Big Data deck
November 2012
What is Big Data?
Many PBs
of data every
day
25+ TBs
of log data
every day
12+ TBs
of tweet data
every day
30 billion
RFID tags
today (1.3B in
2005)
4.6 billion
camera
phones world
wide
100s of
millions of
GPS enabled
devices sold
annually
2+ billion
people on the
Web by end
2011
76 million
smart meters
in 2009…
200m by
2014
80%
Of world’s data
is unstructured
The 3Vs : Volume, Variety & Velocity
Business Analytics
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© 2012 Capgemini. All rights reserved.
2
The real 3V explanation
 Volume : Exponential as
• More and more devices and
• Each device generates more and more data
 Variety : This is not about structured / unstructured
• This is about an interconnected world with many external
partners
• And so working with no or low-modeled data
 Velocity : This is not about technical speed
• This is about data value
• Data value decreases every minute!
Business Analytics
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© 2012 Capgemini. All rights reserved.
3
Big Data – some samples
Financial Services
•
Propose “Next Best Actions” for customer service.
•
Discover fraud patterns based on multi-years worth of credit card
transactions and in a time scale that does not allow new patterns
to accumulate significant losses.
•
Measure transaction processing latency across many business
processes by processing and correlating system log data.
•
Analyze and report on trade execution across multiple desks.
Analyze trade execution parameters and why trades were lost.
Smart Energy
A major TV event ends and everyone reaches for the kettle. A substation fails or a pylon is knocked down. The challenge in next
generation smart energy systems is being able to weight supply against
demand and actively change the way the network is configured. With
millions of residents sending their latest demand and tens of thousands
of network points reporting on their current status the ability to forecast
future demand as well as adapting present configuration represents
challenges in the amount of data to handle in real time and to build
effective forecast models
Business Analytics
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© 2012 Capgemini. All rights reserved.
4
Burberry, a digitalized end-to-end company
Business Analytics
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© 2012 Capgemini. All rights reserved.
5
In-memory is changing the game
An in-memory appliance
40 x86 cores, 1TB of RAM
For only 100 K EUR !
Performance improvement means :
1 to 10 ratio : 10’’ and 20’’ become instantaneous
1 to 100 ratio : 2 minutes become 1 second
1 to 1000 : 2 hours are only 10 seconds
 48 hours process should run in 3 minutes !
Business Analytics
Insert “Title, Author, Date"
© 2012 Capgemini. All rights reserved.
6
Conclusion
Big Data technologies allow you to handle data with no limit
Why shouldn’t you be able to handle with Terabytes of data of less?
In-memory means performance is no more an issue
Why not having BI analytics during the transactional Business process?
Cloud seams to be designed for Big Data!
Why waiting months to have new Hardware?
Your business processes may be redesigned using
IT as an accelerator, no more as a constraint!
Business Analytics
Insert “Title, Author, Date"
© 2012 Capgemini. All rights reserved.
7
Any questions
Manuel Sevilla
Capgemini - Global BIM CTO
manuel.sevilla@capgemini.com
twitter.com/msevillatweets
Business Analytics
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© 2012 Capgemini. All rights reserved.
8
Agenda





