TRENDS. November 10, 2006 Introducing Master Data Management. by Rob Karel. Helping Business Thrive On Technology Change

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1 November 10, 2006 Introducing Master Data Management by Rob Karel TRENDS Helping Business Thrive On Technology Change

2 TRENDS Client Choice topic November 10, 2006 Introducing Master Data Management Achieving A Single Version Of The Truth This is the first document in the Master Data Management series. by Rob Karel with Chris Mines, R Ray Wang, Kyle McNabb, and Jamie Barnett EXECUTIVE SUMMARY Master data management (MDM) is a business capability enabled through the alignment of multiple information management technologies, business process improvements, and organizational commitments. MDM is much more than a single technology solution; it requires an ecosystem of technologies to allow the creation, management, and distribution of high-quality master data throughout the organization. This master data can be leveraged on both sides of your firewall wherever and whenever it s needed. Enterprise architects and information management leaders must filter through the vendor hype surrounding MDM and use a checklist that considers the current state of the master data landscape and the technology, resource, and organizational commitment required to successfully implement this business capability across it. TABLE OF CONTENTS 2 Master Data Management: Addressing Real Business Issues 3 Master Data Management Defined 6 Get Started By Scoping Your MDM Ecosystem 9 RECOMMENDATIONS Use The MDM Audit Guide To Support Your Master Data Strategy WHAT IT MEANS 10 There Is A Future For MDM; It s Just Not Here Yet NOTES & RESOURCES Forrester discussed MDM priorities, strategies, and pain points with numerous clients and vendors through the inquiry and consulting processes. Related Research Documents Trends 2006: Master Data Management March 6, 2006, Trends The Forrester Wave : Customer Data Integration, Q June 13, 2005, Tech Choices Product Information Management Leaders Emerge December 6, 2004, Tech Choices 2006, Forrester Research, Inc. All rights reserved. Forrester, Forrester Wave, WholeView 2, Technographics, and Total Economic Impact are trademarks of Forrester Research, Inc. All other trademarks are the property of their respective companies. Forrester clients may make one attributed copy or slide of each figure contained herein. Additional reproduction is strictly prohibited. For additional reproduction rights and usage information, go to Information is based on best available resources. Opinions reflect judgment at the time and are subject to change. To purchase reprints of this document, please resourcecenter@forrester.com.

3 2 Trends Introducing Master Data Management TARGET AUDIENCE Enterprise architecture professional, information and knowledge management professional RESEARCH CATALYST Clients selected this topic for Client Choice research. MASTER DATA MANAGEMENT: ADDRESSING REAL BUSINESS ISSUES When a midsize company grows big enough to capture similar customer, product, or employee information in more than one database, some employee inevitably asks the question, Which version of this data is the source of truth? Most senior executives ignore this philosophical question until the answer becomes business-critical costs are out of control, revenue opportunities are being missed, regulatory pressures are bearing down, or the firm is losing market share to its competitors. Common business issues include: Underperforming direct marketing. Duplicate, incomplete, or out-of-date customer profiles often result in direct marketing campaigns with lower than expected response rates. For example, not recognizing that your customer John R. Smith has moved three times in the past five years and also goes by the name J.R. may result in multiple direct-mail pieces targeting the same customer. At best, only one of the mailings will reach John, and the money spent on the other mailings will be wasted. At worst, multiple mailings (or s or sales calls) reach him, and he feels harassed and opts out of future marketing communication. Breakdowns in the supply chain. Outdated, inconsistent, or incomplete product data can increase the likelihood of incorrect order fulfillment, bar-code scanning pricing inaccuracies at retail points of sale, and costly overestimation of inventory requirements. For example, a tire manufacturer was having a problem with dealers and retail outlets that were often ordering outdated tires because the introduction of new products was inconsistent. The result? These incorrect orders could not be fulfilled and often led to reorders, low dealer inventory levels, and dealer frustration. This firm implemented a product information management (PIM) solution to keep multiple instances of product data in sync. Violations of consumer privacy laws. , phone, or direct-mail contact with customers who have proactively opted out of these marketing communication channels will result in negative customer satisfaction and loyalty and can result in costly state or federal penalties. Your privacy data may tell you not to market to John R. Smith, but how sure are you that the J.R. Smith customer record should still be open to communication? November 10, , Forrester Research, Inc. Reproduction Prohibited

