The Internet of Things (IoT) presents an opportunity to collect real-time information about every physical operation of a business. From the temperature of equipment to the performance of a fleet of wind turbines, IoT sensors can deliver this information in real time. There is tremendous opportunity for those businesses that can convert raw IoT data into business insights, and the key to doing so lies within effective data analytics.<\/p>\n
Characteristics of IoT Data and Analytics<\/strong><\/p>\n Innovations such as in-memory computing and massively distributed processing frameworks (like Hadoop) are important IoT catalysts for organizations that don\u2019t want to compromise speed when analyzing millions of data points. As these technologies have progressed in tandem with reduced sensor and wireless network connectivity costs, IoT analytics initiatives have approached a threshold point making investments more accessible to a broader population of organizations. The IoT data that organizations collect comes from machine sensors and logs that are not part of the traditional corporate data analysis lexicon. As such IoT data feeds tend to have a number of unique characteristics that must be accounted for. Given the persistent nature of IoT data feeds, successful streaming analytics is at the core of fundamentally unlocking value from connected devices. Table 2 details the most salient characteristics of IoT data.<\/p>\n Choosing the Right Analytics Solution<\/strong><\/p>\n In each observed instance, value to the organization \u2013 whether from cost savings or new revenue opportunities \u2013 was unlocked from the analysis of the collected data. That is to say, the process through which individual signals from sensors becomes actionable is through analytics. Broadly, Blue Hill observes four basic stages that represent the maturity of IoT analytics projects. These stages range from replacing manual data collection efforts with sensors to automatically making changes to operations without any human intervention. Each stage builds off of the previous one, and provides tradeoffs in terms of their complexity and their potential value to the organization.<\/p>\n Descriptive Analysis<\/strong> Descriptive analysis offers an opportunity for organizations to understand the state of affairs of their operations. Whether through data discovery efforts or building dashboards, descriptive analysis allows business decision makers to drill into specific areas of the business to identify performance levels, anomalies, and root causes of top-line outcomes. IoT initiatives present a distinction as this analysis can be handled both on \u2018at-rest\u2019 data and live \u2018in-stream\u2019 data. With IoT sensors, organizations have the opportunity to monitor key metrics and performance numbers in real-time by bringing the live data feeds directly into their analysis. This allows organizations to have the most up-to-date understanding of operations, and ensures that decisions are being made on the most relevant data points. However, not all IoT analytics initiatives rely on real-time decision-making. Even if the analytics systems bring in live streams of data from the field, organizations may prefer to review the data in aggregate at the end of specified time periods. As an example, the California based oil & gas company is able to monitor the performance of oil wells at the end of every day or week. This allows them to identify opportunities for improvement (such as increasing production levels) and areas of potential concern. Ultimately, they are able to take this information and disseminate it to their field crew to make adjustments or repairs. The result is reduced downtime and increased production levels. The company estimates that they lose $500 for every hour that a single oil well is not in operation. After analyzing the initial impacts of sensor deployment they estimate that quicker oil well repairs saves approximately $145,000 in cost avoidance per month per field.<\/p>\n Predictive Analysis<\/strong><\/p>\n Using available data to create forecasts and predict future outcomes presents a next step for extending the value proposition of analytics efforts. In this regard, Blue Hill observes important implications across a spectrum of outcomes, from increasing asset uptime to strategic decision-making. For example, a manufacturer monitoring their production line can gain significant advantages by forecasting likely equipment failures before they happen. They may have sensors that monitor characteristics such as temperature, uptime, and output levels of a key piece of equipment. Predictive models built from these monitored sensor readings, combined with historical performance data, can identify leading indicators to future breakdowns. Making adjustments or repairs during scheduled downtime is far more cost-effective than disrupting planned production cycles. The result from predictive analysis in this sense is a reduced number of breakdowns and a higher level of production. This, in turn, may mean additional revenue as well as lower costs. Predictive modeling and machine learning algorithms are built and trained upon past observations, and must be continually refined based on realized outcomes. Again, IoT initiatives presents a distinction in that predictive analysis can be performed based on already-stored data at rest and with live in-stream data. Analytics environments that can