BIM Global Service Line presentation
Big Data Survey
Our Big Data model
Credentials
Still more
Business Analytics
Insert “Title, Author, Date"
© 2012 Capgemini. All rights reserved.
9
The BIM Global Service Line is now 3 years old
(BIM = Business Information Management)
 Capgemini ‘s global reach with operations in 36
countries and a focus on BIM with over 7,400 BIM
practitioners.
Austria
Finland
France
Italy
Germany
Norway
Canada
Netherlands
Poland
Spain
Sweden
Switzerland
UK
United States
 A uniquely integrated approach to Information
Strategy based around the Capgemini “Intelligence
Enterprise”.
 Deep Industry sector knowledge supported by Sector
Specific BIM offerings.
 Capgemini’s best-in-class Rightshore® capability for
BIM for development and management of BIM –
2200 BIM experts in India CoE.
 A unmatched (and vendor independent) depth of
technology experience. Capgemini works with all the
major BI software vendors to deliver solutions
appropriate to the customer’s needs.
More than half a billion EUR revenue
China
Morocco
Mexico
India
Brazil
Australia
Argentina
Pinnacle Awards
2 in 2012
1 in 2011, 2 in 2010
Software's Most
Innovative Alliance
Partner of the Year
2011
IIG President’s
award for Customer
Satisfaction 2012
Diamond Partner
BI Specialized Partner
Global Applications
Partner of the Year
Award 2012
Outstanding
Collaboration
Award 2011
Services Partner
of the Year award
2012
Most valuable
partner award
2010
Innovation award 2009 & 2010
Epic award for Contribution
Revenue 2011
Global Partner
Business Analytics
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© 2012 Capgemini. All rights reserved.
10
The BIM TLI Who’s Who
Alliances
Connie Cservenyak
BIM Global Leader
BIM CTO
Manuel Sevilla
Paul Nannetti
BIM Alliances
Hélène Mery
Marketing
Madeleine Lewis
MDM Global Lead
Steve Jones
Program Lead
PMO & KM
Richard Brown
Atanu Saha
NA
UK
France
Benelux
FS
India
Scott
Schlesinger
Rob Toguri
Christian
Becht
Kees
Birkhoff
Marc
Zimmerman
Kiran Cavale
Analyst Relations
Sukanya Chakraborty
India leaders
Brazil
DACH
Nicola
Mazzi
Kai –Oliver
Schaefer
Apps1
Sundar Bala
Global Practice Networks
Information
Strategy
Master Data
Management
Business
Analytics
CTO
Big Data
Enterprise
Content
Management
HANA
Apps2
Venkat Iyer
CTO & CoE
Sesh
Rangarajan
Business Analytics
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© 2012 Capgemini. All rights reserved.
11
BIM is naturally strong in Big Data as...
Business
Analytics
Financial
Services
Business
Engagement
Consumer
Products
Energy,
Utilities &
Chemicals
& Retail
Telco
Media &
Entertainment
Public
Sector
Life
Sciences
Manufacturing
& Transport
Performance Management Excellence
Information Strategy
Technology
Foundation
BI Service Centre
Enterprise Delivery Model
BI & Analytics
Master Data
Management
Data Warehousing
Enterprise Content
Management
Data Management
Business Analytics
Insert “Title, Author, Date"
© 2012 Capgemini. All rights reserved.
12
... Big Data is precisely BIM scope!
Financial
Services
Business
Engagement
Consumer
Products
Energy,
Utilities &
Chemicals
& Retail
Telco
Media &
Entertainment
Public
Sector
Life
Sciences
Performance Management Excellence
Information Strategy
BI Service Centre
Enterprise Delivery Model
Technology
Foundation
Manufacturing
& Transport
BI & Analytics
Master Data
Management
BIG Warehousing
DATA
Data
Business Process
Outsourcing
Capgemini Consulting
Business
Analytics
Enterprise Content
Management
Data Management
Business Analytics
Insert “Title, Author, Date"
© 2012 Capgemini. All rights reserved.
13
Agenda





BIM Global Service Line presentation
Big Data Survey
Our Big Data model
Credentials
Still more
Business Analytics
Insert “Title, Author, Date"
© 2012 Capgemini. All rights reserved.
14
The Big Data Survey
Capgemini commissioned the Economist Intelligence Unit to
survey over 600 business leaders, across the globe and industry
sector, about the use of Big Data in their organizations.
Specifically looking at:
 Their use of big data today and planned in the next 3 years
 The advantages they have seen
 The issues they have in using it
43%
of participants are C-level
and board executives
Business Analytics
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© 2012 Capgemini. All rights reserved.
15
The Economist Intelligence Unit Survey:
(1 of 2)
The Deciding Factor: Big Data and Decision Making
What we found:
Believe their organizations
75% to be data-driven
but
say the decisions they’ve made in the past 3 years would
9 out of10 have been better if they’d had all the relevant information
Survey respondents say that unstructured
content is too difficult to interpret
42%
Say the issue is not about volume but the ability
85% to analyse and act on the data in real time
Business Analytics
Insert “Title, Author, Date"
© 2012 Capgemini. All rights reserved.
16
The Economist Intelligence Unit Survey:
(2 of 2)
The Deciding Factor: Big Data and Decision Making
is the level of performance improvement already
26% seen from the application of big data analytics
is the level of performance improvement
41% expected in the next 3 years
cited “organization silos” in the top three
56% impediment to effective decision making
cited “shortage of
50% data analyst”
Dispute the proposition that most operational / tactical
62% decisions that can be automated have been automated
http://frgnbqv.fr.capgemini.com/qlikview/index.htm
Business Analytics
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© 2012 Capgemini. All rights reserved.
17
Extract from The study infographics
Business Analytics
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© 2012 Capgemini. All rights reserved.
18
Agenda





