Boost the Value of Information With Master Data Management

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A White Paper

Boost the Value of Information With Master

Data Management

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J.T. Taylor is the chief technology officer of iWay Software, an innovator of enterprise integration solutions. Mr. Taylor brings more than 25 years of experience in information technology to his position, with 14 years in the commercial software industry developing and marketing enterprise products. Strategically focused and internationally experienced, Mr. Taylor is a recognized industry leader with extensive experience in the application integration, systems management, and Internet e-commerce markets. Prior to joining iWay Software, Mr. Taylor was the founder and CEO of Semantic Solutions, an independent software vendor and reseller targeting the application development market. Previously, he held the position of senior director of Global Product Marketing and chief software architect at Software AG, a $2 billion independent software vendor in the database, application development, and application integration markets. Prior to Software AG, Mr. Taylor was vice president and chief technology officer of SingleShop, an Internet e-commerce startup, responsible for development and support of the company’s innovative software as a service (SaaS) e-commerce products. Mr. Taylor is a decorated veteran of the U.S. Army. J.T. Taylor

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Master Data Management: What It Is and Why You Need It How Master Data Management Works

The Primary Business Drivers and Requirements Behind MDM Synchronization: One of MDM’s Greatest Challenges

Data Quality Management and MDM: What Is the Correlation? Master Data Management: A Real-World Scenario

iWay Master Data Center Conclusion

Master Data Management: What It Is and Why You Need It

Master data management (MDM) is a comprehensive, enterprise-wide technique that comprises data governance, distribution, and administration. It is designed to improve accuracy and streamline sharing by seamlessly linking together and synchronizing the disparate information that exists across a business. With MDM, universally accepted values for key data elements are created, providing all users throughout an organization with a single, correct, up-to-date version of vital business information that can be instantly retrieved and leveraged at any time.

As companies expand, merge, and/or globalize, corporate technology environments continue to become more and more sophisticated. They contain a larger and more complex network of diverse applications and databases than ever before – systems that are frequently housed in geographically dispersed locations, maintained by different teams, and seldom well-integrated or synchronized. The results of this can be disastrous. Information that’s outdated or inaccurate will be used to make critical business decisions or develop important corporate strategies. Users won’t be able to access the data they need, when they need it, to perform their day-to-day business activities. Cross-department collaboration will be nearly impossible due to inconsistent versions of the same information (multiple versions of the truth).

An effective MDM plan can change all that. Companies who define and implement broad-reaching master data management strategies can realize significant benefits, including greater visibility into core operations, improved information accuracy and consistency, increased productivity (particularly for those processes that are highly information-centric), and enhanced data exchange within and beyond the company. Perhaps the most important advantage of MDM is its ability to mitigate corporate risk, increasing the success of compliance initiatives by ensuring the quality of information delivered in response to regulatory reporting guidelines.

Master Data Defined

There are many vague and unclear definitions of master data available from a variety of sources, leaving companies unsure exactly what constitutes master data. In the simplest terms, master data is a single set of information elements and all related metadata and attributes (definitions, roles, taxonomies, etc.) that have been synchronized from similar records located in various systems and applications across a business. Once master data is generated, it is closely governed to ensure consistency among all sources where equivalent data is stored.

Master data objects should be created for those common data sets that exist – in some form or another – within numerous databases and sources. For example, client information is often stored in customer relationship management (CRM) systems, as well as accounting and billing applications, help desk systems, etc. The creation of a master data object for each customer would help ensure that the information about that customer is correct and consistent across those multiple sources.

