Making IoT Data Actionable Using Predictive Analytics
•
3 gefällt mir•156 views
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.
Empfohlen
Empfohlen
Weitere ähnliche Inhalte
Was ist angesagt?
Was ist angesagt? (20)
Ähnlich wie Making IoT Data Actionable Using Predictive Analytics
Ähnlich wie Making IoT Data Actionable Using Predictive Analytics (20)
Mehr von HPCC Systems
Mehr von HPCC Systems (20)
Kürzlich hochgeladen
Kürzlich hochgeladen (20)
Making IoT Data Actionable Using Predictive Analytics
- 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. Making IoT Data Actionable Using Predictive Analytics 2
- 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. 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. 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. 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. 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. 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. Today, let’s discuss some examples Occupancy: Monitoring/Prevention Water Leak: Monitoring/Alert 9
- 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. 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. 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. 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. 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. Broward Miami-Dade Palm Beach Assignment of Benefits – Tri Counties Making IoT Data Actionable Using Predictive Analytics Source: LexisNexis Internal Research 15
- 16. Broward Miami-Dade Palm Beach Assignment of Benefits – Tri Counties Making IoT Data Actionable Using Predictive Analytics Source: LexisNexis Internal Research 16
- 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. Harvey: Tweets Containing “Flood” Making IoT Data Actionable Using Predictive Analytics 18
- 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. 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. 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. How to start unlocking these insights now Technology/Analytics to develop and deploy a pilot program
- 23. HPCC Systems Architecture Making IoT Data Actionable Using Predictive Analytics 23
- 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. 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
Hinweis der Redaktion
- Devices in the Internet of Things communicate with each other, only a human isn’t directly prompting the interaction. Today we call this “The Internet of Things,” but that’s only because it’s new. In five years we’ll probably just call it “the internet.”
- Gartner put the number of IoT devices at 8 billion in 2017. For 2020, they estimate TWENTY billion. Cisco estimates 50 billion. We can be sure they’re both wrong, but one of them might be close. The point is, there will be tens of billions of devices generating data. And on the data side, what’s interesting is that humans have generated the majority of the data out there today, from pictures and texts, to movies, to scholarly articles. But soon the data created by “things” will dwarf the data created by humans.
- There has been a lot of activity over the past year but these same key questions are still largely unanswered. [Walk through points] And I’ll add one more --- Consumer engagement. What gets the consumer to push through setup challenges, encourage them to replace batteries, or even engage with the device through an app? There is still a lot of ambivalence and complexity out there so instead of taking a step back like we did last year, let’s take a step in and look at some specific use cases. Who will be the winners and loser in the devices and platforms. There will continue to be consolidation, new entries and exits. This makes partnerships and data agreements complicated. Who is driving? Is it the Consumer, the insurer or the infrastructure. As I showed on the previous slide… You may want to prevent water losses, but that doesn’t mean your policyholder shares that concern. He or she may be more likely to opt for voice activated mood lighting. Discounts or carrier device buys may help to remedy this over time. Connected utility meters, built in capabilities may influence in time. Cyber risk: In 2016 there was a major Distributed Denial of Service attack that shut down a number of websites. Wifi enable baby monitors have been hacked. Carriers do have to consider this when potentially connected their brand with a device. Do you want that connected thermostat you encouraged your customer to buy to be susceptible to ransomware that extort a payment to keep the heat on during the winter? .. . The good news is that there are good companies out there today working on building more sophisticated technology to protect connected devices. Much of the purported benefit of the connected home is speculation. How does this data really play out? Does the connected water sensor really prevent loss payments to a significant degree. Does it reduce frequency? Just Severity? How much? We need a lot more data to know for sure. And multiply that across the dozens of devices that are available. How big is the disruption? If at the end of the day we end up with a lot of new data sources that allow us to offer another 5% discount, or that help us validate the home security system discounts carriers are already giving . . . Then it’s still useful but not revolutionary. On the other hand, being able to price a risk from the ground-up using a multitude of IoT real time data becomes a reality then maybe it does. The other question here is loss mitigation versus loss avoidance. Finally, is cost. Particularly the cost of the device. As we discussed above, the consumer may not buy the devices you want them to have, which means the insurer would potentially need to foot the bill (either directly or through discounting and/or rate). That math needs to work, and a $5 device will be a lot more attractive to mitigate flood risk under a give sink then an $80 device.
