As more companies grow their business in global markets, they discover the need to capture new opportunities in a matter of days rather than months to have competitive advantage and to capture new market share. Their machines are producing terabytes of various data types — video, audio, Microsoft® SharePoint®, sensor data, Microsoft Excel® files — and leaders are searching for the right technologies to capture this data and help provide a better understanding of their business. The HDS big data product roadmap will help customers build a big data enterprise plan that ingests data faster and correlate meaningful data sets to create intelligence that’s easy to consume and helps leaders make the right business decisions. View this webcast to learn about Hitachi’s product roadmap to big data. For more information on HDS Big Data Solutions please visit: http://www.hds.com/solutions/it-strategies/big-data/?WT.ac=us_mg_sol_bigdat

1. INVENTING THE FUTURE
HITACHI DATA SYSTEMS BIG DATA ROADMAP
MICHAEL HAY
CTO AND VP, GLOBAL SOLUTIONS STRATEGY
AND DEVELOPMENT
CHIEF ENGINEER, INTEGRATED PLATFORM
STRATEGY @ ITPD

2. As more companies grow their business in global markets, they discover the
need to capture new opportunities in a matter of days rather than months to
have competitive advantage and to capture new market share. Their machines
are producing terabytes of various data types — video, audio, Microsoft®
SharePoint®, sensor data, Microsoft Excel® files — and leaders are searching
for the right technologies to capture this data and help provide a better
understanding of their business.
The HDS big data product roadmap will help customers build a big data
enterprise plan that ingests data faster and correlate meaningful data sets to
create intelligence that’s easy to consume and helps leaders make the right
business decisions.
Join this webcast to learn about Hitachi’s product roadmap to big data.
INVENTING THE FUTURE: HDS BIG DATA ROADMAP
WEBTECH EDUCATIONAL SERIES

3. DEEP INNOVATION RESOURCES
INNOVATION BUDGET
 Founded in 1910
 US$118B FY11
 900 subsidiaries
 324,000 employees
 More than 760 PhDs
#38 in the 2012 FORTUNE® Global 500

4. 2003 HITACHI DATA SYTEMS (HDS)
PORTFOLIO

5. OUR JOURNEY
HDS WAS A STORAGE
HARDWARE VENDOR
COMPETING ON PRICE Redesigned and expanded software suite
Acquisition of Archivas for content software
2011-12
2003
2007
2009 Redesign of midrange hardware,
packaged as solution
Launch of verticals
SOFTWARE
DRAGS
HARDWARE
IMPROVED SOFTWARE
VIRTUALIZATION
FILE AND CONTENT
SOLUTIONS 2010
SOLUTIONS
DRAG
SOFTWARE
Acquisitions of BlueArc,
Cofio
2013
ACCELERATION

6. Infrastructure
 Converged solution stacks
 Rapid and on-demand
provisioning and deployment
HDS INTEGRATED STRATEGY
 HIGHER VALUE  HIGHER MARGIN  HIGHER STICKINESS
Data Intelligence
 Data lifecycle management
 Index, search, and discover
independent of application
Information Analytics
 Data reuse for new business
 Data analytics independent of
application and media
INFORMATION
Information Virtualization
Analytics
Integration
Integrated
Information-as-a-
service
Text
CONTENT
Content Virtualization
Search, discover, repurpose
Link to vertical/SI markets
Content-on-demand
Archiving-as-a-service
INFRASTRUCTURE
Data, Storage, File, Server, Network Virtualization
Virtualization, mobility
Integrated management
Data center convergence
Infrastructure and
platform-as-a-service

7. Life Sciences
Research
Location-Based
Advertising
One to One
Marketing
On-Demand
Maintenance
Satellite
Images
Every industry, every geo, companies big and small
BIG DATA OPPORTUNITY IS EVERYWHERE
Fraud
Detection
Churn
Analysis
Risk
Analysis
Sentiment
Analysis
One to One
Marketing
Geomation
Farming
Location-Based
Advertising
Oil
Exploration
Network
Monitoring
Asset
Tracking
On-Demand
Maintenance
Traffic Flow
Optimization
Seismic
Monitoring
Satellite
Images
Fraud
Detection
Churn
Analysis
Risk
Analysis
Sentiment
Analysis

8. CONTENTINFRASTRUCTURE
IP AND STORAGE NETWORKING
SYSTEMS MANAGEMENT
SMART INGEST
HDI | HDD-MS
COMMAND SUITE
UCP DIRECTOR
CLOUD/OBJECT
HCP
UCP SELECT
NAS/FILE
HNAS
SEARCH
HDDS
BLOCK/UNIFED STORAGE PLATFORMS
UNIFIED COMPUTE
PLATFORM PRO
COMPUTE PLATFORMS
INSTANCE MGMT.
UCP for SAP HANA | UCP for
Oracle | UCP for MS Exchange |
UCP for MS SQL | UCP for
VMware | Etc.
OUR PORTFOLIO

9. BIG DATA JOURNEY
OVERALL HITACHI VISION AND
STRATEGY FOR BIG DATA
Extending traditional analytics
with Hadoop
Rich media analytics
Expanded vertical solutions
Advanced analytics
orchestration
Smart ingest (e.g. JDSU, HDI)
Hadoop ref. architecture
Big Data ISV ecosystem
UCP for SAP HANA
Infrastructure layer
Content layer
UCP for Oracle, Microsoft
Hitachi Clinical Repository Expanded Big Data services
Managing data growth
High performance DB analytics
Real time
Metadata driven content
analysis
Machine data
Data science mainstream
adoption
Image, audio, video analytics
Complex data mashups
TODAY EVOLVING TOMORROW
Social innovation
Vertical solutions
Market Requirements: Mainstream Use Cases
Hitachi Portfolio
Big Data services
Scale-out architectures

