1. Big-Data IoT, VORA = Digital Enterprise
Hari Guleria
VP Big-Data & SAP HANA
Roadmap to Digital Monitization

2. Start and End With Business Benefits
The success of any new technology lies in the impact it has on the Company’s operations

3. Agenda The Digital Reality Today
ROI
The Digital Reality Today
Big-Data Explosion  Digital Disruption
Inverting the Pyramid
Business Needs
Business Case 1- O&G
Business case 2- Retail
The ball is now in your court

4. - BIG-DATA- Digital Disruption
I d e a !
- BIG-DATA- Digital Disruption
Digital Disruption Starts with an Idea
What Disruption!

5. Accelerating Data Creation
PaperHost
SLI Systems
NetSuite
OpSource
Joyent
Hosting.com
Tata Communications
Datapipe
PPM
Alterian
Hyland
NetDocuments
NetReach
OpenText
Xerox
Google
Microsoft
IntraLinks
Qvidian
Sage
SugarCRM
Volusion
Zoho
Adobe
Avid
Corel
Serif
Yahoo
CyberShift
Saba
Softscape
Sonar6
Ariba
Yahoo!
Quadrem
Elemica
Kinaxis
CCC
DCC
SCM
ADP VirtualEdge
Cornerstone onDemand
Kenexa
Workscape
Exact Online
FinancialForce.com
Intacct
Plex Systems
Quickbooks
eBay
YouTube
Viber
Qzone
Amazon Web Services
GoGrid
Rackspace
LimeLight
Jive Software
salesforce.com
Xactly
Paint.NET
Business
Education
Entertainment
Games
Lifestyle
Music
Navigation
News
Photo & Video
Productivity
Reference
Social Networking
Sport
Travel
Utilities
Workbrain
SuccessFactors
Taleo
Workday
Finance
box.net
Facebook
LinkedIn
TripIt
Pinterest
Zynga
Baidu
Twitter
Yammer
Atlassian
MobilieIron
SmugMug
Amazon
iHandy
PingMe
Associatedcontent
Flickr
Snapfish
Answers.com
Tumblr.
Urban
Scribd.
Pandora
MobileFrame.com
Mixi
CYworld
Renren
Xing
Yandex
Heroku
RightScale
New Relic
AppFog
Bromium
Splunk
CloudSigma
cloudability
kaggle
nebula
Parse
ScaleXtreme
SolidFire
Zillabyte
dotCloud
BeyondCore
Mozy
Fring
Toggl
MailChimp
Hootsuite
Foursquare
buzzd
Dragon Diction
SuperCam
UPS Mobile
Fed Ex Mobile
Scanner Pro
DocuSign
HP ePrint
iSchedule
Khan Academy
BrainPOP
myHomework
Cookie Doodle
Ah! Fasion Girl
Every 60 seconds
148,000+ tweets
MRM
Claim Processing
Payroll
Sales tracking & Marketing
Commissions
Database
ERP
CRM
SCM
HCM
PLM HP
EMC
Cost Management
Order Entry
Product Configurator
Bills of Material
Engineering
Inventory
Manufacturing Projects
Quality Control
SAP
Cash Management
Accounts Receivable
Fixed Assets
Costing
Billing
Time and Expense
Activity Management
Training
Time & Attendance
Rostering
Service
Data Warehousing
1,245,000 status updates
IBM
Unisys
Burroughs
Hitachi
NEC
Bull
Fijitsu
22 million instant messages
DIGITAL DISRUPTION
Mobile, Social, IoT,
Big Data & The Cloud
ERP BUSINESS
Client/Server
CONNECTIVITY
The Internet
Mainframe
1,523,216 Google searches
368 million+ s sent
3,820TB of data created
916 new mobile web users 
Every 7-10 years, technology delivery undergoes a tectonic shift; one that opens up new business and access models. A shift that changes the way technology is consumed and the value that it can bring. A change in what is possible. A removal of inhibitors that unleash the power of innovation.
Today, mobility, social, big data, and the advent of cloud computing are representative of such shifts offering a new means for IT to help organizations accelerate progress towards solving their most pressing challenges (including speeding innovation, enhancing agility, improving financial management). These shifts can unleash the power of IT to not only support but help shape the business.
Some of it about your products

6. The Digital Data Explosion
A poor fit for the traditional relational database
As-Is
To-Be
2005
2018
2010
More than 90% is unstructured data
Approx. 500 quadrillion files
Quantity doubles every 2 years
Most unstructured data is neither stored nor analyzed!
1.8 trillion gigabytes of data was created in 2011:
10,000
GB of Data
(IN BILLIONS)
STRUCTURED DATA – MIDSTREAM
(Repetitive Data)
UNSTRUCTURED DATA UPSTREAM AND DOWNSTREAM (Non-Repetitive Data)
90% of the DIGITAL ‘Business Value Attainment’ lies
10% / 90%
99% of CURRENT Focus lies
90% / 10%
Source: Cloudera

