Master Data Management Driving Industry Convergence

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Industry Convergence

James Kobielus

Principal Analyst, Data Management April 16, 2007

Summary

Master data management (MDM) is critical to enterprise success. MDM refers to the infrastructure, tools and best practices for governance of master data sets that may be consolidated in data warehouses, or scattered across diverse databases and applications. MDM helps companies assure that business data has been generated, moved, cleansed, processed and protected according to a consistent set of policies and controls. Traditionally, the data management (DM) industry has contributed to MDM solution fragmentation by proliferating point products that address specific requirements. However, enterprise IT groups are increasingly requiring multi-application MDM solutions that can be deployed for customer data integration (CDI), product information management (PIM), supply chain management and other critical requirements.

Recognizing this demand, vendors are introducing integrated MDM solution families that offer a full range of infrastructure, tools and functionality, and can be leveraged across a broad range of master data sets. Much of the recent DM industry consolidation is driven by the need for vendors to field ever more comprehensive MDM-enabling solution portfolios. The MDM market is converging on a common multi-application reference architecture. Vendors are differentiating their MDM solution portfolios around a broad range of product and service offerings, and strategic technology and channel partnerships play a strong role in their MDM value proposition.

Current Perspective

MDM is a relatively new term for a timeless concern. It refers to the need for corporate systems of record that are authoritative, consolidated, current and internally consistent. Just as organizations need to keep precise tabs on their finances, they must also maintain current, cleansed, structured master datasets on customers, employees, suppliers,

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MDM is both a best-practices paradigm and an information technology (IT) solution category. As discussed in Current Analysis’ new MDM Solution Assessments (SAs), MDM refers to the infrastructure, tools and workflows for lifecycle governance of master reference data. Many enterprises are already implementing MDM to a degree, though they may refer to it by such near-synonyms as data warehouses (DWs), data governance (DG) or information lifecycle management (ILM). Many firms have implemented such MDM use cases as customer data integration (CDI) and PIM, under which those particular corporate master data sets are managed under policy-driven governance workflows. And many have implemented the underlying data integration (DI) technologies upon which MDM depends, such as extract transform load (ETL), enterprise information integration (EII), enterprise application integration (EAI), change data capture (CDC) and data synchronization. The great promise of MDM is that it enables organizations to maintain a “single version of the truth.” In practice, what this usually means is that a company has chosen to populate one or more central databases—sometimes called DWs, data hubs or data marts--with the latest official versions of records of particular types, such as customer profiles and product engineering configurations. Prior to consolidation into the designated repositories, master data is usually validated, matched, corrected, enhanced, and otherwise scrubbed in accordance with corporate-standard “data governance” policies. In the process, it becomes the operational “reference data”—essentially, the gospel—upon which business decisions depend. MDM is also a key component of corporate governance, risk, and compliance (GRC) management, enabling organizations to assure that data has been generated, vetted, processed, protected and transmitted according to a consistent set of policies and controls. Unfortunately, the reality of MDM in today’s corporate environments is a bit messier than that. In the hyper-siloed real world of enterprise networking, master data is scattered all over creation and subjected to a fragmented, inconsistent, rickety set of manual and automated processes. Multiple versions of the same operational data may and often do permeate many organizations, hurting their ability to serve customers, manage the external supply chain or prove to the authorities that they are complying with regulatory mandates. To the extent that enterprises are implementing MDM today, it is often in conjunction with CDI and single-subject data hubs, rather than as a general-purpose infrastructure for managing all classes of reference data.

Traditionally, the DM industry has contributed to MDM solution fragmentation by proliferating point products that address specific requirements. For decades, DM vendors have focused on becoming best-of-breed in specific niches, such as database management systems (DBMS), online analytical processing (OLAP), business intelligence (BI), corporate performance management (CPM), DW, DQ, EII and ETL. In the past few years, however, an increasing number of vendors have introduced integrated solution families that offer a full range of MDM infrastructure, tools and functionality. MDM solution providers are offering multifunctional, flexible, extensible solution portfolios that support current and evolving requirements, and that play well in the SOA universe.

Vendor consolidation will continue as the top-tier DM software providers assemble best-of-breed components into comprehensive MDM suites that leverage common metadata and middleware. DM vendors that are already well along in assembling these MDM suites (through acquisitions, partnering, and/or internal development) include IBM, Oracle/ Hyperion, Teradata, SAS Institute, TIBCO and SAP. Other major DM vendors—such as Business Objects, Informatica and Microsoft—have many of the enabling components of

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Master Data Management Driving Industry

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MDM, though they haven’t integrated them under the MDM banner. Of course, a broad range of strong MDM pure-play vendors have come along in recent years, such as Kalido, Siperian, Purisma, Initiate Systems, Cordys, Innovative Systems, Stratature, VisionWare and Orchestra Networks.