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In addition to clients/customers, most companies maintain the following data in multiple disparate sources, and should create master data objects for each related record:

Products. ■

■ Product data often exists in systems, such as engineering, production, sales, and inventory management

Parts/materials. ■

■ Information about the countless components used in the manufacturing process or the office supplies a company keeps on hand to support day-to-day activities can typically be found in inventory, production, and procurement systems

Suppliers/vendors. ■

■ Accounting teams, as well as engineering and production departments, often keep data about suppliers and vendors in their various business applications

Employees. ■

■ Employee data is stored in human resource management applications, IT help desk systems, payroll packages, and other disparate databases

Branches/stores/locations. ■

■ Data about the various offices and branches a company

operates may exist in sales, shipping and distribution, human resource management, and accounting applications

These items are ideal candidates for master data and can typically be found in virtually any organization. However, master data strategies are flexible and can be easily made to conform to any unique or specific business need. In fact, nearly any item, person, or process described in corporate information sets can be incorporated into a master data plan.

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Master data management is a combination of processes and technologies that enable the creation of a single system of record. A golden record that provides a set of validated, universally recognized values, derived from the various sources that house similar information, and that has been reconciled and stored in a centralized hub to be used as a primary frame of reference by all enterprise users and systems. It will feed complete, consistent, and correct data back to applications and databases across an entire business.

For example, a healthcare organization may create a golden record for each patient, which would then feed all correct, consistent contact details and insurance information back to billing, clinical, and other systems. A manufacturing firm may need one golden record for each product, including components, design and engineering specifications, and pricing, which would be seamlessly shared among sales, inventory, procurement, production, and other related applications. Before we continue discussing how it works, it is important to stress the importance of

maintaining the integrity of golden records once they are created. Keep in mind that a single error in master data, no matter how small or seemingly insignificant, will multiply exponentially, causing errors in every system that uses it.

Once golden records have been created, there are five primary ways to manage master data throughout a business:

Consolidation

A single, physical instance of master data is created and maintained. Updates to master data are made at source systems, then transferred back to the central repository.

How Master Data Management Works

Source

Master

Source

Source Source

Master is single version of truth ■

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Data quality management at master ■

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Updates occur at sources ■

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Updates propagated to master ■

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Registry

All master data resides in its original databases, while a virtual repository of keys is maintained to aid in the synchronization of MDM records across different information assets. When an update is made to master data at its source, it is harmonized with the associated key and then redistributed among other related back-end systems.

Coexistence

All master data attributes are stored in a central repository, but the master data itself can be generated, stored, and updated either in the MDM database or within the individual applications and back-end systems. Changes, regardless of where they are made, are dynamically disseminated among related sources.

Master

Multiple versions of truth ■

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Data quality management is ongoing ■

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Updates occur at sources ■

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Keys and metadata updated in registry ■

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Updates propagated to other sources (optional) ■

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Master

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Data quality management is ongoing ■

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Updates occur at sources or master ■

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Updates propagated to other sources ■

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Transaction

Master data is read and written to a central repository in its transactional context, in real time upon event execution. Updates are also managed at the source and propagated to the MDM database.

Data Synchronization

Asynchronous harmonization of master data dynamically occurs, in batch, among systems across the entire infrastructure. Updates are recorded at the source, then shared with other sources at pre-determined intervals.

There is no right method when it comes to master data management. What will work best for one company may be the wrong approach for another. In order to choose, according to renowned industry blog BeyeNETWORK,1_{firms need to carefully evaluate their unique and specific master} data management needs, such as:

Timeliness: the speed at which new master data must be available within the environment ■

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Latency: the desired time for master data to be delivered when requested ■

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Currency: the frequency at which master data should be refreshed ■

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Consistency: the degree to which each application’s view is like others across the enterprise, ■

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and/or the ability of the environment to return the same results each time data is requested, no matter which sources are queried

No matter which style of MDM an organization chooses, a common, standardized master data model is a crucial element. Although it will serve different purposes in different strategies, an MDM model ensures seamless synchronization of all master data among the countless operational systems that use it. For example, when the consolidation method is used, the master data

model acts as the hub database, which shares its information with various back-end systems. In the registry approach, the master data model is used for building queries that will collect and transform related application data. For companies that choose the coexistence model, the MDM model will serve as a hub for storing common information attributes. Unique data characteristics

Master

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Data quality management at master ■

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Updates occur at master ■

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Updates propagated to sources ■

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A recent analyst survey ranked the top business drivers behind the adoption of master data management. Respondents cited cost optimization and efficiencies, compliance and risk management, and revenue and profit growth as the key reasons behind their MDM strategies.2 A similar report by the Aberdeen Group cites the following as the primary reasons behind many of today’s MDM initiatives: 3

Competitive growth and profitability ■

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Increasing customer demands ■

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High data management costs ■

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Regulatory compliance ■

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Low sales conversions ■

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Customer defections ■

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The requirements that create a true urgency for MDM across an organization can be viewed from three distinct perspectives – business, IT, and data.