7. Disruption just needs an idea
Digital connections are changing the definition of an enterprise
Image: The Economist
Attribute
Old
New
Leadership
Innovation
World Class
Higher Quality + Lower Cost
Data Store
On-Premise
Cloud’s
Operations
Assets
Self Owned
Crowd Sourced (Uber, airbnb)
Customer Survey
Annual
Instant - Real-Rime
Decisions
Periodic
Real-Time
Trade-Promotion
Real-Time (Gaming)
Welcome to the world of ‘IoQ’

8. Customer Centric Decisions Operational Management
Inverting the Pyramid..
Legacy Organizations
On-Premise IT
Manager is always right
Quarterly/Annual review of customers
100% Focus inside the Midstream
Low connectivity to Upstream
Low connectivity to Downstream
Very Little Predictive Analysis
Field Information Flows Down-to-Up
Most decision flow Top-to-Down
DIGITAL Pyramid
Customer Centric Decisions
Operational
User Feedback
Power Users & CONNECTED Consumers = Customer Loyalty
Operational Management
‘C’ level
Executives
Leaders
Need
Audit
Customer
Expectations
Flow down
Real-Time
deployed to
Exceed
IMPROVE
Executives become
Conductors to the
Symphony of
Customer Satisfaction
Customer Inclusive DIGITAL Connectivity
Identifying, Prioritizing & Meeting Expectations
Real-time Customer Satisfaction Admin
Connect
Leaders
Instructions
Flow down
Level-by-Level
Operational Reality
Summarized up
Level-by-Level
‘C’ Level
Executives
Disconnected
Periodic
Operational Management
Operational Assets
Operational Supply Chains
Operational Stocks and Services
Sales persons driven Quotas
Disconnected
Operational
Users
Customer Exclusive Methodology
The Digital disruption is inverting traditional ‘Command & Control’ organizations existing in ‘Brick & Mortar’ enterprises with a tear tht is totally services and customer focused. In this new world ‘customers’ are the new CIO’s
LEGACY Decision Pyramid
Enterprise Centric Decisions

9. Data-Explosion + Real-time ‘Value-Chain’ Decisions
I d e a !
What is THIS CReating
Data-Explosion + Real-time ‘Value-Chain’ Decisions
Emerging Pressure of..

10. IoT- The Connected Enterprise
Midstream
This is our enterprise
Our Plants
Our Systems
Our Employees
Behind our Firewall
Our most familiar place
Upstream
Our Vendors & Suppliers
Our Manufacturing Units
Downstream
Our Retail
Our customers
Our Buyers

11. The Connected Enterprise = The Digital Enterprise
Disrupt or Be Disrupted
The digital economy is disrupting everything.
In every industry, data is being created in places it never has before.
Creating hyper-distributed data environments.
It is becoming ever increasingly hard to reach that data, secure that data, and much less draw an insight and enable a person or process to take action on the data.
But data is not the problem, connected data is the problem.
If every single employee is a decision-maker, organizations must focus on enhancing the quality of each decision taken.
The ability to secure, aggregate, automate, and draw insights from an organization’s own data – with speed – will define value for that organization.
When you connect People, Process, Data and Things, new opportunities emerge:
New market opportunities
New business models
New way to operate
New ways to consume technology
Technology becomes an enabler

12. The Digital Enterprise Evolution
MIDSTREAM
VENDORS
SUPPLIERS
COMPANY
PLANTS
Inbound
LOGISTICS
DRIVERS
EMPLOYEES
VEHICLES
SENSORS
DOWNSTREAM
CUSTOMERS
RETAIL
WHOLESALE
CONTRACT
MFGS
Outbound
LOGISTICS
SENSORS
UPSTREAM
CORP HQ
VP Sales
VP Service
CIO
Planners
VP BU
VP Supply Chain
Customer Service
45,000+
CONNECTED
PARTNERS
~100
FACTORIES
4,000+
DEALERS
100M+
VEHICLES AND DRIVERS ON THE ROAD
500+
APPLICATIONS
450,000+ CONNECTED
USERS
What do we mean by hyper distributed operations? Let’s take an automobile manufacturers…they may have hundreds of factories, thousands of dealers, tens of thousands of partners, and millions of vehicles on the road…it doesn’t get much more distributed then this…and they want and need to share systems and data across all of these participants in their value chain…