One can also expect to see the leading SOA platform and enterprise service bus (ESB) middleware vendors target the MDM space aggressively, evolving their existing DM product portfolios and engaging in strategic acquisition and partnering. Most of the leading SOA platform/middleware vendors, including IBM, Microsoft, Oracle, Progress, SAP, Software AG and TIBCO, have placed a high priority on expanding their presence in BI, DW, DQ and other components of the MDM universe. Several of them are already well-established in the DBMS and DI markets, and they can easily leverage those positions as they address customers’ evolving MDM requirements.

Some vendors are quite advanced in their efforts to assemble comprehensive SOA-based MDM suites through acquisitions, partnerships, internal development, and other means. For example, IBM already fields one of the strongest MDM suites on the market, having made several DM vendor acquisitions in recent years—most notably, EII/ETL pioneer Ascential— to build up its portfolio, while providing packaged MDM offerings targeted at the CDI and PIM markets. Oracle’s acquisition of Hyperion combines two established MDM vendors into a formidable solution provider. Also, Business Objects, a BI market leader, now fields a comprehensive MDM-enabling suite, having recently added to its portfolio critical EII, data cleansing, metadata management, data lineage assessment and data-modification impact analysis features. In 2006, it acquired Firstlogic, a leading vendor of DQ tools, stressing the importance of cleansed, consistent and accurate data to the compliance equation. Other DQ pure plays have been acquired in recent years by comprehensive DM vendors (e.g., Similarity Systems by Informatica), signaling the critical role of data profiling and cleansing to MDM. Regardless of background and strategic direction, vendors are converging on a common MDM reference architecture that includes the following key solution elements:

• DI: These consist of all tools, runtime components, and services needed to retrieve, extract, and move data from origin repositories; validate and transform the data; and deliver it to target databases, data warehouses, data marts and applications.

• DQ: These consist of all tools, runtime components and services needed to discover and profile source data; validate, de-duplicate, match, merge and cleanse that data; and enhance, enrich and augment it with additional, related data.

• DBMS: These consist of all tools, runtime components, and services needed to organize, index, store, query and administer structured data sets.

• DW: These consist of all tools, runtime components, and services necessary to consolidate structured master data into subject-oriented, integrated, non-volatile and time-variant repositories under unified governance. DW environments consolidate master data from source data stores through various DI approaches and govern its controlled distribution to various operational data stores, data marts, access databases and BI applications.

• Domain Models: These consist of all packaged master data definitions, schemas, models and objects, plus data governance infrastructure necessary to tailor an MDM or DW environment for a particular horizontal application (such as customer data integration, product information management or financial consolidation) or vertical, industry-specific deployment (such as retailing, financial services, consumer packaged goods or healthcare).

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Europe +33 (0) 1 41 14 83 14. Or visit our Web site: www.currentanalysis.com 4 • Data Modeling and Mapping: These consist of all tools necessary to create business and technical definitions of master data sets; model, design, index and cross-reference one or more semantically distinct master data sets; and define and manage hierarchies, mappings and transformations among master data sets.

• DG: This encompasses all repositories (metadata, policy etc.); collaboration environments (workflow, task management, exception handling, event-driven alerting, calendar-driven reminders, priority escalation etc.); controls (authentication, authorization, mapping/ translation, version, validation, monitoring, auditing etc.); and other tools, components and services necessary to define, approve and administer the domain models, data definitions, and DI/DQ/DW rules upon which MDM environments depend. This is sometimes known as a “data stewardship” environment.

The principal differentiators among commercial MDM solutions are:

• Functionality: An MDM solution should encompass the full range of functionality necessary for lifecycle management of master data set. It must include and/or integrate with best-of-breed solution elements in all the key categories, including DI, DQ, DW, DBMS, pre-built domain models, data modeling and mapping, and DG.

• Pre-built Domain Models: An MDM solution should include a broad range of pre-built, extensible domain models to address the leading horizontal and vertical requirements. It must include and/or integrate with prebuilt domain models for key horizontal applications such as customer data integration and product information management, and for industries such as financial services, healthcare, telecommunications and so forth.

• Development and Integration: An MDM solution should incorporate a wide range of development and integration tools and interfaces. It must be accessible through wide range of programmatic interfaces (e.g., Java, .NET) and middleware and integration connectors, standards and protocols (such as SOA/Web services). And it must support integration in downstream BI applications (i.e., for analytical MDM) and upstream line-of-business and business process management (BPM) applications (i.e., for operational MDM).

