{"id":17223,"date":"2016-11-18T01:05:58","date_gmt":"2016-11-18T06:05:58","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/the-drone-buzz-how-kespry-transforms-physical-jobsites-into-big-data-sets\/"},"modified":"2016-11-18T01:05:58","modified_gmt":"2016-11-18T06:05:58","slug":"the-drone-buzz-how-kespry-transforms-physical-jobsites-into-big-data-sets","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/the-drone-buzz-how-kespry-transforms-physical-jobsites-into-big-data-sets\/","title":{"rendered":"The Drone Buzz: How Kespry Transforms Physical Jobsites into Big Data Sets"},"content":{"rendered":"<p>Have you ever walked past the construction gate circling the soon-to-be Klarman Hall and wondered: what\u2019s <em>really<\/em> happening on the other side of the fence? A sign on the gate\u00a0sketches a broad\u00a0vision of the future building. But how is the project progressing on the ground? What if \u2013 from the safety of your laptop \u2013 you could log into myHBS, open a link to the Klarman project site, and virtually explore a scaled 3D model of the full grounds, gaining richer and more accurate insight than would be possible by\u00a0crossing the fence? You could answer:<\/p>\n<ul>\n<li>Whether every base point of that hole in the ground is at the right depth and levelness to safely begin\u00a0building;<\/li>\n<li>If the volume and mass of a new pile of raw material reflects what HBS paid for;<\/li>\n<li>Whether today\u2019s progress \u2013 at every spatial point \u2013 matches the plan;<\/li>\n<li>How many hand tools have been accidentally dropped across the site \u2013 oops!<\/li>\n<\/ul>\n<p>These questions and beyond are what construction, mining, and insurance companies ask and answer through Kespry, a first-mover digitizing drones that turn the physical jobsite into a big data set.<\/p>\n<p>While numerous companies large and small have tried to capitalize on the drone hype with varying success, Kespry is winning customer acquisition in time-old industries previously set in their ways by leveraging the power of digitization.<\/p>\n<p>How exactly does this work? How has Kespry digitized what it does, beyond\u00a0being a commercial eye in the sky? After all, the hardware just looks like a quadcopter with a camera, right?<\/p>\n<p><strong><u>No Joysticks: Safety and Efficiency <\/u><\/strong><\/p>\n<p>While Kespry began as a remote-controlled widget, the end user now merely pushes \u201cgo\u201d in Kespry\u2019s iPad app to make the drone take off, fly, collect imagery and telemetry, transfer that data to the cloud, and land \u2013 all on its own. The app uses simple inputs from the user including flight area and height to algorithmically generate an optimal flight path.[1] Through localized wireless internet and a computer with embedded software on the drone, the app and drone maintain communication to display in-app flight progress and send collected data to the cloud for automated processing.[2]<\/p>\n<p>Behind the software are engineers who have dabbled in flying cars, R&amp;D for BMW\u2019s Connected Cars Group, as well as SpaceX\u2019s first software developers, formerly handpicked by Elon Musk himself.[3]<\/p>\n<p><strong><u>From Physical Stuff to\u00a0Actionable Data<\/u><\/strong><\/p>\n<p>Once flight data is transferred to the \u201cKespry Cloud,\u201d it is stitched into various\u00a0virtual, interactive maps (topographical, elevational, 3D view) with millions of mineable data points as well as downloadable files and reports.[4] A jobsite manager can not only pinpoint the latitude, longitude, and altitude of any visible point on the site, but aggregate these points to instantaneously calculate volume and mass of objects; once an object is measured, the Kespry Cloud automatically generates reports for management and sharing.[5]<\/p>\n<p><a href=\"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2016\/11\/Screen-Shot-2016-11-17-at-10.26.51-PM.png\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-17150\" src=\"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2016\/11\/Screen-Shot-2016-11-17-at-10.26.51-PM-300x128.png\" alt=\"screen-shot-2016-11-17-at-10-26-51-pm\" width=\"382\" height=\"163\" srcset=\"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2016\/11\/Screen-Shot-2016-11-17-at-10.26.51-PM-300x128.png 300w, https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2016\/11\/Screen-Shot-2016-11-17-at-10.26.51-PM-768x328.png 768w, https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2016\/11\/Screen-Shot-2016-11-17-at-10.26.51-PM-1024x437.png 1024w, https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2016\/11\/Screen-Shot-2016-11-17-at-10.26.51-PM-600x256.png 600w, https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2016\/11\/Screen-Shot-2016-11-17-at-10.26.51-PM.png 1102w\" sizes=\"auto, (max-width: 382px) 100vw, 382px\" \/><\/a><\/p>\n<p>Source: Kespry, Inc. website: http:\/\/kespry.com\/products\/kespry-cloud<\/p>\n<p>Through a partnership with NVIDIA, Kespry is pioneering the intersection of drones and artificial intelligence with the Jetson TX1, a \u201cdeep-learning\u201d chip that enables recognition, classification, and quantification of objects and patterns.