13. Data Processing Must Evolve Too
CENTRALIZED
DECENTRALIZED
FEDERATED
HEADQUARTERS
PARTNERS
VEHICLES
DRIVERS
FACTORIES
DEALERS
FIELD SALES
Enterprise Applications
Dealer Access
Machine Apps
Customer Mobile Apps
Mobile Workforce Applications
On-board Computer
Supplier Exchanges
This evolution of the network requiring a new approach to computing…
Starting with the need to support computing at the edge. Today’s environment requires that you support application development and hosting across fog, cloud and mobile.
It requires enabling an Application-centric Infrastructure with application-based policies that decouple application requirements from network configurations to reduce the impact of application changes on performance, security, availability and scale
Enabling Streaming Analytics and Aggregation…streaming analytics across data-in-motion and data-at-rest and rapid logical aggregation of data
It requires Secure Interaction – the growing ecosystem of partners requires pervasive security policies that support the new B2B and B2C interactions.
And last but not least, it requires application integration across hybrid computing environments
Cisco is uniquely positioned to help our clients address these new requirements with:
• an intelligent network and IoX that supports network distributed workload and edge computing
• new streaming analytics and data virtualization capabilities with our Connected Analytics software
• and best in class application integration with our Cisco Integration Platform that allows you to connect disparate applications across hybrid environments.
To further support these evolving requirements, we are announcing a number of new software capabilities that serve as the foundation for digital business.
across hybrid computing environments

14. Digital Enterprises Will Process Data at the Edge
Widely Distributed, Streaming, Short Shelf Life, Too Big to Move
“By 2020 Most Decision-Data
will be processed at the edge”
(mobile devices, appliances, routers)
86%
HYPER FEDERATED
(Inter-Cloud)
Three years from now, where will most data generated by IoT solutions be processed?

15. What is Changing?
New data processing must take the processing to the data and send subsets for Decisions & Analytics
Traditional data processing moved the data for Decisions & Analytics
Fog Node
Edge Node
IoT Device
Real-Time
Decision
Processing
Hind-Sight
Decision Processing
Data
All Select Data
Only Filtered Data

16. A real world example: Sensor data from a Boeing jet flying from New York to SFO
2,600+ sensors per engine
30 TB
30 TB of Data per engine every hour 2
twin-engine Boeing 737
60TB / Hour 5
five-hour, flight from New York to San Francisco
300 TB / flight
28,537
# of commercial flights in the sky in the United States on any given day.
days in a year
365
30 GB
8.5 TB
312.4 TB
300 TB /flight
8,561,100 TB/day
3,124,801,500 TB/year
Predictive Patterns: 99.9% of this data simply states ‘I am OK’

17. Real-Time Decisions & Predictive Analytics
Business NOW!
Iot
Internet of Things & Everything
Real-Time Decisions & Predictive Analytics

18. IoT vs. IoE From Internet of Things to Internet of Everything
Enterprise
Unit 1
Unit 2
Unit ‘Z’
Unit ‘N’
Step 1: IoT
Hub & Spoke
Step ‘N’: IoE
Intra-Connected
Unit 1
Enterprise
Unit 2
Unit ‘Z’
Unit ‘N’
*Unit= Sensor, Car, component, Person, Patient, Product
Meter, Gauge, Engine, Bearing in an engine
= any thing that that can be digitized
CONNECTED TO A HUB
*Unit= Sensor, Car, component, Person, Patient, Product
Meter, Gauge, Engine, Bearing in an engine
= any thing that that can be digitized
HYPER-CONNECTED

19. Business Needs by Priority – Digital Enterprise
REAL-TIME Decision Feedback Loop
What is happening in the field Right-Now
Automated Reality Checks and Alerts systems
Predictive Analytics
Link History to Predict the Future
Prescriptive Solutions
How do I fix that I can now Predict
Highest Quality & Lowest Cost Solutions
Do it Right the First Time, Every Time

20. Deconstructing Predictive Analytics
Those who do not learn History, tend to make the same mistakes again
Present
Historical Cone
PAST
Unlikely
FUTURE
Likely
Algorithms
Patterns
Data
Time
Predictable
Experience
Patterns
Data
&
Patterns
Algorithms
OBSERVER
Likely
Probability
Future Cone
Unlikely

21. Preventive Maintenance & Monitor Condition
Cost to Repair
IIoT - Predictive Maintenance and Service Visuals: The P-F Interval Curve
Preventive Maintenance & Monitor Condition
Repair or Replace
Equipment Unusable
“Can”
Effect of PdMS
Potential Failure P
Early Signal 1 – Ultrasonic Energy Detected
Machine Capability / Resistance to Failure
Early Signal 2 – Vibration Analysis Fault
Early Signal 3 - Oil Contamination Detected
Audible Noise
Hot to Touch
Mechanically Loose F
Functional Failure
“Want”
Total Failure
Ancillary Damage
Time

