By leveraging both big data and master data, organizations can provide a true 360-degree view of critical master data entities and produce actionable insights gleaned from big data sources.

1. Mastering Big Data: The Next Big
Leap for Master Data Management
By leveraging big data, organizations can provide a true
360-degree view of critical master data entities; meanwhile,
master data can help convert information gleaned from big
data sources into actionable insight.
Master data management (MDM) is critical to
the success of many big data analytics projects.
Why? Because it encompasses people, processes
and technologies that ensure the accuracy,
uniformity and uniqueness of data entities shared
by multiple business units within an organiza-
tion. (See our white paper “Innovations in MDM
Implementation: Success via a Boxed Approach”
for additional insights.) Big data, on the other
hand, refers to the large quantities of human-
or machine-generated bits and bytes that are
generated at high speeds from multiple sources,
including not only posts and comments on social
media but also images, videos, PDFs, RFID data,
point of sale data, call center comments, data
generated by enterprise systems and more. A big
data analytics strategy involves collecting this
data in order to create meaningful information.
All this data, in various shapes, sizes and forms,
represents valuable information that needs to
be standardized and stored in a meaningful
way within a central repository accessible by all
business units. MDM provides a trusted, central
point of storage and access to this information.
This whitepaper describes how master data
management and big data analysis need to work
hand-in-hand to provide a 360-degree view of
an organization’s critical data entities. (While
this paper is largely focused on social data and
customer MDM, there are many other applica-
tions of big data not explored here.)
The Relationship Between
Big Data and MDM
MDM provides a meaningful context to big data
and a panoramic view of critical data entities,
such as product, customer, employee, agent,
location, etc. (see Figure 1, next page). With all this
information stored in a central trusted repository,
all business units have access to a single version
of the truth, on demand. In the absence of such a
master data hub, records are typically stored in
silos, such as loyalty systems, CRM systems, POS
systems, Web portals and big data repositories.
Such systems usually have their own version of
the truth, causing headaches that ripple across
the organization, from substandard customer
service and bad business intelligence, through
impaired operational efficiency (see Figure 2,
next page).
While MDM is crucial to the success of many big
data projects, it is important to note that not all
big data initiatives require an MDM solution. MDM
is needed only for big data initiatives that involve
• Cognizant 20-20 Insights
cognizant 20-20 insights | may 2013

2. 2
master data entities, such as customer, employee,
agent, product, location, etc.
The Scope of Big Data and MDM
When data is extracted from most big data
sources, it contains two parts. The first part is
entity attribute-level data, or data that can be
used to identify an entity. The other is transac-
tion-level data that is generated by each entity.
For instance, if the big data source is Facebook,
the two types of data include:
• Attribute-level data: Facebook ID, phone
number, name, address, etc.
• Transaction-level data: Conversations,
comments, likes, etc.
The big data extraction layer will pass the attri-
bute-level data to the MDM system and the
transaction-level data to the big data hub, to
be analyzed by a big data analysis tool. After
analysis, the brand advocate score, purchase
intent, etc. will be passed to the MDM system to
create the 360-degree view of the customer.
To help define the scope of big data, consider the
following:
• MDM will act as a repository of the following
information types obtained from big data
sources:
>> Attribute-level data.
>> Information derived from transaction-level
data.
cognizant 20-20 insights
Figure 1
A 360-Degree View of Jane Doe
Figure 2
With or Without MDM
?
Customer store
purchase information
and the information
entered in loyalty card.
Basic customer information,
campaign information,
targeted mailer, couponing
information.
Social conversations,
whether the customer is
a net promoter, on the
verge of switching,
network reach, etc.
Web purchase history,
information entered
during checkout.
Central reliable
repository
Customer store purchase
information and the
information entered in
loyalty card.
Basic customer
information, campaign
information, targeted
mailer, couponing
information.
Social conversations,
whether the customer is a
net promoter, on the
verge of switching,
network reach, etc.
Web purchase history,
information entered
during checkout.
Call center conversation,
number of problems
resolved, dissatisfied
customers.
No reliable,
360-degree view
CRM System
CRM System
Social Data – Big Data
Social Data – Big Data
Web Portal Web PortalIVR Portal IVR Portal
POS System POS System
Call center conversation,
number of problems
resolved, dissatisfied
customers.
Unstructured Internal Data
Unstructured External Data
Third-partyInformation
NominalInformation
• Call center conversations
• Point of sale data
• Conversations
• Reviews
• Blogs
• Images
• Videos
• Credit score
• Professional affiliations
• Memberships
Name: Jane Doe
Address: 1 Acme Road,
Kiwi NJ 00000
Phone: 555-555-5555
Age: 23

