Market Offering:

Package(s): Oracle

Authors: Rick Olson, Luke Tay

Date: January 13, 2012

NCOE whitepaper

Master Data Deployment

and Management in a

Global ERP

Implementation

NCOE leading practices

Contents

Executive summary 1

Introduction 2

Problem definition 3

High-level solution 4

Solution details 8

Business benefits 18

Conclusion 19

About Deloitte Consulting LLP 20

Executive summary

1

Executive summary

In limited geographical Enterprise Resource Planning (ERP) implementations, Master Data

Management (MDM) is not formally addressed with a dedicated MDM team, instead the functional

teams are tasked to manage MDM efforts, but using this approach may cause overall data quality

issues for the larger and more complex organizations attempting a global implementation.

Furthermore, in a multiinstance implementation, data is often neglected and its complexity is

underestimated.

Consideration to MDM data domain, organization structure, process, and conceptual design can be

the key to an effective ERP implementation. Prior to embarking on a MDM initiative, an analysis of

business drivers and current state assessment should be undertaken. The ultimate goal is to add to

shareholder value. Improvements in efficiency, improvements in customer and supplier

relationships, and enhanced execution from accurate product structures can facilitate the

development of the business case to support the MDM and ERP initiatives (Figure 1).

Figure 1

In many cases, businesses have not developed MDM governance practices at the same rate of

progress as other key business processes. As an enterprise expands globally, it soon finds that

underfunded data management practices can restrict the enterprise expansion and even delay

desired ERP initiatives.

This paper describes the domain options, the required organization structure, business processes,

and data design of master data management to facilitate the implementation and subsequent

rollouts of a global ERP initiative.

Introduction

2

Introduction

A master data deployment initiative requires technical infrastructure, organizational alignment,

business processes, and a detailed data design to be effective on a global level. This effort should

take the form of a business transformation project since it will require sponsorship, project

management, and cross functional deployment teams. Depending on the number of sites and

geography in scope, a global deployment project can take multiple months to implement.

Initially, the infrastructure domain needs to be established. First, the budget for the investment of

additional technology needs to be determined. This will define the vision of the governance maturity

model to be implemented. In addition, it provides the framework to determine the origin of the data

sources, define the necessary integrations, and determine the sequence and timing of updates. The

maturity model will also have a direct impact on the organizational model and the business

processes necessary for proper support.

A critical success factor for an effective rollout is a cross functional master data organization with

executive-level sponsorship. This is necessary to manage the development, execution, and quality

of the master data lifecycle and demonstrate the significance of data quality to the enterprise. The

organization levels should have a global, regional, and local structure incorporating information

technology, finance, marketing, sales, engineering, and supply chain planning representatives. The

size of the organization is directly related to the volume, quality, and complexity of data to be

managed. Representatives of this organization should have defined roles in each operating location.

In order to manage the lifecycle of master data and integrate properly into the enterprise, the

organization should develop detailed end-to-end MDM business processes. These processes should

be incorporated into the project scope, built into the configuration design of the implementation and

become a permanent part of the key business processes. This should encompass customer,

supplier, item, and financial related data elements. Due to the cross functional nature of the master

data structure, sequencing and timing of the data lifecycle maintenance tasks should be mapped out

in detail including specifications, policies, and process performance indicators.

Data design and definition will potentially impact hundreds of data field elements potentially across

multiple applications. Each element must have a defined definition, defined values, and an owner.

The data design should be segregated into global, regional, and local requirements and each data

element assigned accordingly. Care should be taken to keep the scope of the definitions as simple

as possible with limited selection options. Over complicating the design or having a wide range of

choices will add complexity to construction, analysis, and reporting. Consolidation of the chart of

accounts (COA), product structure taxonomy, and the use of parent/child relationships are some of

the more frequently used concepts.

When proper focus is applied to managing master data in an ERP implementation, it can result in a

smoother rollout, more efficient maintenance, and a higher degree of data integrity across the

enterprise. The initial startup cost and maintenance of the MDM organization can be justified with

higher quality analysis and decision making, reliable reports, improved productivity, fewer

transactional errors, reduced financial variances improved revenue opportunities, and enhanced

acquisition possibilities.

