This document provides an overview of master data management (MDM) and how organizations can treat data as a key asset. It discusses how as companies grow, their data becomes fragmented across different systems. This leads to inconsistencies and a lack of a single view of critical data like customers. The document defines MDM and explains how it can integrate master data to provide a consistent view. It argues that as data drives business operations, organizations should manage data as strategically as they do physical assets to maximize its value.

1. Master Data
Management
Managing Data as an Asset
By
Bandish Gupta
Consultant
CIBER Global Enterprise Integration Practice
Abstract:
Organizations used to depend on business practices to differentiate them in the market; then
various technology systems and applications came along, bringing in automation of business
processes. Today we see that most organizations are automating their business processes,
and that more mature organizations differentiate themselves from others in how they use and
manage their data. The advent of Service Oriented Architecture and advancements such as
Cloud Computing have begun a shift in the IT industry from application-centric solutions to data-
centric solutions.
With an increase in the pace of business, organizations have already built up Business
Intelligence systems to aid efficient decision-making. Globalization and mergers and acquisitions
have served as catalysts for organizations to realize the criticality of data and data integration.
With this background, many organizations have begun to treat data as one of their key assets.
This white paper explains how core business entities known as master data can be considered
organizational assets and how to manage these entities holistically.
A CIBER Data Management Best Practices Whitepaper

2. 2 The What’s and How’s of ETL Architecture
Introduction
As organizations have expanded, acquired, and
merged, their systems and applications have grown
increasingly complex. Often, organizations realize
that something is going wrong or out of control. To
understand this, consider the following situation:
“Due to an economic downfall leading to
cost cutting, a retail company’s business
head decided to send physical product
promotional catalogs to only their
top customers to maintain profitable Application
relationships with them. With this in
mind, he conveyed this assignment to
his executive team and asked them to
make it happen. The executive team
contacted their Customer Relationship
Management (CRM), billing and sales
Application Application
systems representatives to find such
customers. Different systems needed to
interoperate to come up with an answer
and they were not able to reconcile and
agree on the customer information they
stored. The systems did not have a
single true view of their customers.” Figure 1 - Fragmented Application Data
What went wrong in this company’s IT systems?
What could be the reason for ambiguity in customer
data? Before answering these questions, let’s
examine the current business scenarios that exist Master Data Management
in enterprises. Master Data Management (MDM) is a set of
Business operations in most enterprises are policies, procedures, tools and infrastructure used
driven by data, and it can be even considered to capture, integrate, and share master data in a
the lifeblood of the business. Data enters into consistent, accurate, complete, and timely manner.
enterprise systems and applications through many Master data are the reference data elements of
channels, such as messages and electronic files. an enterprise; customer, product, and employee
It then flows through their various systems, such are all master data as opposed to transactional
as Customer Relationship Management (CRM), data such as order, reservation, or claim. Some
sales and billing, gets transformed, and is stored data, such as a list of states, units, and production
in those systems in a variety of formats either composition. remains static and may not require
partially or wholly, as required by the system. When management at an enterprise level.
a new enterprise application is added to carry
out a new requirement, this data gets migrated MDM deals with the issue of scattered and
and stored there as well. The result is a set of fractured master data from a business and
enterprise applications with their own sets of data, technical perspective.
encapsulated within their systems, even when the
scope of these data is enterprise-wide. Data as an Organizational Asset
“The retail company soon figured out What is an organizational asset?
that while focusing on their growth, they In any organization an asset is considered to be:
lost focus on their data as an enterprise
asset and the need for an enterprise- • Something that has value. For example a
wide data management approach.” company’s inventory has value.

3. CIBER, Inc. 3
• Something whose value can be measured. A assets for their inherent contribution to business
computer system has some quantifiable value success, but must actively and carefully consider
on its own. Even when it’s not in use, its cost the intangible data asset as one of the key
can be measured. differentiators for the implementation of business
goals.
• Something that is required for day-to-day
operations and helps an organization to
achieve its objectives. Why treat data as an asset?
As organizations move quickly to adopt new
Usually the term assets brings financial and technologies, trends and techniques as a way of
tangible assets to one’s mind. The focus of responding faster to business needs, the one thing
asset management has always been for tangible that remains unchanged is data. This gives a valid
objects like cash, inventory, tools and equipment. reason for data to be given more importance rather
There are several factors that make it difficult for than treating it as only a piece of information.
organizations to treat their data the same way When an organization starts treating its data as an
they treat other assets. Data is not tangible – it asset, it turns its focus from the effort and expense
is not locked physically in a vault. It does not have associated with only storing and processing data,
intrinsic value; the value comes from how you use towards a full strategic lifecycle of data as an asset
it. The generally accepted accounting principles do and the business value that can be obtained from
not recognize data as an asset in an organization’s using it. Master Data Management emphasizes the
financial record unless it has been purchased. data as an asset paradigm and its various facets
Again, different users have different perceptions of instead of just business process perspectives.
the importance of data so it’s not managed and
valued consistently across the enterprise. MDM – Master Data as an Asset
Managing data as an asset requires data to be
So while many organizations will readily agree
that their data is an important asset, when they defined, secured, and controlled in a business
are asked what they are actually doing to put environment. The following diagram illustrates
this belief in action, the reality doesn’t match the a solution for master data management with the
claims. Organizations must not only value tangible data as an asset perspective.
ETL ESB
a Governan
D at c Portals,
Portlets
Views
e
Master Data
Change
Stored Notifications
Procedures at c
e
a Go rnan
D
ve
Web
Maintenance Services
Figure 2 – Data as an Asset

