................. 11

Mega vendor offerings ...................................................................................................................................................... 12 IBM Master Data Policy Manager (MDPM) .................................................................................................................. 12 Informatica Data Director (IDD) ................................................................................................................................... 12 Oracle Data Governance Manager (DGM) .................................................................................................................. 12 SAP Master Data Governance (MDG) ......................................................................................................................... 13

Boutique / Best-of-Breed Offerings .................................................................................................................................. 13 Critique of Current Master Data Governance Solutions ................................................................................................... 16

CASE STUDY: Master Data Governance at Merck KGaA ................................................ 17

Bottom Line ........................................................................................................................ 18

Appendix A - About the MDM Institute ............................................................................. 19

LIST OF FIGURES

Figure 1 – "Top 10" Evaluation Criteria for MDG Solutions ............................................. 7

Figure 2 – "Top 15" Currently Marketed MDG Solutions ................................................ 12


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Executive Summary Data is an important asset to most companies and leveraging that data properly can result in operational and IT cost savings as well as drive business growth. Furthermore, managing and governing strategic data assets is foundational to a service-oriented architecture (SOA), which in turn facilitates business process management (BPM). Master Data Management (MDM) provides a trusted, consistent view of key information assets across the enterprise – ranging from “customers,” “products,” and “suppliers” to “locations” and more. In large corporations, MDM is becoming a business transformation strategy as the cornerstone of every critical business process and business decision. MDM is an enticing proposition for many enterprises but to achieve these results, a proper “Master Data Governance" strategy must be in place. When deploying MDM, a proper Data Governance discipline should consider the business drivers, project scope, roles and people filling each role, policies and procedures, data quality, inheritability, social norms, and the business operating model. Master Data Governance (MDG) is more than a single product or process, rather, it is an ecosystem of products, processes, people, and information. Based on recognition of issues at hand, an improving economy, and increasing regulatory requirements, businesses are now recognizing the opportunity to take a more strategic view of enterprise Data Governance. Clearly, MDM and MDG are codependent/interdependent. During 2012-15, market-leading enterprises must invest upfront in MDG to realize MDM sustainability and ROI. Master Data Governance is a major IT initiative being undertaken by a large number of market-leading global 5000 enterprises. Both as an IT discipline and an integrated set of technology solutions, MDM continues to evolve at a rapid pace. Research analysts at the MDM Institute annually produce a set of ten milestones to help global 5000 enterprises focus efforts for their own large-scale, mission-critical MDM projects. For software solution evaluation purposes, the MDM Institute provides a set of evaluation criteria that provide a framework of feature/functions we see in increasingly wide practice at IT organizations tasked with evaluating charting and executing their enterprise's MDG programs.

Master Data Governance Survey Finding (2H2012

Over 90% of large enterprises surveyed report that the “top 5” evaluation criteria for selecting an implementation partner (consultancy) for MDM programs includes "Extensible Data Governance Methodology & Accelerators (91%)".

Source: The MDM Institute


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This MDM Institute Market Pulse™ report provides a synopsis of:

Why MDG is essential to the success of your MDM programs

How an enterprise might evaluate the capabilities of potential MDG solutions (software and implementation partners)

An overview of the MDG market landscape to identify the market leaders as well as a look at the new technologies expected to dramatically influence and shape the market during the next 1-3 years

Our gratitude and special thanks go out to those early adopters and industry leaders who shared their insights with us as we captured the “best practices” of these pioneers. Specifically, during 2H2012, in-depth survey interviews were held with IT management of 50+ North American, European and Asia-Pacific MDG programs. All surveyed organizations had annual revenues in excess of US$1 billion. Based on attendee data from recent MDM & Data Governance Summit conferences held during 2012 (London, New York City, San Francisco, Singapore, Sydney, Tokyo, Toronto) the data from an additional 1,500 organizations were reviewed for their experiences in engaging consultancies for the multiple phases of MDG.

During 2012-13, most enterprises will struggle with cross-enterprise Data Governance scope as they initially focus on customer, vendor, or product. Moreover, in our experience we increasingly find that enterprise-level MDG that includes the entire master data lifecycle will be mandated as a core phase zero or phase one deliverable of most large-scale MDM projects during 2012-13

Through 2013, major SIs and boutique consultancies will focus on productizing their Data Governance frameworks while MDM software providers concurrently struggle to link governance process with process hub technologies (i.e. unify master data management and business process management). In our experience, Global 5000 size enterprises will continue to struggle to evolve enterprise MDG both in cost-effective and practical ways that shift from “passive” to “active” Data Governance modes.

BOTTOM LINE: Given the substantial investment businesses undertake with their MDM programs, Master Data Governance is clearly necessary not only to contain costs, but also to insure success and sustainability of this key corporate initiative.


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Business Benefits of Master Data Governance Based on recognition of issues at hand, an improving economy, and increasing regulatory requirements, businesses are now recognizing the opportunity to take a more strategic view of MDG. Industry pundits agree that market demand for ‘Data Governance’ accelerators and services will explode during 2013-14.