BIM Global Service Line presentation
Big Data Survey
Our Big Data model
Credentials
Still more
Business Analytics
Insert “Title, Author, Date"
© 2012 Capgemini. All rights reserved.
19
Business Analytics & Big Data – A Prime focus
An Integrated Capgemini Approach
Nordics
Canada
UK
Europe
United States
People’s Republic
of China
Morocco
Mexico
India
CC
APPS
Technology Business
Expertise & Industry
BPO
Run
Analytics
Brazil
Argentina
Business Analytics Services






Big Data & Analytics strategy
Big Data technology advice
Proof-of-Value
Analytics-as-a-Service
Analytical Model build & deployment
Big Data & Analytics Process
Outsourcing
 Operational Support & Run
Business Analytics Solutions
9 analytical solutions and a series of
industry sector specific analytical models
The 9 solutions focus on the major issues
facing most businesses such as
customer, risk, performance, fraud etc.
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
Argentina
14.
Australia
15.
Austria
16.
Belgium
17.
Brazil
18.
Canada
19.
China / Hong Kong20.
Czech Republic 21.
Denmark
22.
Finland
23.
France
24.
Germany
25.
India
Italy
Mexico
Morocco
Norway
Poland
Portugal
Spain
Sweden
Switzerland
The Netherlands
UK
US
Australia
Big Data & Business
Analytics Technology
Extensive experience in all the leading
business information management,
analytics and big data technologies
We have all the major technologies
installed in our BIM CUBE Lab
Business Analytics
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© 2012 Capgemini. All rights reserved.
20
We have a Big Data Methodology
We have developed a Big Data strategy, methodology and delivery
capability to help clients take advantage of big data:
 Big Data Process Model
New Business Model or Business Process Improvement
Acquisition
Collection of data
Marshalling
Organization and
storing of data
Analysis
Action
Finding insights
Predictive modelling
Changing business
outcomes
Big Data PoV
Data Governance
 Development and Implementation Considerations
Managing
Data
Integration integration of
Data
Integrity
Master data,
governance &
data quality &
filters
Privacy &
Security
Dealing with
new customer
data sources
Architecture Tool’s choice
Data
Storing
Structured, non
structured
modelling, ...
Action
M2M, ERP
injection, dialog
with suppliers...
data sources
Analytics
Value
Models that
deliver
business value
First use
Be sure the
first project
step will be a
success !
Business Analytics
Insert “Title, Author, Date"
© 2012 Capgemini. All rights reserved.
21
We have a Big Data Process Model
Acquisition
Marshalling
Analysis
Action
Collection of data from
sources
Organization (and
storing) of data
Finding Insight /
predictive modelling
Using Insights to change
business outcomes
 Traditional ETL but often
real-time “constant
acquisition” due to volume &
velocity
 Large volumes / constant
feed
 Need to consider how it will
be consumed (real-time,
ASAP, history) and filtered
appropriately
 Format – structured, semistructured on unstructured
 Modelling – from raw form to
highly structured depending
on source and use
 Deletion
 Forward (prediction) rather
than historic
Outputs are:
 Human (e.g. reports and
analysis that people then act
on)
 Machine (more common
with big data) – e.g.
automatic assessment of
customer to adjust offer (e.g.
Amazon proposed products
based on customer profile)
 BPM technology / Real-time