3. • MDM will cleanse, standardize and de-duplicate
this entity-level data and maintain audit trails
and hierarchies.
• MDM will not store entity-level transactional
data, such as conversations, transactions, etc.
• MDM will not analyze big data.
In the absence of an MDM system, both attribute-
level and transaction-level data can be stored in
big data hubs. But such data stores are meant for
heterogeneous, unstructured or semi-structured
data that is intended for analytics purposes;
hence, they are not optimal for managing shared
enterprise master data entities. Additionally:
• Data models in big data hubs are optimized
for analytics, while MDM data models are
optimized for entity resolution.
• A big data hub stores all information gathered
from a big data source; however, these records
are neither unique nor cleansed and rectified.
Hence, finding an authoritative golden copy
can be difficult and time-consuming.
• The sheer volume of data in the big data hub
will not allow a quick search for entity-level
attributes for customer service or decision-
making purposes.
• Big data hubs are built for a specific purpose
— big data analysis. Hence, strong entitlement
and hierarchy management are not included.
MDM for Big Data Analytics
To handle the demands of big data, next-genera-
tion MDM systems must successfully handle the
volume, speed and formats across all data types.
Required fundamental changes to MDM include:
• A data model to store big data-specific
attributes, such as social media IDs, opt-ins,
brand advocate scores, social network hierar-
chies, product preferences, purchase intent,
churn possibility, etc. As previously mentioned,
some of these attributes will be fed into the
MDM system from big data analytics systems;
others will be fed directly from the source.
• Improved MDM integration architectures to
handle the speed at which this data is both
added and updated.
• Advanced match and merge processes,
especially for social analytics, which is among
the most widely applied use cases in big data.
The biggest challenge with social analytics is
successfully identifying the social profiles of
an organization’s existing customers. This is
especially true since multiple results can be
returned when searching for common names.
Also, many people use aliases rather than real
names in their social profiles, making it even
more difficult to identify these profiles.
• Advanced data governance policies involve
rules around not only data usage, ownership,
etc. but also around big data-specific data
governance and data stewardship. MDM
3cognizant 20-20 insights
Figure 3
MDM and Social Architecture
+
Twitter
CRM
Others
Permissions/dataextractionlayer
Social conversations
are passed on to the
analytics tool.
Entity information such as
Facebook IDs, Twitter IDs
and other extractable entity
information is syndicated
to MDM.
Company
Web site
Facebook
Post-analysis
attributes like
net-promoter flag,
at-risk flag, Influencer
flag etc., are passed
on to MDM.
Attensity, Pentaho, Google’s Social
Interaction Analytics, Cognizant
Social Prism and many more.
MDM-on-Cloud
• Data cleansing
• Data standardization
• De-duplication
• Workflows
• Hierarchy, etc.
Social Analytics Tool
• Sentiment analysis
• Lead generation
• Customer acquisition
• Net promoter identification
Analytics
Tools

4. cognizant 20-20 insights 4
needs to enforce policies and procedures that
ensure:
>> External data does not affect the integrity of
the internal data.
>> Data is relevant for the purpose for which it
is mined.
>> Escalation paths exist for data conflicts.
>> Privacy policies are enforced.
When Big Data and MDM Combine to
Achieve a Business Result
Airlines
With enormous customer acquisition costs,
customer centricity is the key to profitability in
this industry, which makes airline carriers ideal
candidates for big data programs.
• Problem statement: An airline wants to send
targeted promotions to brand advocates and
influencers to increase coupon redemption and
positive word of mouth.
• Solution:
>> Route a social media campaign through the
airline’s loyalty portal to capture customers’
social media presence.
>> Analyze big data to identify brand advocates
and influencers among these customers and
tag them appropriately in the MDM hub.
>> A downstream campaign management sys-
tem can readily obtain this data from the
MDM hub to identify brand advocates among
an organization’s customers and — based on
the brand advocates’ preferences, opt-ins,
etc. — send targeted promotions to them.
Multibrand Retailers
Due to cost pressures, severe competition and per-
ishability of certain products, couponing is among
the most commonly used marketing tactics in
retail. Targeted couponing enabled by big data
analytics would help retailers improve coupon
redemption rates and increase profitability.
• Problem statement: A retailer wants to
identify heavy users of certain products and
send them targeted coupons to enable quick
inventory clearance.
• Solution:
>> Implement big data analysis software to
analyze point of sale data for loyalty card
holders, and tag frequent purchasers of a
product in the MDM system as heavy users.
>> A downstream couponing system can readily
obtain this data from the MDM hub to identify
heavy users of certain products and — based
on the preferences, opt-ins, etc. of these cus-
tomers — send them targeted coupons.
Fashion Retailers
With rapidly shifting styles, fashion retailers need
to update their product assortment continuous-
ly. Garments that are in vogue one season can
go out of style the next, which means retailers
must clear their racks using discount pricing
strategies. The longer that old merchandise sits
in the warehouse, the more it loses value. So it’s
imperative for retailers to sell their merchandise
as quickly as possible.
• Problem statement: A retailer wants to
build a complete profile of all customers so it
can recommend ideal products and discount
offers, at the right time and place, to improve
customer service and profitability.
• Solution:
>> Implement big data analysis software to
analyze point of sale data, purchase history
and data from social media (Polyvore, Pin-
terest, Facebook, etc.) and create a complete
customer profile in the customer hub.
>> Analyze data in the customer hub in real
time to recommend products, coupons, etc.
at the time of checkout or as part of market-
ing campaigns that send targeted coupons
and promotions to these customers.
Satellite TV and Cable Providers
These providers can analyze customer channel-
surfing behavior for many purposes.
• Problem statement: A satellite TV/cable
provider wants to show targeted ads to
customers to increase advertiser ROI.
• Solution:
>> Analyze individual customer channel-surf-
ing behavior to understand which customers
are likely to watch which types of ads during
which shows.
>> Enrich customer data in the MDM hub based
on this intelligence.
>> Use downstream ad placement software to
obtain channel-surfing information, along
with viewer household information, opt-ins,
etc. from the MDM hub on-demand, and
based on that insight, target ads to viewers.