Increasingly, enterprises have determined that “You can't protect data if you don't know what it is worth.” To know what it is worth, you have to know where it is, how it is used, and where and when to integrate and federate it. In short, it is not possible to evaluate data from a business perspective if you don't have a commanding or oversight-level IT perspective.

“Master Data Management” – i.e., getting information that can be trusted to the right people and processes at the right time – is thus an essential ingredient of the IT and business processes collectively known as Data Governance.

Once the value of corporate data is determined, the enterprise needs to calculate the probability for risk in a business process. When management understands the value of data and the probability of risk, it is then possible to evaluate how much to spend to protect and manage it, as well as where investments should be made in adequate controls.

This risk assessment is the basis of modern underwriting and increasingly a focus of IT portfolio management as businesses look to manage IT as assets – while understanding costs, risks, and controls. Doing this systematically requires a combination of organizational structures, business processes, and software technologies.

In other words, management of corporate data assets requires an “MDG blueprint” for:

Data quality Information integration Business intelligence

Therefore, a best practice within companies successfully implementing MDG is the collaboration between IT management and business leadership to design and refine “future state” business processes associated with Data Governance commitments. Moreover, a strong MDG function is integral to delivering reliable and usable decision-making and operational execution. Such an MDG function can help enterprises avoid these symptoms of poorly executing IT organizations:

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, and pricing masters Poor data accuracy within and across business areas


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Line-of-business-focused data with inefficient or nonexistent ability to leverage information assets across lines of business (LOBs)

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

With an operationalized MDG program, enterprises are more likely to benefit from:

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

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

Rapid cross-business implementation of new application solutions requiring shared access to master data

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

Continuous data quality improvement as data quality processes are embedded upstream rather than downstream

Increased synergy between horizontal business functions via cross- business data usage – e.g., each LOB is able to cross-sell and upsell its products to the other LOBs’ customers

Based on recognition of the current lack of focus on managing master data as a corporate asset, an improving economy, and ever-increasing regulatory requirements, companies are now clearly identifying an opportunity to take a more strategic view of MDG as both a tactical and a strategic investment.

One of the greatest challenges in MDM is the political arena (“governance”) which necessarily accelerates or brakes the critical momentum of both tactical and enterprise MDM. While tactical MDM marts may be successful via judicious efforts of data stewards who focus on the data quality of a singular domain, when the business utilization of master data expands across departments and lines of business then the governance framework is essential. Each and every consuming and producing organization has a duty or role in the governance of master data. For example, once it is determined that master data is “a corporate asset” then that data must be protected across its life cycle from creation/capture through its retirement including such critical issues as accessibility and compliance.

Unfortunately most of the marketed MDM solutions do not adequately address this formal requirement. And to make matters worse, the vendor community has engaged in its own "scope creep" by usurping and permutating every possible variance of "passive", "active", "proactive" and "integrated" when it comes to marketing adjectives for their data governance offerings.


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In fact, most solution providers still will point to their data steward console as the acme of their Data Governance capabilities. In reality, what’s needed are formal processes, assisted by workflow software, to enable formalized decision making, documentation, and delegation, regarding the rules rendered as part of the end-to-end (E2E) governance lifecycle. Another gaping hole in Data Governance capabilities of the majority of MDM vendors is their inability to directly store and execute such governance-generated procedures as part of the MDM logic that controls the software which in turn should enforce the governance. Effective and sustainable master data governance mandates the integration of people, process, and technologies via a formalized framework. These formal structures are inevitable as they are the key enablers of MDG policy functions – much more so than the

current generation of manual, paper-based methodologies and accelerator/frameworks.

A quick canvas of the marketing terminology currently in wide use will more or less provide the below descriptive terms:

1. Data Governance -- The formal orchestration of people, process, and technology to enable an organization to leverage data as an enterprise asset across different lines-of-business and IT systems.

2. Passive Data Governance -- Data steward consoles and other reactive data management capabilities focused on after-the-fact data compliance; often batch-like and not integrated with MDM.

3. Active Data Governance -- Metrics-enabled, upstream data policy enablement; replaces manual data admin processes with role-based, real-time subject matter expert (SME) empowerment.

4. Proactive Data Governance -- Metrics-driven, crowd-sourced capability for business users and IT to actively control their shared data across different lines of business and IT systems.

The MDG market is actually just beginning its momentum toward modest adoption and penetration. During the next five years, we will also see buyer behavior migrating from point products (i.e., to address customer or product MDG only) to enterprise data governance (supporting multiple entities/data domains such as customer, product, supplier, location, price, etc.) — and, to a lesser extent, from software products to hosted/managed services (cloud-based software as a hosted service). Concurrently, the mega application package vendors will increasingly bundle in industry- and application-specific operational MDG offerings.

"Passive-Aggressive" Data Governance

Vendors and other solution providers who market one version (typically "active" data governance) but deliver a different version (typically "passive" data governance) have provided us the opportunity to coin yet another descriptive term for such offerings.

In the world-at-large, when a person says one thing to you, yet does another it is generally known as "passive-aggressive behavior". Hence our term "Passive-Aggressive" Data Governance for many of the currently marketed MDG solutions.