decisioning
 Partners Information System
 As data is often external –
there are issues of security
and trust
 Licence for data, / privacy
issues for external data
 Open Data (publicly
available sources like
http://data.gov.uk/)
 Modelling behaviour – how
will customers react? When
is the optimum time to
replace parts….
 Probabilistic rather than
definitive
 Text, voice and video
analysis
Data Governance
Business Analytics
Insert “Title, Author, Date"
© 2012 Capgemini. All rights reserved.
22
Big Data Process Model technology
Acquisition
Marshalling
• Extraction
• Hadoop / MapReduce
• ETL
• Other distributed storage using
SQL-like access (eg AsterData,
Neo4J, MongoDB, MarkLogic…)
• Real-time integration
o SOA / Web Services
(eg Facebook)
o Events
o Enterprise Service Bus (ESB)
o Change Data Capture (CDC)
o RSS feeds
• Large Data Warehousing
• Large Content Management
Solutions
• inMemory (eg Oracle Endeca,
SAP HANA, or caching)
• Open data
• Streaming (ESB / Information
Service Bus)
• Social Network
• Removing non useful data
• Data is already here !
Analysis
• Classic BI (SAP Business
Objects, IBM Cognos,
Microstrategy, Exalytics…)
• Predictive analytics (SAS, IBM
SPSS, R)
• Mathematical modelling (eg
Mathematica)
• Text, audio and video mining
(Autonomy, Attensity...)
Action
• BPM (Pega...)
• Real Time Analytical tools
(Oracle RTD – real time
decision, IBM SPSS, SAS)
• BAM (Business Activity
Monitoring)
• Push (mail / mobile BI)
• ESB, SOA... with partners or
internal tools
• Complex Event Processing
(CEP) – ETL tools eg
Informatica, IBM Streams..
Master Data Management + Data Quality tools + Metadata + Data Lifecycle Management
Business Analytics
Insert “Title, Author, Date"
© 2012 Capgemini. All rights reserved.
23
Business Analytics
Insert “Title, Author, Date"
© 2012 Capgemini. All rights reserved.
24
In-memory is changing the game
An in-memory appliance
40 x86 cores, 1TB of RAM
For only 100 K EUR !
Performance improvement means :
1 to 10 ratio : 10’’ and 20’’ become instantaneous
1 to 100 ratio : 2 minutes become 1 second
1 to 1000 : 2 hours are only 10 seconds
 48 hours process should run in 3 minutes !
Business Analytics
Insert “Title, Author, Date"
© 2012 Capgemini. All rights reserved.
25
We deliver Analytics through Business & Industry Solutions
 9 Business Analytical Solutions:
Marketing
Analytics
Customer
Analytics
Predictive
Asset
Maintenance
Enterprise
Performance
Social Media
Analytics
Advanced
Planning &
Scheduling
Fraud
Management
Risk
Analysis
BPO CFO
Analytics
 Industry sector specific analytical solutions in:
 Telecom
 Utilities & Energy
 Financial Services
 Public Sector
 Consumer Products & Retail
Business Analytics
Insert “Title, Author, Date"
© 2012 Capgemini. All rights reserved.
26
Our global approach combined with Big Data revolution is
changing the game
 Big Data technologies are able to handle hundreds of petabytes of data
 In-memory means performance is no more an issue
 Cloud seems to be designed for Big Data
 Our customers business processes
may be redesigned, using IT as an
accelerator, no more as a constraint!
 Together, we are able to improve our
customers revenue and margin !
An Integrated Capgemini Approach
CC
APPS
Technology Business
Expertise & Industry
BPO
Run
Analytics
Business Analytics
Insert “Title, Author, Date"
© 2012 Capgemini. All rights reserved.
27
Agenda