Business

Master data management can solve many business challenges and goals. By enhancing the consistency and correctness of information as it flows across and beyond a business, organizations can dramatically improve:

Risk and fraud mitigation ■

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Compliance with Sarbanes-Oxley, Basel II, and other regulations ■

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Functional initiatives, like sales and marketing programs ■

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Corporate-wide performance management ■

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Mergers, consolidations, and acquisitions ■

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IT

Master data management can play a crucial role in a variety of mission-critical IT initiatives. In fact, many companies deploy their MDM solutions in conjunction with important technology projects, including:

Infrastructure overhauls such as the creation of service-oriented architectures (SOAs) or ■

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the implementation of enterprise resource planning (ERP) packages and other front-end applications. Analysts predict that by 2012, 70 percent of complex SOA projects will compromise business goals unless MDM is implemented

Consolidation of disparate architectures as a result of mergers and acquisitions ■

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Systems integration efforts ■

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Data

Companies who embark on master data management plans will realize more significant return on many data-related initiatives, such as:

Governance ■ ■ Quality management ■ ■

Data enrichment and extension via third-party sources ■

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Single view of customer, patient, etc. ■

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Application migrations ■

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Effective data management doesn’t end once a golden record is created. One of the most important aspects of MDM – and one of the biggest issues companies face when it comes to implementing their policies and associated framework – is finding the best way to maintain those golden records on a continuous basis and synchronize them with the various systems that exist across the business.

To achieve enterprise-wide coordination and propagation of correct and consistent information, formal policies for making changes to existing master data as well as adding new master data to the environment, then distributing those updates across the countless sources that likely exist, must be well-defined and implemented.

Two of the key processes that must be addressed include:

Changes and Updates to Records

Information environments are highly dynamic and changes and updates are frequently made to master data as well as to data contained in back-end applications. Companies must determine exactly how those modifications will impact the rest of the infrastructure. For example, which system is considered the system of record for all others to be validated against, or updated as needed, for the purposes of synchronization? How will changes to master data be propagated across source databases? Or, how will alterations made by end users directly in transactional applications be verified against master data and shared among master data repositories and other enterprise systems?

Rules that address these questions must be outlined as part of the overall master data strategy. Additionally, the technologies chosen to support the MDM plan must automate the definition and invocation of these rules the instant information is modified, to ensure ongoing accuracy and integrity, even as enterprise information continuously changes.

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Splitting Records

In many scenarios, duplicate records are uncovered during the course of master data management efforts and are then joined together for the sake of consistency. For example, various systems that house customer information may contain records for a John W. Smith. If these records contain similar details, it may be determined that they are, indeed, the same client. The golden record will then be created, unifying the information that existed in the separate records, and distributing it among all enterprise source systems.

However, as new information enters the environment, it may become clear that there are, in fact, two separate and distinct customers named John W. Smith. Supporting technologies must then be able to split these records back up, using a full data lineage to determine where each portion of the joined record came from, so the right information makes its way back into the right systems.

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Few companies incorporate data quality management into their MDM strategies. What they don’t realize is that while the efficiency in which master data is generated and maintained is important, it is the quality of that master data that will make or break an MDM initiative. In fact, in a recent article, Rob Karel, a principal analyst at Forrester Research, claimed that “data quality is core to delivering effective master data.”4_{Therefore, in order to succeed with MDM, companies need to} include data quality and validation techniques as a key part of their overall plan.