• Deployment and Administration: An MDM solution should incorporate a rich set of deployment and administration interfaces. It must be deployable on diverse platforms. It should leverage common data dictionary, metadata management, security and policy management, orchestration, monitoring, job scheduling and control, change and configuration management, reporting/auditing, resource connection, parallel processing and storage services. It should support batch and real-time operation. It should support deployment in a hub-and-spoke topology (i.e., DW as master data repository) or federated topology (i.e., source/transaction applications and databases as master data repositories). And it should be deployable into both intra-enterprise and business-to-business (B2B) MDM environments.

• Packaging and Sourcing: An MDM solution should be deliverable through flexible packaging and sourcing options. It should be available through licensed software, integrated hardware/software appliances/bundles and/or hosted services. It should also be provided through global professional services and systems integrators. And it should be provided in cost-effective solutions that are targeted at various customer requirements. SOA is an important driver of industry evolution in the MDM arena. An SOA-based MDM environment enables organizations to treat all master reference data and all corporate data DM infrastructure services as reusable functions that can be leveraged across distributed applications. DM functions, such as DI, DQ and DW, should be implemented as Web

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Convergence

services via the same WS-* standards that permeate the SOA at large. Lacking ubiquitous SOA, enterprises cannot achieve the MDM vision of a “single version of the truth” that permeates all business transactions.

To further the convergence of MDM and SOA, organizations will need to expand interoperability among the corresponding metadata management infrastructures. In the broader architectural context, metadata is the golden thread that links MDM and SOA. Metadata—in the form of WSDL service contracts, WS-Policy policies and other service attributes—sits at the heart of SOA. In an SOA context, metadata describes the structure, behavior, scope, context, reference, use and processing of any resource—including data, services, systems and application components—within a distributed environment. As such, metadata is obviously critical to lifecycle SOA governance of these resources. A unified metadata environment would allow organizations to treat their corporate MDM infrastructure services as shareable, reusable resources that can leveraged across all SOA-facing applications. In that fundamental sense, a comprehensive MDM infrastructure—one that spans diverse entities, domains, data sets, applications and deployment topologies—could serve as the backplane of an intra- or inter-organizational “semantic Web.”

However, MDM is no magic bullet for enterprise integration requirements. In practical terms, MDM is equivalent to SOA or ESB in the sense that it defines an ideal high-level architecture that can be complex, costly and difficult to implement and administer. Consequently, the notion of “shrink-wrapped MDM solutions” is a pipe dream, and professional services organizations are an essential element in the MDM delivery chain. MDM solution vendors cannot succeed in the long run unless they build extensive MDM services organizations, and also cultivate strong relationships with the consulting, systems integration (SI), value-added resellers (VARs), and other channel partners with MDM domain expertise.

Recommended Actions

Recommended Vendor Actions

• Vendors should--through strategic acquisitions, partnerships, and internal development--assemble MDM solution portfolios that encompass best-of-breed solution elements in DI, DQ, DW, DBMS, pre-built domain models, data modeling and mapping, and DG.

• Vendors should—through their professional services team and strategic consulting, SI, and other channel partners—develop a full range of pre-built, extensible domain models to serve as templates and accelerators for customer MDM deployments. Vendors’ pre-built domain models should address the leading horizontal and vertical requirements, both horizontal (e.g., CDI, PIM, financial consolidation) and vertical (financial services, healthcare and telecommunications).

• Vendors should support a wide range of development and integration interfaces in their MDM solution portfolios, especially the full range of WS-* and other standards to enable SOA-based integration across customers’ ESBs, both internal and B2B. They should also provide a rich metadata, modeling, mapping, and semantic layer to provide support a broad range of data entities, schemas, definitions, domains, catalogs and hierarchies in a common MDM infrastructure.

• Vendors should incorporate a rich set of deployment and administration options in their 

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MDM solution portfolio interfaces. Their MDM solutions should be deployed on diverse operating and hardware platforms, leverage common infrastructures and tools, and support both batch and real-time operations. Their MDM infrastructure should support deployment in a hub-and-spoke or federated, both within enterprises and across B2B environments. • Vendors should provide their MDM solutions through flexible packaging, licensing and pricing options. They should provide all or most of their MDM components through licensed software, integrated hardware/software appliances/bundles, and/or hosted services. Vendors should tailor the functionality, licensing and pricing MDM hardware, software, and service offerings for diverse customer horizontal and vertical segments, and also for enterprise and small-to-midsized business (SMB) organizations.

• Vendors should build extensive MDM professional services organizations and also cultivate strong relationships with consulting, SI, VARs and other channel partners that have the necessary MDM domain expertise for various horizontal and vertical markets.