Problem definition

3

Problem definition

The areas of concern for deploying global MDM can include:

IT investments to support leading MDM designs are necessary, but can become significant in a

global approach if not assessed properly

Improper global master data management and governance results in inconsistent financial

reporting and reduced supply chain efficiency

Lack of data consistency and integrity in item, customer, supplier, and financial data contribute to

chronic process barriers and excessive costs between facilities within the organization

Inadequate master data business processes cause delays of product introduction and an

increased cost of quality

Data domains are typically defined within a maturity model framework. The framework identifies

models from a low cost complexity to a high cost and high complexity domain. The total investment

is based upon the value of the data to the business. Typically, global organizations tend to have

higher maturity models than national organizations. As a result, organizations expanding their global

footprint will need to carefully consider cost versus value of the maturity model.

In most regional or national organizations data is managed locally. As the organization grows, it

typically finds that attempting to consolidate local data between facilities consumes valuable time.

There are many data conflicts and the data source and owner is unclear. If a defined master data

organization exists, it usually is centralized at the organization’s headquarters with limited authority

or structure at satellite facilities resulting in delayed or flawed business decisions and reporting.

Data inconsistencies include multiple addresses for both, customers and suppliers, missing or

incorrect attributes, item records that are inconsistent between facilities and facility-specific COA.

This ultimately leads to order processing delays, inaccurate shipments, invoicing and payment

discrepancies, higher inventory obsolesce reserves, and higher degrees of effort to generate

consolidated financial period reports.

When a new product, customer, vendor, or business unit is created they are typically initiated by

different functional areas within each facility. The data maintenance processes which include the

process to create, maintain, and inactivate master data is designed in isolation from other business

processes and its impact to downstream business processes is not considered. In addition,

formalized cross functional process management and performance metrics for master data are not

monitored as closely as supply chain or finance business processes, despite the fact that these key

business processes depend on correct master data to function properly. ERP implementations that

neglect MDM or design data maintenance processes in isolation from key business processes will

find challenges as transactions and operations are negatively impacted.

High-level solution

4

High-level solution

Master data domain

Current and future data domains will define the platform from which the MDM initiative will be

developed. Data domains are defined by maturity level. Data domains are based off of the enabling

technology available to the enterprise. This in turn determines how the technology will be utilized

(Figure 2).

Figure 2

An enterprise should assess the current and future state architecture including executive

sponsorship, data governance, existing capabilities, and data processes to determine the maturity

level, the future state solution, and investment required. The ‘target’ architecture and maturity level

should be the scope for the master data design and the starting point for the data modeling which

will determine the amount and complexity of the data integrations to legacy applications required.

Data governance approach

A centralized data governance methodology is necessary to provide the structure that supports a

global data infrastructure. This is supported by an MDM governance organization. This organization

should be comparable to any of the other functional organizations within the company.

The global data strategy incorporated provides reduced operating costs, enhanced execution, and

improved response to market demands. The benefits of this approach include:

A global governance model can

– Provide standardized data and processes to enhance supply chain network capabilities and

consistent financial reporting across global regions

– Establish a consistent methodology for product lifecycle management

Establishing an MDM organization can

– Improve operational efficiencies across markets

High-level solution

5

– Enable continuous improvement processes and culture

Create a single entity for standards integrity and execution models

The typical IT strategy incorporates the policies that support the MDM activities. It is the delivery

processes that are coordinated with the ERP initiative to provide the intended business driver

results. The primary pathways to integrate MDM and ERP initiatives are the enterprise content and

data management structures (Figure 3).

Figure 3

The content governance model defines the amount and type of data required to operate each

business function. Enterprise content management establishes the standards and procedures as to

how the enterprise will execute the MDM processes. This results in providing the requirements for

the prioritization, sequencing, and timing of data content throughout the enterprise (Figure 4).

Figure 4

The data governance model utilizes input requirements from the IT and business functions to align

data with organizational functions with business objectives. The data is typically defined by its

geographical nature. The purpose of global data is primarily for strategic sourcing and planning.

Regional data is for product planning and deployment into local markets. Local data is primarily for

indirect sourcing and local sales promotions. This results in well-defined data parameters for the

business function to utilize and makes data transacting more efficient (Figure 5).

