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8/3/2019 wp_iway_mdm

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A White Paper

Boost the Value of Information With Master

Data Management

by J.T. Taylor


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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 sof tware 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 companys 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 MDMs Greatest Challenges

Data Quality Management and MDM: What Is the Correlation?

Master Data Management: A Real-World Scenario

iWay Master Data Center

Conclusion

Table of Contents

1

3

6

8

10

11

12

15


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Information Builders1

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 dif ferent teams, and seldom well-integrated or

synchronized. The results of this can be disastrous. Information thats outdated or inaccurate will

be used to make critical business decisions or develop important corporate strategies. Users wontbe 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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Boost the Value of Information With Master Data Manageme2

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

Data quality management at master

Updates occur at sources

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

Source Source

Source Source

Multiple versions of truth

Data quality management is ongoing

Updates occur at sources

Keys and metadata updated in registry

Updates propagated to other sources (optional)

Master

Source Source

Source Source


Data quality management is ongoing

Updates occur at sources or master

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 uponevent 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

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

Currency: the frequency at which master data should be refreshed

Consistency: the degree to which each applications view is like others across the enterprise,

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

are mapped and transformed back to the data model, much in the same way that queries gather

source data in the registry approach.

1 Loshin, David. Master Data Synchronization Assessing Requirements, BeyeNETWORK, January 2008.

Master

Source Source

Source Source


Data quality management at master

Updates occur at master

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 ef ficiencies, 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

todays MDM initiatives: 3

Competitive growth and profitability

Increasing customer demands

High data management costs

Regulatory compliance

Low sales conversions

Customer defections

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 f lows across and beyond a business, organizations

can dramatically improve:

Risk and fraud mitigation

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

Functional initiatives, like sales and marketing programs

Corporate-wide performance management

Mergers, consolidations, and acquisitions

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

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

Systems integration efforts

The Primary Business Drivers and Requirements Behind MDM

2 Master Data Management Business Drivers, All Things Data, May 2009.

3 Master Data Management: The Approach Determines the Results, Aberdeen Group, May 2008.


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DataCompanies 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

Single view of customer, patient, etc.

Application migrations


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Effective data management doesnt 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.

Synchronization: One of MDMs Greatest Challenges


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Splitting RecordsIn 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 dont

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 thebusiness 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

companys 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 toprevent 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.

Data Quality Management and MDM: What Is the Correlation?

4 Kelly, Jeff. Master Data Management Market Trends and Expert Forecasts for 2009, SearchDataManagement.com,

January 2009.

5 Loshin, David. Master Data Management Readiness, BeyeNETWORK, June 2008.


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Now that weve covered what master data management is, what the benefits are, and how it

works, lets 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 well 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 lets 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 stepwas 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.

Master Data Management: A Real-World Scenario


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

iWay Softwares 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 iWays

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

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

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

verification, designed to improve information integrity

iWay Master Data Center


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

any application or data across an enterprise

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

intuitive visualization and job management

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

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

systems and metadata repositories

MDC Server, a Java 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 servicesMDC Repository, an internal repository used in on-demand application mode to optimize and

accelerate data access

Todays 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

been optimized for customer, contact, and product. These leverage pre-defined and pre-

configured master data management services, including profile searches and unification with

additional business entitiesAdvanced data quality and unification techniques designed specifically for customer data

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

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

need for third-party tools

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

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 StrategyMaster 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 keycandidates 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 Softwares 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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