10. TRENDS AND
PORTFOLIO
DIRECTIONS

11. THE EXA-SCALE ERA IS ON ITS WAY
“We are planning for 100EB systems by 2020.” Advanced Customer

12. THE TECH GOLDFISH BOWL THEORY
 Seems counter to
rational thinking, yet
if you look at human
behavior we tend not
to delete anything.
 With all of that data
now available, there
is a movement
contemplating how to
transform unused
data into an
appreciating asset:
Big Data!
 The Hadoop people
are right, but not in
the way they think. 
In economics, Jevons paradox (sometimes Jevons effect) is the
proposition that technological progress that increases the
efficiency with which a resource is used tends to increase
(rather than decrease) the rate of consumption of that resource.

13. WIDE AREA DATA SERVICES PLATFORM
f
private
CORE @ SITE 2
Apps & Ingestors
Object Store
Hitachi
Content
Platform
CORE @ SITE 1
HDDS/Search
CORE @ SITE 3
Apps & Ingestors
Scale-Up NAS
Hitachi
Network
Attached
Storage
private
public
SMART
INGEST
Hitachi Data Ingestor
SMART INGESTION
APPLICATIONS
metadata warehousing
Object
Store
Hitachi
Content
Platform
Scale-
Up NAS
Hitachi
Network
Attached
Storage
NFS File
Server
3rd –
Party
SMART
INGEST
Hitachi Data Ingestor

14. CONSOLIDATED
RACK
THE EVOLUTION OF THE STACK
systemsmanagement
network
storage
compute
os/vm
application
DIY
today 2011-2013
Beyond Converged
2014-Future
RACK
CENTRALIZED
Converged Stacks/Offerings
RACKRACK
RACK
CustomerOR
CommonESMstack
CONSOLIDATED
RESTful
GUI
CLI

15. BUT WHY TAKE
THIS APPROACH?

16. THE FUTURE OF BIG DATA
HITACHI – BIG DATA DRIVES BIG INNOVATION
Machine data is in our DNA
We think more like users

17. BIG DATA DRIVES BIG INNOVATION TODAY
Hitachi
Transportation
Bullet Trains
Demand based maintenance
Early warning improves
safety
More efficient asset utilization
Telemetry from seismic
sensors
Efficient capture of time
series data
Hitachi Power
Power
Stations
Operational data from
sensors
Insight for fleet managers
Competitive differentiation
Hitachi
Construction
Excavators

18. BIG DATA ANALYTICS – VARIETY DOMINATES
RELEVANTTECHNOLOGIESRELEVANTTECHNOLOGIES

19. BIG DATA ANALYTICS – ARCHITECTURES
MODERN 3-TIER APPLICATION
database
application
presentation
COMPONENTS FOR FUTURE BIG DATA, ANALYTICS APPS
search
analytic studio
kvs
Complex
event
processing
visualization
dwh
hive
Extract,
Transform,
Load
machine learning
Graph
databasemany more

20. ANALYTICS ORCHESTRATION AND
THE ANALYTICS STUDIO
UCP Orchestration
Resource management (e.g. provisioning)
+ Analytics Orchestration VISION
(Machine readable documents to auto-deploy multi-step analytics applications)
The Analytics Studio VISION
(A Visio-like interface for humans to create complex multi-step
analytics processes and applications.)

21. DECISION ASSISTS USING
EVENT PROCESSING
GOAL: Help brokers recommend to
clients buy/sell decisions based upon
corporate social sentiment
IMPLEMENTATION: Multiple
technologies orchestrated in
vSphere

22. FOOD FOR
THOUGHT

23.  Granular views into network,
content and subscriber experience
 Move from reactive to predictive
problem management
 The combination of JDSU
PacketPortal and Hitachi
streaming data platform
 Leverage Big Data class
technologies for penetrating
insight
IN-MEMORY PREDICTIVE ANALYTICS
FOR TELCO ENVIRONMENTS

24. BUSINESS MICROSCOPE
A home improvement store
was evaluated using a human
attached sensor platform and
in-store sensors
 Resulted in increased
revenues after
observations and
reconfiguration of staff
 Facial matching
techniques derived
from EMIEW2 from
CCTV feeds could
replace/augment
sensor platforms

25.  EMIEW2 developed as part of
Hitachi's efforts to create a
service robot with diverse
communication functions that
could safely coexist with humans.
 The new iteration combines
research being explored for
Hitachi content and information
layers to illustrate these
technologies in action.
 EMIEW2 uses both visual object
detection and recognition to
identify and find objects.
EMIEW2 – APPLIED AUDIO AND VISUAL
OBJECT RECOGNITION

26. QUESTIONS AND
DISCUSSION

27. Cloud/Object Store
‒ Hitachi Cloud Strategy, Enabling Technologies, and Solutions, Part 1, May
21, 9 a.m. PT, noon ET
‒ Environmental Pressures are Driving an Evolution in File Storage, Part 2,
May 23, 9 a.m. PT, noon ET
Big Data Webcast Series continues
‒ Hitachi Data Systems Hadoop Reference Architecture, June 12, 9 a.m.
PT, noon ET
Check www.hds.com/webtech for:
 Links to the recording, the presentation and Q&A (available next week)
 Schedule and registration for upcoming WebTech sessions
UPCOMING WEBTECHS

28. THANK YOU