BIM Global Service Line presentation
Big Data Survey
Our Big Data model
Credentials
Still more
Business Analytics
Insert “Title, Author, Date"
© 2012 Capgemini. All rights reserved.
28
Case Studies 1
Global
Investment
Bank
Hadoop/R
Global
Investment
Bank
KDB/Q+
Flow Analytics
 Executing proprietary flow models on the Market Data to compare the trades executed
by the traders to that “bid wanted” received by the desk. This supports the Core
Business/Management team to do “What-If” analysis using Hadoop & R-Analytics.
Type Ahead
 Type Ahead functionality for Financial Analyst Dashboard to retrieve client Information
(across millions of accounts).
Failed Use Case: Price Discovery
 Executing complex algorithms (using R- Analytics/Matlab) to discover price for traders
on a real time basis on Fixed Income products
 Kx Systems (Palo Alto) is the author of KDB and Q+.
 KDB/Q+ is high-speed columnar/time-series database used extensively by Wall Street
firms.
 KDB/Q+ is used for advanced analytics, algorithmic trading, market making, high speed
trading, etc.
 We established a KDB/Q+ COE for the client based in Pune - first KDB/Q+ COE in
India.
 Our COE currently provides 16 specialized resources. Our resources have analytical
and technical backgrounds.
 The COE support multiple business units for the client.
 Our client is looking to grow this COE to 50+ resources in 18-24 months.
Business Analytics
Insert “Title, Author, Date"
© 2012 Capgemini. All rights reserved.
29
Case Studies 2:
Advance Planning & Scheduling
Global
Healthcare
ClickSchedule
 The client, the healthcare division of a global consumer products company, delivers,
maintains, and repairs medical equipment all over the world, with around 6,000 field
service engineers across 32 countries. The engineers are centrally scheduled for each
country. For scheduling and dispatching, the client uses one global instance of ClickSchedule; this is the largest single deployment of this product in the world.
 Increases in operational efficiencies led to a reduced workload for planners and
dispatchers & 10-fold improvement. In response times over a three-month period.
Customer & Marketing Analytics
A Major
European
Bank
 The client required a Customer & Marketing Analytics Solution that required growth in its
customer base. They were looking for a Customer and Marketing Analytics solution that
enable the client to implement various customer strategies – up-sell, cross-sell,
customer total value, next best product, etc. The solution integrated click- stream data,
customer account data, and channel data to enable comprehensive strategies.
SAS
 The solution supported integrated marketing strategies across channels – direct, web,
branches, insurance subsidiary.
* Case studies for all the Capgemini analytical solutions are available
Business Analytics
Insert “Title, Author, Date"
© 2012 Capgemini. All rights reserved.
30
Case Studies : Global Customer Products Company
Global
Consumer
Products
Company
Jive
Radian6
Adobe
Omniture
Eloqua
 Social media analytics services. Listen, monitor and engage to the social
conversation with Jive and Radian6 cloud solutions. Don’t miss a word
customers say about you, no matter in what language or location. Open
24x7x365 around the globe
 Web analytics services. Transform website traffic data into intelligence and
actionable insights with Adobe Omniture, Discover and Insight. Built to transform
large amounts of off- & online data
 Email and web-based marketing campaign services. Automate and align multi
channel marketing campaigns with Eloqua campaign management. From lead
nurturing to multi channel effectiveness all by one cloud-based marketing
automation experience
 Search-marketing management services. Manage multiple advertisement
accounts across multiple platforms as Google, Bing and others form a single
interface with Adobe SearchCenter. Manage ad spend, click through, conversion
and add creatives directly
Business Analytics
Insert “Title, Author, Date"
© 2012 Capgemini. All rights reserved.
31
Global Consumer Products Company
Selected business services
Social Media &
front-end
Monitor & analyse
customer
loyalty
Track and record
customer behaviour.
Build customer profile
Drive ‘eyes’
to
website
Execute
multi-channel
campaigns
Email, direct mail,
Facebook, telemarketing
Personalised
landing
pages + offers
Social media
Monitoring
& Engagement
Content mngt
Web Analytics
Marketing
Automation
Segment & profile
based on web activity
and score leads
Process
Orchestration
Move to automated
lead nurturing/
retention program
Move ‘hot’
prospect
to SFA
CRM
Analytics &
Reporting
Reading
digital body
language
Pulling
‘every
Minute’
Measure & Optimise
Program
builder
workflow
Analyse
marketing
performance
Business Analytics
Insert “Title, Author, Date"
© 2012 Capgemini. All rights reserved.
32
Big Data Deployment : From Satellite to Mobile device
New Business Model or Business Model Improvement
Big Data Solution
Satellite creates Earth
Observation (EO) data
Delivery Consortium
Scientific community provides
with scientific algorithms
Capgemini Austria
Capgemini
The FAAPS processing chain
accesses EO data and
generates geo-coded flood
information
Aerospace & Defense
TU Vienna / IPF
GeoVille
(Exemplary) End Users / “Clients”
Office of the Styrian Government
Dept. for Protective Hydraulic Eng.
& Soil Water Management
Federal government of Lower Austria
Department for fire department and civil
protection
• Disaster Management Centers access flood
information
• Rescue Teams access flood information via
mobile devices
Business Analytics
ESA is advertising
us : http://iap.esa.int/projects/security/faaps
Insert “Title, Author, Date"
© 2012 Capgemini. All rights reserved.
33
La Poste Courrier
 Traceo project
 Their previous solution in charge of
following the mail thru their logistic chain
was unable to handle with activity peaks
like Christmas and was limited to 3 days of
historical data
 Current solution uses a Hadoop /
Cassandra cluster jointed with a Memory
cache solution for mySQL to guarantee
needed performance thanks to a
guaranteed scalability and is not anymore
limited in term of historical length.
 On production since 10th of September
Business Analytics
Insert “Title, Author, Date"
© 2012 Capgemini. All rights reserved.
34
France Telecom – Orange
Symphonie Data Warehouse
Customer Requirements