Data quality is so important to master data management because so many disparate sources and types of data will be involved. Application data from back-end systems, transaction data collected during the course of live events, and even the metadata and reference data used to describe the attributes of all this information, will play a huge role in the creation and administration of a single set of data values across the business. If the source data is bad, the master data generated from it will be too. This, in turn, will negate the benefits of MDM, causing problems throughout the business as incorrect or outdated information is leveraged during the course of core operations. BeyeNETWORK stresses the importance of data quality in MDM efforts, citing the maturity of a company’s data quality and governance policies as a key indicator of its MDM readiness.5 The companies who derive the greatest value from their MDM initiatives are those that make comprehensive data validation and quality management solutions part of their MDM framework. To be truly effective and promote data integrity across all back-end systems as well as all master data repositories, these tools must do more than just scan existing data to uncover quality problems. In fact, they must even go beyond the automatic correction of issues once they are uncovered, such as dynamically reformatting or restructuring values based on predefined guidelines. Instead, they must use advanced business rules and quality control techniques to prevent bad data from entering the environment in the first place. Only through this type of proactive data governance can organizations fully ensure the quality of master data throughout the enterprise.

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Now that we’ve covered what master data management is, what the benefits are, and how it works, let’s see what it can really do when applied to real-world business environments. In our experience, “customer” is one of the most common – and most critical – master data objects, so we’ll follow a fictional business, Company XYZ, looking to create a single, accurate record for each of its clients. Since the financial services industry is experiencing massive consolidation, globalization, and merger activity – all of which make master data management a necessity in order to maintain information consistency across disparate, geographically dispersed offices and business units with diverse technology infrastructures – let’s assume that Company XYZ is a bank. In an effort to expand customer “wallet share,” Company XYZ wishes to embark on targeted promotional campaigns designed to up-sell and cross-sell various products and services to existing business account holders. Because the company recently merged with two other financial institutions, it is struggling to unify disparate technology architectures with highly diverse sales, marketing, and customer relationship management systems. The main challenge they face is obtaining a single, consistent view of each client. Questions such as “who is the primary contact?”, “where is headquarters located?”, and “what products and services do they already have?” are difficult to answer, because information varies greatly from one source to the next.

An initial attempt to consolidate information from various systems into a centralized data warehouse failed to minimize inconsistencies across data records and resulted in numerous duplicate entries. The problematic information contained in the warehouse would have made effective campaign planning and execution nearly impossible, and the marketing team would have been challenged to achieve its desired goals in terms of response and conversion rates. So, Company XYZ embarked on a broad-reaching master data management effort. The first step was to cleanse and standardize customer contact information and other details across all back-end databases – more than a dozen in total. A “hub” solution, complete with a single, centralized master data repository that feeds complete, accurate information back to source systems in batch mode, was implemented. The strategy also included fully integrated data quality process support, using such features as merging and matching to cleanse and validate existing data, as well as advanced business rules to ensure the integrity of new data as it enters the environment. By enhancing the consistency and quality of customer data across the enterprise, Company XYZ was able to provide its marketing team with the information it needed – a single, correct view of each customer across all business units – to deploy targeted, effective up-sell and cross- sell programs.

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Cutting-Edge Solution for Comprehensive, Broad-Reaching Master Data

Management

iWay Software’s Master Data Center (MDC) is an innovative, specialized technology that can help companies overcome data consistency challenges by delivering the highest quality master data. Powerful, reliable, and designed to integrate seamlessly with existing systems through iWay’s extensive suite of adapters, iWay MDC leverages advanced, yet easy to define business rules that aid in the consolidation of millions of records. As a result, unified and validated master data is instantly available to ERP and CRM systems, self-service portals, analytical tools, data warehouses, and other enterprise applications.

iWay MDC is built on a component architecture that can easily “plug and play” into any technology environment. Its main modules include:

MDC Storage, which houses and manages original records waiting to be processed, as well as ■

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records that have been validated and consolidated. Pre-defined and customizable vertical data models for key master data objects, such as customers, contacts, and products, are utilized MDC Master Data Services, used for processing and maintaining master data. It is comprised of ■

