Big Data Analytics @ Munich ReSAS Global Forum Executive Program - Orlando

Wolfgang Hauner Marc Wewers

Chief Data Officer, Munich Re IT Architect, Munich Re

Center for International Earth Science Information Network - CIESIN - Columbia University. 2016. Gridded Population of the World,

Version 4 (GPWv4): Population Count. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC).

http://dx.doi.org/10.7927/H4X63JVC.

http://dx.doi.org/10.7927/H4X63JVC

Agenda

2

Data Analytics Framework

1Technology

2

Method Example: AI

3Case Study: Cross Selling

4

Loc-based services

Smart Home

Telematics

Virtual

Assistant

Systems

Haptic

Technologies

Integrated

Systems

Autonomous Systems

and Devices

Automated

Decision

TakingCloud/Client

ArchitectureNew Payment

Models

Big Data

Internet

of Things

Cybersecurity

Digitalization

Computing

Everywhere

Robotics/Drones

Wearable Devices

Risk-based

Security

Context-aware

Computing

Open Data

Collaborative

Consumption

Predictive

Analytics

Industrialization 4.0

Web 4.0

Web-Scale IT

Software-defined

Anything

Crowdsourcing

Mobile Health

Services

3D Printing

Augmented and virtual worlds

Citizen Development

User Centered Design

Digital

Identity

On-Demand-Everything

Big Data in Trend Radar

3

Big Data

Digitization

Internet of Things

When does it become BIG Data

40,000,000,000,000,000,000,000

ByteKilobyteMegabyteGigabyteTerabytePetabyteExabyteZettabyte

Source: IBM

4 KB Commodore

VC 203.5 inch

floppy disk

4 TB in Memory

Big Data Platform

MR

Petabyte storage

big data platform

Google,

Facebook,

Microsoft,

All words ever

spoken by

humans

Yes or No

4

43 zettabytes of data will probably be generated by 2020

300 times the volume in 2005

Data contained

in a library floor

Big Data

Analytics

Methods

Regression Models

Machine Learning

Models

Text Mining

Technology

Hardware

(Compute power)

Software

(SAS, R, Spark, )

Data

Internal Data

External Data

Structured Data

Unstructured Data

People

Data Scientists

Data Engineers

Business People

IT Architects

Big Data Analytics is a Combination of Methods,

Technology, Data and People

5

Building the Team, and the Environment

Programming

Story-telling

Statistics

Visualization

System

Implemen-

tation

DB Administration

Maths

Modelling Data Storage

Business-/

Domain

knowledge

6

Business-

UnitsIT

Building the infrastructure

BI Lab Production

7

Data Lake (HDFS)

Long term unstructured and structured data

Industry icon, tool box and image: used under license from shutterstock.com

Other icons: Munich Re

SASHANA Hadoop Stack

HANA

User InterfaceUser Interface User Interface

SASHANA Hadoop Stack

HANA

User InterfaceUser Interface User Interface

A2P

Wolfgang Hauner / Big Data Analytics & Artificial Intelligence 8Source: Munich Re

Which topics drive our clients?

Up-/Cross-Selling

Data

Sources

Textmining Churn

Analysis

Supply

Chain

Social Media

Analysis

Fraud

Detection

Big Data

Technology

Predictive

UW

Telematics

Sensor

Data/IoT

Geospatial

9

Big Data use cases in insurance

Make the uninsurable insurable

Diabetics

Wind Energy

Consolidate the information and process

Automated underwriting

Risk management platform

Artificial Intelligence supported workflow

Early Loss Detection

Visual Loss Adjustment

10

Image: dpa Picture Alliance Image: Getty Images

Image: Getty Images

Image: used under license from shutterstock.com

Image: used under license from shutterstock.com

Image: used under license from shutterstock.com

Agenda

11

Data Analytics Framework

1Technology

2

Method Example: AI

3Case Study: Cross Selling

4

Design principles for Big Data & Analytics Platform

12

SAS & Hortonworks Self-Service Multi Tenancy

One Central Datalake

DevOps On-Prem & CloudContinious improvement

Automation

Hybrid

Roadmap to Production via Lab environment

Q2 - 2015 Q3 - 2015 Q4 - 2015

Setup of new

BI-Lab Hadoop Cluster

On-boarding & support of

Big Data & Analytics pilots

Stabilization of BI-Lab Hadoop cluster

Authentication & Security

AutomationNew BI-Lab Hadoop Cluster available

Large shared cluster

Dedicated clusters

Single-Node cluster

Pilot SAS Hadoop Integration

13

Design Setup / Build Run

Setup of first

BI-Lab Hadoop

Cluster

Enhance / Optimize

Enhance / Optimize

Building the Big Data & Analytics Platform

Production Environment

14

Design Setup / Build Run

2016

Release v1.0 Release v2.0 Release v3.0

SAS 9.4 M3

SAS Visual Analytics (VA)