High-level solution

6

Figure 5

Information management

Information Management (IM) capability resulting from the efforts of content and data governance

includes tasks to plan, design, implement and monitor data in applications that confirm the fitness of

data for use across the enterprise. The business objective of IM in an ERP initiative is to produce

high quality master data that can be distributed throughout the enterprise for applications, analytical

tools, and directly to business stakeholders.

IM in the master data organization is primarily driven by the data governance model and includes the

lower level tasks of the model. Essentially, it is the information quality and data migration efforts that

are the dependency for the ERP implementation.

These two components comprise the future state data design that will provide the operating

framework for the master data organization (Figure 6).

Figure 6

High-level solution

7

Business processes and data definition

Standardized business processes have been developed across industries over the years. They have

become the building blocks for many sublevel process developments. Most organizations have

employed some degree of these processes with varying degrees of automation. Beginning the

master data initiative with key level 2 business processes defines the scope of the master data

organizations responsibility (Figure 7).

Source: Deloitte Consulting

Figure 7

These processes will impact key data elements in the CRM, ERP, SCM, etc. applications. Within the

master data domain, these critical applications are integrated with master data tables and data

elements. Using the ERP application as an example, since it utilizes master data to perform

business transactions, there are four main data domains for a global initiative (Figure 8).

Figure 8

Designing cross functional processes to the task level, identifying the data fields to control and

defining them with global, regional, and local responsibilities will provide the detailed level necessary

to manage the master data properly.

Solution details

8

Solution details

Master data domain

There are multiple technology solutions available to hold, integrate, and analyze master data. The

more sophisticated the technology solution the more cost that is connected with it. In general, efforts

should be made to have a data domain(s) to retain the master data with selectively synchronized

information exchange between the appropriate applications.

Organization maturity

Introducing a global master data management initiative requires a reasonable level of maturity. It is

not necessary to be at the high-quality maturity level. Certainly, a Level 1 will add a lot more effort to

the initiative. However, starting at a Level 2 with a target of Level 3 is a reasonable maturity to build

from (Figure 9).

Figure 9

The applications domain that determines the difference between a Level 3 and Level 4 is essentially

moving the master data from a transactional application to a specific data only application (Figure

10). As the maturity level increases, the number of data integration between application increases.

However, by segregating master data to specifically developed data applications will improve

management capabilities and security of the data.

Solution details

9

Figure 10

The enabling technology selected for this architecture establishes the baseline definition of the data

model and data.

Data model

A well-defined data model defines the location of data elements and the relationships among them

and how the data elements can be accessed. In either a Level 3 or Level 4 maturity the source

system, target system, and data element definition should be diagrammed (Figure 11).

Figure 11

This model will facilitate the constructions of the technical specifications and development and

testing of the conversions and integrations. It is also fairly common that item, customer, and supplier

numbers and descriptions will require some level of transformation to become harmonized across

the global footprint.

Data governance approach

It is necessary that a cross functional MDM organization is created with executive-level sponsorship.

This is to maintain the timing and quality of the master data lifecycle. The size of the organization is

directly related to the volume, quality, and complexity of data to be managed.

Global governance model

The global governance design is centered on a model, which can provide a basis for defining

business processes, infrastructure, and configured software to meet the majority of an organization’s

common global requirements while leaving room for localization during rollouts.

• System name

• Field name

• Table name

• Data type

• Data length

• Migration frequency

• Description

• Location

• Algorithm

• System name

• Field name

• Table name

• Data type

• Data length

SourceTransformation

(If required)Target

Solution details

10

The governance model is made up of:

High-level approach strategy

A scope of responsibility

A centralized organizational structure with responsibilities at the global, regional, and local level

A common rollout strategy is necessary to attain the business objectives and benefits associated

with it. Application Program Integrations (API) defines the technical scope of the global model

(Figure 12).

Approach

A common global implementation model is required to attain the

majority of the supply chain and financial improvements from

business case benefits associated with it. Application Program

Integrations (API) defines the technical scope of the global model as

80% fit of the global requirements.

Result

Standardized and compliant processes

Common technology

Enables regional and global growth strategies

Knowledge sharing and transfer

Figure 12

The scope of the global model incorporates key business functions built on a foundation of

transformational change, information technology, and master data. These are the key business

functions of ERP initiatives (Figure 13).

