The business models across industries around the world are becoming Customer Centric. Recent studies show that “knowing” customers based on internal as well as external data is one of the top priorities of business leaders. On the other hand various surveys also reveal that customers do not mind to share their semi-personal data for the benefit of differentiated service. In that context, the 360 degree view of customer – which was once thought to be a business process, master data management, data integration and data warehouse / business intelligence related problem has now entered into the whole new big world of BIG data including integration with unstructured data sources. Impact of big data on Customer Master Data Management is spread across - from Integration and linkage of unstructured or semi-structured data with structured master data that is maintained within enterprise; to analyze and visualization of the same to generate useful insight about the customers. There are various patterns to handle the challenges across the steps i.e. acquire, link, manage, analyze and distribute the enhanced customer data for differentiated product or services.

1. Impact of BIG Data
on Master Data
Management
AN INDUSTRY PERSPECTIVE
Subhendu Dey | Senior Consultant / IT Architect

2. Subhendu’s brain dump
What I intend
to cover
Setting the context for Master Data Management
Why impact of Big Data is important
How to deal with the challenge
Business and technical patterns
Q&A
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3. Subhendu’s brain dump
Reduced
Call-center
Cost
Mistargeted
Marketing
Spend
WHAT and
WHY
of
Master Data
Management
(MDM)
Master Data Management (MDM) refers to the disciplines,
technologies, and solutions that are used to create and
maintain consistent and accurate master data (E.g. Customer)
for all stakeholders across and beyond the enterprise.
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Cost of
bad data
Business
benefits
Increased
Customer
Churn
IT
Maintenance
Cost
Cost of
non-
compliance
Lost
Revenue
Opportunity
Reduced IT
Maintenance
Cost
Reduced
Cost to the
Compliance
Improved
Cross-sell /
up-sell
Greater
Customer
Loyalty
Billing and
Invoicing
Errors
Sales Rep
Inefficiency
The goal should be
to reduce the cost
of bad data and
then move forward
to yield business
benefits.

4. Subhendu’s brain dump
Why am I
talking of MDM
in an Analytics
Symposium?
In todays analytical
problem space as we
want to be more
targeted and micro-
segmented the usage
of master data
becomes all the more
important
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Business
Analytics
Master Data
Management
Data
warehouse
Marketing
Mart
Campaign
DB
Insuranc
e
Loans
and
Cards
Baking
products
Claims
Life &
Annuiti
es
Clean & accurate
master data
• In-line / real-time
decisioning
• From Segmented to
Targeted campaign
• Fraud Discovery
• Any other…
As we augment the customer data with more information,
MDM is the ideal place to match / link the data

5. Subhendu’s brain dump
Regional
Business Unit
Change in
dynamics:
recent changes in the
MDM requirements
these years are
taking the problem
space to more
complex scenarios
leading to the world
of BIG data
(characterized by 4
V’s – volume, variety,
velocity and veracity)
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CRM
Customer
ERP
Customer Customer
Customer Customer
Customer Customer
Customer Data
Divisional MDM. Possible
patterns: Consolidation,
Registry, Coexistence or
Transactional
Regional MDM. Possible
patterns: Consolidation or
Registry. May be Coexistence
(rare)
Global MDM. Possible
patterns: Registry.
Global MDM is not very
typical – the final
aggregation could be in
regional level as well
Silo-ed copies of
Customer Data in
Source Systems.
Customer Related
Content
Digital footprint of the customer outside firewall
Chat | Blog | Other transaction | Social presence | Geospatial data
Demographic | Relationship | Accounts | Profile data | Product Holdings
Enhanced360
degreeview
Withinfirewall
customercontent
(mails,chatsetc.)