Customer Benefits
Consultation with France Telecom's sales outlets
Specifically, prevention of unpaid bills. Generally, customer risk
management
Fraud prevention and detection. Juridical requisitions
Network and traffic analysis
Marketing analysis of customer behavior
Unified Data warehouse for fix and mobile usage, calls and sms
Due to legal constraints, every call has to be loaded in less than 1 hour




Direct access to the traffic database by means of an Intranet
application
A flexible solution to supply data to datamarts in order to satisfy
various business area requirements
Symphonie is the reference database for traffic information related
to France Telecom and Orange France
Quasi Real-Time BI (1 hour) has allowed better reactivity
Project Environment
Our Solution: Build and roll out of Symphonie DW









Symphonie collects, transforms and loads up to 200 million tickets per
hour split in loadings launched about every 5 minutes.
Storage of 1 year of tickets (CDR) and 3 years of customer log info (60
Million of Customers)
On average, 25 requests per second with response time of less than 4
seconds for 95% of predefined requests (most common case)
Over 100 aggregates and indicators generated each day
Intranet interface to access the list of calls made or received by a
customer, and access to the following: list of calls made or received by
the customer; customer data log; reply to juridical requisition
Traffic analysis for Orange France (quality of service, optimization of
network infrastructure, monitoring the development of new services)
Traffic analysis fixed-line network, & monitoring of Internet traffic on
RTC network
Ad hoc requests activated by a dedicated team in the office department
Built in 2000 with success, Symphonie is still working on 2011 even if
volume has exploded from 30 TB of data up to 180 TB





Package and Roll-out
Built in 1999-2000
Roll-out and evolutions by Capgemini until now
Capgemini Roll-out signed until 2015
Next large upgrade / migration planned in 2012
Technical environment







DB Hardware : HP Superdome
RDBMS : Oracle 8i in 2000,
Upgraded to Oracle 10gR2 in 2008
Some datamarts in Oracle 11gR2
Database volume : 180 TB
ETL : AB Initio
8000 Users, up to 650 simultaneous users
Business Analytics
Insert “Title, Author, Date"
© 2012 Capgemini. All rights reserved.
35
OPERA – Enterprise DWH
Requirements
Client environment
 Rationalize the multiple existing business data marts into a single homogeneous
warehouse
 > 9 millions customers (20% market share in june
2007)
 Enlarge data access capacity to detailed data / Guarantee data quality
 Important difficulties in cross analytics between
business units
 Going further to Operational BI with Multiple Customer Relationship Media
 Rationalize costs of legacy systems : 4 systems to kill (2 DWH, 1 Data retention
system (DR), 1 Revenue Assurance system (RA))
 High development / maintenance Delay and Cost
 Multiples DWH and Datamarts (IT and business)
 Introduce agility into the development process
Project features / content
 Create a convergent data model – cLDM based, 1st mobile then ISP-ready, B2B &
B2C
Project context
 Type :
Fixed price
 Complete business scope : CDRs, customer, contract, offer, product, loyalty, score &
segmentation, billing, PP recharge
 Date :
2007-2010
 Duration :
2 years (Build : 4 releases)
 Manage different data usage types : Regulation Constraints for Data Retention needs,
BI Functions for other Business teams
 Workload :
8 000 md
 Users :
300+
 Manage high volume and near real time updates (non rated CDRs : loaded every 5
minutes, rated CDRs : every hour) - <5mn query SLA for tactical requests on the last
24h data
 Provide 360° Customer view to support near real time in CRM Front office
Technical environment
 OS :
UNIX
 RDBMS :
Teradata
 Data volume :
130 terabytes end 2010
 ELT :
ODI
 2 legacy systems killed 1,5 year after beginning (DR+RA)
 Analytics :
OBI EE 10g
 Robustness to high volume (400 M CDRs loaded in 1.1.2010)
 Data sources :
20 source systems
 Single version of truth for Business & IT BI initiatives => consolidation
 Data model :
200 core entities
 Use TASM & Datalab concept to ensure agility and better accuracy between business
needs and solutions implemented
Benefits
Business Analytics
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© 2012 Capgemini. All rights reserved.
36
Agenda