Figure 13

The three foundation platforms require global representation and sponsorship within the enterprise

to minimize regionalization of the model. Once the foundation is established, the business within the

global initiative can operate in an autonomous environment networked together by organization,

technology, and data resulting in sustainable and consistent business operations supported by

organization sponsorship.

Solution details

11

Master data management organization

The MDM organization is aligned to the global governance structure and functions similar to a matrix

organization model as it matures. This organization consists of global, regional, and site

representatives with reporting responsibilities to each. The primary responsibility of the MDM

organization is to manage, coordinate, support, and deliver global, local, and regional business

initiatives within its scope (Figure 14).

Figure 14

The purpose of managing master data by geography is to provide:

Consistency of data between geographies and business units

Global standardization of master data driven from the top down

Improved automation of data processing

Information management

IM in an ERP initiative is primarily focused on data information quality and data migration. Prior to

initiating the information improvements, a risk and impact assessment should be conducted. The

objective of the risk assessment is to identify risks posed by data quality issues. The objective of the

impact assessment is to identify the business impact caused by data quality issues and remediation

activities needed to address them. These assessments will inform decisions on the extent of

remediation and cleansing required.

The data quality risk assessment defines the overall risks posed by data quality issues. It evaluates

potential risks in specific categories i.e., financial, compliance, governance, operational, etc. It

defines the master data component, the data source, and the level of risk to the business associated

with it. The primary drivers for risk are:

Data volume

Regulatory requirements

Audit controls

A risk assessment should be conducted on each master data component impacted by the ERP

initiative.

Solution details

12

The data quality business impact assessment documents the impact of data quality on the business

in regards to resources, effort, and organizational impact. It evaluates potential risks by geography,

location, department, etc. It defines the master data component, the data source, and the level of

risk to the business associated with it. It defines the master data component, the data source, and

the business functions associated with it. The primary drivers for risk are:

Level of automation

Number of users

Impacted processes

An impact assessment should be conducted on each master data component and key business

function included in the ERP initiative.

Information quality

Information quality addresses the value, usefulness, accessibility, and security of an organization’s

data and information assets. It includes tasks related to data and information requirements,

standards, management, and security and controls (Figure 15).

Source: Deloitte consulting

Figure 15

In the initial plan phase, a detailed assessment is performed across many applications to determine

the data accuracy and any gaps that exist. This is followed by a review of the data requirements

across the enterprise.

In the design phase, the data cleansing and migration plan is assembled for each geography.

Timelines are determined to minimize data conflicts during transaction processing. This includes any

functional specifications for conversion programs.

During the build phase, the cleansing activities take place. This usually impacts the global, regional,

and local team members. This activity consists of initial validation testing with the business

applications and the testing of security accessibility. The testing consists of processing the cleansed

data through the key transaction processing for the facility utilizing detailed testing scripts and

appropriate success criteria.

Any technical updates to data in mass volume and security deployment occur in the deliver phase,

including detailed validation testing. Typically, these mass updates occur over a limited time span to

minimize any disruption to the business. Additionally, data metrics are deployed to monitor the

success of the update.

Finally, during the operate phase data quality reports are monitored and any final adjustments are

made before the global deployment begins.

Solution details

13

Data migration

The objective of master data migration is to identify the logical structure for the master data in scope.

This includes geographies, alignment to business processes, and defined values for user entry. The

identification, design, and development of conversions and interfaces are based on this effort

(Figure 16).

Source: Deloitte Consulting LLP

Figure 16

The process of globalizing data consists of identifying requirements, verifying the completeness of

the data, and analyzing the data accuracy and integrity. This can provide consistent and reliable

information exchange between businesses. The sustained results of standardized data can provide:

Centralized supplier and item creation policies

Data-driven automated processes

Global general ledger (GL)

In the plan phase, the global, regional, and local data sources and requirements are defined. This

begins the process of aligning data across the global footprint and can require additional business

support as it relates to product introduction timing and inventory relabeling.

During the design phase, the data synchronization plan is developed with the conversion and

integration specifications. These specifications should utilize a consistent template or organizational

standard to enhance the efficiency of the development cycle. The synchronization configuration

provides documented rules and logic for harmonizing master data across the source systems.

