Billions of connected IoT devices are generating a massive amount of data every second. Meanwhile, as the IoT is booming this data generation has exponential growth. This data needs to be analyzed in order to retrieve insights out of this data. Further, these insights that help enterprises draw important decisions regarding their business. Firstly analytics on IoT data involves sensor-generated datasets. This data is now both cheap as well as sophisticated enough to support an endless variety of use cases.
We should improve our questions about our relationship with smart devices and start using the data that we’re collecting in IoT analytics use cases. Companies that ignore this abundance of IoT data are going to fall behind, and the ones that do not are going to rise to the top.
Use case 1- Increased life quality with ‘Smart cities’
These ‘smart cities’ are a future trend in urban design and are key to solve urban problems. In fact, Countries around the whole world are taking steps towards “smart cities”.
Crime prevention
With analytics, it is easy for urban planners and criminologists to identify areas as well as times with high crime frequency. Police can be deployed to these areas to prevent crime before it even happens. London, Chicago, and Los Angeles are already using this idea with great success.
Transport
Data analytics can make the entire network more efficient than before by reducing the degree of congestion, frequency of delays as well as increasing the efficiency of transport networks. Predictive analytics can forecast the demand for transport during weekends or special events.
More efficient spending
It is not uncommon to see a big chunk of money allocated to vanity projects, and not enough in vital services. With data analytics money spending is efficient, so urban planners and elected officials can make better use of resources.
Use cases 2- Data Analysis for Manufacturing Sector
Manufacturers are generating a vast amount of data through their systems. Whether you look at your shop floor, your supply chain, or procurement, advanced analytics helps you identify patterns and dependencies within your systems. By doing that you can make the right decisions or optimize the whole process. Typical use cases for manufacturing are:
Predictive maintenance
Knowing when a part is going to break reduces downtime and waste. By analyzing factors that drive the wear of your devices, you gain transparency on the real-life of your products.
Automatic quality testing
Automating this task saves time and helps to avoid human errors. Instead of using manual checks, quality can be tested incorporating data from special test devices, X-ray scans, photography, etc.
Supply chain optimization
Anticipating the right time to produce orders or plan shipping dates enables on-time delivery as well as resolves storage issues. Analyzing the duration of individual processes and the complex interdependencies among them provides information about transportation times and the impact of disruptions.
Use case 3- Advancement in Healthcare
The significant changes made by healthcare through IoT are remarkable. People and apps are connected in a way that was never deemed possible before. This can, not only improve healthcare outcomes but, will also drastically reduce healthcare costs as well. Sensors embedded in medical devices will help doctors understand medical emergencies even before they arise.
Medical image analysis
The healthcare sector is receiving great benefits because of the data science application in medical imaging. The most popular image-processing techniques focus on enhancement, denoising, as well as segmentation that provides deep analysis of organ anatomy, and detection of diverse disease conditions.
Creation of drugs
It very difficult to discover a new drug and it involves many disciplines. Billions of testing, huge financial as well as time expenditure are the biggest hurdles to new ideas. Using this data, unsupervised learning enables scientists to build models that can predict the outcome from several independent variables simultaneously.
Use case 4- Retail Industry
Personalization for customers
Understanding customer behavior and combining it with consumer demography is the first step in the deployment of predictive analytics. It is now possible to track behavior across channels, i.e. monitor a person who researches in the online store and then purchases the item physically. Such insights coupled with predictive analytics now give merchants the option to make highly personalized offers to customers at a very granular level.
Customer behavior or behavioral analytics
People-tracking technology has now made it easy for retailers to find ways of analyzing online shopping behavior. Various consumer interaction points can provide data. Using this and even data points captured from earlier marketing and advertising campaigns, retailers can now build predictive models to link past behavior and demographics. The aim of such models is to score every customer according to the likelihood of them buying certain products.
Use case 5– Video Analytics
The traditional use of video surveillance was restricted for security purposes. But everything has changed and now, video analytics has more widespread uses. Compressed video streams are being analyzed in real-time to find patterns, anomalies, motion detection, behavior, events, and much more. This information can benefit organizations in many ways like increased sales, time savings, reduction in losses, as well as better productivity.
Security and Surveillance
- It is one of the primary use cases of video surveillance indeed. Face detection technology can identify a person who enters the premises in CCTV video streams. Hence, This information is used to decide whether the identified individual should get access to the premise.
- Allows a security team to designate a zone and monitor if people move into or get too close to that area. In fact, Any motion in the selected area will provoke an alert and every movement outside of the area will be ignored.
Transport Monitoring System
- video analytics can be applied to monitor the traffic flow speed on highways that could be used to predict travel time as well as dynamically calculate toll values.
- video from CCTV cameras can detect road accidents, vehicle breakdowns, as well as bad road conditions. Level spacing curves are derived for the traffic flow of each lane.
