The Power of your Data Achieved - Next Gen Modernization

1Copyright © 2016, Saama Technologies and Hortonworks Inc. All Rights Reserved
The Power of your Data Achieved –
Next Gen Modernization
October 2016

Karim Damji, Saama
Karim is VP, Product Management, and joined Saama from
Plantronics, where he led software strategy and product
management, focusing on driving developer platforms,
strategic partner integrations and contextually enabled UC
solutions.
Prior to joining Plantronics, Karim served in leadership
positions spanning business development, product
management, sales and network engineering at Cisco, Vocera
Communications, MobileIron and DiVitas. Karim spent 7 years
at Cisco building global VoIP and WAN networks, eventually
transitioning to product architecture positions. At Vocera
Communications, Karim was the founding product manager
responsible for driving product concept to market-leading
solutions.
Eric Thorsen, Hortonworks
Eric Thorsen is VP, Industry Solutions at Hortonworks,
with a specialty in Retail and Consumer Products.
Eric holds over 25 years of technology expertise. Prior to
joining Hortonworks, Eric was a VP with SAP, managing
strategic customers in Retail and CP industries. Focusing
on business value and impact of technology on business
imperatives, Eric has counseled grocers, e-commerce,
durables and hardline manufacturers, as well as fashion
and specialty retailers.
Eric’s focus on open source big data provides strategic
direction for revenue and margin gain, greater consumer
loyalty, and cost-takeout opportunities.
Today’s Speakers

Agenda
Modern Data Trends
What a Modern Data Platform Looks Like
Case Studies
Key Takeaways

4ZB
DATA
44ZB
DATA
TOMORROW
INTERNET
OF
ANYTHING

Polling #1 – Maturity Curve
How mature is your organization regarding Hadoop – open source data management?
1. Aware – Big data is discussed but not reflected in the business strategy. There is
general awareness of the benefits of Big Data.
2. Exploring – The enterprise recognizes the potential for data to be used to generate
business insights.
3. Optimizing – The enterprise business strategy encourages the use of insights from data
within business processes.
4. Transforming – Data drives continuous business model innovation and competitive
advantage.

Data Drives the Connected Car
Insurance
Premiums
Warranties
Recalls
Pricing
Models
Design
Innovation
Autonomous
Driving
Connected
City Infotainment
Sensors
Scheduled
Maintenance
Predictive
Maintenance
Route
Optimization
INSURANCE
COMPANIES
GOVERNMENT
AGENCIES
INFOTAINMENT
PROVIDERS
SOFTWARE
COMPANIES
AUTO
MAKERS

7Copyright © 2016, Saama Technologies and Hortonworks Inc. All Rights Reserved 7Copyright © 2016, Saama Technologies and Hortonworks Inc. All Rights Reserved
Modern Data Applications
deliver the value of actionable
intelligence only possible with
both data in motion and data at
rest.
The Connected Car is
an example of
a Modern Data
Application
DATA IN MOTION DATA AT REST

Modern Data
Applications require
architectures that
connect the cloud with
the data center.
CONNECTING THE CLOUD
WITH THE DATA CENTER
Modern Data
Applications
and the Data
Architecture

Survey #2 - Cloud
How does your organization approach cloud?
1. All on-site and intend to stay that way
2. Primarily on-premise, but have some cloud applications
3. Hybrid mode between cloud and on-premise but trending towards cloud
4. “Cloud-first” strategy

Hortonworks solutions come
with enterprise-ready security,
governance,
and operations, to deliver
Actionable Intelligence with
confidence
Ready for
Any Enterprise

Merck’s Journey
Improving Life Sciences Manufacturing Yields
Presents a Complex Data Discovery Challenge
Vaccine manufacturing requires precise control of complex fermentation processes
Two batches of a vaccine, produced using an identical manufacturing process, can exhibit
significant yield variances
Batches that fail quality standards can cost $1 million each
Merck analyzed one vaccine: 10 years of manufacturing data stored across 16 systems

Merck’s Journey
Scientific
Search
Sensor Data
Storage
Vaccine Yield
Optimization
Innovate
Renovate
The Journey to
the Golden Batch
Combined 10 years data
amounted to 1 billion records
5.5 million batch comparisons
1st year yield boost of 40K more
doses  $10M profit impact
McKinsey: 50% yield improvement
Epidemiology
D ATA
D I S C OV E RY
A C T I V E
A R C H I V E
D A T A
D I S C O V E R Y
D A T A
D I S C O V E R Y
The Golden
Batch

