Many enterprises have intertwined internet-of-things (IoT) solutions to enhance operational processes, augment digital capabilities, and segregate products & services.<\/p>\n
However, as enterprises install a varied array of IoT projects and use cases, each with unique requirements, many encounter challenges with using centralized cloud-based and data center analytic strategies.<\/p>\n
To transmute vast streams of IoT data into insights in a secure, fast, and cost-effective way, enterprises must revisit their IoT strategies, architecture, and skills.<\/p>\n
Here is where Edge IoT Analytics seeks attention.<\/p>\n
Edge IoT Analytics is a process that takes analytic computations for certain IoT use cases out of the data center or cloud and passes it as adjacent to the data sources as is essential and viable to alleviate security and compliance risks, empower real-time decisions and decrease costs.<\/p>\n
According to Forrester<\/em><\/strong>, \u201cIncorporating Edge IoT analytics can help organizations overcome the limitations of a fully centralized analytics approach. 69% say that prioritizing edge IoT for analytics for certain use cases would improve their ability to meet IoT objectives. 73% have already executed or are currently steering IoT programs across a wide variety of use cases<\/em><\/strong>\u201d.<\/p>\n The key reason why the edge has become so prevalent in today\u2019s digital era is because the \u201cedge\u201d as we know it is becoming progressively intelligent.<\/p>\n Intelligent edge computing IoT analytics typically have more influential processing proficiencies designed to examine data at the edge.<\/p>\n According to a latest research on Industrial IoT edge computing<\/em><\/strong> conducted by IoT Analytics<\/em><\/strong>, \u201cIntelligent edge compute resources are swapping \u201cdumb\u201d legacy edge compute resources at a rapid pace. While Intelligent edge compute makes up a small portion of the market today, it is expected to grow much faster than the overall market and thus gain share on the latter. The propaganda about edge computing is necessary as the replacement of \u201cdumb\u201d edge computing with intelligent edge computing has major consequences for businesses in all sectors, from manufacturing facilities, consumer electronics and oil and gas wells to machinery OEMs.<\/em><\/strong>\u201d<\/p>\n Many businesses have used cloud platforms to analyze their IoT data by applying cutting-edge analytics models and leveraging clouds\u2019 extensive processing power, connectivity, and storage capabilities.<\/p>\n Nevertheless, they have recognized limitations to cloud analytics which are predominantly significant for efficacious IoT use case deployment including: lack of real-time analytic capabilities, security or compliance concerns, and high data transit costs.<\/p>\n Many enterprises have moved to cloud platforms to connect rich device data with low cost, elastic global infrastructure.<\/p>\n This tactic originally permitted businesses to expedite their development of connected products and industrial internet solutions.<\/p>\n However, as enterprises proliferate their IoT efforts, fully centralized or cloud-only approaches are expected to stumble.<\/p>\n Intensive IoT applications for data and work loads are subject to challenges while using cloud computing platforms.<\/p>\n According to a research conducted by Forrester<\/em><\/strong>,\u201d98% of the respondents cite challenges with analyzing IoT data in the cloud. Maximum pitfalls of analyzing IoT data in the cloud include abridged accessibility and ability to make real-time decisions, security worries, and increasing costs.<\/em><\/strong>\u201d<\/p>\n It is not always economic, practical, or even lawful to move, store, and analyze IoT data into a core cloud infrastructure as IoT use cases often have unique real-time data analysis requirements.<\/p>\n While businesses face limitations with IoT data analysis in the cloud as they proliferate, enterprises are leveraging IoT data as a powerful source of insight.<\/p>\n The expansion of interconnected IoT devices coupled with continuous innovation enable enterprises to collect an unprecedented amount of IoT data.<\/p>\n With data driven decision-making regulating every aspect of our lives, data and insights have become treasured assets for any enterprise.<\/p>\n The value of this data is measured by how enterprises analyze and draw insights for better outcomes.<\/p>\n According to a recent study by Forrester<\/em><\/strong>, \u201cIoT solutions frequently allow digital transformation by encompassing software control of physical assets and providing a rich source of data including location, status, and presence of connected assets, products, and processes. 56% of surveyed global enterprises have already adopted or are expanding deployment of IoT solutions, another 17% of enterprises are currently piloting IoT programs. These IoT initiatives are applied by enterprises across geographic regions in various industries and spanning a wide range of use cases.<\/em><\/strong>\u201d<\/p>\n While enterprises are leveraging IoT data as a powerful source of insight, addressing this data, analytics, and security issues remain a challenge.<\/p>\n Gaps in analytical skills, security concerns, and real-time data analysis challenges are some of the key IoT data analysis barriers.<\/p>\n According to Gartner<\/em><\/strong>, \u201cThe staple inputs for IoT analytics are streams of sensor data from machines, medical devices, environmental sensors and other physical entities. The challenge is going to be how to manage and store that data.<\/em><\/strong> Managing and storing this data in an efficient and timely manner sometimes requires event stream processing platforms, time series database management systems and specialized analytical algorithms. However, many BI and analytics practitioners lack expertise in the streaming analytics, time series data management and other technologies used in IoT analytics.<\/em><\/strong>\u201d<\/p>\n Amidst these challenges, Intelligent Edge IoT Analytics has been a game changer to many enterprises and has brought in several business and operational benefits.<\/p>\n According to a recent survey conducted by Forrester<\/em><\/strong>, \u201cIrrespective of their level of acceptance, all IoT decision makers agree prioritizing edge IoT analytics for certain use cases can improve their capability to meet IoT objectives and 43% describe the level of improvement as significant<\/em><\/strong>.\u201d<\/p>\n Cost reduction, improved real-time decision making, better business processes, preservation of privacy, enhanced security capabilities, and superior customer experience are few of the important benefits realized by businesses that deployed edge IoT analytics.<\/p>\n Key recommendations gleaned from reliable research reports include \u2013<\/p>\n Espousing Edge IoT Analytics can improve IoT outcomes as agreed by many IoT decision makers.<\/p>\n According to a latest report published on IoT Analytics<\/em><\/strong>, \u201cIoT edge computing resources are becoming increasingly intelligent and are estimated to reach $30.8B by 2025, up from $11.6B in 2020<\/em><\/strong>\u201d.<\/p>\n Enterprises that manage physical assets can reap tremendous cost savings and unlock new opportunities by switching to modern, intelligent edge computing architectures.<\/p>\n Cigniti is a pioneer in providing IoT testing services<\/a> for seamless performance and functionality of your intelligent products.<\/p>\nBusinesses face limitations with IoT data analysis in the Cloud<\/h2>\n
Enterprises are leveraging IoT data as a powerful source of insight<\/h2>\n
Challenges in addressing IoT data<\/h2>\n
Edge IoT Analytics – Business benefits and key recommendations<\/h2>\n
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Closing thoughts<\/h2>\n