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Making IoT Data Actionable Using Predictive Analytics

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As part of the 2018 HPCC Systems Summit Community Day event:

This is a proof-of-concept where an HPCC Systems cluster is used to gather current IoT device data from opt-in subscribers. The cluster's architecture and collected data will be described in the presentation, as well as the additional datasets (e.g. property characteristics, weather, etc.) brought in to enhance the data for analysis using predictive analytics for potential applications in the insurance industry.

Dan Camper has been with LexisNexis Risk for four years and is a Senior Architect in the Solutions Lab Group. He has worked for Apple and Dun & Bradstreet, and he ran his own custom programming shop for a decade. He's been writing software professionally for over 35 years and has worked on a myriad of systems, using a lot of different programming languages. He thinks ECL is pretty neat.

Hicham Elhassani is VP Modeling with LexisNexis Risk Solutions.

Published in: Data & Analytics
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Making IoT Data Actionable Using Predictive Analytics

  1. 1. Innovation and Reinvention Driving Transformation OCTOBER 9, 2018 2018 HPCC Systems® Community Day Hicham Elhassani – VP Modeling Vertical Support Dan S. Camper – Sr. Architect, HPCC Solutions Lab Making IoT Data Actionable Using Predictive Analytics
  2. 2. Making IoT Data Actionable Using Predictive Analytics 2
  3. 3. If you think connected “things” are everywhere NOW . . . Making IoT Data Actionable Using Predictive Analytics 2016 2017 2018 2020 Consumer 3,963 5,244 7,036 12,863 Business:Cross-Industry 1,102 1,501 2,133 4,381 Business:Vertical-Specific 1,317 1,635 2,028 3,171 Grand Total 6,382 8,381 11,197 20,415 Source: Gartner (January 2017) IoT Units Installed Base by Category (Millions of Units) 3
  4. 4. Value proposition? Cyber risk? What does the data say? Who is driving? Incremental or revolutionary? Cost vs. Benefit? Making IoT Data Actionable Using Predictive Analytics BIG QUESTIONS FOR INSURANCE 4
  5. 5. Making IoT Data Actionable Using Predictive Analytics Importance of collecting Iot data to company’s insurance strategy (n=120) 8% 70% 22% Very / Somewhat Important Neither important or unimportant Not at all/not very important Importance for insurers to collect IoT data today 5
  6. 6. Making IoT Data Actionable Using Predictive Analytics Collection and/or Purchase of Connected Home Data (n=120) 1% 4% 19% 38% 38% Collect/purchase, use in decision-making Collect/purchase, plan to use Collect/purchase, but not sure how to use Don’t collect/purchase, but plan to Don’t collect/purchase, don’t plan to Collect today = 24% Don’t Collect today = 76% Collection of Connected Home Data 6
  7. 7. Making IoT Data Actionable Using Predictive Analytics Timeline to begin collecting Connected Home data Anticipated Timeline for Collecting and/or Using Connected Homes Data (among those not currently using, but planning to use connected homes, n=73) In next year In next 2-3 years In next 4-5 years In 6+ years Not sure 4% 52% 34% 7% 3% Next 3Years = 56% 4+Years = 41% 7
  8. 8. Home Loss Statistics and IOT opportunities Making IoT Data Actionable Using Predictive Analytics 11 % OTHERTHEFT 25 % 21% 22% 21% WIND HAIL FIRE WATER NON- WEATHERWATER WEATHER LIABILITY Internals data Security Freeze detection Leak detection Smoke/CO Temp/Humidity Motion sensor Appliances Audio/video External data Weather API Social M events Loss history Property info Geo information Internals data Security Freeze detection Leak detection Smoke/CO Temp/Humidity Motion sensor Appliances Audio/Video External data Weather API Social M events Loss history Property info Geo information Internals data Security Freeze detection Leak detection Smoke/CO Temp/Humidity Motion sensor Appliances Audio/video External data Weather API Social M events Loss history Property info Geo information Internals data Security Freeze detection Leak detection Smoke/CO Temp/Humidity Motion sensor Appliances Audio/video External data Weather API Social M events Loss history Property info Geo information Internals data Security Freeze detection Leak detection Smoke/CO Temp/Humidity Motion sensor Appliances Audio/video External data Weather API Social M events Loss history Property info Geo information 8
  9. 9. Today, let’s discuss some examples Occupancy: Monitoring/Prevention Water Leak: Monitoring/Alert 9
  10. 10. Making IoT Data Actionable Using Predictive Analytics Smart Thermostat Data: Primary Residence HVAC Mode Observations 0 50 100 150 200 250 300 350 Eco July 4th Weekend Source: Nest 10
  11. 11. Making IoT Data Actionable Using Predictive Analytics Smart Thermostat Data: Vacation Home 0 20 40 60 80 100 120 Eco HVAC Mode Observations July 4th Weekend Source: Nest 11
