P 01 paw_shiny_methods_2017_10_31_v14
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Operationalizing Analytics and AI
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P 01 paw_shiny_methods_2017_10_31_v14
- 1. OPERATIONALIZING ANALYTICS AND AI Vishwa Kolla Head, Advanced Analytics John Hancock Insurance
- 2. TOPICS 2 Drivers Levers Case Studies
- 3. A JOURNEY TO DELIVER VALUE MIGHT INCLUDE HELPING … 3 REDUCE COMPLAINTS GROW WALLET- SHARE GROW CSAT REDUCE CHURN REDUCE COST TO TARGET GROW BOTTOM-LINE GROW TOP-LINE REDUCE COST TO ACQUIRE
- 4. 1997 2011 2016 ANALYTICS / AI IS AN ENABLER 4
- 5. Smart Human Smart Human Normal Human Normal Human Smart Machine OK Machine Smart Machine OK Machine Weak Process Weak Process Weak Process Strong Process WHAT WILL YIELD THE BEST OUTCOME? 5
- 7. Normal Human OK Machine Strong ProcessSteven Crampton and Zackary Stephen New Hampshire Beat Grand Masters + Machine 2005 THE OUTCOME (THOUGH NON-INTUITIVE) IS REMARKABLE 7
- 8. “Weak human + machine + better process was superior to a strong computer alone and, more remarkably, superior to a strong human + machine + inferior process.” – Gary Kasprov WE EMPHASIZE PROCESS (OVER PURPOSE) FOR THE MOST PART 8
- 9. ITERATIONS VALUE ; INCREASING ADOPTION IS TODAY’S FOCUS 9 Data Models Insights ADOPTIONIterations VALUE TO FIRM FOCUS
- 10. TOPICS 10 Drivers Levers Case Studies
- 11. AI IS BUILT 11 AI is not something you buy; it’s something you build. It’s not something you outsource; it’s something you cultivate internally, until it becomes a trusted core capability. And it is not, counter- intuitively, just about technology; it is, truly, about machines learning from humans. Developing a successful AI algorithm today requires the presence of humans in the learning loop, especially during the process of training the algorithm—a resource-consuming undertaking that many companies woefully underestimate. -- Brad Fisher, KPMG Source: https://www.forbes.com/sites/kpmg/2017/08/09/realizing-the-promise-of-artificial-intelligence/#7362edfb485e
- 12. BIG DECISIONS 12 BUY BUILD IT FOR ME BUILD IN-HOUSE Cost Customization Man-Hours Build-time Support Knowledge gain Cost Customization Man-Hours Build-time Support Knowledge gain
- 13. NO DEARTH OF BUILD OPTIONS 13 EXCEL SHINY R HOME GROWN LOW CODE
- 14. TOPICS 14 Drivers Levers Case Studies
- 15. EXCEL IS OUR DEFACTO STARTING POINT 15 PROFILING AUTOMATED EXPLORATION DATA CLEANSING ACCELERATORS RULES ENGINE FRONT-END ADHOC ANALYSES MODEL EXPERIMENTS FRONT Variable Name Start Value End Value Capped Value ELSE VAL Capped Var Desc ELSE_VAL_DESC DEMO_AGE 0 18 1 55 01 - 0 to 18 05 - 50 - 60 DEMO_AGE 18 30 2 55 02 - 18 to 30 05 - 50 - 60 DEMO_AGE 30 40 3 55 03 - 30 to 40 05 - 50 - 60 DEMO_AGE 40 50 4 55 04 - 40 to 50 05 - 50 - 60 DEMO_AGE 50 60 5 55 05 - 50 to 60 05 - 50 - 60 DEMO_AGE 60 71 6 55 06 - 60 to 71 05 - 50 - 60 DEMO_AGE 71 81 7 55 07 - 71 to 81 05 - 50 - 60 DEMO_AGE 81 91 8 55 08 - 81 to 91 05 - 50 - 60 DEMO_AGE 91 999 9 55 09 - 91 to 999 05 - 50 - 60
- 16. EXCEL SERVES US WELL FOR RAPID POC 16 THE GOOD EASY ADAPTABLE THE BAD DATA CONNECTION LATENCY SINGLE THREADED THE UGLY VISUALIZATION LIBRARY ALGORITHMS
- 17. SHINY IS REALLY SHINY ; ACCELERATES ADOPTION 17 Shiny R App Web scrapping Sentiment Analysis Word clouds Topic Modelling Word Frequency
- 18. WE FIND SHINY IS AN AGILE WAY TO DATA SCIENCE 18 1. Shiny Dashboard 2. Plotly 3. Ggplot 4. Render 5. Ggvis 6. Shiny.Semantic 7. ShinyBS 8. ShinyJqui 9. Leaflet 10. ShinyCCSloaders 11. Ggmaps 1. Caret 2. Tm 3. Dplyr 4. Tidyr 5. Stringr 6. Car 7. Vcd 8. Rccp 9. Jsonlite 10. Httr 11. Devtools UI.R SERVER.R VISUAL ENHANCE MENTS STRATEGY CHANGE BUISNESS INPUT UI SMOTHING DATA ENGG. ALGO RECALI BRATION SCRUBBING EDA BIVARIATE ANALYSIS TEXT ANALYTICS CLASSIFI CATION AI SCRUBBING IMAGE PROCESSING ML TIME SERIES ANALYSIS
- 19. USE SHINY ONLY IF YOU HAVE A COMMUNITY OF ADOPTERS 19 THE GOOD VISUAL DATA CONNECTIVITY POWER OF R DS ALGORITHMS THE BAD HARDER TO LEARN VISUALS THE UGLY LIMITED SCALABILITY MEMORY HOG
- 20. HOME GROWN IS A DOUBLE-EDGED SWORD 20 FOUNDATIONAL USP CORE-IP ONE-OFF RE-INVENTING THE WHEEL NON-CORE IP
- 21. LOW CODE CAN ACCELERATE ADOPTION FOR A LOW(ER) COST 21 BI IN EXCEL STAND- ALONE BI BI ON WEB BI ON MOBILE BI FOR MOBILE & WEB LOW CODE
- 22. USE “FIT FOR PURPOSE” WHEN CHOOSING AN OPTION 22 POC PROTOTYPE PROD