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Understanding the “Top 10” Evaluation Criteria for MDG Solutions Other than the soft criteria for evaluating software solution providers, such as reputation, ease of doing business, etc., the more important criteria for evaluating MDG solutions is the feature/functionality set. This section's discussion of the "top 10" evaluation criteria for such products is derived from reviews of and discussions with our MDM Institute's advisory council members as they executed their MDG evaluation processes during 2012. These organizations, along with 1,800+ enterprises who attended the MDM & Data Governance Summits during 2012, shared their experiences and in evaluating such solutions -- ranging from mega vendor offerings (IBM, Informatica, Oracle, SAP) to specialty vendors (Collibra, Kalido, Orchestra Networks, etc.). The cardinal order of the evaluation criteria is structured along the lines of the typical MDG evaluation process -- but does not represent the relative importance of the criteria. Organizations need to prioritize and weight the criteria as per their specific requirements.

One key observation is that the market is eager to conflate the terms "data stewardship and "data governance" – e.g., the act of enforcing policies vs. the act of making the policies. Examples of products focused on stewardship include Informatica IDD and Oracle Data Governance Manager. Examples of MDG products focused on policy include Collibra and Kalido-- where a further distinction can be made with Collibra excelling in the Data Model/Policy Model category whereas Kalido’s strength is In Rules and Policy Management.

Methodology

The purpose of a methodology is to provide systemic rigor in designing, measuring and managing processes, in this case, the lifecycle of master data as an enterprise asset. Ideally, an MDG solution provider provides accelerators in addition to pre-defined process and data models to help kickstart (or map to existing) governance processes and related organizational structures. Such a methodology should support less- or more-centralized functions, as well as distributed or federated governance models. In relation to evaluation criteria #5 (decision rights management), the methodology can also provide standard templates for agreement approval, etc.

Criticism of most currently marketed MDG solutions include the observation that there is a mismatch of applying project-oriented methodology rather than asset-focused methodology.

Figure 1-

“Top 10” Evaluation Criteria for MDG Solutions

1. Methodology

2. Data exploration/profiling

3. Data model, policy model & business glossary management

4. Rules/policy management

5. Decision rights management

6. MDM hub integration

7. Enterprise application integration

8. Multi-level, role-based security

9. Integrated metrics

10. E2E data lifecycle support


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

#4 – Evolve key stakeholders from “data ownership” to “data stewardship”

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



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That is, current MDG solutions' methodologies are missing the "asset" aspect of data, i.e. cost, decaying value, ROI for cleansing data, etc. Another critique or current MDG solutions is that such frameworks are not addressing the “community” aspect of shared asset development – e.g. wikis for global corporate business vocabulary, etc.

In addition, all too often our clients complain that the top tier consultancies overly focus on "maturity assessments" as their methodology wherein a busload of freshly indoctrinated MBA school graduates are dropped off at the front door to document "how bad things are" via "assessment by inquisition".

Data Exploration/Profiling

MDG does benefit from traditional data profiling in that it is a necessary step to inventory existing data assets. In addition, however, MDG profiling also is different from traditional data profiling in that is not solely focused on "structure" of sources, but is also focused on the "state" of the data. For example, how well are data matching rules executing. Another consideration is the need to map processes as well as data. The output should provide a visual and hierarchic mapping framework of not just what are the systems, but also what business processes do they support. Lastly, such data profiling capabilities should also capture and measure lineage.

Data Model, Policy Model & Business Glossary Management

As data hubs are increasingly recognized as policy hubs, and business process management (BPM) is increasingly integrated with MDM, it is important to map sources, targets and processes across departments, lines-of-business, and corporate functions. Such a policy-centric approach to governance of data model management would allow for a single representation to track lineage. Many enterprises have already marshaled a number of IT resources such as data models, BPM models and business glossaries to improve the quality of master data and related business processes. One such typical "use case" is where a Pharmaceutical firm creates a master physician list whereby the business needs to consolidate syndicated data to manage subscription costs. Business glossaries are also critical to the enablement of standard reference data (taxonomies) to drive key business processes and application logic. Errors or omissions in reference data can have a major negative and multiplicative business impact. Mismatches in reference data impact on data quality affect the integrity of BI reports and also are a common source of application integration failure.

Rules/Policy Management

While many MDM software vendors have some notion of MDG innately enabled; the leading vanguard of MDG vendors is focused on policy, policy, policy. With MDG's policy-centric approach, there exists a higher order for definition and compliance which exists above the data model's simpler set of rules such as whether a field is required or not. Increasingly, such business rules are exported into the MDM hub to manage the policies in real-time. For MDM to succeed and provide sustainable business value, such a top-down approach is necessary. This in turn enables monitoring and measurements


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to ensure compliance and identify Data Quality problems before master data is published for enterprise use.

Decision Rights Management

An MDG solution must enable and enhance the organizational aspects of governance by enabling and documenting the debating, escalating, arbitrating, etc. of data policy management by the numerous styles of organizational decision making -- ranging from data stewards/trustees as highly empowered subject matter experts (SMEs) to full blown Data Governance councils and committees. The MDG solution should provide templates for management of such decision-making processes while enabling and enforcing role-based participation and responsibilities.

