What is IoT Analytics?
Internet of Things (IoT) analytics is a data analysis tool that assesses the wide range of data collected from IoT devices. IoT analytics assesses vast quantities of data and produces useful information from it.
IoT analytics are usually discussed in tandem with Industrial IoT (IIoT). Data is collected from a wide range of sensors on manufacturing infrastructure, weather stations, smart meters, delivery trucks, and all forms of machinery. IoT analytics can be applied to managing data centers and applications handling retail and healthcare.
In many ways, IoT data is similar to big data. The key difference between the two is not just the quantity of data but the range of sources it is obtained from. All this data has to be processed into one comprehensible, single stream of data. Considering the several kinds of sources of information, data integration becomes quite difficult, and this is where IoT analytics makes a difference, though it can be tough to develop and implement.
Devices that power IoT Analytics
There are a wide range of IoT devices that collect data:
Dedicated trackers such as Fitbit or other smartwatches have gone beyond tracking steps. You can track your friends' fitness activities, compete with them, message, and even answer the phone by connecting your devices through the Internet. This information is tracked by fitness companies, enabling them to create customized packages if you sign up. This can include exercise routines, diet, goals, and more. The newest smart watches even monitor heart rates and rhythms and have accurately diagnosed heart problems in their wearers.
Smart homes have security systems you can access and control when you are away from home, to appliances you can turn on and off with digital assistance. There is a wide range of devices that you can incorporate into your home and a wide range of data that can be collected to assess usage patterns, the efficacy of systems, and more.
Healthcare has a wide range of IoT devices. Bluetooth technology creates hearing aids, records heart and blood pressure, and monitors pulse-based alarm systems that can call for help. This has helped enhance healthcare to a large extent. The data collected is invaluable in terms of creating newer and better technology.
Digital assistants are a form of IoT devices. Alexa, Siri, and Google take notes, find information, play music, order cabs, tell the weather, set alarms, and everything else. The internet regularly updates these digital assistants to improve functionality. Their data helps companies tailor their services for you, based on your everyday interaction with digital assistants.
How IoT Analytics Work and the Applications
With a wide range of devices, there is an endless stream of data in enormous quantities. IoT analytics helps analyze this data across all connected devices without hardware or infrastructure. As the needs of your organization change, computing power and data storage scales up or down accordingly, ensuring your IoT analysis has the requisite capacity.
- The first step is to collect data aggregated from a variety of sources, in a range of formats, and at multiple frequencies.
- This data is then processed with a wide range of external sources.
- The information is then stored in a time-series for analysis.
- The analysis can be done in multiple ways--with custom analysis systems, with standard SQL queries, or with machine learning analysis techniques. The results can be used to make a wide range of predictions.
- With the information received, organizations can build several systems and applications to ease business processes.
Business Use Cases of IoT Analytics
Smart agriculture: With IoT analytics, connected field machinery works based on information derived from IoT analysis. Analysis factors include time, geographical location, weather, altitude, and local environmental conditions. For example, irrigation systems can be optimized to deliver the exact amount of water as rainfall predictions.
Regular restocking of supplies: Monitor inventories in real-time. A food vending company, with connected machines, can have their machines request restocking based on the depletion of products. This can be triggered when stocks in the machine reach a particular level.
Predictive maintenance: Varying infrastructure needs regular maintenance. With IoT analysis, pre-set templates can help determine quality predictive maintenance models applied to specific needs. For example, in long-distance transport vehicles with heating and cooling systems--IoT analytics can determine when vehicles need an overhaul to ensure cargos are not damaged.
Process efficiency scoring: Every company works with a range of processes in place. IoT analytics can measure the efficiency of these processes and make the necessary changes in them. Results from IoT analytics can identify bottlenecks--both current and potential--and can increase efficiencies.
The Benefits and Challenges of IoT Analytics
IoT analytics brings a wide range of benefits, such as actionable intelligence and invaluable insights. These can result in:
- Increased visibility and better control, resulting in quicker decision-making
- Flexible scaling of business needs and expansion in new markets
- Reduction in operational costs from automation and better resource utilization
- New revenue streams, resulting from the clearing of operational challenges
- Quicker solutions from accurate pinpointing of problems
- Quicker resolution of issues, preventing recurrence
- Enhanced customer experience based on analysis of purchase history
- Quicker and more relevant product development.
Challenges in Implementation
While the many benefits of IoT analytics are quite clear, it does not come without its share of implementation difficulties. Some of the major challenges of IoT analytics include:
Ascertaining time series and data structures: Sensors are a part of IoT analytics and often have a barrage of static data thrown at them. This data remains the same until something happens to change it. What influences the change during these long periods is difficult to ascertain and its impact on analysis cannot always be determined. This can impact diagnostic and predictive efforts.
Balancing speed and storage: Companies often struggle with storing the right amount of data and analyzing it quickly. Scaling these two processes up, particularly in the case of time-sensitive data, can be a challenge, especially when historical data is needed to make comparisons. With historical data, it is essential to store data for a long time, which increases the cost of storage, putting a strain on financial resources.
Finding the right professionals: To run IoT analytics, a company needs to hire professionals in several fields. You will need developers, database specialists, and data scientists as well as data processing specialists and other specific skill sets, depending on your organization and the kind of work it does.
With an increase in popularity for IoT devices, data is abundant. Organizations have data flowing continuously from personal devices, smart homes devices, automobiles, and more. For organizations looking to capitalize on this, building a robust IoT analysis-based system is key. IoT analytics can unlock the true potential of IoT data, opening up several opportunities a company can leverage to get ahead of the competition.
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