[6] The following image depicts how this can be done with large objects; for subtler objects, Kespry is helping insurance giants such as Allstate inspect various types of roof damages and adjust claims.[7]<\/p>\n<p><a href=\"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2016\/11\/Screen-Shot-2016-11-17-at-10.27.28-PM.png\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-medium wp-image-17153\" src=\"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2016\/11\/Screen-Shot-2016-11-17-at-10.27.28-PM-300x170.png\" alt=\"screen-shot-2016-11-17-at-10-27-28-pm\" width=\"300\" height=\"170\" srcset=\"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2016\/11\/Screen-Shot-2016-11-17-at-10.27.28-PM-300x170.png 300w, https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2016\/11\/Screen-Shot-2016-11-17-at-10.27.28-PM-600x341.png 600w, https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2016\/11\/Screen-Shot-2016-11-17-at-10.27.28-PM.png 690w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/a><\/p>\n<p>Source: NVIDIA Company Blog: https:\/\/blogs.nvidia.com\/blog\/2015\/11\/19\/kespry-jetson-tx1\/<\/p>\n<p>With an end-to-end digitized solution, Kespry ultimately helps its clients operate more efficiently, cost-effectively, accurately, and safely. For example, a client Whitaker Construction tracked\u00a0that with Kespry, they can take inventory twice as often, in a quarter of the time, reducing costs by 22%.[8]<\/p>\n<p><strong><u>What\u2019s Next for Kespry?<\/u><\/strong><\/p>\n<p>Kespry is literally on the up-and-up. With more than $26 million in venture capital raised and $4 million in revenue earned in its first year on the market alone, [9] what could Kespry possibly do next?<\/p>\n<p><strong>Network Effects <\/strong><\/p>\n<p>As Kespry\u2019s customer use scales, and as each new drone flight captures millions of data points, Kespry could (with customer willingness) aggregate this data to deliver valuable industry insights to clients. If clients were open to contributing aggregated, anonymized data for the purpose of network benefits, clients could use this data benchmark themselves, whether it be material density inputs, how ahead of or behind schedule their projects are compared to industry standard, or use cases others have discovered and gained additional value from.<\/p>\n<p><strong>Other Enterprise Software Integration<\/strong><\/p>\n<p>Kespry is capturing critical information about assets, operations, and projects for its clients. Kespry\u2019s data is rich, but\u00a0in a silo. The company should consider integrating, for example, with ERP tools its mining customers may use, or AutoCAD-like programs its construction clients use to seamlessly ship value-adding data between programs that could generate increased customer value and stickiness.<\/p>\n<p>Word Count:\u00a0792<\/p>\n<p>[1] Interview with John Jessen, Head of Mobile at Kespry, Inc.<\/p>\n<p>[2] Ibid.<\/p>\n<p>[3] Paul Doersch, \u201cOpen Letter from Kespry Founder.\u201d http:\/\/kespry.com\/careers<\/p>\n<p>[4] Company website. http:\/\/kespry.com\/products\/kespry-cloud<\/p>\n<p>[5] Ibid.<\/p>\n<p>[6] Brian Caulfield, \u201cKespry Shows How Jetson TX1-Powered Drones Can Keep an Eye on Construction Sites,\u201d November 19, 2015. <a href=\"https:\/\/blogs.nvidia.com\/blog\/2015\/11\/19\/kespry-jetson-tx1\/\">https:\/\/blogs.nvidia.com\/blog\/2015\/11\/19\/kespry-jetson-tx1\/<\/a><\/p>\n<p>[7] Jonathan Vanian, \u201cAllstate Just Used Drones to Inspect Homes in Texas,\u201d September 2, 2016. Fortune. http:\/\/fortune.com\/2016\/09\/02\/allstate-drone-home-inspection-insurance\/<\/p>\n<p>[8] Company website. http:\/\/www.kespry.com\/drone-stockpile-cost-savings\/?utm_content=bufferbe783&amp;utm_medium=social&amp;utm_source=twitter.com&amp;utm_campaign=buffer<\/p>\n<p>[9] Ryan Mac, &#8220;Enterprise Drone Maker Kespry Closes $16 Million Round With New FAA Regulations In Sight,&#8221; June 21, 2016. Forbes. http:\/\/www.forbes.com\/sites\/ryanmac\/2016\/06\/21\/enterprise-drone-maker-kespry-closes-16-million-round-with-new-faa-regulations-in-sight\/#460708e27ef0<\/p>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>What happens when you give a miner, construction worker, and roof inspector a drone?<\/p>\n","protected":false},"author":2200,"featured_media":17232,"comment_status":"open","ping_status":"closed","template":"","categories":[2468,1909,298,428,2465,1253,730,2019,1503,5,212,2466,2467],"class_list":["post-17223","hck-submission","type-hck-submission","status-publish","has-post-thumbnail","hentry","category-aggregates","category-artificial-intelligence","category-big-data","category-construction","category-drone","category-drones","category-insurance","category-internet-of-things","category-iot","category-mining","category-tech","category-uav","category-uavs"],"connected_submission_link":"https:\/\/d3.harvard.edu\/platform-rctom\/assignment\/digitization-challenge-2016\/","yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.3 - 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