{"id":35862,"date":"2025-05-07T03:45:02","date_gmt":"2025-05-07T11:45:02","guid":{"rendered":"https:\/\/www.linquip.com\/blog\/?p=35862"},"modified":"2026-02-24T00:11:55","modified_gmt":"2026-02-24T08:11:55","slug":"smarter-facilities-predictive-analytics-in-mechanical-maintenance","status":"publish","type":"post","link":"https:\/\/www.linquip.com\/blog\/smarter-facilities-predictive-analytics-in-mechanical-maintenance\/","title":{"rendered":"Smarter Facilities: Predictive Analytics in Mechanical Maintenance"},"content":{"rendered":"<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_83 counter-hierarchy ez-toc-counter ez-toc-grey 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-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/www.linquip.com\/blog\/smarter-facilities-predictive-analytics-in-mechanical-maintenance\/#The_Evolution_from_Reactive_to_Predictive_Maintenance\" >The Evolution from Reactive to Predictive Maintenance<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/www.linquip.com\/blog\/smarter-facilities-predictive-analytics-in-mechanical-maintenance\/#The_Strategic_Value_of_Predictive_Analytics\" >The Strategic Value of Predictive Analytics<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/www.linquip.com\/blog\/smarter-facilities-predictive-analytics-in-mechanical-maintenance\/#Core_Technologies_Powering_Predictive_Maintenance\" >Core Technologies Powering Predictive Maintenance<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/www.linquip.com\/blog\/smarter-facilities-predictive-analytics-in-mechanical-maintenance\/#The_Practical_Impact_of_Predictive_Maintenance\" >The Practical Impact of Predictive Maintenance<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/www.linquip.com\/blog\/smarter-facilities-predictive-analytics-in-mechanical-maintenance\/#Implementation_Roadmap_for_Facilities_Managers\" >Implementation Roadmap for Facilities Managers<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/www.linquip.com\/blog\/smarter-facilities-predictive-analytics-in-mechanical-maintenance\/#FAQs\" >FAQs<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/www.linquip.com\/blog\/smarter-facilities-predictive-analytics-in-mechanical-maintenance\/#The_Future_of_Mechanical_Maintenance\" >The Future of Mechanical Maintenance<\/a><\/li><\/ul><\/nav><\/div>\n<p><span style=\"font-weight: 400;\">In today&#8217;s fast-paced business environment, facility managers are constantly looking for ways to minimize disruptions, reduce costs, and extend the life of mechanical systems. The traditional way of fixing things after they break isn&#8217;t cutting it anymore.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">When equipment fails unexpectedly, it&#8217;s not just annoying, it&#8217;s expensive. That\u2019s where predictive analytics comes in. It revolutionizes how we approach mechanical maintenance by helping us see problems before they happen.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">This shift from reactive to proactive maintenance isn&#8217;t just a trend, it&#8217;s becoming essential for competitive, efficient operations. The growing importance of this shift is evident in global markets, with Brazil\u2019s maintenance and repair sector for mechanical engineering machinery projected to reach nearly <\/span><a href=\"https:\/\/www.statista.com\/forecasts\/424506\/maintenance-and-repair-of-mechanical-engineering-machinery-and-equipment-revenue-in-brazil#:~:text=It%20is%20projected%20that%20the%20revenue%20of%20Maintenance%20and%20repair%20of%20mechanical%20engineering%20machinery%20and%20equipment%20in%20Brazil%20will%20amount%20to%20approximately%20173.86%20million%20U.S.%20Dollars%20by%202025\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\">174 million U.S. dollars<\/span><\/a><span style=\"font-weight: 400;\"> in revenue by 2026.<\/span><\/p>\n<h2><span class=\"ez-toc-section\" id=\"The_Evolution_from_Reactive_to_Predictive_Maintenance\"><\/span><b>The Evolution from Reactive to Predictive Maintenance<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">The journey from reactive to predictive approaches represents a fundamental shift in maintenance philosophy. This evolution changes not just how maintenance gets done, but its entire strategic role within an organization.<\/span><\/p>\n<h3><b>The Cost of Waiting Until Something Breaks<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Reactive maintenance, waiting until equipment fails before addressing issues. It can create cascading problems throughout an organization. According to recent research, organizations relying primarily on reactive maintenance typically spend 30% more on repairs than those using predictive approaches. These unexpected failures not only drain budgets but also interrupt operations, sometimes for days or weeks.<\/span><\/p>\n<h3><b>How Data Changes the Game<\/b><\/h3>\n<p><b>Predictive analytics<\/b><span style=\"font-weight: 400;\"> transforms raw operational data into actionable insights. By collecting and analyzing information from sensors, historical maintenance records, and equipment performance metrics, <\/span><a href=\"https:\/\/mpulsesoftware.com\/what-is-facility-maintenance\/\" target=\"_blank\" rel=\"noopener\"><b>facility maintenance<\/b><\/a><span style=\"font-weight: 400;\"> teams can spot subtle signs of deterioration long before they lead to failures. <\/span><b>\u00a0<\/b><span style=\"font-weight: 400;\">The economic argument for implementing <\/span><b>predictive maintenance tools<\/b><span style=\"font-weight: 400;\"> is compelling.