  12. 12. Making IoT Data Actionable Using Predictive Analytics 0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 3/12/20180:00 3/12/20186:00 3/12/201812:00 3/12/201818:00 3/13/20180:00 3/13/20186:00 3/13/201812:00 3/13/201818:00 3/14/20180:00 3/14/20186:00 3/14/201812:00 3/14/201818:00 3/15/20180:00 3/15/20186:00 3/15/201812:00 3/15/201818:00 3/16/20180:00 3/16/20186:00 3/16/201812:00 3/16/201818:00 3/17/20180:00 3/17/20187:00 3/17/201813:00 3/17/201819:00 3/18/20181:00 3/18/20187:00 3/18/201813:00 3/18/201819:00 3/19/20181:00 3/19/20187:00 3/19/201813:00 3/19/201819:00 3/20/20181:00 3/20/20187:00 3/20/201813:00 3/20/201819:00 3/21/20181:00 3/21/20187:00 3/21/201813:00 3/21/201819:00 3/22/20181:00 3/22/20187:00 3/22/201813:00 3/22/201819:00 3/23/20181:00 3/23/20187:00 3/23/201813:00 3/23/201819:00 3/24/20181:00 3/24/20187:00 3/24/201813:00 3/24/201819:00 3/25/20181:00 3/25/20187:00 3/25/201813:00 3/25/201819:00 3/26/20181:00 3/26/20187:00 3/26/201813:00 3/26/201819:00 Shower Restroo m Laundry x3 Dishwasher x2 Child’s bath Dishwasher Child’s bath Child’s bath Child’s bath Child’s bath Child’s bath Child’s bath Source: Streamlabs Example: Water Leak Detection 12
  13. 13. Example: Water Leak & Assignment of Benefits Making IoT Data Actionable Using Predictive Analytics File it Assign of benefits (AOB) is a legal tool that allows the homeowner to transfer their rights to collect from an insurance claim to a third party. Fix It AOB is commonly used when a homeowner employs a contractor or water remediation company to fix water damage from pipe and appliance leaks Fake it This arrangement has permitted some contractors to overinflate claims, resulting in a dramatic increase in frequency and severity in Florida water non-weather claims Source: Office of Insurance Consumer Advocate, Florida Office of Insurance Regulation 13
  14. 14. Assignment of Benefits – Florida vs USA (Excl. Florida) Making IoT Data Actionable Using Predictive Analytics 30 25 20 15 10 5 0 LossCost($) 2011 2012 2013 2014 2015 2016 Accidental Water Discharge and Appliance Leakage Loss Cost USA (Excl. Florida) FloridaSource: LexisNexis Internal Research 14
  15. 15. Broward Miami-Dade Palm Beach Assignment of Benefits – Tri Counties Making IoT Data Actionable Using Predictive Analytics Source: LexisNexis Internal Research 15
  16. 16. Broward Miami-Dade Palm Beach Assignment of Benefits – Tri Counties Making IoT Data Actionable Using Predictive Analytics Source: LexisNexis Internal Research 16
  17. 17. Water Leak and Geo-located losses Making IoT Data Actionable Using Predictive Analytics 0.50% 0.45% 0.40% 0.35% 0.30% 0.25% 0.20% 0.15% 0.10% 0.05% 0.00% Frequency 2011 2012 2013 2014 2015 2016 Accidental Water Discharge and Appliance Leakage Frequency Broward County Miami-Dade County Palm Beach County Florida (Excl. Tri Counties) Source: LexisNexis Internal Research 17
  18. 18. Harvey: Tweets Containing “Flood” Making IoT Data Actionable Using Predictive Analytics 18
  19. 19. Weather Events Digital Trail • Elk City tornado by the NOAA:yesterday 17/05/2017 • Flood • Hail • Lightning • Tornado • Wildfire Making IoT Data Actionable Using Predictive Analytics 19
  20. 20. Stream Analytics: Push and Pull data sources Making IoT Data Actionable Using Predictive Analytics Wind Fire Water (non- weather) Water (weather ) Theft Liability Other Hail 20
  21. 21. Data platforms will be key to unlocking the full potential of this opportunity Making IoT Data Actionable Using Predictive Analytics MARKETING CONTACT QUOTE UNDERWRITIN G RENEWAL COMPLIANCE CLAIM IoT Platform Insurer Automatio n Mitigation Utilities Connected Home Securit y Connecte d Car Connecte d Self Connecte d Business 21
  22. 22. How to start unlocking these insights now Technology/Analytics to develop and deploy a pilot program
  23. 23. HPCC Systems Architecture Making IoT Data Actionable Using Predictive Analytics 23
  24. 24. HPCC Systems – Pull Architecture • Device users register at a web portal • Authentication and authorization via device manufacturer’s web site • Authorization response includes an access token • All registration information saved • Thor queries devices for all registered users in parallel • Ancillary data, such as weather conditions local to every device, is periodically gathered • Analytics are also run periodically, as often as needed • ROXIE updated with analytics results and are made available to external services Making IoT Data Actionable Using Predictive Analytics 24
  25. 25. HPCC Systems – Push Architecture • Authorized devices whitelisted via master device management • Remote devices send their data to ROXIE • After validation and normalization, message stored in Kafka and Couchbase • Thor periodically pulls new messages from Kafka for processing • Ancillary data, such as weather conditions local to every device, is periodically gathered • Analytics are also run periodically, as often as needed • ROXIE updated with analytics results and are made available to external services Making IoT Data Actionable Using Predictive Analytics 25

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