4 Trends Introducing Master Data Management 3 Lack of business insight. A common catalyst for MDM prioritization is low confidence in the reporting and analysis coming from existing business intelligence (BI) and data warehousing (DW) systems. When your data warehouse is pulling similar information from many disparate sources, end users need a single, consolidated view of this information. If the upstream processes don t provide it, they will do it themselves very often with manual review of data pulled into spreadsheets. For example, a North American electric utility company faced regulatory and strategic pressure to optimize its power generation, transmission, and distribution assets. The firm has many separate application suites and systems that manage these assets, and its existing BI solutions were targeting tactical and departmental demands but not supporting strategic asset management requirements. This firm is now focusing its efforts on implementing a MDM capability that can effectively cross-reference its critical capital assets to support its enterprise reporting needs. Loss of market share. Companies that provide multiple services to the same customer face rising user experience expectations. Many providers already offer consolidated views of disparate accounts and activities during billing and support interactions. Examples include diversified financial service firms like USAA that provide a single-user Web site that allows their customers to manage their checking, savings, credit card, loan, home and auto insurance, and investment accounts together. In addition, communications companies like AT&T provide billing statements and account access that combine voice, Internet, data services, and local, longdistance and wireless calling plans. When the business demands information management capabilities using buzzwords like single version of truth and 360-degree view, enterprise architects and information management professionals must tackle the MDM enigma. MASTER DATA MANAGEMENT DEFINED Forrester defines MDM as a business capability enabling an organization to: 1. Identify trusted master data. MDM defines and/or derives the most trusted and unique version of important enterprise data (e.g., customer, product, employee, asset, material, location, etc.). Often, this enterprise data is captured, maintained, and used across disparate systems and business processes on both sides of an organization s firewall. 2. Leverage master data to improve business processes and decisions. MDM incorporates this master version of the data within functional business processes (sales, marketing, finance, support, etc.) that will provide direct benefit to employees, customers, partners, or other relevant stakeholders within an organization. Master data alone provides little value. Hence, anticipation of how the data will be consumed by other applications or systems within the context of a business process provides the most value. 2006, Forrester Research, Inc. Reproduction Prohibited November 10, 2006

5 4 Trends Introducing Master Data Management Technology vendors, global system integrators, and internal constituents make it difficult for enterprise architects and information management professionals to understand the scope of MDM. Technology vendors like SAP frame MDM as a software challenge to be addressed by applications, while global system integrators like Tata Consultancy Services naturally focus on the integration complexities. Enterprises may consider MDM to be an IT project, a functional initiative, or a long-term enterprise strategy, depending on who s driving the project. When driven by a direct marketing executive, MDM runs the risk of being underscoped as a customer data deduplication exercise that can be outsourced to a database marketing service provider. At the same time, an IT-driven effort may successfully create a central repository storing master data with sights only on the data warehouse and no larger vision on how to deliver the information to support business operations and improve customer experience. Enterprise architects and information management professionals must ensure that they have included the entire MDM ecosystem in their vision (see Figure 1). Upstream technologies and processes create and consume master data. Master data will only be as good as the sources of information that feed it. These systems, and the business processes developed to operate and maintain them, are not only the front line for ensuring useful raw data is captured but are also often real-time consumers of the master version of this data once it is defined and delivered by the MDM solution. There are several sources that feed an MDM solution. Enterprise applications include your enterprise resource planning (ERP), customer relationship management (CRM), supply chain management, and HR management system (HRMS). Common customer- or employee-facing Web applications include external ecommerce, customer self-service, and product registration Web sites, as well as internal employee intranet sites. External data feeds include data received from external partners such as supply chain product data and customer data from resale and distribution partners, as well as purchased content such as credit scores from Experian or corporate hierarchies from Dun & Bradstreet. The MDM core connects upstream and downstream processes. The MDM core consists of the technologies that manage both the creation of master data and the enabling technologies that ensure the effective delivery of the master data capabilities. This data management factory delivers services that collect, standardize, consolidate, aggregate, and apply business rules to create the finished product (i.e., master data or single version of truth ). These services will determine that John R. Smith at 123 North Main Street, Apartment 3 is actually the same unique customer as J.R. Smith at 123 Main Street, #3 by first standardizing the name and address data and then using a logical algorithm to recognize the match. November 10, , Forrester Research, Inc. Reproduction Prohibited