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more than 100 elementary and component methods that can be fully configured directly from the administration console

MDC Data Quality Services, data process, and quality measures, such as automated record ■

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MDC Adapters, industry-standard interfaces that enable bidirectional connectivity to virtually ■

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any application or data across an enterprise

MDC Master Data Manager, an interface for business users and data stewards that provides ■

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intuitive visualization and job management

MDC Hierarchy Manager, a specialized interface for more advanced end users who need to ■

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oversee jobs dealing with hierarchal master data, such as product categories or organizational structures

MDC Metadata Interface, which allows users to export metadata from iWay MDC to other ■

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systems and metadata repositories MDC Server, a Java

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■ TM server platform for on-demand processing that integrates with iWay Service Manager, IBM WebSphere, BEA WebLogic, SAP NetWeaver, and a variety of other IT infrastructure logging and monitoring services

MDC Repository, an internal repository used in on-demand application mode to optimize and ■

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accelerate data access

Today’s Most Robust, Feature-Rich MDM Environment

iWay MDC is packed with powerful, state-of the art functionality to address even the most sophisticated and complex MDM needs. Key features include:

Vertical business models for healthcare, financial services, insurance, and government that have ■

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been optimized for customer, contact, and product. These leverage defined and pre-configured master data management services, including profile searches and unification with additional business entities

Advanced data quality and unification techniques designed specifically for customer data ■

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A Java-based infrastructure based on open standards that is fully compatible with a wide range ■

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of popular platforms. This accelerates deployment by simplifying integration with existing IT infrastructures

Bundled administration applications for fast and easy configuration and operation, without the ■

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need for third-party tools

Parallel data processing methods that ensure maximum scalability, as well as full support for ■

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incremental data processing in batch or on-demand online modes using ETL, ESB, or SOA- style integration

And best of all, iWay MDC combines power and functionality with affordability and budget-friendliness – simplifying project complexity and reducing the time from initial development to delivery, at a fraction of the cost of other tools.

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A Critical Part of Any Information Management Strategy

Master data management is a key component of any broad-reaching enterprise information management (EIM) strategy. EIM combines the principles and technologies of enterprise integration, business intelligence, and content management to streamline and formalize the activities associated with data generation, storage, access, and handling. As a result, companies can boost the value of their corporate information, tapping into it to gain a substantial competitive edge through increased operational productivity, reduced overhead costs, and better business performance. iWay Software offers a comprehensive portfolio of tools and solutions to support all aspects of EIM.

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Conclusion

Master data management (MDM) is a comprehensive technique that synchronizes similar information across disparate sources to ensure enterprise-wide information consistency, no matter how complex or multifaceted the underlying technology environment is. By creating a single, accurate reference point for all common data elements – such as customers, products, or employees – MDM can help organizations to enhance productivity and operational performance by improving information accuracy and data exchange within and beyond the company. Additionally, MDM can significantly mitigate corporate risk and boost the effectiveness of compliance efforts.

There are many challenges when it comes to designing, deploying, and administering an MDM strategy. There are many different MDM models to choose from and companies must closely evaluate each, compare them with their unique business and information requirements, and determine which approach is the best for them. They also must outline and prioritize the key candidates for master data within their business.

Additionally, firms must think beyond the initial creation, storage, and dissemination of master data, and clearly define how it will be updated, expanded, and maintained on an ongoing basis, to ensure continued success of the plan. Finally, they must select the right tools and technologies to support their MDM initiative.

iWay Software’s Master Data Center (MDC) is a powerful, comprehensive MDM platform that addresses data consistency challenges by delivering the highest quality master data. With robust features, a modular “plug and play” design that facilitates seamless integration with existing infrastructures, and the flexibility to support virtually any MDM approach, iWay MDC makes unified and validated master data instantly available to ERP and CRM systems, self-service portals, analytical tools, data warehouses, and other applications throughout an enterprise.

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Boost the Value of Information With Master Data Management

A White Paper