Enterprises are faced by information overload. Big data appears as an opportunity, but has no relevance until enterprises can put it in context of their activities, processes, and organizations, Applying MDM principles to Big Data is therefore an opportunity that enterprises should target. This presentation covers the following topics : - what is MDM and Information Management - what is Big Data and what are the use cases - why and how Big Data can take advantage of MDM ? why and how MDM can take advantage of Big Data ?

1. MDM and Big Data :
the wining association
Jean-Michel Franco
Innovation Director
Copyright 2007, Information
Builders. Slide 1

2. Business & Decision : a global Consulting and
System Integration company
Founded in 1992
Revenue 2012 :
221,9 M€
2 500 employees
16 countries
5 expertise recognized by global independent industry analysts
BI / EPM
CRM
Digital Marketing
BI & EPM Services
Europe MarketScope
Magic Quadrant
for CRM Services
Interactive Design Agency
Overview, Europe, 2013
EIM
MDM / BRMS / Search / ECM
CONSULTING
Consulting,
Change Management , enterprise
software design, training
2

3. Business & Decision and MDM
MDM and Information
Management
specialist
Proven iterative
Design &
implementation
methodology
Innovation,
engagement ,
expertise




Master Data Management dedicated practice
80 consultants globally
Strong market research and partnership relationship management
Closely linked to other group expertise (CRM, BI, e-bus)
 Proven Agile MDM approach, closely linked to the business with
engagement on costs and time to deliver
 Versatile consultants with BI and IT skills




50 % delivered through a fixed priced approach
90% of the business targeting large accounts
End to end engagement
Deep knowledge of technology solutions
Expertise
across industries
and MDM domains
3

4. Are you ready to get value from your data assets ?
lessons learned from Amazon.com.
Know your
customer
Source : Faber Novel
Expand your
service portfolio
Value Your
ecosystem

5. From data retention to data sharing
New organizational and technological paradigm
Knowledge is Power
Knowledge is Profit
• Retention and data
silos
• Heterogeneity and
« best-of-breed »
• Decentralization et
autonomy
• Vertical
organization
• Opaqueness
• Communication
and global sharing
• Mutualization
• Centralization/
federation and
collaboration
•Horizontal
Organization
• Transparency
Some answers are in your data
– if only you could take advantage of them
5

6. Business needs are becoming more and more precise
and urgent
Leveraging your data assets are a must, not an option,
to tackle current Business challenges
IT system
convergence &
consolidation
Deinterleaving of
IT systems for
deregulated
markets
Governance, Risk
and Compliance
Time to market
Customer
Knowledge
Information
Hub
Business
Glossary
Master data
Repository
Data
Quality
Integration
Extended
enterprise

7. From data integration to Information Governance
From a siloed, IT driven model (Data
Management)…
IT
…to a federated,
and shared responsibility model
(Information Governance)
Lines of
businesses
 Business define their need and use information
within enterprise applications
 IT designs, implements, runs and manages
 Ongoing conflicts on data quality and relevancy,
lack of autonomy, slow time to market...
Métiers
 Line of businesses define their needs,
administrate the information, document
them, sometimes mashes them up and
contribute to their relevancy and maintenance
 IT accompanies, controls, rolls out, delivers “as
a service”, secure and manages

8. Master Data Management 101
Master data management (MDM) is a comprehensive method of enabling an enterprise
to link all of its critical data to one file, called a master file, that provides a common
point of reference. When properly done, MDM streamlines data sharing among
personnel and departments. In addition, MDM can facilitate computing in multiple
system architectures, platforms and applications.
The Master Data Management aims to develop the processes, organization and tools to
collect, reference, manage and share the Master Data and links between them across
organizations, people, processes and systems
The party/
persons
The products
Les
The places
Lieux
The
organizations
The Shared
assets
Clients
Citizen
Products
User
Services
Points de
Countries
vente
Charts of
accounts
Entrepots
Warehouse
Stores
Supplier
33%
44%
3%
Business rules
SLA
Territories
21 %
Subsidiaries
Assets
Configurations
Employees
Operational and
legal organization
Partners
Rates
Real estate
Standard
codifications
8

9. MDM isn’t self sufficient : disciplines of Enterprise
Information Management
Document,
eliminate
data redundancies
Improve and
certify data
quality and
relevance
Transfer
information,
secure it, trace it
Expose the
information and
make it accessible
« as a service »
Transfer
information,
secure it, trace it
Master
Data
Management,
Meta Data
Management
Data Quality
Information
Governance
Enterprise
Information
Integration,
Info Lifecycle
Management,
Data Loss
Prevention
Data
Services
SOA, enterprise search,
BI,
Enterprise
portals

10. What are the data types to consider ?
Describe
 Data types :
Analytical data
Meta
Data
Transactional data
use
Master and reference data
 Example:
Transactional data
Meta-data
Customer_Id
First name
Name
Product
Supplier
Date
Amount
92584789
Ann
B.
TXF98
Dell
24/12/2013
650 $
92584789
Ann
B.
AXC54
Maped
24/12/2013
2,44 $
92584789
Ann
B.
TRE56
Playmobil
24/12/2013
….
Master Data
Analytical data
scoring
RFM
CLV
129,36 $

11. From data integration to information governance :
Where to start?
Design the
platform
Define the roles
Engage the programs,
domains per domain
Product
Customer
Organization
Sites
Platform design
-> Needs assessment
workshop
-> Proof of concept
-> Roadmaps and budgets
Orga. blueprint
-> Information Governance
competency center
-> Data Stewardships
-> Service Center
Roll out
« Fast delivery »
-> Iterative modeling
-> Data mappings
-> Data quality maps