MDM Hub Integration

As MDM vendors increasingly deliver innate MDG-like workflows, for certain vendors therefore, integration is a moot point as they are one and the same platform. Many MDM solutions still, however, provide basically a data steward console UI which typically is a one-way push to export Data Quality rules, not policies, into the MDM hub. Such integration must provide for staging of batch, offline data to transform "low quality" master data into "high quality" published master data

Enterprise Application Integration

MDG policy management goes far beyond MDM policies and even beyond MDM in practice as certain MDG solutions derive their extended functionality from the enterprise content management (ECM) market and have strong capabilities for document/email management, etc. Yet other MDG solutions (especially in the capital markets) provide straight-through-processing (STP) capable performance and/or OLCP (on-line complex processing) capabilities to be integrated. In any case, MDG-managed master data must also be shared with the rest of IT fabric such as other enterprise OLTP systems and even departmental analytical applications.

Multi-Level, Role-Based Security

Given that there will be multiple producers and consumers of the various levels of Data Quality rules being created and managed by an MDG solution, there must also be support for comprehensive audit trails (access, update, etc.) More advanced MDG tools make distinction between the governance and stewardship functions as these are two different processes undertaken by two different sets of people: (1) policy-level governance, and (2) instance-level stewardship. Specifically, data stewards can be process-oriented over data model (e.g., Capgemini BPO, Kalido DGD).


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Integrated Metrics

Essentially, there are three key measurement areas: (1) Business, (2) Technical, and (3) Philosophical.

Philosophical metrics - As an organization tracks its maturity level movement against certain maturity models (SEI, Gartner, IBM Information Governance, SAS/DataFlux)

Technical/IT metrics - Measured directly out of the MDM hub and its related subsystems (data quality tools, etc.) where quality level of objects (completeness, accuracy, etc.) are measured; this can also be extended into "process metrics" not just "data metrics"; for example, to measure stewardship processes as well

Business metrics - An MDG solution should measure the business context, e.g., reduction in Days Sales Outstanding (DSO); whereas a single technical metric could be used in multiple different business processes

Ideally, an MDG solution will not only provide such diverse metricsbut actually be centered on the definition, administration and execution of such metrics. Furthermore, the notion of round-trip metrics (feedback loop) should be provided. For example, once an MDM hub produces its set of master data, it must be monitored and measured whether certain downstream systems violate what the MDM system is enforcing. In a proactive integrated metrics scenario, MDG solutions should require downstream systems to feed metrics back to match against the original rules.

E2E Data Lifecycle Support

One of the prime directives for an MDG solution is to recognizethat corporate information is an "asset" and needs to be treated so, Therefore, an MDG product should provide for systemic end-to-end, full lifecycle accountability of information as an asset -- including new account creation, onboarding, promotion, retirement, and redaction. Any changed master data must be fully audited, and checked to see whether it should be published for consumption. Further, the policy-centric orientation of MDG mandates that policies monitor master data use not just in the MDM hub but also in other applications. For example, information asset retirement should be managed at the MDG perspective, not just the MDM hub level.


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Appraising the Market Leaders in Master Data Governance

Market Overview

Prior to the release of commercial-off-the-shelf solutions, organizations that desired enterprise-strength Data Quality/Governance as part of their MDM programs relied upon consultancies for such critical capabilities. As a result, major systems integrators (SIs)such as Accenture, Deloitte, Tata, and Wipro developed very significant skills and pools of staff to develop custom MDG frameworks to solve these challenges. Concurrently, a major specialty market for boutique consultancies also thrived which in turn helped drive the growth of firms such as BackOffice Associates, Cognizant, EMC Consulting, and Utopia while also spawning upstarts such as Black Watch Data, DATUM, and eCenta. The vast majority of SIs developed a Data Governance practice (e.g., HCL Axon, iGATE Patni, L&T Infotech, Mahindra Satyam, etc.). Despite the costs and risks associated with such custom MDG frameworks, the market thrived and the vast majority of the purchasers were very satisfied. With the formal delivery of MDG solutions such as IBM's Master Data Policy Manager, Informatica's Data Director and SAP's Master Data Governance for Finance, these consultancies had to rapidly remake themselves as "value add" contributors to the increasing install base of commercial MDG sites. During 2012-13, the market will modestly but broadly embrace the mega vendors' MDG solutions1 which will enable SIs and consultancies to continue marketing their custom solutions, while concurrently building out offerings layered upon the mega vendor MDG platforms (e.g., BackOffice Associates and Utopia providing add-on capabilities to SAP MDG-F/V/S, etc.). Concurrently, heterogeneous MDG software solution providers (not aligned with a specific mega vendor MDM or MDG platform) such as Ataccama, Allen Systems Group, Collibra, Kalido, and SAS DataFlux will thrive in their respective industry-specific or use case-oriented markets. During the modest freeze in MDM procurements during the Global Financial Crisis of 2008-10, we observed that while enterprises were slow to fund multi-million dollar MDM programs, they were not slow in funding $250K-$500K Data Governance initiatives. This provided a great incentive and fillip to MDM software vendors and consultancies to rapidly re-badge and re-orient their delivery capabilities to address this market both as a revenue opportunity and as a survival strategy during the lean quarters of the recession.