6. Subhendu’s brain dump
Change in
dynamics:
when asked to rank
their top three priorities
for big data, nearly half
of the respondents in
a 2012 IBM Institute for
Business Value study
named customer-
centric objectives such
as improving customer
satisfaction and
reducing churn as the
top concern.
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In another IBM Institute for
Business Value study, CIOs
identified the top five
capabilities that enable
translation of data into
meaningful and useful
intelligence:
• Master data management
• Client analytics
• Warehousing
• Dashboards
• Search

7. Subhendu’s brain dump
Impact of BIG
data on
Customer
MDM
Volume
Existing storage may not be
ideal for high volume of
external data (mostly
unstructured).
Data retention strategy is
likely to be impacted due to
sheer volume.
Velocity
The high velocity of data
demands revisit on data
ingestion pattern.
There could be need for
real-time analytics on high
velocity data which is not
typical in traditional case.
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Having single view of truth on
customer data based on
internal data in more essential
before jumping into the world
of Big data.
Existing matching software
may prove insufficient.
Veracity
Different analysis techniques
for various types of data to
generate useful insight
Strategy for both data at rest
as well as in motion
Variety
Who, What, How
&
WHY
factor
Security and Governance
Capture & Link
Manage
Analyze
Visualize
Distribute

8. Subhendu’s brain dump
How to swim
in the sea of
change:
let us look for
patterns and
reference
architecture for a
faster response to
clients.
Build Business
Case for BIG
data
Select Business
Architecture
Pattern
Choose
Technical
Pattern
Derive
architecture
using RA
Follow the customer
Let the customer
follow
Buy data externally
Cost of Capture
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Usefulness
Correctness
Acceptability to
Customers
One pattern may
not be sufficient
and it could be a
combination of
business patterns
for capturing
data.
Capture/link Manage Analyze Visualize Distribute

9. Subhendu’s brain dump
Business
Patterns:
possible means to
get extra information
on customer that can
add value to the
business.
Set up an evaluation
matrix to generate
heat-map to find best
suitable one.
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Business
Architecture
Patterns
Follow the
customer
Individual
Following
Virtual
Real
Pervasive
Mass following
Let the
customer
follow
Buy data
externally
Patterns
Evaluationcriteria
ROI Scores
This is one of the most
popular form of data capture
on customer behavior, usage
pattern given the fact that it
is pretty much indirect means
of data capture.
Given the popularity of cloud
based service models – this
is gaining increased
acceptance.
This has good potential given
the footprint of the customer
through various system of
engagement.

10. Subhendu’s brain dump
Technical
patterns:
through the lifecycle
of enhanced
information about
customer, there are
various IT challenges
leading to adoption of
various patterns
The identity of the
potential
customer
information
available from
external sources
may not be
matching with the
same applicable
for on-premise
customer data
(master).
 Identity
resolution
 Entity Analytics
 Probabilistic
matching
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For the lack of 1:1
identity matching
capture process
could often
include data of
too many
unmatched
entities. Those
need to be filtered
appropriately.
Not always the
raw information is
of interest, but a
combination of
events together
makes a
meaningful
insight. Special
care needs to be
taken from cost
perspective.
 Storage
 Retention
Need to find the
meaningful
insight from the
customer related
information
received from the
external sources
and also to store
the same for
efficient future
use.
The master data
for customer and
the extra
information
received from
BIG data sources
are likely to
reside in different
places (structured
vs. unstructured).
Also the
information from
external sources
can be faster
changing than
Master Data.
 Composite
visualization
including both
sources
 Integrated
access to
on/off-premise
data
Distribution of
customer data is
not always batch,
but could be in
real-time as well
(e.g. a mobile
service company
monetizes the
location with a
food chain
company).
 Change is
minimum here
as the existing
patterns mostly
support
distribution.
Capture/link Manage Analyze Visualize Distribute

11. Subhendu’s brain dump
Capture / Link
BIG Data:
there are various
patterns to capture the
digital footprint of the
customer inside/outside
the context of the
organization of interest.
In fact the shared
service component
also plays a role, which
is relevant only in case
of BIG data domain.
Subscription based
 Receive customer details
based on subscription
 Back-end interface sends
customer related data
based on subscription
token created in
registration process.
 Maps to mass following or
individual following (virtual
or real) pattern
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Channel-end
App Component
Platform
Component
Intermediate layer
Shared Services
Component
Back-end
Interface
Application
Process
Data Component
Track & tap actions
 Data sharing across app
components by platform
component
 Maps to pervasive
computing, individual
following (virtual)
Channel initiated
 In this pattern the front-end application component at the
channel side sends back information about the
customers’ feedback, behaviors or experience.
 Maps to customer following organization, pervasive
computing and individual following (virtual)