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Organizations that implement these technologies typically see maintenance costs decrease by 5-15% while equipment uptime increases by 10-20%. For a midsize facility, this can translate to hundreds of thousands in annual savings.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Transitioning from reactive to predictive approaches requires an initial investment, but the long-term benefits of more strategic <\/span><b>maintenance optimization<\/b><span style=\"font-weight: 400;\"> make this a worthwhile expenditure for forward-thinking organizations.<\/span><\/p>\n<h2><span class=\"ez-toc-section\" id=\"The_Strategic_Value_of_Predictive_Analytics\"><\/span><b>The Strategic Value of Predictive Analytics<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Beyond immediate cost savings, <\/span><b>predictive analytics<\/b><span style=\"font-weight: 400;\"> enables <\/span><b>facilities management<\/b><span style=\"font-weight: 400;\"> teams to make more informed, strategic decisions about their mechanical systems. This broader perspective transforms maintenance from a cost center into a strategic asset.<\/span><\/p>\n<h3><b>Current State of Maintenance Practices<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Most commercial and industrial facilities still operate with a hybrid approach\u2014some preventive maintenance combined with plenty of reactive fixes. Surprisingly, even in 2026, approximately 40% of maintenance activities remain reactive, responding to failures rather than preventing them. This creates unpredictable budgets, staffing challenges, and equipment reliability issues that ripple throughout organizations.<\/span><\/p>\n<h3><b>The Data-Driven Revolution<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Modern <\/span><b>facility maintenance<\/b><span style=\"font-weight: 400;\"> systems leverage interconnected sensors, cloud computing, and sophisticated algorithms to create a comprehensive picture of mechanical system health. These systems don&#8217;t just collect data\u2014they contextualize it, spotting patterns human supervisors might miss and identifying the early warning signs of potential failures.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">When unusual patterns emerge, maintenance teams receive alerts with specific recommendations\u2014check this bearing, replace that filter, or adjust operating parameters. This precision eliminates the guesswork from maintenance planning.<\/span><\/p>\n<h3><b>Bridging Preventive and Condition-Based Approaches<\/b><\/h3>\n<p><b>Predictive analytics<\/b><span style=\"font-weight: 400;\"> serves as the bridge between traditional time-based preventive maintenance and newer condition-based approaches. Unlike preventive maintenance, which follows fixed schedules regardless of actual need, predictive maintenance determines when intervention is necessary based on real-time condition monitoring.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">This hybrid approach combines the reliability of systematic maintenance with the efficiency of only performing work when equipment needs it. The result is a maintenance program that maximizes uptime while minimizing unnecessary labor and parts costs.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Moving beyond theoretical benefits, let&#8217;s examine the practical technologies that make predictive maintenance possible in today&#8217;s facilities.<\/span><\/p>\n<h2><span class=\"ez-toc-section\" id=\"Core_Technologies_Powering_Predictive_Maintenance\"><\/span><b>Core Technologies Powering Predictive Maintenance<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Several key technologies work together to create effective <\/span><b>predictive maintenance tools<\/b><span style=\"font-weight: 400;\">. Understanding these components helps facilities managers evaluate potential solutions and build systems tailored to their needs.<\/span><\/p>\n<h3><b>IoT Sensors and Real-Time Monitoring<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">The foundation of any predictive maintenance system is data collection through strategically placed sensors. These devices monitor critical parameters like:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Vibration patterns in motors and pumps<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Temperature fluctuations in electrical systems<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Pressure changes in hydraulic equipment<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Energy consumption patterns across mechanical systems<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Modern IoT sensors are increasingly affordable and compact, allowing for comprehensive monitoring even in complex mechanical environments. The most effective implementations create a connected ecosystem where data flows seamlessly from equipment to analytics platforms.<\/span><\/p>\n<h3><b>Advanced Analytics and Machine Learning<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Raw data alone doesn&#8217;t provide value\u2014it must be interpreted. This is where advanced analytics and machine learning algorithms transform information into insights. These systems:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Establish operational baselines for each piece of equipment<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Identify subtle deviations that might indicate developing problems<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Learn from historical failures to improve future predictions<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Generate increasingly accurate forecasts as more data accumulates<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">The power of <\/span><b>predictive analytics<\/b><span style=\"font-weight: 400;\"> comes from identifying patterns too subtle or complex for human observation. These algorithms can detect minute changes in vibration signatures, temperature patterns, or energy consumption that frequently precede mechanical failures.