The next section of this study provides capsule descriptions of the four mega vendors offerings as well as the next tier of the top 15 software vendor and consultancy offerings.

1 Based on our conversations with SAP customers who attended the ASUG Data Governance SIG

meeting in Houston, as well as meetings at SAPPHIRE EMEA in Madrid 2H2012, we believe there are already 200+ SAP MDG in various stages of implementation.


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Current Market Offerings

Mega vendor offerings

IBM Master Data Policy Manager (MDPM)

During 2H2012, IBM shipped a family of active Data Governance products which build upon the 2H2011 beta testing of its Master Data Policy Manager solution. MDPM provides a common Data Governance front-end for both IBM MDM Server Standard Edition (formerly Initiate Systems Master Data Services) and IBM MDM Server Advanced Edition (formerly WebSphere Customer Center). The focus of MDPM is to help enterprises establish and sustain effective operations of Data Governance

councils with the primary focus on MDM data hub implementations. MDPM also provides proactive policy-driven data stewardship on such MDM initiatives and MDM-powered projects and programs. Additionally, MDPM will be used by IBM GBS/BAO service organizations to help IBM customers implement MDG programs to achieve high levels of Data Governance and stewardship maturity (Levels 4 and 5) as defined by IBM Information Governance Council.

Informatica Data Director (IDD)

Informatica offers a separately-priced, passive Data Governance capability which is descended from the prior Siperian Business Data Director. Informatica Data Director (IDD) is cited as good for fundamental MDM administration as data steward console due to its task-oriented and business process-driven approach. While optimized around data quality anomalies, it does provide access to history and data lineage as well. As a business user interface for managing and monitoring master data, it provides a flexible security model but is critiqued for allowing users to unduly degrade MDM hub performance. note also that Informatica’s data profiling solution is not IDD, but rather IDQ; also their E2E data lifecycle solution is the Informatica Application Information Lifecycle Management (ILM).

Oracle Data Governance Manager (DGM)

Oracle's standalone offerings for Data Governance are passive products which primarily provide metrics for the data steward to manage Data Quality of Oracle MDM customer hubs. It is designed to simplify and streamline data stewardship tasks such as analyze hub technical metrics, perform merge/unmerge, etc. Oracle DGM also links to reference documents to view data definitions, etc. and also provides a dashboard to access data

Figure 2-

“Top 15” Currently Marketed MDG Solutions

1. BackOffice Associates

2. Cognizant Governance-in-a-Box

3. Collibra

4. Datum

5. IBM Master Data Policy Manager

6. Informatica Data Director

7. Information Builders

8. Kalido Data Governance Director

9. Oracle Data Governance Manager

10. Orchestra Networks EBX

11. Platon Insight

12. SAP Master Data Governance

13. SAS DataFlux

14. Software AG WebMethods OneData

15. Utopia


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

#4 – Evolve key stakeholders from “data ownership” to “data stewardship”

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



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remediation tools such as Oracle's Data Watch and Repair monitoring tool. It is important to note that as Oracle's Fusion family of applications come to market, each of the applications will increasingly incorporate workflows to support the notion of Data Governance-driven Data Quality within those specific applications-- both horizontal (e.g., enterprise performance management, human resource management) and vertical (e.g., higher education, high tech manufacturing). Additionally, the cross-application hubs such as Fusion Customer Hub and Fusion Product Hub already incorporate nuances of Data Governance workflows-- however, these next generation Oracle MDM hubs are just beginning to roll out and very slowly at that.

SAP Master Data Governance (MDG)

SAP MDG-X (where "X" is Customer, Finance, Materials, or Vendors) is an integrated and active Data Governance application -- via its governance capabilities for creation, maintenance and replication of master data "upfront" (upstream) of line-of-business applications. Specifically, SAP MDG provides out-of-the-box, domain-specific governance to centrally create, change and distribute master data across the entire SAP landscape -- with a focus on the SAP Business Suite. SAP MDG is an MDM application purpose-built and natively integrated with key SAP solutions. While such "upfront" governance of SAP MDG differs from “reactive” governance (e.g., consolidation and/or harmonization governance models) both are often needed within an organization's landscape for different use cases. Currently SAP MDG is offered for customer, financial, material, and vendor master data with "reference data" on the road map..

Boutique / Best-of-Breed Offerings

BackOffice Associates

BackOffice Associates is very well known for SAP-specific Data Migration, Data Quality and Data Governance capabilities using purpose-built tools, proven methodology and 380+ SAP expert consultants. Although the company has been delivering MDG solutions to their clients since 2005 the company was slow to evolve into a software company. As a recent SAP OEM partner, BackOffice has fully embraced MDG and is collaboratively working with SAP product and field teams to bring these innovations to market as well as actively engaging on joint sales opportunities for SAP MDG and BackOffice content and tools. Specifically, BackOffice is planning to bring its experience, content. and reference libraries for complex business processes as new service offerings to combine with SAP choices -- most notably SAP MDG, Information Steward and Data Services. BackOffice's Active Data Governance is a 7-year mature application suite that has overlaps with elements of SAP MDG. As an SAP Premier Consulting and Services partner, BOA is in the process of re-aligning their product strategy to complement SAP MDG as a family of more pro-active Data Governance capabilities.

