{"id":15613,"date":"2016-08-16T08:37:55","date_gmt":"2016-08-16T05:37:55","guid":{"rendered":"http:\/\/www.altoros.com\/blog\/?p=15613"},"modified":"2018-06-22T14:48:37","modified_gmt":"2018-06-22T11:48:37","slug":"a-challenging-path-to-building-predictive-analytics-into-iiot-deployments","status":"publish","type":"post","link":"https:\/\/www.altoros.com\/blog\/a-challenging-path-to-building-predictive-analytics-into-iiot-deployments\/","title":{"rendered":"A Challenging Path to Building Predictive Analytics into IIoT Deployments"},"content":{"rendered":"<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_82_2 counter-hierarchy ez-toc-counter ez-toc-transparent ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/www.altoros.com\/blog\/a-challenging-path-to-building-predictive-analytics-into-iiot-deployments\/#Huge_IIoT_potential\" >Huge IIoT potential<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/www.altoros.com\/blog\/a-challenging-path-to-building-predictive-analytics-into-iiot-deployments\/#Its_about_transformation_not_just_data\" >It&#8217;s about transformation, not just data<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/www.altoros.com\/blog\/a-challenging-path-to-building-predictive-analytics-into-iiot-deployments\/#The_need_for_real-time_architecture\" >The need for real-time architecture<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/www.altoros.com\/blog\/a-challenging-path-to-building-predictive-analytics-into-iiot-deployments\/#Cleansing_the_data\" >Cleansing the data<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/www.altoros.com\/blog\/a-challenging-path-to-building-predictive-analytics-into-iiot-deployments\/#Intelligent_integration\" >Intelligent integration<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/www.altoros.com\/blog\/a-challenging-path-to-building-predictive-analytics-into-iiot-deployments\/#Cloudy_edgy_forecast\" >Cloudy, edgy forecast<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/www.altoros.com\/blog\/a-challenging-path-to-building-predictive-analytics-into-iiot-deployments\/#Where_are_we_on_the_path\" >Where are we on the path?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/www.altoros.com\/blog\/a-challenging-path-to-building-predictive-analytics-into-iiot-deployments\/#Related_slides\" >Related slides<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/www.altoros.com\/blog\/a-challenging-path-to-building-predictive-analytics-into-iiot-deployments\/#Related_reading\" >Related reading<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/www.altoros.com\/blog\/a-challenging-path-to-building-predictive-analytics-into-iiot-deployments\/#About_the_speaker\" >About the speaker<\/a><\/li><\/ul><\/nav><\/div>\n<h3><span class=\"ez-toc-section\" id=\"Huge_IIoT_potential\"><\/span>Huge IIoT potential<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<div id=\"attachment_15636\" style=\"width: 210px\" class=\"wp-caption alignleft\"><a href=\"https:\/\/www.altoros.com\/blog\/wp-content\/uploads\/2016\/08\/Venu-Vasudevan.jpg\"><img decoding=\"async\" aria-describedby=\"caption-attachment-15636\" src=\"https:\/\/www.altoros.com\/blog\/wp-content\/uploads\/2016\/08\/Venu-Vasudevan.jpg\" alt=\"Venu Vasudevan\" width=\"200\" class=\"size-full wp-image-15636\" \/><\/a><p id=\"caption-attachment-15636\" class=\"wp-caption-text\"><small>Dr. Venu Vasudevan<\/small><\/p><\/div>\n<p>Why is the Industrial Internet of Things different from other types of modern deployments (or is it)? <a href=\"https:\/\/www.linkedin.com\/in\/venuvasudevan\" target=\"_blank\">Dr. Venu Vasudevan<\/a>, a Chicago-based IoT consultant, who also serves as an adjunct professor at Rice University, addressed this question at a recent <a href=\"https:\/\/www.meetup.com\/GE-Predix-Chicago-Industrial-Internet\/events\/229688811\/\" target=\"_blank\">Predix meetup<\/a> in Chicago that was co-organized by Altoros.