22. Real-Time Decision Enablement
VORA Business CASE 1
O&G
Real-Time Decision Enablement

23. What is SAP VORA Real-Time Streaming Data Integrator Between For
Streaming Unstructured Data (Non-Repetitive Data)
Streaming Semi-Structured Data (Semi Repetitive Data)
Enterprise Structured Data (Predictable+ Repetitive Data)
For
Real-Time
Alerts
Predictive & Prescriptive Analytics
Operational Analytics

24. Operational Facts 4 Unplanned Downtime
‘Unplanned Downtime’ Cost per unit Instance
On-Shore Drilling
$3,000-5,000 per hour = $72k to 120k per day
Off-Shore Drilling Rigs
$500,000 to $2 million per day or $15,000/hr
Average Unplanned downtime is 1-5 days
Depending on the severity of the damage

25. Asset failure for Predictable Maintenance
Acquisition Start time
Acquisition end time
Tool Status ( Temp)
Scintillator detector Parameters
Data
Inputs
Depth
Angle of Penetration
Internal temperature
External Temperature
RPM
Internal Pressure
External Pressure
Torque
Video + Audio

26. Solution Architecture
On Cloud 1
Scintillator Detector
Temperature
Pressure
HDFS
SAP HANA VORA
SCALA Programming
SPARK SQL
SPARK 2
On- Premise
SAP EAM
SAP ERP
SAP FI
SAP HANA Information Model
VORA Virtual Tables
VORA Remote Data Source
SAP HANA 3
Assets Master data
ECC operational Data
OLAP – SAP Lumera
OLAP Analytics
Predictive Analytics
Predictive engine
Algorithms 4
Self-Help Analytics
HANA
SPARK
Adapter

27. Data Flow Real-time Streaming Data Cloud HANA Modeler HDFS Alerts
For ‘Zero-Unplanned Downtime’ Predictive Analytics
Real-time
Streaming Data
Alerts
Cloud
SPARK
VORA
SAP ECC
HDFS
HANA Modeler
SPARK
VORA
Predictions
SPARK
VORA
SAP EAM
Analytics

28. Business Benefits Unplanned Downtime reduced by 62%
9 Month Cost savings of $7.6 million
Zero Downtime increased from 72% to 93%
OSHA 300A scores improved by 13%

29. Real-Time Decision Enablement
VORA Business CASE 2
Retail
Real-Time Decision Enablement

30. Operational Facts Per Store Indirect Costs Sales Target vs. Actuals
From total 760 stores - ’12 Stores data’ Cost per unit store
Per Store Indirect Costs
$3,000-$4,000 per day
Sales Target vs. Actuals
$17,000 good day ; $ 5,000 bad day
Store Manager concerns
Delighting a customer
Finding what customer needs
Keeping store employees motivated- Decreasing Employee Churn
Training / on-boarding costs are around $3,000 to $10,000
Store employee concerns
Normally a temp job- waiting for a better opportunity
Unable to predict earnings from commissions
Average employee employment is 4-6 months

31. Analytics for Customer Experience
Store ID, + Geo Loc
Location Heat Map, Traffic, Locality, etc
Customer
Loyalty Card
Store Opening time
Store Closing Time
Data
Inputs
Location
&
Movement Analytics
Store Manager
Store Employees + Invoice
Employee Attendance In/Out
Store Racks
Store Stocks
Store Sales, Order Line Items
Permission based services
Store POS Data
Weather
Temperature

32. Data Flow Real-Time Customer Real-time Feedback Alerts Cloud
For ‘Store Employee Real time’ Retention Analytics – Grew Customer satisfaction by 16% in 3 months
Real-Time Customer
Feedback
Real-time
Alerts
Cloud
SPARK
VORA
SAP ECC
HDFS
HANA Modeler
SPARK
VORA
Predictions
SPARK
VORA
Analytics
Real-Time POS
Streaming Data
POS
SAP ECC
SAP EAM

33. Business Benefits Employee Retention increased by 43%
Per store sales increased by 17%
Customer satisfaction went up by 13%
Overall Per Store Sales went up by 21%

34. The ball is now in your Court
Option 1
Option 2
Stay as you are!
Hope for the Best
Disrupt – Don’t get Disrupted
Become the competitive Disrupter
Undertake a Design thinking workshop (80% Buss/20% IT)
Follow the Digital Enterprise Structured Framework
Build the Digital Enterprise for the Future
Ask us for our
D-BAS (Digital- Business Assurance Services)
B-QAS (ERP selection- Business Quality Assurance Services)

35. Thank You
The Base for Business Success is a strong foundation of Technology

36. Gartner’s Digital Marketing Hub

37. Actionable Roadmap to Digital Delivery
Proven and Repetitive
Scientific approach to digital success
Structured Stepping Stones