With evolvement of the Internet of Things (IoT) that allows to implement the model of operating on connected devices in any domain, edge analytics has become predominant. By capturing, storing and analyzing the data with an algorithm at the edge of a network, organizations, companies and individuals unlock the option of selective storing and sharing the information and enhance analytical powers.
According to Gartner Report on technologies expected to emerge in 2019, edge analytics is the method that will enable users to leverage data analytics ‘beyond those of traditional business insights’ and increase operating efficiency multifold by zooming into the smallest detail with exceptional analysis precision and relevancy.
Edge analytics market dynamics demonstrates steady growth of the edge computing adoption with the biggest expected expansion of the market size in North America and overall increase of the market to almost $8 billion by 2021.
What is Edge Analytics?
Edge analytics is the advanced data analysis method that enables users to get access to real-time processing and extracting the unstructured data captured and stored on the edge of network devices. Edge analytics provides the automatic analytical computation of generated data in real-time mode without sending the data back to the centralized data store or server.
While gathering information locally, edge computing employs AI and ‘makes sense’ of the collected data, which turns this method from purely technical into strategic as it sets a new tone to a much faster decision-making process.
Analyzing data on the edge is innovative in terms of perspective it opens for the IoT consumers. In addition to considerable decrease of probable decision-taking latency on connected devices, it saves time by processing data faster and, thus, increasing deployment scalability. Edge analytics can be applied for the purpose of description or failure prevention, thus, there is a differentiation of:
- diagnostic edge analytics
- predictive edge analytics.
However, descriptive and predictive edge analytics can blend: for instance, you can customize to interpret the data at the network edge according to pre-set parameters, which means real-time diagnosis and predictive response to certain operation.
It leads to enabling automation of performance and activating the streamlined feedback not only from the outside source but within the device.
Another considerable benefit of edge analytics is reducing the cost on data management as it ensures a cost-effective way of extracting exclusively relevant information within data processing.
IoT Edge Analytics
The significant increase on edge analytics application results from the excessive adoption of internet of things (IoT) universally recognized by industries as the most important tech trend due to the wide spectrum of IoT services and their domain capabilities.
In particular, edge analytics if taken as the data-driven approach to extracting tangible and measurable metrics from the IoT, helps companies to get more advanced data faster by employing advanced analytics and machine learning at the point of data collection through connected devices and via real-time intelligence.
Speaking of industrial IoT market, it can be predicted that sectors utilizing the edge computing are projected to expand further due to the edge analytics benefits of distributed aggregation and providing the shop-floor data collection across all the industrial sectors.
With 13 billion connected IoT devices available on the market now, IoT demands edge analytics because connected devices generate a huge amount of decentralized data, which is especially applicable to large-scale industries operating in low-bandwidth environments.
Furthermore, IoT growth is observed across logistics, travel, retail, manufacturing, healthcare industries where data collection is the most efficient way of leveraging technology for multiple business needs.
Even the global cloud providers have already taken steps in bringing edge analytics to their networks and connected devices. For example, Google has extended its Cloud IoT software platform features to edge networking.
Edge Analytics Applications
The concept of edge analytics opens the possibility of creating an optimal model of managing the data transfer from the edge and ‘smart’ data storage at data centers. Advanced analytics methods regardless of the domain of their application, work behind the scenes to predict failure of the system components, maintain functioning the devices and keep the smooth workflow unbroken. Overall, edge analytics capabilities are actively employed in creation of smart machines operating on self-responsive actions, and predictive outcomes.
Edge AI video-based analytics found a wide application in lots of domains, mainly in education. Indeed, it can transform teaching-learning process by giving revolutionary solutions in terms of presenting learning videos in a new way. For example, Sony presented its AI edge analytics based solution for creating the video content: its main unit in combination with various licensed appliances proves to elevate video streaming to a new dimension.
With just an edge analytics box and a camera, educators and presenters can employ handwriting extraction, gesture recognition, focus cropping and audio tracking in real time, which means unleashing the full potential of video content while discovering the new ways of audience engagement.
Healthcare is another area where the IoT continuously makes a profound impact on all the industry players. On average, a hospital deploys up to 85000 connected devices, creating a considerable pressure for the cloud network. Edge analytics is the optimal method of eliminating overload and addressing storage security and connectivity issues in the cost-effective way.
Analytics on Cloud vs Analytics on Edge
Analytics on cloud has established itself due to tech giants continuously offering new cloud solutions like cheaper cloud storages, enhanced computing powers and wider spread of broadband, which translated into the steady growth and increase by 22% of the cloud solutions market yearly.
Cloud-based analytics can do all the ‘heavy-lifting’ like adding historical data to streaming data as well as analyzing the output information from all the devices. Cloud analytics can be presented in real time or retrospectively. However, the industry recognizes the shortages of cloud analytics capabilities pertaining to the modern enterprise architecture supporting numerous IoT applications.
Analytics on cloud can be very fast but not instant compared to extracting relevant data close to the device, where it can ‘skip’ on network latency, concerns for cost effectiveness or any issues in regards to connectivity.
According to IDC (International Data Corporation), the increase of the number of IoT devices is predicted to result in 79.4ZB data generation by 2025. Not to mention the considerable expenses associated with data transfer and storage, non-stop data synchronization on the cloud can potentially drain resources.
Shifting most of computing workload to the edge delivers best results in terms of reducing the communications costs and enabling the operation in instant action mode.
Therefore, analytics on edge is a must in case devices or machines operate more effectively upon activation of self-responding mode. Then, analytics at the edge becomes a key factor of development strategy. Instead of trying to bring data to the cloud faster, it is wise to apply data processing at the edge and bring analytics to the data-generating devices.
Apparently, edge analytics providing data insights at the edge, is in great demand in retail, transportation, logistics and any vertical-governed sectors relying on activated option of instant feedback locally. Other fields that might greatly benefit from deploying edge analytics are customer engagement analysis, online banking, and monitoring of logistics and manufacturing equipment, and remote controlling in healthcare.
Concluding Thought
Despite possible challenges of implementation, the edge analytics model presents long-lasting trend enabling users to obtain valuable and actionable insights in real-time, bring order to unstructured content and feed the cognitive-oriented systems on relevant data.
To make the most of the collected data, market players are investing in storage and networking gear at the edge for fail-proof facilitation of the work processes and strategic exploration of advanced analytics capabilities.
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