Journey to analytics in the cloud

Copyright © 2016, Saama Technologies
Journey to Analytics in the Cloud
October 5, 2016

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Copyright © 2016, Saama Technologies | Confidential
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Speakers
Alan Byers, Motorists
As 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 age.
alan.byers@motoristsgroup.com
Skip Shaw, Saama
As 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.
skip.shaw@saama.com
@saamatechinc

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Believe the Hype…

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The “Right Now” Disruption
Weather Patterns
Connected World
Safer Driving Ecosystem
Safety First
Eco Friendly, Shared Economy
Autonomous Vehicles
Wearables, More Informed, More
Connected
Smart / Connected Homes

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The “Right Now” Disruption
• Peer-to-Peer
• Emerging Business Models
Channel Disruption
• Digital customer experience
• Connected auto, home and self
• The Internet Of “Me”
Digitization
• Traditional models disrupted
• Innovation by partnering with “technology” companies;
VC funding
Change in Eco System
• Predictive and automated
• Customer 360 views
Embracing 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 results
Cloud makes deployment easier, infrastructure more scalable, and enables self-service
analytics

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It’s a Cloud Era
Deployment in the cloud report saving 20% to 60% over on-premises
infrastructure cost
Up to 85% of new data is unstructured; competitive advantage mandates
use of real-time advanced analytics
Scalability and Elasticity – Accelerate the analysis by scaling nodes
rapidly to run workloads in minutes rather than hours or days on a few
nodes
Self-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

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Inefficiencies in Commodity Infrastructure
TIME
ITCAPACITY
Actual Load
Allocated
IT-capacities
“Waste“ of
capacities
“Under-supply“ of
capacities
Fixed cost of IT-
capacities
Load Forecast
Barrier for
innovations
Source: Microsoft Cloud Continuum Presentation

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

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Hybrid Compatibility
HDInsight in Azure
123456 4712

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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
Aggregation,Queries,Services,BusinessLogic
Dashboards
Scorecards
API
Integration
Embedded
Analytics
Data Feeds
Ad-hoc
Analysis
Prescriptive
Models
Predictive
Models
Report
Subscriptions Self-service
Discovery,
Self-service
DataRefinery-DataManagementandGovernance
• 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

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Which Road to Take?
On-
premise
IaaS or
PaaS

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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

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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

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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

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Key Takeaways
Companies that unlock the value within their data will establish a competitive advantage
Leveraging the Cloud can provide operational efficiencies but not without proper due
diligence
Creating the right team is critical to success
– Business
– IT
– External Partners

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The Existing Analytics Model is Overwhelmed
Today
2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
40,000
20,000
30,000
IOT
Users Devices
Unstructured
Transactional
Digital
Industrialization
Big
Data
Social
50xGrowth in data
from 2010 to 2020
Source: IDC
Machine
to Machine
TOO SLOW
Time
to create
a custom
analytic
solution
shorter
longer
NOT ENOUGH
SPECIFICITY

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

Journey to analytics in the cloud

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