<\/p>\n<p>Venu provided some market research by IC Market Drivers showing the IIoT as a $12-billion market in 2015, with a growth rate that experiences a doubling every three years or so. (This tracks well with anticipated overall data growth rates from the Cisco <a href=\"https:\/\/www.cisco.com\/c\/dam\/en\/us\/solutions\/collateral\/service-provider\/global-cloud-index-gci\/white-paper-c11-738085.pdf\" target=\"_blank\">research<\/a>.)<\/p>\n<p><a href=\"https:\/\/www.altoros.com\/blog\/wp-content\/uploads\/2016\/08\/Venu-Vasudevan-IIoT-Predix-Predictive-Analytics-1-Market-Potential.jpg\"><img decoding=\"async\" src=\"https:\/\/www.altoros.com\/blog\/wp-content\/uploads\/2016\/08\/Venu-Vasudevan-IIoT-Predix-Predictive-Analytics-1-Market-Potential.jpg\" alt=\"Venu Vasudevan IIoT Predix Predictive Analytics 1 Market Potential\" width=\"640\" class=\"aligncenter size-full wp-image-15680\" \/><\/a><\/p>\n<p>&nbsp;<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Its_about_transformation_not_just_data\"><\/span>It&#8217;s about transformation, not just data<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>However, the heart of this discussion focused on transformation rather than simply growth. \u201cAn enterprise&#8217;s data needs to move from (mere) reliability to optimization,\u201d he said. \u201cIt must migrate from being descriptive and reactive to predictive.\u201d<\/p>\n<p>He cited a survey from an IoT pioneer Parstream (a German-based company that Cisco acquired in November 2015) that states \u201c<strong>IoT is not delivering its full potential because of data challenges.<\/strong> (Whereas) 86% of enterprises say IoT data is important, only 8% of them are capturing it in a timely fashion.\u201d The survey also found out that 70% of respondents said improved IoT data collection would improve their decision making.<\/p>\n<blockquote><p><em>&#8220;Only 8% of enterprises are capturing IoT data in a timely fashion.&#8221; \u2014Dr. Venu Vasudevan<\/em><\/p><\/blockquote>\n<p>As much of newly created IIoT data is coming from network edges (along factory floors, etc.), it&#8217;s critical to focus on optimal ways to handle this data, Venu pointed out. He outlined a transition from the popular conception of \u201cslow\u201d data lakes to \u201cfast data streams,\u201d mentioning an acceleration of 30 to 100 times.<\/p>\n<p>&nbsp;<\/p>\n<h3><span class=\"ez-toc-section\" id=\"The_need_for_real-time_architecture\"><\/span>The need for real-time architecture<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>Venu espoused the use of (admittedly complex) <a href=\"https:\/\/en.wikipedia.org\/wiki\/Lambda_architecture\" target=\"_blank\">lambda architecture<\/a>, involving the use of Apache Spark, as the path to success in deploying and successfully managing these new, fast data streams. He further recommended edge filtering systems as a way to get a good grip on useful data from these streams early in the process, as abstracted and illustrated in the following diagram:<\/p>\n<p><a href=\"https:\/\/www.altoros.com\/blog\/wp-content\/uploads\/2016\/08\/Venu-Vasudevan-IIoT-Predix-Predictive-Analytics-2-Edge-Filtering-lambda-architecture.jpg\"><img decoding=\"async\" src=\"https:\/\/www.altoros.com\/blog\/wp-content\/uploads\/2016\/08\/Venu-Vasudevan-IIoT-Predix-Predictive-Analytics-2-Edge-Filtering-lambda-architecture.jpg\" alt=\"Venu Vasudevan IIoT Predix Predictive Analytics 2 Edge Filtering lambda architecture\" width=\"640\" class=\"aligncenter size-full wp-image-15681\" \/><\/a><\/p>\n<p>Massive IIoT data production and handling systems imply the idea of \u201cmachine learning at scale,\u201d he noted, as companies work to establish the procedures that will produce the clean data that&#8217;s needed for predictive analytics to meet its potential.<\/p>\n<p>&nbsp;<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Cleansing_the_data\"><\/span>Cleansing the data<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>He noted the significant differences in gathering and using consumer data versus IoT data\u2014consumer data capture is hard, IoT (and by extension, IIoT) easy; yet consumer-data sanitizing is only moderately difficult while IoT-data sanitization is difficult, and modeling of the two types of data is easy and difficult, respectively.