Cognizant Governance-in-a-Box

Cognizant Technology Solutions is a premier systems integrator with demonstrated leadership in the areas of MDM and Data Governance with over 100 implementations to its credit. Cognizant's Governance-in-a-Box eases the process of defining governance


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and maturity models, SOPs and metrics to measure Data Governance. Since its launch in 2009, Governance-in-a-Box has expanded to address the needs of life sciences, retail, banking, healthcare and insurance. Cognizant has also implemented joint solutions with leading Data Governance vendor applications such as Collibra and SAP, among others.

Collibra

Collibra is a software company focused solely on Data Governance. Its Data Governance Center software provides all the necessary components needed for the implementation of a Data Governance program. Typically, Collibra clients begin with a business glossary (business terms and definitions) and then evolve this towards a complete data stewardship solution by linking up with reference data, metadata, policies, rules, issue management, and (ultimately) master data. The business outcome goal is to install a collaborative governance process, based on roles and responsibilities to involve both business and IT, which captures, reviews, approves and publishes “artifacts” and "policies". Collibra is the sole vendor that is only active in the Data Governance domain and as such is agnostic in its integrations with other MDM, Data Quality and Metadata vendors. Currently, Collibra provides out of the box integrations with IBM InfoSphere MDM and Informatica Metadata Manager (IMM), with more integrations on the roadmap. In 1H2012, Collibra partnered with Cognizant to integrate Data Governance Center with Cognizant's Data Governance-in-a-Box.

DATUM

DATUM is a small independent consultancy that provides a great set up for SAP Professional Services and others to execute on enterprise MDG strategies. By framing the customer's expectations into the business cycle that Data Governance can fit into, DATUM provides an elevated awareness of the risks while enhancing communications across the lines-of-business and IT that generates results. By focusing on high-level Data Governance strategy and train-the-trainer accelerators, DATUM is well known for providing incremental wins without over-burdening the organization with a hefty ROI exercise based on inflated concepts of governance that break the bank and sour the mood. Like other consultancies that have previously developed Data Quality and Data Governance processes for SAP ERP and SAP NW MDM, DATUM is also focused now on leveraging the lifting tide that SAP MDG will provide for a very large number of SAP customers with the introduction of MDG design capabilities in the form of their Information Value Management (IVM) platform.

Information Builders Inc.

Information Builders Inc. (IBI) is a mid-size software firm (US$300M+ revenues)with a large customer base that looks to IBI for "late majority" access to software trends. Currently, IBI is building and OEMing various piece parts that comprise a passive framework for Data Governance-- including a Data Steward console, Data Profiling software, and integrated frameworks for both Data Quality (DQ Center) and MDM (MDM Center).


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Kalido

Kalido is a software firm best known for its Dynamic Information Warehouse and MDM software. Data Governance Director is its offering to manage data quality policies by operationalizing Data Governance processes and measurements. Primarily sold into the existing Kalido customer base of 150+ global businesses, Kalido’s “Roles and Responsibilities” MDG engine provides process owners a framework to align policies with specific organizational roles and their responsibilities at each stage of the data lifecycle.

Orchestra Networks EBX

As with other 4th generation MDM platforms, EBX provides integrated pro-active Data Governance processes. Primarily focused on reference data management (RDM), EBX has

architectural constraints that limit its use as a high-end transactional MDM solution, but provide a well integrated solution for other use cases.

Platon Insight

A large Nordic consultancy that has modestly gone global by specializing in Business Intelligence. As one of the more successful agnostic Data Governance methodologies, Platon Insight unifies MDM, Data Quality, and Data Governance

SAS Institute DataFlux

Using the DataFlux brand, SAS Institute offers a graduated approach to MDM which offers three entry points: Data Quality, Data Governance and MDM. This phased implementation option enables enterprises to start small and expand their requirements as they proceed. The browser-based Data Governance and data stewardship console provide for business glossary, reference data, SAS's DataFlux Data Governance also can provide high-quality data to serve as the foundation for SAS Business Analytics-- such as SAS Customer Intelligence, SAS Fraud Management, SAS Risk Management and others. Additionally, SAS's prior acquisition of Baseline Consulting provides a core group of 20+ associates who have solid Data Governance strategy development credentials.

Software AG WebMethods OneData

Well known for its WebMethods business process management (BPM) capabilities and ARIS business process modeling framework for SAP environments, Software AG also acquired OneData in 2010-- a 4th generation MDM platform with integrated Data Governance workflows. Primarily focused on reference data use case, OneData is planned

to provide an integrated BPM solution such that this may be arguably one of the first integrated BPM/MDM platforms. Already this software solution provides a common front-end and workflows for designing shared data and process services.

