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 ModernizationOctober 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, HortonworksEric 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


4ZBDATA

44ZB

DATATOMORROW

INTERNETOF

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

RecallsPricing Models

DesignInnovation

Autonomous Driving

Connected City Infotainment

Sensors

Scheduled Maintenance

Predictive Maintenance

RouteOptimization

INSURANCECOMPANIES

GOVERNMENTAGENCIES

INFOTAINMENTPROVIDERS

SOFTWARECOMPANIES

AUTOMAKERS



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 CLOUDWITH 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 way2. Primarily on-premise, but have some cloud applications3. Hybrid mode between cloud and on-premise but

trending towards cloud4. “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 processesTwo batches of a vaccine, produced using an identical manufacturing process, can exhibit significant yield variancesBatches that fail quality standards can cost $1 million eachMerck 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 comparisons1st year yield boost of 40K more doses $10M profit impact McKinsey: 50% yield improvement

Epidemiology

DATADISCOVER

Y

A C T I V EA R C H I V E

D A T AD 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 clinicsLimited visibility into the entire supply chain prevented suppliers from understanding how their drugs were prescribedAcute 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 Inges

t

Drug Cost Optimization

Single Patien

t Recor

d

Cardinal HealthLaunched a New Lineof Business

Fuse by Cardinal Health aims to make healthcare safer and more cost-effectiveTeam enriches supply chain data with public sources – bringing suppliers, providers and patients closer togetherData processing speeds doubledFuse shows suppliers how their drugs are used

Innovate

Renovate Balanced Medical Supply Chain

PREDICTIVEANALYTICS

P R E D I C T I V EA N A LY T I C S

E T LO N B O A R D


D A T AE N R I C H M E N T

D A T AE N R I C H M E N

TA C T I V E

A R C H I V E

P R E D I C T I V EA N A L Y T I C S

S I N G L EV 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 clinicsMoving 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 HDPImproved billing processes resulted in $1M additional annual revenuefrom newly documented secondary diagnoses and care


PREDICTIVEANALYTICS

Mercy’s Journey

Billing Vital Signs

SinglePatient Record

Lab Notes

PrivacyDatabase

Medical Decision Support

DeviceData

Ingest

PreventiveCare

Epic Enrichment

OPEX Efficienc

y

Epic EMR Replicatio

n

Better HealthThrough Data

Searches of free-text lab notes, speed researcher insight from “never” to “seconds” Ingest of ICU vital signsincreased by 900X, letting clinicians respond more quicklyMercy is building real-timetools to support surgical decisions and preventive care

Innovate

Renovate

Better Health

D A T AD I S C O V E R

YS I N G L E

V I E WD A T A

D I S C O V E R YS I N G L E

V I E W




D A T AE N R I C H M E N T

E T LO N B O A R D

P R E D I C T I V EA N A L Y T I C S



Payment Tracking

DueDiligence

SocialMapping

ProductDesign M & ACall

AnalysisMachine

Data

DefectDetecting

FactoryYields

CustomerSupport

BasketAnalysis Segments

CustomerRetention

SentimentAnalysis

OptimizeInventorie

s

SupplyChain

Cross-Sell

VendorScorecards

AdPlacement

CyberSecurity

DisasterMitigation

Investment

Planning

AdPlacement

RiskModeling

ProactiveRepair

InventoryPredictions

NextProduct

Recs

OPEXReduction

HistoricalRecords

MainframeOffloads

Device Data

Ingest

Rapid Reporting

DigitalProtection

Dataas a

Service

FraudPrevention

PublicData

Capture

INNOVATE

RENOVATE

EXPLORE OPT IM IZE TRANSFORM

ACTIVEARCHIVE

ETLONBOARD

DATAENRICHMENT

DATADISCOVERY

SINGLEVIEW

PREDICTIVEANALYTICS

M&A StorageBlending

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 Attributes1

High Data Fidelityfor Modeling2

AnalyzingUnstructured Data3

UnknownUnknowns

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 needs2. Well Enough – Able to serve 50 to 60% of the changing

business needs3. Not so well – Able to serve 10 to 20% of the changing business

needs4. 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 dataDistributed 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

DAT

A AC

QU

ISIT

ION

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

DAT

A W

AREH

OU

SE/D

ATA

LAKE


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 StorageRaw Streaming

Data


Key Takeaways

Modernizing Analytics is more of a need than a wantModern Analytics are based on the Hadoop EcosystemTo 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+Engagement

s

900+Employees

50+Global 250

3000+

Algorithms

1Purpose

Accelerating Business Outcomes using Data Driven Insights


About Hortonworks

Leader in Connected Data Platforms

Publicly traded on NASDAQ: HDPHortonworks DataFlow for data in motionHortonworks Data Platform for data at restPowering new modern data applications

Partnering for Customer SuccessLeader in open-source community, focused on innovation to meet enterprise needsUnrivaled support subscriptions

Founded in 2011 Original 24 Architects, Developers,

Operators of Hadoop from Yahoo!

950+EMP LOY EE S

1500+EC OS Y S T EM PARTN ER S


Q&A

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The Power of your Data Achieved - Next Gen Modernization

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