<\/p>\n<p><a href=\"https:\/\/www.altoros.com\/blog\/wp-content\/uploads\/2016\/08\/Venu-Vasudevan-IIoT-Predix-Predictive-Analytics-3-sensing-data-challenge.jpg\"><img decoding=\"async\" src=\"https:\/\/www.altoros.com\/blog\/wp-content\/uploads\/2016\/08\/Venu-Vasudevan-IIoT-Predix-Predictive-Analytics-3-sensing-data-challenge.jpg\" alt=\"Venu Vasudevan IIoT Predix Predictive Analytics 3 sensing data challenge\" width=\"640\" class=\"aligncenter size-full wp-image-15682\" \/><\/a><br \/>\n&nbsp;<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Intelligent_integration\"><\/span>Intelligent integration<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>The resultant \u201cinsight-data gap\u201d with IoT data behooves the use of a two-tier IT structure, in which a machine learning (ML) approach to \u201cwrangling\u201d of IoT data is present.<\/p>\n<p><a href=\"https:\/\/www.altoros.com\/blog\/wp-content\/uploads\/2016\/08\/Venu-Vasudevan-IIoT-Predix-Predictive-Analytics-4-two-tier-machine-learning.jpg\"><img decoding=\"async\" src=\"https:\/\/www.altoros.com\/blog\/wp-content\/uploads\/2016\/08\/Venu-Vasudevan-IIoT-Predix-Predictive-Analytics-4-two-tier-machine-learning.jpg\" alt=\"Venu Vasudevan IIoT Predix Predictive Analytics 4 two-tier machine learning\" width=\"640\" class=\"aligncenter size-full wp-image-15683\" \/><\/a><\/p>\n<p>Nevertheless, an intelligent integration to a target model is still needed.<\/p>\n<p>&nbsp;<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Cloudy_edgy_forecast\"><\/span>Cloudy, edgy forecast<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>In conclusion he abstracted the present and future of the IoT as follows:<\/p>\n<p><strong>Present:<\/strong> Cloudy<\/p>\n<ul>\n<li><strong>Embrace:<\/strong> leverage cutting edge cloud and ML services.<\/li>\n<li><strong>Extend:<\/strong> adapt to IIoT business processes.<\/li>\n<\/ul>\n<p><strong>Future:<\/strong> Edgy<\/p>\n<ul>\n<li>Hyper-decentralized intelligence and data.<\/li>\n<li>Systems that understand \u201cnormal\u201d and \u201cdeviation\u201d.<\/li>\n<li>Predictive systems that have both response velocity and<br \/>\ndepth of insight.<\/li>\n<\/p>\n<\/ul>\n<p>&nbsp;<br \/>\n<div id=\"\" class=\"clearfix padded-panel\"><div class=\"details-box-wrapper\" style=\"\/* background-image:url(http:\/\/www.altoros.com\/blog\/wp-content\/uploads\/2015\/04\/cf-after-dark-apache-brooklyn.gif); *\/\"><div class=\"details-box-overlay details-box-overlay-a\"><\/div><div class=\"details-box\"><div class=\"section-title\">Altoros Take<\/div><div class=\"details-wrapper\"><div class=\"detail-item\"><div class=\"take\"><span>\n<h3><span class=\"ez-toc-section\" id=\"Where_are_we_on_the_path\"><\/span>Where are we on the path?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>Venu\u2019s presentation and synopsis struck a few deep chords in me:<\/p>\n<ol>\n<li>\n<p>It was another demonstration that shows how early we are in the Predix era. Altoros has now sponsored more than a dozen of Predix meetups, yet we remain in sort of a \"pre-Predix\" era, in which developers and enterprises need to figure out what they\u2019re doing and what they want to achieve before they can even think of building a PoC, pilot project, or use case for Predix.<\/p>\n<\/li>\n<li>\n<p>The sanitizing (or cleansing) raw data is an enormous issue. Venu outlined his two-tier approach, something that will be complex, developing, and probably tedious. Existing problems of finding the gold in the ore are only magnified in an IIoT installation that features a rapid data stream instead of a placid data lake.<\/p>\n<\/li>\n<blockquote><p><em>\"Modeling consumer data is easy, modeling IIoT data is difficult.