Utopia

Utopia's 800+ consultants focus on Data Migrations for the SAP market with an emphasis on their Enterprise Data Lifecycle Management (EDLM) which provides a full end-to-end roadmap with multiple entry points-- either as a custom MDG framework or as an add-on to


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SAP's own MDG solution. Utopia's 100+ templates and workshops were initially built on SAP Data Services (formerly Business Objects Data Integration ETL) but are being updated to support current SAP solutions such as HANA and hosted MDG (SaaS, cloud-based). This tactical approach to MDG provides a popular means to repurpose rules and logic for Data Migration cleansing as part of a "Real-Time Data Governance" solution.

Critique of Current Master Data Governance Solutions

Due to the historical lack of commercial off-the-shelf software to support MDG programs, organizations either: (a) built their own processes using off-the-shelf components such as BPM workflow (Microsoft SharePoint) and hierarchy managers (Oracle Hyperion Data Relationship Manager), or (b) turned to consultancies or systems integrators to design and build custom MDG frameworks.

During the past 12-18 months, however, a number of software vendors have begun to increase the MDG capabilities of their Data Steward consoles or otherwise bring to market more robust MDG capabilities aligned with commercial MDM software.

As a result, the market momentum is now in favor off commercial-off-the-shelf software solutions for MDG becoming more broadly available and increasingly dictated within MDM RFPs.

"Manual" Data Governance is error prone, time-consuming and unable to ensure compliance or measure business impact. Regardless whether a custom-built MDG solution is crafted using low-level or high-level software tools, the common fragile nature of such custom solutions is counter to the notion of robust, auditable governance capabilities. The current "gap" between most commercial offerings and the MDG requirements gathered from meetings with the MDM Institute's Customer Advisory Council can be summarized as:

Most MDG methodologies lack master data focus and do not leverage contemporary MDM hub specifics

There is a tendency to apply project-oriented methodology rather than an asset-focused methodology; overall, such methodologies missing the asset aspect of data … e.g., cost, decaying value, ROI for cleansing data, etc.

Very few current MDG solutions provide operational systemic rigor (round trip support from policy to implementation) nor E2E lifecycle support

Clearly, there currently exists a major gap or chasm between an enterprise's requirements for operational MDG and what is currently offered by the majority of currently proffered MDG solutions. "Mind the gap" is the watch phrase as organizations look to invest in "operational" MDG to drive MDM sustainability and ROI.


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CASE STUDY: Master Data Governance at Merck KGaA

Merck is the oldest pharmaceutical and chemical company in the world -- and also one of the earliest adopters of MDM with significant investments in both SAP MDM 3.0 and SAP NetWeaver MDM 7.1. As a pioneer in MDM, Merck was challenged to manage its product lifecycle data across both its global reporting systems and its localized transaction processing systems. The desired solution was to provide an oversight layer of data stewardship to manage the data quality rules and ownership. Recently, Merck decided to streamline its master data landscape as part of its strategy to become a "better run" life science business.

By adopting SAP's latest MDM family member SAP MDG for Materials (SAP MDG-M), Merck was able to leverage the SAP Materials Master (MM) data model to share common ERP rules for data quality and ownership. SAP MDG-M also provided a solid framework to extend the MM data model and to define business rules using the SAP standard business rules facility (BRF). In addition, SAP MDG-M also provided for easy creation of browser-based user interfaces to provide the workflow processes to stage Materials master data for approval and publishing into the master data hub -- i.e., REQUEST for new Material from Requestor, followed by APPROVAL from the Product Owner, ENRICHMENT from various field personnel, APPROVAL from the Business Process Owner/Approver, and lastly DISTRIBUTION (publish) by the SAP MDG-M system.

SAP MDG-M leverages the standard SAP subsystems including such flexible middleware integration connector/layer as provided by SAP Process Integrator (PI)for interfacing with other SAP subsystems via IDOCs, ALE, and XML.

Unlike many, if not most, commercial MDM solutions, Merck was able to install and configure SAP MDG-M in less than two days. Key benefits arise from the leverage of existing SAP subsystems and processes/rules within the SAP ECC platform ... something which the previous SAP NetWeaver MDM product family had difficulties with. As a comparison, Merck found the SAP MDG solution to be better suited for complex operational scenarios (versus the simpler analytical capabilities of SAP MDM). Another key advantage of SAP MDG vs. SAP NW MDM is its ability to manage data governance as a central function. Merck’s partner in designing and implementing their MDG-M solution is Camelot Management Consultants AG, a Germany-based boutique consultancy.

Future plans for the Merck rollout of its new Data Governance-driven MDM capabilities (via SAP MDG) include upgraded "process visibility" via global cross-system processes to control the complete Merck product lifecycle as well as measure end-to-end process execution. Additionally, Merck plans to measure data quality visually via the SAP Information Steward (formerly Business Objects Information Steward) dashboard capabilities. Clearly, SAP MDG excels when the requirement is for strong governance processes in an SAP master data landscape.


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Bottom Line The Master Data Governance juggernaut cannot be stopped. Data governance is in the process of moving from “de rigueur” to “de facto” to “de jure”.

