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

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9de BI congres van het BICC-Thomas More: 24 maart 2016

Data analytical platform, new generation. In this presentation Miloud Belkacem shows you how to structure your infrastructure and data sources so they can be available not to just data analysts, but also to the whole organization. It’s an insight into a modern data analytical platform.

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

  1. 1. Insurance comparison engine Information Management Project Speaker: Miloud Belkacem 24 March 2016
  2. 2. Agenda Project Presentation B&D Answer Benefits and Conclusion 2
  3. 3. 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 ? 305-04-16 Startup
  4. 4. Client’s Business Model 405-04-16 Site visitors fill-in forms to compare insurances The company sells the visitors’ forms to partners Performance of the organisation relies on  Website Traffic  Conversion Rate
  5. 5. Project Objectives 505-04-16 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
  6. 6. Initial Situation 605-04-16 Platform Site DatabaseWeb Logs Data Scientist Studio Excel file Management Line & decision takers Deliver Results IT Department Relay Decisions Implement Large volumes of Raw data Slow and heavy analysis Limited insights and analysis capabilities Slow cycle to market Ads and Targeting Slow adaptations of the model ExtractGenerate Analyze
  7. 7. Challenges 705-04-16 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
  8. 8. Agenda Project Presentation B&D Answer Benefits and Conclusion 8
  9. 9. B&D’s Mission 905-04-16 Query & Analysis Solutions 03 Self-Service Limited BI / Spreadsheet 05 Limited Strategic 02 Dashboards management Operational 04 Operational Reporting Data Mining & Predictive Analysis 01 Analytics Excellence Client
  10. 10. 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 10
  11. 11. Azure Machine Learning in a Nutshell 1105-04-16 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
  12. 12. Empowered Insight platform 1205-04-16 Insurance Comparison Platform Site Database Web Logs Data Scientist Studio Generate Business Intelligence Load Large volumes storage Machine Learning Consume Data Lab Automated Real-Time Targeting and Ads selection HDInsight Consume
  13. 13. Single Data Platform 1305-04-16 In-House Sources Consumption Platform Data HubStaging area ML StudioWebLogs Dataretrieval Reference Files ManualCnsolidation External Sources Insurance Files ExtractTransformLoadConsolidate Mirror LZ MER Staging BigData Stage (Hive metastore) TransformMergeLoad Cube Process MER DirectAccess
  14. 14. Zoom on Azure ML 1405-04-16
  15. 15. Agenda Project Presentation B&D Answer Benefits and Conclusion 15
  16. 16. FUNCTIONAL Benefits of the solution 16 More relevant recommendations On-time recommendations New requests for contact (MER) Increase conversion rate 1 2 3 4
  17. 17. 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 16 More relevant recommendations On-time recommendations New requests for contact (MER) Increase conversion rate 1 2 3 4
  18. 18. 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 17