12. Big Data : définition
Big Data is high volume,
high-velocity and highvariety information assets
that demand cost-effective
innovative forms of
information processing for
enhanced insight and
decision making
From “the 451 Group” et Gartner
Source : Wall Street Journal
“The challenges include capture, curation, storage, search,
sharing, analysis and visualization..” (wikipedia)
Inspired by Wikipedia

13. Big Data is the long tail of information management
“Today, we sold more books that didn’t sell at all yesterday
than we sold today of all the books that did sell yesterday”
(amazon.com, via Josh Petersen & Wikipedia)
BI as we know it
Popularity
- Information sourced from internal IT Systems
- information provisioned in batch mode
- Static information modeling
BI as we’d like it to be
BI as we know
+ externally sourced data
+ « just in time » data
+ un-structured, semi-structured data
+ information as it comes (schema less)
Available information

14. Data Warehousing on steroids for better pricing, planning
and customer facing policies
Retailers pioneered Enterprise Data Warehouses, especially for market basket
analysis.
But retailers are now pressured to get more value from their data assets, to deepen
and sharpen analytical capabilities and make them « actionable » .
Dynamic and micro-segmented pricing policies
Personalization of the offers for loyalty programs
Adjustment of offers to demand by locations
Consistency across channels (e-commerce, stores , drive)

15. Transparency for supply chain efficiencies
and superior customer services
In France, 25% of the water that flows into the distribution networks is lost
due to leaks or frauds ; This accounts for 2,4 billions Euros per year. (*)
Digital channels and internet of objects open new opportunities to bring
transparency into the supply chain, and deliver superior customer service
(*) Source : SIA conseil
Real time information on water flows and quality
A value added service for consumers and institutions
Detection of leaks as they occur along the network and
at the end of the chain
A common engagement between supplier and
customers in terms of sustainability
Automation of the collection process for billing

16. Innovation in insurance industry and agritech
Innovate with data centric new offers
A start-up to manage risks and insure farmers through online services that
predicts how climate affects crop yield and personalized insurance offers.
A predictive platform that combines hyper local weather data with agronomic
yield data down to the field level, all undergirded by weather simulations.
* acquired by Monsanto on October
2013 for 950 millions $
Trusted advizorship through online personalized services to help
farmers better predict manage the climate conditions
Services can be deployed globally without limits, allowing to tackle
new markets
Claims management processes fully automated from observation
to payment
Huge opportunities to transform best practices in agriculture and
climate management

17. Innovating in the insurance industry :
Fraud Management
Apply the principle of Credit Scoring for claims management.
Integrate unstructured and semi-structured data to highlight inconsistencies in claims
declarations.
Push the analytics on the field, close to the customer and when an where the claim is declared .
Success rate of investigations : from 50 to 85%
25% of claims are closed immediately at the first step, against 4%
before -> better service for honest customers
Scoring drives the claims process and improves its efficiency
(Actionable analytics)

18. Elevating the good old Data Warehouse with
Big Data : Searching for your « long tail »
Extend the founding principles of
Data Warehousing and Information
Management for more:
On line transactional processing
Immediacy
Data
Warehouse
Big
Data
Precision
Create new source insights
through new data flows
Sensors, Internet of things
Business
Intelligence a
analytics
Agility
External data, shared
data, open data
Capture and share unstructured data
Documents, rich content…
Public data sourced from
social networks and
internet

19. Big Data by industry and by activity
IBM : the real world use of Big Data

20. What do you need to manage your Big Data?

21. MDM and Big Data : The wining association
21

22. Why Big Data needs MDM?
Example : Digitalizing the ordering process to Santa Claus
Entity extraction
Data Quality management
Reconciliation with master data
Data enrichment
Customer
_Id
First name
92584789
Ann
B.
92584789
Ann
92584789
Ann
….
Name
Product
Supplier
Date
Amount
TXF98
Dell
24/12/2013
650 $
B.
AXC54
Maped
24/12/2013
2,44 $
B.
TRE56
Playmobil
24/12/2013
129,36 $
22

23. Why MDM needs Big Data ? Ex.: From Customer Data
Integration to an active and real time 360° Customer View
Master Data
Contact Center
Transactional Data
Face to face
Interactions
SMS/Mail/Chat…
Mobile
Applications
3rd party
Data Platform
Customer
Journey Data
Customer
Data
Platform
ROI
Analytical data
(stores, agencies…)
Web Site
*Source : H Sorensen
23

24. Innovation in the hospitality industry: real time
recommendations
• From attention to intention economy
• Test offers and challenge their efficiency on an ongoing basis
• Provide a consistent quality of service across channels
• Better manage recommendations across the brands, together
with interactions with customers, promoters, detractors…
Improve click rate (+43%) and transformation rate
Ability to test new offers, and to stop or improve them as
soon as needed
Ability to listen and react to promoters and detractors in
social networks
Personalized offers and personalized interactions
Federation of customer knowledge across brands to
adapt to organizational changes

25. Innovation in the banking industry:
Multi channel customer journeys
• Acquire/Enrich Customer knowledge
• Recommend the next best offer according to the context
• Manage end to end purchasing journey from intent to payment
• Monitor real time the relevance and success of offers
Personalized interactions with unknown internauts based on
their click stream
Personalized interactions with known customers based on
their profile and current /past on line and offline journey
Ability to track the timeline of customer interactions both
offline and online
Definition of new customer segments based on analytics
around customer journeys

26. Next step: Towards « predictive/prescriptive apps » :
Next generation of apps that can anticipate user need and recommend
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