journey to analytics in the cloud

Copyright © 2016, Saama Technologies

Journey to Analytics in the CloudOctober 5, 2016

2Copyright © 2016, Saama Technologies | Confidential 2Copyright © 2016, Saama Technologies 2Copyright © 2016, Saama Technologies

SpeakersAlan Byers, MotoristsAs AVP of Data Analytics, Alan Byers is responsible for strategic Enterprise Data Management combined with tactical development of data solutions that support Analytics and systems integration. Alan is focused on reducing the company’s time-to-information from being measured in days, weeks, or even months down to seconds by using an Agile BI approach that provides quick delivery of data services and self-service analytics. He believes that effective use of data assets by combining wisdom and advanced analytics methodologies is a key driver for success in the insurance industry during the digital [email protected]

Skip Shaw, SaamaAs Regional Director of Saama's eastern region, Skip Shaw is responsible for the sales and delivery of Big Data solutions to Fortune 500 companies. Prior to joining Saama in 2016, Shaw was a Director of Sales at Oracle where he was responsible for driving strategy for core technology solutions. Before that, he spent 15 years at Microsoft in various sales roles where he helped customers develop solutions focused on business intelligence, advanced analytics, and data management. [email protected] @saamatechinc


Believe the Hype…


The “Right Now” Disruption

Weather Patterns

Connected World

Safer Driving Ecosystem

Safety FirstEco Friendly, Shared EconomyAutonomous Vehicles

Wearables, More Informed, More

Connected

Smart / Connected Homes


The “Right Now” Disruption

• Peer-to-Peer• Emerging Business ModelsChannel Disruption

• Digital customer experience• Connected auto, home and self• The Internet Of “Me”

Digitization

• Traditional models disrupted• Innovation by partnering with

“technology” companies; VC fundingChange in Eco

System

• Predictive and automated• Customer 360 viewsEmbracing Big Data

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How do we Use all this Data in the Disruptive Era?

Leading companies are moving towards consolidated data management Introduction of an enterprise data hub built on open-source Apache Hadoop provides a cost-effective way for insurers to aggregate and store ALL their data, in any format, in a highly secure environment Users can access rich data sources, blend and analyze data from any source, in any amount, detect patterns, model risk and gain valuable real-time insights that deliver resultsCloud makes deployment easier, infrastructure more scalable, and enables self-service analytics


It’s a Cloud Era

Deployment in the cloud report saving 20% to 60% over on-premises infrastructure costUp to 85% of new data is unstructured; competitive advantage mandates use of real-time advanced analyticsScalability and Elasticity – Accelerate the analysis by scaling nodes rapidly to run workloads in minutes rather than hours or days on a few nodesSelf-Service Analytics Platform – Provision flexi advanced analytics tools designed for a varied skill levels Simplified deployment – Minimize costs by provisioning resources on demand in minutes

https://ncmedia.azureedge.net/ncmedia/2016/05/The_Forrester_Wave__Big_D.pdf

7

https://ncmedia.azureedge.net/ncmedia/2016/05/The_Forrester_Wave__Big_D.pdf


Inefficiencies in Commodity Infrastructure

8TIME

IT C

APAC

ITY

Actual Load

Allocated IT-

capacities

“Waste“ of capacities

“Under-supply“ of capacities

Fixed cost of IT-capacities

Load Forecast

Barrier forinnovations

Source: Microsoft Cloud Continuum Presentation


Source: Forrester Wave™: Big Data Hadoop Cloud, Q1 2016


Hybrid Compatibility

HDInsight in AzureHadoop On Premises

Name= Sarah Pnid=123456

123456 4712


The Situation

In early 2014, Motorists Insurance Group with under $1b in Net Written Premiums and operation in 20+ states had a few business challenges:

– Aging systems run by an aging workforce– Reduced customer loyalty + pricing pressures– Many operational data sources: DB2, VSAM, IMS, SQL, documents, and others– Needed to analyze new types of data: clickstream, social media, and telematics– No single version of truth: KPIs were inconsistent, information for decision-making was unreliable– Integration of data from new affiliate companies with their own systems and structures– Needed real-time analysis, that required processing of massive amounts of data faster – Need of scalable, integrated, secure data in a cost effective way

Motorists wanted to embark on a transformation program to consolidate and modernize its existing IT systems, which support core Insurance processes – Policy Admin, Claims, and

Billing but was faced with some questions/decisions about its data ecosystem

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Data Warehouse Ecosystem Features

New Affiliate Data

3rd Party Data

Social Media, UBI,

Clickstream, ...

Guidewire

Analytics Engines

Data Warehouse

Data Lake

Agg

rega

tion, Q

uerie

s, S

ervi

ces,

Bus

ines

s Lo

gic

Dashboards

Scorecards

API Integration

Embedded Analytics

Data Feeds

Ad-hoc Analysis

PrescriptiveModels

PredictiveModels

Report Subscriptions Self-service

Discovery, Self-service

Dat

a R

efin

ery -

Dat

a M

anag

emen

t and

Gov

erna

nce

• Fast data ingest• Agile data refinery• Data discovery• Searchable

information catalog• Rapid solution

delivery• Multi-stage data

governance• Workload-optimized

architecture• Distributed

architecture• Data as a Service


Which Road to Take?

On-premise

IaaS or PaaS


Radical Shifts in Cloud Strategy

On-Prem• Comfortable• Cloud-leary

culture• Simpler

security• Appears less

expensive

IaaS• On-prem

hardware config not aligned with IT principles

• Better elasticity

• Leverage existing relationships

On-prem• Still comfy• Simpler

security management

• PaaS looks expensive

PaaS POC• Elastic• Price

differential smaller

• DR delivered

• Reduced corporate datacenter dependency


Outstanding Questions

– Bandwidth and connectivity requirements to be determined– Analytics leading the charge to the cloud with internal data; will need

to refine cloud data management principles and practices– Audit and security teams need "warm and fuzzy" feeling– Validate that we can maintain portability of solutions - different

providers or in-house– Refine understanding of cost forecasts based on real-world

implementation through POC– Identify needed changes in development practices and team skill sets


Lessons Learned

– Partner with trusted external resources to help guide you– Begin evaluating "production" platform options very early– Collect and document real-world business use cases early to help

refine infrastructure needs– Partner with the right people inside the organization as you begin

evaluating options– Know the company's current cloud appetite and understand the

changing tides


Key Takeaways

Companies that unlock the value within their data will establish a competitive advantageLeveraging the Cloud can provide operational efficiencies but not without proper due diligenceCreating the right team is critical to success

– Business– IT– External Partners


The Existing Analytics Model is Overwhelmed

Today

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

40,000

20,000

30,000IOT

Users Devices

Unstructured

TransactionalDigital

Industrialization

Big Data

Social

50xGrowth in datafrom 2010 to 2020Source: IDC

Machine to Machine

TOO SLOWTime to create a customanalytic solution

shorter

longer

NOT ENOUGH SPECIFICITY


About Saama

5000+Engagements

900+Employees

50+Global 250

3000+Algorithms

1Purpose

Accelerating Business Outcomes using Data Driven Insights

journey to analytics in the cloud

Data & Analytics