BI congres 2016-3: Insurance comparison engine - Miloud Belkacem - Business & Decision

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Insurance comparison engine Information Management Project Speaker: Miloud Belkacem 24 March 2016

Transcript of BI congres 2016-3: Insurance comparison engine - Miloud Belkacem - Business & Decision

Page 1: BI congres 2016-3: Insurance comparison engine - Miloud Belkacem - Business & Decision

Insurance comparison engine Information Management ProjectSpeaker: Miloud Belkacem

24 March 2016

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Agenda

Project Presentation

B&D Answer

Benefits and Conclusion

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Amongst the market’s top 4

platforms

Activity exclusively web

oriented

Significant monthly web traffic

Insurance comparison platform active on the French Market

Who is the Client ?

Startup

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Client’s Business Model

Site visitors fill-in forms to compare insurancesThe company sells the visitors’ forms to partnersPerformance of the organisation relies on Website Traffic Conversion Rate

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Project Objectives

Business & Decision Belgium was selected to define & execute the client’s data strategy

Two parallel tracks: Big Data & Analytics and Business Intelligence

The key high level objectives being:

Competitive Edge

Helping the client gain competitive edge and foster its

market position

Boost Insights

Leverage analytics practices to better understand what

happened before, what is happening now and what could

happen in the future

Modernize the Data Platform

Set up a modern data lab based on top-notch technological

solutions and powerful practices

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Initial Situation

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Platform

Site DatabaseWeb Logs

Data Scientist

Studio Excel file

Management Line & decision takers

Deliver Results

IT Department

Relay Decisions

Implement

Large volumes of Raw dataSlow and heavy analysis

Limited insights and analysis capabilitiesSlow cycle to market Ads and Targeting

Slow adaptations of the model

ExtractGenerate

Analyze

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Challenges

Difficulty to exploit large sized web logs which is key to understanding the behavior of users

Tedious manual data extraction to perform analysis due to performance and the need to perform data transformations

Slow Analysis Life-Cycle as: Data Scientist delivers information manually and irregularly to business Decision takers assess the analysis results and take decisions IT builds new recommendation Ads and targeting rules into the platform based on the input

of the management which creates latency

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Agenda

Project Presentation

B&D Answer

Benefits and Conclusion

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B&D’s Mission

Query & Analysis Solutions03Self-Service

Limited BI / Spreadsheet05Limited

Strategic02 Dashboards management

Operational04 Operational Reporting

Data Mining & Predictive Analysis01Analytics

Excellence

Client

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Approach Overview

In order to overcome the challenges detailed earlier, B&D has:

Insurance Comparison Platform

Business Intelligence

Big Data & Analytics

Selected Microsoft as the technology provider Set up a full-featured Data platform hosted on Microsoft Azure Define data governance to streamline reporting efforts

Design a BI solution to Deliver traditional BI outputs (reports, Ad-Hoc, etc.) Serve as the destination of aggregated Big Data

Set up a data lab on the cloud to Load and make available large sized web logs and external files Provide data scientist tools for analysis purposes Deploy Machine Learning platform and mechanisms Plug & Play

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Azure Machine Learning in a Nutshell

Machine Learning cloud based component

Provides trained & enriched predictive models

Provides web service based interface to integrate with third party tools Implement Real-Time targeting and Ads selection Real time suggestions Automatic referrals Churn calculations Customer segmentations Next best offer ...

Azure ML

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Empowered Insight platform

Insurance Comparison Platform

Site Database

Web Logs

Data Scientist

Studio

BI Load

Generate

Business Intelligence

Load

Large volumes storage

Machine Learning

Consume

Analyze

Data Lab

Automated Real-Time Targeting and Ads selection

HDInsight

Cons

ume

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Single Data Platform

In-HouseSources

ConsumptionPlatformData HubStaging area

ML StudioWebLogs

Data retrieval

ReferenceFiles

Manual Cnsolidation

External Sources

Insurance Files

Extract Transform Load Consolidate

Mirror

LZ MERStaging

BigData Stage(Hive metastore)

Transform M

erge Load

Cube

Process

MER

Direct Access

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Zoom on Azure ML

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Agenda

Project Presentation

B&D Answer

Benefits and Conclusion

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FUNCTIONAL

Benefits of the solution

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More relevant recommendations

On-time recommendations

New requests for contact (MER)

Increase conversion rate

1

2

3

4

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FUNCTIONAL TECHNICAL

Improved data integration (data flow)

Fully automated recommendation system

Usage of state-of-the-art ML technology

Usage of the cloud infrastructure

SocialAnalytics-Ready

Benefits of the solution

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More relevant recommendations

On-time recommendations

New requests for contact (MER)

Increase conversion rate

1

2

3

4

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Conclusions

Combine traditional BI & Big Data capabilities

Project hosted in the cloud

Project initiated overnight & first results presented after a few weeks

« Data Lab » solution to validate use cases, then industrialization

Machine Learning capabilities activated

BIBigData

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