Cardinal Health’s Journey
Data Ingest Constrained Analysis of the Medical Supply Chain at Fuse by Cardinal
Health
Cardinal Health supplies equipment and medicines to 85% of US hospitals and clinics
Limited visibility into the entire supply chain prevented suppliers from understanding how their
drugs were prescribed
Acute pharmacists couldn’t see all the product options that they could prescribe for various
conditions

Cardinal Health’s Journey
Drug Supply
Chain Analytics
Sensor Data
Ingest
Prescription
Archive
Pandemic
Response
Outcome-based
Medicine
Clinical Decision
Support
Public
Data
Ingest
Drug Cost
Optimization
Single
Patient
Record
Cardinal Health
Launched a New Line
of Business
Fuse by Cardinal Health aims to
make healthcare safer and more
cost-effective
Team enriches supply chain data with
public sources – bringing suppliers,
providers and patients closer together
Data processing speeds doubled
Fuse shows suppliers how their drugs
are used
Innovate
Renovate
Balanced
Medical Supply
Chain
PREDICTIVE
ANALYTICS
P R E D I C T I V E
A N A L Y T I C S
E T L
O N B O A R D
D A T A
D I S C O V E R Y
D A T A
E N R I C H M E N T
D A T A
E N R I C H M E N T
A C T I V E
A R C H I V E
P R E D I C T I V E
A N A L Y T I C S
S I N G L E
V I E W

Mercy’s Journey
Mercy Medical System Sought a Data Lake for a Single View of its Patients – “One
Patient, One Record”
Existing platform impeded goal of enriching Epic data for 1 million patients over 35
Hospitals and 500 clinics
Moving Epic EMR data to Clarity EDW took 24 hours and was “never going to enable
real-time analytics”. Now that takes 3-5 minutes with HDP
Improved billing processes resulted in $1M additional annual revenue
from newly documented secondary diagnoses and care

PREDICTIVE
ANALYTICS
Mercy’s Journey
Billing Vital
Signs
Single
Patient
Record
Lab
Notes
Privacy
Database
Medical
Decision
Support
Device
Data
Ingest
Preventive
Care
Epic
Enrichment
OPEX
Efficiency
Epic EMR
Replication
Better Health
Through Data
Searches of free-text lab notes,
speed researcher insight from
“never” to “seconds”
Ingest of ICU vital signs
increased by 900X, letting clinicians
respond more quickly
Mercy is building real-time
tools to support surgical decisions
and preventive care
Innovate
Renovate
Better Health
D A T A
D I S C O V E R Y
S I N G L E
V I E W
D A T A
D I S C O V E R Y
S I N G L E
V I E W
A C T I V E
A R C H I V E
A C T I V E
A R C H I V E
A C T I V E
A R C H I V E
D A T A
E N R I C H M E N T
E T L
O N B O A R D
P R E D I C T I V E
A N A L Y T I C S

Payment
Tracking
Due
Diligence
Social
Mapping
Product
Design
M & ACall
Analysis
Machine
Data
Defect
Detecting
Factory
Yields
Customer
Support
Basket
Analysis
Segments
Customer
Retention
Sentiment
Analysis
Optimize
Inventories
Supply
Chain
Cross-
Sell
Vendor
Scorecards
Ad
Placement
Cyber
Security
Disaster
Mitigation
Investment
Planning
Ad
Placement
Risk
Modeling
Proactive
Repair
Inventory
Predictions
Next
Product Recs
OPEX
Reduction
Historical
Records
Mainframe
Offloads
Device
Data
Ingest
Rapid
Reporting
Digital
Protection
Data
as a
Service
Fraud
Prevention
Public
Data
Capture
INNOVATE
RENOVATE
E X P LOR E OP T I M I Z E T R A N S F OR M
ACTIVE
ARCHIVE
ETL
ONBOARD
DATA
ENRICHMENT
DATA
DISCOVERY
SINGLE
VIEW
PREDICTIVE
ANALYTICS
M&A
Storage
Blending
M&A
Ingest
Integration

Practical Approach to Modernization
We’ve looked at the reasons for modernization
Following slides cover a real customer success story and our approach to modernization in
a real life scenario
Including what the customers tell us about the benefits they achieved