<\/span><\/p>\n<h3><b>Cloud-Based Management Platforms<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Modern <\/span><b>predictive maintenance tools<\/b><span style=\"font-weight: 400;\"> typically leverage cloud computing to store, process, and deliver insights. These platforms offer several advantages:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Centralized data access for distributed maintenance teams<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Scalable computing resources for complex analytics<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Integration capabilities with existing maintenance management systems<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Mobile accessibility for technicians in the field<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">The transition to cloud-based maintenance platforms represents a significant shift from older, siloed maintenance management approaches. Today&#8217;s solutions enable real-time collaboration, consistent processes across multiple locations, and data-driven decision making at all levels.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">By combining these technologies, facilities can create a comprehensive <\/span><b>maintenance optimization<\/b><span style=\"font-weight: 400;\"> strategy that addresses both immediate concerns and long-term equipment reliability.<\/span><\/p>\n<h2><span class=\"ez-toc-section\" id=\"The_Practical_Impact_of_Predictive_Maintenance\"><\/span><b>The Practical Impact of Predictive Maintenance<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">The real value of <\/span><b>predictive analytics<\/b><span style=\"font-weight: 400;\"> emerges when these technologies are applied to specific mechanical systems within facilities. Let&#8217;s examine how predictive approaches transform maintenance for critical equipment.<\/span><\/p>\n<h3><b>HVAC Systems Optimization<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">HVAC systems represent some of the highest-value targets for <\/span><b>predictive analytics<\/b><span style=\"font-weight: 400;\"> in <\/span><b>facilities management<\/b><span style=\"font-weight: 400;\">. These complex systems have multiple potential failure points and significant operational costs.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">One manufacturing facility implemented vibration sensors on its critical air handlers and achieved a 30% reduction in <\/span><a href=\"https:\/\/www.petro.com\/resource-center\/what-is-hvac\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\">HVAC<\/span><\/a><span style=\"font-weight: 400;\"> failures within the first year, saving approximately $45,000 in emergency repair costs.<\/span><\/p>\n<h3><b>Electrical System Predictive Maintenance<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Electrical distribution equipment benefits dramatically from predictive approaches. Thermal imaging sensors can detect hotspots in panels and connections long before they cause failures, while power quality monitors identify harmful harmonics or voltage fluctuations that might damage sensitive equipment.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">These implementations not only prevent failures but also extend equipment life. Properly maintained electrical systems typically last 15-20% longer than those managed reactively, representing significant capital expense deferrals.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">With the technological foundation and applications clear, let&#8217;s explore how organizations can begin implementing these capabilities.<\/span><\/p>\n<h2><span class=\"ez-toc-section\" id=\"Implementation_Roadmap_for_Facilities_Managers\"><\/span><b>Implementation Roadmap for Facilities Managers<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Implementing <\/span><b>predictive analytics<\/b><span style=\"font-weight: 400;\"> doesn&#8217;t happen overnight. Successful programs typically follow a phased approach that builds capabilities over time while demonstrating value at each stage.<\/span><\/p>\n<h3><b>Assessing Organizational Readiness<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Before investing in sophisticated analytics platforms, facilities managers should evaluate their organization&#8217;s readiness for predictive maintenance. Key readiness factors include:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Current data collection capabilities and sensor infrastructure<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Maintenance team&#8217;s technical skills and openness to new approaches<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Integration possibilities with existing maintenance management systems<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Executive support for the initial investment required<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">This assessment helps identify gaps that need addressing before implementation and establishes realistic expectations for the transition timeline.