stream data live enable dynamic forecasts to be delivered to critical decision makers in real time. This affords the potential for substantial competitive advantages and efficiency gains by reducing response times and generating more accurate predictions. Predictive analytics requires both an understanding of what statistical insights are possible and the tools to create accurate projections. As a starting point, this means avoiding basic statistical fallacies associated with error, interpolation, extrapolation, and accurate regression modeling to build a portfolio of outcomes. This can also mean using predictive analytic tools not only for operational data, but also for relevant text inputs, work schedules, and other external data that can provide context to the prediction at hand. Predictive analytics also require the same clean data needed to conduct historical analysis as a starting point to accurately benchmark and project success.<\/p>\n","protected":false},"excerpt":{"rendered":" The Internet of Things (IoT) presents an opportunity to collect real-time information about every physical operation of a business. From the temperature of equipment to the performance of a fleet of wind turbines, IoT sensors can deliver this information in real time. There is tremendous opportunity for those businesses that<\/p>\n","protected":false},"author":3,"featured_media":757,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[5],"tags":[],"class_list":["post-756","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-knowledgebase"],"jetpack_publicize_connections":[],"jetpack_featured_media_url":"https:\/\/i0.wp.com\/www.virtono.com\/community\/wp-content\/uploads\/2016\/11\/IoT.jpg?fit=2160%2C1080&ssl=1","jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/p7ISfL-cc","jetpack_likes_enabled":true,"jetpack-related-posts":[{"id":3717,"url":"https:\/\/www.virtono.com\/community\/tutorial-how-to\/how-to-deploy-influxdb-on-kubernetes\/","url_meta":{"origin":756,"position":0},"title":"How to Deploy InfluxDB on Kubernetes","author":"George B.","date":"August 4, 2023","format":false,"excerpt":"InfluxDB has become a popular choice for time-series databases, especially for DevOps monitoring, real-time analytics, and IoT applications. In this guide, we will explore how to deploy InfluxDB on Kubernetes cluster using K3s and Helm. We will assume that you already have K3s installed and Helm configured.\u00a0If K3s and Helm\u2026","rel":"","context":"In "Tutorials"","block_context":{"text":"Tutorials","link":"https:\/\/www.virtono.com\/community\/category\/tutorial-how-to\/"},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/www.virtono.com\/community\/wp-content\/uploads\/2023\/08\/Deploy-InfluxDB-on-Kubernetes.png?fit=360%2C240&ssl=1&resize=350%2C200","width":350,"height":200},"classes":[]},{"id":836,"url":"https:\/\/www.virtono.com\/community\/knowledgebase\/the-future-of-big-data-what-we-learned-from-2016\/","url_meta":{"origin":756,"position":1},"title":"The Future Of Big Data, What We Learned From 2016.","author":"Daniel Draga","date":"January 6, 2017","format":false,"excerpt":"#1: Cloud-Based BDA Technology Complexity in managing on- and off-premises data from disparate sources and types in a hybrid environment will persist. Integration, orchestration, provisioning, and data management requirements will increase during the transition to cloud. IT needs to work closely with business to identify and prioritize workloads to move\u2026","rel":"","context":"In "Knowledgebase"","block_context":{"text":"Knowledgebase","link":"https:\/\/www.virtono.com\/community\/category\/knowledgebase\/"},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/www.virtono.com\/community\/wp-content\/uploads\/2017\/01\/bigdata.jpg?fit=1200%2C821&ssl=1&resize=350%2C200","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/www.virtono.com\/community\/wp-content\/uploads\/2017\/01\/bigdata.jpg?fit=1200%2C821&ssl=1&resize=350%2C200 1x, https:\/\/i0.wp.com\/www.virtono.com\/community\/wp-content\/uploads\/2017\/01\/bigdata.jpg?fit=1200%2C821&ssl=1&resize=525%2C300 1.5x, https:\/\/i0.wp.com\/www.virtono.com\/community\/wp-content\/uploads\/2017\/01\/bigdata.jpg?fit=1200%2C821&ssl=1&resize=700%2C400 2x, https:\/\/i0.wp.com\/www.virtono.com\/community\/wp-content\/uploads\/2017\/01\/bigdata.jpg?fit=1200%2C821&ssl=1&resize=1050%2C600 3x"},"classes":[]},{"id":93,"url":"https:\/\/www.virtono.com\/community\/knowledgebase\/business-intelligence-and-data-challenges\/","url_meta":{"origin":756,"position":2},"title":"Business Intelligence And Data Challenges","author":"Virtono","date":"July 18, 2016","format":false,"excerpt":"One growing demand in the business and analytics world is that of something called Business Intelligence (BI). BI is a combination of data subjected over analytical tools to produce complex and relevant information to the business\u2019s decision makers. The objective is simple, to gain a competitive edge over others, and\u2026","rel":"","context":"In "Knowledgebase"","block_context":{"text":"Knowledgebase","link":"https:\/\/www.virtono.com\/community\/category\/knowledgebase\/"},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/www.virtono.com\/community\/wp-content\/uploads\/2016\/07\/Business-Intelligence.jpg?fit=1200%2C675&ssl=1&resize=350%2C200","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/www.virtono.com\/community\/wp-content\/uploads\/2016\/07\/Business-Intelligence.jpg?fit=1200%2C675&ssl=1&resize=350%2C200 