4. 4 The What’s and How’s of ETL Architecture
Data Governance
A key tenet of MDM states that the business must be Awareness, Sponsorship and Training
an integral part of any MDM project. Data Governance Finally, the governance team has the responsibility
is the manifestation of that involvement in the of promoting data governance awareness and act
process; where business and IT come together. Data as a key sponsor of governance-based initiatives.
governance is where the policies and procedures are As the governance processes are applied to each
created to regulate data creation and maintenance. business area, the governance team, in conjunction
The governance committee develops rules for data with the training organization, provides executive,
quality and stewardship, and ultimately, drives the stakeholder, steward, and user training to support
enterprise towards treating data as an enterprise the governance activities.
asset. To get the full benefit from a data centric
approach, data governance must be the foundation MDM Architecture
of your data management strategy. Two primary architectures have emerged for MDM,
System of Record and System of Reference. An
Data Architecture organization may adopt one of these approaches
As master data is identified, it is important to establish or a combination to manage its master data.
a common business vocabulary for all business Both architectures consolidate master data and
entities. This business vocabulary can be developed make it available to the enterprise in a form that
through enterprise data modeling and results in is standardized according to the agreed upon
understandable and shared data definitions for all guidelines.
users across the enterprise.
System of Reference
Data Ownership The System of Reference architecture views
Creating a master data repository creates a single master data as continuously updated reference
version of truth, but to maintain this data, every domain data. This architecture aggregates master data
specific data and its associated data elements should in a central repository that acts as a reference
have a clear operational owner. It’s the responsibility across the enterprise. The data may enter through
of the business data owner to oversee the definitions, any business system, and is accessible to other
terminology, calculations and usage of their data. The systems through the central reference repository.
data owners ensure the processes used to maintain
and modify their domain data result in consistent data
while satisfying business needs. They also monitor
data security and privacy and data quality levels.
Application
Data Stewardship Application
Data stewards are established to provide “on the
ground” coordination for governance activities. These
stewards work with the governance team, business
data owners, and data governors to support their
directives for data creation and usage, data quality,
and data security.
MDM
Data Quality, Security and Privacy
The data governance team also oversees the accuracy,
integrity, cleanliness, correctness, completeness, and
consistency of data across the organization. Security
issues such as network security, physical control,
Application
Data Integration
systems logs, incident response, and security audits
are addressed. Based on their analysis, problem Reference
reports, and other feedback, the governance team
will work with the business data owners to establish
reactive and proactive activities to maintain and Figure 3 – System of Reference
improve the quality of the enterprise’s data.

5. CIBER, Inc. 5
In this style of implementation, a copy of master Hybrid Architecture
data remains in the transactional systems. As a As the name suggests, this architecture is a
variation, a registry can be created which maps the combination of both the System of Record and
master data creating a common key for reference System of Reference. In reality, not all the
across the Enterprise. applications may be able to offload record-keeping
functionalities to another system. Such systems
System of Record will use the MDM system as a reference and while
The System of Record architecture assumes record- other systems may offload record-keeping and
keeping functionalities for master data, maintaining use the System of Record capabilities of the MDM
tight control of Create, Read, Update, and Delete system.
(CRUD) actions. The MDM system becomes the
point of entry, custodian and the authoritative Metadata
reference for master data. Alternatively, systems All master data entities identified for an organization
and applications that receive master data may should capture the descriptive information about
collaborate with the MDM system to author master their Enterprise data known as metadata. This
data in the centralized repository. includes:
• Business Metadata This includes a
In the System of Record architecture, individual
dictionary or glossary of business terms,
applications no longer maintain master data in
data elements, acronyms and abbreviations.
their environment, except for technical reasons
It is all about making meaning explicit and
(such as caching for performance). Note that
providing business description, terminology,
each of these applications retains a dedicated
aliases, limits, constraints, calculations,
data store for application specific data such as
privacy, and usage of information.
transactions or logs.
• Technical Metadata Technical metadata
includes the internal data types and
structures, its storage location, the systems
Application that affect the information and more.
Application • Operational Metadata This includes
operational run-time and performance
statistics.
Data Services
To control and secure the MDM repository, it
should be accessed and updated by a collection
MDM of data services. These services implement the
business operations and support authentication,
security, access control and audits in support
of organizational goals. Any change to the
data repository has to flow through the data
Application services layer. This layer can contain services
Data Entry like ETL (Extract, Transform & Load), views, ESB
(Enterprise Service Bus Adapter), web services,
Reference
portals, notification services, maintenance and
enhancement services. All of these services can
be implemented without affecting other services
Figure 4 – System of Record and their functionality. If the organization wishes
to switch to some emerging trend or technology,
the existing service can be modified to adapt to the