Source Systems
Case Study – A Large Insurance Company
RDBMS
DATA Files
Enterprise Data
Warehouse
Data Mart
Data Mart
Data Mart
Business Requirements
10-12% Rationalized and
Transformed
Aggregated
Need Additional Attributes
1
High Data Fidelity
for Modeling
2
Analyzing
Unstructured Data
3
Unknown
Unknowns
Landing
Zone

Survey #3 – Business Agility
Are you able serve your business’ ever evolving analytics need?
1. Very Well – Architecture is very flexible and able to serve most fast changing business
needs
2. Well Enough – Able to serve 50 to 60% of the changing business needs
3. Not so well – Able to serve 10 to 20% of the changing business needs
4. Not at all – Business requirements driven, new needs require a full change
management process

Saama’s Modern Analytics Framework
Connectors for
multiple sources:
• Structured
• Unstructured
• Real Time
geospatial
• Syndicated
• Social Media
Elastic Search and
Indexing
Fast (Millisecond)
Search
Highly Scalable for
massive amounts of
data
Distributed and
Scalable Processing
Consuming and
Storing Large Data
Sets
Access to Raw
Granular Data
Self-Service for
Modeling and Data
Science
Fluid Analytics
Data Extraction Layer Business Intelligence/Analytics Layer
Data Aggregation Layer Layer
Raw Transactional Data Layer – Lowest Grain
Data Integration
Data Storage
COBOL / VSAM / DB2, PIG, SQOOP, STORM, MAP - REDUCE
DATAACQUISITION Profit Stats Loss Stats Scorecards Quote Conversions Ratios Customer 360 Product 360 Others
FILE SYSTEM
Sales
Legacy Data
Marketing Booking Quotes Customer Service IVR Web Logs D&B Syndicated
Structured Data Unstructured Data External Data
Reference Data
Master Data
Hierarchies
Notes Images IVR
Web & Mobile System Logs
D&B
Social Others
PUSH AND/OR PULL PUSH AND/OR PULL
Billing Backlog
Syndicated
PUSH AND/OR PULL
Schema on Read
Future Proofing
Analytics
Highly flexible for
exploring unknown-
unknowns
DATAWAREHOUSE/DATALAKE

What our Customers Told Us
Fraud Analytics
We are now focusing on the
right claims to investigate
and catch more fraudulent
claims based on the
predictive scoring model,
the uptick is already +0.5%
on actual fraud caught
Agency Dashboard
Real-time search of the
customer/ prospect has
hugely helped us in serving
and upselling, while
substantially increasing
customer satisfaction and
engagement
Decommission
The platform has enabled
us to accelerate the
decommissioning to legacy
systems while adhering to
regulatory requirements,
saving us 10s of millions
Subrogation
We are already see an
uptick in subrogation claims
by 5%, saving us close to
$10 million, thanks to
machine learning
algorithms and analytics
Special Investigation Unit Insurance Agent CIO Head of Claims
Machine Learning and
Modeling
Elastic Search
Low Cost Hot/Warm
Storage
Raw Streaming Data

Key Takeaways
Modernizing Analytics is more of a need than a want
Modern Analytics are based on the Hadoop Ecosystem
To deliver Modern Analytics, Saama’s Framework builds on top of the Hadoop ecosystem and
adds
• Schema on Read - Data Models
• Elastic Search
• Data Flows
• Algorithms
• Aggregation
• Analytics
Saama can deliver a complete Modernization of Analytics using Saama’s Framework and
Hortonworks HDP

About Saama
5000+
Engagements
900+
Employees
50+
Global 250
3000+
Algorithms
1
Purpose
Accelerating Business Outcomes using
Data Driven Insights

About Hortonworks
Leader in Connected Data Platforms
Publicly traded on NASDAQ: HDP
Hortonworks DataFlow for data in motion
Hortonworks Data Platform for data at rest
Powering new modern data applications
Partnering for Customer Success
Leader in open-source community, focused
on innovation to meet enterprise needs
Unrivaled support subscriptions
Founded in 2011
Original 24 Architects, Developers,
Operators of Hadoop from Yahoo!
950+
E M P L O Y E E S
1500+
E C O S Y S T E M
PA R T N E R S

Q&A
Or questions to
crystal.black@saama.com

The Power of your Data Achieved - Next Gen Modernization

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