12. Subhendu’s brain dump
Manage BIG
Data:
create your data
management
strategy for all
possible types of
information of
interest. So that any
new project can
follow the pattern as
reference
architecture.
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x x x x x
x x x x x
x x x x x
x x x
x
x
Stagingareabeforeloading
customerdata
Masterdatastoreforcustomer
OperationalDataStore(ODS)
TimePersistentRepositories
(TPR)
AtomicDataWarehouse
DimensionalData
UnstructuredData(Bigdata/
Content)
Sandbox
Master details related to customer (name,
address, etc.) from siloed sources
Internal transaction data
External transaction (structured)
External unstructured data
Data for exploratory analysis
Internal content data
Customer data store in analytical space
Customerdata
forAnalytics
Have strategy made on:
• Database choice – RDBMS vs.
NoSQL DB
• Cost optimization on H/W
selection
• Data item selection (full vs.
subset)

13. Subhendu’s brain dump
Analyze BIG
Data:
there are different
architecture patterns to
follow based upon the
business context and
latency in which the
analysis is applied.
Also, the decision on
on-premise vs. could
based analysis
changes the
operational perspective
of architecture.
Taking long time for
analysis may make
the high velocity
data out of context
Analysis may
become more of
exploration
Analysis is done on
sandbox
environment before
moving to run on
real data
Master data is
augmented with
more insight if
required.
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This is getting real
traction with analytics
on cloud. Both PaaS
as well as SaaS
offerings are in place.
Use of the RTAP (real-
time analytic
processing) for in-line
decisioning. Sink only
the data you need.
Not very different from
usual data analytics.
This could be
descriptive,
prescriptive as well as
predictive.
N/A
Real-timeBatch
On-premise Off-premise
Select your right
quadrant(s)

14. Subhendu’s brain dump
Factorsfor
Comparison
Visualize BIG
Data:
what differentiates
from traditional
visualization
techniques is the
ability to explore data
from various sources
for exploration.
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AggregatedLayout
ApplicationAggregation
ServiceComposition
SearchComposition
Turn around time
Change in supporting
Applications
Look and Feel
Support for Inter Application
Communication
Security
Search and Explore
Capabilities
Purpose
• Create specification
• Map to goals
Content
• Data and relationship that
are of interest
Structure
• The layout for revealing
the knowledge
Formatting
• Final look and feel based
on other three
Patterns to visualize data
from various sources
Four pillars of visualization
Content along
with existing
technology
asset dictate
the options

15. Subhendu’s brain dump
References:  Article: Mandy Chessell, 2013. Big Data reference architecture: Enhanced
360 Degree View of the Customer
 Article: Stacy Leidwinger, 2013. Big Data use case: Enhanced view of
customer, IBM Bid Data Hub (www.ibmbigdatahub.com)
 Article: Sven Casteleyn, William Van Woensel, Olga De Troyer. Assisting
Mobile Web Users: Client-Side Injection of Context-Sensitive Cues into
Websites, http://www.academia.edu.
 Book: Whei-Jen Chen, Bruce Adams, Arun Manoharan, Luke Palamara,
Jennifer Reed, Bill von Hagen, 2014. Building 360 degree information
applications, IBM Redbook.
 Article: Noah Iliinsky, 2013. Choosing Visual properties for successful
visualizations (http://www-01.ibm.com/common/ssi/cgi-
bin/ssialias?subtype=WH&infotype=SA&appname=SWGE_YT_YT_USEN&h
tmlfid=YTW03323USEN&attachment=YTW03323USEN.PDF#loaded)
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16. Subhendu’s brain dump
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17. Subhendu’s brain dump
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