\" \u2014Dr. Venu Vasudevan<\/em><\/p><\/blockquote>\n<li>\n<p>Venu\u2019s closing remarks about understanding the difference between \"normal\" and \"deviation\" is similar to many conversations I\u2019ve had recently about turning \"anomalies\" into \"patterns.\" With a massive, ongoing data flow\u2014an archetype being the data that GE collects from its jet engines\u2014things that in the past that were unlikely therefore aberrational reveal themselves to be uncommon but predictable.<\/p>\n<\/li>\n<li>\n<p>This strikes me as a big thought, as we contemplate all of the new insights that the era of the IIoT and big data can reveal. Will it turn out that there\u2019s nothing truly aberrational, that in fact all data has some order to it within the famed <a href=https:\/\/en.wikipedia.org\/wiki\/Chaos_theory target=_blank>Lorenz Attractor<\/a> (i.e., \"butterfly\") within chaos theory?<\/p>\n<\/li>\n<\/ol>\n<\/span><\/div><\/div><\/div><\/div><\/div><\/div>\n<p>&nbsp;<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Related_slides\"><\/span>Related slides<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><center><iframe loading=\"lazy\" src=\"\/\/www.slideshare.net\/slideshow\/embed_code\/key\/LUa5huZ9DRiq9v\" width=\"595\" height=\"485\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\" style=\"border:1px solid #CCC; border-width:1px; margin-bottom:5px; max-width: 100%;\" allowfullscreen><\/iframe><\/center><br \/>\n&nbsp;<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Related_reading\"><\/span>Related reading<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<ul>\n<li><a href=\"https:\/\/www.altoros.com\/blog\/going-loco-ge-predix-siemens-analytics\/\">Going Loco with GE Predix and Siemens Analytics<\/a><\/li>\n<li><a href=\"https:\/\/www.altoros.com\/blog\/ge-predix-and-the-dds-standard-transform-healthcare-control-robots\/\">GE Predix and the DDS Standard Transform Healthcare, Control Robots<\/a><\/li>\n<\/ul>\n<hr>\n<h3><span class=\"ez-toc-section\" id=\"About_the_speaker\"><\/span>About the speaker<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<div>\n<div style=\"float: right;\"><a href=\"https:\/\/www.linkedin.com\/in\/venuvasudevan\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/www.altoros.com\/blog\/wp-content\/uploads\/2016\/08\/Venu-Vasudevan-bio.png\" alt=\"Venu Vasudevan bio\" width=\"150\" height=\"150\" class=\"aligncenter size-full wp-image-15645\" \/><\/a><\/div>\n<div style=\"width: 600px;\"><small><a href=\"https:\/\/www.linkedin.com\/in\/venuvasudevan\">Dr. Venu Vasudevan<\/a> is a technology entrepreneur with a track record of successful products at the intersection of (I)IoT, big data, and media. His early work on the Industrial Internet of Things led to key contributions to the Zigbee IoT standard. Venu holds a Ph.D. (Databases and Artificial Intelligence) in Computer Science from The Ohio State University.<\/small><\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>Huge IIoT potential<\/p>\n<p id=\"caption-attachment-15636\" class=\"wp-caption-text\">Dr. Venu Vasudevan<\/p>\n<p>Why is the Industrial Internet of Things different from other types of modern deployments (or is it)? Dr. Venu Vasudevan, a Chicago-based IoT consultant, who also serves as an adjunct professor at Rice University, addressed this question at a recent Predix meetup in Chicago [&#8230;]<\/p>\n","protected":false},"author":9,"featured_media":15649,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","footnotes":"","_links_to":"","_links_to_target":""},"categories":[7],"tags":[873,117,748,647],"class_list":["post-15613","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news-and-opinion","tag-cloud-native","tag-iot","tag-machine-learning","tag-predix"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>A Challenging Path to Building Predictive Analytics into IIoT Deployments | Altoros<\/title>\n<meta name=\"description\" content=\"Dr. Venu Vasudevan outlined the complex nature of building predictive analytics into the Industrial Internet of Things (IIoT). 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