6 Trends Introducing Master Data Management 5 Many customer data integration (CDI) and PIM solutions provide a technology bundle that allows a single vendor to support much of the MDM core. 1 While the data models, data quality tools, and integration services built within these offerings cannot often be reused to solve other master data domains, CDI and PIM are effective solutions for enterprises that are only looking to solve for the creation of customer or product master data, respectively. 2 Broad software suppliers such as IBM and Oracle have effective CDI and PIM offerings but are still working to develop a more cohesive and reusable set of master data components across their offerings. Downstream technologies and processes. These are the tools that consume and package the master data to support business demand for, and usage of, this information. Analytic and BI environments continue to rely heavily upon master data. There are several kinds of downstream systems. Accessing master data via a data warehousing environment for reporting, analysis, and data mining is often a primary driver for early-phase MDM initiatives. But data warehouses are not just downstream consumers of master data; they can also contribute to the enrichment of the master data used across the enterprise. Operational data stores (ODSes) are often used when business stakeholders require master data to be made available throughout the day to support daily operations. Business intelligence tools, dashboards, and portals are just a few examples of the end-user-facing master data information delivery mechanisms. 3 Integration technologies. Data integration technologies ensure that the data can seamlessly navigate through the components listed above. Architects rely on an alphabet soup of technologies, including extract, transform and load (ETL); enterprise information integration (EII); enterprise application integration (EAI); change data capture (CDC); and application connectors and Web services that can be developed to manage the primary create, read, update, and delete (CRUD) data management capabilities. While some CDI and PIM solutions offer proprietary connectors and data management services into and out of their hubs, enterprise architects need to consider the ultimate vision for their enterprise s demand for this master data. It s extremely likely that master data will need to be accessible and delivered to a broad range of enterprise applications, data warehouses, and business services, as well as within business processes spanning multiple business functions. Building out a portfolio of data integration techniques that can support a variety of business needs is the optimal approach. Organizational change management. MDM requires focus on data governance, organization alignment, and business process optimization. The technology infrastructure introduced above is worthless without first ensuring that key roles in your organization are ready for the responsibility of maintaining a MDM capability. From a governance perspective, it begins with one or more C-level executive sponsors taking full ownership akin to a fiduciary responsibility for key data domains like customers or products. These sponsors must ensure that stewardship roles are defined throughout the organization to not only provide the business requirements for the master data capability but also to assess and improve the relevant business processes that affect the quality and usefulness of the master data. Relevant processes may include the capture, maintenance, access, and enrichment of data. 2006, Forrester Research, Inc. Reproduction Prohibited November 10, 2006

7 6 Trends Introducing Master Data Management Figure1 The MDM Ecosystem Organizational change management Upstream Data is created and consumed MDM core Downstream Data is consumed and appended Integration Source: Forrester Research, Inc. GET STARTED BY SCOPING YOUR MDM ECOSYSTEM Enterprise architects and information management professionals who are responsible for implementing a MDM capability should follow these prerequisite steps to framing any MDM initiative: 1. Define and validate the business need. Identify the specific business processes that need to be improved or enabled with master data, and map those processes both to the systems upstream and downstream and the enterprise data that is critical to supporting each process. Focus on in the people in the organization who will be benefiting and how, not only on the capabilities delivered. 2. Ensure data governance and business sponsorship. Your MDM initiative must be supported at the highest levels possible, and your executive sponsors should recognize it as a priority investment that will require ongoing oversight and management from both a business and an IT perspective it s not a one-time project. 3. Scope your unique MDM ecosystem. Take a first pass at reviewing your organization s MDM ecosystem by reviewing the systems identified in the first step when validating the business need. With the MDM ecosystem audit guide as a template, this review will allow you to identify the systems within your enterprise that you need to include in your MDM ecosystem (see Figure 2). This will be the first step in identifying where you expect your organization s master data to be sourced and consumed; what existing technology infrastructure investments, skills, and resources exist in-house; and where new technology and resource investments may be required. November 10, , Forrester Research, Inc. Reproduction Prohibited

8 Trends Introducing Master Data Management 7 For example, if the entire scope of your enterprise master data vision is to reduce direct marketing costs by eliminating duplicate customers from campaigns, then your MDM ecosystem may only consist of a CRM and ERP system upstream, a marketing data warehouse and campaign management system downstream, and an information quality product within the data warehouse to match and dedupe customer records. This is a light MDM vision, to be sure, but it may be the only business need your organization has been able to identify at this time. 4. Analyze your data. Your master data will only be as useful and trusted as the source data being used to derive it. Ensure that your executive sponsor has identified the subject matter experts or data stewards across the enterprise who can best articulate what quality means to your business, identify process gaps where improvements are necessary, and define the quality metrics that should be monitored ongoing. 5. Make technology decisions and kick off phased implementation. Once the business vision for your MDM capability is defined, the organizational support is clear, the current state of your infrastructure is understood, and the relevant data has been scoped and assessed, you are ready to take the next step. 2006, Forrester Research, Inc. Reproduction Prohibited November 10, 2006

9 8 Trends Introducing Master Data Management Figure 2 MDM Ecosystem Audit Guide Organizational change management Data governance Organizational alignment Business process optimization Upstream Enterprise apps Web apps External data Supply chain data Legacy apps MDM core technologies CDI PIM Data governance and hierarchy management Data model management Information quality management Third-party data enrichment Data quality assessment and profiling Metadata management Downstream Data warehouse Operational data stores BI marts BI tools Dashboards/portals Integration acronym buffet (pick one or more) ETL EAI CDC EII Customdeveloped code Source: Forrester Research, Inc. November 10, , Forrester Research, Inc. Reproduction Prohibited

