Verisk Analytics Keynote

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Verisk Analytics Keynote
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CC Attribution - ShareAlike 3.0 Unported:
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2017
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English

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Automation
Human migration Service (economics) Strategy game Video game Analytic set Bit
Standard deviation Presentation of a group Service (economics) Serial port Line (geometry) Decision theory Device driver Database Data analysis Area Protein folding Bit rate Software Autonomic computing Software testing Information Endliche Modelltheorie Category of being Cybersex Service (economics) Arm Regulator gene Decision theory Building Uniqueness quantification Point (geometry) Computer program Sound effect Computer network Bit Formal language Category of being Latent heat Arithmetic mean Event horizon Extreme programming Estimation Vertex (graph theory) Charge carrier Computing platform Catastrophism Volume Telematik Spacetime
Simulation Event horizon Maxima and minima Computer simulation Endliche Modelltheorie Metadata Task (computing) Permutation
INTEGRAL Decision theory Analytic set Database transaction Perspective (visual) Data management Bit rate Hypermedia Single-precision floating-point format Hazard (2005 film) Information Information security Physical system Data integrity Point cloud Service (economics) Purchasing Observational study Touchscreen Regulator gene Channel capacity View (database) Point (geometry) Moment (mathematics) Shared memory Sound effect Streaming media Digital signal Measurement Demoscene Process modeling Tablet computer Mechanism design Data mining Arithmetic mean Chain Self-organization Endliche Modelltheorie Smartphone Right angle Information security Boiling point Physical system Spacetime Service (economics) Algorithm Channel capacity Disintegration Rule of inference 2 (number) Product (business) Number Time domain Goodness of fit Natural number Uniqueness quantification MiniDisc Domain name Scale (map) Pay television Scaling (geometry) Information Civil engineering Analytic set Client (computing) Scalability Hypermedia Business Intelligence Personal digital assistant Revision control Charge carrier Data center Point cloud
Human migration INTEGRAL Direction (geometry) Software developer Self-organization Automation Point cloud Cartesian coordinate system Routing Point cloud
like to introduce our 1 of our big successful customers on 4 for shuffle automate but which is Erich Schneider from various in a little bit of
my fellowship life evidence of my name's eric Shire currently service CTO
there's analytics company most you probably don't know much about so I thought I'd talk it's a little bit is about is the journey that might have been on for the last year and a half which has been developing and delivering upon our migration strategy as well as a little bit about various his what we do and how we do it the technology challenge that creates for us and how we're solving that challenges with some more technology partners today so who is merits but most people don't know it but I can
guarantee everybody in this room has been touched by bears in some way and I think you'll see some of that as I go through the presentation so who are weak but where data analytics company but simply said are goal is to provide insights to our customers so that they can make better decisions we serve 3 primary verticals we serve the property casualty insurance vertical don't falsely here please of we serve the Natural Resources and Energy vertical and we also serve the financial services vertical were about 60 500 people in 27 different countries around the world we grown by acquisition in each 1 of those verticals you can see some of our major brands here and throughout the course that journey we've amassed about 14 petabytes of unique and proprietary data assets that we use every day by those insights our customers so what is that mean and how do we use that data and a proper account of the space yes were involved in all aspects of the rating in claims processing it within the insurance industry were evolved 11 new places and a lot of innovative places that probably wouldn't think of when you think of the insurance industry and it's how the shaping those industries were involved in data related to vehicle telematics the I O T 2 drones the and their impact on the
industry autonomous cars on the test the driver so I'm very interested in that 1 myself but were also involved in catastrophe modeling and we take a look at the impact of earthquakes and we help insurance carriers predict the impact of that and the ripple effects of that throughout the world that we take a look at whether the were actually of the go back to Commons leader not are where the business is I ta compliance don't ask me why but the government uses the weather data that we provide and is governed by the same rules for Arms Regulations of were involved in cyber risk of thank you government serial of and were also involved in 1 of my favorite pandemic modelling and if you notice was started the elbow a bomb on a bit of a germ freak but its data let us show you in a 2nd that drives me that so there's uh the germ of folds in the audience you may wanna look away for a minute but here to make it a little more real for everybody but imagine the outbreak of a pandemic in Austin Texas among 15 hundred people that happened to be attacked attending a technology conference so what would that look like I call it the DAB
outside its outbreak and of over the course of 3 days each 1 of us have the ability to infect 1 . 6 susceptible people and there was no cure for the disease what you see here is a real simulation based on real data and over the
