Experience the power of data analytics on information to meet the digital transformation requirement.

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Fujitsu World Tour 2017
Power of
Information
Analytics in Digital
world

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Experience the power of information
Pankaj Sharma
Sr. Solution Architect – High perfromance
Computing & Big Data Solutions
Abhishek Dixit
Head of High Performance Computing and Big
Data Business
Experience the power of data analytics on information to meet the
digital transformation requirement.

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Actuate
Data
Algorithms,
Al technology
• Learning, Insights
• Decision support
The Internet
of Things (IoT)
• Connect physical world
• Sensing
Today’s view of technology in digital world
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 The IoT digitalizes
physical objects,
enabling the collection
of digital data
 The Big data does
analysis of this data &
produces useful insights,
enabling better control
2

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Key industry touch points
PainPoint#1PainPoint#2
Being able to collect & store data effectively
 The Internet of Things
 Unstructured data
 Database infrastructure
Being able to draw insights from data
 Real-time & predictive analytics
 Data visualization
 Using data to solve a problem
PainPoint#3
Being able to leverage data across the business
 Businesses need to be data-driven
 Data experts need to collaborate
 Data leaders need to be appointed

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Digitization – The Challenges
 Organizations are facing different challenges in shifting to digital
world
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Leadership & Talent Security Complexity Adaption

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Our approach
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People Information Infrastructure (Things)
 Bring together 3 dimensions to create innovation

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 Fast Time-to-Information
 Data Discovery
 Real-time Analytics
 Visualization
Big Data solutions empower innovation
Traditional analytics focus on analyzing historical data. Big Data extends this concept to include real-time
analytics of in-flight transitory data
CIOs recognize the importance of data but understand that data is useless without having the tools to make
sense of it

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Big Data value chain
Big Data
Collect
Stream
Structured &
unstructured data
Devices,
sensors,
Internet of Things
Cleanse
Transform
Analyze
Find
Decide
Navigate
Research &
development,
science
Operation,
automation,
production
Interactive
reporting,
advertising
Use cases, solution architecture, processes: Fujitsu’s approach
Social media,
open data,
linked data

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Pure-play Hadoop distribution vendors
Hadoop becoming mainstream
 Cloudera, MapR and Hortonworks innovate
to meet enterprise demands
 Enterprise IT vendors offering Hadoop
distributions:
Hadoop strategy & eco system in place
 Big Data solution providers: Managed
hosting and cloud vendors offer Big Data
solutions
 In-memory technology and streaming
concepts accelerate
Market momentum:
50B connected devices by 2020
2016: Hadoop no longer a hype
“The broad and active utilization of big data requires the
empowerment of business users and citizens by tools
answering their questions and interests in a self-service way.
Such environments will unveil huge values from big data and
will help to democratize
the access to information.”
Dr. Fritz Schinkel, Fujitsu
- Distinguished Engineer
- Head of Big Data Competence Center

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Vertical snapshot
Gain knowledge
from information
Transportation
• Predictive maintenance for logistics
• Analytics allows airlines to predict
flight times with greater accuracy
Retail
• Stores use analytics to streamline
inventory management
• Algorithms enable retailers to generate
appropriate offerings in real-time
Target Industries
Finance
• High-speed trading
• Improve risk
management
Public Sector
• Fraud detection to
catch benefit cheats
• Next level traffic
management
Automotive
• Foundation for
driverless cars
• Cars contributing to IoT
(Internet of Things)

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Cybersecurity
Target Industries: Large Public Networks, Government, Financial, Telecom, Utilities, etc.
Real Time
Detect
Attacks in
Real Time
Prevent
Threats in
Real Time

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Fraud Detection
Target Industries: Financial, Insurance, Government, etc.
Patterns
Words
External
Information
Internal
Information

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Targeted Advertising
Target Industries: Retail, Manufacturing, Service, Financial, etc.
Personalized Offers to Consumers
Data sources:
• Social networks
• CRM
• Sales Orders
• POS
• Web Logs
• Online profiles
• etc.
Social Media
Data
Multi-
Structured
Data

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 Trace and interprets online
discussions in social media
to Gather customer feelings
on:
 Your organization
 Your Brand
 Your Products and Services
 Your Competition
 Other events that may impact
corporate performance: politics,
disasters, large-scale events, etc.
Sentiment Analysis
 or ?
Decide /
Improve
Target Industries: Retail, Manufacturing, Service, Financial, etc.
Like
Dislike
Want
Need

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Predictive Maintenance
Target Industries: Manufacturing, Natural Resources, Utilities, Telecommunication
Real Time
Improve Operations, Reduce Waste,
Identify Bottlenecks
Control Manufactured Product Quality
Monitor Asset Performance
Avoid Operations Downtime
Predict Asset Failure
Machine / Sensor Data
Predictive Models/Machine Learning

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Usage Monitoring
Target Industries: Manufacturing , Natural Resources, Transport, Utilities
Monetize Data
Monitor/Control
Optimize
Machine / Sensor Data