Self-Service Data Upload

SAS Embedded Process

for Hadoop

SAS Enterprise Guide (EG)

SAS MS Office Add-in

Data Access to SAP HANA,

Oracle & MS SQL-Server

SAS Enterprise Miner

SAS Contextual Analysis

SAS Mobile BI iOS App

HDP 2.3

Hue

Hive

Ambari

Ranger with LDAP

Sqoop

Pig

Spark 1.4

Oozie

HDP 2.4.2

Ambari Views

Spark 1.6

Solr Cloud

Tesseract

Start setup platform Release v4.0

01 02 03 04 05 06 07 0812 09 10 11 122017

SAS VA Row Level Security

SAS HA

HDP 2.5

Atlas

Zeppelin

Data Catalogue Tool

Data Lineage

Compliance & Security

Optimize

2-week iterations withRolling Upgrades

Enhance / Optimize

Enhance

Big Data & Analytics Production Environments

IT and Business Deployment

Sandbox

Integration

Production

Self-Service

Ad-hoc Analytics

Scheduled

Analytics

Business

Deployment

IT D

eplo

ym

ent

Big Data & Analytics Production Environments

Scalability

Sandbox

Integration

Production

SAS

HWX

SAS

YARN

EP

LASR

Hive

YARN

HiveYARN

Hive

YARN

Hive

Scalability

LASR LASR

LASR

LASR

LASR

EP EP

EP

Hive

YARN

Hive

YARN

Hive

YARN YARN

EP

EP

Hive

Hive

YARN

Hive

YARN

Hive

YARN

Hive

YARN

Hive

YARN

Hive

YARN

EP EP EP EP

EP EP EP EP

Simplified Server Architecture SAS and HDP

Data Node 1 Data Node 2 Data Node 3 Data Node x Data Node x+1 Data Node x+2 Data Node y

SAS

In-Memory

LASR

SAS

In-Memory

LASR

SAS

In-Memory

LASR

SAS

In-Memory

LASR

SAS EP SAS EP SAS EP SAS EP SAS EP SAS EP SAS EP

HDFS HDFS HDFS HDFS HDFS HDFS HDFS

SAS

Mmgt &

Metadata

Hadoop

Mmgt &

Metadata

Hive Hive HiveHive Hive Hive Hive

Hadoop

Frontend

SERVER / OS SERVER / OS SERVER / OS

x < y

EP = Embedded Process

bring calculation to data

Ambari Views,

Zeppelin,

17

YARN YARN YARNYARN YARN YARN YARN

Solr Solr SolrSolr Sol Solr Solr

Spark Spark SparkSpark Spark Spark Spark

Lessons learned

Make use of Lab environment

Enable Self-Service

Agile IT-Project management

18

1

2

Automatization

Security

YARN queue management3

4

5

6

Agenda

19

Data Analytics Framework

1Technology

2

Method Example: AI

3Case Study: Cross Selling

4

Artificial Intelligence (AI) is coming

20

Automation of physical tasks

1st Machine Age

Automation of cognitive tasks

2nd Machine Age

Image: used under license from shutterstock.com

Image: dpa Picture Alliance

AI Evolution

21

1997

IBMs deep blue defeats world

chess champion

Purely rule based

2011

IBMs Watson AI system wins

Jeopardy match against

human players

Mixed machine learning and

rule based

2016

Googles DeepMind defeats top

ranked Go player (Lee Se-dol)

Purely machine learning based

Rule based Hybrid Learning based

Image: dpa Picture Alliance / Stan_Honda Image: dpa Picture Alliance / Seth Wenig Image: dpa Picture Alliance / Lee Jin-man

Insurance specific AI

22

Munich Re as industry

leader in insurance-

specific-AI

Insurance

specific AI

General AI Google, Facebook, Microsoft, Open AI