Leveraging IoT Analytics: From Edge to Business Insights

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Leveraging IoT Analytics: From Edge to Business Insights

By combining IT and OT data, IoT analytics helps to extract information that improves business value. Read on to find out how IoT analytics can provide business insights.

· IoT Zone ·
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With IoT data being generated in zettabytes today, enterprises need to leverage this data to perform various monitoring, reporting, and decision-making tasks. By combining the IT and OT data, IoT analytics helps extract actionable and meaningful insights to help improve the overall business value.

With the Internet and wireless technology reaching billions, IoT has become ubiquitous. It has reached our homes, vehicles, and cities. Hence, each of us is generating a very high volume of data with the Internet of Things. Cisco predicts that, by the end of 2021, IoT will generate 847 zettabytes of data per year, and this number will continue to grow exponentially.

Considering the rapid growth of IoT and connected devices, there is a thriving need among organizations to make sense of this IoT data with IoT analytics to extract important business insights that can be used as a competitive advantage and informed decision-making. For example, visualizations, reports, and alerts generated with real-time IoT data of an e-commerce website can be directly used to provide better customer experience. According to the Grand View Research, the global IoT analytics market size is expected to reach USD 57.3 billion by 2025 at a CAGR of 29.7 percent. However, when it comes to analyzing IoT data, it has its own challenges.

Challenges of Analyzing IoT Data

IoT data comes in huge volumes, is highly unstructured, and differ in terms of variety (text, image, or videos). Moreover, while the operational technology relates to the data collected from temperature sensors, pressure sensors, tablets, smart manufacturing devices/tools, etc., the information technology relates to the data collected from enterprise systems, legacy systems, ERP, CRM, and finance systems. Looking at only the OT or IT data in silos will not provide the necessary results. The OT and IT data have to be combined to make sense. Unfortunately, traditional analytics tools and technologies are designed to look at only the IT data and do not work directly on this combined dataset.

Moreover, cleansing of OT data from the noise, corrupt, and false readings is one of the major challenges in the IoT environment. For example, in a smart home system, to know when to proactively replace sensor batteries at the customer’s place for better service, a vendor will require customer transaction data from the enterprise systems as well as sensor data, like device status, metadata, etc. Securely managing the IoT data, before passing for dashboard generation, is another challenge posed when carrying out IoT analytics.

How IoT Analytics Helps Business Insights

IoT analytics encompasses data collection from OT and IT data sources and subsequent storage, processing, integration, visualization, and analytics to generate useful and actionable business insights.

There are various use cases for which IoT analytics can be applied. Things like home security and automation, data collected from different smoke, temperature, humidity, and alarm sensors can be combined with customer data to enhance customer lifestyle experience, provide proactive data-driven services, and bring in more operational efficiencies through remote monitoring, visualization, and troubleshooting.

In a smart building area, energy utilization dashboards can aid in sensor control, identifying specific times to heat or cool rooms, finding air quality threats, and deploying predictive fixes and maintenance. A study by Texas Instruments indicates that in HVAC and lighting IoT solutions, energy use can be cut by 40 percent just by sensor control. The same study also indicates that thermal comfort improves human productivity by 3 percent, impacting the bottom line.

In the retail industry, video analytics on the data collected from IP cameras can be used to check for theft detection, sweet heartening, and monitoring entries and exits. Also, the data from temperature tags and humidity sensors in the retail store can be used for forecasting shelf space replenishment, perishables, etc. Data from digital shelves can be used for real-time price and discount offers to customers.

Thus, there is a huge opportunity and value to be unlocked in combination with enterprise data to mine valuable intelligence through IoT analytics from the edge.

business insights ,data ,edge ,edge computing ,iot ,iot analytics ,iot data

Published at DZone with permission of Urvashi Babaria . See the original article here.

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