BIM Global Service Line presentation
Big Data Survey
Our Big Data model
Credentials
Still more
Business Analytics
Insert “Title, Author, Date"
© 2012 Capgemini. All rights reserved.
37
BI Appliances Hadoop
 Expensive dedicated HW
 Uses commodity PCs
 Built for performance
 Built for extreme scalability
 Designed for high volumes (eg 10s of TB)
 Designed for extreme volumes (10s of PB and more)
 High availability
 Very high availability
 Initially developed using Relational Data bases
 Initially developed for web ranking
 Very mature solutions (skills, SW, HW, administration)
 Not yet fully mature
 Designed for modelled and structured data
 Hadoop = Data is distributed over many machines
 Business As Usual ways to design, build and deliver
 MapReduce = Computing is distributed and executed
where data is (grid solution)
 Teradata, Exadata, Netezza, HANA, ...
Business Analytics
Insert “Title, Author, Date"
© 2012 Capgemini. All rights reserved.
38
Hadoop High Level Architecture
Lucene / SolR used as
search solutions over
Hadoop
Hadoop ecosystem
To improve MapReduce
usage and provide SQL
and analytics (with R)
capablities
Language
JS
JDBC
ODBC
Languages
Search Engine
PIG
ETL
Large data
extraction
Distributed
Coordination
Workflow
Coordination
ZooKeeper
Hbase to store
structured data in a
columnar database
Row / column data
File data
Distributed data processing
Distributed File System
MapReduce is used to
do do grid computing
over Hadoop
HDFS to store any
unstructured data
Business Analytics
Insert “Title, Author, Date"
© 2012 Capgemini. All rights reserved.
39
Here we are !
Country
Contact Italic means no photo
Australia
Brett Miles
Brazil
Fernando Fornazieri
China
Jun Shen
Finland
Harri Johansson
France
Olivier Flebus
Germany
Rudiger Eberlein
India
Hemant Kulkarni
India FS
Rajas Gokhale
Mexico
Manuel Cornille
Jon Hoverson
+ Scott Schlesinger
North America
US - FS
Jeffrey Shmain
Netherlands
Leo Baltus
Norway
Lasse Bache Mathisen
Spain
Juan Carlos Martínez
Sweden
Björn Lillebekk
United Kingdom Tony Harper
UK - FS
Simon Gratton
Global
Jojy Mathew
Sesh Rangarajan
BusinessManuel
Analytics Sevilla
Insert “Title, Author, Date"
© 2012 Capgemini. All rights reserved.
40
Capgemini BIM + Big Data CUBE Lab
Our BIM CUBE hosts the Big Data lab
We are able to show and to build PoCs on these technologies:
What is the BIM CUBE:
Customers can :



Located at Capgemini Mumbai and occupying a space of over 400
sq feet, the CUBE features an interactive kiosk that outlines our BIM
Service Model
Customers can navigate themselves, or have a guided tour, to help
them gain greater insight into the broad spectrum of BIM Solutions



Experience innovative Business Information Management
solutions
Interact with BIM Subject Matter Experts
Witness the solutions created for similar customers
Review proof of concepts and technology innovations, as well as
productivity tools
We are at the forefront of the technology disruptions fuelling information led transformation
Business Analytics
Insert “Title, Author, Date"
© 2012 Capgemini. All rights reserved.
41
One of our Internal POCs: Yammer!
Business Analytics
Insert “Title, Author, Date"
© 2012 Capgemini. All rights reserved.
42
Some Big Data stuff
The Deciding Factor (A global survey from the Economist Intelligence Unit
commissioned by Capgemini)
http://www.capgemini.com/services-and-solutions/technology/business-informationmanagement/publications/the-deciding-factor-big-data-decision-making/
Lot of blog posts are dedicated to big data (URL link to Capping IT off)
http://www.capgemini.com/technology-blog/
More generally : BIM publications
http://www.capgemini.com/services-and-solutions/technology/business-informationmanagement/publications/
Business Analytics
Insert “Title, Author, Date"
© 2012 Capgemini. All rights reserved.
43
Business Information Management
Better Intelligence, Smarter Decisions
44