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Using Tableau in the Insurance Industry

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  2. 2 Massachusetts: Chart Champ!/vizhome/TBW_FINAL_CAHRTCHAMP_SUBMISSION_JAI_20160904/D_01_Fina l_Dashboard Dashboard analyzing MA school districts: • Finding the schools with the highest SAT Scores in the neighborhoods with the lowest house prices • We found that schools  Away from Boston,  Low population densities,  Low crime rate and  Highly skilled teachers, tend to outperform other schools on our SAT/house-price metric
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  4. 4 Where How So What? Where do we use Tableau ?
  5. 5 Where do we use Tableau ? EDA on historic data Underwriting Long term care Marketing Reserving Monitor real-time Performance Predictive Analytics Automated Underwriting Claims trends UW Decision Monitor Historic Performance Market Segmentation Market Mix Modelling Who will buy, lie and die models Fraud Prevention
  6. 6 Where How So What? How do we use Tableau ?
  7. 7 Creating a 360 view of our customers • Following the data journey of a Vitality customer • Tableau allows for quick integration from multiple data systems allowing us to generate 360 insights
  8. 8 Visualizing the journey of data • Finding gaps in historic data allowed us to prioritize data scrubbing more efficiently Data 1 Data 2 Data 3 Data 4 Data 5 Data 6
  9. 9 Viewing data with Geospatial capabilities within Tableau • Maps within tableau allow for quick geo-analysis. • Inbuilt data sources inside tableau store a wealth of data Ability to select zip’s within x miles
  10. 10 Monitoring Data Health • The match rate after combining multiple real time datasets can be quickly monitored from dashboards uploaded on our internal tableau server Data 1 Data 3 Data 2 Data 4 Data 5 Data 6
  11. 11 In combination with R for advanced analytics • The advanced analytical capabilities missing in tableau can be substituted by integrating R- Shiny based web apps • These R web apps can be added to a story thus allowing quick decision trees
  12. 12 In comparing predictive model performance • We frequently use tableau to compare different predictive models • Integration with SAS, Python and R outputs allows heterogenous modelling comparisons in Tableau
  13. 13 Creating user guides allows for faster dashboard ingestion • A quick cross-tab allows for faster bi-variate analysis in tableau • We have created in-house ‘how to guides’ enabling faster ingestion of our analytical dashboards
  14. 14 Where How So What? Source: Tweets for # Careers in Finance Analytics; # Careers in Health Analytics;# Careers in Insurance Analytics
  15. 15 What has Tableau done for us Time saved Make the world of data beautiful again Get to actionable insights faster
  16. 16 Nuggets of wisdom • Use the tools to their strengths Data Engineering Data Visualization Modelling • Good visualization practices saves $$$ Save the Pies for dinner Soothing MVR White spaces make everything better Data to Ink ratio is king Space bars appropriatelyStart axis at 0 Tableau Instant Karma
  17. Things Data Visualization rookies say.. 17 • I have two numbers, I’ll still make a graph anyway… • Vertical bars are way better than Horizontal bars... • 3D graphs are cool… No, the human eye tends to compare horizontal bars better! No, just write the numbers instead Not cool at all..
  18. How to lie with visuals.. 18 Remove scales altogether Use 3-D Pie charts Scale don’t need to start at 0 Shapes don’t need to match data
  19. Increased Impact 19 Increased Insights Reduced Software Spending Reduced ETL time What has Tableau done for us
  20. 20 3D Pie Chart Fin!/