10 Trends Introducing Master Data Management 9 R E C O M M E N D A T I O N S USE THE MDM AUDIT GUIDE TO SUPPORT YOUR MASTER DATA STRATEGY MDM is an immature yet fast-evolving capability. The secret to MDM is not so much an introduction of new technology but more the ability to leverage and integrate existing information integration, information management, and supporting technologies to deliver an integrated business service. It also doesn t end with the audit guide service-oriented architecture (SOA), Web services, information security, business process management suites, storage solutions, and other technology platforms may also play a major role in building out your organization s unique MDM capabilities and ecosystem. And the sooner enterprise architects determine how, the better suited they will be to delivering the MDM capabilities that your business stakeholders are demanding. Don t rush to solve for all master data. Most organizations would benefit from improved management of their master data, but large technology investments may not be required today. Many organizations can achieve significant return by implementing a focused CDI and/or PIM solution or even by leveraging existing information management investments (e.g., information quality, data warehousing, or data mining tools) to improve targeted data and not feel the immediate need to extend to other master data entities. While many early adopters of MDM will eventually be rewarded for their investments, they will also be the guinea pigs for an evolving and turbulent market. Get started now, but look ahead. Cut your teeth on a single data domain like customer or product, and focus on a subset of stakeholders. Working with CDI and PIM vendors remains the correct approach, but ensure that their product road map and current offerings match your larger MDM vision. Placing your bet on some of the larger vendors like IBM or Oracle would be the safest approach but stay tuned for other emerging players. MDM cannot be implemented in a silo. The enterprise architects or information management professionals who are responsible for delivering a MDM capability must coordinate the solution architecture with SOA architects, data integration architects, enterprise application architects, data warehouse architects, business process architects, and, of course, the business leaders responsible for the business capabilities that are being affected or enabled. Start an integration competency center. This increasingly popular project management best practice brings business and technical data integration stakeholders together to provide a knowledge base and coordination mechanism for complex information integration initiatives MDM surely qualifies , Forrester Research, Inc. Reproduction Prohibited November 10, 2006

11 10 Trends Introducing Master Data Management W H A T I T M E A N S THERE IS A FUTURE FOR MDM; IT S JUST NOT HERE YET While the vendor community across many technology disciplines continues to use MDM as a marketing tool to engage prospects, a few focused technology vendors are putting their product road maps where their mouth is and have visions of building the wrapper through acquisition, ecosystem partnerships, and internal development that will eventually deliver a unified experience that can leverage the processes and technologies required to enable MDM. Today, IBM and Oracle have the largest span of components required within the MDM ecosystem, and they also currently have offerings that solve for both customer and product data domains, which are still the highest demand master data targets. IBM and Oracle also provide database and integration experience that they are configuring to complement their MDM offerings. Although they are not yet as comprehensive, i2, SAP, Siperian, and TIBCO Software are also contributing some significant components into the ecosystem. More niche vendors, such as Hyperion Solutions, Kalido, and Stratature, are focusing their efforts on being completely data-domain-agnostic and are targeting the data governance, modeling, and hierarchy management components of the ecosystem. Today, a combination of vendor-supported technologies will be required to build out your MDM ecosystem with single-vendor-supported solutions on the horizon. ENDNOTES 1 To reduce the cost and time of creating high-quality data, a new category of vendors is delivering solutions that combine data cleansing, data matching, and data management with integrated stewardship and administration, offloading expensive systems integration. See the June 13, 2005, Tech Choices The Forrester Wave : Customer Data Integration, Q Growth is anticipated in the pure-play CDI and PIM markets, as well as the broader MDM category. See the March 6, 2006, Trends Trends 2006: Master Data Management. 3 An example of a master data attribute derived in the data warehouse would be a lifetime value (LTV) score. For some companies, LTV is a critical attribute managing customer relationships in sales, marketing, service, and support functions. Calculating a LTV score often requires a customer s order, support, and contact history captured across disparate systems. The consolidation of this information, hence the derivation of the lifetime value score, is the job of the data warehouse. While the MDM solution may define the single version of the customer, the data warehouse would in turn append the master customer record with a trusted LTV score that could be shared and leveraged across the enterprise. 4 The ICC should be staffed with personnel with expertise in several areas, including technical, business, and other soft skill areas. See the April 15, 2004, Quick Take Staffing The Integration Competency Center, and see the February 1, 2005, Best Practices Integration Competency Center Best Practices. November 10, , Forrester Research, Inc. Reproduction Prohibited

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