6. 6 The What’s and How’s of ETL Architecture
new technology without affecting other business business processes
functionality.
- Better control over data by implementing
Some of the services are:
ownership and stewardship of data –
modification, flow and maintenance of data
Extract, Transform and Load (ETL) happens in a controlled manner
These services are utilized for batch processing of
data. They can extract data from source systems, - Avoids duplicated effort in maintaining and
stage them for cleansing, standardization, storing data – saves money and
enhancement and other data quality checks, and management overhead
finally load the cleansed data to the repository. • Stakeholder Satisfaction
Notification Services - Better engagement and satisfaction
These are outbound services that provide a common levels from customers, business users, and
mechanism to notify subscribers (application technical teams
systems) of changes to the Master Data Repository. • Risk Management
Using these services, an application can assure
- Better compliance to business, technical
that it is aware of the latest information of any
and legal requirements
master data entity, regardless of which application
recorded the updated information initially.
Conclusion
Web Services Data has always been the life blood of organizations,
Web services provide input/output interface but typically the business processes have been
to the data repository. These can be utilized by getting more attention. The perspective of data
middleware technologies to access and update as an asset will provide seamless control over
data in near real time. Web services use XML the quality, security, management and lifecycle of
formatted messages to communicate; a data data, which in turn provides improved capabilities
model is defined to exchange data to and from the to the business. With the increasing pace of
data repository. business operations and demands for quality data
and high availability, it is time to focus on data as
Maintenance and Enhancement Services the backbone for organizations.
These services perform periodic operations to
ensure quality and integrity of the Data Repository. Though the initial effort of establishing data
Examples of such services are governance and data management disciplines
involves lot of time and effort from business and
• Data Profiling
IT stakeholders, once all policies, procedures and
• Entity Matching infrastructure is in place, the business becomes
• Data Enrichment and Enhancement more nimble when meeting customer needs and
business objectives. Treating data as an asset
• Data Quality Audits provides a single and centralized point of control,
• Backup and Recovery easier maintenance and a single version of truth.
Data management along with data governance
• Archival and Purging provides a framework to achieve complex business
functions effectively and can be tracked to
Key Benefits of MDM completion successfully. And finally, treating data
Properly implemented, MDM promises to improve as corporate asset gives a sense of satisfaction
an enterprise’s: from top management to IT stakeholders while
• Operational Efficiency satisfying clients and customers as the same time.
- Clean, unambiguous and consolidated
view of data helps to improve efficiency of

7. CIBER, Inc. 7
About The Author
Bandish Gupta has been
involved with the technical and
business aspects of building
Data warehouses. She has a
good exposure to various tools
and techniques in BI/DW space,
including tools like extract-transform-load(ETL)
and database. She has worked with organizations
in retail and healthcare domains. Her current
interests include business intelligence, data
profiling, data quality, data governance and
metadata. She is based out of Bangalore, India.

8. CIBER, Inc. (NYSE: CBR) is a pure-play international system integration
consultancy and outsourcing company with superior value-priced
services and reliable delivery for both private and government sector
clients. CIBER’s services are offered globally on a project- or strategic-
staffing basis, in both custom and enterprise resource planning (ERP)
package environments, and across all technology platforms, operating
systems and infrastructures.
Founded in 1974 and headquartered in Greenwood Village, Colo., CIBER
now serves client businesses from over 40 U.S. offices, 25 European
offices and seven offices in Asia/Pacific. Operating in 18 countries,
with more than 8,500 employees and annual revenue approximately
$1.2 billion, CIBER and its IT specialists continuously build and upgrade
clients’ systems to “competitive advantage status.” CIBER is included in
the Russell 2000 Index and the S&P Small Cap 600 Index. CIBER, the
Reliable Global IT Services Partner.
www.ciber.com
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