1x, https:\/\/i0.wp.com\/www.virtono.com\/community\/wp-content\/uploads\/2016\/07\/Business-Intelligence.jpg?fit=1200%2C675&ssl=1&resize=525%2C300 1.5x, https:\/\/i0.wp.com\/www.virtono.com\/community\/wp-content\/uploads\/2016\/07\/Business-Intelligence.jpg?fit=1200%2C675&ssl=1&resize=700%2C400 2x, https:\/\/i0.wp.com\/www.virtono.com\/community\/wp-content\/uploads\/2016\/07\/Business-Intelligence.jpg?fit=1200%2C675&ssl=1&resize=1050%2C600 3x"},"classes":[]},{"id":3687,"url":"https:\/\/www.virtono.com\/community\/tutorial-how-to\/how-to-deploy-mongodb-on-kubernetes\/","url_meta":{"origin":756,"position":3},"title":"How to Deploy MongoDB on Kubernetes","author":"George B.","date":"July 29, 2023","format":false,"excerpt":"This tutorial will walk you through installing MongoDB on Kubernetes cluster. We'll assume that you've already got K3s and Helm set up on your machine. The deployment of applications like MongoDB on Kubernetes cluster is made easier by the Kubernetes package manager Helm. Before getting too technical, it's important to\u2026","rel":"","context":"In "Tutorials"","block_context":{"text":"Tutorials","link":"https:\/\/www.virtono.com\/community\/category\/tutorial-how-to\/"},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/www.virtono.com\/community\/wp-content\/uploads\/2023\/07\/How-to-Deploy-MongoDB-on-Kubernetes.png?fit=360%2C240&ssl=1&resize=350%2C200","width":350,"height":200},"classes":[]},{"id":739,"url":"https:\/\/www.virtono.com\/community\/knowledgebase\/introduction-to-siem-security-information-and-event-management\/","url_meta":{"origin":756,"position":4},"title":"Introduction To SIEM : Security information and event management","author":"Daniel Draga","date":"November 5, 2016","format":false,"excerpt":"Systems we call SIEMs have never stopped evolving, and they\u2019re at it again, with new capabilities essential to 21st-century security. So it\u2019s time to get to know SIEMs again. Security information and event management products first appeared in the mid-2000s, a merging of SIM (security information management) and SEM (security\u2026","rel":"","context":"In "Knowledgebase"","block_context":{"text":"Knowledgebase","link":"https:\/\/www.virtono.com\/community\/category\/knowledgebase\/"},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/www.virtono.com\/community\/wp-content\/uploads\/2016\/11\/siem-image-wp.jpg?fit=690%2C355&ssl=1&resize=350%2C200","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/www.virtono.com\/community\/wp-content\/uploads\/2016\/11\/siem-image-wp.jpg?fit=690%2C355&ssl=1&resize=350%2C200 1x, https:\/\/i0.wp.com\/www.virtono.com\/community\/wp-content\/uploads\/2016\/11\/siem-image-wp.jpg?fit=690%2C355&ssl=1&resize=525%2C300 1.5x"},"classes":[]},{"id":781,"url":"https:\/\/www.virtono.com\/community\/tutorial-how-to\/cross-function-workforce-optimization-processes\/","url_meta":{"origin":756,"position":5},"title":"Cross-Function Workforce Optimization Processes.","author":"Daniel Draga","date":"December 2, 2016","format":false,"excerpt":"Key Challenges: Workforce optimization (WFO) suites are superior to integrated point solutions, but old ways of thinking hold back innovation within larger and more complex organizations. Departmental siloes that occur within larger organizations \u2013 especially those linked to specific WFO functions like quality management and workforce planning \u2013 encourage a\u2026","rel":"","context":"In "Knowledgebase"","block_context":{"text":"Knowledgebase","link":"https:\/\/www.virtono.com\/community\/category\/knowledgebase\/"},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/www.virtono.com\/community\/wp-content\/uploads\/2016\/12\/video-image-2-desktop.png?fit=940%2C493&ssl=1&resize=350%2C200","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/www.virtono.com\/community\/wp-content\/uploads\/2016\/12\/video-image-2-desktop.png?fit=940%2C493&ssl=1&resize=350%2C200 1x, https:\/\/i0.wp.com\/www.virtono.com\/community\/wp-content\/uploads\/2016\/12\/video-image-2-desktop.png?fit=940%2C493&ssl=1&resize=525%2C300 1.5x, https:\/\/i0.wp.com\/www.virtono.com\/community\/wp-content\/uploads\/2016\/12\/video-image-2-desktop.png?fit=940%2C493&ssl=1&resize=700%2C400 2x"},"classes":[]}],"_links":{"self":[{"href":"https:\/\/www.virtono.com\/community\/wp-json\/wp\/v2\/posts\/756","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.virtono.com\/community\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.virtono.com\/community\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.virtono.com\/community\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/www.virtono.com\/community\/wp-json\/wp\/v2\/comments?post=756"}],"version-history":[{"count":1,"href":"https:\/\/www.virtono.com\/community\/wp-json\/wp\/v2\/posts\/756\/revisions"}],"predecessor-version":[{"id":758,"href":"https:\/\/www.virtono.com\/community\/wp-json\/wp\/v2\/posts\/756\/revisions\/758"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.virtono.com\/community\/wp-json\/wp\/v2\/media\/757"}],"wp:attachment":[{"href":"https:\/\/www.virtono.com\/community\/wp-json\/wp\/v2\/media?parent=756"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.virtono.com\/community\/wp-json\/wp\/v2\/categories?post=756"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.virtono.com\/community\/wp-json\/wp\/v2\/tags?post=756"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}