- Insurers can explore many ways to avoid and limit losses So where does LexisNexis fit in the IoT world? We can analyze, normalize, and score this data for our customers (WITH THE CONSUMERS PERMISSION, OF COURSE). We can solve the many to many challenge, not only for insurers, but for IoT companies, too. We can take millions of datapoints and turn them into something digestible and meaningful to the industry. I hope this all sounds familiar, because it’s what we do every day already. And the normalization can take many forms. It’s not hard to imagine that the Nest, the Ecobee, the Lyric, and the Sensi - all smart thermostats which use occupancy to make decisions – might produce different data. It might come at different intervals, at different levels of granularity, and there may be differences in sensitivity between them. Clearly there’s an opportunity for us to normalize that data on the way in so that we can produce occupancy score or attribute from thermostats that works for ALL popular models of thermostat. This is not too different from what we’ve done in the UBI space to normalize driver scores across phone types.
- This is one piece of the data that we can collect from Nest thermostats. In this case I once again got one of my co-workers to agree to let me use his data – but he won’t let me use his real name because he is paranoid that his rates will go up. We are going to call him “Shawn” Shawn has two Nest thermostats and they each send data nearly 150 times a day. This data stream has dozens of field including everything from the actual temperature in the home, the desired temperature, the location of the thermostat the consumer has specified and whether someone has locked in a temperature other than those in the settings. The nest thermostat switches to “Eco” mode when it doesn’t detect anyone present in the home and this data is captured as well.
- Here is Shawn’s lake House. Only one thermostat in this house but it is consistently reporting “Eco Status” until we get to the Holiday weekend. Now this is a very clear example and not every example will be this clear but it is evident.
- Assignment of Benefits mainly impacts water non-weather claims associated with leaking pipes and damaged appliances
- Small circles are tweets containing ‘tornado’, large circles are official sightings So we are starting to harvest based on keywords to 1: build up data to have a baseline (i.e. background noise) 2: ‘hoping’ for an event to see spikes Right now we are grabbing tweets with words (also partial) containing the keywords Flood Hail Lightning Tornado Wildfire
- So where does LexisNexis fit in the IoT world? We can analyze, normalize, and score this data for our customers (WITH THE CONSUMERS PERMISSION, OF COURSE). We can solve the many to many challenge, not only for insurers, but for IoT companies, too. We can take millions of datapoints and turn them into something digestible and meaningful to the industry. I hope this all sounds familiar, because it’s what we do every day already. And the normalization can take many forms. It’s not hard to imagine that the Nest, the Ecobee, the Lyric, and the Sensi - all smart thermostats which use occupancy to make decisions – might produce different data. It might come at different intervals, at different levels of granularity, and there may be differences in sensitivity between them. Clearly there’s an opportunity for us to normalize that data on the way in so that we can produce occupancy score or attribute from thermostats that works for ALL popular models of thermostat. This is not too different from what we’ve done in the UBI space to normalize driver scores across phone types.
- For a carrier that wants to get started in IoT the first objective is to get data, and this can be a challenge by yourself. However, LexisNexis offers to be your partner in collecting and interpreting this data. An easy place to start is by leveraging the devices that are already in your customer’s homes. LexisNexis is in the process of rolling out internal pilots with our employees to collect Nest thermostat data via an API connection. As we move into phase II of this program by early next year, we invite you to join us. For your customers that opt in, and have a Nest in their home, you will be able to simply supply them with a URL to begin collecting data. LexisNexis will then collect and process data, including pooling with participants should you choose to participate in data sharing and share the aggregate results with the broader group. If you are interested in a water device pilot, we are happy to work with you as well and are happy to facilitate conversations with device makers that fit your needs.