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Big Data infrastructure reference architecture:
Choose platform according to business case
Consolidated data Distilled essence Applied knowledgeVarious data
Extract, Collect Cleanse, Transform Decide, ActAnalyze, Visualize
Data Sources Analytics Platform Access
Batch
processing
platform
Event
processing
platform
Fast response
platform
Data bases
Application
server
Web
content
Sensor
data
Apps
Services
Queries
Visualization
Reporting
Notification

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Big Data infrastructure reference architecture
Consolidated data Distilled essence Applied knowledgeVarious data
Extract, Collect Cleanse, Transform Decide, ActAnalyze, Visualize
Data Sources Analytics Platform Access
Batch
processing
platform
Event
processing
platform
Fast response
platform
Data bases
Application
server
Web
content
Sensor
data
Apps
Services
Queries
Visualization
Reporting
Notification

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Hadoop readiness
What is the industry saying about Hadoop?
 A one-size-fits-all approach does not work with Hadoop
 IT departments are unsure how to manage it
 Businesses need people with the right expertise
 Hadoop is an undiscovered concept to many non IT-experts
Solution
 FUJITSU PRIMEFLEX helps to solve these business needs per
inclusion of analytic features and services

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PRIMEFLEX for Hadoop solution stack
Software stack
 Business oriented analytic (spreadsheets)
 Hadoop core: Map Reduce / file system
 Streaming and In-memory technologies
Hadoop platform sourcing options
 On-premise: Entry or Rack option
 Storage or compute intensive workloads
 Off-premise: Cloud offering
 Designed to connect to SAP HANA
Service and Consulting
 Consulting, Hadoop and Analytic Services
 Tool supported sizing
 Integration Service
Entry Rack Cloud
Big Data Management
Analytics
Analytic Services
Integration Service
and
Sizing
ServicesSoftwareInfrastructure
Smart

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PRIMEFLEX for Hadoop stackAnalyticsVisualization
 End-to-end data analytics
 Iterative spreadsheet
 Self-service data integration
 Explore data to identify patterns
 Segment data and use decision trees
 Move beyond static dashboards
 Extensive library of visualization tools
 Visualize and communicate data according to
business needs
 Integrate data visualizations

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Fujitsu Consulting and Services for Big Data
 Big Data Assessment Workshop
Understand the opportunities Big Data can bring to your organization
through the assessment of your organization’s strategic objectives,
processes, and technical assets.
 Strategy Consulting
Develop the comprehensive Strategy Plan and optimal road map
needed to efficiently introduce Big Data into your business.
 Analytic Service
Fujitsu Big Data Analytics Services assist our customers to quickly
implement new Big Data analytics workflows through a proven Use
Case driven approach
 Hadoop Service
Pragmatic, efficient and assured services for integrating complex
Hadoop customizations into your business.
 Integration Service
Deploy the solution in your environment and connect to IT services.
•Fujitsu Big Data Assessment Workshop
•Fujitsu Big Data Strategy Consulting
•Fujitsu Big Data Analytics Services
Big Data Consulting Services
•Fujitsu Services for Hadoop
Fujitsu Services for Hadoop
•Fujitsu PRIMEFLEX for Hadoop Deployment Service
Fujitsu Integration Services

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PRIMEFLEX for Hadoop highlights
Integrated System combining the convenience of pre-configured and pre-tested
hardware with the economic advantages of open source software
Analytic Capabilities: Self-service data analytics,
benchmarking, historic comparisons,
forecast functions
Visualization Features: Powerful and
transparent visualization of big data scenarios
from versatile data sources at highly-
productive level
In essence: Derive value from information

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PRIMEFLEX for Hadoop testimonials
The principle benefit is the speed at which big data can be analyzed. What once would have taken weeks to
process can now be completed within an hour or two.
Dr Martin Fengler, CEO, Meteomatics
PRIMEFLEX for Hadoop is the ideal platform to foster innovation in Big Data use cases. For instance, in an
analytics-based real-time shopping assistant, it is mission critical to manage and analyze Big Data volumes at
high speed. At our industry-joint Software Innovation Campus Paderborn, we help to develop innovative models
for all user types to enable new shopping experiences.
Dr Stefan Sauer, Senior Researcher, University of Paderborn
With Hadoop, we can run parallel on large datasets, enabling us to skip the reduction step and work without
performance degradation…The parallelization approach can even speed up the analysis of a single genome by a
factor of four or more.
Dr Matthias Schlesner, German Cancer Research Center (DKFZ)
The purpose of the CoE is to solve local urban challenges; bringing together expertise across three organizations
to develop solutions that can be applied globally. Fujitsu is playing a key role in the joint research and
commercialization of the outcome.
Dr Hazel Khoo, Deputy Executive Director of the Science and Engineering Research Council, A*STAR

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Use case: Hadoop-based genome research
Cancer research focuses on genomic data, which involves DNA sequencing,
assembly and analysis. The human genome, with three billion base pairs, is a
classic example of Big Data, as multiple patient samples can generate
petabytes of data.
Benefits
 By leveraging parallel processing power, the PRIMEFLEX for Hadoop
platform analyzes genomes faster and more efficiently
 Integrated self-service analytics software allows biologists or physicians to
work with raw genome data
 No risk of data degradation as the system minimizes the number of
processing steps and the need for data transport, while considering the full
data set
 Zero downtime hardware of the Big Data platform

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