The build phase develops and tests the conversion and integration programs and the businesses

across the enterprise initiate the inventory, customer, supplier reliable, and communication process.

The testing consists of processing the harmonized data through the key business processes across

enterprise utilizing detailed testing scripts and appropriate success criteria. This testing should be in

coordination with the ERP testing phases and follow the same methodology.

In the deliver phase the harmonization process occurs. At this point the global data for items,

customers, suppliers, plants, and financial data gets deployed and the business units begin to use

the global elements. Depending on the business units involved and the magnitude of change that

can occur, this phase can take a significant amount of time relative to the other phases on the

migration process. The organization of MDM will be highly complex and challenging until the

harmonization effort is integrated.

During the operate phase, the full MDM team assumes control of the master data and the policies

and master data business processes are initiated.

Solution details

14

Business processes and data definition

MDM processes should be treated as key to the business. Modifications to data can occur across

the organization at any time. Process must be robustly designed to consider master data release,

synchronization, and obsolescence within many facilities. As with any process, they should contain

well defined tasks, owners, and performance measures. In addition, master data processes require

approvals from the appropriate global, regional, or local representatives. The typical master data

process includes a request, an approval process, and the update (Figure 17).

Figure 17

This process will take different paths depending on if the data is global, regional, or local. Local

updates are typically the responsibility of the local team, regional data the regional team, and global

data the global team.

Create and maintain customer master data

This is the overall process of requesting and creating and maintaining customer master data to

establish global, regional, and local relationships. Customer information is collected from various

channels and is ultimately located in the customer information domain. Sales input is the primary

source for information. Local attributes include contact names, phone numbers, local reporting

codes, etc. Regional data includes country-specific data such as tax codes, bank information,

regional reporting codes, etc. Global data includes creation of the customer number, terms, business

unit assignment, etc. (Figure 19). The review process is coordinated through the customer master

data organization and the updates are performed by the owner of the data.

Create and maintain material master data

This is the overall process of requesting and creating and maintaining material master data to

establish global, regional, and local relationships. Material master information is primarily developed

through the product lifecycle management process and is typically segmented by finished goods,

subassemblies, raw materials, and incorporated into bill of materials. This process is managed

through utilizing revision levels and effectively dating so it can be managed across the entire supply

chain. Local attributes include costs, local planning parameters, warehouse locations, local inventory

parameters, etc. Regional data includes distribution planning parameters, business unit assignment,

bill of material definition, regional segmentation codes, etc. Global data includes supply chain

planning parameters, item number creation, descriptions, global segmentation codes, etc. (Figure

19). The review process is coordinated through the material master data organization and updates

are usually time dependent.

Create and maintain supplier master data

This is the overall process of requesting and creating and maintaining vendor master data to

establish global, regional, and local relationships. The creation and maintenance of vendor master

data should support each function's business drivers, which can provide synergies across affected

business areas and benefits the organization as a whole. Local attributes include indirect supplier

information, contact names, phone numbers, local reporting codes, etc. Regional data includes bank

Solution details

15

information, tax codes, regional reporting codes, etc. Global data includes creating the vendor

number, terms and conditions, business unit assignment, etc. (Figure 19). The review process is

coordinated through the vendor master data organization and the updates are performed by the

owner of the data.

Create and maintain employee master data

This is the overall process of creating and maintaining vendor master data to establish global,

regional, and local relationships. Employee data is used to support time reporting, item assignments,

reimbursement processing, etc. (Figure 19). The HR function typically coordinates and owns this

process due to potential confidentiality conflicts.

Create and maintain plant master data

This is the overall process of requesting and creating plant master data to establish global, regional,

and local relationships. Plant data supports the operations in new and existing facilitates in a timely

manner. It can include a new company creation, the business unit definition, COA incorporation, etc.

(Figure 19). The review process is coordinated through the finance and business unit master data

organization and the updates are performed by the owner of the data.