Data Governance Is Becoming “De Rigueur.” During 2012, MDG will become a mainstay of large MDM projects such as "single view of the customer" initiatives. For the Global 5000, every MDM-specific RFP that we have been privy to mandates that the solutions provider formally specify a full MDG program – from program definition and skill profiles to execution and communication. Clearly, the duopoly of Data Governance and data stewardship has become not only fashionable, but also standard in large MDM projects.

Data Governance Must Become “De Facto.” As indicated by the results of our most recent survey, as well as other analyst research, an “end-to-end data lifecycle” process is the goal for data stewards to manage master data over the full lifecycle – from creating or importing customer information, to deduplicating, standardizing, validating, enhancing, and (ultimately) retiring it.

Data Governance Will Become “De Jure.” By 2013-14, director or VP level of IT professionals in market-leading Global 5000 size enterprises will be tasked with executing MDG. As governments focus more on individuals’ rights and businesses’ obligations concerning customer data, for example, we can expect increased regulations and reporting requirements. Moreover, management and regulators will want to know who creates new customer information, who has the right to do so, who can update it, who can access which views of the unified customer master data, how to merge/unmerge accounts and under what circumstances, etc. MDG will not be solely focused on the large enterprises but will increasingly be mandated upon all organizations that manage data as a corporate asset. Not only are data quality stewards or sales territory managers on the hook for the veracity of the data they manage, but also the executive ranks are aware of fines and other penalties that await those that are running a large business poorly. Clearly, MDG will become an ever greater legal and financial liability for those large businesses that do not formalize their governance processes and

positions.

Based on recognition of issues at hand, an improving economy, and increasing regulatory requirements, enterprises are now recognizing the opportunity to take a more strategic view of Master Data Governance with the understanding that MDM and Master Data Governance are codependent/interdependent.

During 2012-15, market-leading enterprises must invest upfront in Master Data Governance to achieve MDM sustainability and ROI.

MDM Institute Customer Advisory Council

Recommendation

Don’t settle for “passive” (downstream) Data Governance but rather demand “proactive” (upstream) Data Governance and avoid "passive-aggressive" vendors' offerings.


http://www.m-w.com/dictionary/de%20rigueur

http://www.m-w.com/dictionary/duopoly

http://www.m-w.com/dictionary/De%20Facto

http://www.m-w.com/dictionary/De%20Jure


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Appendix A - About the MDM Institute The MDM Institute is the world's leading research and advisory consultancy exclusively focused on master data management (MDM). As chief research officer, Aaron Zornes delivers the technology-related insight necessary for its clients to make the right decisions in their use of MDM, customer data integration (CDI), reference data management (RDM) and data governance solutions to achieve their customer-centric business goals. The MDM Institute provides authoritative, independent and relevant consulting advice to senior IT leaders in corporations and government agencies, to business leaders in high-tech enterprises and professional services firms, and to technology investors. The MDM Institute delivers its research and advice to more than 60,000 clients in 10,500 distinct enterprises via Twitter, Linked In, Xing, Google+ and email newsletters. Additionally, each year more than 2,000 paid delegates attend its MDM & Data Governance Summit conference series held in London, New York City, San Francisco, Singapore, Sydney, Tokyo and Toronto (now in its seventh year). Founded in 2004, the MDM Institute is headquartered in San Francisco and has clients primarily in North America, Europe and Asia-Pacific. For more information, visit www.the-mdm-institute.com.

Aaron Zornes is chief research officer of the MDM Institute. For additional info on this topic or other MDM Institute offerings, please contact [email protected].

To provide feedback on our client's MDM initiatives we have two levels of sponsorship for IT organizations: (1) free membership (by invitation) in our MDM Institute Advisory Council providing unlimited MDM consultation by phone, and (2) free membership in our MDM Institute Business Council (survey base) which provides bi-weekly updates on key MDM trends and issues via an email newsletter.

MDM Advisory Council™ of 150 organizations who receive unlimited MDM and data governance advice to key individuals, e.g. CTOs, CIOs, and MDM project leads

MDM Business Council™ of 25,000+ Global 5000 IT executives who receive a limited distribution, bi-weekly newsletter with MDM industry updates

MDM Alert™ newsletter provides IT organizations, MDM vendors, and investors hard-hitting insights into best practices as well as market observations derived from interactions with the MDM Institute Advisory Council™ and the MDM Institute Business Council™. The intended audience includes: enterprise architects, data architects, solution architects, MDM project managers, directors of data governance, CIOs, CISOs, CTOs, chief customer officers, chief data officers, chief privacy officers, data quality managers, data stewards, and project teams responsible for MDM solutions and infrastructure.

MDM MarketPulse™ monthly survey results, e.g. budgets, success/failure rates, mindshare based on ongoing surveys of the MDM Advisory Council and the MDM Business Council

MDM Fast Track™ quarterly 1-day workshop – fee-based and rotating through the major North American, European and Asia-Pacific metropolitan areas

MDM & Data Governance Summit™ annual 3-day conference and exposition – London, New York City, San Francisco, Shanghai, Singapore, Sydney, Tokyo, and Toronto.

mailto:[email protected]

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