<\/span><\/p>\n<h3><b>Selecting the Right Predictive Maintenance Tools<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">With readiness established, the next step involves selecting appropriate technologies. This decision should consider:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">The criticality of different equipment types within the facility<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Available budget for sensors, software, and implementation services<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Internal technical capabilities for system management<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Integration requirements with existing systems<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Most implementations start small, focusing on the highest-value equipment, then expand as the team gains experience and demonstrates ROI.<\/span><\/p>\n<h2><span class=\"ez-toc-section\" id=\"FAQs\"><\/span><b>FAQs<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><b>What&#8217;s the difference between predictive analytics and preventive maintenance in mechanical systems?<\/b><\/p>\n<p><span style=\"font-weight: 400;\">Preventive maintenance follows fixed time-based schedules, while <\/span><b>predictive analytics<\/b><span style=\"font-weight: 400;\"> in mechanical maintenance uses real-time data to determine when equipment actually needs attention. This data-driven approach reduces unnecessary interventions while catching potential issues that might develop between scheduled preventive maintenance.<\/span><\/p>\n<p><b>What typical ROI can facilities expect from predictive maintenance implementation?<\/b><\/p>\n<p><span style=\"font-weight: 400;\">Most facilities see ROI within 12-18 months, with typical returns including 10-20% reduction in maintenance costs, 20-30% decrease in unexpected failures, and 10-15% increase in equipment lifespan. These benefits compound over time as the system collects more data and refines its predictive capabilities.<\/span><\/p>\n<p><b>Which mechanical systems benefit most from predictive analytics?<\/b><\/p>\n<p><span style=\"font-weight: 400;\">High-value equipment with significant downtime costs provides the greatest return, including HVAC systems, electrical distribution equipment, pumps, motors, and critical production machinery. Systems with clear failure indicators (like vibration patterns) typically yield the most accurate predictions.<\/span><\/p>\n<h2><span class=\"ez-toc-section\" id=\"The_Future_of_Mechanical_Maintenance\"><\/span><b>The Future of Mechanical Maintenance<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Forward-thinking facilities managers are already moving beyond basic predictive capabilities to create truly intelligent maintenance systems that not only predict failures but also autonomously schedule and optimize maintenance activities across entire portfolios of assets. This evolution promises even greater efficiency gains as <\/span><b>maintenance optimization<\/b><span style=\"font-weight: 400;\"> becomes increasingly sophisticated.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">As sensor costs continue to decrease and analytics capabilities advance, the barriers to implementing <\/span><b>predictive analytics<\/b><span style=\"font-weight: 400;\"> in <\/span><b>facilities management<\/b><span style=\"font-weight: 400;\"> will continue to fall, making these approaches accessible to organizations of all sizes. Those who embrace these changes now will gain significant competitive advantages through more reliable operations and optimized maintenance costs.<\/span><\/p>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In today&#8217;s fast-paced business environment, facility managers are constantly looking for ways to minimize disruptions, reduce costs, and extend the life of mechanical systems. The traditional way of fixing things after they break isn&#8217;t cutting it anymore.\u00a0 When equipment fails unexpectedly, it&#8217;s not just annoying, it&#8217;s expensive. That\u2019s where predictive analytics comes in. It revolutionizes &#8230;<\/p>\n","protected":false},"author":14,"featured_media":35863,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"default","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"default","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","footnotes":""},"categories":[325],"tags":[341],"class_list":["post-35862","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-sponsored","tag-sponsored"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.linquip.com\/blog\/wp-json\/wp\/v2\/posts\/35862","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.linquip.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.linquip.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.linquip.com\/blog\/wp-json\/wp\/v2\/users\/14"}],"replies":[{"embeddable":true,"href":"https:\/\/www.linquip.com\/blog\/wp-json\/wp\/v2\/comments?post=35862"}],"version-history":[{"count":2,"href":"https:\/\/www.linquip.com\/blog\/wp-json\/wp\/v2\/posts\/35862\/revisions"}],"predecessor-version":[{"id":37671,"href":"https:\/\/www.linquip.com\/blog\/wp-json\/wp\/v2\/posts\/35862\/revisions\/37671"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.linquip.com\/blog\/wp-json\/wp\/v2\/media\/35863"}],"wp:attachment":[{"href":"https:\/\/www.linquip.com\/blog\/wp-json\/wp\/v2\/media?parent=35862"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.linquip.com\/blog\/wp-json\/wp\/v2\/categories?post=35862"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.linquip.com\/blog\/wp-json\/wp\/v2\/tags?post=35862"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}