Create and maintain financial data

This is the overall process of requesting and creating financial configurations to establish global,

regional, and local relationships. This process starts with the global COA design to define the GL for

the enterprise. The COA size should be kept to a minimum and subledgers and reporting codes

should be used for enhancing the detail and analysis of object accounts. Managing the GL includes

the activities related to designing, implementing, and maintaining the accounting framework. This

includes coding structure, interrelationships with primary information gathering systems (e.g.,

accounts payable, accounts receivable, payroll, fixed assets, etc.) and such GL structures as the

use of single or multiple COAs and the use of subledgers. Some additional financial master data

elements include profit centers, cost centers, equipment masters, cost elements, customer, and

vendor financial views should be built into the account level detail (Figure 18).

Figure 18

Solution details

16

Define and maintain customer groups and hierarchy

This is the overall process of requesting and creating customer hierarchies to establish global,

regional, and local relationships. Customer hierarchies are based upon the legal structure of the

customer. The creation of the customer hierarchy is typically owned by the global or regional team to

minimize duplicates, coordinate contact information, and define pricing parameters. Local data

attributes are usually limited to local reporting parameters. Regional and global data include creation

and changes of the hierarchy, account assignment, report parameters, etc. (Figure 19). The review

process is coordinated through the customer master data organization and the updates are

performed by the owner of the data.

Define and maintain vendor hierarchy

This is the overall process of requesting and creating vendor hierarchies to establish global,

regional, and local relationships. Vendor hierarchies will be based on the legal structure of the

vendors. The creation of the customer hierarchy is typically owned by the global or regional team to

minimize duplicates, coordinate contact information, and define certification. Local attributes are

usually limited to local reporting and quality parameters. Regional and global data include creation

and changes of the hierarchy, certification parameters, rebate management, report parameters, etc.

(Figure 19). The review process is coordinated through the vendor master data organization and the

updates are performed by the owner of the data.

Release and rollout new products/changes to existing products

This is the overall process of releasing new products or changes to existing products to meet

customer requirements. This is the last subprocess in the product development cycle and can

include product obsolescence. Prior to the process, analysis development and prototype testing

have occurred including items, bill of material, routing, costing, storage, and transportation designs

potentially incorporating the previous processes. The release process is coordinated through sales

and marketing with support from the entire master data organization. Any updates occur as a result

of a combination of customer requirements or cost savings.

Data elements

Within the customer, vendor, item and plant data masters there are hundreds of possible data

elements that can qualify for incorporation into the data management organization. Typically, any

element that is not defined as global or regional defaults to local ownership and the site can

determine if they need to utilize any specific element that remains. Once a data element is classified

geographically, it is flagged in the data master domain accordingly so the applications supported

with master data are aligned to the classification. Identifying data elements this way will enable the

MDM organization to limit and control the attributes.

Solution details

17

Figure 19

Business benefits

18

Business benefits

There are many potential areas of “soft” cost reduction areas once proper MDM is exercised. This

can be measured through customer service level improvements, inventory turns, productivity gains,

revenue per employee, and reduced cost of goods just to name a few (Figure 20).

Source: Deloitte Global Benchmark Survey

Figure 20

Within the scope of an MDM and ERP initiative the business drivers can be directly impacted and

improvements can be recognized in as little as 3 years. In addition to the “hard” benefits it can also

enhance customer and supplier relationships by providing improved data accuracy and content

(Figure 21).

Source: Deloitte Consulting FIT template benchmarks

Figure 21

Conclusion

19

Conclusion

With a broad approach to data management, an organization can position itself more competitively

and take steps towards improving customer and supplier relationships. The key components of an

effective management initiative include:

Domain

Governance

Information management

Business processes

In organizations where data is not recognized as a key business driver, order fill rates, revenue per

employee, item costs, and the cost of quality may lag behind high performing companies.

For enterprises with a global footprint or expanding into one, MDM can help them in their efforts to

sustain business activities and provide the foundation for growth and acquisition.

About Deloitte Consulting

20

About Deloitte Consulting

For more information, please contact:

Rick Olson

Specialist Leader

Deloitte Consulting, LLP

550 S. Tryon Street, Suite 2500

Charlotte, NC 28217

+1 704 277 7044

rolson@deloitte.com

Luke Tay

Specialist Leader

Deloitte Consulting, LLP

191 Peachtree St, NE #2000

Atlanta, GA 30303

+1 404 631 3790

luketay@deloitte.com

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