Recommended User Actions

• Enterprises should standardize on comprehensive SOA-based MDM product suites from leading vendors while retaining the flexibility of integrating and/or federating with best-of-breed DM tools in particular niches, as necessary.

• Enterprises should evaluate MDM vendors based on their ability to provide

multifunctional, extensible product suites that support current and evolving requirements in DI, DQ, DW, DG and other key solution areas and that play well in the SOA universe. • Enterprises should evaluate MDM vendors based on their ability to provide DM functionality as both licensed software packages and on-demand services, and on diverse computing platforms, in keeping with the heterogeneous nature of most enterprise SOA/ESB environments.

• Enterprises should evaluate MDM vendors based on their ability to engage ecosystems of professional services partners to deepen their presence in the various markets to which MDM has traditionally been marketed, especially CDI and PIM.

• Enterprises should evaluate MDM solutions based on the vendor’s packaging of diverse components through pre-built domain models into templates, accelerators or bundles geared to particular use cases or deployment scenarios, such as CDI and PIM.

• Enterprises should deploy ESBs that support whatever interaction patterns—hub-and-spoke, decentralized or peer-to-peer—are consistent with the topology of their MDM environments. They should deploy traditional centralized DWs as their core enterprise MDM repositories, while at the same time retaining the flexibility of deploying their MDM applications and DG environments across distributed master data stores through an EII fabric.

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Master data management (MDM) is critical to enterprise success.

MDM refers to the infrastructure, tools and best practices for

governance of official corporate reference data that may be

scattered across diverse databases and other repositories.

MDM helps companies assure that business data has been

generated, vetted, processed, protected and transmitted

according to a consistent set of policies and controls.

Increasingly, enterprise IT groups are laying out MDM

strategies that encompass data warehouses, data quality

tools, extract transform load (ETL) and enterprise information

integration (EII) middleware, business intelligence applications,

and much more. Vendors are racing to reposition their data

management products under the MDM umbrella.

SERVICE DESCRIPTION

Delivered via our web-based platform, CurrentCOMPETE, Current Analysis’ MDM solution assessments track and analyze events, technologies and companies shaping the enterprise MDM market. Comprehensive coverage includes vendor Solution Assessments and analysis of competitor events in the marketplace, covering announcements of new product and strategies, as well as mergers and partnerships. MDM solutions are rated on the following selection criteria:

• Ability to manage master data as a “single version of the truth” that permeates all business transactions and feeds the corporate business intelligence (BI) environment.

• Ability to consolidate master data into data warehouses, data marts, and other hubs associated with particular critical data sets, such as customers, products, finances, and human resources.

• Ability to enforce a common set of policies across the MDM life cycle, allowing master data to be reused over and over within corporate-standard business intelligence applications.

• Ability to treat all master data and all corporate data management (DM) infrastructure services—such as data quality and warehousing--

as reusable resources that can be leveraged across service-oriented architectures (SOAs).

• Ability to manage master data in centralized or decentralized repositories,

SERVICE FEATURES

4 Solution Assessments of Product Portfolios

4 Industry Event Reports

4 Market Assessments

4 Advisory Reports

Master Data Management

(MDM)

Solution Assessments

Data Management MARkeT AReA

COMPANIES COVERED

4 IBM 4 NCR/Teradata 4 Oracle 4 SAS 4 Business Objects 4 TIBCO 4 Hyperion 4 SAP 4 Microsoft 4 Informatica

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• Evolution of MDM solutions to enable standards-based integration within service-oriented architecture (SOA) and enterprise service bus (ESB) environments.

• Delivery of MDM functionality as both licensed software and on-demand services, accross diverse computing platforms.

• Support for flexible MDM interaction patterns required by global enterprises, including such integration topologies as hub-and-spoke, decentralized, and peer-to-peer.

• Packaging of MDM product portfolios for deployment as data hubs in particular subject areas, including customer data integration (CDI) and product information management (PIM).

• Role of professional services in customizing MDM solutions to the needs of diverse horizontal, vertical, and compliance market segments.

MARKET SEGMENTS COVERED

Market Segment

Solution Opportunities

Business Intelligence 4 Reporting

4 Query

4 Online analytical processing

4 Dashboards

4 Scorecards

4 Data mining and predictive analytics

4 Corporate performance management Data Warehousing 4 Database management systems

4 Data marts

4 Operational data stores

4 Data governance

4 Metadata management

4 Data monitoring Data Integration 4 Extract transform load

4 Enterprise information integration

4 Event stream processing

4 Data synchronization

4 Data profiling, cleansing, and enrichment

4 Customer data integration