5. cognizant 20-20 insights 5
Multiproduct Manufacturing Companies
Such companies must manage a large number of
suppliers and have a 360-degree view of them
via a central repository. This is important not
only for supplier selection purposes but also for
regulatory compliance.
• Problem statement: A multiproduct manufac-
turing company seeks a 360-degree view of
its global supplier base for quick and efficient
supplier selection.
• Solution:
>> Analyze available information about each
supplier via the media, the Web and from
notes transcribed by procurement manag-
ers during transactions.
>> Enrich supplier data based on this analysis
to build a 360-degree view of each supplier,
including violations cited in the news, delay
in past shipments, etc.
Automobile Manufacturers
Car makers tend to spend millions of dollars
each year on market research for product line
extensions or feature enhancements. Big data
can be analyzed to understand what customers,
partners and competitors think about a car
and more.
• Problem statement: In an attempt to expand
its business, a car manufacturer seeks to
aggregate all feature-related commentary
about its models and use these insights to
shape future vehicles.
• Solution:
>> Analyze social media commentary, calls to
the call center related to each model and
data from automobile monitoring systems,
if available.
>> Enrich the data about each car in the prod-
uct information management system, in-
cluding features to be added in the next
model, so that all information about the ve-
hicle resides in the same place.
Critical Move-Forward Considerations
While big data is already the next big thing in busi-
ness-technology, and holds the key to achieving
the next level of customer centricity, organiza-
tions that want to make sense of huge, prolifer-
ating pools of structured, unstructured and semi-
structured data need to keep the following in
mind:
• A big data strategy should be a companywide
initiative, with complete buy-in from senior
leaders.
• Big data will impact not only MDM but almost
all enterprise systems in any size company (i.e.,
businessintelligencesystems,datawarehouses,
ERP, CRM, campaign management, etc.).
Therefore, a proper IT integration strategy is
crucial.
• It is important to establish a clear understand-
ing of the expectations from big data analytics.
The social Web holds terabytes of data for most
brands; acquiring, storing and attempting to
mine all of this data is not only impossible but
also unnecessary.
• Finally, brands need to maintain a balance
between targeted promotions that consumers
find useful vs. targeted promotions that
consumers may find intrusive or an encroach-
ment on their privacy.
Final Thoughts
In this era of customer centricity, superior
customer service and operational efficiency are
two major differentiators that can set a company
apart from its competitors. Big data analytics can
help improve customer service, develop operation-
al efficiencies and increase profitability. However,
big data analytics cannot provide actionable
insights unless the data and related information
derived from big data sources can be traced back
to the appropriate entity. Therefore, to get the full
value from big data initiatives, MDM is not just an
option but a critical necessity.
About the Author
Reshmi Nath is a Business Consultant within Cognizant’s Customer Solutions Practice, focused on
master data management. She has seven-plus years of experience in the technology and consulting
industries in the U.S. and India. Reshmi earned her M.B.A. in marketing and finance at Michigan State
University and a bachelor’s degree in computer science at Bemidji State University. She can be reached
at Reshmi.Nath@cognizant.com.

6. About Cognizant
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