IoT devices are proliferating quickly—by the end of 2021, more than 35 billion IoT devices had been installed around the world. The data they produce can understandably tell us a considerable amount about how we live, enabling a wide array of improvements to service delivery and customer experience. Though still at the early stages of its integration into data operations (and our lives in general), IoT data analytics already provides some very interesting examples of its potential.
These IoT data analytics use cases range across virtually all areas of industry and, as data operations become more streamlined and effective in dealing with masses of data, are only a sign of things to come. Currently, the biggest obstacles to fully utilizing IoT data analytics are the costs and speed of storing, processing, and analyzing the extensive flows of data from distributed IoT devices. Despite these issues, we’ll take a look at some of the most important areas where IoT data analytics are proving to be valuable.
Nine IoT data analytics use cases
For manufacturers, utility service providers, and any other industry that uses large critical machinery or equipment, IoT sensors can measure the wear and tear of the machinery. The resulting IoT data analysis combines metrics such as load, usage, material throughputs, temperature, and other stressors to identify and schedule maintenance visits. Per McKinsey, using IoT data analytics for predictive maintenance can reduce maintenance costs by 40% and equipment downtime by 50%.
Reduced energy consumption is one of the key advantages that smart home devices and systems, such as Google Nest, deliver for users. By creating a centralized dashboard where homeowners can easily view all their energy usage, they can identify and optimize their energy consumption. When combined with smart metering, analytics can happen in both directions. That is to say, energy providers can use the data to customize tailor-made packages to meet specific users’ needs.
Improving customer experience
Businesses are naturally turning to IoT data analysis insights to understand how customers interact with a product or service and how to improve their experience. There are numerous ways to use IoT data analytics in the context of improving business offerings. In one example, AGT International analyzed fan movements, reactions, and heatmaps at basketball games. They then used this data to optimize seating placement and facility access, overall improving fans’ gameday experience.
Supply chain management
Small, cheap, and powerful IoT sensors have a big impact on how supply chains work. By being able to track not only the exact location of goods but also their travel speed and storage conditions, such as ambient temperature, humidity, and light, organizations can use IoT data analytics to ensure their products arrive on time and in optimal condition.
Remote IoT devices already help improve patient outcomes with features like insulin monitoring and fall detection. With IoT data analytics, health service delivery can be improved even further. As more insights are gained into certain conditions and the effectiveness of certain medications, medical practitioners can use IoT data analytics to modify or improve current treatment plans.
The ability of remote IoT sensors to power the smart cities of the future is already being shown in areas such as smart parking, where sensors detect available parking spaces around the city and upload this real-time information to an app. This allows users to quickly and easily locate a spot rather than having to drive around looking for one. Already in use in many cities worldwide, this use of IoT data analytics is also beneficial for the city itself—when car parking is maximized, it can lead to 20% to 30% increases in parking revenue.
Smart building monitoring
Sudden natural disasters, as well as consistent environmental change, can create a considerable risk to buildings and infrastructure, potentially costing lives. Using data gathered from IoT devices and IoT data analysis, changes can be tracked in important metrics over time, such as tremors that may otherwise have gone unnoticed, moisture levels in walls, or deterioration in brickwork or plaster. Collating this data and using IoT data analytics can monitor structural health and predict necessary remediation work.
Smart grids collect IoT and other data from all energy producers and consumers to create a full picture of energy use across areas ranging from the size of cities to full continents. This usage of IoT data analytics then allows all stakeholders to make decisions around the most efficient ways energy can be used, such as lowering prices during peak solar production times, allowing two-way energy flows so users can sell back to the grid, and ensuring energy infrastructure is built in the most effective locations.
There are numerous factors in agricultural production that can be improved by IoT data analytics. From environmental sensors that measure weather changes, direct sunlight, and rainfall to calculating the presence of vital nutrients and moisture levels in the soil, the future of successful farming relies on big data and IoT data analytics.
Performing IoT data analytics
All of these data analytics use cases and many more are of great benefit to businesses around the world. It’s no surprise, then, that the market for IoT data management is projected to grow to $147 billion by 2026. However, as mentioned before, there are still considerable obstacles to the full utilization of IoT data analytics. The biggest of these are the costs and capability to store, process, and analyze the wealth of data that IoT devices produce. In addition, there is also increasing regulatory oversight over the IoT data analysis, such as the CCPA in California and GDPR in Europe.
A solution to all of these issues is using a secure, interoperable data platform, such as Intertrust Platform. Intertrust Platform creates a virtualized data layer that brings together all the data that’s needed for query and analysis, no matter where it resides, effectively avoiding both time-consuming data migration and more costly storage options. The IoT data analytics are then performed in secure containers with fine-grained access controls, which decreases the potential for data leakage or security breaches. These security measures and others also help to ensure regulatory compliance, even when collaborating with partners or using third-party Iot data analysis.
About Abhishek Prabhakar
Abhishek Prabhakar is a Senior Manager ( Marketing Strategy and Product Planning ) at Intertrust Technologies Corporation, and is primarily involved in the global product marketing and planning function for The Intertrust Platform. He has extensive experience in the field of new age enterprise transformation technologies and is actively involved in market research and strategic partnerships in the field.