coming 8 months we would actually fact the entire world so I'll leave the ending to you based on your personality were either all dead or we've infected everybody with the about scientists and so all this looked very easy the amount of simulations that we run the permutations of these models and the metadata that goes behind this is actually no small task but so what else do we do in the
natural resources space we actually look at what what's in the ground and we believe we have more data about what's in the ground than anybody else and we use that data not just understand the value of what's in the ground we provide that to our customers in the cost of pulling that data the ground we look a lot of other interesting things as well but 1 that I find particularly compelling is humanitarian aspects involved in mining and minerals also so 3 Tg is referred to in industry which stands for taking young teen tungsten and gold for minerals that make their way into just about every single smartphone and tablet computer or a piece of technology infrastructure that we use every single day and we look at human rights violations that may actually be occurring within the sourcing of those minerals whether that's children and is Adam rightly pointed out we don't want any children near were crushes that's no place for them of and we also take a look at whether in fact militant organizations are involved in the sale and if in the sale of that they're using the profits that defines violence and terror and we advise our customers of those human rights violations so they can actually make more informed and from my perspective health your decisions about all the products that they get there in their supply chain what else do we do financial services in the financial-services space we track 6 . 7 trillion dollars worth of consumer spending every year and we do a lot with that data you can probably guess but again some interesting things media effectiveness so about a year ago we formed a partnership with Nielsen ratings Nielsen wanted to improve the quality of the analytic that they provided to their customers and they partnered with various analytics for that so the typical measure that Nielsen had in the past was eyeballs on screen and while ineffective and poured measure because if you're not watching it what good is the ad it wasn't everything that they wanted to be they brought the demographics data that they have about who's watching the shows we married that with the consumer spending data that we have and were actually able to provide real insights now is that actually the fact if the folks watching those shows actually a interest in those advertisements purchase them how often they purchase them and perhaps there should be different advertisements running during the shows so a pretty diverse skill set so that we have in a pretty diverse data set but for us we think that makes us pretty unique we boil it down into 4 distinct a unique assets that we have that's the data assets that I talked about the deep domain expertise we have we have thousands of data scientists across the world working on these problems every day we pride ourselves in the markets that we serve on being 1st to market with many new products and innovations and were deeply integrated into our customer workflows from a good example that you probably all familiar with is our ties and you see on TV you can use your smart phone you take a picture your license plate you take a picture the number on your car and you get car insurance a quote in moments were actually very often behind the scenes on that date is actually uploaded to various we in seconds provide back and less than seconds often provide back to the carrier a lot of information about you the car the risk associated with you where that car exists and the carrier can in turn make a more informed decision about whether or not they want or for you insurance and the rate at so what is all that mean from a technology perspective the challenges that I'm talking about I'm sure not unique I'm sure every put everybody in this room shares the same for me it was a company that actually encompass the mall which was the 1st in my career the security of our systems is paramount to us we pride ourselves on being good stewards of the data that we have whether it's export data or whether it's data that we're processing on behalf of our customers right so that integrity it could be more important the diversity the business this scale the assets are need to be agile as you've heard here over and over today already the ability to move at new speed is very different even in the insurance space we need the capacity within countries that we operate the rules regulations around data import export and if at all it it can happen are very different in the markets that we operate so we need the ability to have capacity throughout the world where our customers are and where we are and availability resiliency in the example I just gave around auto insurance you can imagine of systems are down and you can't get back what what that means our customers so for us that's transformed us into kind
of a simple yes but 3 main pillars we've been focused on 1 is migrate to the cloud we cannot operate on from data centers at the case which which do which we need to move anymore of we been focused
highly on instilling a dev arts culture throughout the Organization giving our developers direct access to infrastructure is something that was very you for organization I'm sure you're all dealing with that but it's been a big big push with embarrassed and automate everything or ability to have integrity that I talked about the availability we talked about is really predicate and a lot of automation for many ways and we've been solving that problem to 3 technology partnerships you can see the logos here Amazon Web Services shaft AWS stops works we bundle that up internally
into something that we call the HOV lanes lane of we believe it's the express route for us to move things to the cloud internally without the typical bottlenecks you get when you're trying to deploy infrastructure deploy your application
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